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scikit-learn
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/* Generated by Cython 0.20.2 on Fri Nov 28 18:58:48 2014 */
#define PY_SSIZE_T_CLEAN
#ifndef CYTHON_USE_PYLONG_INTERNALS
#ifdef PYLONG_BITS_IN_DIGIT
#define CYTHON_USE_PYLONG_INTERNALS 0
#else
#include "pyconfig.h"
#ifdef PYLONG_BITS_IN_DIGIT
#define CYTHON_USE_PYLONG_INTERNALS 1
#else
#define CYTHON_USE_PYLONG_INTERNALS 0
#endif
#endif
#endif
#include "Python.h"
#ifndef Py_PYTHON_H
#error Python headers needed to compile C extensions, please install development version of Python.
#elif PY_VERSION_HEX < 0x02040000
#error Cython requires Python 2.4+.
#else
#define CYTHON_ABI "0_20_2"
#include <stddef.h> /* For offsetof */
#ifndef offsetof
#define offsetof(type, member) ( (size_t) & ((type*)0) -> member )
#endif
#if !defined(WIN32) && !defined(MS_WINDOWS)
#ifndef __stdcall
#define __stdcall
#endif
#ifndef __cdecl
#define __cdecl
#endif
#ifndef __fastcall
#define __fastcall
#endif
#endif
#ifndef DL_IMPORT
#define DL_IMPORT(t) t
#endif
#ifndef DL_EXPORT
#define DL_EXPORT(t) t
#endif
#ifndef PY_LONG_LONG
#define PY_LONG_LONG LONG_LONG
#endif
#ifndef Py_HUGE_VAL
#define Py_HUGE_VAL HUGE_VAL
#endif
#ifdef PYPY_VERSION
#define CYTHON_COMPILING_IN_PYPY 1
#define CYTHON_COMPILING_IN_CPYTHON 0
#else
#define CYTHON_COMPILING_IN_PYPY 0
#define CYTHON_COMPILING_IN_CPYTHON 1
#endif
#if CYTHON_COMPILING_IN_PYPY && PY_VERSION_HEX < 0x02070600
#define Py_OptimizeFlag 0
#endif
#if PY_VERSION_HEX < 0x02050000
typedef int Py_ssize_t;
#define PY_SSIZE_T_MAX INT_MAX
#define PY_SSIZE_T_MIN INT_MIN
#define PY_FORMAT_SIZE_T ""
#define CYTHON_FORMAT_SSIZE_T ""
#define PyInt_FromSsize_t(z) PyInt_FromLong(z)
#define PyInt_AsSsize_t(o) __Pyx_PyInt_As_int(o)
#define PyNumber_Index(o) ((PyNumber_Check(o) && !PyFloat_Check(o)) ? PyNumber_Int(o) : \
(PyErr_Format(PyExc_TypeError, \
"expected index value, got %.200s", Py_TYPE(o)->tp_name), \
(PyObject*)0))
#define __Pyx_PyIndex_Check(o) (PyNumber_Check(o) && !PyFloat_Check(o) && \
!PyComplex_Check(o))
#define PyIndex_Check __Pyx_PyIndex_Check
#define PyErr_WarnEx(category, message, stacklevel) PyErr_Warn(category, message)
#define __PYX_BUILD_PY_SSIZE_T "i"
#else
#define __PYX_BUILD_PY_SSIZE_T "n"
#define CYTHON_FORMAT_SSIZE_T "z"
#define __Pyx_PyIndex_Check PyIndex_Check
#endif
#if PY_VERSION_HEX < 0x02060000
#define Py_REFCNT(ob) (((PyObject*)(ob))->ob_refcnt)
#define Py_TYPE(ob) (((PyObject*)(ob))->ob_type)
#define Py_SIZE(ob) (((PyVarObject*)(ob))->ob_size)
#define PyVarObject_HEAD_INIT(type, size) \
PyObject_HEAD_INIT(type) size,
#define PyType_Modified(t)
typedef struct {
void *buf;
PyObject *obj;
Py_ssize_t len;
Py_ssize_t itemsize;
int readonly;
int ndim;
char *format;
Py_ssize_t *shape;
Py_ssize_t *strides;
Py_ssize_t *suboffsets;
void *internal;
} Py_buffer;
#define PyBUF_SIMPLE 0
#define PyBUF_WRITABLE 0x0001
#define PyBUF_FORMAT 0x0004
#define PyBUF_ND 0x0008
#define PyBUF_STRIDES (0x0010 | PyBUF_ND)
#define PyBUF_C_CONTIGUOUS (0x0020 | PyBUF_STRIDES)
#define PyBUF_F_CONTIGUOUS (0x0040 | PyBUF_STRIDES)
#define PyBUF_ANY_CONTIGUOUS (0x0080 | PyBUF_STRIDES)
#define PyBUF_INDIRECT (0x0100 | PyBUF_STRIDES)
#define PyBUF_RECORDS (PyBUF_STRIDES | PyBUF_FORMAT | PyBUF_WRITABLE)
#define PyBUF_FULL (PyBUF_INDIRECT | PyBUF_FORMAT | PyBUF_WRITABLE)
typedef int (*getbufferproc)(PyObject *, Py_buffer *, int);
typedef void (*releasebufferproc)(PyObject *, Py_buffer *);
#endif
#if PY_MAJOR_VERSION < 3
#define __Pyx_BUILTIN_MODULE_NAME "__builtin__"
#define __Pyx_PyCode_New(a, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos) \
PyCode_New(a+k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos)
#define __Pyx_DefaultClassType PyClass_Type
#else
#define __Pyx_BUILTIN_MODULE_NAME "builtins"
#define __Pyx_PyCode_New(a, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos) \
PyCode_New(a, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos)
#define __Pyx_DefaultClassType PyType_Type
#endif
#if PY_VERSION_HEX < 0x02060000
#define PyUnicode_FromString(s) PyUnicode_Decode(s, strlen(s), "UTF-8", "strict")
#endif
#if PY_MAJOR_VERSION >= 3
#define Py_TPFLAGS_CHECKTYPES 0
#define Py_TPFLAGS_HAVE_INDEX 0
#endif
#if (PY_VERSION_HEX < 0x02060000) || (PY_MAJOR_VERSION >= 3)
#define Py_TPFLAGS_HAVE_NEWBUFFER 0
#endif
#if PY_VERSION_HEX < 0x02060000
#define Py_TPFLAGS_HAVE_VERSION_TAG 0
#endif
#if PY_VERSION_HEX < 0x02060000 && !defined(Py_TPFLAGS_IS_ABSTRACT)
#define Py_TPFLAGS_IS_ABSTRACT 0
#endif
#if PY_VERSION_HEX < 0x030400a1 && !defined(Py_TPFLAGS_HAVE_FINALIZE)
#define Py_TPFLAGS_HAVE_FINALIZE 0
#endif
#if PY_VERSION_HEX > 0x03030000 && defined(PyUnicode_KIND)
#define CYTHON_PEP393_ENABLED 1
#define __Pyx_PyUnicode_READY(op) (likely(PyUnicode_IS_READY(op)) ? \
0 : _PyUnicode_Ready((PyObject *)(op)))
#define __Pyx_PyUnicode_GET_LENGTH(u) PyUnicode_GET_LENGTH(u)
#define __Pyx_PyUnicode_READ_CHAR(u, i) PyUnicode_READ_CHAR(u, i)
#define __Pyx_PyUnicode_KIND(u) PyUnicode_KIND(u)
#define __Pyx_PyUnicode_DATA(u) PyUnicode_DATA(u)
#define __Pyx_PyUnicode_READ(k, d, i) PyUnicode_READ(k, d, i)
#else
#define CYTHON_PEP393_ENABLED 0
#define __Pyx_PyUnicode_READY(op) (0)
#define __Pyx_PyUnicode_GET_LENGTH(u) PyUnicode_GET_SIZE(u)
#define __Pyx_PyUnicode_READ_CHAR(u, i) ((Py_UCS4)(PyUnicode_AS_UNICODE(u)[i]))
#define __Pyx_PyUnicode_KIND(u) (sizeof(Py_UNICODE))
#define __Pyx_PyUnicode_DATA(u) ((void*)PyUnicode_AS_UNICODE(u))
#define __Pyx_PyUnicode_READ(k, d, i) ((void)(k), (Py_UCS4)(((Py_UNICODE*)d)[i]))
#endif
#if CYTHON_COMPILING_IN_PYPY
#define __Pyx_PyUnicode_Concat(a, b) PyNumber_Add(a, b)
#define __Pyx_PyUnicode_ConcatSafe(a, b) PyNumber_Add(a, b)
#else
#define __Pyx_PyUnicode_Concat(a, b) PyUnicode_Concat(a, b)
#define __Pyx_PyUnicode_ConcatSafe(a, b) ((unlikely((a) == Py_None) || unlikely((b) == Py_None)) ? \
PyNumber_Add(a, b) : __Pyx_PyUnicode_Concat(a, b))
#endif
#define __Pyx_PyString_FormatSafe(a, b) ((unlikely((a) == Py_None)) ? PyNumber_Remainder(a, b) : __Pyx_PyString_Format(a, b))
#define __Pyx_PyUnicode_FormatSafe(a, b) ((unlikely((a) == Py_None)) ? PyNumber_Remainder(a, b) : PyUnicode_Format(a, b))
#if PY_MAJOR_VERSION >= 3
#define __Pyx_PyString_Format(a, b) PyUnicode_Format(a, b)
#else
#define __Pyx_PyString_Format(a, b) PyString_Format(a, b)
#endif
#if PY_MAJOR_VERSION >= 3
#define PyBaseString_Type PyUnicode_Type
#define PyStringObject PyUnicodeObject
#define PyString_Type PyUnicode_Type
#define PyString_Check PyUnicode_Check
#define PyString_CheckExact PyUnicode_CheckExact
#endif
#if PY_VERSION_HEX < 0x02060000
#define PyBytesObject PyStringObject
#define PyBytes_Type PyString_Type
#define PyBytes_Check PyString_Check
#define PyBytes_CheckExact PyString_CheckExact
#define PyBytes_FromString PyString_FromString
#define PyBytes_FromStringAndSize PyString_FromStringAndSize
#define PyBytes_FromFormat PyString_FromFormat
#define PyBytes_DecodeEscape PyString_DecodeEscape
#define PyBytes_AsString PyString_AsString
#define PyBytes_AsStringAndSize PyString_AsStringAndSize
#define PyBytes_Size PyString_Size
#define PyBytes_AS_STRING PyString_AS_STRING
#define PyBytes_GET_SIZE PyString_GET_SIZE
#define PyBytes_Repr PyString_Repr
#define PyBytes_Concat PyString_Concat
#define PyBytes_ConcatAndDel PyString_ConcatAndDel
#endif
#if PY_MAJOR_VERSION >= 3
#define __Pyx_PyBaseString_Check(obj) PyUnicode_Check(obj)
#define __Pyx_PyBaseString_CheckExact(obj) PyUnicode_CheckExact(obj)
#else
#define __Pyx_PyBaseString_Check(obj) (PyString_CheckExact(obj) || PyUnicode_CheckExact(obj) || \
PyString_Check(obj) || PyUnicode_Check(obj))
#define __Pyx_PyBaseString_CheckExact(obj) (PyString_CheckExact(obj) || PyUnicode_CheckExact(obj))
#endif
#if PY_VERSION_HEX < 0x02060000
#define PySet_Check(obj) PyObject_TypeCheck(obj, &PySet_Type)
#define PyFrozenSet_Check(obj) PyObject_TypeCheck(obj, &PyFrozenSet_Type)
#endif
#ifndef PySet_CheckExact
#define PySet_CheckExact(obj) (Py_TYPE(obj) == &PySet_Type)
#endif
#define __Pyx_TypeCheck(obj, type) PyObject_TypeCheck(obj, (PyTypeObject *)type)
#if PY_MAJOR_VERSION >= 3
#define PyIntObject PyLongObject
#define PyInt_Type PyLong_Type
#define PyInt_Check(op) PyLong_Check(op)
#define PyInt_CheckExact(op) PyLong_CheckExact(op)
#define PyInt_FromString PyLong_FromString
#define PyInt_FromUnicode PyLong_FromUnicode
#define PyInt_FromLong PyLong_FromLong
#define PyInt_FromSize_t PyLong_FromSize_t
#define PyInt_FromSsize_t PyLong_FromSsize_t
#define PyInt_AsLong PyLong_AsLong
#define PyInt_AS_LONG PyLong_AS_LONG
#define PyInt_AsSsize_t PyLong_AsSsize_t
#define PyInt_AsUnsignedLongMask PyLong_AsUnsignedLongMask
#define PyInt_AsUnsignedLongLongMask PyLong_AsUnsignedLongLongMask
#define PyNumber_Int PyNumber_Long
#endif
#if PY_MAJOR_VERSION >= 3
#define PyBoolObject PyLongObject
#endif
#if PY_VERSION_HEX < 0x030200A4
typedef long Py_hash_t;
#define __Pyx_PyInt_FromHash_t PyInt_FromLong
#define __Pyx_PyInt_AsHash_t PyInt_AsLong
#else
#define __Pyx_PyInt_FromHash_t PyInt_FromSsize_t
#define __Pyx_PyInt_AsHash_t PyInt_AsSsize_t
#endif
#if (PY_MAJOR_VERSION < 3) || (PY_VERSION_HEX >= 0x03010300)
#define __Pyx_PySequence_GetSlice(obj, a, b) PySequence_GetSlice(obj, a, b)
#define __Pyx_PySequence_SetSlice(obj, a, b, value) PySequence_SetSlice(obj, a, b, value)
#define __Pyx_PySequence_DelSlice(obj, a, b) PySequence_DelSlice(obj, a, b)
#else
#define __Pyx_PySequence_GetSlice(obj, a, b) (unlikely(!(obj)) ? \
(PyErr_SetString(PyExc_SystemError, "null argument to internal routine"), (PyObject*)0) : \
(likely((obj)->ob_type->tp_as_mapping) ? (PySequence_GetSlice(obj, a, b)) : \
(PyErr_Format(PyExc_TypeError, "'%.200s' object is unsliceable", (obj)->ob_type->tp_name), (PyObject*)0)))
#define __Pyx_PySequence_SetSlice(obj, a, b, value) (unlikely(!(obj)) ? \
(PyErr_SetString(PyExc_SystemError, "null argument to internal routine"), -1) : \
(likely((obj)->ob_type->tp_as_mapping) ? (PySequence_SetSlice(obj, a, b, value)) : \
(PyErr_Format(PyExc_TypeError, "'%.200s' object doesn't support slice assignment", (obj)->ob_type->tp_name), -1)))
#define __Pyx_PySequence_DelSlice(obj, a, b) (unlikely(!(obj)) ? \
(PyErr_SetString(PyExc_SystemError, "null argument to internal routine"), -1) : \
(likely((obj)->ob_type->tp_as_mapping) ? (PySequence_DelSlice(obj, a, b)) : \
(PyErr_Format(PyExc_TypeError, "'%.200s' object doesn't support slice deletion", (obj)->ob_type->tp_name), -1)))
#endif
#if PY_MAJOR_VERSION >= 3
#define PyMethod_New(func, self, klass) ((self) ? PyMethod_New(func, self) : PyInstanceMethod_New(func))
#endif
#if PY_VERSION_HEX < 0x02050000
#define __Pyx_GetAttrString(o,n) PyObject_GetAttrString((o),((char *)(n)))
#define __Pyx_SetAttrString(o,n,a) PyObject_SetAttrString((o),((char *)(n)),(a))
#define __Pyx_DelAttrString(o,n) PyObject_DelAttrString((o),((char *)(n)))
#else
#define __Pyx_GetAttrString(o,n) PyObject_GetAttrString((o),(n))
#define __Pyx_SetAttrString(o,n,a) PyObject_SetAttrString((o),(n),(a))
#define __Pyx_DelAttrString(o,n) PyObject_DelAttrString((o),(n))
#endif
#if PY_VERSION_HEX < 0x02050000
#define __Pyx_NAMESTR(n) ((char *)(n))
#define __Pyx_DOCSTR(n) ((char *)(n))
#else
#define __Pyx_NAMESTR(n) (n)
#define __Pyx_DOCSTR(n) (n)
#endif
#ifndef CYTHON_INLINE
#if defined(__GNUC__)
#define CYTHON_INLINE __inline__
#elif defined(_MSC_VER)
#define CYTHON_INLINE __inline
#elif defined (__STDC_VERSION__) && __STDC_VERSION__ >= 199901L
#define CYTHON_INLINE inline
#else
#define CYTHON_INLINE
#endif
#endif
#ifndef CYTHON_RESTRICT
#if defined(__GNUC__)
#define CYTHON_RESTRICT __restrict__
#elif defined(_MSC_VER) && _MSC_VER >= 1400
#define CYTHON_RESTRICT __restrict
#elif defined (__STDC_VERSION__) && __STDC_VERSION__ >= 199901L
#define CYTHON_RESTRICT restrict
#else
#define CYTHON_RESTRICT
#endif
#endif
#ifdef NAN
#define __PYX_NAN() ((float) NAN)
#else
static CYTHON_INLINE float __PYX_NAN() {
/* Initialize NaN. The sign is irrelevant, an exponent with all bits 1 and
a nonzero mantissa means NaN. If the first bit in the mantissa is 1, it is
a quiet NaN. */
float value;
memset(&value, 0xFF, sizeof(value));
return value;
}
#endif
#ifdef __cplusplus
template<typename T>
void __Pyx_call_destructor(T* x) {
x->~T();
}
#endif
#if PY_MAJOR_VERSION >= 3
#define __Pyx_PyNumber_Divide(x,y) PyNumber_TrueDivide(x,y)
#define __Pyx_PyNumber_InPlaceDivide(x,y) PyNumber_InPlaceTrueDivide(x,y)
#else
#define __Pyx_PyNumber_Divide(x,y) PyNumber_Divide(x,y)
#define __Pyx_PyNumber_InPlaceDivide(x,y) PyNumber_InPlaceDivide(x,y)
#endif
#ifndef __PYX_EXTERN_C
#ifdef __cplusplus
#define __PYX_EXTERN_C extern "C"
#else
#define __PYX_EXTERN_C extern
#endif
#endif
#if defined(WIN32) || defined(MS_WINDOWS)
#define _USE_MATH_DEFINES
#endif
#include <math.h>
#define __PYX_HAVE__sklearn__tree___tree
#define __PYX_HAVE_API__sklearn__tree___tree
#include "string.h"
#include "stdio.h"
#include "stdlib.h"
#include "numpy/arrayobject.h"
#include "numpy/ufuncobject.h"
#include "math.h"
#include "pythread.h"
#ifdef _OPENMP
#include <omp.h>
#endif /* _OPENMP */
#ifdef PYREX_WITHOUT_ASSERTIONS
#define CYTHON_WITHOUT_ASSERTIONS
#endif
#ifndef CYTHON_UNUSED
# if defined(__GNUC__)
# if !(defined(__cplusplus)) || (__GNUC__ > 3 || (__GNUC__ == 3 && __GNUC_MINOR__ >= 4))
# define CYTHON_UNUSED __attribute__ ((__unused__))
# else
# define CYTHON_UNUSED
# endif
# elif defined(__ICC) || (defined(__INTEL_COMPILER) && !defined(_MSC_VER))
# define CYTHON_UNUSED __attribute__ ((__unused__))
# else
# define CYTHON_UNUSED
# endif
#endif
typedef struct {PyObject **p; char *s; const Py_ssize_t n; const char* encoding;
const char is_unicode; const char is_str; const char intern; } __Pyx_StringTabEntry; /*proto*/
#define __PYX_DEFAULT_STRING_ENCODING_IS_ASCII 0
#define __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT 0
#define __PYX_DEFAULT_STRING_ENCODING ""
#define __Pyx_PyObject_FromString __Pyx_PyBytes_FromString
#define __Pyx_PyObject_FromStringAndSize __Pyx_PyBytes_FromStringAndSize
#define __Pyx_fits_Py_ssize_t(v, type, is_signed) ( \
(sizeof(type) < sizeof(Py_ssize_t)) || \
(sizeof(type) > sizeof(Py_ssize_t) && \
likely(v < (type)PY_SSIZE_T_MAX || \
v == (type)PY_SSIZE_T_MAX) && \
(!is_signed || likely(v > (type)PY_SSIZE_T_MIN || \
v == (type)PY_SSIZE_T_MIN))) || \
(sizeof(type) == sizeof(Py_ssize_t) && \
(is_signed || likely(v < (type)PY_SSIZE_T_MAX || \
v == (type)PY_SSIZE_T_MAX))) )
static CYTHON_INLINE char* __Pyx_PyObject_AsString(PyObject*);
static CYTHON_INLINE char* __Pyx_PyObject_AsStringAndSize(PyObject*, Py_ssize_t* length);
#define __Pyx_PyByteArray_FromString(s) PyByteArray_FromStringAndSize((const char*)s, strlen((const char*)s))
#define __Pyx_PyByteArray_FromStringAndSize(s, l) PyByteArray_FromStringAndSize((const char*)s, l)
#define __Pyx_PyBytes_FromString PyBytes_FromString
#define __Pyx_PyBytes_FromStringAndSize PyBytes_FromStringAndSize
static CYTHON_INLINE PyObject* __Pyx_PyUnicode_FromString(const char*);
#if PY_MAJOR_VERSION < 3
#define __Pyx_PyStr_FromString __Pyx_PyBytes_FromString
#define __Pyx_PyStr_FromStringAndSize __Pyx_PyBytes_FromStringAndSize
#else
#define __Pyx_PyStr_FromString __Pyx_PyUnicode_FromString
#define __Pyx_PyStr_FromStringAndSize __Pyx_PyUnicode_FromStringAndSize
#endif
#define __Pyx_PyObject_AsSString(s) ((signed char*) __Pyx_PyObject_AsString(s))
#define __Pyx_PyObject_AsUString(s) ((unsigned char*) __Pyx_PyObject_AsString(s))
#define __Pyx_PyObject_FromUString(s) __Pyx_PyObject_FromString((const char*)s)
#define __Pyx_PyBytes_FromUString(s) __Pyx_PyBytes_FromString((const char*)s)
#define __Pyx_PyByteArray_FromUString(s) __Pyx_PyByteArray_FromString((const char*)s)
#define __Pyx_PyStr_FromUString(s) __Pyx_PyStr_FromString((const char*)s)
#define __Pyx_PyUnicode_FromUString(s) __Pyx_PyUnicode_FromString((const char*)s)
#if PY_MAJOR_VERSION < 3
static CYTHON_INLINE size_t __Pyx_Py_UNICODE_strlen(const Py_UNICODE *u)
{
const Py_UNICODE *u_end = u;
while (*u_end++) ;
return (size_t)(u_end - u - 1);
}
#else
#define __Pyx_Py_UNICODE_strlen Py_UNICODE_strlen
#endif
#define __Pyx_PyUnicode_FromUnicode(u) PyUnicode_FromUnicode(u, __Pyx_Py_UNICODE_strlen(u))
#define __Pyx_PyUnicode_FromUnicodeAndLength PyUnicode_FromUnicode
#define __Pyx_PyUnicode_AsUnicode PyUnicode_AsUnicode
#define __Pyx_Owned_Py_None(b) (Py_INCREF(Py_None), Py_None)
#define __Pyx_PyBool_FromLong(b) ((b) ? (Py_INCREF(Py_True), Py_True) : (Py_INCREF(Py_False), Py_False))
static CYTHON_INLINE int __Pyx_PyObject_IsTrue(PyObject*);
static CYTHON_INLINE PyObject* __Pyx_PyNumber_Int(PyObject* x);
static CYTHON_INLINE Py_ssize_t __Pyx_PyIndex_AsSsize_t(PyObject*);
static CYTHON_INLINE PyObject * __Pyx_PyInt_FromSize_t(size_t);
#if CYTHON_COMPILING_IN_CPYTHON
#define __pyx_PyFloat_AsDouble(x) (PyFloat_CheckExact(x) ? PyFloat_AS_DOUBLE(x) : PyFloat_AsDouble(x))
#else
#define __pyx_PyFloat_AsDouble(x) PyFloat_AsDouble(x)
#endif
#define __pyx_PyFloat_AsFloat(x) ((float) __pyx_PyFloat_AsDouble(x))
#if PY_MAJOR_VERSION < 3 && __PYX_DEFAULT_STRING_ENCODING_IS_ASCII
static int __Pyx_sys_getdefaultencoding_not_ascii;
static int __Pyx_init_sys_getdefaultencoding_params(void) {
PyObject* sys;
PyObject* default_encoding = NULL;
PyObject* ascii_chars_u = NULL;
PyObject* ascii_chars_b = NULL;
const char* default_encoding_c;
sys = PyImport_ImportModule("sys");
if (!sys) goto bad;
default_encoding = PyObject_CallMethod(sys, (char*) (const char*) "getdefaultencoding", NULL);
Py_DECREF(sys);
if (!default_encoding) goto bad;
default_encoding_c = PyBytes_AsString(default_encoding);
if (!default_encoding_c) goto bad;
if (strcmp(default_encoding_c, "ascii") == 0) {
__Pyx_sys_getdefaultencoding_not_ascii = 0;
} else {
char ascii_chars[128];
int c;
for (c = 0; c < 128; c++) {
ascii_chars[c] = c;
}
__Pyx_sys_getdefaultencoding_not_ascii = 1;
ascii_chars_u = PyUnicode_DecodeASCII(ascii_chars, 128, NULL);
if (!ascii_chars_u) goto bad;
ascii_chars_b = PyUnicode_AsEncodedString(ascii_chars_u, default_encoding_c, NULL);
if (!ascii_chars_b || !PyBytes_Check(ascii_chars_b) || memcmp(ascii_chars, PyBytes_AS_STRING(ascii_chars_b), 128) != 0) {
PyErr_Format(
PyExc_ValueError,
"This module compiled with c_string_encoding=ascii, but default encoding '%.200s' is not a superset of ascii.",
default_encoding_c);
goto bad;
}
Py_DECREF(ascii_chars_u);
Py_DECREF(ascii_chars_b);
}
Py_DECREF(default_encoding);
return 0;
bad:
Py_XDECREF(default_encoding);
Py_XDECREF(ascii_chars_u);
Py_XDECREF(ascii_chars_b);
return -1;
}
#endif
#if __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT && PY_MAJOR_VERSION >= 3
#define __Pyx_PyUnicode_FromStringAndSize(c_str, size) PyUnicode_DecodeUTF8(c_str, size, NULL)
#else
#define __Pyx_PyUnicode_FromStringAndSize(c_str, size) PyUnicode_Decode(c_str, size, __PYX_DEFAULT_STRING_ENCODING, NULL)
#if __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT
static char* __PYX_DEFAULT_STRING_ENCODING;
static int __Pyx_init_sys_getdefaultencoding_params(void) {
PyObject* sys;
PyObject* default_encoding = NULL;
char* default_encoding_c;
sys = PyImport_ImportModule("sys");
if (!sys) goto bad;
default_encoding = PyObject_CallMethod(sys, (char*) (const char*) "getdefaultencoding", NULL);
Py_DECREF(sys);
if (!default_encoding) goto bad;
default_encoding_c = PyBytes_AsString(default_encoding);
if (!default_encoding_c) goto bad;
__PYX_DEFAULT_STRING_ENCODING = (char*) malloc(strlen(default_encoding_c));
if (!__PYX_DEFAULT_STRING_ENCODING) goto bad;
strcpy(__PYX_DEFAULT_STRING_ENCODING, default_encoding_c);
Py_DECREF(default_encoding);
return 0;
bad:
Py_XDECREF(default_encoding);
return -1;
}
#endif
#endif
/* Test for GCC > 2.95 */
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#define likely(x) __builtin_expect(!!(x), 1)
#define unlikely(x) __builtin_expect(!!(x), 0)
#else /* !__GNUC__ or GCC < 2.95 */
#define likely(x) (x)
#define unlikely(x) (x)
#endif /* __GNUC__ */
static PyObject *__pyx_m;
static PyObject *__pyx_d;
static PyObject *__pyx_b;
static PyObject *__pyx_empty_tuple;
static PyObject *__pyx_empty_bytes;
static int __pyx_lineno;
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static const char * __pyx_cfilenm= __FILE__;
static const char *__pyx_filename;
#if !defined(CYTHON_CCOMPLEX)
#if defined(__cplusplus)
#define CYTHON_CCOMPLEX 1
#elif defined(_Complex_I)
#define CYTHON_CCOMPLEX 1
#else
#define CYTHON_CCOMPLEX 0
#endif
#endif
#if CYTHON_CCOMPLEX
#ifdef __cplusplus
#include <complex>
#else
#include <complex.h>
#endif
#endif
#if CYTHON_CCOMPLEX && !defined(__cplusplus) && defined(__sun__) && defined(__GNUC__)
#undef _Complex_I
#define _Complex_I 1.0fj
#endif
static const char *__pyx_f[] = {
"_tree.pyx",
"_tree.pxd",
"__init__.pxd",
"type.pxd",
"bool.pxd",
"complex.pxd",
"_utils.pxd",
};
#define IS_UNSIGNED(type) (((type) -1) > 0)
struct __Pyx_StructField_;
#define __PYX_BUF_FLAGS_PACKED_STRUCT (1 << 0)
typedef struct {
const char* name; /* for error messages only */
struct __Pyx_StructField_* fields;
size_t size; /* sizeof(type) */
size_t arraysize[8]; /* length of array in each dimension */
int ndim;
char typegroup; /* _R_eal, _C_omplex, Signed _I_nt, _U_nsigned int, _S_truct, _P_ointer, _O_bject, c_H_ar */
char is_unsigned;
int flags;
} __Pyx_TypeInfo;
typedef struct __Pyx_StructField_ {
__Pyx_TypeInfo* type;
const char* name;
size_t offset;
} __Pyx_StructField;
typedef struct {
__Pyx_StructField* field;
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__Pyx_StructField root;
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/* "/Users/ajoly/.virtualenvs/research/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":723
* # in Cython to enable them only on the right systems.
*
* ctypedef npy_int8 int8_t # <<<<<<<<<<<<<<
* ctypedef npy_int16 int16_t
* ctypedef npy_int32 int32_t
*/
typedef npy_int8 __pyx_t_5numpy_int8_t;
/* "/Users/ajoly/.virtualenvs/research/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":724
*
* ctypedef npy_int8 int8_t
* ctypedef npy_int16 int16_t # <<<<<<<<<<<<<<
* ctypedef npy_int32 int32_t
* ctypedef npy_int64 int64_t
*/
typedef npy_int16 __pyx_t_5numpy_int16_t;
/* "/Users/ajoly/.virtualenvs/research/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":725
* ctypedef npy_int8 int8_t
* ctypedef npy_int16 int16_t
* ctypedef npy_int32 int32_t # <<<<<<<<<<<<<<
* ctypedef npy_int64 int64_t
* #ctypedef npy_int96 int96_t
*/
typedef npy_int32 __pyx_t_5numpy_int32_t;
/* "/Users/ajoly/.virtualenvs/research/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":726
* ctypedef npy_int16 int16_t
* ctypedef npy_int32 int32_t
* ctypedef npy_int64 int64_t # <<<<<<<<<<<<<<
* #ctypedef npy_int96 int96_t
* #ctypedef npy_int128 int128_t
*/
typedef npy_int64 __pyx_t_5numpy_int64_t;
/* "/Users/ajoly/.virtualenvs/research/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":730
* #ctypedef npy_int128 int128_t
*
* ctypedef npy_uint8 uint8_t # <<<<<<<<<<<<<<
* ctypedef npy_uint16 uint16_t
* ctypedef npy_uint32 uint32_t
*/
typedef npy_uint8 __pyx_t_5numpy_uint8_t;
/* "/Users/ajoly/.virtualenvs/research/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":731
*
* ctypedef npy_uint8 uint8_t
* ctypedef npy_uint16 uint16_t # <<<<<<<<<<<<<<
* ctypedef npy_uint32 uint32_t
* ctypedef npy_uint64 uint64_t
*/
typedef npy_uint16 __pyx_t_5numpy_uint16_t;
/* "/Users/ajoly/.virtualenvs/research/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":732
* ctypedef npy_uint8 uint8_t
* ctypedef npy_uint16 uint16_t
* ctypedef npy_uint32 uint32_t # <<<<<<<<<<<<<<
* ctypedef npy_uint64 uint64_t
* #ctypedef npy_uint96 uint96_t
*/
typedef npy_uint32 __pyx_t_5numpy_uint32_t;
/* "/Users/ajoly/.virtualenvs/research/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":733
* ctypedef npy_uint16 uint16_t
* ctypedef npy_uint32 uint32_t
* ctypedef npy_uint64 uint64_t # <<<<<<<<<<<<<<
* #ctypedef npy_uint96 uint96_t
* #ctypedef npy_uint128 uint128_t
*/
typedef npy_uint64 __pyx_t_5numpy_uint64_t;
/* "/Users/ajoly/.virtualenvs/research/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":737
* #ctypedef npy_uint128 uint128_t
*
* ctypedef npy_float32 float32_t # <<<<<<<<<<<<<<
* ctypedef npy_float64 float64_t
* #ctypedef npy_float80 float80_t
*/
typedef npy_float32 __pyx_t_5numpy_float32_t;
/* "/Users/ajoly/.virtualenvs/research/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":738
*
* ctypedef npy_float32 float32_t
* ctypedef npy_float64 float64_t # <<<<<<<<<<<<<<
* #ctypedef npy_float80 float80_t
* #ctypedef npy_float128 float128_t
*/
typedef npy_float64 __pyx_t_5numpy_float64_t;
/* "/Users/ajoly/.virtualenvs/research/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":747
* # The int types are mapped a bit surprising --
* # numpy.int corresponds to 'l' and numpy.long to 'q'
* ctypedef npy_long int_t # <<<<<<<<<<<<<<
* ctypedef npy_longlong long_t
* ctypedef npy_longlong longlong_t
*/
typedef npy_long __pyx_t_5numpy_int_t;
/* "/Users/ajoly/.virtualenvs/research/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":748
* # numpy.int corresponds to 'l' and numpy.long to 'q'
* ctypedef npy_long int_t
* ctypedef npy_longlong long_t # <<<<<<<<<<<<<<
* ctypedef npy_longlong longlong_t
*
*/
typedef npy_longlong __pyx_t_5numpy_long_t;
/* "/Users/ajoly/.virtualenvs/research/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":749
* ctypedef npy_long int_t
* ctypedef npy_longlong long_t
* ctypedef npy_longlong longlong_t # <<<<<<<<<<<<<<
*
* ctypedef npy_ulong uint_t
*/
typedef npy_longlong __pyx_t_5numpy_longlong_t;
/* "/Users/ajoly/.virtualenvs/research/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":751
* ctypedef npy_longlong longlong_t
*
* ctypedef npy_ulong uint_t # <<<<<<<<<<<<<<
* ctypedef npy_ulonglong ulong_t
* ctypedef npy_ulonglong ulonglong_t
*/
typedef npy_ulong __pyx_t_5numpy_uint_t;
/* "/Users/ajoly/.virtualenvs/research/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":752
*
* ctypedef npy_ulong uint_t
* ctypedef npy_ulonglong ulong_t # <<<<<<<<<<<<<<
* ctypedef npy_ulonglong ulonglong_t
*
*/
typedef npy_ulonglong __pyx_t_5numpy_ulong_t;
/* "/Users/ajoly/.virtualenvs/research/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":753
* ctypedef npy_ulong uint_t
* ctypedef npy_ulonglong ulong_t
* ctypedef npy_ulonglong ulonglong_t # <<<<<<<<<<<<<<
*
* ctypedef npy_intp intp_t
*/
typedef npy_ulonglong __pyx_t_5numpy_ulonglong_t;
/* "/Users/ajoly/.virtualenvs/research/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":755
* ctypedef npy_ulonglong ulonglong_t
*
* ctypedef npy_intp intp_t # <<<<<<<<<<<<<<
* ctypedef npy_uintp uintp_t
*
*/
typedef npy_intp __pyx_t_5numpy_intp_t;
/* "/Users/ajoly/.virtualenvs/research/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":756
*
* ctypedef npy_intp intp_t
* ctypedef npy_uintp uintp_t # <<<<<<<<<<<<<<
*
* ctypedef npy_double float_t
*/
typedef npy_uintp __pyx_t_5numpy_uintp_t;
/* "/Users/ajoly/.virtualenvs/research/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":758
* ctypedef npy_uintp uintp_t
*
* ctypedef npy_double float_t # <<<<<<<<<<<<<<
* ctypedef npy_double double_t
* ctypedef npy_longdouble longdouble_t
*/
typedef npy_double __pyx_t_5numpy_float_t;
/* "/Users/ajoly/.virtualenvs/research/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":759
*
* ctypedef npy_double float_t
* ctypedef npy_double double_t # <<<<<<<<<<<<<<
* ctypedef npy_longdouble longdouble_t
*
*/
typedef npy_double __pyx_t_5numpy_double_t;
/* "/Users/ajoly/.virtualenvs/research/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":760
* ctypedef npy_double float_t
* ctypedef npy_double double_t
* ctypedef npy_longdouble longdouble_t # <<<<<<<<<<<<<<
*
* ctypedef npy_cfloat cfloat_t
*/
typedef npy_longdouble __pyx_t_5numpy_longdouble_t;
/* "sklearn/tree/_utils.pxd":12
* cimport numpy as np
*
* ctypedef np.npy_intp SIZE_t # Type for indices and counters # <<<<<<<<<<<<<<
*
*
*/
typedef npy_intp __pyx_t_7sklearn_4tree_6_utils_SIZE_t;
/* "sklearn/tree/_tree.pxd":14
* cimport numpy as np
*
* ctypedef np.npy_float32 DTYPE_t # Type of X # <<<<<<<<<<<<<<
* ctypedef np.npy_float64 DOUBLE_t # Type of y, sample_weight
* ctypedef np.npy_intp SIZE_t # Type for indices and counters
*/
typedef npy_float32 __pyx_t_7sklearn_4tree_5_tree_DTYPE_t;
/* "sklearn/tree/_tree.pxd":15
*
* ctypedef np.npy_float32 DTYPE_t # Type of X
* ctypedef np.npy_float64 DOUBLE_t # Type of y, sample_weight # <<<<<<<<<<<<<<
* ctypedef np.npy_intp SIZE_t # Type for indices and counters
* ctypedef np.npy_int32 INT32_t # Signed 32 bit integer
*/
typedef npy_float64 __pyx_t_7sklearn_4tree_5_tree_DOUBLE_t;
/* "sklearn/tree/_tree.pxd":16
* ctypedef np.npy_float32 DTYPE_t # Type of X
* ctypedef np.npy_float64 DOUBLE_t # Type of y, sample_weight
* ctypedef np.npy_intp SIZE_t # Type for indices and counters # <<<<<<<<<<<<<<
* ctypedef np.npy_int32 INT32_t # Signed 32 bit integer
* ctypedef np.npy_uint32 UINT32_t # Unsigned 32 bit integer
*/
typedef npy_intp __pyx_t_7sklearn_4tree_5_tree_SIZE_t;
/* "sklearn/tree/_tree.pxd":17
* ctypedef np.npy_float64 DOUBLE_t # Type of y, sample_weight
* ctypedef np.npy_intp SIZE_t # Type for indices and counters
* ctypedef np.npy_int32 INT32_t # Signed 32 bit integer # <<<<<<<<<<<<<<
* ctypedef np.npy_uint32 UINT32_t # Unsigned 32 bit integer
*
*/
typedef npy_int32 __pyx_t_7sklearn_4tree_5_tree_INT32_t;
/* "sklearn/tree/_tree.pxd":18
* ctypedef np.npy_intp SIZE_t # Type for indices and counters
* ctypedef np.npy_int32 INT32_t # Signed 32 bit integer
* ctypedef np.npy_uint32 UINT32_t # Unsigned 32 bit integer # <<<<<<<<<<<<<<
*
*
*/
typedef npy_uint32 __pyx_t_7sklearn_4tree_5_tree_UINT32_t;
#if CYTHON_CCOMPLEX
#ifdef __cplusplus
typedef ::std::complex< float > __pyx_t_float_complex;
#else
typedef float _Complex __pyx_t_float_complex;
#endif
#else
typedef struct { float real, imag; } __pyx_t_float_complex;
#endif
#if CYTHON_CCOMPLEX
#ifdef __cplusplus
typedef ::std::complex< double > __pyx_t_double_complex;
#else
typedef double _Complex __pyx_t_double_complex;
#endif
#else
typedef struct { double real, imag; } __pyx_t_double_complex;
#endif
/*--- Type declarations ---*/
struct __pyx_obj_7sklearn_4tree_6_utils_Stack;
struct __pyx_obj_7sklearn_4tree_6_utils_PriorityHeap;
struct __pyx_obj_7sklearn_4tree_5_tree_Criterion;
struct __pyx_obj_7sklearn_4tree_5_tree_Splitter;
struct __pyx_obj_7sklearn_4tree_5_tree_Tree;
struct __pyx_obj_7sklearn_4tree_5_tree_TreeBuilder;
struct __pyx_obj_7sklearn_4tree_5_tree_ClassificationCriterion;
struct __pyx_obj_7sklearn_4tree_5_tree_Entropy;
struct __pyx_obj_7sklearn_4tree_5_tree_Gini;
struct __pyx_obj_7sklearn_4tree_5_tree_RegressionCriterion;
struct __pyx_obj_7sklearn_4tree_5_tree_MSE;
struct __pyx_obj_7sklearn_4tree_5_tree_FriedmanMSE;
struct __pyx_obj_7sklearn_4tree_5_tree_BaseDenseSplitter;
struct __pyx_obj_7sklearn_4tree_5_tree_BestSplitter;
struct __pyx_obj_7sklearn_4tree_5_tree_RandomSplitter;
struct __pyx_obj_7sklearn_4tree_5_tree_PresortBestSplitter;
struct __pyx_obj_7sklearn_4tree_5_tree_BaseSparseSplitter;
struct __pyx_obj_7sklearn_4tree_5_tree_BestSparseSplitter;
struct __pyx_obj_7sklearn_4tree_5_tree_RandomSparseSplitter;
struct __pyx_obj_7sklearn_4tree_5_tree_DepthFirstTreeBuilder;
struct __pyx_obj_7sklearn_4tree_5_tree_BestFirstTreeBuilder;
/* "/Users/ajoly/.virtualenvs/research/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":762
* ctypedef npy_longdouble longdouble_t
*
* ctypedef npy_cfloat cfloat_t # <<<<<<<<<<<<<<
* ctypedef npy_cdouble cdouble_t
* ctypedef npy_clongdouble clongdouble_t
*/
typedef npy_cfloat __pyx_t_5numpy_cfloat_t;
/* "/Users/ajoly/.virtualenvs/research/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":763
*
* ctypedef npy_cfloat cfloat_t
* ctypedef npy_cdouble cdouble_t # <<<<<<<<<<<<<<
* ctypedef npy_clongdouble clongdouble_t
*
*/
typedef npy_cdouble __pyx_t_5numpy_cdouble_t;
/* "/Users/ajoly/.virtualenvs/research/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":764
* ctypedef npy_cfloat cfloat_t
* ctypedef npy_cdouble cdouble_t
* ctypedef npy_clongdouble clongdouble_t # <<<<<<<<<<<<<<
*
* ctypedef npy_cdouble complex_t
*/
typedef npy_clongdouble __pyx_t_5numpy_clongdouble_t;
/* "/Users/ajoly/.virtualenvs/research/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":766
* ctypedef npy_clongdouble clongdouble_t
*
* ctypedef npy_cdouble complex_t # <<<<<<<<<<<<<<
*
* cdef inline object PyArray_MultiIterNew1(a):
*/
typedef npy_cdouble __pyx_t_5numpy_complex_t;
struct __pyx_t_7sklearn_4tree_6_utils_StackRecord;
struct __pyx_t_7sklearn_4tree_6_utils_PriorityHeapRecord;
/* "sklearn/tree/_utils.pxd":20
*
* # A record on the stack for depth-first tree growing
* cdef struct StackRecord: # <<<<<<<<<<<<<<
* SIZE_t start
* SIZE_t end
*/
struct __pyx_t_7sklearn_4tree_6_utils_StackRecord {
__pyx_t_7sklearn_4tree_6_utils_SIZE_t start;
__pyx_t_7sklearn_4tree_6_utils_SIZE_t end;
__pyx_t_7sklearn_4tree_6_utils_SIZE_t depth;
__pyx_t_7sklearn_4tree_6_utils_SIZE_t parent;
int is_left;
double impurity;
__pyx_t_7sklearn_4tree_6_utils_SIZE_t n_constant_features;
};
/* "sklearn/tree/_utils.pxd":46
*
* # A record on the frontier for best-first tree growing
* cdef struct PriorityHeapRecord: # <<<<<<<<<<<<<<
* SIZE_t node_id
* SIZE_t start
*/
struct __pyx_t_7sklearn_4tree_6_utils_PriorityHeapRecord {
__pyx_t_7sklearn_4tree_6_utils_SIZE_t node_id;
__pyx_t_7sklearn_4tree_6_utils_SIZE_t start;
__pyx_t_7sklearn_4tree_6_utils_SIZE_t end;
__pyx_t_7sklearn_4tree_6_utils_SIZE_t pos;
__pyx_t_7sklearn_4tree_6_utils_SIZE_t depth;
int is_leaf;
double impurity;
double impurity_left;
double impurity_right;
double improvement;
};
struct __pyx_t_7sklearn_4tree_5_tree_SplitRecord;
struct __pyx_t_7sklearn_4tree_5_tree_Node;
struct __pyx_opt_args_7sklearn_4tree_5_tree_4Tree__resize_c;
struct __pyx_opt_args_7sklearn_4tree_5_tree_4Tree_compute_feature_importances;
struct __pyx_opt_args_7sklearn_4tree_5_tree_11TreeBuilder_build;
struct __pyx_opt_args_7sklearn_4tree_5_tree_21DepthFirstTreeBuilder_build;
struct __pyx_opt_args_7sklearn_4tree_5_tree_20BestFirstTreeBuilder_build;
/* "sklearn/tree/_tree.pxd":67
* # =============================================================================
*
* cdef struct SplitRecord: # <<<<<<<<<<<<<<
* # Data to track sample split
* SIZE_t feature # Which feature to split on.
*/
struct __pyx_t_7sklearn_4tree_5_tree_SplitRecord {
__pyx_t_7sklearn_4tree_5_tree_SIZE_t feature;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t pos;
double threshold;
double improvement;
double impurity_left;
double impurity_right;
};
/* "sklearn/tree/_tree.pxd":145
* # =============================================================================
*
* cdef struct Node: # <<<<<<<<<<<<<<
* # Base storage structure for the nodes in a Tree object
*
*/
struct __pyx_t_7sklearn_4tree_5_tree_Node {
__pyx_t_7sklearn_4tree_5_tree_SIZE_t left_child;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t right_child;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t feature;
__pyx_t_7sklearn_4tree_5_tree_DOUBLE_t threshold;
__pyx_t_7sklearn_4tree_5_tree_DOUBLE_t impurity;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t n_node_samples;
__pyx_t_7sklearn_4tree_5_tree_DOUBLE_t weighted_n_node_samples;
};
/* "sklearn/tree/_tree.pxd":183
* double weighted_n_samples) nogil
* cdef void _resize(self, SIZE_t capacity) except *
* cdef int _resize_c(self, SIZE_t capacity=*) nogil # <<<<<<<<<<<<<<
*
* cdef np.ndarray _get_value_ndarray(self)
*/
struct __pyx_opt_args_7sklearn_4tree_5_tree_4Tree__resize_c {
int __pyx_n;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t capacity;
};
/* "sklearn/tree/_tree.pxd":193
* cdef np.ndarray _apply_sparse_csr(self, object X)
*
* cpdef compute_feature_importances(self, normalize=*) # <<<<<<<<<<<<<<
*
*
*/
struct __pyx_opt_args_7sklearn_4tree_5_tree_4Tree_compute_feature_importances {
int __pyx_n;
PyObject *normalize;
};
/* "sklearn/tree/_tree.pxd":215
* cdef SIZE_t max_depth # Maximal tree depth
*
* cpdef build(self, Tree tree, object X, np.ndarray y, # <<<<<<<<<<<<<<
* np.ndarray sample_weight=*)
* cdef _check_input(self, object X, np.ndarray y, np.ndarray sample_weight)
*/
struct __pyx_opt_args_7sklearn_4tree_5_tree_11TreeBuilder_build {
int __pyx_n;
PyArrayObject *sample_weight;
};
/* "sklearn/tree/_tree.pyx":69
* cdef int IS_NOT_LEFT = 0
*
* cdef enum: # <<<<<<<<<<<<<<
* # Max value for our rand_r replacement (near the bottom).
* # We don't use RAND_MAX because it's different across platforms and
*/
enum {
__pyx_e_7sklearn_4tree_5_tree_RAND_R_MAX = 0x7FFFFFFF
};
/* "sklearn/tree/_tree.pyx":2617
* self.max_depth = max_depth
*
* cpdef build(self, Tree tree, object X, np.ndarray y, # <<<<<<<<<<<<<<
* np.ndarray sample_weight=None):
* """Build a decision tree from the training set (X, y)."""
*/
struct __pyx_opt_args_7sklearn_4tree_5_tree_21DepthFirstTreeBuilder_build {
int __pyx_n;
PyArrayObject *sample_weight;
};
/* "sklearn/tree/_tree.pyx":2771
* self.max_leaf_nodes = max_leaf_nodes
*
* cpdef build(self, Tree tree, object X, np.ndarray y, # <<<<<<<<<<<<<<
* np.ndarray sample_weight=None):
* """Build a decision tree from the training set (X, y)."""
*/
struct __pyx_opt_args_7sklearn_4tree_5_tree_20BestFirstTreeBuilder_build {
int __pyx_n;
PyArrayObject *sample_weight;
};
/* "sklearn/tree/_utils.pxd":29
* SIZE_t n_constant_features
*
* cdef class Stack: # <<<<<<<<<<<<<<
* cdef SIZE_t capacity
* cdef SIZE_t top
*/
struct __pyx_obj_7sklearn_4tree_6_utils_Stack {
PyObject_HEAD
struct __pyx_vtabstruct_7sklearn_4tree_6_utils_Stack *__pyx_vtab;
__pyx_t_7sklearn_4tree_6_utils_SIZE_t capacity;
__pyx_t_7sklearn_4tree_6_utils_SIZE_t top;
struct __pyx_t_7sklearn_4tree_6_utils_StackRecord *stack_;
};
/* "sklearn/tree/_utils.pxd":58
* double improvement
*
* cdef class PriorityHeap: # <<<<<<<<<<<<<<
* cdef SIZE_t capacity
* cdef SIZE_t heap_ptr
*/
struct __pyx_obj_7sklearn_4tree_6_utils_PriorityHeap {
PyObject_HEAD
struct __pyx_vtabstruct_7sklearn_4tree_6_utils_PriorityHeap *__pyx_vtab;
__pyx_t_7sklearn_4tree_6_utils_SIZE_t capacity;
__pyx_t_7sklearn_4tree_6_utils_SIZE_t heap_ptr;
struct __pyx_t_7sklearn_4tree_6_utils_PriorityHeapRecord *heap_;
};
/* "sklearn/tree/_tree.pxd":25
* # =============================================================================
*
* cdef class Criterion: # <<<<<<<<<<<<<<
* # The criterion computes the impurity of a node and the reduction of
* # impurity of a split on that node. It also computes the output statistics
*/
struct __pyx_obj_7sklearn_4tree_5_tree_Criterion {
PyObject_HEAD
struct __pyx_vtabstruct_7sklearn_4tree_5_tree_Criterion *__pyx_vtab;
__pyx_t_7sklearn_4tree_5_tree_DOUBLE_t *y;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t y_stride;
__pyx_t_7sklearn_4tree_5_tree_DOUBLE_t *sample_weight;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t *samples;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t start;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t pos;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t end;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t n_outputs;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t n_node_samples;
double weighted_n_samples;
double weighted_n_node_samples;
double weighted_n_left;
double weighted_n_right;
};
/* "sklearn/tree/_tree.pxd":79
*
*
* cdef class Splitter: # <<<<<<<<<<<<<<
* # The splitter searches in the input space for a feature and a threshold
* # to split the samples samples[start:end].
*/
struct __pyx_obj_7sklearn_4tree_5_tree_Splitter {
PyObject_HEAD
struct __pyx_vtabstruct_7sklearn_4tree_5_tree_Splitter *__pyx_vtab;
struct __pyx_obj_7sklearn_4tree_5_tree_Criterion *criterion;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t max_features;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t min_samples_leaf;
double min_weight_leaf;
PyObject *random_state;
__pyx_t_7sklearn_4tree_5_tree_UINT32_t rand_r_state;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t *samples;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t n_samples;
double weighted_n_samples;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t *features;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t *constant_features;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t n_features;
__pyx_t_7sklearn_4tree_5_tree_DTYPE_t *feature_values;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t start;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t end;
__pyx_t_7sklearn_4tree_5_tree_DOUBLE_t *y;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t y_stride;
__pyx_t_7sklearn_4tree_5_tree_DOUBLE_t *sample_weight;
};
/* "sklearn/tree/_tree.pxd":157
*
*
* cdef class Tree: # <<<<<<<<<<<<<<
* # The Tree object is a binary tree structure constructed by the
* # TreeBuilder. The tree structure is used for predictions and
*/
struct __pyx_obj_7sklearn_4tree_5_tree_Tree {
PyObject_HEAD
struct __pyx_vtabstruct_7sklearn_4tree_5_tree_Tree *__pyx_vtab;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t n_features;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t *n_classes;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t n_outputs;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t max_n_classes;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t max_depth;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t node_count;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t capacity;
struct __pyx_t_7sklearn_4tree_5_tree_Node *nodes;
double *value;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t value_stride;
};
/* "sklearn/tree/_tree.pxd":200
* # =============================================================================
*
* cdef class TreeBuilder: # <<<<<<<<<<<<<<
* # The TreeBuilder recursively builds a Tree object from training samples,
* # using a Splitter object for splitting internal nodes and assigning
*/
struct __pyx_obj_7sklearn_4tree_5_tree_TreeBuilder {
PyObject_HEAD
struct __pyx_vtabstruct_7sklearn_4tree_5_tree_TreeBuilder *__pyx_vtab;
struct __pyx_obj_7sklearn_4tree_5_tree_Splitter *splitter;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t min_samples_split;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t min_samples_leaf;
double min_weight_leaf;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t max_depth;
};
/* "sklearn/tree/_tree.pyx":150
*
*
* cdef class ClassificationCriterion(Criterion): # <<<<<<<<<<<<<<
* """Abstract criterion for classification."""
* cdef SIZE_t* n_classes
*/
struct __pyx_obj_7sklearn_4tree_5_tree_ClassificationCriterion {
struct __pyx_obj_7sklearn_4tree_5_tree_Criterion __pyx_base;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t *n_classes;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t label_count_stride;
double *label_count_left;
double *label_count_right;
double *label_count_total;
};
/* "sklearn/tree/_tree.pyx":368
*
*
* cdef class Entropy(ClassificationCriterion): # <<<<<<<<<<<<<<
* """Cross Entropy impurity criteria.
*
*/
struct __pyx_obj_7sklearn_4tree_5_tree_Entropy {
struct __pyx_obj_7sklearn_4tree_5_tree_ClassificationCriterion __pyx_base;
};
/* "sklearn/tree/_tree.pyx":459
*
*
* cdef class Gini(ClassificationCriterion): # <<<<<<<<<<<<<<
* """Gini Index impurity criteria.
*
*/
struct __pyx_obj_7sklearn_4tree_5_tree_Gini {
struct __pyx_obj_7sklearn_4tree_5_tree_ClassificationCriterion __pyx_base;
};
/* "sklearn/tree/_tree.pyx":553
*
*
* cdef class RegressionCriterion(Criterion): # <<<<<<<<<<<<<<
* """Abstract criterion for regression.
*
*/
struct __pyx_obj_7sklearn_4tree_5_tree_RegressionCriterion {
struct __pyx_obj_7sklearn_4tree_5_tree_Criterion __pyx_base;
double *mean_left;
double *mean_right;
double *mean_total;
double *sq_sum_left;
double *sq_sum_right;
double *sq_sum_total;
double *var_left;
double *var_right;
double *sum_left;
double *sum_right;
double *sum_total;
};
/* "sklearn/tree/_tree.pyx":836
*
*
* cdef class MSE(RegressionCriterion): # <<<<<<<<<<<<<<
* """Mean squared error impurity criterion.
*
*/
struct __pyx_obj_7sklearn_4tree_5_tree_MSE {
struct __pyx_obj_7sklearn_4tree_5_tree_RegressionCriterion __pyx_base;
};
/* "sklearn/tree/_tree.pyx":877
*
*
* cdef class FriedmanMSE(MSE): # <<<<<<<<<<<<<<
* """Mean squared error impurity criterion with improvement score by Friedman
*
*/
struct __pyx_obj_7sklearn_4tree_5_tree_FriedmanMSE {
struct __pyx_obj_7sklearn_4tree_5_tree_MSE __pyx_base;
};
/* "sklearn/tree/_tree.pyx":1031
*
*
* cdef class BaseDenseSplitter(Splitter): # <<<<<<<<<<<<<<
* cdef DTYPE_t* X
* cdef SIZE_t X_sample_stride
*/
struct __pyx_obj_7sklearn_4tree_5_tree_BaseDenseSplitter {
struct __pyx_obj_7sklearn_4tree_5_tree_Splitter __pyx_base;
__pyx_t_7sklearn_4tree_5_tree_DTYPE_t *X;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t X_sample_stride;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t X_fx_stride;
};
/* "sklearn/tree/_tree.pyx":1062
*
*
* cdef class BestSplitter(BaseDenseSplitter): # <<<<<<<<<<<<<<
* """Splitter for finding the best split."""
* def __reduce__(self):
*/
struct __pyx_obj_7sklearn_4tree_5_tree_BestSplitter {
struct __pyx_obj_7sklearn_4tree_5_tree_BaseDenseSplitter __pyx_base;
};
/* "sklearn/tree/_tree.pyx":1361
*
*
* cdef class RandomSplitter(BaseDenseSplitter): # <<<<<<<<<<<<<<
* """Splitter for finding the best random split."""
* def __reduce__(self):
*/
struct __pyx_obj_7sklearn_4tree_5_tree_RandomSplitter {
struct __pyx_obj_7sklearn_4tree_5_tree_BaseDenseSplitter __pyx_base;
};
/* "sklearn/tree/_tree.pyx":1563
*
*
* cdef class PresortBestSplitter(BaseDenseSplitter): # <<<<<<<<<<<<<<
* """Splitter for finding the best split, using presorting."""
* cdef DTYPE_t* X_old
*/
struct __pyx_obj_7sklearn_4tree_5_tree_PresortBestSplitter {
struct __pyx_obj_7sklearn_4tree_5_tree_BaseDenseSplitter __pyx_base;
__pyx_t_7sklearn_4tree_5_tree_DTYPE_t *X_old;
PyArrayObject *X_argsorted;
__pyx_t_7sklearn_4tree_5_tree_INT32_t *X_argsorted_ptr;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t X_argsorted_stride;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t n_total_samples;
unsigned char *sample_mask;
};
/* "sklearn/tree/_tree.pyx":1811
*
*
* cdef class BaseSparseSplitter(Splitter): # <<<<<<<<<<<<<<
* # The sparse splitter works only with csc sparse matrix format
* cdef DTYPE_t* X_data
*/
struct __pyx_obj_7sklearn_4tree_5_tree_BaseSparseSplitter {
struct __pyx_obj_7sklearn_4tree_5_tree_Splitter __pyx_base;
__pyx_t_7sklearn_4tree_5_tree_DTYPE_t *X_data;
__pyx_t_7sklearn_4tree_5_tree_INT32_t *X_indices;
__pyx_t_7sklearn_4tree_5_tree_INT32_t *X_indptr;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t n_total_samples;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t *index_to_samples;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t *sorted_samples;
};
/* "sklearn/tree/_tree.pyx":2130
*
*
* cdef class BestSparseSplitter(BaseSparseSplitter): # <<<<<<<<<<<<<<
* """Splitter for finding the best split, using the sparse data."""
*
*/
struct __pyx_obj_7sklearn_4tree_5_tree_BestSparseSplitter {
struct __pyx_obj_7sklearn_4tree_5_tree_BaseSparseSplitter __pyx_base;
};
/* "sklearn/tree/_tree.pyx":2348
*
*
* cdef class RandomSparseSplitter(BaseSparseSplitter): # <<<<<<<<<<<<<<
* """Splitter for finding a random split, using the sparse data."""
*
*/
struct __pyx_obj_7sklearn_4tree_5_tree_RandomSparseSplitter {
struct __pyx_obj_7sklearn_4tree_5_tree_BaseSparseSplitter __pyx_base;
};
/* "sklearn/tree/_tree.pyx":2605
* # Depth first builder ---------------------------------------------------------
*
* cdef class DepthFirstTreeBuilder(TreeBuilder): # <<<<<<<<<<<<<<
* """Build a decision tree in depth-first fashion."""
*
*/
struct __pyx_obj_7sklearn_4tree_5_tree_DepthFirstTreeBuilder {
struct __pyx_obj_7sklearn_4tree_5_tree_TreeBuilder __pyx_base;
};
/* "sklearn/tree/_tree.pyx":2751
*
*
* cdef class BestFirstTreeBuilder(TreeBuilder): # <<<<<<<<<<<<<<
* """Build a decision tree in best-first fashion.
*
*/
struct __pyx_obj_7sklearn_4tree_5_tree_BestFirstTreeBuilder {
struct __pyx_obj_7sklearn_4tree_5_tree_TreeBuilder __pyx_base;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t max_leaf_nodes;
};
/* "sklearn/tree/_utils.pxd":29
* SIZE_t n_constant_features
*
* cdef class Stack: # <<<<<<<<<<<<<<
* cdef SIZE_t capacity
* cdef SIZE_t top
*/
struct __pyx_vtabstruct_7sklearn_4tree_6_utils_Stack {
int (*is_empty)(struct __pyx_obj_7sklearn_4tree_6_utils_Stack *);
int (*push)(struct __pyx_obj_7sklearn_4tree_6_utils_Stack *, __pyx_t_7sklearn_4tree_6_utils_SIZE_t, __pyx_t_7sklearn_4tree_6_utils_SIZE_t, __pyx_t_7sklearn_4tree_6_utils_SIZE_t, __pyx_t_7sklearn_4tree_6_utils_SIZE_t, int, double, __pyx_t_7sklearn_4tree_6_utils_SIZE_t);
int (*pop)(struct __pyx_obj_7sklearn_4tree_6_utils_Stack *, struct __pyx_t_7sklearn_4tree_6_utils_StackRecord *);
};
static struct __pyx_vtabstruct_7sklearn_4tree_6_utils_Stack *__pyx_vtabptr_7sklearn_4tree_6_utils_Stack;
/* "sklearn/tree/_utils.pxd":58
* double improvement
*
* cdef class PriorityHeap: # <<<<<<<<<<<<<<
* cdef SIZE_t capacity
* cdef SIZE_t heap_ptr
*/
struct __pyx_vtabstruct_7sklearn_4tree_6_utils_PriorityHeap {
int (*is_empty)(struct __pyx_obj_7sklearn_4tree_6_utils_PriorityHeap *);
int (*push)(struct __pyx_obj_7sklearn_4tree_6_utils_PriorityHeap *, __pyx_t_7sklearn_4tree_6_utils_SIZE_t, __pyx_t_7sklearn_4tree_6_utils_SIZE_t, __pyx_t_7sklearn_4tree_6_utils_SIZE_t, __pyx_t_7sklearn_4tree_6_utils_SIZE_t, __pyx_t_7sklearn_4tree_6_utils_SIZE_t, int, double, double, double, double);
int (*pop)(struct __pyx_obj_7sklearn_4tree_6_utils_PriorityHeap *, struct __pyx_t_7sklearn_4tree_6_utils_PriorityHeapRecord *);
};
static struct __pyx_vtabstruct_7sklearn_4tree_6_utils_PriorityHeap *__pyx_vtabptr_7sklearn_4tree_6_utils_PriorityHeap;
/* "sklearn/tree/_tree.pyx":97
* # =============================================================================
*
* cdef class Criterion: # <<<<<<<<<<<<<<
* """Interface for impurity criteria."""
*
*/
struct __pyx_vtabstruct_7sklearn_4tree_5_tree_Criterion {
void (*init)(struct __pyx_obj_7sklearn_4tree_5_tree_Criterion *, __pyx_t_7sklearn_4tree_5_tree_DOUBLE_t *, __pyx_t_7sklearn_4tree_5_tree_SIZE_t, __pyx_t_7sklearn_4tree_5_tree_DOUBLE_t *, double, __pyx_t_7sklearn_4tree_5_tree_SIZE_t *, __pyx_t_7sklearn_4tree_5_tree_SIZE_t, __pyx_t_7sklearn_4tree_5_tree_SIZE_t);
void (*reset)(struct __pyx_obj_7sklearn_4tree_5_tree_Criterion *);
void (*update)(struct __pyx_obj_7sklearn_4tree_5_tree_Criterion *, __pyx_t_7sklearn_4tree_5_tree_SIZE_t);
double (*node_impurity)(struct __pyx_obj_7sklearn_4tree_5_tree_Criterion *);
void (*children_impurity)(struct __pyx_obj_7sklearn_4tree_5_tree_Criterion *, double *, double *);
void (*node_value)(struct __pyx_obj_7sklearn_4tree_5_tree_Criterion *, double *);
double (*impurity_improvement)(struct __pyx_obj_7sklearn_4tree_5_tree_Criterion *, double);
};
static struct __pyx_vtabstruct_7sklearn_4tree_5_tree_Criterion *__pyx_vtabptr_7sklearn_4tree_5_tree_Criterion;
/* "sklearn/tree/_tree.pyx":922
*
*
* cdef class Splitter: # <<<<<<<<<<<<<<
* def __cinit__(self, Criterion criterion, SIZE_t max_features,
* SIZE_t min_samples_leaf, double min_weight_leaf,
*/
struct __pyx_vtabstruct_7sklearn_4tree_5_tree_Splitter {
void (*init)(struct __pyx_obj_7sklearn_4tree_5_tree_Splitter *, PyObject *, PyArrayObject *, __pyx_t_7sklearn_4tree_5_tree_DOUBLE_t *);
void (*node_reset)(struct __pyx_obj_7sklearn_4tree_5_tree_Splitter *, __pyx_t_7sklearn_4tree_5_tree_SIZE_t, __pyx_t_7sklearn_4tree_5_tree_SIZE_t, double *);
void (*node_split)(struct __pyx_obj_7sklearn_4tree_5_tree_Splitter *, double, struct __pyx_t_7sklearn_4tree_5_tree_SplitRecord *, __pyx_t_7sklearn_4tree_5_tree_SIZE_t *);
void (*node_value)(struct __pyx_obj_7sklearn_4tree_5_tree_Splitter *, double *);
double (*node_impurity)(struct __pyx_obj_7sklearn_4tree_5_tree_Splitter *);
};
static struct __pyx_vtabstruct_7sklearn_4tree_5_tree_Splitter *__pyx_vtabptr_7sklearn_4tree_5_tree_Splitter;
/* "sklearn/tree/_tree.pyx":2955
* # =============================================================================
*
* cdef class Tree: # <<<<<<<<<<<<<<
* """Array-based representation of a binary decision tree.
*
*/
struct __pyx_vtabstruct_7sklearn_4tree_5_tree_Tree {
__pyx_t_7sklearn_4tree_5_tree_SIZE_t (*_add_node)(struct __pyx_obj_7sklearn_4tree_5_tree_Tree *, __pyx_t_7sklearn_4tree_5_tree_SIZE_t, int, int, __pyx_t_7sklearn_4tree_5_tree_SIZE_t, double, double, __pyx_t_7sklearn_4tree_5_tree_SIZE_t, double);
void (*_resize)(struct __pyx_obj_7sklearn_4tree_5_tree_Tree *, __pyx_t_7sklearn_4tree_5_tree_SIZE_t);
int (*_resize_c)(struct __pyx_obj_7sklearn_4tree_5_tree_Tree *, struct __pyx_opt_args_7sklearn_4tree_5_tree_4Tree__resize_c *__pyx_optional_args);
PyArrayObject *(*_get_value_ndarray)(struct __pyx_obj_7sklearn_4tree_5_tree_Tree *);
PyArrayObject *(*_get_node_ndarray)(struct __pyx_obj_7sklearn_4tree_5_tree_Tree *);
PyArrayObject *(*predict)(struct __pyx_obj_7sklearn_4tree_5_tree_Tree *, PyObject *, int __pyx_skip_dispatch);
PyArrayObject *(*apply)(struct __pyx_obj_7sklearn_4tree_5_tree_Tree *, PyObject *, int __pyx_skip_dispatch);
PyArrayObject *(*_apply_dense)(struct __pyx_obj_7sklearn_4tree_5_tree_Tree *, PyObject *);
PyArrayObject *(*_apply_sparse_csr)(struct __pyx_obj_7sklearn_4tree_5_tree_Tree *, PyObject *);
PyObject *(*compute_feature_importances)(struct __pyx_obj_7sklearn_4tree_5_tree_Tree *, int __pyx_skip_dispatch, struct __pyx_opt_args_7sklearn_4tree_5_tree_4Tree_compute_feature_importances *__pyx_optional_args);
};
static struct __pyx_vtabstruct_7sklearn_4tree_5_tree_Tree *__pyx_vtabptr_7sklearn_4tree_5_tree_Tree;
static PyArrayObject *__pyx_f_7sklearn_4tree_5_tree_4Tree__apply_dense(struct __pyx_obj_7sklearn_4tree_5_tree_Tree *, PyObject *);
static PyArrayObject *__pyx_f_7sklearn_4tree_5_tree_4Tree__apply_sparse_csr(struct __pyx_obj_7sklearn_4tree_5_tree_Tree *, PyObject *);
/* "sklearn/tree/_tree.pyx":2566
* # Tree builders
* # =============================================================================
* cdef class TreeBuilder: # <<<<<<<<<<<<<<
* """Interface for different tree building strategies. """
*
*/
struct __pyx_vtabstruct_7sklearn_4tree_5_tree_TreeBuilder {
PyObject *(*build)(struct __pyx_obj_7sklearn_4tree_5_tree_TreeBuilder *, struct __pyx_obj_7sklearn_4tree_5_tree_Tree *, PyObject *, PyArrayObject *, int __pyx_skip_dispatch, struct __pyx_opt_args_7sklearn_4tree_5_tree_11TreeBuilder_build *__pyx_optional_args);
PyObject *(*_check_input)(struct __pyx_obj_7sklearn_4tree_5_tree_TreeBuilder *, PyObject *, PyArrayObject *, PyArrayObject *);
};
static struct __pyx_vtabstruct_7sklearn_4tree_5_tree_TreeBuilder *__pyx_vtabptr_7sklearn_4tree_5_tree_TreeBuilder;
static PyObject *__pyx_f_7sklearn_4tree_5_tree_11TreeBuilder__check_input(struct __pyx_obj_7sklearn_4tree_5_tree_TreeBuilder *, PyObject *, PyArrayObject *, PyArrayObject *);
/* "sklearn/tree/_tree.pyx":150
*
*
* cdef class ClassificationCriterion(Criterion): # <<<<<<<<<<<<<<
* """Abstract criterion for classification."""
* cdef SIZE_t* n_classes
*/
struct __pyx_vtabstruct_7sklearn_4tree_5_tree_ClassificationCriterion {
struct __pyx_vtabstruct_7sklearn_4tree_5_tree_Criterion __pyx_base;
};
static struct __pyx_vtabstruct_7sklearn_4tree_5_tree_ClassificationCriterion *__pyx_vtabptr_7sklearn_4tree_5_tree_ClassificationCriterion;
/* "sklearn/tree/_tree.pyx":368
*
*
* cdef class Entropy(ClassificationCriterion): # <<<<<<<<<<<<<<
* """Cross Entropy impurity criteria.
*
*/
struct __pyx_vtabstruct_7sklearn_4tree_5_tree_Entropy {
struct __pyx_vtabstruct_7sklearn_4tree_5_tree_ClassificationCriterion __pyx_base;
};
static struct __pyx_vtabstruct_7sklearn_4tree_5_tree_Entropy *__pyx_vtabptr_7sklearn_4tree_5_tree_Entropy;
/* "sklearn/tree/_tree.pyx":459
*
*
* cdef class Gini(ClassificationCriterion): # <<<<<<<<<<<<<<
* """Gini Index impurity criteria.
*
*/
struct __pyx_vtabstruct_7sklearn_4tree_5_tree_Gini {
struct __pyx_vtabstruct_7sklearn_4tree_5_tree_ClassificationCriterion __pyx_base;
};
static struct __pyx_vtabstruct_7sklearn_4tree_5_tree_Gini *__pyx_vtabptr_7sklearn_4tree_5_tree_Gini;
/* "sklearn/tree/_tree.pyx":553
*
*
* cdef class RegressionCriterion(Criterion): # <<<<<<<<<<<<<<
* """Abstract criterion for regression.
*
*/
struct __pyx_vtabstruct_7sklearn_4tree_5_tree_RegressionCriterion {
struct __pyx_vtabstruct_7sklearn_4tree_5_tree_Criterion __pyx_base;
};
static struct __pyx_vtabstruct_7sklearn_4tree_5_tree_RegressionCriterion *__pyx_vtabptr_7sklearn_4tree_5_tree_RegressionCriterion;
/* "sklearn/tree/_tree.pyx":836
*
*
* cdef class MSE(RegressionCriterion): # <<<<<<<<<<<<<<
* """Mean squared error impurity criterion.
*
*/
struct __pyx_vtabstruct_7sklearn_4tree_5_tree_MSE {
struct __pyx_vtabstruct_7sklearn_4tree_5_tree_RegressionCriterion __pyx_base;
};
static struct __pyx_vtabstruct_7sklearn_4tree_5_tree_MSE *__pyx_vtabptr_7sklearn_4tree_5_tree_MSE;
/* "sklearn/tree/_tree.pyx":877
*
*
* cdef class FriedmanMSE(MSE): # <<<<<<<<<<<<<<
* """Mean squared error impurity criterion with improvement score by Friedman
*
*/
struct __pyx_vtabstruct_7sklearn_4tree_5_tree_FriedmanMSE {
struct __pyx_vtabstruct_7sklearn_4tree_5_tree_MSE __pyx_base;
};
static struct __pyx_vtabstruct_7sklearn_4tree_5_tree_FriedmanMSE *__pyx_vtabptr_7sklearn_4tree_5_tree_FriedmanMSE;
/* "sklearn/tree/_tree.pyx":1031
*
*
* cdef class BaseDenseSplitter(Splitter): # <<<<<<<<<<<<<<
* cdef DTYPE_t* X
* cdef SIZE_t X_sample_stride
*/
struct __pyx_vtabstruct_7sklearn_4tree_5_tree_BaseDenseSplitter {
struct __pyx_vtabstruct_7sklearn_4tree_5_tree_Splitter __pyx_base;
};
static struct __pyx_vtabstruct_7sklearn_4tree_5_tree_BaseDenseSplitter *__pyx_vtabptr_7sklearn_4tree_5_tree_BaseDenseSplitter;
/* "sklearn/tree/_tree.pyx":1062
*
*
* cdef class BestSplitter(BaseDenseSplitter): # <<<<<<<<<<<<<<
* """Splitter for finding the best split."""
* def __reduce__(self):
*/
struct __pyx_vtabstruct_7sklearn_4tree_5_tree_BestSplitter {
struct __pyx_vtabstruct_7sklearn_4tree_5_tree_BaseDenseSplitter __pyx_base;
};
static struct __pyx_vtabstruct_7sklearn_4tree_5_tree_BestSplitter *__pyx_vtabptr_7sklearn_4tree_5_tree_BestSplitter;
/* "sklearn/tree/_tree.pyx":1361
*
*
* cdef class RandomSplitter(BaseDenseSplitter): # <<<<<<<<<<<<<<
* """Splitter for finding the best random split."""
* def __reduce__(self):
*/
struct __pyx_vtabstruct_7sklearn_4tree_5_tree_RandomSplitter {
struct __pyx_vtabstruct_7sklearn_4tree_5_tree_BaseDenseSplitter __pyx_base;
};
static struct __pyx_vtabstruct_7sklearn_4tree_5_tree_RandomSplitter *__pyx_vtabptr_7sklearn_4tree_5_tree_RandomSplitter;
/* "sklearn/tree/_tree.pyx":1563
*
*
* cdef class PresortBestSplitter(BaseDenseSplitter): # <<<<<<<<<<<<<<
* """Splitter for finding the best split, using presorting."""
* cdef DTYPE_t* X_old
*/
struct __pyx_vtabstruct_7sklearn_4tree_5_tree_PresortBestSplitter {
struct __pyx_vtabstruct_7sklearn_4tree_5_tree_BaseDenseSplitter __pyx_base;
};
static struct __pyx_vtabstruct_7sklearn_4tree_5_tree_PresortBestSplitter *__pyx_vtabptr_7sklearn_4tree_5_tree_PresortBestSplitter;
/* "sklearn/tree/_tree.pyx":1811
*
*
* cdef class BaseSparseSplitter(Splitter): # <<<<<<<<<<<<<<
* # The sparse splitter works only with csc sparse matrix format
* cdef DTYPE_t* X_data
*/
struct __pyx_vtabstruct_7sklearn_4tree_5_tree_BaseSparseSplitter {
struct __pyx_vtabstruct_7sklearn_4tree_5_tree_Splitter __pyx_base;
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void (*extract_nnz)(struct __pyx_obj_7sklearn_4tree_5_tree_BaseSparseSplitter *, __pyx_t_7sklearn_4tree_5_tree_SIZE_t, __pyx_t_7sklearn_4tree_5_tree_SIZE_t *, __pyx_t_7sklearn_4tree_5_tree_SIZE_t *, int *);
};
static struct __pyx_vtabstruct_7sklearn_4tree_5_tree_BaseSparseSplitter *__pyx_vtabptr_7sklearn_4tree_5_tree_BaseSparseSplitter;
static CYTHON_INLINE __pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_f_7sklearn_4tree_5_tree_18BaseSparseSplitter__partition(struct __pyx_obj_7sklearn_4tree_5_tree_BaseSparseSplitter *, double, __pyx_t_7sklearn_4tree_5_tree_SIZE_t, __pyx_t_7sklearn_4tree_5_tree_SIZE_t, __pyx_t_7sklearn_4tree_5_tree_SIZE_t);
static CYTHON_INLINE void __pyx_f_7sklearn_4tree_5_tree_18BaseSparseSplitter_extract_nnz(struct __pyx_obj_7sklearn_4tree_5_tree_BaseSparseSplitter *, __pyx_t_7sklearn_4tree_5_tree_SIZE_t, __pyx_t_7sklearn_4tree_5_tree_SIZE_t *, __pyx_t_7sklearn_4tree_5_tree_SIZE_t *, int *);
/* "sklearn/tree/_tree.pyx":2130
*
*
* cdef class BestSparseSplitter(BaseSparseSplitter): # <<<<<<<<<<<<<<
* """Splitter for finding the best split, using the sparse data."""
*
*/
struct __pyx_vtabstruct_7sklearn_4tree_5_tree_BestSparseSplitter {
struct __pyx_vtabstruct_7sklearn_4tree_5_tree_BaseSparseSplitter __pyx_base;
};
static struct __pyx_vtabstruct_7sklearn_4tree_5_tree_BestSparseSplitter *__pyx_vtabptr_7sklearn_4tree_5_tree_BestSparseSplitter;
/* "sklearn/tree/_tree.pyx":2348
*
*
* cdef class RandomSparseSplitter(BaseSparseSplitter): # <<<<<<<<<<<<<<
* """Splitter for finding a random split, using the sparse data."""
*
*/
struct __pyx_vtabstruct_7sklearn_4tree_5_tree_RandomSparseSplitter {
struct __pyx_vtabstruct_7sklearn_4tree_5_tree_BaseSparseSplitter __pyx_base;
};
static struct __pyx_vtabstruct_7sklearn_4tree_5_tree_RandomSparseSplitter *__pyx_vtabptr_7sklearn_4tree_5_tree_RandomSparseSplitter;
/* "sklearn/tree/_tree.pyx":2605
* # Depth first builder ---------------------------------------------------------
*
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*
*/
struct __pyx_vtabstruct_7sklearn_4tree_5_tree_DepthFirstTreeBuilder {
struct __pyx_vtabstruct_7sklearn_4tree_5_tree_TreeBuilder __pyx_base;
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static struct __pyx_vtabstruct_7sklearn_4tree_5_tree_DepthFirstTreeBuilder *__pyx_vtabptr_7sklearn_4tree_5_tree_DepthFirstTreeBuilder;
/* "sklearn/tree/_tree.pyx":2751
*
*
* cdef class BestFirstTreeBuilder(TreeBuilder): # <<<<<<<<<<<<<<
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*
*/
struct __pyx_vtabstruct_7sklearn_4tree_5_tree_BestFirstTreeBuilder {
struct __pyx_vtabstruct_7sklearn_4tree_5_tree_TreeBuilder __pyx_base;
int (*_add_split_node)(struct __pyx_obj_7sklearn_4tree_5_tree_BestFirstTreeBuilder *, struct __pyx_obj_7sklearn_4tree_5_tree_Splitter *, struct __pyx_obj_7sklearn_4tree_5_tree_Tree *, __pyx_t_7sklearn_4tree_5_tree_SIZE_t, __pyx_t_7sklearn_4tree_5_tree_SIZE_t, double, int, int, struct __pyx_t_7sklearn_4tree_5_tree_Node *, __pyx_t_7sklearn_4tree_5_tree_SIZE_t, struct __pyx_t_7sklearn_4tree_6_utils_PriorityHeapRecord *);
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static struct __pyx_vtabstruct_7sklearn_4tree_5_tree_BestFirstTreeBuilder *__pyx_vtabptr_7sklearn_4tree_5_tree_BestFirstTreeBuilder;
static CYTHON_INLINE int __pyx_f_7sklearn_4tree_5_tree_20BestFirstTreeBuilder__add_split_node(struct __pyx_obj_7sklearn_4tree_5_tree_BestFirstTreeBuilder *, struct __pyx_obj_7sklearn_4tree_5_tree_Splitter *, struct __pyx_obj_7sklearn_4tree_5_tree_Tree *, __pyx_t_7sklearn_4tree_5_tree_SIZE_t, __pyx_t_7sklearn_4tree_5_tree_SIZE_t, double, int, int, struct __pyx_t_7sklearn_4tree_5_tree_Node *, __pyx_t_7sklearn_4tree_5_tree_SIZE_t, struct __pyx_t_7sklearn_4tree_6_utils_PriorityHeapRecord *);
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static PyTypeObject *__Pyx_ImportType(const char *module_name, const char *class_name, size_t size, int strict); /*proto*/
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static PyTypeObject *__pyx_ptype_7sklearn_4tree_5_tree_Splitter = 0;
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static PyTypeObject *__pyx_ptype_7sklearn_4tree_5_tree_Gini = 0;
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static PyTypeObject *__pyx_ptype_7sklearn_4tree_5_tree_BaseSparseSplitter = 0;
static PyTypeObject *__pyx_ptype_7sklearn_4tree_5_tree_BestSparseSplitter = 0;
static PyTypeObject *__pyx_ptype_7sklearn_4tree_5_tree_RandomSparseSplitter = 0;
static PyTypeObject *__pyx_ptype_7sklearn_4tree_5_tree_DepthFirstTreeBuilder = 0;
static PyTypeObject *__pyx_ptype_7sklearn_4tree_5_tree_BestFirstTreeBuilder = 0;
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static __pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_v_7sklearn_4tree_5_tree__TREE_UNDEFINED;
static __pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_v_7sklearn_4tree_5_tree_INITIAL_STACK_SIZE;
static __pyx_t_7sklearn_4tree_5_tree_DTYPE_t __pyx_v_7sklearn_4tree_5_tree_MIN_IMPURITY_SPLIT;
static __pyx_t_7sklearn_4tree_5_tree_DTYPE_t __pyx_v_7sklearn_4tree_5_tree_FEATURE_THRESHOLD;
static __pyx_t_7sklearn_4tree_5_tree_DTYPE_t __pyx_v_7sklearn_4tree_5_tree_EXTRACT_NNZ_SWITCH;
static int __pyx_v_7sklearn_4tree_5_tree_IS_FIRST;
static int __pyx_v_7sklearn_4tree_5_tree_IS_NOT_FIRST;
static int __pyx_v_7sklearn_4tree_5_tree_IS_LEFT;
static int __pyx_v_7sklearn_4tree_5_tree_IS_NOT_LEFT;
static CYTHON_INLINE void __pyx_f_7sklearn_4tree_5_tree__init_split(struct __pyx_t_7sklearn_4tree_5_tree_SplitRecord *, __pyx_t_7sklearn_4tree_5_tree_SIZE_t); /*proto*/
static CYTHON_INLINE void __pyx_f_7sklearn_4tree_5_tree_sort(__pyx_t_7sklearn_4tree_5_tree_DTYPE_t *, __pyx_t_7sklearn_4tree_5_tree_SIZE_t *, __pyx_t_7sklearn_4tree_5_tree_SIZE_t); /*proto*/
static CYTHON_INLINE void __pyx_f_7sklearn_4tree_5_tree_swap(__pyx_t_7sklearn_4tree_5_tree_DTYPE_t *, __pyx_t_7sklearn_4tree_5_tree_SIZE_t *, __pyx_t_7sklearn_4tree_5_tree_SIZE_t, __pyx_t_7sklearn_4tree_5_tree_SIZE_t); /*proto*/
static CYTHON_INLINE __pyx_t_7sklearn_4tree_5_tree_DTYPE_t __pyx_f_7sklearn_4tree_5_tree_median3(__pyx_t_7sklearn_4tree_5_tree_DTYPE_t *, __pyx_t_7sklearn_4tree_5_tree_SIZE_t); /*proto*/
static void __pyx_f_7sklearn_4tree_5_tree_introsort(__pyx_t_7sklearn_4tree_5_tree_DTYPE_t *, __pyx_t_7sklearn_4tree_5_tree_SIZE_t *, __pyx_t_7sklearn_4tree_5_tree_SIZE_t, int); /*proto*/
static CYTHON_INLINE void __pyx_f_7sklearn_4tree_5_tree_sift_down(__pyx_t_7sklearn_4tree_5_tree_DTYPE_t *, __pyx_t_7sklearn_4tree_5_tree_SIZE_t *, __pyx_t_7sklearn_4tree_5_tree_SIZE_t, __pyx_t_7sklearn_4tree_5_tree_SIZE_t); /*proto*/
static void __pyx_f_7sklearn_4tree_5_tree_heapsort(__pyx_t_7sklearn_4tree_5_tree_DTYPE_t *, __pyx_t_7sklearn_4tree_5_tree_SIZE_t *, __pyx_t_7sklearn_4tree_5_tree_SIZE_t); /*proto*/
static int __pyx_f_7sklearn_4tree_5_tree_compare_SIZE_t(void const *, void const *); /*proto*/
static CYTHON_INLINE void __pyx_f_7sklearn_4tree_5_tree_binary_search(__pyx_t_7sklearn_4tree_5_tree_INT32_t *, __pyx_t_7sklearn_4tree_5_tree_INT32_t, __pyx_t_7sklearn_4tree_5_tree_INT32_t, __pyx_t_7sklearn_4tree_5_tree_SIZE_t, __pyx_t_7sklearn_4tree_5_tree_SIZE_t *, __pyx_t_7sklearn_4tree_5_tree_INT32_t *); /*proto*/
static CYTHON_INLINE void __pyx_f_7sklearn_4tree_5_tree_extract_nnz_index_to_samples(__pyx_t_7sklearn_4tree_5_tree_INT32_t *, __pyx_t_7sklearn_4tree_5_tree_DTYPE_t *, __pyx_t_7sklearn_4tree_5_tree_INT32_t, __pyx_t_7sklearn_4tree_5_tree_INT32_t, __pyx_t_7sklearn_4tree_5_tree_SIZE_t *, __pyx_t_7sklearn_4tree_5_tree_SIZE_t, __pyx_t_7sklearn_4tree_5_tree_SIZE_t, __pyx_t_7sklearn_4tree_5_tree_SIZE_t *, __pyx_t_7sklearn_4tree_5_tree_DTYPE_t *, __pyx_t_7sklearn_4tree_5_tree_SIZE_t *, __pyx_t_7sklearn_4tree_5_tree_SIZE_t *); /*proto*/
static CYTHON_INLINE void __pyx_f_7sklearn_4tree_5_tree_extract_nnz_binary_search(__pyx_t_7sklearn_4tree_5_tree_INT32_t *, __pyx_t_7sklearn_4tree_5_tree_DTYPE_t *, __pyx_t_7sklearn_4tree_5_tree_INT32_t, __pyx_t_7sklearn_4tree_5_tree_INT32_t, __pyx_t_7sklearn_4tree_5_tree_SIZE_t *, __pyx_t_7sklearn_4tree_5_tree_SIZE_t, __pyx_t_7sklearn_4tree_5_tree_SIZE_t, __pyx_t_7sklearn_4tree_5_tree_SIZE_t *, __pyx_t_7sklearn_4tree_5_tree_DTYPE_t *, __pyx_t_7sklearn_4tree_5_tree_SIZE_t *, __pyx_t_7sklearn_4tree_5_tree_SIZE_t *, __pyx_t_7sklearn_4tree_5_tree_SIZE_t *, int *); /*proto*/
static CYTHON_INLINE void __pyx_f_7sklearn_4tree_5_tree_sparse_swap(__pyx_t_7sklearn_4tree_5_tree_SIZE_t *, __pyx_t_7sklearn_4tree_5_tree_SIZE_t *, __pyx_t_7sklearn_4tree_5_tree_SIZE_t, __pyx_t_7sklearn_4tree_5_tree_SIZE_t); /*proto*/
static CYTHON_INLINE int __pyx_f_7sklearn_4tree_5_tree__add_to_frontier(struct __pyx_t_7sklearn_4tree_6_utils_PriorityHeapRecord *, struct __pyx_obj_7sklearn_4tree_6_utils_PriorityHeap *); /*proto*/
static CYTHON_INLINE __pyx_t_7sklearn_4tree_5_tree_UINT32_t __pyx_f_7sklearn_4tree_5_tree_our_rand_r(__pyx_t_7sklearn_4tree_5_tree_UINT32_t *); /*proto*/
static CYTHON_INLINE PyArrayObject *__pyx_f_7sklearn_4tree_5_tree_sizet_ptr_to_ndarray(__pyx_t_7sklearn_4tree_5_tree_SIZE_t *, __pyx_t_7sklearn_4tree_5_tree_SIZE_t); /*proto*/
static CYTHON_INLINE __pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_f_7sklearn_4tree_5_tree_rand_int(__pyx_t_7sklearn_4tree_5_tree_SIZE_t, __pyx_t_7sklearn_4tree_5_tree_SIZE_t, __pyx_t_7sklearn_4tree_5_tree_UINT32_t *); /*proto*/
static CYTHON_INLINE double __pyx_f_7sklearn_4tree_5_tree_rand_uniform(double, double, __pyx_t_7sklearn_4tree_5_tree_UINT32_t *); /*proto*/
static CYTHON_INLINE double __pyx_f_7sklearn_4tree_5_tree_log(double); /*proto*/
static __pyx_t_7sklearn_4tree_5_tree_DTYPE_t *__pyx_fuse_0__pyx_f_7sklearn_4tree_5_tree_safe_realloc(__pyx_t_7sklearn_4tree_5_tree_DTYPE_t **, size_t); /*proto*/
static __pyx_t_7sklearn_4tree_5_tree_SIZE_t *__pyx_fuse_1__pyx_f_7sklearn_4tree_5_tree_safe_realloc(__pyx_t_7sklearn_4tree_5_tree_SIZE_t **, size_t); /*proto*/
static unsigned char *__pyx_fuse_2__pyx_f_7sklearn_4tree_5_tree_safe_realloc(unsigned char **, size_t); /*proto*/
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static __Pyx_TypeInfo __Pyx_TypeInfo_nn___pyx_t_7sklearn_4tree_5_tree_DTYPE_t = { "DTYPE_t", NULL, sizeof(__pyx_t_7sklearn_4tree_5_tree_DTYPE_t), { 0 }, 0, 'R', 0, 0 };
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#define __Pyx_MODULE_NAME "sklearn.tree._tree"
int __pyx_module_is_main_sklearn__tree___tree = 0;
/* Implementation of 'sklearn.tree._tree' */
static PyObject *__pyx_builtin_range;
static PyObject *__pyx_builtin_MemoryError;
static PyObject *__pyx_builtin_ValueError;
static PyObject *__pyx_builtin_RuntimeError;
static int __pyx_pf_7sklearn_4tree_5_tree_23ClassificationCriterion___cinit__(struct __pyx_obj_7sklearn_4tree_5_tree_ClassificationCriterion *__pyx_v_self, __pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_v_n_outputs, PyArrayObject *__pyx_v_n_classes); /* proto */
static void __pyx_pf_7sklearn_4tree_5_tree_23ClassificationCriterion_2__dealloc__(struct __pyx_obj_7sklearn_4tree_5_tree_ClassificationCriterion *__pyx_v_self); /* proto */
static PyObject *__pyx_pf_7sklearn_4tree_5_tree_23ClassificationCriterion_4__reduce__(struct __pyx_obj_7sklearn_4tree_5_tree_ClassificationCriterion *__pyx_v_self); /* proto */
static PyObject *__pyx_pf_7sklearn_4tree_5_tree_23ClassificationCriterion_6__getstate__(CYTHON_UNUSED struct __pyx_obj_7sklearn_4tree_5_tree_ClassificationCriterion *__pyx_v_self); /* proto */
static PyObject *__pyx_pf_7sklearn_4tree_5_tree_23ClassificationCriterion_8__setstate__(CYTHON_UNUSED struct __pyx_obj_7sklearn_4tree_5_tree_ClassificationCriterion *__pyx_v_self, CYTHON_UNUSED PyObject *__pyx_v_d); /* proto */
static int __pyx_pf_7sklearn_4tree_5_tree_19RegressionCriterion___cinit__(struct __pyx_obj_7sklearn_4tree_5_tree_RegressionCriterion *__pyx_v_self, __pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_v_n_outputs); /* proto */
static void __pyx_pf_7sklearn_4tree_5_tree_19RegressionCriterion_2__dealloc__(struct __pyx_obj_7sklearn_4tree_5_tree_RegressionCriterion *__pyx_v_self); /* proto */
static PyObject *__pyx_pf_7sklearn_4tree_5_tree_19RegressionCriterion_4__reduce__(struct __pyx_obj_7sklearn_4tree_5_tree_RegressionCriterion *__pyx_v_self); /* proto */
static PyObject *__pyx_pf_7sklearn_4tree_5_tree_19RegressionCriterion_6__getstate__(CYTHON_UNUSED struct __pyx_obj_7sklearn_4tree_5_tree_RegressionCriterion *__pyx_v_self); /* proto */
static PyObject *__pyx_pf_7sklearn_4tree_5_tree_19RegressionCriterion_8__setstate__(CYTHON_UNUSED struct __pyx_obj_7sklearn_4tree_5_tree_RegressionCriterion *__pyx_v_self, CYTHON_UNUSED PyObject *__pyx_v_d); /* proto */
static int __pyx_pf_7sklearn_4tree_5_tree_8Splitter___cinit__(struct __pyx_obj_7sklearn_4tree_5_tree_Splitter *__pyx_v_self, struct __pyx_obj_7sklearn_4tree_5_tree_Criterion *__pyx_v_criterion, __pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_v_max_features, __pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_v_min_samples_leaf, double __pyx_v_min_weight_leaf, PyObject *__pyx_v_random_state); /* proto */
static void __pyx_pf_7sklearn_4tree_5_tree_8Splitter_2__dealloc__(struct __pyx_obj_7sklearn_4tree_5_tree_Splitter *__pyx_v_self); /* proto */
static PyObject *__pyx_pf_7sklearn_4tree_5_tree_8Splitter_4__getstate__(CYTHON_UNUSED struct __pyx_obj_7sklearn_4tree_5_tree_Splitter *__pyx_v_self); /* proto */
static PyObject *__pyx_pf_7sklearn_4tree_5_tree_8Splitter_6__setstate__(CYTHON_UNUSED struct __pyx_obj_7sklearn_4tree_5_tree_Splitter *__pyx_v_self, CYTHON_UNUSED PyObject *__pyx_v_d); /* proto */
static PyObject *__pyx_pf_7sklearn_4tree_5_tree_8Splitter_9criterion___get__(struct __pyx_obj_7sklearn_4tree_5_tree_Splitter *__pyx_v_self); /* proto */
static int __pyx_pf_7sklearn_4tree_5_tree_8Splitter_9criterion_2__set__(struct __pyx_obj_7sklearn_4tree_5_tree_Splitter *__pyx_v_self, PyObject *__pyx_v_value); /* proto */
static int __pyx_pf_7sklearn_4tree_5_tree_8Splitter_9criterion_4__del__(struct __pyx_obj_7sklearn_4tree_5_tree_Splitter *__pyx_v_self); /* proto */
static PyObject *__pyx_pf_7sklearn_4tree_5_tree_8Splitter_12max_features___get__(struct __pyx_obj_7sklearn_4tree_5_tree_Splitter *__pyx_v_self); /* proto */
static int __pyx_pf_7sklearn_4tree_5_tree_8Splitter_12max_features_2__set__(struct __pyx_obj_7sklearn_4tree_5_tree_Splitter *__pyx_v_self, PyObject *__pyx_v_value); /* proto */
static PyObject *__pyx_pf_7sklearn_4tree_5_tree_8Splitter_16min_samples_leaf___get__(struct __pyx_obj_7sklearn_4tree_5_tree_Splitter *__pyx_v_self); /* proto */
static int __pyx_pf_7sklearn_4tree_5_tree_8Splitter_16min_samples_leaf_2__set__(struct __pyx_obj_7sklearn_4tree_5_tree_Splitter *__pyx_v_self, PyObject *__pyx_v_value); /* proto */
static PyObject *__pyx_pf_7sklearn_4tree_5_tree_8Splitter_15min_weight_leaf___get__(struct __pyx_obj_7sklearn_4tree_5_tree_Splitter *__pyx_v_self); /* proto */
static int __pyx_pf_7sklearn_4tree_5_tree_8Splitter_15min_weight_leaf_2__set__(struct __pyx_obj_7sklearn_4tree_5_tree_Splitter *__pyx_v_self, PyObject *__pyx_v_value); /* proto */
static int __pyx_pf_7sklearn_4tree_5_tree_17BaseDenseSplitter___cinit__(struct __pyx_obj_7sklearn_4tree_5_tree_BaseDenseSplitter *__pyx_v_self, CYTHON_UNUSED struct __pyx_obj_7sklearn_4tree_5_tree_Criterion *__pyx_v_criterion, CYTHON_UNUSED __pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_v_max_features, CYTHON_UNUSED __pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_v_min_samples_leaf, CYTHON_UNUSED double __pyx_v_min_weight_leaf, CYTHON_UNUSED PyObject *__pyx_v_random_state); /* proto */
static PyObject *__pyx_pf_7sklearn_4tree_5_tree_12BestSplitter___reduce__(struct __pyx_obj_7sklearn_4tree_5_tree_BestSplitter *__pyx_v_self); /* proto */
static PyObject *__pyx_pf_7sklearn_4tree_5_tree_14RandomSplitter___reduce__(struct __pyx_obj_7sklearn_4tree_5_tree_RandomSplitter *__pyx_v_self); /* proto */
static int __pyx_pf_7sklearn_4tree_5_tree_19PresortBestSplitter___cinit__(struct __pyx_obj_7sklearn_4tree_5_tree_PresortBestSplitter *__pyx_v_self, CYTHON_UNUSED struct __pyx_obj_7sklearn_4tree_5_tree_Criterion *__pyx_v_criterion, CYTHON_UNUSED __pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_v_max_features, CYTHON_UNUSED __pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_v_min_samples_leaf, CYTHON_UNUSED double __pyx_v_min_weight_leaf, CYTHON_UNUSED PyObject *__pyx_v_random_state); /* proto */
static void __pyx_pf_7sklearn_4tree_5_tree_19PresortBestSplitter_2__dealloc__(struct __pyx_obj_7sklearn_4tree_5_tree_PresortBestSplitter *__pyx_v_self); /* proto */
static PyObject *__pyx_pf_7sklearn_4tree_5_tree_19PresortBestSplitter_4__reduce__(struct __pyx_obj_7sklearn_4tree_5_tree_PresortBestSplitter *__pyx_v_self); /* proto */
static int __pyx_pf_7sklearn_4tree_5_tree_18BaseSparseSplitter___cinit__(struct __pyx_obj_7sklearn_4tree_5_tree_BaseSparseSplitter *__pyx_v_self, CYTHON_UNUSED struct __pyx_obj_7sklearn_4tree_5_tree_Criterion *__pyx_v_criterion, CYTHON_UNUSED __pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_v_max_features, CYTHON_UNUSED __pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_v_min_samples_leaf, CYTHON_UNUSED double __pyx_v_min_weight_leaf, CYTHON_UNUSED PyObject *__pyx_v_random_state); /* proto */
static void __pyx_pf_7sklearn_4tree_5_tree_18BaseSparseSplitter_2__dealloc__(struct __pyx_obj_7sklearn_4tree_5_tree_BaseSparseSplitter *__pyx_v_self); /* proto */
static PyObject *__pyx_pf_7sklearn_4tree_5_tree_18BestSparseSplitter___reduce__(struct __pyx_obj_7sklearn_4tree_5_tree_BestSparseSplitter *__pyx_v_self); /* proto */
static PyObject *__pyx_pf_7sklearn_4tree_5_tree_20RandomSparseSplitter___reduce__(struct __pyx_obj_7sklearn_4tree_5_tree_RandomSparseSplitter *__pyx_v_self); /* proto */
static PyObject *__pyx_pf_7sklearn_4tree_5_tree_11TreeBuilder_build(struct __pyx_obj_7sklearn_4tree_5_tree_TreeBuilder *__pyx_v_self, struct __pyx_obj_7sklearn_4tree_5_tree_Tree *__pyx_v_tree, PyObject *__pyx_v_X, PyArrayObject *__pyx_v_y, PyArrayObject *__pyx_v_sample_weight); /* proto */
static int __pyx_pf_7sklearn_4tree_5_tree_21DepthFirstTreeBuilder___cinit__(struct __pyx_obj_7sklearn_4tree_5_tree_DepthFirstTreeBuilder *__pyx_v_self, struct __pyx_obj_7sklearn_4tree_5_tree_Splitter *__pyx_v_splitter, __pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_v_min_samples_split, __pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_v_min_samples_leaf, double __pyx_v_min_weight_leaf, __pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_v_max_depth); /* proto */
static PyObject *__pyx_pf_7sklearn_4tree_5_tree_21DepthFirstTreeBuilder_2build(struct __pyx_obj_7sklearn_4tree_5_tree_DepthFirstTreeBuilder *__pyx_v_self, struct __pyx_obj_7sklearn_4tree_5_tree_Tree *__pyx_v_tree, PyObject *__pyx_v_X, PyArrayObject *__pyx_v_y, PyArrayObject *__pyx_v_sample_weight); /* proto */
static int __pyx_pf_7sklearn_4tree_5_tree_20BestFirstTreeBuilder___cinit__(struct __pyx_obj_7sklearn_4tree_5_tree_BestFirstTreeBuilder *__pyx_v_self, struct __pyx_obj_7sklearn_4tree_5_tree_Splitter *__pyx_v_splitter, __pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_v_min_samples_split, __pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_v_min_samples_leaf, PyObject *__pyx_v_min_weight_leaf, __pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_v_max_depth, __pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_v_max_leaf_nodes); /* proto */
static PyObject *__pyx_pf_7sklearn_4tree_5_tree_20BestFirstTreeBuilder_2build(struct __pyx_obj_7sklearn_4tree_5_tree_BestFirstTreeBuilder *__pyx_v_self, struct __pyx_obj_7sklearn_4tree_5_tree_Tree *__pyx_v_tree, PyObject *__pyx_v_X, PyArrayObject *__pyx_v_y, PyArrayObject *__pyx_v_sample_weight); /* proto */
static PyObject *__pyx_pf_7sklearn_4tree_5_tree_4Tree_9n_classes___get__(struct __pyx_obj_7sklearn_4tree_5_tree_Tree *__pyx_v_self); /* proto */
static PyObject *__pyx_pf_7sklearn_4tree_5_tree_4Tree_13children_left___get__(struct __pyx_obj_7sklearn_4tree_5_tree_Tree *__pyx_v_self); /* proto */
static PyObject *__pyx_pf_7sklearn_4tree_5_tree_4Tree_14children_right___get__(struct __pyx_obj_7sklearn_4tree_5_tree_Tree *__pyx_v_self); /* proto */
static PyObject *__pyx_pf_7sklearn_4tree_5_tree_4Tree_7feature___get__(struct __pyx_obj_7sklearn_4tree_5_tree_Tree *__pyx_v_self); /* proto */
static PyObject *__pyx_pf_7sklearn_4tree_5_tree_4Tree_9threshold___get__(struct __pyx_obj_7sklearn_4tree_5_tree_Tree *__pyx_v_self); /* proto */
static PyObject *__pyx_pf_7sklearn_4tree_5_tree_4Tree_8impurity___get__(struct __pyx_obj_7sklearn_4tree_5_tree_Tree *__pyx_v_self); /* proto */
static PyObject *__pyx_pf_7sklearn_4tree_5_tree_4Tree_14n_node_samples___get__(struct __pyx_obj_7sklearn_4tree_5_tree_Tree *__pyx_v_self); /* proto */
static PyObject *__pyx_pf_7sklearn_4tree_5_tree_4Tree_23weighted_n_node_samples___get__(struct __pyx_obj_7sklearn_4tree_5_tree_Tree *__pyx_v_self); /* proto */
static PyObject *__pyx_pf_7sklearn_4tree_5_tree_4Tree_5value___get__(struct __pyx_obj_7sklearn_4tree_5_tree_Tree *__pyx_v_self); /* proto */
static int __pyx_pf_7sklearn_4tree_5_tree_4Tree___cinit__(struct __pyx_obj_7sklearn_4tree_5_tree_Tree *__pyx_v_self, int __pyx_v_n_features, PyArrayObject *__pyx_v_n_classes, int __pyx_v_n_outputs); /* proto */
static void __pyx_pf_7sklearn_4tree_5_tree_4Tree_2__dealloc__(struct __pyx_obj_7sklearn_4tree_5_tree_Tree *__pyx_v_self); /* proto */
static PyObject *__pyx_pf_7sklearn_4tree_5_tree_4Tree_4__reduce__(struct __pyx_obj_7sklearn_4tree_5_tree_Tree *__pyx_v_self); /* proto */
static PyObject *__pyx_pf_7sklearn_4tree_5_tree_4Tree_6__getstate__(struct __pyx_obj_7sklearn_4tree_5_tree_Tree *__pyx_v_self); /* proto */
static PyObject *__pyx_pf_7sklearn_4tree_5_tree_4Tree_8__setstate__(struct __pyx_obj_7sklearn_4tree_5_tree_Tree *__pyx_v_self, PyObject *__pyx_v_d); /* proto */
static PyObject *__pyx_pf_7sklearn_4tree_5_tree_4Tree_10predict(struct __pyx_obj_7sklearn_4tree_5_tree_Tree *__pyx_v_self, PyObject *__pyx_v_X); /* proto */
static PyObject *__pyx_pf_7sklearn_4tree_5_tree_4Tree_12apply(struct __pyx_obj_7sklearn_4tree_5_tree_Tree *__pyx_v_self, PyObject *__pyx_v_X); /* proto */
static PyObject *__pyx_pf_7sklearn_4tree_5_tree_4Tree_14compute_feature_importances(struct __pyx_obj_7sklearn_4tree_5_tree_Tree *__pyx_v_self, PyObject *__pyx_v_normalize); /* proto */
static PyObject *__pyx_pf_7sklearn_4tree_5_tree_4Tree_10n_features___get__(struct __pyx_obj_7sklearn_4tree_5_tree_Tree *__pyx_v_self); /* proto */
static int __pyx_pf_7sklearn_4tree_5_tree_4Tree_10n_features_2__set__(struct __pyx_obj_7sklearn_4tree_5_tree_Tree *__pyx_v_self, PyObject *__pyx_v_value); /* proto */
static PyObject *__pyx_pf_7sklearn_4tree_5_tree_4Tree_9n_outputs___get__(struct __pyx_obj_7sklearn_4tree_5_tree_Tree *__pyx_v_self); /* proto */
static int __pyx_pf_7sklearn_4tree_5_tree_4Tree_9n_outputs_2__set__(struct __pyx_obj_7sklearn_4tree_5_tree_Tree *__pyx_v_self, PyObject *__pyx_v_value); /* proto */
static PyObject *__pyx_pf_7sklearn_4tree_5_tree_4Tree_13max_n_classes___get__(struct __pyx_obj_7sklearn_4tree_5_tree_Tree *__pyx_v_self); /* proto */
static int __pyx_pf_7sklearn_4tree_5_tree_4Tree_13max_n_classes_2__set__(struct __pyx_obj_7sklearn_4tree_5_tree_Tree *__pyx_v_self, PyObject *__pyx_v_value); /* proto */
static PyObject *__pyx_pf_7sklearn_4tree_5_tree_4Tree_9max_depth___get__(struct __pyx_obj_7sklearn_4tree_5_tree_Tree *__pyx_v_self); /* proto */
static int __pyx_pf_7sklearn_4tree_5_tree_4Tree_9max_depth_2__set__(struct __pyx_obj_7sklearn_4tree_5_tree_Tree *__pyx_v_self, PyObject *__pyx_v_value); /* proto */
static PyObject *__pyx_pf_7sklearn_4tree_5_tree_4Tree_10node_count___get__(struct __pyx_obj_7sklearn_4tree_5_tree_Tree *__pyx_v_self); /* proto */
static int __pyx_pf_7sklearn_4tree_5_tree_4Tree_10node_count_2__set__(struct __pyx_obj_7sklearn_4tree_5_tree_Tree *__pyx_v_self, PyObject *__pyx_v_value); /* proto */
static PyObject *__pyx_pf_7sklearn_4tree_5_tree_4Tree_8capacity___get__(struct __pyx_obj_7sklearn_4tree_5_tree_Tree *__pyx_v_self); /* proto */
static int __pyx_pf_7sklearn_4tree_5_tree_4Tree_8capacity_2__set__(struct __pyx_obj_7sklearn_4tree_5_tree_Tree *__pyx_v_self, PyObject *__pyx_v_value); /* proto */
static PyObject *__pyx_pf_7sklearn_4tree_5_tree__realloc_test(CYTHON_UNUSED PyObject *__pyx_self); /* proto */
static int __pyx_pf_5numpy_7ndarray___getbuffer__(PyArrayObject *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags); /* proto */
static void __pyx_pf_5numpy_7ndarray_2__releasebuffer__(PyArrayObject *__pyx_v_self, Py_buffer *__pyx_v_info); /* proto */
static PyObject *__pyx_tp_new_7sklearn_4tree_5_tree_Criterion(PyTypeObject *t, PyObject *a, PyObject *k); /*proto*/
static PyObject *__pyx_tp_new_7sklearn_4tree_5_tree_Splitter(PyTypeObject *t, PyObject *a, PyObject *k); /*proto*/
static PyObject *__pyx_tp_new_7sklearn_4tree_5_tree_Tree(PyTypeObject *t, PyObject *a, PyObject *k); /*proto*/
static PyObject *__pyx_tp_new_7sklearn_4tree_5_tree_TreeBuilder(PyTypeObject *t, PyObject *a, PyObject *k); /*proto*/
static PyObject *__pyx_tp_new_7sklearn_4tree_5_tree_ClassificationCriterion(PyTypeObject *t, PyObject *a, PyObject *k); /*proto*/
static PyObject *__pyx_tp_new_7sklearn_4tree_5_tree_Entropy(PyTypeObject *t, PyObject *a, PyObject *k); /*proto*/
static PyObject *__pyx_tp_new_7sklearn_4tree_5_tree_Gini(PyTypeObject *t, PyObject *a, PyObject *k); /*proto*/
static PyObject *__pyx_tp_new_7sklearn_4tree_5_tree_RegressionCriterion(PyTypeObject *t, PyObject *a, PyObject *k); /*proto*/
static PyObject *__pyx_tp_new_7sklearn_4tree_5_tree_MSE(PyTypeObject *t, PyObject *a, PyObject *k); /*proto*/
static PyObject *__pyx_tp_new_7sklearn_4tree_5_tree_FriedmanMSE(PyTypeObject *t, PyObject *a, PyObject *k); /*proto*/
static PyObject *__pyx_tp_new_7sklearn_4tree_5_tree_BaseDenseSplitter(PyTypeObject *t, PyObject *a, PyObject *k); /*proto*/
static PyObject *__pyx_tp_new_7sklearn_4tree_5_tree_BestSplitter(PyTypeObject *t, PyObject *a, PyObject *k); /*proto*/
static PyObject *__pyx_tp_new_7sklearn_4tree_5_tree_RandomSplitter(PyTypeObject *t, PyObject *a, PyObject *k); /*proto*/
static PyObject *__pyx_tp_new_7sklearn_4tree_5_tree_PresortBestSplitter(PyTypeObject *t, PyObject *a, PyObject *k); /*proto*/
static PyObject *__pyx_tp_new_7sklearn_4tree_5_tree_BaseSparseSplitter(PyTypeObject *t, PyObject *a, PyObject *k); /*proto*/
static PyObject *__pyx_tp_new_7sklearn_4tree_5_tree_BestSparseSplitter(PyTypeObject *t, PyObject *a, PyObject *k); /*proto*/
static PyObject *__pyx_tp_new_7sklearn_4tree_5_tree_RandomSparseSplitter(PyTypeObject *t, PyObject *a, PyObject *k); /*proto*/
static PyObject *__pyx_tp_new_7sklearn_4tree_5_tree_DepthFirstTreeBuilder(PyTypeObject *t, PyObject *a, PyObject *k); /*proto*/
static PyObject *__pyx_tp_new_7sklearn_4tree_5_tree_BestFirstTreeBuilder(PyTypeObject *t, PyObject *a, PyObject *k); /*proto*/
static char __pyx_k_B[] = "B";
static char __pyx_k_C[] = "C";
static char __pyx_k_H[] = "H";
static char __pyx_k_I[] = "I";
static char __pyx_k_L[] = "L";
static char __pyx_k_O[] = "O";
static char __pyx_k_Q[] = "Q";
static char __pyx_k_X[] = "X";
static char __pyx_k_b[] = "b";
static char __pyx_k_d[] = "d";
static char __pyx_k_f[] = "f";
static char __pyx_k_g[] = "g";
static char __pyx_k_h[] = "h";
static char __pyx_k_i[] = "i";
static char __pyx_k_l[] = "l";
static char __pyx_k_p[] = "p";
static char __pyx_k_q[] = "q";
static char __pyx_k_y[] = "y";
static char __pyx_k_Zd[] = "Zd";
static char __pyx_k_Zf[] = "Zf";
static char __pyx_k_Zg[] = "Zg";
static char __pyx_k_np[] = "np";
static char __pyx_k_inf[] = "inf";
static char __pyx_k_max[] = "max";
static char __pyx_k_sum[] = "sum";
static char __pyx_k_axis[] = "axis";
static char __pyx_k_clip[] = "clip";
static char __pyx_k_copy[] = "copy";
static char __pyx_k_data[] = "data";
static char __pyx_k_intp[] = "intp";
static char __pyx_k_main[] = "__main__";
static char __pyx_k_mode[] = "mode";
static char __pyx_k_ndim[] = "ndim";
static char __pyx_k_take[] = "take";
static char __pyx_k_test[] = "__test__";
static char __pyx_k_tree[] = "tree";
static char __pyx_k_DTYPE[] = "DTYPE";
static char __pyx_k_apply[] = "apply";
static char __pyx_k_build[] = "build";
static char __pyx_k_dtype[] = "dtype";
static char __pyx_k_flags[] = "flags";
static char __pyx_k_int32[] = "int32";
static char __pyx_k_names[] = "names";
static char __pyx_k_nodes[] = "nodes";
static char __pyx_k_numpy[] = "numpy";
static char __pyx_k_order[] = "order";
static char __pyx_k_range[] = "range";
static char __pyx_k_shape[] = "shape";
static char __pyx_k_tocsc[] = "tocsc";
static char __pyx_k_zeros[] = "zeros";
static char __pyx_k_DOUBLE[] = "DOUBLE";
static char __pyx_k_import[] = "__import__";
static char __pyx_k_indptr[] = "indptr";
static char __pyx_k_values[] = "values";
static char __pyx_k_argsort[] = "argsort";
static char __pyx_k_asarray[] = "asarray";
static char __pyx_k_feature[] = "feature";
static char __pyx_k_float32[] = "float32";
static char __pyx_k_float64[] = "float64";
static char __pyx_k_formats[] = "formats";
static char __pyx_k_indices[] = "indices";
static char __pyx_k_offsets[] = "offsets";
static char __pyx_k_predict[] = "predict";
static char __pyx_k_randint[] = "randint";
static char __pyx_k_reshape[] = "reshape";
static char __pyx_k_strides[] = "strides";
static char __pyx_k_getstate[] = "__getstate__";
static char __pyx_k_impurity[] = "impurity";
static char __pyx_k_issparse[] = "issparse";
static char __pyx_k_itemsize[] = "itemsize";
static char __pyx_k_splitter[] = "splitter";
static char __pyx_k_TREE_LEAF[] = "TREE_LEAF";
static char __pyx_k_criterion[] = "criterion";
static char __pyx_k_max_depth[] = "max_depth";
static char __pyx_k_n_classes[] = "n_classes";
static char __pyx_k_n_outputs[] = "n_outputs";
static char __pyx_k_normalize[] = "normalize";
static char __pyx_k_threshold[] = "threshold";
static char __pyx_k_NODE_DTYPE[] = "NODE_DTYPE";
static char __pyx_k_ValueError[] = "ValueError";
static char __pyx_k_contiguous[] = "contiguous";
static char __pyx_k_csc_matrix[] = "csc_matrix";
static char __pyx_k_csr_matrix[] = "csr_matrix";
static char __pyx_k_left_child[] = "left_child";
static char __pyx_k_n_features[] = "n_features";
static char __pyx_k_node_count[] = "node_count";
static char __pyx_k_pyx_vtable[] = "__pyx_vtable__";
static char __pyx_k_MemoryError[] = "MemoryError";
static char __pyx_k_right_child[] = "right_child";
static char __pyx_k_RuntimeError[] = "RuntimeError";
static char __pyx_k_c_contiguous[] = "c_contiguous";
static char __pyx_k_max_features[] = "max_features";
static char __pyx_k_random_state[] = "random_state";
static char __pyx_k_realloc_test[] = "_realloc_test";
static char __pyx_k_scipy_sparse[] = "scipy.sparse";
static char __pyx_k_sort_indices[] = "sort_indices";
static char __pyx_k_pyx_getbuffer[] = "__pyx_getbuffer";
static char __pyx_k_sample_weight[] = "sample_weight";
static char __pyx_k_TREE_UNDEFINED[] = "TREE_UNDEFINED";
static char __pyx_k_asfortranarray[] = "asfortranarray";
static char __pyx_k_max_leaf_nodes[] = "max_leaf_nodes";
static char __pyx_k_n_node_samples[] = "n_node_samples";
static char __pyx_k_min_weight_leaf[] = "min_weight_leaf";
static char __pyx_k_min_samples_leaf[] = "min_samples_leaf";
static char __pyx_k_ascontiguousarray[] = "ascontiguousarray";
static char __pyx_k_min_samples_split[] = "min_samples_split";
static char __pyx_k_pyx_releasebuffer[] = "__pyx_releasebuffer";
static char __pyx_k_resizing_tree_to_d[] = "resizing tree to %d";
static char __pyx_k_sklearn_tree__tree[] = "sklearn.tree._tree";
static char __pyx_k_weighted_n_node_samples[] = "weighted_n_node_samples";
static char __pyx_k_X_should_be_in_csc_format[] = "X should be in csc format";
static char __pyx_k_could_not_allocate_d_bytes[] = "could not allocate %d bytes";
static char __pyx_k_compute_feature_importances[] = "compute_feature_importances";
static char __pyx_k_ndarray_is_not_C_contiguous[] = "ndarray is not C contiguous";
static char __pyx_k_could_not_allocate_d_d_bytes[] = "could not allocate (%d * %d) bytes";
static char __pyx_k_Users_ajoly_git_scikit_learn_sk[] = "/Users/ajoly/git/scikit-learn/sklearn/tree/_tree.pyx";
static char __pyx_k_unknown_dtype_code_in_numpy_pxd[] = "unknown dtype code in numpy.pxd (%d)";
static char __pyx_k_Did_not_recognise_loaded_array_l[] = "Did not recognise loaded array layout";
static char __pyx_k_Format_string_allocated_too_shor[] = "Format string allocated too short, see comment in numpy.pxd";
static char __pyx_k_No_support_for_np_int64_index_ba[] = "No support for np.int64 index based sparse matrices";
static char __pyx_k_Non_native_byte_order_not_suppor[] = "Non-native byte order not supported";
static char __pyx_k_X_dtype_should_be_np_float32_got[] = "X.dtype should be np.float32, got %s";
static char __pyx_k_X_should_be_in_csr_matrix_format[] = "X should be in csr_matrix format, got %s";
static char __pyx_k_X_should_be_in_np_ndarray_format[] = "X should be in np.ndarray format, got %s";
static char __pyx_k_You_have_loaded_Tree_version_whi[] = "You have loaded Tree version which cannot be imported";
static char __pyx_k_ndarray_is_not_Fortran_contiguou[] = "ndarray is not Fortran contiguous";
static char __pyx_k_Format_string_allocated_too_shor_2[] = "Format string allocated too short.";
static PyObject *__pyx_n_s_C;
static PyObject *__pyx_n_s_DOUBLE;
static PyObject *__pyx_n_s_DTYPE;
static PyObject *__pyx_kp_s_Did_not_recognise_loaded_array_l;
static PyObject *__pyx_kp_u_Format_string_allocated_too_shor;
static PyObject *__pyx_kp_u_Format_string_allocated_too_shor_2;
static PyObject *__pyx_n_s_MemoryError;
static PyObject *__pyx_n_s_NODE_DTYPE;
static PyObject *__pyx_kp_s_No_support_for_np_int64_index_ba;
static PyObject *__pyx_kp_u_Non_native_byte_order_not_suppor;
static PyObject *__pyx_n_s_RuntimeError;
static PyObject *__pyx_n_s_TREE_LEAF;
static PyObject *__pyx_n_s_TREE_UNDEFINED;
static PyObject *__pyx_kp_s_Users_ajoly_git_scikit_learn_sk;
static PyObject *__pyx_n_s_ValueError;
static PyObject *__pyx_n_s_X;
static PyObject *__pyx_kp_s_X_dtype_should_be_np_float32_got;
static PyObject *__pyx_kp_s_X_should_be_in_csc_format;
static PyObject *__pyx_kp_s_X_should_be_in_csr_matrix_format;
static PyObject *__pyx_kp_s_X_should_be_in_np_ndarray_format;
static PyObject *__pyx_kp_s_You_have_loaded_Tree_version_whi;
static PyObject *__pyx_n_s_apply;
static PyObject *__pyx_n_s_argsort;
static PyObject *__pyx_n_s_asarray;
static PyObject *__pyx_n_s_ascontiguousarray;
static PyObject *__pyx_n_s_asfortranarray;
static PyObject *__pyx_n_s_axis;
static PyObject *__pyx_n_s_build;
static PyObject *__pyx_n_s_c_contiguous;
static PyObject *__pyx_n_s_clip;
static PyObject *__pyx_n_s_compute_feature_importances;
static PyObject *__pyx_n_s_contiguous;
static PyObject *__pyx_n_s_copy;
static PyObject *__pyx_kp_s_could_not_allocate_d_bytes;
static PyObject *__pyx_kp_s_could_not_allocate_d_d_bytes;
static PyObject *__pyx_n_s_criterion;
static PyObject *__pyx_n_s_csc_matrix;
static PyObject *__pyx_n_s_csr_matrix;
static PyObject *__pyx_n_s_data;
static PyObject *__pyx_n_s_dtype;
static PyObject *__pyx_n_s_feature;
static PyObject *__pyx_n_s_flags;
static PyObject *__pyx_n_s_float32;
static PyObject *__pyx_n_s_float64;
static PyObject *__pyx_n_s_formats;
static PyObject *__pyx_n_s_getstate;
static PyObject *__pyx_n_s_import;
static PyObject *__pyx_n_s_impurity;
static PyObject *__pyx_n_s_indices;
static PyObject *__pyx_n_s_indptr;
static PyObject *__pyx_n_s_inf;
static PyObject *__pyx_n_s_int32;
static PyObject *__pyx_n_s_intp;
static PyObject *__pyx_n_s_issparse;
static PyObject *__pyx_n_s_itemsize;
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/* "sklearn/tree/_tree.pyx":100
* """Interface for impurity criteria."""
*
* cdef void init(self, DOUBLE_t* y, SIZE_t y_stride, DOUBLE_t* sample_weight, # <<<<<<<<<<<<<<
* double weighted_n_samples, SIZE_t* samples, SIZE_t start,
* SIZE_t end) nogil:
*/
static void __pyx_f_7sklearn_4tree_5_tree_9Criterion_init(CYTHON_UNUSED struct __pyx_obj_7sklearn_4tree_5_tree_Criterion *__pyx_v_self, CYTHON_UNUSED __pyx_t_7sklearn_4tree_5_tree_DOUBLE_t *__pyx_v_y, CYTHON_UNUSED __pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_v_y_stride, CYTHON_UNUSED __pyx_t_7sklearn_4tree_5_tree_DOUBLE_t *__pyx_v_sample_weight, CYTHON_UNUSED double __pyx_v_weighted_n_samples, CYTHON_UNUSED __pyx_t_7sklearn_4tree_5_tree_SIZE_t *__pyx_v_samples, CYTHON_UNUSED __pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_v_start, CYTHON_UNUSED __pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_v_end) {
/* function exit code */
}
/* "sklearn/tree/_tree.pyx":107
* pass
*
* cdef void reset(self) nogil: # <<<<<<<<<<<<<<
* """Reset the criterion at pos=start."""
* pass
*/
static void __pyx_f_7sklearn_4tree_5_tree_9Criterion_reset(CYTHON_UNUSED struct __pyx_obj_7sklearn_4tree_5_tree_Criterion *__pyx_v_self) {
/* function exit code */
}
/* "sklearn/tree/_tree.pyx":111
* pass
*
* cdef void update(self, SIZE_t new_pos) nogil: # <<<<<<<<<<<<<<
* """Update the collected statistics by moving samples[pos:new_pos] from
* the right child to the left child."""
*/
static void __pyx_f_7sklearn_4tree_5_tree_9Criterion_update(CYTHON_UNUSED struct __pyx_obj_7sklearn_4tree_5_tree_Criterion *__pyx_v_self, CYTHON_UNUSED __pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_v_new_pos) {
/* function exit code */
}
/* "sklearn/tree/_tree.pyx":116
* pass
*
* cdef double node_impurity(self) nogil: # <<<<<<<<<<<<<<
* """Evaluate the impurity of the current node, i.e. the impurity of
* samples[start:end]."""
*/
static double __pyx_f_7sklearn_4tree_5_tree_9Criterion_node_impurity(CYTHON_UNUSED struct __pyx_obj_7sklearn_4tree_5_tree_Criterion *__pyx_v_self) {
double __pyx_r;
/* function exit code */
__pyx_r = 0;
return __pyx_r;
}
/* "sklearn/tree/_tree.pyx":121
* pass
*
* cdef void children_impurity(self, double* impurity_left, # <<<<<<<<<<<<<<
* double* impurity_right) nogil:
* """Evaluate the impurity in children nodes, i.e. the impurity of
*/
static void __pyx_f_7sklearn_4tree_5_tree_9Criterion_children_impurity(CYTHON_UNUSED struct __pyx_obj_7sklearn_4tree_5_tree_Criterion *__pyx_v_self, CYTHON_UNUSED double *__pyx_v_impurity_left, CYTHON_UNUSED double *__pyx_v_impurity_right) {
/* function exit code */
}
/* "sklearn/tree/_tree.pyx":127
* pass
*
* cdef void node_value(self, double* dest) nogil: # <<<<<<<<<<<<<<
* """Compute the node value of samples[start:end] into dest."""
* pass
*/
static void __pyx_f_7sklearn_4tree_5_tree_9Criterion_node_value(CYTHON_UNUSED struct __pyx_obj_7sklearn_4tree_5_tree_Criterion *__pyx_v_self, CYTHON_UNUSED double *__pyx_v_dest) {
/* function exit code */
}
/* "sklearn/tree/_tree.pyx":131
* pass
*
* cdef double impurity_improvement(self, double impurity) nogil: # <<<<<<<<<<<<<<
* """Weighted impurity improvement, i.e.
*
*/
static double __pyx_f_7sklearn_4tree_5_tree_9Criterion_impurity_improvement(struct __pyx_obj_7sklearn_4tree_5_tree_Criterion *__pyx_v_self, double __pyx_v_impurity) {
double __pyx_v_impurity_left;
double __pyx_v_impurity_right;
double __pyx_r;
/* "sklearn/tree/_tree.pyx":143
* cdef double impurity_right
*
* self.children_impurity(&impurity_left, &impurity_right) # <<<<<<<<<<<<<<
*
* return ((self.weighted_n_node_samples / self.weighted_n_samples) *
*/
((struct __pyx_vtabstruct_7sklearn_4tree_5_tree_Criterion *)__pyx_v_self->__pyx_vtab)->children_impurity(__pyx_v_self, (&__pyx_v_impurity_left), (&__pyx_v_impurity_right));
/* "sklearn/tree/_tree.pyx":145
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*
* return ((self.weighted_n_node_samples / self.weighted_n_samples) * # <<<<<<<<<<<<<<
* (impurity - self.weighted_n_right / self.weighted_n_node_samples * impurity_right
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goto __pyx_L0;
/* "sklearn/tree/_tree.pyx":131
* pass
*
* cdef double impurity_improvement(self, double impurity) nogil: # <<<<<<<<<<<<<<
* """Weighted impurity improvement, i.e.
*
*/
/* function exit code */
__pyx_L0:;
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/* "sklearn/tree/_tree.pyx":158
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* def __cinit__(self, SIZE_t n_outputs, # <<<<<<<<<<<<<<
* np.ndarray[SIZE_t, ndim=1] n_classes):
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*/
/* Python wrapper */
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* self.weighted_n_samples = weighted_n_samples
*/
__pyx_v_self->__pyx_base.end = __pyx_v_end;
/* "sklearn/tree/_tree.pyx":238
* self.start = start
* self.end = end
* self.n_node_samples = end - start # <<<<<<<<<<<<<<
* self.weighted_n_samples = weighted_n_samples
* cdef double weighted_n_node_samples = 0.0
*/
__pyx_v_self->__pyx_base.n_node_samples = (__pyx_v_end - __pyx_v_start);
/* "sklearn/tree/_tree.pyx":239
* self.end = end
* self.n_node_samples = end - start
* self.weighted_n_samples = weighted_n_samples # <<<<<<<<<<<<<<
* cdef double weighted_n_node_samples = 0.0
*
*/
__pyx_v_self->__pyx_base.weighted_n_samples = __pyx_v_weighted_n_samples;
/* "sklearn/tree/_tree.pyx":240
* self.n_node_samples = end - start
* self.weighted_n_samples = weighted_n_samples
* cdef double weighted_n_node_samples = 0.0 # <<<<<<<<<<<<<<
*
* # Initialize label_count_total and weighted_n_node_samples
*/
__pyx_v_weighted_n_node_samples = 0.0;
/* "sklearn/tree/_tree.pyx":243
*
* # Initialize label_count_total and weighted_n_node_samples
* cdef SIZE_t n_outputs = self.n_outputs # <<<<<<<<<<<<<<
* cdef SIZE_t* n_classes = self.n_classes
* cdef SIZE_t label_count_stride = self.label_count_stride
*/
__pyx_t_1 = __pyx_v_self->__pyx_base.n_outputs;
__pyx_v_n_outputs = __pyx_t_1;
/* "sklearn/tree/_tree.pyx":244
* # Initialize label_count_total and weighted_n_node_samples
* cdef SIZE_t n_outputs = self.n_outputs
* cdef SIZE_t* n_classes = self.n_classes # <<<<<<<<<<<<<<
* cdef SIZE_t label_count_stride = self.label_count_stride
* cdef double* label_count_total = self.label_count_total
*/
__pyx_t_2 = __pyx_v_self->n_classes;
__pyx_v_n_classes = __pyx_t_2;
/* "sklearn/tree/_tree.pyx":245
* cdef SIZE_t n_outputs = self.n_outputs
* cdef SIZE_t* n_classes = self.n_classes
* cdef SIZE_t label_count_stride = self.label_count_stride # <<<<<<<<<<<<<<
* cdef double* label_count_total = self.label_count_total
*
*/
__pyx_t_1 = __pyx_v_self->label_count_stride;
__pyx_v_label_count_stride = __pyx_t_1;
/* "sklearn/tree/_tree.pyx":246
* cdef SIZE_t* n_classes = self.n_classes
* cdef SIZE_t label_count_stride = self.label_count_stride
* cdef double* label_count_total = self.label_count_total # <<<<<<<<<<<<<<
*
* cdef SIZE_t i = 0
*/
__pyx_t_3 = __pyx_v_self->label_count_total;
__pyx_v_label_count_total = __pyx_t_3;
/* "sklearn/tree/_tree.pyx":248
* cdef double* label_count_total = self.label_count_total
*
* cdef SIZE_t i = 0 # <<<<<<<<<<<<<<
* cdef SIZE_t p = 0
* cdef SIZE_t k = 0
*/
__pyx_v_i = 0;
/* "sklearn/tree/_tree.pyx":249
*
* cdef SIZE_t i = 0
* cdef SIZE_t p = 0 # <<<<<<<<<<<<<<
* cdef SIZE_t k = 0
* cdef SIZE_t c = 0
*/
__pyx_v_p = 0;
/* "sklearn/tree/_tree.pyx":250
* cdef SIZE_t i = 0
* cdef SIZE_t p = 0
* cdef SIZE_t k = 0 # <<<<<<<<<<<<<<
* cdef SIZE_t c = 0
* cdef DOUBLE_t w = 1.0
*/
__pyx_v_k = 0;
/* "sklearn/tree/_tree.pyx":251
* cdef SIZE_t p = 0
* cdef SIZE_t k = 0
* cdef SIZE_t c = 0 # <<<<<<<<<<<<<<
* cdef DOUBLE_t w = 1.0
* cdef SIZE_t offset = 0
*/
__pyx_v_c = 0;
/* "sklearn/tree/_tree.pyx":252
* cdef SIZE_t k = 0
* cdef SIZE_t c = 0
* cdef DOUBLE_t w = 1.0 # <<<<<<<<<<<<<<
* cdef SIZE_t offset = 0
*
*/
__pyx_v_w = 1.0;
/* "sklearn/tree/_tree.pyx":253
* cdef SIZE_t c = 0
* cdef DOUBLE_t w = 1.0
* cdef SIZE_t offset = 0 # <<<<<<<<<<<<<<
*
* for k in range(n_outputs):
*/
__pyx_v_offset = 0;
/* "sklearn/tree/_tree.pyx":255
* cdef SIZE_t offset = 0
*
* for k in range(n_outputs): # <<<<<<<<<<<<<<
* memset(label_count_total + offset, 0,
* n_classes[k] * sizeof(double))
*/
__pyx_t_1 = __pyx_v_n_outputs;
for (__pyx_t_4 = 0; __pyx_t_4 < __pyx_t_1; __pyx_t_4+=1) {
__pyx_v_k = __pyx_t_4;
/* "sklearn/tree/_tree.pyx":256
*
* for k in range(n_outputs):
* memset(label_count_total + offset, 0, # <<<<<<<<<<<<<<
* n_classes[k] * sizeof(double))
* offset += label_count_stride
*/
memset((__pyx_v_label_count_total + __pyx_v_offset), 0, ((__pyx_v_n_classes[__pyx_v_k]) * (sizeof(double))));
/* "sklearn/tree/_tree.pyx":258
* memset(label_count_total + offset, 0,
* n_classes[k] * sizeof(double))
* offset += label_count_stride # <<<<<<<<<<<<<<
*
* for p in range(start, end):
*/
__pyx_v_offset = (__pyx_v_offset + __pyx_v_label_count_stride);
}
/* "sklearn/tree/_tree.pyx":260
* offset += label_count_stride
*
* for p in range(start, end): # <<<<<<<<<<<<<<
* i = samples[p]
*
*/
__pyx_t_1 = __pyx_v_end;
for (__pyx_t_4 = __pyx_v_start; __pyx_t_4 < __pyx_t_1; __pyx_t_4+=1) {
__pyx_v_p = __pyx_t_4;
/* "sklearn/tree/_tree.pyx":261
*
* for p in range(start, end):
* i = samples[p] # <<<<<<<<<<<<<<
*
* if sample_weight != NULL:
*/
__pyx_v_i = (__pyx_v_samples[__pyx_v_p]);
/* "sklearn/tree/_tree.pyx":263
* i = samples[p]
*
* if sample_weight != NULL: # <<<<<<<<<<<<<<
* w = sample_weight[i]
*
*/
__pyx_t_5 = ((__pyx_v_sample_weight != NULL) != 0);
if (__pyx_t_5) {
/* "sklearn/tree/_tree.pyx":264
*
* if sample_weight != NULL:
* w = sample_weight[i] # <<<<<<<<<<<<<<
*
* for k in range(n_outputs):
*/
__pyx_v_w = (__pyx_v_sample_weight[__pyx_v_i]);
goto __pyx_L7;
}
__pyx_L7:;
/* "sklearn/tree/_tree.pyx":266
* w = sample_weight[i]
*
* for k in range(n_outputs): # <<<<<<<<<<<<<<
* c = <SIZE_t> y[i * y_stride + k]
* label_count_total[k * label_count_stride + c] += w
*/
__pyx_t_6 = __pyx_v_n_outputs;
for (__pyx_t_7 = 0; __pyx_t_7 < __pyx_t_6; __pyx_t_7+=1) {
__pyx_v_k = __pyx_t_7;
/* "sklearn/tree/_tree.pyx":267
*
* for k in range(n_outputs):
* c = <SIZE_t> y[i * y_stride + k] # <<<<<<<<<<<<<<
* label_count_total[k * label_count_stride + c] += w
*
*/
__pyx_v_c = ((__pyx_t_7sklearn_4tree_5_tree_SIZE_t)(__pyx_v_y[((__pyx_v_i * __pyx_v_y_stride) + __pyx_v_k)]));
/* "sklearn/tree/_tree.pyx":268
* for k in range(n_outputs):
* c = <SIZE_t> y[i * y_stride + k]
* label_count_total[k * label_count_stride + c] += w # <<<<<<<<<<<<<<
*
* weighted_n_node_samples += w
*/
__pyx_t_8 = ((__pyx_v_k * __pyx_v_label_count_stride) + __pyx_v_c);
(__pyx_v_label_count_total[__pyx_t_8]) = ((__pyx_v_label_count_total[__pyx_t_8]) + __pyx_v_w);
}
/* "sklearn/tree/_tree.pyx":270
* label_count_total[k * label_count_stride + c] += w
*
* weighted_n_node_samples += w # <<<<<<<<<<<<<<
*
* self.weighted_n_node_samples = weighted_n_node_samples
*/
__pyx_v_weighted_n_node_samples = (__pyx_v_weighted_n_node_samples + __pyx_v_w);
}
/* "sklearn/tree/_tree.pyx":272
* weighted_n_node_samples += w
*
* self.weighted_n_node_samples = weighted_n_node_samples # <<<<<<<<<<<<<<
*
* # Reset to pos=start
*/
__pyx_v_self->__pyx_base.weighted_n_node_samples = __pyx_v_weighted_n_node_samples;
/* "sklearn/tree/_tree.pyx":275
*
* # Reset to pos=start
* self.reset() # <<<<<<<<<<<<<<
*
* cdef void reset(self) nogil:
*/
((struct __pyx_vtabstruct_7sklearn_4tree_5_tree_ClassificationCriterion *)__pyx_v_self->__pyx_base.__pyx_vtab)->__pyx_base.reset(((struct __pyx_obj_7sklearn_4tree_5_tree_Criterion *)__pyx_v_self));
/* "sklearn/tree/_tree.pyx":226
* pass
*
* cdef void init(self, DOUBLE_t* y, SIZE_t y_stride, # <<<<<<<<<<<<<<
* DOUBLE_t* sample_weight, double weighted_n_samples,
* SIZE_t* samples, SIZE_t start, SIZE_t end) nogil:
*/
/* function exit code */
}
/* "sklearn/tree/_tree.pyx":277
* self.reset()
*
* cdef void reset(self) nogil: # <<<<<<<<<<<<<<
* """Reset the criterion at pos=start."""
* self.pos = self.start
*/
static void __pyx_f_7sklearn_4tree_5_tree_23ClassificationCriterion_reset(struct __pyx_obj_7sklearn_4tree_5_tree_ClassificationCriterion *__pyx_v_self) {
__pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_v_n_outputs;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t *__pyx_v_n_classes;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_v_label_count_stride;
double *__pyx_v_label_count_total;
double *__pyx_v_label_count_left;
double *__pyx_v_label_count_right;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_v_k;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_t_1;
double __pyx_t_2;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t *__pyx_t_3;
double *__pyx_t_4;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_t_5;
/* "sklearn/tree/_tree.pyx":279
* cdef void reset(self) nogil:
* """Reset the criterion at pos=start."""
* self.pos = self.start # <<<<<<<<<<<<<<
*
* self.weighted_n_left = 0.0
*/
__pyx_t_1 = __pyx_v_self->__pyx_base.start;
__pyx_v_self->__pyx_base.pos = __pyx_t_1;
/* "sklearn/tree/_tree.pyx":281
* self.pos = self.start
*
* self.weighted_n_left = 0.0 # <<<<<<<<<<<<<<
* self.weighted_n_right = self.weighted_n_node_samples
*
*/
__pyx_v_self->__pyx_base.weighted_n_left = 0.0;
/* "sklearn/tree/_tree.pyx":282
*
* self.weighted_n_left = 0.0
* self.weighted_n_right = self.weighted_n_node_samples # <<<<<<<<<<<<<<
*
* cdef SIZE_t n_outputs = self.n_outputs
*/
__pyx_t_2 = __pyx_v_self->__pyx_base.weighted_n_node_samples;
__pyx_v_self->__pyx_base.weighted_n_right = __pyx_t_2;
/* "sklearn/tree/_tree.pyx":284
* self.weighted_n_right = self.weighted_n_node_samples
*
* cdef SIZE_t n_outputs = self.n_outputs # <<<<<<<<<<<<<<
* cdef SIZE_t* n_classes = self.n_classes
* cdef SIZE_t label_count_stride = self.label_count_stride
*/
__pyx_t_1 = __pyx_v_self->__pyx_base.n_outputs;
__pyx_v_n_outputs = __pyx_t_1;
/* "sklearn/tree/_tree.pyx":285
*
* cdef SIZE_t n_outputs = self.n_outputs
* cdef SIZE_t* n_classes = self.n_classes # <<<<<<<<<<<<<<
* cdef SIZE_t label_count_stride = self.label_count_stride
* cdef double* label_count_total = self.label_count_total
*/
__pyx_t_3 = __pyx_v_self->n_classes;
__pyx_v_n_classes = __pyx_t_3;
/* "sklearn/tree/_tree.pyx":286
* cdef SIZE_t n_outputs = self.n_outputs
* cdef SIZE_t* n_classes = self.n_classes
* cdef SIZE_t label_count_stride = self.label_count_stride # <<<<<<<<<<<<<<
* cdef double* label_count_total = self.label_count_total
* cdef double* label_count_left = self.label_count_left
*/
__pyx_t_1 = __pyx_v_self->label_count_stride;
__pyx_v_label_count_stride = __pyx_t_1;
/* "sklearn/tree/_tree.pyx":287
* cdef SIZE_t* n_classes = self.n_classes
* cdef SIZE_t label_count_stride = self.label_count_stride
* cdef double* label_count_total = self.label_count_total # <<<<<<<<<<<<<<
* cdef double* label_count_left = self.label_count_left
* cdef double* label_count_right = self.label_count_right
*/
__pyx_t_4 = __pyx_v_self->label_count_total;
__pyx_v_label_count_total = __pyx_t_4;
/* "sklearn/tree/_tree.pyx":288
* cdef SIZE_t label_count_stride = self.label_count_stride
* cdef double* label_count_total = self.label_count_total
* cdef double* label_count_left = self.label_count_left # <<<<<<<<<<<<<<
* cdef double* label_count_right = self.label_count_right
*
*/
__pyx_t_4 = __pyx_v_self->label_count_left;
__pyx_v_label_count_left = __pyx_t_4;
/* "sklearn/tree/_tree.pyx":289
* cdef double* label_count_total = self.label_count_total
* cdef double* label_count_left = self.label_count_left
* cdef double* label_count_right = self.label_count_right # <<<<<<<<<<<<<<
*
* cdef SIZE_t k = 0
*/
__pyx_t_4 = __pyx_v_self->label_count_right;
__pyx_v_label_count_right = __pyx_t_4;
/* "sklearn/tree/_tree.pyx":291
* cdef double* label_count_right = self.label_count_right
*
* cdef SIZE_t k = 0 # <<<<<<<<<<<<<<
*
* for k in range(n_outputs):
*/
__pyx_v_k = 0;
/* "sklearn/tree/_tree.pyx":293
* cdef SIZE_t k = 0
*
* for k in range(n_outputs): # <<<<<<<<<<<<<<
* memset(label_count_left, 0, n_classes[k] * sizeof(double))
* memcpy(label_count_right, label_count_total,
*/
__pyx_t_1 = __pyx_v_n_outputs;
for (__pyx_t_5 = 0; __pyx_t_5 < __pyx_t_1; __pyx_t_5+=1) {
__pyx_v_k = __pyx_t_5;
/* "sklearn/tree/_tree.pyx":294
*
* for k in range(n_outputs):
* memset(label_count_left, 0, n_classes[k] * sizeof(double)) # <<<<<<<<<<<<<<
* memcpy(label_count_right, label_count_total,
* n_classes[k] * sizeof(double))
*/
memset(__pyx_v_label_count_left, 0, ((__pyx_v_n_classes[__pyx_v_k]) * (sizeof(double))));
/* "sklearn/tree/_tree.pyx":295
* for k in range(n_outputs):
* memset(label_count_left, 0, n_classes[k] * sizeof(double))
* memcpy(label_count_right, label_count_total, # <<<<<<<<<<<<<<
* n_classes[k] * sizeof(double))
*
*/
memcpy(__pyx_v_label_count_right, __pyx_v_label_count_total, ((__pyx_v_n_classes[__pyx_v_k]) * (sizeof(double))));
/* "sklearn/tree/_tree.pyx":298
* n_classes[k] * sizeof(double))
*
* label_count_total += label_count_stride # <<<<<<<<<<<<<<
* label_count_left += label_count_stride
* label_count_right += label_count_stride
*/
__pyx_v_label_count_total = (__pyx_v_label_count_total + __pyx_v_label_count_stride);
/* "sklearn/tree/_tree.pyx":299
*
* label_count_total += label_count_stride
* label_count_left += label_count_stride # <<<<<<<<<<<<<<
* label_count_right += label_count_stride
*
*/
__pyx_v_label_count_left = (__pyx_v_label_count_left + __pyx_v_label_count_stride);
/* "sklearn/tree/_tree.pyx":300
* label_count_total += label_count_stride
* label_count_left += label_count_stride
* label_count_right += label_count_stride # <<<<<<<<<<<<<<
*
* cdef void update(self, SIZE_t new_pos) nogil:
*/
__pyx_v_label_count_right = (__pyx_v_label_count_right + __pyx_v_label_count_stride);
}
/* "sklearn/tree/_tree.pyx":277
* self.reset()
*
* cdef void reset(self) nogil: # <<<<<<<<<<<<<<
* """Reset the criterion at pos=start."""
* self.pos = self.start
*/
/* function exit code */
}
/* "sklearn/tree/_tree.pyx":302
* label_count_right += label_count_stride
*
* cdef void update(self, SIZE_t new_pos) nogil: # <<<<<<<<<<<<<<
* """Update the collected statistics by moving samples[pos:new_pos] from
* the right child to the left child."""
*/
static void __pyx_f_7sklearn_4tree_5_tree_23ClassificationCriterion_update(struct __pyx_obj_7sklearn_4tree_5_tree_ClassificationCriterion *__pyx_v_self, __pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_v_new_pos) {
__pyx_t_7sklearn_4tree_5_tree_DOUBLE_t *__pyx_v_y;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_v_y_stride;
__pyx_t_7sklearn_4tree_5_tree_DOUBLE_t *__pyx_v_sample_weight;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t *__pyx_v_samples;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_v_pos;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_v_n_outputs;
CYTHON_UNUSED __pyx_t_7sklearn_4tree_5_tree_SIZE_t *__pyx_v_n_classes;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_v_label_count_stride;
CYTHON_UNUSED double *__pyx_v_label_count_total;
double *__pyx_v_label_count_left;
double *__pyx_v_label_count_right;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_v_i;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_v_p;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_v_k;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_v_label_index;
__pyx_t_7sklearn_4tree_5_tree_DOUBLE_t __pyx_v_w;
__pyx_t_7sklearn_4tree_5_tree_DOUBLE_t __pyx_v_diff_w;
__pyx_t_7sklearn_4tree_5_tree_DOUBLE_t *__pyx_t_1;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_t_2;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t *__pyx_t_3;
double *__pyx_t_4;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_t_5;
int __pyx_t_6;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_t_7;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_t_8;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_t_9;
/* "sklearn/tree/_tree.pyx":305
* """Update the collected statistics by moving samples[pos:new_pos] from
* the right child to the left child."""
* cdef DOUBLE_t* y = self.y # <<<<<<<<<<<<<<
* cdef SIZE_t y_stride = self.y_stride
* cdef DOUBLE_t* sample_weight = self.sample_weight
*/
__pyx_t_1 = __pyx_v_self->__pyx_base.y;
__pyx_v_y = __pyx_t_1;
/* "sklearn/tree/_tree.pyx":306
* the right child to the left child."""
* cdef DOUBLE_t* y = self.y
* cdef SIZE_t y_stride = self.y_stride # <<<<<<<<<<<<<<
* cdef DOUBLE_t* sample_weight = self.sample_weight
*
*/
__pyx_t_2 = __pyx_v_self->__pyx_base.y_stride;
__pyx_v_y_stride = __pyx_t_2;
/* "sklearn/tree/_tree.pyx":307
* cdef DOUBLE_t* y = self.y
* cdef SIZE_t y_stride = self.y_stride
* cdef DOUBLE_t* sample_weight = self.sample_weight # <<<<<<<<<<<<<<
*
* cdef SIZE_t* samples = self.samples
*/
__pyx_t_1 = __pyx_v_self->__pyx_base.sample_weight;
__pyx_v_sample_weight = __pyx_t_1;
/* "sklearn/tree/_tree.pyx":309
* cdef DOUBLE_t* sample_weight = self.sample_weight
*
* cdef SIZE_t* samples = self.samples # <<<<<<<<<<<<<<
* cdef SIZE_t pos = self.pos
*
*/
__pyx_t_3 = __pyx_v_self->__pyx_base.samples;
__pyx_v_samples = __pyx_t_3;
/* "sklearn/tree/_tree.pyx":310
*
* cdef SIZE_t* samples = self.samples
* cdef SIZE_t pos = self.pos # <<<<<<<<<<<<<<
*
* cdef SIZE_t n_outputs = self.n_outputs
*/
__pyx_t_2 = __pyx_v_self->__pyx_base.pos;
__pyx_v_pos = __pyx_t_2;
/* "sklearn/tree/_tree.pyx":312
* cdef SIZE_t pos = self.pos
*
* cdef SIZE_t n_outputs = self.n_outputs # <<<<<<<<<<<<<<
* cdef SIZE_t* n_classes = self.n_classes
* cdef SIZE_t label_count_stride = self.label_count_stride
*/
__pyx_t_2 = __pyx_v_self->__pyx_base.n_outputs;
__pyx_v_n_outputs = __pyx_t_2;
/* "sklearn/tree/_tree.pyx":313
*
* cdef SIZE_t n_outputs = self.n_outputs
* cdef SIZE_t* n_classes = self.n_classes # <<<<<<<<<<<<<<
* cdef SIZE_t label_count_stride = self.label_count_stride
* cdef double* label_count_total = self.label_count_total
*/
__pyx_t_3 = __pyx_v_self->n_classes;
__pyx_v_n_classes = __pyx_t_3;
/* "sklearn/tree/_tree.pyx":314
* cdef SIZE_t n_outputs = self.n_outputs
* cdef SIZE_t* n_classes = self.n_classes
* cdef SIZE_t label_count_stride = self.label_count_stride # <<<<<<<<<<<<<<
* cdef double* label_count_total = self.label_count_total
* cdef double* label_count_left = self.label_count_left
*/
__pyx_t_2 = __pyx_v_self->label_count_stride;
__pyx_v_label_count_stride = __pyx_t_2;
/* "sklearn/tree/_tree.pyx":315
* cdef SIZE_t* n_classes = self.n_classes
* cdef SIZE_t label_count_stride = self.label_count_stride
* cdef double* label_count_total = self.label_count_total # <<<<<<<<<<<<<<
* cdef double* label_count_left = self.label_count_left
* cdef double* label_count_right = self.label_count_right
*/
__pyx_t_4 = __pyx_v_self->label_count_total;
__pyx_v_label_count_total = __pyx_t_4;
/* "sklearn/tree/_tree.pyx":316
* cdef SIZE_t label_count_stride = self.label_count_stride
* cdef double* label_count_total = self.label_count_total
* cdef double* label_count_left = self.label_count_left # <<<<<<<<<<<<<<
* cdef double* label_count_right = self.label_count_right
*
*/
__pyx_t_4 = __pyx_v_self->label_count_left;
__pyx_v_label_count_left = __pyx_t_4;
/* "sklearn/tree/_tree.pyx":317
* cdef double* label_count_total = self.label_count_total
* cdef double* label_count_left = self.label_count_left
* cdef double* label_count_right = self.label_count_right # <<<<<<<<<<<<<<
*
* cdef SIZE_t i
*/
__pyx_t_4 = __pyx_v_self->label_count_right;
__pyx_v_label_count_right = __pyx_t_4;
/* "sklearn/tree/_tree.pyx":323
* cdef SIZE_t k
* cdef SIZE_t label_index
* cdef DOUBLE_t w = 1.0 # <<<<<<<<<<<<<<
* cdef DOUBLE_t diff_w = 0.0
*
*/
__pyx_v_w = 1.0;
/* "sklearn/tree/_tree.pyx":324
* cdef SIZE_t label_index
* cdef DOUBLE_t w = 1.0
* cdef DOUBLE_t diff_w = 0.0 # <<<<<<<<<<<<<<
*
* # Note: We assume start <= pos < new_pos <= end
*/
__pyx_v_diff_w = 0.0;
/* "sklearn/tree/_tree.pyx":328
* # Note: We assume start <= pos < new_pos <= end
*
* for p in range(pos, new_pos): # <<<<<<<<<<<<<<
* i = samples[p]
*
*/
__pyx_t_2 = __pyx_v_new_pos;
for (__pyx_t_5 = __pyx_v_pos; __pyx_t_5 < __pyx_t_2; __pyx_t_5+=1) {
__pyx_v_p = __pyx_t_5;
/* "sklearn/tree/_tree.pyx":329
*
* for p in range(pos, new_pos):
* i = samples[p] # <<<<<<<<<<<<<<
*
* if sample_weight != NULL:
*/
__pyx_v_i = (__pyx_v_samples[__pyx_v_p]);
/* "sklearn/tree/_tree.pyx":331
* i = samples[p]
*
* if sample_weight != NULL: # <<<<<<<<<<<<<<
* w = sample_weight[i]
*
*/
__pyx_t_6 = ((__pyx_v_sample_weight != NULL) != 0);
if (__pyx_t_6) {
/* "sklearn/tree/_tree.pyx":332
*
* if sample_weight != NULL:
* w = sample_weight[i] # <<<<<<<<<<<<<<
*
* for k in range(n_outputs):
*/
__pyx_v_w = (__pyx_v_sample_weight[__pyx_v_i]);
goto __pyx_L5;
}
__pyx_L5:;
/* "sklearn/tree/_tree.pyx":334
* w = sample_weight[i]
*
* for k in range(n_outputs): # <<<<<<<<<<<<<<
* label_index = (k * label_count_stride +
* <SIZE_t> y[i * y_stride + k])
*/
__pyx_t_7 = __pyx_v_n_outputs;
for (__pyx_t_8 = 0; __pyx_t_8 < __pyx_t_7; __pyx_t_8+=1) {
__pyx_v_k = __pyx_t_8;
/* "sklearn/tree/_tree.pyx":335
*
* for k in range(n_outputs):
* label_index = (k * label_count_stride + # <<<<<<<<<<<<<<
* <SIZE_t> y[i * y_stride + k])
* label_count_left[label_index] += w
*/
__pyx_v_label_index = ((__pyx_v_k * __pyx_v_label_count_stride) + ((__pyx_t_7sklearn_4tree_5_tree_SIZE_t)(__pyx_v_y[((__pyx_v_i * __pyx_v_y_stride) + __pyx_v_k)])));
/* "sklearn/tree/_tree.pyx":337
* label_index = (k * label_count_stride +
* <SIZE_t> y[i * y_stride + k])
* label_count_left[label_index] += w # <<<<<<<<<<<<<<
* label_count_right[label_index] -= w
*
*/
__pyx_t_9 = __pyx_v_label_index;
(__pyx_v_label_count_left[__pyx_t_9]) = ((__pyx_v_label_count_left[__pyx_t_9]) + __pyx_v_w);
/* "sklearn/tree/_tree.pyx":338
* <SIZE_t> y[i * y_stride + k])
* label_count_left[label_index] += w
* label_count_right[label_index] -= w # <<<<<<<<<<<<<<
*
* diff_w += w
*/
__pyx_t_9 = __pyx_v_label_index;
(__pyx_v_label_count_right[__pyx_t_9]) = ((__pyx_v_label_count_right[__pyx_t_9]) - __pyx_v_w);
}
/* "sklearn/tree/_tree.pyx":340
* label_count_right[label_index] -= w
*
* diff_w += w # <<<<<<<<<<<<<<
*
* self.weighted_n_left += diff_w
*/
__pyx_v_diff_w = (__pyx_v_diff_w + __pyx_v_w);
}
/* "sklearn/tree/_tree.pyx":342
* diff_w += w
*
* self.weighted_n_left += diff_w # <<<<<<<<<<<<<<
* self.weighted_n_right -= diff_w
*
*/
__pyx_v_self->__pyx_base.weighted_n_left = (__pyx_v_self->__pyx_base.weighted_n_left + __pyx_v_diff_w);
/* "sklearn/tree/_tree.pyx":343
*
* self.weighted_n_left += diff_w
* self.weighted_n_right -= diff_w # <<<<<<<<<<<<<<
*
* self.pos = new_pos
*/
__pyx_v_self->__pyx_base.weighted_n_right = (__pyx_v_self->__pyx_base.weighted_n_right - __pyx_v_diff_w);
/* "sklearn/tree/_tree.pyx":345
* self.weighted_n_right -= diff_w
*
* self.pos = new_pos # <<<<<<<<<<<<<<
*
* cdef double node_impurity(self) nogil:
*/
__pyx_v_self->__pyx_base.pos = __pyx_v_new_pos;
/* "sklearn/tree/_tree.pyx":302
* label_count_right += label_count_stride
*
* cdef void update(self, SIZE_t new_pos) nogil: # <<<<<<<<<<<<<<
* """Update the collected statistics by moving samples[pos:new_pos] from
* the right child to the left child."""
*/
/* function exit code */
}
/* "sklearn/tree/_tree.pyx":347
* self.pos = new_pos
*
* cdef double node_impurity(self) nogil: # <<<<<<<<<<<<<<
* pass
*
*/
static double __pyx_f_7sklearn_4tree_5_tree_23ClassificationCriterion_node_impurity(CYTHON_UNUSED struct __pyx_obj_7sklearn_4tree_5_tree_ClassificationCriterion *__pyx_v_self) {
double __pyx_r;
/* function exit code */
__pyx_r = 0;
return __pyx_r;
}
/* "sklearn/tree/_tree.pyx":350
* pass
*
* cdef void children_impurity(self, double* impurity_left, # <<<<<<<<<<<<<<
* double* impurity_right) nogil:
* pass
*/
static void __pyx_f_7sklearn_4tree_5_tree_23ClassificationCriterion_children_impurity(CYTHON_UNUSED struct __pyx_obj_7sklearn_4tree_5_tree_ClassificationCriterion *__pyx_v_self, CYTHON_UNUSED double *__pyx_v_impurity_left, CYTHON_UNUSED double *__pyx_v_impurity_right) {
/* function exit code */
}
/* "sklearn/tree/_tree.pyx":354
* pass
*
* cdef void node_value(self, double* dest) nogil: # <<<<<<<<<<<<<<
* """Compute the node value of samples[start:end] into dest."""
* cdef SIZE_t n_outputs = self.n_outputs
*/
static void __pyx_f_7sklearn_4tree_5_tree_23ClassificationCriterion_node_value(struct __pyx_obj_7sklearn_4tree_5_tree_ClassificationCriterion *__pyx_v_self, double *__pyx_v_dest) {
__pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_v_n_outputs;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t *__pyx_v_n_classes;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_v_label_count_stride;
double *__pyx_v_label_count_total;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_v_k;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_t_1;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t *__pyx_t_2;
double *__pyx_t_3;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_t_4;
/* "sklearn/tree/_tree.pyx":356
* cdef void node_value(self, double* dest) nogil:
* """Compute the node value of samples[start:end] into dest."""
* cdef SIZE_t n_outputs = self.n_outputs # <<<<<<<<<<<<<<
* cdef SIZE_t* n_classes = self.n_classes
* cdef SIZE_t label_count_stride = self.label_count_stride
*/
__pyx_t_1 = __pyx_v_self->__pyx_base.n_outputs;
__pyx_v_n_outputs = __pyx_t_1;
/* "sklearn/tree/_tree.pyx":357
* """Compute the node value of samples[start:end] into dest."""
* cdef SIZE_t n_outputs = self.n_outputs
* cdef SIZE_t* n_classes = self.n_classes # <<<<<<<<<<<<<<
* cdef SIZE_t label_count_stride = self.label_count_stride
* cdef double* label_count_total = self.label_count_total
*/
__pyx_t_2 = __pyx_v_self->n_classes;
__pyx_v_n_classes = __pyx_t_2;
/* "sklearn/tree/_tree.pyx":358
* cdef SIZE_t n_outputs = self.n_outputs
* cdef SIZE_t* n_classes = self.n_classes
* cdef SIZE_t label_count_stride = self.label_count_stride # <<<<<<<<<<<<<<
* cdef double* label_count_total = self.label_count_total
* cdef SIZE_t k
*/
__pyx_t_1 = __pyx_v_self->label_count_stride;
__pyx_v_label_count_stride = __pyx_t_1;
/* "sklearn/tree/_tree.pyx":359
* cdef SIZE_t* n_classes = self.n_classes
* cdef SIZE_t label_count_stride = self.label_count_stride
* cdef double* label_count_total = self.label_count_total # <<<<<<<<<<<<<<
* cdef SIZE_t k
*
*/
__pyx_t_3 = __pyx_v_self->label_count_total;
__pyx_v_label_count_total = __pyx_t_3;
/* "sklearn/tree/_tree.pyx":362
* cdef SIZE_t k
*
* for k in range(n_outputs): # <<<<<<<<<<<<<<
* memcpy(dest, label_count_total, n_classes[k] * sizeof(double))
* dest += label_count_stride
*/
__pyx_t_1 = __pyx_v_n_outputs;
for (__pyx_t_4 = 0; __pyx_t_4 < __pyx_t_1; __pyx_t_4+=1) {
__pyx_v_k = __pyx_t_4;
/* "sklearn/tree/_tree.pyx":363
*
* for k in range(n_outputs):
* memcpy(dest, label_count_total, n_classes[k] * sizeof(double)) # <<<<<<<<<<<<<<
* dest += label_count_stride
* label_count_total += label_count_stride
*/
memcpy(__pyx_v_dest, __pyx_v_label_count_total, ((__pyx_v_n_classes[__pyx_v_k]) * (sizeof(double))));
/* "sklearn/tree/_tree.pyx":364
* for k in range(n_outputs):
* memcpy(dest, label_count_total, n_classes[k] * sizeof(double))
* dest += label_count_stride # <<<<<<<<<<<<<<
* label_count_total += label_count_stride
*
*/
__pyx_v_dest = (__pyx_v_dest + __pyx_v_label_count_stride);
/* "sklearn/tree/_tree.pyx":365
* memcpy(dest, label_count_total, n_classes[k] * sizeof(double))
* dest += label_count_stride
* label_count_total += label_count_stride # <<<<<<<<<<<<<<
*
*
*/
__pyx_v_label_count_total = (__pyx_v_label_count_total + __pyx_v_label_count_stride);
}
/* "sklearn/tree/_tree.pyx":354
* pass
*
* cdef void node_value(self, double* dest) nogil: # <<<<<<<<<<<<<<
* """Compute the node value of samples[start:end] into dest."""
* cdef SIZE_t n_outputs = self.n_outputs
*/
/* function exit code */
}
/* "sklearn/tree/_tree.pyx":382
* cross-entropy = - \sum_{k=0}^{K-1} pmk log(pmk)
* """
* cdef double node_impurity(self) nogil: # <<<<<<<<<<<<<<
* """Evaluate the impurity of the current node, i.e. the impurity of
* samples[start:end]."""
*/
static double __pyx_f_7sklearn_4tree_5_tree_7Entropy_node_impurity(struct __pyx_obj_7sklearn_4tree_5_tree_Entropy *__pyx_v_self) {
double __pyx_v_weighted_n_node_samples;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_v_n_outputs;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t *__pyx_v_n_classes;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_v_label_count_stride;
double *__pyx_v_label_count_total;
double __pyx_v_entropy;
double __pyx_v_total;
double __pyx_v_tmp;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_v_k;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_v_c;
double __pyx_r;
double __pyx_t_1;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_t_2;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t *__pyx_t_3;
double *__pyx_t_4;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_t_5;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_t_6;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_t_7;
int __pyx_t_8;
/* "sklearn/tree/_tree.pyx":385
* """Evaluate the impurity of the current node, i.e. the impurity of
* samples[start:end]."""
* cdef double weighted_n_node_samples = self.weighted_n_node_samples # <<<<<<<<<<<<<<
*
* cdef SIZE_t n_outputs = self.n_outputs
*/
__pyx_t_1 = __pyx_v_self->__pyx_base.__pyx_base.weighted_n_node_samples;
__pyx_v_weighted_n_node_samples = __pyx_t_1;
/* "sklearn/tree/_tree.pyx":387
* cdef double weighted_n_node_samples = self.weighted_n_node_samples
*
* cdef SIZE_t n_outputs = self.n_outputs # <<<<<<<<<<<<<<
* cdef SIZE_t* n_classes = self.n_classes
* cdef SIZE_t label_count_stride = self.label_count_stride
*/
__pyx_t_2 = __pyx_v_self->__pyx_base.__pyx_base.n_outputs;
__pyx_v_n_outputs = __pyx_t_2;
/* "sklearn/tree/_tree.pyx":388
*
* cdef SIZE_t n_outputs = self.n_outputs
* cdef SIZE_t* n_classes = self.n_classes # <<<<<<<<<<<<<<
* cdef SIZE_t label_count_stride = self.label_count_stride
* cdef double* label_count_total = self.label_count_total
*/
__pyx_t_3 = __pyx_v_self->__pyx_base.n_classes;
__pyx_v_n_classes = __pyx_t_3;
/* "sklearn/tree/_tree.pyx":389
* cdef SIZE_t n_outputs = self.n_outputs
* cdef SIZE_t* n_classes = self.n_classes
* cdef SIZE_t label_count_stride = self.label_count_stride # <<<<<<<<<<<<<<
* cdef double* label_count_total = self.label_count_total
*
*/
__pyx_t_2 = __pyx_v_self->__pyx_base.label_count_stride;
__pyx_v_label_count_stride = __pyx_t_2;
/* "sklearn/tree/_tree.pyx":390
* cdef SIZE_t* n_classes = self.n_classes
* cdef SIZE_t label_count_stride = self.label_count_stride
* cdef double* label_count_total = self.label_count_total # <<<<<<<<<<<<<<
*
* cdef double entropy = 0.0
*/
__pyx_t_4 = __pyx_v_self->__pyx_base.label_count_total;
__pyx_v_label_count_total = __pyx_t_4;
/* "sklearn/tree/_tree.pyx":392
* cdef double* label_count_total = self.label_count_total
*
* cdef double entropy = 0.0 # <<<<<<<<<<<<<<
* cdef double total = 0.0
* cdef double tmp
*/
__pyx_v_entropy = 0.0;
/* "sklearn/tree/_tree.pyx":393
*
* cdef double entropy = 0.0
* cdef double total = 0.0 # <<<<<<<<<<<<<<
* cdef double tmp
* cdef SIZE_t k
*/
__pyx_v_total = 0.0;
/* "sklearn/tree/_tree.pyx":398
* cdef SIZE_t c
*
* for k in range(n_outputs): # <<<<<<<<<<<<<<
* entropy = 0.0
*
*/
__pyx_t_2 = __pyx_v_n_outputs;
for (__pyx_t_5 = 0; __pyx_t_5 < __pyx_t_2; __pyx_t_5+=1) {
__pyx_v_k = __pyx_t_5;
/* "sklearn/tree/_tree.pyx":399
*
* for k in range(n_outputs):
* entropy = 0.0 # <<<<<<<<<<<<<<
*
* for c in range(n_classes[k]):
*/
__pyx_v_entropy = 0.0;
/* "sklearn/tree/_tree.pyx":401
* entropy = 0.0
*
* for c in range(n_classes[k]): # <<<<<<<<<<<<<<
* tmp = label_count_total[c]
* if tmp > 0.0:
*/
__pyx_t_6 = (__pyx_v_n_classes[__pyx_v_k]);
for (__pyx_t_7 = 0; __pyx_t_7 < __pyx_t_6; __pyx_t_7+=1) {
__pyx_v_c = __pyx_t_7;
/* "sklearn/tree/_tree.pyx":402
*
* for c in range(n_classes[k]):
* tmp = label_count_total[c] # <<<<<<<<<<<<<<
* if tmp > 0.0:
* tmp /= weighted_n_node_samples
*/
__pyx_v_tmp = (__pyx_v_label_count_total[__pyx_v_c]);
/* "sklearn/tree/_tree.pyx":403
* for c in range(n_classes[k]):
* tmp = label_count_total[c]
* if tmp > 0.0: # <<<<<<<<<<<<<<
* tmp /= weighted_n_node_samples
* entropy -= tmp * log(tmp)
*/
__pyx_t_8 = ((__pyx_v_tmp > 0.0) != 0);
if (__pyx_t_8) {
/* "sklearn/tree/_tree.pyx":404
* tmp = label_count_total[c]
* if tmp > 0.0:
* tmp /= weighted_n_node_samples # <<<<<<<<<<<<<<
* entropy -= tmp * log(tmp)
*
*/
__pyx_v_tmp = (__pyx_v_tmp / __pyx_v_weighted_n_node_samples);
/* "sklearn/tree/_tree.pyx":405
* if tmp > 0.0:
* tmp /= weighted_n_node_samples
* entropy -= tmp * log(tmp) # <<<<<<<<<<<<<<
*
* total += entropy
*/
__pyx_v_entropy = (__pyx_v_entropy - (__pyx_v_tmp * __pyx_f_7sklearn_4tree_5_tree_log(__pyx_v_tmp)));
goto __pyx_L7;
}
__pyx_L7:;
}
/* "sklearn/tree/_tree.pyx":407
* entropy -= tmp * log(tmp)
*
* total += entropy # <<<<<<<<<<<<<<
* label_count_total += label_count_stride
*
*/
__pyx_v_total = (__pyx_v_total + __pyx_v_entropy);
/* "sklearn/tree/_tree.pyx":408
*
* total += entropy
* label_count_total += label_count_stride # <<<<<<<<<<<<<<
*
* return total / n_outputs
*/
__pyx_v_label_count_total = (__pyx_v_label_count_total + __pyx_v_label_count_stride);
}
/* "sklearn/tree/_tree.pyx":410
* label_count_total += label_count_stride
*
* return total / n_outputs # <<<<<<<<<<<<<<
*
* cdef void children_impurity(self, double* impurity_left,
*/
__pyx_r = (__pyx_v_total / __pyx_v_n_outputs);
goto __pyx_L0;
/* "sklearn/tree/_tree.pyx":382
* cross-entropy = - \sum_{k=0}^{K-1} pmk log(pmk)
* """
* cdef double node_impurity(self) nogil: # <<<<<<<<<<<<<<
* """Evaluate the impurity of the current node, i.e. the impurity of
* samples[start:end]."""
*/
/* function exit code */
__pyx_L0:;
return __pyx_r;
}
/* "sklearn/tree/_tree.pyx":412
* return total / n_outputs
*
* cdef void children_impurity(self, double* impurity_left, # <<<<<<<<<<<<<<
* double* impurity_right) nogil:
* """Evaluate the impurity in children nodes, i.e. the impurity of the
*/
static void __pyx_f_7sklearn_4tree_5_tree_7Entropy_children_impurity(struct __pyx_obj_7sklearn_4tree_5_tree_Entropy *__pyx_v_self, double *__pyx_v_impurity_left, double *__pyx_v_impurity_right) {
CYTHON_UNUSED double __pyx_v_weighted_n_node_samples;
double __pyx_v_weighted_n_left;
double __pyx_v_weighted_n_right;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_v_n_outputs;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t *__pyx_v_n_classes;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_v_label_count_stride;
double *__pyx_v_label_count_left;
double *__pyx_v_label_count_right;
double __pyx_v_entropy_left;
double __pyx_v_entropy_right;
double __pyx_v_total_left;
double __pyx_v_total_right;
double __pyx_v_tmp;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_v_k;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_v_c;
double __pyx_t_1;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_t_2;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t *__pyx_t_3;
double *__pyx_t_4;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_t_5;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_t_6;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_t_7;
int __pyx_t_8;
/* "sklearn/tree/_tree.pyx":417
* left child (samples[start:pos]) and the impurity the right child
* (samples[pos:end])."""
* cdef double weighted_n_node_samples = self.weighted_n_node_samples # <<<<<<<<<<<<<<
* cdef double weighted_n_left = self.weighted_n_left
* cdef double weighted_n_right = self.weighted_n_right
*/
__pyx_t_1 = __pyx_v_self->__pyx_base.__pyx_base.weighted_n_node_samples;
__pyx_v_weighted_n_node_samples = __pyx_t_1;
/* "sklearn/tree/_tree.pyx":418
* (samples[pos:end])."""
* cdef double weighted_n_node_samples = self.weighted_n_node_samples
* cdef double weighted_n_left = self.weighted_n_left # <<<<<<<<<<<<<<
* cdef double weighted_n_right = self.weighted_n_right
*
*/
__pyx_t_1 = __pyx_v_self->__pyx_base.__pyx_base.weighted_n_left;
__pyx_v_weighted_n_left = __pyx_t_1;
/* "sklearn/tree/_tree.pyx":419
* cdef double weighted_n_node_samples = self.weighted_n_node_samples
* cdef double weighted_n_left = self.weighted_n_left
* cdef double weighted_n_right = self.weighted_n_right # <<<<<<<<<<<<<<
*
* cdef SIZE_t n_outputs = self.n_outputs
*/
__pyx_t_1 = __pyx_v_self->__pyx_base.__pyx_base.weighted_n_right;
__pyx_v_weighted_n_right = __pyx_t_1;
/* "sklearn/tree/_tree.pyx":421
* cdef double weighted_n_right = self.weighted_n_right
*
* cdef SIZE_t n_outputs = self.n_outputs # <<<<<<<<<<<<<<
* cdef SIZE_t* n_classes = self.n_classes
* cdef SIZE_t label_count_stride = self.label_count_stride
*/
__pyx_t_2 = __pyx_v_self->__pyx_base.__pyx_base.n_outputs;
__pyx_v_n_outputs = __pyx_t_2;
/* "sklearn/tree/_tree.pyx":422
*
* cdef SIZE_t n_outputs = self.n_outputs
* cdef SIZE_t* n_classes = self.n_classes # <<<<<<<<<<<<<<
* cdef SIZE_t label_count_stride = self.label_count_stride
* cdef double* label_count_left = self.label_count_left
*/
__pyx_t_3 = __pyx_v_self->__pyx_base.n_classes;
__pyx_v_n_classes = __pyx_t_3;
/* "sklearn/tree/_tree.pyx":423
* cdef SIZE_t n_outputs = self.n_outputs
* cdef SIZE_t* n_classes = self.n_classes
* cdef SIZE_t label_count_stride = self.label_count_stride # <<<<<<<<<<<<<<
* cdef double* label_count_left = self.label_count_left
* cdef double* label_count_right = self.label_count_right
*/
__pyx_t_2 = __pyx_v_self->__pyx_base.label_count_stride;
__pyx_v_label_count_stride = __pyx_t_2;
/* "sklearn/tree/_tree.pyx":424
* cdef SIZE_t* n_classes = self.n_classes
* cdef SIZE_t label_count_stride = self.label_count_stride
* cdef double* label_count_left = self.label_count_left # <<<<<<<<<<<<<<
* cdef double* label_count_right = self.label_count_right
*
*/
__pyx_t_4 = __pyx_v_self->__pyx_base.label_count_left;
__pyx_v_label_count_left = __pyx_t_4;
/* "sklearn/tree/_tree.pyx":425
* cdef SIZE_t label_count_stride = self.label_count_stride
* cdef double* label_count_left = self.label_count_left
* cdef double* label_count_right = self.label_count_right # <<<<<<<<<<<<<<
*
* cdef double entropy_left = 0.0
*/
__pyx_t_4 = __pyx_v_self->__pyx_base.label_count_right;
__pyx_v_label_count_right = __pyx_t_4;
/* "sklearn/tree/_tree.pyx":427
* cdef double* label_count_right = self.label_count_right
*
* cdef double entropy_left = 0.0 # <<<<<<<<<<<<<<
* cdef double entropy_right = 0.0
* cdef double total_left = 0.0
*/
__pyx_v_entropy_left = 0.0;
/* "sklearn/tree/_tree.pyx":428
*
* cdef double entropy_left = 0.0
* cdef double entropy_right = 0.0 # <<<<<<<<<<<<<<
* cdef double total_left = 0.0
* cdef double total_right = 0.0
*/
__pyx_v_entropy_right = 0.0;
/* "sklearn/tree/_tree.pyx":429
* cdef double entropy_left = 0.0
* cdef double entropy_right = 0.0
* cdef double total_left = 0.0 # <<<<<<<<<<<<<<
* cdef double total_right = 0.0
* cdef double tmp
*/
__pyx_v_total_left = 0.0;
/* "sklearn/tree/_tree.pyx":430
* cdef double entropy_right = 0.0
* cdef double total_left = 0.0
* cdef double total_right = 0.0 # <<<<<<<<<<<<<<
* cdef double tmp
* cdef SIZE_t k
*/
__pyx_v_total_right = 0.0;
/* "sklearn/tree/_tree.pyx":435
* cdef SIZE_t c
*
* for k in range(n_outputs): # <<<<<<<<<<<<<<
* entropy_left = 0.0
* entropy_right = 0.0
*/
__pyx_t_2 = __pyx_v_n_outputs;
for (__pyx_t_5 = 0; __pyx_t_5 < __pyx_t_2; __pyx_t_5+=1) {
__pyx_v_k = __pyx_t_5;
/* "sklearn/tree/_tree.pyx":436
*
* for k in range(n_outputs):
* entropy_left = 0.0 # <<<<<<<<<<<<<<
* entropy_right = 0.0
*
*/
__pyx_v_entropy_left = 0.0;
/* "sklearn/tree/_tree.pyx":437
* for k in range(n_outputs):
* entropy_left = 0.0
* entropy_right = 0.0 # <<<<<<<<<<<<<<
*
* for c in range(n_classes[k]):
*/
__pyx_v_entropy_right = 0.0;
/* "sklearn/tree/_tree.pyx":439
* entropy_right = 0.0
*
* for c in range(n_classes[k]): # <<<<<<<<<<<<<<
* tmp = label_count_left[c]
* if tmp > 0.0:
*/
__pyx_t_6 = (__pyx_v_n_classes[__pyx_v_k]);
for (__pyx_t_7 = 0; __pyx_t_7 < __pyx_t_6; __pyx_t_7+=1) {
__pyx_v_c = __pyx_t_7;
/* "sklearn/tree/_tree.pyx":440
*
* for c in range(n_classes[k]):
* tmp = label_count_left[c] # <<<<<<<<<<<<<<
* if tmp > 0.0:
* tmp /= weighted_n_left
*/
__pyx_v_tmp = (__pyx_v_label_count_left[__pyx_v_c]);
/* "sklearn/tree/_tree.pyx":441
* for c in range(n_classes[k]):
* tmp = label_count_left[c]
* if tmp > 0.0: # <<<<<<<<<<<<<<
* tmp /= weighted_n_left
* entropy_left -= tmp * log(tmp)
*/
__pyx_t_8 = ((__pyx_v_tmp > 0.0) != 0);
if (__pyx_t_8) {
/* "sklearn/tree/_tree.pyx":442
* tmp = label_count_left[c]
* if tmp > 0.0:
* tmp /= weighted_n_left # <<<<<<<<<<<<<<
* entropy_left -= tmp * log(tmp)
*
*/
__pyx_v_tmp = (__pyx_v_tmp / __pyx_v_weighted_n_left);
/* "sklearn/tree/_tree.pyx":443
* if tmp > 0.0:
* tmp /= weighted_n_left
* entropy_left -= tmp * log(tmp) # <<<<<<<<<<<<<<
*
* tmp = label_count_right[c]
*/
__pyx_v_entropy_left = (__pyx_v_entropy_left - (__pyx_v_tmp * __pyx_f_7sklearn_4tree_5_tree_log(__pyx_v_tmp)));
goto __pyx_L7;
}
__pyx_L7:;
/* "sklearn/tree/_tree.pyx":445
* entropy_left -= tmp * log(tmp)
*
* tmp = label_count_right[c] # <<<<<<<<<<<<<<
* if tmp > 0.0:
* tmp /= weighted_n_right
*/
__pyx_v_tmp = (__pyx_v_label_count_right[__pyx_v_c]);
/* "sklearn/tree/_tree.pyx":446
*
* tmp = label_count_right[c]
* if tmp > 0.0: # <<<<<<<<<<<<<<
* tmp /= weighted_n_right
* entropy_right -= tmp * log(tmp)
*/
__pyx_t_8 = ((__pyx_v_tmp > 0.0) != 0);
if (__pyx_t_8) {
/* "sklearn/tree/_tree.pyx":447
* tmp = label_count_right[c]
* if tmp > 0.0:
* tmp /= weighted_n_right # <<<<<<<<<<<<<<
* entropy_right -= tmp * log(tmp)
*
*/
__pyx_v_tmp = (__pyx_v_tmp / __pyx_v_weighted_n_right);
/* "sklearn/tree/_tree.pyx":448
* if tmp > 0.0:
* tmp /= weighted_n_right
* entropy_right -= tmp * log(tmp) # <<<<<<<<<<<<<<
*
* total_left += entropy_left
*/
__pyx_v_entropy_right = (__pyx_v_entropy_right - (__pyx_v_tmp * __pyx_f_7sklearn_4tree_5_tree_log(__pyx_v_tmp)));
goto __pyx_L8;
}
__pyx_L8:;
}
/* "sklearn/tree/_tree.pyx":450
* entropy_right -= tmp * log(tmp)
*
* total_left += entropy_left # <<<<<<<<<<<<<<
* total_right += entropy_right
* label_count_left += label_count_stride
*/
__pyx_v_total_left = (__pyx_v_total_left + __pyx_v_entropy_left);
/* "sklearn/tree/_tree.pyx":451
*
* total_left += entropy_left
* total_right += entropy_right # <<<<<<<<<<<<<<
* label_count_left += label_count_stride
* label_count_right += label_count_stride
*/
__pyx_v_total_right = (__pyx_v_total_right + __pyx_v_entropy_right);
/* "sklearn/tree/_tree.pyx":452
* total_left += entropy_left
* total_right += entropy_right
* label_count_left += label_count_stride # <<<<<<<<<<<<<<
* label_count_right += label_count_stride
*
*/
__pyx_v_label_count_left = (__pyx_v_label_count_left + __pyx_v_label_count_stride);
/* "sklearn/tree/_tree.pyx":453
* total_right += entropy_right
* label_count_left += label_count_stride
* label_count_right += label_count_stride # <<<<<<<<<<<<<<
*
* impurity_left[0] = total_left / n_outputs
*/
__pyx_v_label_count_right = (__pyx_v_label_count_right + __pyx_v_label_count_stride);
}
/* "sklearn/tree/_tree.pyx":455
* label_count_right += label_count_stride
*
* impurity_left[0] = total_left / n_outputs # <<<<<<<<<<<<<<
* impurity_right[0] = total_right / n_outputs
*
*/
(__pyx_v_impurity_left[0]) = (__pyx_v_total_left / __pyx_v_n_outputs);
/* "sklearn/tree/_tree.pyx":456
*
* impurity_left[0] = total_left / n_outputs
* impurity_right[0] = total_right / n_outputs # <<<<<<<<<<<<<<
*
*
*/
(__pyx_v_impurity_right[0]) = (__pyx_v_total_right / __pyx_v_n_outputs);
/* "sklearn/tree/_tree.pyx":412
* return total / n_outputs
*
* cdef void children_impurity(self, double* impurity_left, # <<<<<<<<<<<<<<
* double* impurity_right) nogil:
* """Evaluate the impurity in children nodes, i.e. the impurity of the
*/
/* function exit code */
}
/* "sklearn/tree/_tree.pyx":474
* = 1 - \sum_{k=0}^{K-1} pmk ** 2
* """
* cdef double node_impurity(self) nogil: # <<<<<<<<<<<<<<
* """Evaluate the impurity of the current node, i.e. the impurity of
* samples[start:end]."""
*/
static double __pyx_f_7sklearn_4tree_5_tree_4Gini_node_impurity(struct __pyx_obj_7sklearn_4tree_5_tree_Gini *__pyx_v_self) {
double __pyx_v_weighted_n_node_samples;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_v_n_outputs;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t *__pyx_v_n_classes;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_v_label_count_stride;
double *__pyx_v_label_count_total;
double __pyx_v_gini;
double __pyx_v_total;
double __pyx_v_tmp;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_v_k;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_v_c;
double __pyx_r;
double __pyx_t_1;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_t_2;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t *__pyx_t_3;
double *__pyx_t_4;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_t_5;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_t_6;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_t_7;
/* "sklearn/tree/_tree.pyx":477
* """Evaluate the impurity of the current node, i.e. the impurity of
* samples[start:end]."""
* cdef double weighted_n_node_samples = self.weighted_n_node_samples # <<<<<<<<<<<<<<
*
* cdef SIZE_t n_outputs = self.n_outputs
*/
__pyx_t_1 = __pyx_v_self->__pyx_base.__pyx_base.weighted_n_node_samples;
__pyx_v_weighted_n_node_samples = __pyx_t_1;
/* "sklearn/tree/_tree.pyx":479
* cdef double weighted_n_node_samples = self.weighted_n_node_samples
*
* cdef SIZE_t n_outputs = self.n_outputs # <<<<<<<<<<<<<<
* cdef SIZE_t* n_classes = self.n_classes
* cdef SIZE_t label_count_stride = self.label_count_stride
*/
__pyx_t_2 = __pyx_v_self->__pyx_base.__pyx_base.n_outputs;
__pyx_v_n_outputs = __pyx_t_2;
/* "sklearn/tree/_tree.pyx":480
*
* cdef SIZE_t n_outputs = self.n_outputs
* cdef SIZE_t* n_classes = self.n_classes # <<<<<<<<<<<<<<
* cdef SIZE_t label_count_stride = self.label_count_stride
* cdef double* label_count_total = self.label_count_total
*/
__pyx_t_3 = __pyx_v_self->__pyx_base.n_classes;
__pyx_v_n_classes = __pyx_t_3;
/* "sklearn/tree/_tree.pyx":481
* cdef SIZE_t n_outputs = self.n_outputs
* cdef SIZE_t* n_classes = self.n_classes
* cdef SIZE_t label_count_stride = self.label_count_stride # <<<<<<<<<<<<<<
* cdef double* label_count_total = self.label_count_total
*
*/
__pyx_t_2 = __pyx_v_self->__pyx_base.label_count_stride;
__pyx_v_label_count_stride = __pyx_t_2;
/* "sklearn/tree/_tree.pyx":482
* cdef SIZE_t* n_classes = self.n_classes
* cdef SIZE_t label_count_stride = self.label_count_stride
* cdef double* label_count_total = self.label_count_total # <<<<<<<<<<<<<<
*
* cdef double gini = 0.0
*/
__pyx_t_4 = __pyx_v_self->__pyx_base.label_count_total;
__pyx_v_label_count_total = __pyx_t_4;
/* "sklearn/tree/_tree.pyx":484
* cdef double* label_count_total = self.label_count_total
*
* cdef double gini = 0.0 # <<<<<<<<<<<<<<
* cdef double total = 0.0
* cdef double tmp
*/
__pyx_v_gini = 0.0;
/* "sklearn/tree/_tree.pyx":485
*
* cdef double gini = 0.0
* cdef double total = 0.0 # <<<<<<<<<<<<<<
* cdef double tmp
* cdef SIZE_t k
*/
__pyx_v_total = 0.0;
/* "sklearn/tree/_tree.pyx":490
* cdef SIZE_t c
*
* for k in range(n_outputs): # <<<<<<<<<<<<<<
* gini = 0.0
*
*/
__pyx_t_2 = __pyx_v_n_outputs;
for (__pyx_t_5 = 0; __pyx_t_5 < __pyx_t_2; __pyx_t_5+=1) {
__pyx_v_k = __pyx_t_5;
/* "sklearn/tree/_tree.pyx":491
*
* for k in range(n_outputs):
* gini = 0.0 # <<<<<<<<<<<<<<
*
* for c in range(n_classes[k]):
*/
__pyx_v_gini = 0.0;
/* "sklearn/tree/_tree.pyx":493
* gini = 0.0
*
* for c in range(n_classes[k]): # <<<<<<<<<<<<<<
* tmp = label_count_total[c]
* gini += tmp * tmp
*/
__pyx_t_6 = (__pyx_v_n_classes[__pyx_v_k]);
for (__pyx_t_7 = 0; __pyx_t_7 < __pyx_t_6; __pyx_t_7+=1) {
__pyx_v_c = __pyx_t_7;
/* "sklearn/tree/_tree.pyx":494
*
* for c in range(n_classes[k]):
* tmp = label_count_total[c] # <<<<<<<<<<<<<<
* gini += tmp * tmp
*
*/
__pyx_v_tmp = (__pyx_v_label_count_total[__pyx_v_c]);
/* "sklearn/tree/_tree.pyx":495
* for c in range(n_classes[k]):
* tmp = label_count_total[c]
* gini += tmp * tmp # <<<<<<<<<<<<<<
*
* gini = 1.0 - gini / (weighted_n_node_samples *
*/
__pyx_v_gini = (__pyx_v_gini + (__pyx_v_tmp * __pyx_v_tmp));
}
/* "sklearn/tree/_tree.pyx":497
* gini += tmp * tmp
*
* gini = 1.0 - gini / (weighted_n_node_samples * # <<<<<<<<<<<<<<
* weighted_n_node_samples)
*
*/
__pyx_v_gini = (1.0 - (__pyx_v_gini / (__pyx_v_weighted_n_node_samples * __pyx_v_weighted_n_node_samples)));
/* "sklearn/tree/_tree.pyx":500
* weighted_n_node_samples)
*
* total += gini # <<<<<<<<<<<<<<
* label_count_total += label_count_stride
*
*/
__pyx_v_total = (__pyx_v_total + __pyx_v_gini);
/* "sklearn/tree/_tree.pyx":501
*
* total += gini
* label_count_total += label_count_stride # <<<<<<<<<<<<<<
*
* return total / n_outputs
*/
__pyx_v_label_count_total = (__pyx_v_label_count_total + __pyx_v_label_count_stride);
}
/* "sklearn/tree/_tree.pyx":503
* label_count_total += label_count_stride
*
* return total / n_outputs # <<<<<<<<<<<<<<
*
* cdef void children_impurity(self, double* impurity_left,
*/
__pyx_r = (__pyx_v_total / __pyx_v_n_outputs);
goto __pyx_L0;
/* "sklearn/tree/_tree.pyx":474
* = 1 - \sum_{k=0}^{K-1} pmk ** 2
* """
* cdef double node_impurity(self) nogil: # <<<<<<<<<<<<<<
* """Evaluate the impurity of the current node, i.e. the impurity of
* samples[start:end]."""
*/
/* function exit code */
__pyx_L0:;
return __pyx_r;
}
/* "sklearn/tree/_tree.pyx":505
* return total / n_outputs
*
* cdef void children_impurity(self, double* impurity_left, # <<<<<<<<<<<<<<
* double* impurity_right) nogil:
* """Evaluate the impurity in children nodes, i.e. the impurity of the
*/
static void __pyx_f_7sklearn_4tree_5_tree_4Gini_children_impurity(struct __pyx_obj_7sklearn_4tree_5_tree_Gini *__pyx_v_self, double *__pyx_v_impurity_left, double *__pyx_v_impurity_right) {
CYTHON_UNUSED double __pyx_v_weighted_n_node_samples;
double __pyx_v_weighted_n_left;
double __pyx_v_weighted_n_right;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_v_n_outputs;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t *__pyx_v_n_classes;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_v_label_count_stride;
double *__pyx_v_label_count_left;
double *__pyx_v_label_count_right;
double __pyx_v_gini_left;
double __pyx_v_gini_right;
CYTHON_UNUSED double __pyx_v_total;
double __pyx_v_total_left;
double __pyx_v_total_right;
double __pyx_v_tmp;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_v_k;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_v_c;
double __pyx_t_1;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_t_2;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t *__pyx_t_3;
double *__pyx_t_4;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_t_5;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_t_6;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_t_7;
/* "sklearn/tree/_tree.pyx":510
* left child (samples[start:pos]) and the impurity the right child
* (samples[pos:end])."""
* cdef double weighted_n_node_samples = self.weighted_n_node_samples # <<<<<<<<<<<<<<
* cdef double weighted_n_left = self.weighted_n_left
* cdef double weighted_n_right = self.weighted_n_right
*/
__pyx_t_1 = __pyx_v_self->__pyx_base.__pyx_base.weighted_n_node_samples;
__pyx_v_weighted_n_node_samples = __pyx_t_1;
/* "sklearn/tree/_tree.pyx":511
* (samples[pos:end])."""
* cdef double weighted_n_node_samples = self.weighted_n_node_samples
* cdef double weighted_n_left = self.weighted_n_left # <<<<<<<<<<<<<<
* cdef double weighted_n_right = self.weighted_n_right
*
*/
__pyx_t_1 = __pyx_v_self->__pyx_base.__pyx_base.weighted_n_left;
__pyx_v_weighted_n_left = __pyx_t_1;
/* "sklearn/tree/_tree.pyx":512
* cdef double weighted_n_node_samples = self.weighted_n_node_samples
* cdef double weighted_n_left = self.weighted_n_left
* cdef double weighted_n_right = self.weighted_n_right # <<<<<<<<<<<<<<
*
* cdef SIZE_t n_outputs = self.n_outputs
*/
__pyx_t_1 = __pyx_v_self->__pyx_base.__pyx_base.weighted_n_right;
__pyx_v_weighted_n_right = __pyx_t_1;
/* "sklearn/tree/_tree.pyx":514
* cdef double weighted_n_right = self.weighted_n_right
*
* cdef SIZE_t n_outputs = self.n_outputs # <<<<<<<<<<<<<<
* cdef SIZE_t* n_classes = self.n_classes
* cdef SIZE_t label_count_stride = self.label_count_stride
*/
__pyx_t_2 = __pyx_v_self->__pyx_base.__pyx_base.n_outputs;
__pyx_v_n_outputs = __pyx_t_2;
/* "sklearn/tree/_tree.pyx":515
*
* cdef SIZE_t n_outputs = self.n_outputs
* cdef SIZE_t* n_classes = self.n_classes # <<<<<<<<<<<<<<
* cdef SIZE_t label_count_stride = self.label_count_stride
* cdef double* label_count_left = self.label_count_left
*/
__pyx_t_3 = __pyx_v_self->__pyx_base.n_classes;
__pyx_v_n_classes = __pyx_t_3;
/* "sklearn/tree/_tree.pyx":516
* cdef SIZE_t n_outputs = self.n_outputs
* cdef SIZE_t* n_classes = self.n_classes
* cdef SIZE_t label_count_stride = self.label_count_stride # <<<<<<<<<<<<<<
* cdef double* label_count_left = self.label_count_left
* cdef double* label_count_right = self.label_count_right
*/
__pyx_t_2 = __pyx_v_self->__pyx_base.label_count_stride;
__pyx_v_label_count_stride = __pyx_t_2;
/* "sklearn/tree/_tree.pyx":517
* cdef SIZE_t* n_classes = self.n_classes
* cdef SIZE_t label_count_stride = self.label_count_stride
* cdef double* label_count_left = self.label_count_left # <<<<<<<<<<<<<<
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/* "sklearn/tree/_tree.pyx":659
* children samples[start:start] and samples[start:end]."""
* # Initialize fields
* self.y = y # <<<<<<<<<<<<<<
* self.y_stride = y_stride
* self.sample_weight = sample_weight
*/
__pyx_v_self->__pyx_base.y = __pyx_v_y;
/* "sklearn/tree/_tree.pyx":660
* # Initialize fields
* self.y = y
* self.y_stride = y_stride # <<<<<<<<<<<<<<
* self.sample_weight = sample_weight
* self.samples = samples
*/
__pyx_v_self->__pyx_base.y_stride = __pyx_v_y_stride;
/* "sklearn/tree/_tree.pyx":661
* self.y = y
* self.y_stride = y_stride
* self.sample_weight = sample_weight # <<<<<<<<<<<<<<
* self.samples = samples
* self.start = start
*/
__pyx_v_self->__pyx_base.sample_weight = __pyx_v_sample_weight;
/* "sklearn/tree/_tree.pyx":662
* self.y_stride = y_stride
* self.sample_weight = sample_weight
* self.samples = samples # <<<<<<<<<<<<<<
* self.start = start
* self.end = end
*/
__pyx_v_self->__pyx_base.samples = __pyx_v_samples;
/* "sklearn/tree/_tree.pyx":663
* self.sample_weight = sample_weight
* self.samples = samples
* self.start = start # <<<<<<<<<<<<<<
* self.end = end
* self.n_node_samples = end - start
*/
__pyx_v_self->__pyx_base.start = __pyx_v_start;
/* "sklearn/tree/_tree.pyx":664
* self.samples = samples
* self.start = start
* self.end = end # <<<<<<<<<<<<<<
* self.n_node_samples = end - start
* self.weighted_n_samples = weighted_n_samples
*/
__pyx_v_self->__pyx_base.end = __pyx_v_end;
/* "sklearn/tree/_tree.pyx":665
* self.start = start
* self.end = end
* self.n_node_samples = end - start # <<<<<<<<<<<<<<
* self.weighted_n_samples = weighted_n_samples
* cdef double weighted_n_node_samples = 0.
*/
__pyx_v_self->__pyx_base.n_node_samples = (__pyx_v_end - __pyx_v_start);
/* "sklearn/tree/_tree.pyx":666
* self.end = end
* self.n_node_samples = end - start
* self.weighted_n_samples = weighted_n_samples # <<<<<<<<<<<<<<
* cdef double weighted_n_node_samples = 0.
*
*/
__pyx_v_self->__pyx_base.weighted_n_samples = __pyx_v_weighted_n_samples;
/* "sklearn/tree/_tree.pyx":667
* self.n_node_samples = end - start
* self.weighted_n_samples = weighted_n_samples
* cdef double weighted_n_node_samples = 0. # <<<<<<<<<<<<<<
*
* # Initialize accumulators
*/
__pyx_v_weighted_n_node_samples = 0.;
/* "sklearn/tree/_tree.pyx":670
*
* # Initialize accumulators
* cdef SIZE_t n_outputs = self.n_outputs # <<<<<<<<<<<<<<
* cdef double* mean_left = self.mean_left
* cdef double* mean_right = self.mean_right
*/
__pyx_t_1 = __pyx_v_self->__pyx_base.n_outputs;
__pyx_v_n_outputs = __pyx_t_1;
/* "sklearn/tree/_tree.pyx":671
* # Initialize accumulators
* cdef SIZE_t n_outputs = self.n_outputs
* cdef double* mean_left = self.mean_left # <<<<<<<<<<<<<<
* cdef double* mean_right = self.mean_right
* cdef double* mean_total = self.mean_total
*/
__pyx_t_2 = __pyx_v_self->mean_left;
__pyx_v_mean_left = __pyx_t_2;
/* "sklearn/tree/_tree.pyx":672
* cdef SIZE_t n_outputs = self.n_outputs
* cdef double* mean_left = self.mean_left
* cdef double* mean_right = self.mean_right # <<<<<<<<<<<<<<
* cdef double* mean_total = self.mean_total
* cdef double* sq_sum_left = self.sq_sum_left
*/
__pyx_t_2 = __pyx_v_self->mean_right;
__pyx_v_mean_right = __pyx_t_2;
/* "sklearn/tree/_tree.pyx":673
* cdef double* mean_left = self.mean_left
* cdef double* mean_right = self.mean_right
* cdef double* mean_total = self.mean_total # <<<<<<<<<<<<<<
* cdef double* sq_sum_left = self.sq_sum_left
* cdef double* sq_sum_right = self.sq_sum_right
*/
__pyx_t_2 = __pyx_v_self->mean_total;
__pyx_v_mean_total = __pyx_t_2;
/* "sklearn/tree/_tree.pyx":674
* cdef double* mean_right = self.mean_right
* cdef double* mean_total = self.mean_total
* cdef double* sq_sum_left = self.sq_sum_left # <<<<<<<<<<<<<<
* cdef double* sq_sum_right = self.sq_sum_right
* cdef double* sq_sum_total = self.sq_sum_total
*/
__pyx_t_2 = __pyx_v_self->sq_sum_left;
__pyx_v_sq_sum_left = __pyx_t_2;
/* "sklearn/tree/_tree.pyx":675
* cdef double* mean_total = self.mean_total
* cdef double* sq_sum_left = self.sq_sum_left
* cdef double* sq_sum_right = self.sq_sum_right # <<<<<<<<<<<<<<
* cdef double* sq_sum_total = self.sq_sum_total
* cdef double* var_left = self.var_left
*/
__pyx_t_2 = __pyx_v_self->sq_sum_right;
__pyx_v_sq_sum_right = __pyx_t_2;
/* "sklearn/tree/_tree.pyx":676
* cdef double* sq_sum_left = self.sq_sum_left
* cdef double* sq_sum_right = self.sq_sum_right
* cdef double* sq_sum_total = self.sq_sum_total # <<<<<<<<<<<<<<
* cdef double* var_left = self.var_left
* cdef double* var_right = self.var_right
*/
__pyx_t_2 = __pyx_v_self->sq_sum_total;
__pyx_v_sq_sum_total = __pyx_t_2;
/* "sklearn/tree/_tree.pyx":677
* cdef double* sq_sum_right = self.sq_sum_right
* cdef double* sq_sum_total = self.sq_sum_total
* cdef double* var_left = self.var_left # <<<<<<<<<<<<<<
* cdef double* var_right = self.var_right
* cdef double* sum_left = self.sum_left
*/
__pyx_t_2 = __pyx_v_self->var_left;
__pyx_v_var_left = __pyx_t_2;
/* "sklearn/tree/_tree.pyx":678
* cdef double* sq_sum_total = self.sq_sum_total
* cdef double* var_left = self.var_left
* cdef double* var_right = self.var_right # <<<<<<<<<<<<<<
* cdef double* sum_left = self.sum_left
* cdef double* sum_right = self.sum_right
*/
__pyx_t_2 = __pyx_v_self->var_right;
__pyx_v_var_right = __pyx_t_2;
/* "sklearn/tree/_tree.pyx":679
* cdef double* var_left = self.var_left
* cdef double* var_right = self.var_right
* cdef double* sum_left = self.sum_left # <<<<<<<<<<<<<<
* cdef double* sum_right = self.sum_right
* cdef double* sum_total = self.sum_total
*/
__pyx_t_2 = __pyx_v_self->sum_left;
__pyx_v_sum_left = __pyx_t_2;
/* "sklearn/tree/_tree.pyx":680
* cdef double* var_right = self.var_right
* cdef double* sum_left = self.sum_left
* cdef double* sum_right = self.sum_right # <<<<<<<<<<<<<<
* cdef double* sum_total = self.sum_total
*
*/
__pyx_t_2 = __pyx_v_self->sum_right;
__pyx_v_sum_right = __pyx_t_2;
/* "sklearn/tree/_tree.pyx":681
* cdef double* sum_left = self.sum_left
* cdef double* sum_right = self.sum_right
* cdef double* sum_total = self.sum_total # <<<<<<<<<<<<<<
*
* cdef SIZE_t i = 0
*/
__pyx_t_2 = __pyx_v_self->sum_total;
__pyx_v_sum_total = __pyx_t_2;
/* "sklearn/tree/_tree.pyx":683
* cdef double* sum_total = self.sum_total
*
* cdef SIZE_t i = 0 # <<<<<<<<<<<<<<
* cdef SIZE_t p = 0
* cdef SIZE_t k = 0
*/
__pyx_v_i = 0;
/* "sklearn/tree/_tree.pyx":684
*
* cdef SIZE_t i = 0
* cdef SIZE_t p = 0 # <<<<<<<<<<<<<<
* cdef SIZE_t k = 0
* cdef DOUBLE_t y_ik = 0.0
*/
__pyx_v_p = 0;
/* "sklearn/tree/_tree.pyx":685
* cdef SIZE_t i = 0
* cdef SIZE_t p = 0
* cdef SIZE_t k = 0 # <<<<<<<<<<<<<<
* cdef DOUBLE_t y_ik = 0.0
* cdef DOUBLE_t w_y_ik = 0.0
*/
__pyx_v_k = 0;
/* "sklearn/tree/_tree.pyx":686
* cdef SIZE_t p = 0
* cdef SIZE_t k = 0
* cdef DOUBLE_t y_ik = 0.0 # <<<<<<<<<<<<<<
* cdef DOUBLE_t w_y_ik = 0.0
* cdef DOUBLE_t w = 1.0
*/
__pyx_v_y_ik = 0.0;
/* "sklearn/tree/_tree.pyx":687
* cdef SIZE_t k = 0
* cdef DOUBLE_t y_ik = 0.0
* cdef DOUBLE_t w_y_ik = 0.0 # <<<<<<<<<<<<<<
* cdef DOUBLE_t w = 1.0
*
*/
__pyx_v_w_y_ik = 0.0;
/* "sklearn/tree/_tree.pyx":688
* cdef DOUBLE_t y_ik = 0.0
* cdef DOUBLE_t w_y_ik = 0.0
* cdef DOUBLE_t w = 1.0 # <<<<<<<<<<<<<<
*
* cdef SIZE_t n_bytes = n_outputs * sizeof(double)
*/
__pyx_v_w = 1.0;
/* "sklearn/tree/_tree.pyx":690
* cdef DOUBLE_t w = 1.0
*
* cdef SIZE_t n_bytes = n_outputs * sizeof(double) # <<<<<<<<<<<<<<
* memset(mean_left, 0, n_bytes)
* memset(mean_right, 0, n_bytes)
*/
__pyx_v_n_bytes = (__pyx_v_n_outputs * (sizeof(double)));
/* "sklearn/tree/_tree.pyx":691
*
* cdef SIZE_t n_bytes = n_outputs * sizeof(double)
* memset(mean_left, 0, n_bytes) # <<<<<<<<<<<<<<
* memset(mean_right, 0, n_bytes)
* memset(mean_total, 0, n_bytes)
*/
memset(__pyx_v_mean_left, 0, __pyx_v_n_bytes);
/* "sklearn/tree/_tree.pyx":692
* cdef SIZE_t n_bytes = n_outputs * sizeof(double)
* memset(mean_left, 0, n_bytes)
* memset(mean_right, 0, n_bytes) # <<<<<<<<<<<<<<
* memset(mean_total, 0, n_bytes)
* memset(sq_sum_left, 0, n_bytes)
*/
memset(__pyx_v_mean_right, 0, __pyx_v_n_bytes);
/* "sklearn/tree/_tree.pyx":693
* memset(mean_left, 0, n_bytes)
* memset(mean_right, 0, n_bytes)
* memset(mean_total, 0, n_bytes) # <<<<<<<<<<<<<<
* memset(sq_sum_left, 0, n_bytes)
* memset(sq_sum_right, 0, n_bytes)
*/
memset(__pyx_v_mean_total, 0, __pyx_v_n_bytes);
/* "sklearn/tree/_tree.pyx":694
* memset(mean_right, 0, n_bytes)
* memset(mean_total, 0, n_bytes)
* memset(sq_sum_left, 0, n_bytes) # <<<<<<<<<<<<<<
* memset(sq_sum_right, 0, n_bytes)
* memset(sq_sum_total, 0, n_bytes)
*/
memset(__pyx_v_sq_sum_left, 0, __pyx_v_n_bytes);
/* "sklearn/tree/_tree.pyx":695
* memset(mean_total, 0, n_bytes)
* memset(sq_sum_left, 0, n_bytes)
* memset(sq_sum_right, 0, n_bytes) # <<<<<<<<<<<<<<
* memset(sq_sum_total, 0, n_bytes)
* memset(var_left, 0, n_bytes)
*/
memset(__pyx_v_sq_sum_right, 0, __pyx_v_n_bytes);
/* "sklearn/tree/_tree.pyx":696
* memset(sq_sum_left, 0, n_bytes)
* memset(sq_sum_right, 0, n_bytes)
* memset(sq_sum_total, 0, n_bytes) # <<<<<<<<<<<<<<
* memset(var_left, 0, n_bytes)
* memset(var_right, 0, n_bytes)
*/
memset(__pyx_v_sq_sum_total, 0, __pyx_v_n_bytes);
/* "sklearn/tree/_tree.pyx":697
* memset(sq_sum_right, 0, n_bytes)
* memset(sq_sum_total, 0, n_bytes)
* memset(var_left, 0, n_bytes) # <<<<<<<<<<<<<<
* memset(var_right, 0, n_bytes)
* memset(sum_left, 0, n_bytes)
*/
memset(__pyx_v_var_left, 0, __pyx_v_n_bytes);
/* "sklearn/tree/_tree.pyx":698
* memset(sq_sum_total, 0, n_bytes)
* memset(var_left, 0, n_bytes)
* memset(var_right, 0, n_bytes) # <<<<<<<<<<<<<<
* memset(sum_left, 0, n_bytes)
* memset(sum_right, 0, n_bytes)
*/
memset(__pyx_v_var_right, 0, __pyx_v_n_bytes);
/* "sklearn/tree/_tree.pyx":699
* memset(var_left, 0, n_bytes)
* memset(var_right, 0, n_bytes)
* memset(sum_left, 0, n_bytes) # <<<<<<<<<<<<<<
* memset(sum_right, 0, n_bytes)
* memset(sum_total, 0, n_bytes)
*/
memset(__pyx_v_sum_left, 0, __pyx_v_n_bytes);
/* "sklearn/tree/_tree.pyx":700
* memset(var_right, 0, n_bytes)
* memset(sum_left, 0, n_bytes)
* memset(sum_right, 0, n_bytes) # <<<<<<<<<<<<<<
* memset(sum_total, 0, n_bytes)
*
*/
memset(__pyx_v_sum_right, 0, __pyx_v_n_bytes);
/* "sklearn/tree/_tree.pyx":701
* memset(sum_left, 0, n_bytes)
* memset(sum_right, 0, n_bytes)
* memset(sum_total, 0, n_bytes) # <<<<<<<<<<<<<<
*
* for p in range(start, end):
*/
memset(__pyx_v_sum_total, 0, __pyx_v_n_bytes);
/* "sklearn/tree/_tree.pyx":703
* memset(sum_total, 0, n_bytes)
*
* for p in range(start, end): # <<<<<<<<<<<<<<
* i = samples[p]
*
*/
__pyx_t_1 = __pyx_v_end;
for (__pyx_t_3 = __pyx_v_start; __pyx_t_3 < __pyx_t_1; __pyx_t_3+=1) {
__pyx_v_p = __pyx_t_3;
/* "sklearn/tree/_tree.pyx":704
*
* for p in range(start, end):
* i = samples[p] # <<<<<<<<<<<<<<
*
* if sample_weight != NULL:
*/
__pyx_v_i = (__pyx_v_samples[__pyx_v_p]);
/* "sklearn/tree/_tree.pyx":706
* i = samples[p]
*
* if sample_weight != NULL: # <<<<<<<<<<<<<<
* w = sample_weight[i]
*
*/
__pyx_t_4 = ((__pyx_v_sample_weight != NULL) != 0);
if (__pyx_t_4) {
/* "sklearn/tree/_tree.pyx":707
*
* if sample_weight != NULL:
* w = sample_weight[i] # <<<<<<<<<<<<<<
*
* for k in range(n_outputs):
*/
__pyx_v_w = (__pyx_v_sample_weight[__pyx_v_i]);
goto __pyx_L5;
}
__pyx_L5:;
/* "sklearn/tree/_tree.pyx":709
* w = sample_weight[i]
*
* for k in range(n_outputs): # <<<<<<<<<<<<<<
* y_ik = y[i * y_stride + k]
* w_y_ik = w * y_ik
*/
__pyx_t_5 = __pyx_v_n_outputs;
for (__pyx_t_6 = 0; __pyx_t_6 < __pyx_t_5; __pyx_t_6+=1) {
__pyx_v_k = __pyx_t_6;
/* "sklearn/tree/_tree.pyx":710
*
* for k in range(n_outputs):
* y_ik = y[i * y_stride + k] # <<<<<<<<<<<<<<
* w_y_ik = w * y_ik
* sum_total[k] += w_y_ik
*/
__pyx_v_y_ik = (__pyx_v_y[((__pyx_v_i * __pyx_v_y_stride) + __pyx_v_k)]);
/* "sklearn/tree/_tree.pyx":711
* for k in range(n_outputs):
* y_ik = y[i * y_stride + k]
* w_y_ik = w * y_ik # <<<<<<<<<<<<<<
* sum_total[k] += w_y_ik
* sq_sum_total[k] += w_y_ik * y_ik
*/
__pyx_v_w_y_ik = (__pyx_v_w * __pyx_v_y_ik);
/* "sklearn/tree/_tree.pyx":712
* y_ik = y[i * y_stride + k]
* w_y_ik = w * y_ik
* sum_total[k] += w_y_ik # <<<<<<<<<<<<<<
* sq_sum_total[k] += w_y_ik * y_ik
*
*/
__pyx_t_7 = __pyx_v_k;
(__pyx_v_sum_total[__pyx_t_7]) = ((__pyx_v_sum_total[__pyx_t_7]) + __pyx_v_w_y_ik);
/* "sklearn/tree/_tree.pyx":713
* w_y_ik = w * y_ik
* sum_total[k] += w_y_ik
* sq_sum_total[k] += w_y_ik * y_ik # <<<<<<<<<<<<<<
*
* weighted_n_node_samples += w
*/
__pyx_t_7 = __pyx_v_k;
(__pyx_v_sq_sum_total[__pyx_t_7]) = ((__pyx_v_sq_sum_total[__pyx_t_7]) + (__pyx_v_w_y_ik * __pyx_v_y_ik));
}
/* "sklearn/tree/_tree.pyx":715
* sq_sum_total[k] += w_y_ik * y_ik
*
* weighted_n_node_samples += w # <<<<<<<<<<<<<<
*
* self.weighted_n_node_samples = weighted_n_node_samples
*/
__pyx_v_weighted_n_node_samples = (__pyx_v_weighted_n_node_samples + __pyx_v_w);
}
/* "sklearn/tree/_tree.pyx":717
* weighted_n_node_samples += w
*
* self.weighted_n_node_samples = weighted_n_node_samples # <<<<<<<<<<<<<<
*
* for k in range(n_outputs):
*/
__pyx_v_self->__pyx_base.weighted_n_node_samples = __pyx_v_weighted_n_node_samples;
/* "sklearn/tree/_tree.pyx":719
* self.weighted_n_node_samples = weighted_n_node_samples
*
* for k in range(n_outputs): # <<<<<<<<<<<<<<
* mean_total[k] = sum_total[k] / weighted_n_node_samples
*
*/
__pyx_t_1 = __pyx_v_n_outputs;
for (__pyx_t_3 = 0; __pyx_t_3 < __pyx_t_1; __pyx_t_3+=1) {
__pyx_v_k = __pyx_t_3;
/* "sklearn/tree/_tree.pyx":720
*
* for k in range(n_outputs):
* mean_total[k] = sum_total[k] / weighted_n_node_samples # <<<<<<<<<<<<<<
*
* # Reset to pos=start
*/
(__pyx_v_mean_total[__pyx_v_k]) = ((__pyx_v_sum_total[__pyx_v_k]) / __pyx_v_weighted_n_node_samples);
}
/* "sklearn/tree/_tree.pyx":723
*
* # Reset to pos=start
* self.reset() # <<<<<<<<<<<<<<
*
* cdef void reset(self) nogil:
*/
((struct __pyx_vtabstruct_7sklearn_4tree_5_tree_RegressionCriterion *)__pyx_v_self->__pyx_base.__pyx_vtab)->__pyx_base.reset(((struct __pyx_obj_7sklearn_4tree_5_tree_Criterion *)__pyx_v_self));
/* "sklearn/tree/_tree.pyx":653
* pass
*
* cdef void init(self, DOUBLE_t* y, SIZE_t y_stride, DOUBLE_t* sample_weight, # <<<<<<<<<<<<<<
* double weighted_n_samples, SIZE_t* samples, SIZE_t start,
* SIZE_t end) nogil:
*/
/* function exit code */
}
/* "sklearn/tree/_tree.pyx":725
* self.reset()
*
* cdef void reset(self) nogil: # <<<<<<<<<<<<<<
* """Reset the criterion at pos=start."""
* self.pos = self.start
*/
static void __pyx_f_7sklearn_4tree_5_tree_19RegressionCriterion_reset(struct __pyx_obj_7sklearn_4tree_5_tree_RegressionCriterion *__pyx_v_self) {
__pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_v_n_outputs;
double *__pyx_v_mean_left;
double *__pyx_v_mean_right;
double *__pyx_v_mean_total;
double *__pyx_v_sq_sum_left;
double *__pyx_v_sq_sum_right;
double *__pyx_v_sq_sum_total;
double *__pyx_v_var_left;
double *__pyx_v_var_right;
double __pyx_v_weighted_n_node_samples;
double *__pyx_v_sum_left;
double *__pyx_v_sum_right;
double *__pyx_v_sum_total;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_v_k;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_t_1;
double __pyx_t_2;
double *__pyx_t_3;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_t_4;
/* "sklearn/tree/_tree.pyx":727
* cdef void reset(self) nogil:
* """Reset the criterion at pos=start."""
* self.pos = self.start # <<<<<<<<<<<<<<
*
* self.weighted_n_left = 0.0
*/
__pyx_t_1 = __pyx_v_self->__pyx_base.start;
__pyx_v_self->__pyx_base.pos = __pyx_t_1;
/* "sklearn/tree/_tree.pyx":729
* self.pos = self.start
*
* self.weighted_n_left = 0.0 # <<<<<<<<<<<<<<
* self.weighted_n_right = self.weighted_n_node_samples
*
*/
__pyx_v_self->__pyx_base.weighted_n_left = 0.0;
/* "sklearn/tree/_tree.pyx":730
*
* self.weighted_n_left = 0.0
* self.weighted_n_right = self.weighted_n_node_samples # <<<<<<<<<<<<<<
*
* cdef SIZE_t n_outputs = self.n_outputs
*/
__pyx_t_2 = __pyx_v_self->__pyx_base.weighted_n_node_samples;
__pyx_v_self->__pyx_base.weighted_n_right = __pyx_t_2;
/* "sklearn/tree/_tree.pyx":732
* self.weighted_n_right = self.weighted_n_node_samples
*
* cdef SIZE_t n_outputs = self.n_outputs # <<<<<<<<<<<<<<
* cdef double* mean_left = self.mean_left
* cdef double* mean_right = self.mean_right
*/
__pyx_t_1 = __pyx_v_self->__pyx_base.n_outputs;
__pyx_v_n_outputs = __pyx_t_1;
/* "sklearn/tree/_tree.pyx":733
*
* cdef SIZE_t n_outputs = self.n_outputs
* cdef double* mean_left = self.mean_left # <<<<<<<<<<<<<<
* cdef double* mean_right = self.mean_right
* cdef double* mean_total = self.mean_total
*/
__pyx_t_3 = __pyx_v_self->mean_left;
__pyx_v_mean_left = __pyx_t_3;
/* "sklearn/tree/_tree.pyx":734
* cdef SIZE_t n_outputs = self.n_outputs
* cdef double* mean_left = self.mean_left
* cdef double* mean_right = self.mean_right # <<<<<<<<<<<<<<
* cdef double* mean_total = self.mean_total
* cdef double* sq_sum_left = self.sq_sum_left
*/
__pyx_t_3 = __pyx_v_self->mean_right;
__pyx_v_mean_right = __pyx_t_3;
/* "sklearn/tree/_tree.pyx":735
* cdef double* mean_left = self.mean_left
* cdef double* mean_right = self.mean_right
* cdef double* mean_total = self.mean_total # <<<<<<<<<<<<<<
* cdef double* sq_sum_left = self.sq_sum_left
* cdef double* sq_sum_right = self.sq_sum_right
*/
__pyx_t_3 = __pyx_v_self->mean_total;
__pyx_v_mean_total = __pyx_t_3;
/* "sklearn/tree/_tree.pyx":736
* cdef double* mean_right = self.mean_right
* cdef double* mean_total = self.mean_total
* cdef double* sq_sum_left = self.sq_sum_left # <<<<<<<<<<<<<<
* cdef double* sq_sum_right = self.sq_sum_right
* cdef double* sq_sum_total = self.sq_sum_total
*/
__pyx_t_3 = __pyx_v_self->sq_sum_left;
__pyx_v_sq_sum_left = __pyx_t_3;
/* "sklearn/tree/_tree.pyx":737
* cdef double* mean_total = self.mean_total
* cdef double* sq_sum_left = self.sq_sum_left
* cdef double* sq_sum_right = self.sq_sum_right # <<<<<<<<<<<<<<
* cdef double* sq_sum_total = self.sq_sum_total
* cdef double* var_left = self.var_left
*/
__pyx_t_3 = __pyx_v_self->sq_sum_right;
__pyx_v_sq_sum_right = __pyx_t_3;
/* "sklearn/tree/_tree.pyx":738
* cdef double* sq_sum_left = self.sq_sum_left
* cdef double* sq_sum_right = self.sq_sum_right
* cdef double* sq_sum_total = self.sq_sum_total # <<<<<<<<<<<<<<
* cdef double* var_left = self.var_left
* cdef double* var_right = self.var_right
*/
__pyx_t_3 = __pyx_v_self->sq_sum_total;
__pyx_v_sq_sum_total = __pyx_t_3;
/* "sklearn/tree/_tree.pyx":739
* cdef double* sq_sum_right = self.sq_sum_right
* cdef double* sq_sum_total = self.sq_sum_total
* cdef double* var_left = self.var_left # <<<<<<<<<<<<<<
* cdef double* var_right = self.var_right
* cdef double weighted_n_node_samples = self.weighted_n_node_samples
*/
__pyx_t_3 = __pyx_v_self->var_left;
__pyx_v_var_left = __pyx_t_3;
/* "sklearn/tree/_tree.pyx":740
* cdef double* sq_sum_total = self.sq_sum_total
* cdef double* var_left = self.var_left
* cdef double* var_right = self.var_right # <<<<<<<<<<<<<<
* cdef double weighted_n_node_samples = self.weighted_n_node_samples
* cdef double* sum_left = self.sum_left
*/
__pyx_t_3 = __pyx_v_self->var_right;
__pyx_v_var_right = __pyx_t_3;
/* "sklearn/tree/_tree.pyx":741
* cdef double* var_left = self.var_left
* cdef double* var_right = self.var_right
* cdef double weighted_n_node_samples = self.weighted_n_node_samples # <<<<<<<<<<<<<<
* cdef double* sum_left = self.sum_left
* cdef double* sum_right = self.sum_right
*/
__pyx_t_2 = __pyx_v_self->__pyx_base.weighted_n_node_samples;
__pyx_v_weighted_n_node_samples = __pyx_t_2;
/* "sklearn/tree/_tree.pyx":742
* cdef double* var_right = self.var_right
* cdef double weighted_n_node_samples = self.weighted_n_node_samples
* cdef double* sum_left = self.sum_left # <<<<<<<<<<<<<<
* cdef double* sum_right = self.sum_right
* cdef double* sum_total = self.sum_total
*/
__pyx_t_3 = __pyx_v_self->sum_left;
__pyx_v_sum_left = __pyx_t_3;
/* "sklearn/tree/_tree.pyx":743
* cdef double weighted_n_node_samples = self.weighted_n_node_samples
* cdef double* sum_left = self.sum_left
* cdef double* sum_right = self.sum_right # <<<<<<<<<<<<<<
* cdef double* sum_total = self.sum_total
*
*/
__pyx_t_3 = __pyx_v_self->sum_right;
__pyx_v_sum_right = __pyx_t_3;
/* "sklearn/tree/_tree.pyx":744
* cdef double* sum_left = self.sum_left
* cdef double* sum_right = self.sum_right
* cdef double* sum_total = self.sum_total # <<<<<<<<<<<<<<
*
* cdef SIZE_t k = 0
*/
__pyx_t_3 = __pyx_v_self->sum_total;
__pyx_v_sum_total = __pyx_t_3;
/* "sklearn/tree/_tree.pyx":746
* cdef double* sum_total = self.sum_total
*
* cdef SIZE_t k = 0 # <<<<<<<<<<<<<<
*
* for k in range(n_outputs):
*/
__pyx_v_k = 0;
/* "sklearn/tree/_tree.pyx":748
* cdef SIZE_t k = 0
*
* for k in range(n_outputs): # <<<<<<<<<<<<<<
* mean_right[k] = mean_total[k]
* mean_left[k] = 0.0
*/
__pyx_t_1 = __pyx_v_n_outputs;
for (__pyx_t_4 = 0; __pyx_t_4 < __pyx_t_1; __pyx_t_4+=1) {
__pyx_v_k = __pyx_t_4;
/* "sklearn/tree/_tree.pyx":749
*
* for k in range(n_outputs):
* mean_right[k] = mean_total[k] # <<<<<<<<<<<<<<
* mean_left[k] = 0.0
* sq_sum_right[k] = sq_sum_total[k]
*/
(__pyx_v_mean_right[__pyx_v_k]) = (__pyx_v_mean_total[__pyx_v_k]);
/* "sklearn/tree/_tree.pyx":750
* for k in range(n_outputs):
* mean_right[k] = mean_total[k]
* mean_left[k] = 0.0 # <<<<<<<<<<<<<<
* sq_sum_right[k] = sq_sum_total[k]
* sq_sum_left[k] = 0.0
*/
(__pyx_v_mean_left[__pyx_v_k]) = 0.0;
/* "sklearn/tree/_tree.pyx":751
* mean_right[k] = mean_total[k]
* mean_left[k] = 0.0
* sq_sum_right[k] = sq_sum_total[k] # <<<<<<<<<<<<<<
* sq_sum_left[k] = 0.0
* var_right[k] = (sq_sum_right[k] / weighted_n_node_samples -
*/
(__pyx_v_sq_sum_right[__pyx_v_k]) = (__pyx_v_sq_sum_total[__pyx_v_k]);
/* "sklearn/tree/_tree.pyx":752
* mean_left[k] = 0.0
* sq_sum_right[k] = sq_sum_total[k]
* sq_sum_left[k] = 0.0 # <<<<<<<<<<<<<<
* var_right[k] = (sq_sum_right[k] / weighted_n_node_samples -
* mean_right[k] * mean_right[k])
*/
(__pyx_v_sq_sum_left[__pyx_v_k]) = 0.0;
/* "sklearn/tree/_tree.pyx":753
* sq_sum_right[k] = sq_sum_total[k]
* sq_sum_left[k] = 0.0
* var_right[k] = (sq_sum_right[k] / weighted_n_node_samples - # <<<<<<<<<<<<<<
* mean_right[k] * mean_right[k])
* var_left[k] = 0.0
*/
(__pyx_v_var_right[__pyx_v_k]) = (((__pyx_v_sq_sum_right[__pyx_v_k]) / __pyx_v_weighted_n_node_samples) - ((__pyx_v_mean_right[__pyx_v_k]) * (__pyx_v_mean_right[__pyx_v_k])));
/* "sklearn/tree/_tree.pyx":755
* var_right[k] = (sq_sum_right[k] / weighted_n_node_samples -
* mean_right[k] * mean_right[k])
* var_left[k] = 0.0 # <<<<<<<<<<<<<<
* sum_right[k] = sum_total[k]
* sum_left[k] = 0.0
*/
(__pyx_v_var_left[__pyx_v_k]) = 0.0;
/* "sklearn/tree/_tree.pyx":756
* mean_right[k] * mean_right[k])
* var_left[k] = 0.0
* sum_right[k] = sum_total[k] # <<<<<<<<<<<<<<
* sum_left[k] = 0.0
*
*/
(__pyx_v_sum_right[__pyx_v_k]) = (__pyx_v_sum_total[__pyx_v_k]);
/* "sklearn/tree/_tree.pyx":757
* var_left[k] = 0.0
* sum_right[k] = sum_total[k]
* sum_left[k] = 0.0 # <<<<<<<<<<<<<<
*
* cdef void update(self, SIZE_t new_pos) nogil:
*/
(__pyx_v_sum_left[__pyx_v_k]) = 0.0;
}
/* "sklearn/tree/_tree.pyx":725
* self.reset()
*
* cdef void reset(self) nogil: # <<<<<<<<<<<<<<
* """Reset the criterion at pos=start."""
* self.pos = self.start
*/
/* function exit code */
}
/* "sklearn/tree/_tree.pyx":759
* sum_left[k] = 0.0
*
* cdef void update(self, SIZE_t new_pos) nogil: # <<<<<<<<<<<<<<
* """Update the collected statistics by moving samples[pos:new_pos] from
* the right child to the left child."""
*/
static void __pyx_f_7sklearn_4tree_5_tree_19RegressionCriterion_update(struct __pyx_obj_7sklearn_4tree_5_tree_RegressionCriterion *__pyx_v_self, __pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_v_new_pos) {
__pyx_t_7sklearn_4tree_5_tree_DOUBLE_t *__pyx_v_y;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_v_y_stride;
__pyx_t_7sklearn_4tree_5_tree_DOUBLE_t *__pyx_v_sample_weight;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t *__pyx_v_samples;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_v_pos;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_v_n_outputs;
double *__pyx_v_mean_left;
double *__pyx_v_mean_right;
double *__pyx_v_sq_sum_left;
double *__pyx_v_sq_sum_right;
double *__pyx_v_var_left;
double *__pyx_v_var_right;
double *__pyx_v_sum_left;
double *__pyx_v_sum_right;
double __pyx_v_weighted_n_left;
double __pyx_v_weighted_n_right;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_v_i;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_v_p;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_v_k;
__pyx_t_7sklearn_4tree_5_tree_DOUBLE_t __pyx_v_w;
__pyx_t_7sklearn_4tree_5_tree_DOUBLE_t __pyx_v_diff_w;
__pyx_t_7sklearn_4tree_5_tree_DOUBLE_t __pyx_v_y_ik;
__pyx_t_7sklearn_4tree_5_tree_DOUBLE_t __pyx_v_w_y_ik;
__pyx_t_7sklearn_4tree_5_tree_DOUBLE_t *__pyx_t_1;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_t_2;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t *__pyx_t_3;
double *__pyx_t_4;
double __pyx_t_5;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_t_6;
int __pyx_t_7;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_t_8;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_t_9;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_t_10;
/* "sklearn/tree/_tree.pyx":762
* """Update the collected statistics by moving samples[pos:new_pos] from
* the right child to the left child."""
* cdef DOUBLE_t* y = self.y # <<<<<<<<<<<<<<
* cdef SIZE_t y_stride = self.y_stride
* cdef DOUBLE_t* sample_weight = self.sample_weight
*/
__pyx_t_1 = __pyx_v_self->__pyx_base.y;
__pyx_v_y = __pyx_t_1;
/* "sklearn/tree/_tree.pyx":763
* the right child to the left child."""
* cdef DOUBLE_t* y = self.y
* cdef SIZE_t y_stride = self.y_stride # <<<<<<<<<<<<<<
* cdef DOUBLE_t* sample_weight = self.sample_weight
*
*/
__pyx_t_2 = __pyx_v_self->__pyx_base.y_stride;
__pyx_v_y_stride = __pyx_t_2;
/* "sklearn/tree/_tree.pyx":764
* cdef DOUBLE_t* y = self.y
* cdef SIZE_t y_stride = self.y_stride
* cdef DOUBLE_t* sample_weight = self.sample_weight # <<<<<<<<<<<<<<
*
* cdef SIZE_t* samples = self.samples
*/
__pyx_t_1 = __pyx_v_self->__pyx_base.sample_weight;
__pyx_v_sample_weight = __pyx_t_1;
/* "sklearn/tree/_tree.pyx":766
* cdef DOUBLE_t* sample_weight = self.sample_weight
*
* cdef SIZE_t* samples = self.samples # <<<<<<<<<<<<<<
* cdef SIZE_t pos = self.pos
*
*/
__pyx_t_3 = __pyx_v_self->__pyx_base.samples;
__pyx_v_samples = __pyx_t_3;
/* "sklearn/tree/_tree.pyx":767
*
* cdef SIZE_t* samples = self.samples
* cdef SIZE_t pos = self.pos # <<<<<<<<<<<<<<
*
* cdef SIZE_t n_outputs = self.n_outputs
*/
__pyx_t_2 = __pyx_v_self->__pyx_base.pos;
__pyx_v_pos = __pyx_t_2;
/* "sklearn/tree/_tree.pyx":769
* cdef SIZE_t pos = self.pos
*
* cdef SIZE_t n_outputs = self.n_outputs # <<<<<<<<<<<<<<
* cdef double* mean_left = self.mean_left
* cdef double* mean_right = self.mean_right
*/
__pyx_t_2 = __pyx_v_self->__pyx_base.n_outputs;
__pyx_v_n_outputs = __pyx_t_2;
/* "sklearn/tree/_tree.pyx":770
*
* cdef SIZE_t n_outputs = self.n_outputs
* cdef double* mean_left = self.mean_left # <<<<<<<<<<<<<<
* cdef double* mean_right = self.mean_right
* cdef double* sq_sum_left = self.sq_sum_left
*/
__pyx_t_4 = __pyx_v_self->mean_left;
__pyx_v_mean_left = __pyx_t_4;
/* "sklearn/tree/_tree.pyx":771
* cdef SIZE_t n_outputs = self.n_outputs
* cdef double* mean_left = self.mean_left
* cdef double* mean_right = self.mean_right # <<<<<<<<<<<<<<
* cdef double* sq_sum_left = self.sq_sum_left
* cdef double* sq_sum_right = self.sq_sum_right
*/
__pyx_t_4 = __pyx_v_self->mean_right;
__pyx_v_mean_right = __pyx_t_4;
/* "sklearn/tree/_tree.pyx":772
* cdef double* mean_left = self.mean_left
* cdef double* mean_right = self.mean_right
* cdef double* sq_sum_left = self.sq_sum_left # <<<<<<<<<<<<<<
* cdef double* sq_sum_right = self.sq_sum_right
* cdef double* var_left = self.var_left
*/
__pyx_t_4 = __pyx_v_self->sq_sum_left;
__pyx_v_sq_sum_left = __pyx_t_4;
/* "sklearn/tree/_tree.pyx":773
* cdef double* mean_right = self.mean_right
* cdef double* sq_sum_left = self.sq_sum_left
* cdef double* sq_sum_right = self.sq_sum_right # <<<<<<<<<<<<<<
* cdef double* var_left = self.var_left
* cdef double* var_right = self.var_right
*/
__pyx_t_4 = __pyx_v_self->sq_sum_right;
__pyx_v_sq_sum_right = __pyx_t_4;
/* "sklearn/tree/_tree.pyx":774
* cdef double* sq_sum_left = self.sq_sum_left
* cdef double* sq_sum_right = self.sq_sum_right
* cdef double* var_left = self.var_left # <<<<<<<<<<<<<<
* cdef double* var_right = self.var_right
* cdef double* sum_left = self.sum_left
*/
__pyx_t_4 = __pyx_v_self->var_left;
__pyx_v_var_left = __pyx_t_4;
/* "sklearn/tree/_tree.pyx":775
* cdef double* sq_sum_right = self.sq_sum_right
* cdef double* var_left = self.var_left
* cdef double* var_right = self.var_right # <<<<<<<<<<<<<<
* cdef double* sum_left = self.sum_left
* cdef double* sum_right = self.sum_right
*/
__pyx_t_4 = __pyx_v_self->var_right;
__pyx_v_var_right = __pyx_t_4;
/* "sklearn/tree/_tree.pyx":776
* cdef double* var_left = self.var_left
* cdef double* var_right = self.var_right
* cdef double* sum_left = self.sum_left # <<<<<<<<<<<<<<
* cdef double* sum_right = self.sum_right
*
*/
__pyx_t_4 = __pyx_v_self->sum_left;
__pyx_v_sum_left = __pyx_t_4;
/* "sklearn/tree/_tree.pyx":777
* cdef double* var_right = self.var_right
* cdef double* sum_left = self.sum_left
* cdef double* sum_right = self.sum_right # <<<<<<<<<<<<<<
*
* cdef double weighted_n_left = self.weighted_n_left
*/
__pyx_t_4 = __pyx_v_self->sum_right;
__pyx_v_sum_right = __pyx_t_4;
/* "sklearn/tree/_tree.pyx":779
* cdef double* sum_right = self.sum_right
*
* cdef double weighted_n_left = self.weighted_n_left # <<<<<<<<<<<<<<
* cdef double weighted_n_right = self.weighted_n_right
*
*/
__pyx_t_5 = __pyx_v_self->__pyx_base.weighted_n_left;
__pyx_v_weighted_n_left = __pyx_t_5;
/* "sklearn/tree/_tree.pyx":780
*
* cdef double weighted_n_left = self.weighted_n_left
* cdef double weighted_n_right = self.weighted_n_right # <<<<<<<<<<<<<<
*
* cdef SIZE_t i
*/
__pyx_t_5 = __pyx_v_self->__pyx_base.weighted_n_right;
__pyx_v_weighted_n_right = __pyx_t_5;
/* "sklearn/tree/_tree.pyx":785
* cdef SIZE_t p
* cdef SIZE_t k
* cdef DOUBLE_t w = 1.0 # <<<<<<<<<<<<<<
* cdef DOUBLE_t diff_w = 0.0
* cdef DOUBLE_t y_ik, w_y_ik
*/
__pyx_v_w = 1.0;
/* "sklearn/tree/_tree.pyx":786
* cdef SIZE_t k
* cdef DOUBLE_t w = 1.0
* cdef DOUBLE_t diff_w = 0.0 # <<<<<<<<<<<<<<
* cdef DOUBLE_t y_ik, w_y_ik
*
*/
__pyx_v_diff_w = 0.0;
/* "sklearn/tree/_tree.pyx":790
*
* # Note: We assume start <= pos < new_pos <= end
* for p in range(pos, new_pos): # <<<<<<<<<<<<<<
* i = samples[p]
*
*/
__pyx_t_2 = __pyx_v_new_pos;
for (__pyx_t_6 = __pyx_v_pos; __pyx_t_6 < __pyx_t_2; __pyx_t_6+=1) {
__pyx_v_p = __pyx_t_6;
/* "sklearn/tree/_tree.pyx":791
* # Note: We assume start <= pos < new_pos <= end
* for p in range(pos, new_pos):
* i = samples[p] # <<<<<<<<<<<<<<
*
* if sample_weight != NULL:
*/
__pyx_v_i = (__pyx_v_samples[__pyx_v_p]);
/* "sklearn/tree/_tree.pyx":793
* i = samples[p]
*
* if sample_weight != NULL: # <<<<<<<<<<<<<<
* w = sample_weight[i]
*
*/
__pyx_t_7 = ((__pyx_v_sample_weight != NULL) != 0);
if (__pyx_t_7) {
/* "sklearn/tree/_tree.pyx":794
*
* if sample_weight != NULL:
* w = sample_weight[i] # <<<<<<<<<<<<<<
*
* for k in range(n_outputs):
*/
__pyx_v_w = (__pyx_v_sample_weight[__pyx_v_i]);
goto __pyx_L5;
}
__pyx_L5:;
/* "sklearn/tree/_tree.pyx":796
* w = sample_weight[i]
*
* for k in range(n_outputs): # <<<<<<<<<<<<<<
* y_ik = y[i * y_stride + k]
* w_y_ik = w * y_ik
*/
__pyx_t_8 = __pyx_v_n_outputs;
for (__pyx_t_9 = 0; __pyx_t_9 < __pyx_t_8; __pyx_t_9+=1) {
__pyx_v_k = __pyx_t_9;
/* "sklearn/tree/_tree.pyx":797
*
* for k in range(n_outputs):
* y_ik = y[i * y_stride + k] # <<<<<<<<<<<<<<
* w_y_ik = w * y_ik
*
*/
__pyx_v_y_ik = (__pyx_v_y[((__pyx_v_i * __pyx_v_y_stride) + __pyx_v_k)]);
/* "sklearn/tree/_tree.pyx":798
* for k in range(n_outputs):
* y_ik = y[i * y_stride + k]
* w_y_ik = w * y_ik # <<<<<<<<<<<<<<
*
* sum_left[k] += w_y_ik
*/
__pyx_v_w_y_ik = (__pyx_v_w * __pyx_v_y_ik);
/* "sklearn/tree/_tree.pyx":800
* w_y_ik = w * y_ik
*
* sum_left[k] += w_y_ik # <<<<<<<<<<<<<<
* sum_right[k] -= w_y_ik
*
*/
__pyx_t_10 = __pyx_v_k;
(__pyx_v_sum_left[__pyx_t_10]) = ((__pyx_v_sum_left[__pyx_t_10]) + __pyx_v_w_y_ik);
/* "sklearn/tree/_tree.pyx":801
*
* sum_left[k] += w_y_ik
* sum_right[k] -= w_y_ik # <<<<<<<<<<<<<<
*
* sq_sum_left[k] += w_y_ik * y_ik
*/
__pyx_t_10 = __pyx_v_k;
(__pyx_v_sum_right[__pyx_t_10]) = ((__pyx_v_sum_right[__pyx_t_10]) - __pyx_v_w_y_ik);
/* "sklearn/tree/_tree.pyx":803
* sum_right[k] -= w_y_ik
*
* sq_sum_left[k] += w_y_ik * y_ik # <<<<<<<<<<<<<<
* sq_sum_right[k] -= w_y_ik * y_ik
*
*/
__pyx_t_10 = __pyx_v_k;
(__pyx_v_sq_sum_left[__pyx_t_10]) = ((__pyx_v_sq_sum_left[__pyx_t_10]) + (__pyx_v_w_y_ik * __pyx_v_y_ik));
/* "sklearn/tree/_tree.pyx":804
*
* sq_sum_left[k] += w_y_ik * y_ik
* sq_sum_right[k] -= w_y_ik * y_ik # <<<<<<<<<<<<<<
*
* diff_w += w
*/
__pyx_t_10 = __pyx_v_k;
(__pyx_v_sq_sum_right[__pyx_t_10]) = ((__pyx_v_sq_sum_right[__pyx_t_10]) - (__pyx_v_w_y_ik * __pyx_v_y_ik));
}
/* "sklearn/tree/_tree.pyx":806
* sq_sum_right[k] -= w_y_ik * y_ik
*
* diff_w += w # <<<<<<<<<<<<<<
*
* weighted_n_left += diff_w
*/
__pyx_v_diff_w = (__pyx_v_diff_w + __pyx_v_w);
}
/* "sklearn/tree/_tree.pyx":808
* diff_w += w
*
* weighted_n_left += diff_w # <<<<<<<<<<<<<<
* weighted_n_right -= diff_w
*
*/
__pyx_v_weighted_n_left = (__pyx_v_weighted_n_left + __pyx_v_diff_w);
/* "sklearn/tree/_tree.pyx":809
*
* weighted_n_left += diff_w
* weighted_n_right -= diff_w # <<<<<<<<<<<<<<
*
* for k in range(n_outputs):
*/
__pyx_v_weighted_n_right = (__pyx_v_weighted_n_right - __pyx_v_diff_w);
/* "sklearn/tree/_tree.pyx":811
* weighted_n_right -= diff_w
*
* for k in range(n_outputs): # <<<<<<<<<<<<<<
* mean_left[k] = sum_left[k] / weighted_n_left
* mean_right[k] = sum_right[k] / weighted_n_right
*/
__pyx_t_2 = __pyx_v_n_outputs;
for (__pyx_t_6 = 0; __pyx_t_6 < __pyx_t_2; __pyx_t_6+=1) {
__pyx_v_k = __pyx_t_6;
/* "sklearn/tree/_tree.pyx":812
*
* for k in range(n_outputs):
* mean_left[k] = sum_left[k] / weighted_n_left # <<<<<<<<<<<<<<
* mean_right[k] = sum_right[k] / weighted_n_right
* var_left[k] = (sq_sum_left[k] / weighted_n_left -
*/
(__pyx_v_mean_left[__pyx_v_k]) = ((__pyx_v_sum_left[__pyx_v_k]) / __pyx_v_weighted_n_left);
/* "sklearn/tree/_tree.pyx":813
* for k in range(n_outputs):
* mean_left[k] = sum_left[k] / weighted_n_left
* mean_right[k] = sum_right[k] / weighted_n_right # <<<<<<<<<<<<<<
* var_left[k] = (sq_sum_left[k] / weighted_n_left -
* mean_left[k] * mean_left[k])
*/
(__pyx_v_mean_right[__pyx_v_k]) = ((__pyx_v_sum_right[__pyx_v_k]) / __pyx_v_weighted_n_right);
/* "sklearn/tree/_tree.pyx":814
* mean_left[k] = sum_left[k] / weighted_n_left
* mean_right[k] = sum_right[k] / weighted_n_right
* var_left[k] = (sq_sum_left[k] / weighted_n_left - # <<<<<<<<<<<<<<
* mean_left[k] * mean_left[k])
* var_right[k] = (sq_sum_right[k] / weighted_n_right -
*/
(__pyx_v_var_left[__pyx_v_k]) = (((__pyx_v_sq_sum_left[__pyx_v_k]) / __pyx_v_weighted_n_left) - ((__pyx_v_mean_left[__pyx_v_k]) * (__pyx_v_mean_left[__pyx_v_k])));
/* "sklearn/tree/_tree.pyx":816
* var_left[k] = (sq_sum_left[k] / weighted_n_left -
* mean_left[k] * mean_left[k])
* var_right[k] = (sq_sum_right[k] / weighted_n_right - # <<<<<<<<<<<<<<
* mean_right[k] * mean_right[k])
*
*/
(__pyx_v_var_right[__pyx_v_k]) = (((__pyx_v_sq_sum_right[__pyx_v_k]) / __pyx_v_weighted_n_right) - ((__pyx_v_mean_right[__pyx_v_k]) * (__pyx_v_mean_right[__pyx_v_k])));
}
/* "sklearn/tree/_tree.pyx":819
* mean_right[k] * mean_right[k])
*
* self.weighted_n_left = weighted_n_left # <<<<<<<<<<<<<<
* self.weighted_n_right = weighted_n_right
*
*/
__pyx_v_self->__pyx_base.weighted_n_left = __pyx_v_weighted_n_left;
/* "sklearn/tree/_tree.pyx":820
*
* self.weighted_n_left = weighted_n_left
* self.weighted_n_right = weighted_n_right # <<<<<<<<<<<<<<
*
* self.pos = new_pos
*/
__pyx_v_self->__pyx_base.weighted_n_right = __pyx_v_weighted_n_right;
/* "sklearn/tree/_tree.pyx":822
* self.weighted_n_right = weighted_n_right
*
* self.pos = new_pos # <<<<<<<<<<<<<<
*
* cdef double node_impurity(self) nogil:
*/
__pyx_v_self->__pyx_base.pos = __pyx_v_new_pos;
/* "sklearn/tree/_tree.pyx":759
* sum_left[k] = 0.0
*
* cdef void update(self, SIZE_t new_pos) nogil: # <<<<<<<<<<<<<<
* """Update the collected statistics by moving samples[pos:new_pos] from
* the right child to the left child."""
*/
/* function exit code */
}
/* "sklearn/tree/_tree.pyx":824
* self.pos = new_pos
*
* cdef double node_impurity(self) nogil: # <<<<<<<<<<<<<<
* pass
*
*/
static double __pyx_f_7sklearn_4tree_5_tree_19RegressionCriterion_node_impurity(CYTHON_UNUSED struct __pyx_obj_7sklearn_4tree_5_tree_RegressionCriterion *__pyx_v_self) {
double __pyx_r;
/* function exit code */
__pyx_r = 0;
return __pyx_r;
}
/* "sklearn/tree/_tree.pyx":827
* pass
*
* cdef void children_impurity(self, double* impurity_left, # <<<<<<<<<<<<<<
* double* impurity_right) nogil:
* pass
*/
static void __pyx_f_7sklearn_4tree_5_tree_19RegressionCriterion_children_impurity(CYTHON_UNUSED struct __pyx_obj_7sklearn_4tree_5_tree_RegressionCriterion *__pyx_v_self, CYTHON_UNUSED double *__pyx_v_impurity_left, CYTHON_UNUSED double *__pyx_v_impurity_right) {
/* function exit code */
}
/* "sklearn/tree/_tree.pyx":831
* pass
*
* cdef void node_value(self, double* dest) nogil: # <<<<<<<<<<<<<<
* """Compute the node value of samples[start:end] into dest."""
* memcpy(dest, self.mean_total, self.n_outputs * sizeof(double))
*/
static void __pyx_f_7sklearn_4tree_5_tree_19RegressionCriterion_node_value(struct __pyx_obj_7sklearn_4tree_5_tree_RegressionCriterion *__pyx_v_self, double *__pyx_v_dest) {
/* "sklearn/tree/_tree.pyx":833
* cdef void node_value(self, double* dest) nogil:
* """Compute the node value of samples[start:end] into dest."""
* memcpy(dest, self.mean_total, self.n_outputs * sizeof(double)) # <<<<<<<<<<<<<<
*
*
*/
memcpy(__pyx_v_dest, __pyx_v_self->mean_total, (__pyx_v_self->__pyx_base.n_outputs * (sizeof(double))));
/* "sklearn/tree/_tree.pyx":831
* pass
*
* cdef void node_value(self, double* dest) nogil: # <<<<<<<<<<<<<<
* """Compute the node value of samples[start:end] into dest."""
* memcpy(dest, self.mean_total, self.n_outputs * sizeof(double))
*/
/* function exit code */
}
/* "sklearn/tree/_tree.pyx":841
* MSE = var_left + var_right
* """
* cdef double node_impurity(self) nogil: # <<<<<<<<<<<<<<
* """Evaluate the impurity of the current node, i.e. the impurity of
* samples[start:end]."""
*/
static double __pyx_f_7sklearn_4tree_5_tree_3MSE_node_impurity(struct __pyx_obj_7sklearn_4tree_5_tree_MSE *__pyx_v_self) {
__pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_v_n_outputs;
double *__pyx_v_sq_sum_total;
double *__pyx_v_mean_total;
double __pyx_v_weighted_n_node_samples;
double __pyx_v_total;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_v_k;
double __pyx_r;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_t_1;
double *__pyx_t_2;
double __pyx_t_3;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_t_4;
/* "sklearn/tree/_tree.pyx":844
* """Evaluate the impurity of the current node, i.e. the impurity of
* samples[start:end]."""
* cdef SIZE_t n_outputs = self.n_outputs # <<<<<<<<<<<<<<
* cdef double* sq_sum_total = self.sq_sum_total
* cdef double* mean_total = self.mean_total
*/
__pyx_t_1 = __pyx_v_self->__pyx_base.__pyx_base.n_outputs;
__pyx_v_n_outputs = __pyx_t_1;
/* "sklearn/tree/_tree.pyx":845
* samples[start:end]."""
* cdef SIZE_t n_outputs = self.n_outputs
* cdef double* sq_sum_total = self.sq_sum_total # <<<<<<<<<<<<<<
* cdef double* mean_total = self.mean_total
* cdef double weighted_n_node_samples = self.weighted_n_node_samples
*/
__pyx_t_2 = __pyx_v_self->__pyx_base.sq_sum_total;
__pyx_v_sq_sum_total = __pyx_t_2;
/* "sklearn/tree/_tree.pyx":846
* cdef SIZE_t n_outputs = self.n_outputs
* cdef double* sq_sum_total = self.sq_sum_total
* cdef double* mean_total = self.mean_total # <<<<<<<<<<<<<<
* cdef double weighted_n_node_samples = self.weighted_n_node_samples
* cdef double total = 0.0
*/
__pyx_t_2 = __pyx_v_self->__pyx_base.mean_total;
__pyx_v_mean_total = __pyx_t_2;
/* "sklearn/tree/_tree.pyx":847
* cdef double* sq_sum_total = self.sq_sum_total
* cdef double* mean_total = self.mean_total
* cdef double weighted_n_node_samples = self.weighted_n_node_samples # <<<<<<<<<<<<<<
* cdef double total = 0.0
* cdef SIZE_t k
*/
__pyx_t_3 = __pyx_v_self->__pyx_base.__pyx_base.weighted_n_node_samples;
__pyx_v_weighted_n_node_samples = __pyx_t_3;
/* "sklearn/tree/_tree.pyx":848
* cdef double* mean_total = self.mean_total
* cdef double weighted_n_node_samples = self.weighted_n_node_samples
* cdef double total = 0.0 # <<<<<<<<<<<<<<
* cdef SIZE_t k
*
*/
__pyx_v_total = 0.0;
/* "sklearn/tree/_tree.pyx":851
* cdef SIZE_t k
*
* for k in range(n_outputs): # <<<<<<<<<<<<<<
* total += (sq_sum_total[k] / weighted_n_node_samples -
* mean_total[k] * mean_total[k])
*/
__pyx_t_1 = __pyx_v_n_outputs;
for (__pyx_t_4 = 0; __pyx_t_4 < __pyx_t_1; __pyx_t_4+=1) {
__pyx_v_k = __pyx_t_4;
/* "sklearn/tree/_tree.pyx":852
*
* for k in range(n_outputs):
* total += (sq_sum_total[k] / weighted_n_node_samples - # <<<<<<<<<<<<<<
* mean_total[k] * mean_total[k])
*
*/
__pyx_v_total = (__pyx_v_total + (((__pyx_v_sq_sum_total[__pyx_v_k]) / __pyx_v_weighted_n_node_samples) - ((__pyx_v_mean_total[__pyx_v_k]) * (__pyx_v_mean_total[__pyx_v_k]))));
}
/* "sklearn/tree/_tree.pyx":855
* mean_total[k] * mean_total[k])
*
* return total / n_outputs # <<<<<<<<<<<<<<
*
* cdef void children_impurity(self, double* impurity_left,
*/
__pyx_r = (__pyx_v_total / __pyx_v_n_outputs);
goto __pyx_L0;
/* "sklearn/tree/_tree.pyx":841
* MSE = var_left + var_right
* """
* cdef double node_impurity(self) nogil: # <<<<<<<<<<<<<<
* """Evaluate the impurity of the current node, i.e. the impurity of
* samples[start:end]."""
*/
/* function exit code */
__pyx_L0:;
return __pyx_r;
}
/* "sklearn/tree/_tree.pyx":857
* return total / n_outputs
*
* cdef void children_impurity(self, double* impurity_left, # <<<<<<<<<<<<<<
* double* impurity_right) nogil:
* """Evaluate the impurity in children nodes, i.e. the impurity of the
*/
static void __pyx_f_7sklearn_4tree_5_tree_3MSE_children_impurity(struct __pyx_obj_7sklearn_4tree_5_tree_MSE *__pyx_v_self, double *__pyx_v_impurity_left, double *__pyx_v_impurity_right) {
__pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_v_n_outputs;
double *__pyx_v_var_left;
double *__pyx_v_var_right;
double __pyx_v_total_left;
double __pyx_v_total_right;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_v_k;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_t_1;
double *__pyx_t_2;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_t_3;
/* "sklearn/tree/_tree.pyx":862
* left child (samples[start:pos]) and the impurity the right child
* (samples[pos:end])."""
* cdef SIZE_t n_outputs = self.n_outputs # <<<<<<<<<<<<<<
* cdef double* var_left = self.var_left
* cdef double* var_right = self.var_right
*/
__pyx_t_1 = __pyx_v_self->__pyx_base.__pyx_base.n_outputs;
__pyx_v_n_outputs = __pyx_t_1;
/* "sklearn/tree/_tree.pyx":863
* (samples[pos:end])."""
* cdef SIZE_t n_outputs = self.n_outputs
* cdef double* var_left = self.var_left # <<<<<<<<<<<<<<
* cdef double* var_right = self.var_right
* cdef double total_left = 0.0
*/
__pyx_t_2 = __pyx_v_self->__pyx_base.var_left;
__pyx_v_var_left = __pyx_t_2;
/* "sklearn/tree/_tree.pyx":864
* cdef SIZE_t n_outputs = self.n_outputs
* cdef double* var_left = self.var_left
* cdef double* var_right = self.var_right # <<<<<<<<<<<<<<
* cdef double total_left = 0.0
* cdef double total_right = 0.0
*/
__pyx_t_2 = __pyx_v_self->__pyx_base.var_right;
__pyx_v_var_right = __pyx_t_2;
/* "sklearn/tree/_tree.pyx":865
* cdef double* var_left = self.var_left
* cdef double* var_right = self.var_right
* cdef double total_left = 0.0 # <<<<<<<<<<<<<<
* cdef double total_right = 0.0
* cdef SIZE_t k
*/
__pyx_v_total_left = 0.0;
/* "sklearn/tree/_tree.pyx":866
* cdef double* var_right = self.var_right
* cdef double total_left = 0.0
* cdef double total_right = 0.0 # <<<<<<<<<<<<<<
* cdef SIZE_t k
*
*/
__pyx_v_total_right = 0.0;
/* "sklearn/tree/_tree.pyx":869
* cdef SIZE_t k
*
* for k in range(n_outputs): # <<<<<<<<<<<<<<
* total_left += var_left[k]
* total_right += var_right[k]
*/
__pyx_t_1 = __pyx_v_n_outputs;
for (__pyx_t_3 = 0; __pyx_t_3 < __pyx_t_1; __pyx_t_3+=1) {
__pyx_v_k = __pyx_t_3;
/* "sklearn/tree/_tree.pyx":870
*
* for k in range(n_outputs):
* total_left += var_left[k] # <<<<<<<<<<<<<<
* total_right += var_right[k]
*
*/
__pyx_v_total_left = (__pyx_v_total_left + (__pyx_v_var_left[__pyx_v_k]));
/* "sklearn/tree/_tree.pyx":871
* for k in range(n_outputs):
* total_left += var_left[k]
* total_right += var_right[k] # <<<<<<<<<<<<<<
*
* impurity_left[0] = total_left / n_outputs
*/
__pyx_v_total_right = (__pyx_v_total_right + (__pyx_v_var_right[__pyx_v_k]));
}
/* "sklearn/tree/_tree.pyx":873
* total_right += var_right[k]
*
* impurity_left[0] = total_left / n_outputs # <<<<<<<<<<<<<<
* impurity_right[0] = total_right / n_outputs
*
*/
(__pyx_v_impurity_left[0]) = (__pyx_v_total_left / __pyx_v_n_outputs);
/* "sklearn/tree/_tree.pyx":874
*
* impurity_left[0] = total_left / n_outputs
* impurity_right[0] = total_right / n_outputs # <<<<<<<<<<<<<<
*
*
*/
(__pyx_v_impurity_right[0]) = (__pyx_v_total_right / __pyx_v_n_outputs);
/* "sklearn/tree/_tree.pyx":857
* return total / n_outputs
*
* cdef void children_impurity(self, double* impurity_left, # <<<<<<<<<<<<<<
* double* impurity_right) nogil:
* """Evaluate the impurity in children nodes, i.e. the impurity of the
*/
/* function exit code */
}
/* "sklearn/tree/_tree.pyx":886
* """
*
* cdef double impurity_improvement(self, double impurity) nogil: # <<<<<<<<<<<<<<
* cdef SIZE_t n_outputs = self.n_outputs
* cdef SIZE_t k
*/
static double __pyx_f_7sklearn_4tree_5_tree_11FriedmanMSE_impurity_improvement(struct __pyx_obj_7sklearn_4tree_5_tree_FriedmanMSE *__pyx_v_self, CYTHON_UNUSED double __pyx_v_impurity) {
__pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_v_n_outputs;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_v_k;
double *__pyx_v_sum_left;
double *__pyx_v_sum_right;
double __pyx_v_total_sum_left;
double __pyx_v_total_sum_right;
double __pyx_v_weighted_n_left;
double __pyx_v_weighted_n_right;
double __pyx_v_diff;
double __pyx_r;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_t_1;
double *__pyx_t_2;
double __pyx_t_3;
/* "sklearn/tree/_tree.pyx":887
*
* cdef double impurity_improvement(self, double impurity) nogil:
* cdef SIZE_t n_outputs = self.n_outputs # <<<<<<<<<<<<<<
* cdef SIZE_t k
* cdef double* sum_left = self.sum_left
*/
__pyx_t_1 = __pyx_v_self->__pyx_base.__pyx_base.__pyx_base.n_outputs;
__pyx_v_n_outputs = __pyx_t_1;
/* "sklearn/tree/_tree.pyx":889
* cdef SIZE_t n_outputs = self.n_outputs
* cdef SIZE_t k
* cdef double* sum_left = self.sum_left # <<<<<<<<<<<<<<
* cdef double* sum_right = self.sum_right
* cdef double total_sum_left = 0.0
*/
__pyx_t_2 = __pyx_v_self->__pyx_base.__pyx_base.sum_left;
__pyx_v_sum_left = __pyx_t_2;
/* "sklearn/tree/_tree.pyx":890
* cdef SIZE_t k
* cdef double* sum_left = self.sum_left
* cdef double* sum_right = self.sum_right # <<<<<<<<<<<<<<
* cdef double total_sum_left = 0.0
* cdef double total_sum_right = 0.0
*/
__pyx_t_2 = __pyx_v_self->__pyx_base.__pyx_base.sum_right;
__pyx_v_sum_right = __pyx_t_2;
/* "sklearn/tree/_tree.pyx":891
* cdef double* sum_left = self.sum_left
* cdef double* sum_right = self.sum_right
* cdef double total_sum_left = 0.0 # <<<<<<<<<<<<<<
* cdef double total_sum_right = 0.0
* cdef double weighted_n_left = self.weighted_n_left
*/
__pyx_v_total_sum_left = 0.0;
/* "sklearn/tree/_tree.pyx":892
* cdef double* sum_right = self.sum_right
* cdef double total_sum_left = 0.0
* cdef double total_sum_right = 0.0 # <<<<<<<<<<<<<<
* cdef double weighted_n_left = self.weighted_n_left
* cdef double weighted_n_right = self.weighted_n_right
*/
__pyx_v_total_sum_right = 0.0;
/* "sklearn/tree/_tree.pyx":893
* cdef double total_sum_left = 0.0
* cdef double total_sum_right = 0.0
* cdef double weighted_n_left = self.weighted_n_left # <<<<<<<<<<<<<<
* cdef double weighted_n_right = self.weighted_n_right
* cdef double diff = 0.0
*/
__pyx_t_3 = __pyx_v_self->__pyx_base.__pyx_base.__pyx_base.weighted_n_left;
__pyx_v_weighted_n_left = __pyx_t_3;
/* "sklearn/tree/_tree.pyx":894
* cdef double total_sum_right = 0.0
* cdef double weighted_n_left = self.weighted_n_left
* cdef double weighted_n_right = self.weighted_n_right # <<<<<<<<<<<<<<
* cdef double diff = 0.0
*
*/
__pyx_t_3 = __pyx_v_self->__pyx_base.__pyx_base.__pyx_base.weighted_n_right;
__pyx_v_weighted_n_right = __pyx_t_3;
/* "sklearn/tree/_tree.pyx":895
* cdef double weighted_n_left = self.weighted_n_left
* cdef double weighted_n_right = self.weighted_n_right
* cdef double diff = 0.0 # <<<<<<<<<<<<<<
*
* for k from 0 <= k < n_outputs:
*/
__pyx_v_diff = 0.0;
/* "sklearn/tree/_tree.pyx":897
* cdef double diff = 0.0
*
* for k from 0 <= k < n_outputs: # <<<<<<<<<<<<<<
* total_sum_left += sum_left[k]
* total_sum_right += sum_right[k]
*/
__pyx_t_1 = __pyx_v_n_outputs;
for (__pyx_v_k = 0; __pyx_v_k < __pyx_t_1; __pyx_v_k++) {
/* "sklearn/tree/_tree.pyx":898
*
* for k from 0 <= k < n_outputs:
* total_sum_left += sum_left[k] # <<<<<<<<<<<<<<
* total_sum_right += sum_right[k]
*
*/
__pyx_v_total_sum_left = (__pyx_v_total_sum_left + (__pyx_v_sum_left[__pyx_v_k]));
/* "sklearn/tree/_tree.pyx":899
* for k from 0 <= k < n_outputs:
* total_sum_left += sum_left[k]
* total_sum_right += sum_right[k] # <<<<<<<<<<<<<<
*
* total_sum_left = total_sum_left / n_outputs
*/
__pyx_v_total_sum_right = (__pyx_v_total_sum_right + (__pyx_v_sum_right[__pyx_v_k]));
}
/* "sklearn/tree/_tree.pyx":901
* total_sum_right += sum_right[k]
*
* total_sum_left = total_sum_left / n_outputs # <<<<<<<<<<<<<<
* total_sum_right = total_sum_right / n_outputs
* diff = ((total_sum_left / weighted_n_left) -
*/
__pyx_v_total_sum_left = (__pyx_v_total_sum_left / __pyx_v_n_outputs);
/* "sklearn/tree/_tree.pyx":902
*
* total_sum_left = total_sum_left / n_outputs
* total_sum_right = total_sum_right / n_outputs # <<<<<<<<<<<<<<
* diff = ((total_sum_left / weighted_n_left) -
* (total_sum_right / weighted_n_right))
*/
__pyx_v_total_sum_right = (__pyx_v_total_sum_right / __pyx_v_n_outputs);
/* "sklearn/tree/_tree.pyx":903
* total_sum_left = total_sum_left / n_outputs
* total_sum_right = total_sum_right / n_outputs
* diff = ((total_sum_left / weighted_n_left) - # <<<<<<<<<<<<<<
* (total_sum_right / weighted_n_right))
*
*/
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/* "sklearn/tree/_tree.pyx":1004
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*/
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/* "sklearn/tree/_tree.pyx":1005
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*/
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/* "sklearn/tree/_tree.pyx":1001
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*
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* double* weighted_n_node_samples) nogil:
* """Reset splitter on node samples[start:end]."""
*/
/* function exit code */
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/* "sklearn/tree/_tree.pyx":1017
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* SIZE_t* n_constant_features) nogil:
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*/
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/* function exit code */
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/* "sklearn/tree/_tree.pyx":1022
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*
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* """Copy the value of node samples[start:end] into dest."""
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*/
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/* "sklearn/tree/_tree.pyx":1024
* cdef void node_value(self, double* dest) nogil:
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*
* cdef double node_impurity(self) nogil:
*/
((struct __pyx_vtabstruct_7sklearn_4tree_5_tree_Criterion *)__pyx_v_self->criterion->__pyx_vtab)->node_value(__pyx_v_self->criterion, __pyx_v_dest);
/* "sklearn/tree/_tree.pyx":1022
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* """Copy the value of node samples[start:end] into dest."""
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*/
/* function exit code */
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/* "sklearn/tree/_tree.pyx":1026
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double __pyx_r;
/* "sklearn/tree/_tree.pyx":1028
* cdef double node_impurity(self) nogil:
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*
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/* "sklearn/tree/_tree.pyx":1026
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* """Copy the impurity of node samples[start:end."""
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* """Find the best split on node samples[start:end]."""
*/
static void __pyx_f_7sklearn_4tree_5_tree_12BestSplitter_node_split(struct __pyx_obj_7sklearn_4tree_5_tree_BestSplitter *__pyx_v_self, double __pyx_v_impurity, struct __pyx_t_7sklearn_4tree_5_tree_SplitRecord *__pyx_v_split, __pyx_t_7sklearn_4tree_5_tree_SIZE_t *__pyx_v_n_constant_features) {
__pyx_t_7sklearn_4tree_5_tree_SIZE_t *__pyx_v_samples;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_v_start;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_v_end;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t *__pyx_v_features;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t *__pyx_v_constant_features;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_v_n_features;
__pyx_t_7sklearn_4tree_5_tree_DTYPE_t *__pyx_v_X;
__pyx_t_7sklearn_4tree_5_tree_DTYPE_t *__pyx_v_Xf;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_v_X_sample_stride;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_v_X_fx_stride;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_v_max_features;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_v_min_samples_leaf;
double __pyx_v_min_weight_leaf;
__pyx_t_7sklearn_4tree_5_tree_UINT32_t *__pyx_v_random_state;
struct __pyx_t_7sklearn_4tree_5_tree_SplitRecord __pyx_v_best;
struct __pyx_t_7sklearn_4tree_5_tree_SplitRecord __pyx_v_current;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_v_f_i;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_v_f_j;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_v_p;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_v_tmp;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_v_n_visited_features;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_v_n_found_constants;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_v_n_drawn_constants;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_v_n_known_constants;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_v_n_total_constants;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_v_partition_end;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t *__pyx_t_1;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_t_2;
__pyx_t_7sklearn_4tree_5_tree_DTYPE_t *__pyx_t_3;
double __pyx_t_4;
int __pyx_t_5;
int __pyx_t_6;
int __pyx_t_7;
int __pyx_t_8;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_t_9;
/* "sklearn/tree/_tree.pyx":1075
* """Find the best split on node samples[start:end]."""
* # Find the best split
* cdef SIZE_t* samples = self.samples # <<<<<<<<<<<<<<
* cdef SIZE_t start = self.start
* cdef SIZE_t end = self.end
*/
__pyx_t_1 = __pyx_v_self->__pyx_base.__pyx_base.samples;
__pyx_v_samples = __pyx_t_1;
/* "sklearn/tree/_tree.pyx":1076
* # Find the best split
* cdef SIZE_t* samples = self.samples
* cdef SIZE_t start = self.start # <<<<<<<<<<<<<<
* cdef SIZE_t end = self.end
*
*/
__pyx_t_2 = __pyx_v_self->__pyx_base.__pyx_base.start;
__pyx_v_start = __pyx_t_2;
/* "sklearn/tree/_tree.pyx":1077
* cdef SIZE_t* samples = self.samples
* cdef SIZE_t start = self.start
* cdef SIZE_t end = self.end # <<<<<<<<<<<<<<
*
* cdef SIZE_t* features = self.features
*/
__pyx_t_2 = __pyx_v_self->__pyx_base.__pyx_base.end;
__pyx_v_end = __pyx_t_2;
/* "sklearn/tree/_tree.pyx":1079
* cdef SIZE_t end = self.end
*
* cdef SIZE_t* features = self.features # <<<<<<<<<<<<<<
* cdef SIZE_t* constant_features = self.constant_features
* cdef SIZE_t n_features = self.n_features
*/
__pyx_t_1 = __pyx_v_self->__pyx_base.__pyx_base.features;
__pyx_v_features = __pyx_t_1;
/* "sklearn/tree/_tree.pyx":1080
*
* cdef SIZE_t* features = self.features
* cdef SIZE_t* constant_features = self.constant_features # <<<<<<<<<<<<<<
* cdef SIZE_t n_features = self.n_features
*
*/
__pyx_t_1 = __pyx_v_self->__pyx_base.__pyx_base.constant_features;
__pyx_v_constant_features = __pyx_t_1;
/* "sklearn/tree/_tree.pyx":1081
* cdef SIZE_t* features = self.features
* cdef SIZE_t* constant_features = self.constant_features
* cdef SIZE_t n_features = self.n_features # <<<<<<<<<<<<<<
*
* cdef DTYPE_t* X = self.X
*/
__pyx_t_2 = __pyx_v_self->__pyx_base.__pyx_base.n_features;
__pyx_v_n_features = __pyx_t_2;
/* "sklearn/tree/_tree.pyx":1083
* cdef SIZE_t n_features = self.n_features
*
* cdef DTYPE_t* X = self.X # <<<<<<<<<<<<<<
* cdef DTYPE_t* Xf = self.feature_values
* cdef SIZE_t X_sample_stride = self.X_sample_stride
*/
__pyx_t_3 = __pyx_v_self->__pyx_base.X;
__pyx_v_X = __pyx_t_3;
/* "sklearn/tree/_tree.pyx":1084
*
* cdef DTYPE_t* X = self.X
* cdef DTYPE_t* Xf = self.feature_values # <<<<<<<<<<<<<<
* cdef SIZE_t X_sample_stride = self.X_sample_stride
* cdef SIZE_t X_fx_stride = self.X_fx_stride
*/
__pyx_t_3 = __pyx_v_self->__pyx_base.__pyx_base.feature_values;
__pyx_v_Xf = __pyx_t_3;
/* "sklearn/tree/_tree.pyx":1085
* cdef DTYPE_t* X = self.X
* cdef DTYPE_t* Xf = self.feature_values
* cdef SIZE_t X_sample_stride = self.X_sample_stride # <<<<<<<<<<<<<<
* cdef SIZE_t X_fx_stride = self.X_fx_stride
* cdef SIZE_t max_features = self.max_features
*/
__pyx_t_2 = __pyx_v_self->__pyx_base.X_sample_stride;
__pyx_v_X_sample_stride = __pyx_t_2;
/* "sklearn/tree/_tree.pyx":1086
* cdef DTYPE_t* Xf = self.feature_values
* cdef SIZE_t X_sample_stride = self.X_sample_stride
* cdef SIZE_t X_fx_stride = self.X_fx_stride # <<<<<<<<<<<<<<
* cdef SIZE_t max_features = self.max_features
* cdef SIZE_t min_samples_leaf = self.min_samples_leaf
*/
__pyx_t_2 = __pyx_v_self->__pyx_base.X_fx_stride;
__pyx_v_X_fx_stride = __pyx_t_2;
/* "sklearn/tree/_tree.pyx":1087
* cdef SIZE_t X_sample_stride = self.X_sample_stride
* cdef SIZE_t X_fx_stride = self.X_fx_stride
* cdef SIZE_t max_features = self.max_features # <<<<<<<<<<<<<<
* cdef SIZE_t min_samples_leaf = self.min_samples_leaf
* cdef double min_weight_leaf = self.min_weight_leaf
*/
__pyx_t_2 = __pyx_v_self->__pyx_base.__pyx_base.max_features;
__pyx_v_max_features = __pyx_t_2;
/* "sklearn/tree/_tree.pyx":1088
* cdef SIZE_t X_fx_stride = self.X_fx_stride
* cdef SIZE_t max_features = self.max_features
* cdef SIZE_t min_samples_leaf = self.min_samples_leaf # <<<<<<<<<<<<<<
* cdef double min_weight_leaf = self.min_weight_leaf
* cdef UINT32_t* random_state = &self.rand_r_state
*/
__pyx_t_2 = __pyx_v_self->__pyx_base.__pyx_base.min_samples_leaf;
__pyx_v_min_samples_leaf = __pyx_t_2;
/* "sklearn/tree/_tree.pyx":1089
* cdef SIZE_t max_features = self.max_features
* cdef SIZE_t min_samples_leaf = self.min_samples_leaf
* cdef double min_weight_leaf = self.min_weight_leaf # <<<<<<<<<<<<<<
* cdef UINT32_t* random_state = &self.rand_r_state
*
*/
__pyx_t_4 = __pyx_v_self->__pyx_base.__pyx_base.min_weight_leaf;
__pyx_v_min_weight_leaf = __pyx_t_4;
/* "sklearn/tree/_tree.pyx":1090
* cdef SIZE_t min_samples_leaf = self.min_samples_leaf
* cdef double min_weight_leaf = self.min_weight_leaf
* cdef UINT32_t* random_state = &self.rand_r_state # <<<<<<<<<<<<<<
*
* cdef SplitRecord best, current
*/
__pyx_v_random_state = (&__pyx_v_self->__pyx_base.__pyx_base.rand_r_state);
/* "sklearn/tree/_tree.pyx":1094
* cdef SplitRecord best, current
*
* cdef SIZE_t f_i = n_features # <<<<<<<<<<<<<<
* cdef SIZE_t f_j, p, tmp
* cdef SIZE_t n_visited_features = 0
*/
__pyx_v_f_i = __pyx_v_n_features;
/* "sklearn/tree/_tree.pyx":1096
* cdef SIZE_t f_i = n_features
* cdef SIZE_t f_j, p, tmp
* cdef SIZE_t n_visited_features = 0 # <<<<<<<<<<<<<<
* # Number of features discovered to be constant during the split search
* cdef SIZE_t n_found_constants = 0
*/
__pyx_v_n_visited_features = 0;
/* "sklearn/tree/_tree.pyx":1098
* cdef SIZE_t n_visited_features = 0
* # Number of features discovered to be constant during the split search
* cdef SIZE_t n_found_constants = 0 # <<<<<<<<<<<<<<
* # Number of features known to be constant and drawn without replacement
* cdef SIZE_t n_drawn_constants = 0
*/
__pyx_v_n_found_constants = 0;
/* "sklearn/tree/_tree.pyx":1100
* cdef SIZE_t n_found_constants = 0
* # Number of features known to be constant and drawn without replacement
* cdef SIZE_t n_drawn_constants = 0 # <<<<<<<<<<<<<<
* cdef SIZE_t n_known_constants = n_constant_features[0]
* # n_total_constants = n_known_constants + n_found_constants
*/
__pyx_v_n_drawn_constants = 0;
/* "sklearn/tree/_tree.pyx":1101
* # Number of features known to be constant and drawn without replacement
* cdef SIZE_t n_drawn_constants = 0
* cdef SIZE_t n_known_constants = n_constant_features[0] # <<<<<<<<<<<<<<
* # n_total_constants = n_known_constants + n_found_constants
* cdef SIZE_t n_total_constants = n_known_constants
*/
__pyx_v_n_known_constants = (__pyx_v_n_constant_features[0]);
/* "sklearn/tree/_tree.pyx":1103
* cdef SIZE_t n_known_constants = n_constant_features[0]
* # n_total_constants = n_known_constants + n_found_constants
* cdef SIZE_t n_total_constants = n_known_constants # <<<<<<<<<<<<<<
* cdef DTYPE_t current_feature_value
* cdef SIZE_t partition_end
*/
__pyx_v_n_total_constants = __pyx_v_n_known_constants;
/* "sklearn/tree/_tree.pyx":1107
* cdef SIZE_t partition_end
*
* _init_split(&best, end) # <<<<<<<<<<<<<<
*
* # Sample up to max_features without replacement using a
*/
__pyx_f_7sklearn_4tree_5_tree__init_split((&__pyx_v_best), __pyx_v_end);
/* "sklearn/tree/_tree.pyx":1118
* # newly discovered constant features to spare computation on descendant
* # nodes.
* while (f_i > n_total_constants and # Stop early if remaining features # <<<<<<<<<<<<<<
* # are constant
* (n_visited_features < max_features or
*/
while (1) {
__pyx_t_5 = ((__pyx_v_f_i > __pyx_v_n_total_constants) != 0);
if (__pyx_t_5) {
/* "sklearn/tree/_tree.pyx":1120
* while (f_i > n_total_constants and # Stop early if remaining features
* # are constant
* (n_visited_features < max_features or # <<<<<<<<<<<<<<
* # At least one drawn features must be non constant
* n_visited_features <= n_found_constants + n_drawn_constants)):
*/
__pyx_t_6 = ((__pyx_v_n_visited_features < __pyx_v_max_features) != 0);
if (!__pyx_t_6) {
/* "sklearn/tree/_tree.pyx":1122
* (n_visited_features < max_features or
* # At least one drawn features must be non constant
* n_visited_features <= n_found_constants + n_drawn_constants)): # <<<<<<<<<<<<<<
*
* n_visited_features += 1
*/
__pyx_t_7 = ((__pyx_v_n_visited_features <= (__pyx_v_n_found_constants + __pyx_v_n_drawn_constants)) != 0);
__pyx_t_8 = __pyx_t_7;
} else {
__pyx_t_8 = __pyx_t_6;
}
__pyx_t_6 = __pyx_t_8;
} else {
__pyx_t_6 = __pyx_t_5;
}
if (!__pyx_t_6) break;
/* "sklearn/tree/_tree.pyx":1124
* n_visited_features <= n_found_constants + n_drawn_constants)):
*
* n_visited_features += 1 # <<<<<<<<<<<<<<
*
* # Loop invariant: elements of features in
*/
__pyx_v_n_visited_features = (__pyx_v_n_visited_features + 1);
/* "sklearn/tree/_tree.pyx":1138
*
* # Draw a feature at random
* f_j = rand_int(n_drawn_constants, f_i - n_found_constants, # <<<<<<<<<<<<<<
* random_state)
*
*/
__pyx_v_f_j = __pyx_f_7sklearn_4tree_5_tree_rand_int(__pyx_v_n_drawn_constants, (__pyx_v_f_i - __pyx_v_n_found_constants), __pyx_v_random_state);
/* "sklearn/tree/_tree.pyx":1141
* random_state)
*
* if f_j < n_known_constants: # <<<<<<<<<<<<<<
* # f_j in the interval [n_drawn_constants, n_known_constants[
* tmp = features[f_j]
*/
__pyx_t_6 = ((__pyx_v_f_j < __pyx_v_n_known_constants) != 0);
if (__pyx_t_6) {
/* "sklearn/tree/_tree.pyx":1143
* if f_j < n_known_constants:
* # f_j in the interval [n_drawn_constants, n_known_constants[
* tmp = features[f_j] # <<<<<<<<<<<<<<
* features[f_j] = features[n_drawn_constants]
* features[n_drawn_constants] = tmp
*/
__pyx_v_tmp = (__pyx_v_features[__pyx_v_f_j]);
/* "sklearn/tree/_tree.pyx":1144
* # f_j in the interval [n_drawn_constants, n_known_constants[
* tmp = features[f_j]
* features[f_j] = features[n_drawn_constants] # <<<<<<<<<<<<<<
* features[n_drawn_constants] = tmp
*
*/
(__pyx_v_features[__pyx_v_f_j]) = (__pyx_v_features[__pyx_v_n_drawn_constants]);
/* "sklearn/tree/_tree.pyx":1145
* tmp = features[f_j]
* features[f_j] = features[n_drawn_constants]
* features[n_drawn_constants] = tmp # <<<<<<<<<<<<<<
*
* n_drawn_constants += 1
*/
(__pyx_v_features[__pyx_v_n_drawn_constants]) = __pyx_v_tmp;
/* "sklearn/tree/_tree.pyx":1147
* features[n_drawn_constants] = tmp
*
* n_drawn_constants += 1 # <<<<<<<<<<<<<<
*
* else:
*/
__pyx_v_n_drawn_constants = (__pyx_v_n_drawn_constants + 1);
goto __pyx_L5;
}
/*else*/ {
/* "sklearn/tree/_tree.pyx":1151
* else:
* # f_j in the interval [n_known_constants, f_i - n_found_constants[
* f_j += n_found_constants # <<<<<<<<<<<<<<
* # f_j in the interval [n_total_constants, f_i[
*
*/
__pyx_v_f_j = (__pyx_v_f_j + __pyx_v_n_found_constants);
/* "sklearn/tree/_tree.pyx":1154
* # f_j in the interval [n_total_constants, f_i[
*
* current.feature = features[f_j] # <<<<<<<<<<<<<<
*
* # Sort samples along that feature; first copy the feature
*/
__pyx_v_current.feature = (__pyx_v_features[__pyx_v_f_j]);
/* "sklearn/tree/_tree.pyx":1160
* # Xf[i] == X[samples[i], j], so the sort uses the cache more
* # effectively.
* for p in range(start, end): # <<<<<<<<<<<<<<
* Xf[p] = X[X_sample_stride * samples[p] +
* X_fx_stride * current.feature]
*/
__pyx_t_2 = __pyx_v_end;
for (__pyx_t_9 = __pyx_v_start; __pyx_t_9 < __pyx_t_2; __pyx_t_9+=1) {
__pyx_v_p = __pyx_t_9;
/* "sklearn/tree/_tree.pyx":1161
* # effectively.
* for p in range(start, end):
* Xf[p] = X[X_sample_stride * samples[p] + # <<<<<<<<<<<<<<
* X_fx_stride * current.feature]
*
*/
(__pyx_v_Xf[__pyx_v_p]) = (__pyx_v_X[((__pyx_v_X_sample_stride * (__pyx_v_samples[__pyx_v_p])) + (__pyx_v_X_fx_stride * __pyx_v_current.feature))]);
}
/* "sklearn/tree/_tree.pyx":1164
* X_fx_stride * current.feature]
*
* sort(Xf + start, samples + start, end - start) # <<<<<<<<<<<<<<
*
* if Xf[end - 1] <= Xf[start] + FEATURE_THRESHOLD:
*/
__pyx_f_7sklearn_4tree_5_tree_sort((__pyx_v_Xf + __pyx_v_start), (__pyx_v_samples + __pyx_v_start), (__pyx_v_end - __pyx_v_start));
/* "sklearn/tree/_tree.pyx":1166
* sort(Xf + start, samples + start, end - start)
*
* if Xf[end - 1] <= Xf[start] + FEATURE_THRESHOLD: # <<<<<<<<<<<<<<
* features[f_j] = features[n_total_constants]
* features[n_total_constants] = current.feature
*/
__pyx_t_6 = (((__pyx_v_Xf[(__pyx_v_end - 1)]) <= ((__pyx_v_Xf[__pyx_v_start]) + __pyx_v_7sklearn_4tree_5_tree_FEATURE_THRESHOLD)) != 0);
if (__pyx_t_6) {
/* "sklearn/tree/_tree.pyx":1167
*
* if Xf[end - 1] <= Xf[start] + FEATURE_THRESHOLD:
* features[f_j] = features[n_total_constants] # <<<<<<<<<<<<<<
* features[n_total_constants] = current.feature
*
*/
(__pyx_v_features[__pyx_v_f_j]) = (__pyx_v_features[__pyx_v_n_total_constants]);
/* "sklearn/tree/_tree.pyx":1168
* if Xf[end - 1] <= Xf[start] + FEATURE_THRESHOLD:
* features[f_j] = features[n_total_constants]
* features[n_total_constants] = current.feature # <<<<<<<<<<<<<<
*
* n_found_constants += 1
*/
__pyx_t_2 = __pyx_v_current.feature;
(__pyx_v_features[__pyx_v_n_total_constants]) = __pyx_t_2;
/* "sklearn/tree/_tree.pyx":1170
* features[n_total_constants] = current.feature
*
* n_found_constants += 1 # <<<<<<<<<<<<<<
* n_total_constants += 1
*
*/
__pyx_v_n_found_constants = (__pyx_v_n_found_constants + 1);
/* "sklearn/tree/_tree.pyx":1171
*
* n_found_constants += 1
* n_total_constants += 1 # <<<<<<<<<<<<<<
*
* else:
*/
__pyx_v_n_total_constants = (__pyx_v_n_total_constants + 1);
goto __pyx_L8;
}
/*else*/ {
/* "sklearn/tree/_tree.pyx":1174
*
* else:
* f_i -= 1 # <<<<<<<<<<<<<<
* features[f_i], features[f_j] = features[f_j], features[f_i]
*
*/
__pyx_v_f_i = (__pyx_v_f_i - 1);
/* "sklearn/tree/_tree.pyx":1175
* else:
* f_i -= 1
* features[f_i], features[f_j] = features[f_j], features[f_i] # <<<<<<<<<<<<<<
*
* # Evaluate all splits
*/
__pyx_t_2 = (__pyx_v_features[__pyx_v_f_j]);
__pyx_t_9 = (__pyx_v_features[__pyx_v_f_i]);
(__pyx_v_features[__pyx_v_f_i]) = __pyx_t_2;
(__pyx_v_features[__pyx_v_f_j]) = __pyx_t_9;
/* "sklearn/tree/_tree.pyx":1178
*
* # Evaluate all splits
* self.criterion.reset() # <<<<<<<<<<<<<<
* p = start
*
*/
((struct __pyx_vtabstruct_7sklearn_4tree_5_tree_Criterion *)__pyx_v_self->__pyx_base.__pyx_base.criterion->__pyx_vtab)->reset(__pyx_v_self->__pyx_base.__pyx_base.criterion);
/* "sklearn/tree/_tree.pyx":1179
* # Evaluate all splits
* self.criterion.reset()
* p = start # <<<<<<<<<<<<<<
*
* while p < end:
*/
__pyx_v_p = __pyx_v_start;
/* "sklearn/tree/_tree.pyx":1181
* p = start
*
* while p < end: # <<<<<<<<<<<<<<
* while (p + 1 < end and
* Xf[p + 1] <= Xf[p] + FEATURE_THRESHOLD):
*/
while (1) {
__pyx_t_6 = ((__pyx_v_p < __pyx_v_end) != 0);
if (!__pyx_t_6) break;
/* "sklearn/tree/_tree.pyx":1182
*
* while p < end:
* while (p + 1 < end and # <<<<<<<<<<<<<<
* Xf[p + 1] <= Xf[p] + FEATURE_THRESHOLD):
* p += 1
*/
while (1) {
__pyx_t_6 = (((__pyx_v_p + 1) < __pyx_v_end) != 0);
if (__pyx_t_6) {
/* "sklearn/tree/_tree.pyx":1183
* while p < end:
* while (p + 1 < end and
* Xf[p + 1] <= Xf[p] + FEATURE_THRESHOLD): # <<<<<<<<<<<<<<
* p += 1
*
*/
__pyx_t_5 = (((__pyx_v_Xf[(__pyx_v_p + 1)]) <= ((__pyx_v_Xf[__pyx_v_p]) + __pyx_v_7sklearn_4tree_5_tree_FEATURE_THRESHOLD)) != 0);
__pyx_t_8 = __pyx_t_5;
} else {
__pyx_t_8 = __pyx_t_6;
}
if (!__pyx_t_8) break;
/* "sklearn/tree/_tree.pyx":1184
* while (p + 1 < end and
* Xf[p + 1] <= Xf[p] + FEATURE_THRESHOLD):
* p += 1 # <<<<<<<<<<<<<<
*
* # (p + 1 >= end) or (X[samples[p + 1], current.feature] >
*/
__pyx_v_p = (__pyx_v_p + 1);
}
/* "sklearn/tree/_tree.pyx":1188
* # (p + 1 >= end) or (X[samples[p + 1], current.feature] >
* # X[samples[p], current.feature])
* p += 1 # <<<<<<<<<<<<<<
* # (p >= end) or (X[samples[p], current.feature] >
* # X[samples[p - 1], current.feature])
*/
__pyx_v_p = (__pyx_v_p + 1);
/* "sklearn/tree/_tree.pyx":1192
* # X[samples[p - 1], current.feature])
*
* if p < end: # <<<<<<<<<<<<<<
* current.pos = p
*
*/
__pyx_t_8 = ((__pyx_v_p < __pyx_v_end) != 0);
if (__pyx_t_8) {
/* "sklearn/tree/_tree.pyx":1193
*
* if p < end:
* current.pos = p # <<<<<<<<<<<<<<
*
* # Reject if min_samples_leaf is not guaranteed
*/
__pyx_v_current.pos = __pyx_v_p;
/* "sklearn/tree/_tree.pyx":1196
*
* # Reject if min_samples_leaf is not guaranteed
* if (((current.pos - start) < min_samples_leaf) or # <<<<<<<<<<<<<<
* ((end - current.pos) < min_samples_leaf)):
* continue
*/
__pyx_t_8 = (((__pyx_v_current.pos - __pyx_v_start) < __pyx_v_min_samples_leaf) != 0);
if (!__pyx_t_8) {
/* "sklearn/tree/_tree.pyx":1197
* # Reject if min_samples_leaf is not guaranteed
* if (((current.pos - start) < min_samples_leaf) or
* ((end - current.pos) < min_samples_leaf)): # <<<<<<<<<<<<<<
* continue
*
*/
__pyx_t_6 = (((__pyx_v_end - __pyx_v_current.pos) < __pyx_v_min_samples_leaf) != 0);
__pyx_t_5 = __pyx_t_6;
} else {
__pyx_t_5 = __pyx_t_8;
}
if (__pyx_t_5) {
/* "sklearn/tree/_tree.pyx":1198
* if (((current.pos - start) < min_samples_leaf) or
* ((end - current.pos) < min_samples_leaf)):
* continue # <<<<<<<<<<<<<<
*
* self.criterion.update(current.pos)
*/
goto __pyx_L9_continue;
}
/* "sklearn/tree/_tree.pyx":1200
* continue
*
* self.criterion.update(current.pos) # <<<<<<<<<<<<<<
*
* # Reject if min_weight_leaf is not satisfied
*/
((struct __pyx_vtabstruct_7sklearn_4tree_5_tree_Criterion *)__pyx_v_self->__pyx_base.__pyx_base.criterion->__pyx_vtab)->update(__pyx_v_self->__pyx_base.__pyx_base.criterion, __pyx_v_current.pos);
/* "sklearn/tree/_tree.pyx":1203
*
* # Reject if min_weight_leaf is not satisfied
* if ((self.criterion.weighted_n_left < min_weight_leaf) or # <<<<<<<<<<<<<<
* (self.criterion.weighted_n_right < min_weight_leaf)):
* continue
*/
__pyx_t_5 = ((__pyx_v_self->__pyx_base.__pyx_base.criterion->weighted_n_left < __pyx_v_min_weight_leaf) != 0);
if (!__pyx_t_5) {
/* "sklearn/tree/_tree.pyx":1204
* # Reject if min_weight_leaf is not satisfied
* if ((self.criterion.weighted_n_left < min_weight_leaf) or
* (self.criterion.weighted_n_right < min_weight_leaf)): # <<<<<<<<<<<<<<
* continue
*
*/
__pyx_t_8 = ((__pyx_v_self->__pyx_base.__pyx_base.criterion->weighted_n_right < __pyx_v_min_weight_leaf) != 0);
__pyx_t_6 = __pyx_t_8;
} else {
__pyx_t_6 = __pyx_t_5;
}
if (__pyx_t_6) {
/* "sklearn/tree/_tree.pyx":1205
* if ((self.criterion.weighted_n_left < min_weight_leaf) or
* (self.criterion.weighted_n_right < min_weight_leaf)):
* continue # <<<<<<<<<<<<<<
*
* current.improvement = self.criterion.impurity_improvement(impurity)
*/
goto __pyx_L9_continue;
}
/* "sklearn/tree/_tree.pyx":1207
* continue
*
* current.improvement = self.criterion.impurity_improvement(impurity) # <<<<<<<<<<<<<<
*
* if current.improvement > best.improvement:
*/
__pyx_v_current.improvement = ((struct __pyx_vtabstruct_7sklearn_4tree_5_tree_Criterion *)__pyx_v_self->__pyx_base.__pyx_base.criterion->__pyx_vtab)->impurity_improvement(__pyx_v_self->__pyx_base.__pyx_base.criterion, __pyx_v_impurity);
/* "sklearn/tree/_tree.pyx":1209
* current.improvement = self.criterion.impurity_improvement(impurity)
*
* if current.improvement > best.improvement: # <<<<<<<<<<<<<<
* self.criterion.children_impurity(&current.impurity_left,
* &current.impurity_right)
*/
__pyx_t_6 = ((__pyx_v_current.improvement > __pyx_v_best.improvement) != 0);
if (__pyx_t_6) {
/* "sklearn/tree/_tree.pyx":1210
*
* if current.improvement > best.improvement:
* self.criterion.children_impurity(&current.impurity_left, # <<<<<<<<<<<<<<
* &current.impurity_right)
* current.threshold = (Xf[p - 1] + Xf[p]) / 2.0
*/
((struct __pyx_vtabstruct_7sklearn_4tree_5_tree_Criterion *)__pyx_v_self->__pyx_base.__pyx_base.criterion->__pyx_vtab)->children_impurity(__pyx_v_self->__pyx_base.__pyx_base.criterion, (&__pyx_v_current.impurity_left), (&__pyx_v_current.impurity_right));
/* "sklearn/tree/_tree.pyx":1212
* self.criterion.children_impurity(&current.impurity_left,
* &current.impurity_right)
* current.threshold = (Xf[p - 1] + Xf[p]) / 2.0 # <<<<<<<<<<<<<<
*
* if current.threshold == Xf[p]:
*/
__pyx_v_current.threshold = (((__pyx_v_Xf[(__pyx_v_p - 1)]) + (__pyx_v_Xf[__pyx_v_p])) / 2.0);
/* "sklearn/tree/_tree.pyx":1214
* current.threshold = (Xf[p - 1] + Xf[p]) / 2.0
*
* if current.threshold == Xf[p]: # <<<<<<<<<<<<<<
* current.threshold = Xf[p - 1]
*
*/
__pyx_t_6 = ((__pyx_v_current.threshold == (__pyx_v_Xf[__pyx_v_p])) != 0);
if (__pyx_t_6) {
/* "sklearn/tree/_tree.pyx":1215
*
* if current.threshold == Xf[p]:
* current.threshold = Xf[p - 1] # <<<<<<<<<<<<<<
*
* best = current # copy
*/
__pyx_v_current.threshold = (__pyx_v_Xf[(__pyx_v_p - 1)]);
goto __pyx_L17;
}
__pyx_L17:;
/* "sklearn/tree/_tree.pyx":1217
* current.threshold = Xf[p - 1]
*
* best = current # copy # <<<<<<<<<<<<<<
*
* # Reorganize into samples[start:best.pos] + samples[best.pos:end]
*/
__pyx_v_best = __pyx_v_current;
goto __pyx_L16;
}
__pyx_L16:;
goto __pyx_L13;
}
__pyx_L13:;
__pyx_L9_continue:;
}
}
__pyx_L8:;
}
__pyx_L5:;
}
/* "sklearn/tree/_tree.pyx":1220
*
* # Reorganize into samples[start:best.pos] + samples[best.pos:end]
* if best.pos < end: # <<<<<<<<<<<<<<
* partition_end = end
* p = start
*/
__pyx_t_6 = ((__pyx_v_best.pos < __pyx_v_end) != 0);
if (__pyx_t_6) {
/* "sklearn/tree/_tree.pyx":1221
* # Reorganize into samples[start:best.pos] + samples[best.pos:end]
* if best.pos < end:
* partition_end = end # <<<<<<<<<<<<<<
* p = start
*
*/
__pyx_v_partition_end = __pyx_v_end;
/* "sklearn/tree/_tree.pyx":1222
* if best.pos < end:
* partition_end = end
* p = start # <<<<<<<<<<<<<<
*
* while p < partition_end:
*/
__pyx_v_p = __pyx_v_start;
/* "sklearn/tree/_tree.pyx":1224
* p = start
*
* while p < partition_end: # <<<<<<<<<<<<<<
* if X[X_sample_stride * samples[p] +
* X_fx_stride * best.feature] <= best.threshold:
*/
while (1) {
__pyx_t_6 = ((__pyx_v_p < __pyx_v_partition_end) != 0);
if (!__pyx_t_6) break;
/* "sklearn/tree/_tree.pyx":1226
* while p < partition_end:
* if X[X_sample_stride * samples[p] +
* X_fx_stride * best.feature] <= best.threshold: # <<<<<<<<<<<<<<
* p += 1
*
*/
__pyx_t_6 = (((__pyx_v_X[((__pyx_v_X_sample_stride * (__pyx_v_samples[__pyx_v_p])) + (__pyx_v_X_fx_stride * __pyx_v_best.feature))]) <= __pyx_v_best.threshold) != 0);
if (__pyx_t_6) {
/* "sklearn/tree/_tree.pyx":1227
* if X[X_sample_stride * samples[p] +
* X_fx_stride * best.feature] <= best.threshold:
* p += 1 # <<<<<<<<<<<<<<
*
* else:
*/
__pyx_v_p = (__pyx_v_p + 1);
goto __pyx_L21;
}
/*else*/ {
/* "sklearn/tree/_tree.pyx":1230
*
* else:
* partition_end -= 1 # <<<<<<<<<<<<<<
*
* tmp = samples[partition_end]
*/
__pyx_v_partition_end = (__pyx_v_partition_end - 1);
/* "sklearn/tree/_tree.pyx":1232
* partition_end -= 1
*
* tmp = samples[partition_end] # <<<<<<<<<<<<<<
* samples[partition_end] = samples[p]
* samples[p] = tmp
*/
__pyx_v_tmp = (__pyx_v_samples[__pyx_v_partition_end]);
/* "sklearn/tree/_tree.pyx":1233
*
* tmp = samples[partition_end]
* samples[partition_end] = samples[p] # <<<<<<<<<<<<<<
* samples[p] = tmp
*
*/
(__pyx_v_samples[__pyx_v_partition_end]) = (__pyx_v_samples[__pyx_v_p]);
/* "sklearn/tree/_tree.pyx":1234
* tmp = samples[partition_end]
* samples[partition_end] = samples[p]
* samples[p] = tmp # <<<<<<<<<<<<<<
*
* # Respect invariant for constant features: the original order of
*/
(__pyx_v_samples[__pyx_v_p]) = __pyx_v_tmp;
}
__pyx_L21:;
}
goto __pyx_L18;
}
__pyx_L18:;
/* "sklearn/tree/_tree.pyx":1239
* # element in features[:n_known_constants] must be preserved for sibling
* # and child nodes
* memcpy(features, constant_features, sizeof(SIZE_t) * n_known_constants) # <<<<<<<<<<<<<<
*
* # Copy newly found constant features
*/
memcpy(__pyx_v_features, __pyx_v_constant_features, ((sizeof(__pyx_t_7sklearn_4tree_5_tree_SIZE_t)) * __pyx_v_n_known_constants));
/* "sklearn/tree/_tree.pyx":1242
*
* # Copy newly found constant features
* memcpy(constant_features + n_known_constants, # <<<<<<<<<<<<<<
* features + n_known_constants,
* sizeof(SIZE_t) * n_found_constants)
*/
memcpy((__pyx_v_constant_features + __pyx_v_n_known_constants), (__pyx_v_features + __pyx_v_n_known_constants), ((sizeof(__pyx_t_7sklearn_4tree_5_tree_SIZE_t)) * __pyx_v_n_found_constants));
/* "sklearn/tree/_tree.pyx":1247
*
* # Return values
* split[0] = best # <<<<<<<<<<<<<<
* n_constant_features[0] = n_total_constants
*
*/
(__pyx_v_split[0]) = __pyx_v_best;
/* "sklearn/tree/_tree.pyx":1248
* # Return values
* split[0] = best
* n_constant_features[0] = n_total_constants # <<<<<<<<<<<<<<
*
*
*/
(__pyx_v_n_constant_features[0]) = __pyx_v_n_total_constants;
/* "sklearn/tree/_tree.pyx":1071
* self.random_state), self.__getstate__())
*
* cdef void node_split(self, double impurity, SplitRecord* split, # <<<<<<<<<<<<<<
* SIZE_t* n_constant_features) nogil:
* """Find the best split on node samples[start:end]."""
*/
/* function exit code */
}
/* "sklearn/tree/_tree.pyx":1253
* # Sort n-element arrays pointed to by Xf and samples, simultaneously,
* # by the values in Xf. Algorithm: Introsort (Musser, SP&E, 1997).
* cdef inline void sort(DTYPE_t* Xf, SIZE_t* samples, SIZE_t n) nogil: # <<<<<<<<<<<<<<
* cdef int maxd = 2 * <int>log(n)
* introsort(Xf, samples, n, maxd)
*/
static CYTHON_INLINE void __pyx_f_7sklearn_4tree_5_tree_sort(__pyx_t_7sklearn_4tree_5_tree_DTYPE_t *__pyx_v_Xf, __pyx_t_7sklearn_4tree_5_tree_SIZE_t *__pyx_v_samples, __pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_v_n) {
int __pyx_v_maxd;
/* "sklearn/tree/_tree.pyx":1254
* # by the values in Xf. Algorithm: Introsort (Musser, SP&E, 1997).
* cdef inline void sort(DTYPE_t* Xf, SIZE_t* samples, SIZE_t n) nogil:
* cdef int maxd = 2 * <int>log(n) # <<<<<<<<<<<<<<
* introsort(Xf, samples, n, maxd)
*
*/
__pyx_v_maxd = (2 * ((int)__pyx_f_7sklearn_4tree_5_tree_log(__pyx_v_n)));
/* "sklearn/tree/_tree.pyx":1255
* cdef inline void sort(DTYPE_t* Xf, SIZE_t* samples, SIZE_t n) nogil:
* cdef int maxd = 2 * <int>log(n)
* introsort(Xf, samples, n, maxd) # <<<<<<<<<<<<<<
*
*
*/
__pyx_f_7sklearn_4tree_5_tree_introsort(__pyx_v_Xf, __pyx_v_samples, __pyx_v_n, __pyx_v_maxd);
/* "sklearn/tree/_tree.pyx":1253
* # Sort n-element arrays pointed to by Xf and samples, simultaneously,
* # by the values in Xf. Algorithm: Introsort (Musser, SP&E, 1997).
* cdef inline void sort(DTYPE_t* Xf, SIZE_t* samples, SIZE_t n) nogil: # <<<<<<<<<<<<<<
* cdef int maxd = 2 * <int>log(n)
* introsort(Xf, samples, n, maxd)
*/
/* function exit code */
}
/* "sklearn/tree/_tree.pyx":1258
*
*
* cdef inline void swap(DTYPE_t* Xf, SIZE_t* samples, SIZE_t i, SIZE_t j) nogil: # <<<<<<<<<<<<<<
* # Helper for sort
* Xf[i], Xf[j] = Xf[j], Xf[i]
*/
static CYTHON_INLINE void __pyx_f_7sklearn_4tree_5_tree_swap(__pyx_t_7sklearn_4tree_5_tree_DTYPE_t *__pyx_v_Xf, __pyx_t_7sklearn_4tree_5_tree_SIZE_t *__pyx_v_samples, __pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_v_i, __pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_v_j) {
__pyx_t_7sklearn_4tree_5_tree_DTYPE_t __pyx_t_1;
__pyx_t_7sklearn_4tree_5_tree_DTYPE_t __pyx_t_2;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_t_3;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_t_4;
/* "sklearn/tree/_tree.pyx":1260
* cdef inline void swap(DTYPE_t* Xf, SIZE_t* samples, SIZE_t i, SIZE_t j) nogil:
* # Helper for sort
* Xf[i], Xf[j] = Xf[j], Xf[i] # <<<<<<<<<<<<<<
* samples[i], samples[j] = samples[j], samples[i]
*
*/
__pyx_t_1 = (__pyx_v_Xf[__pyx_v_j]);
__pyx_t_2 = (__pyx_v_Xf[__pyx_v_i]);
(__pyx_v_Xf[__pyx_v_i]) = __pyx_t_1;
(__pyx_v_Xf[__pyx_v_j]) = __pyx_t_2;
/* "sklearn/tree/_tree.pyx":1261
* # Helper for sort
* Xf[i], Xf[j] = Xf[j], Xf[i]
* samples[i], samples[j] = samples[j], samples[i] # <<<<<<<<<<<<<<
*
*
*/
__pyx_t_3 = (__pyx_v_samples[__pyx_v_j]);
__pyx_t_4 = (__pyx_v_samples[__pyx_v_i]);
(__pyx_v_samples[__pyx_v_i]) = __pyx_t_3;
(__pyx_v_samples[__pyx_v_j]) = __pyx_t_4;
/* "sklearn/tree/_tree.pyx":1258
*
*
* cdef inline void swap(DTYPE_t* Xf, SIZE_t* samples, SIZE_t i, SIZE_t j) nogil: # <<<<<<<<<<<<<<
* # Helper for sort
* Xf[i], Xf[j] = Xf[j], Xf[i]
*/
/* function exit code */
}
/* "sklearn/tree/_tree.pyx":1264
*
*
* cdef inline DTYPE_t median3(DTYPE_t* Xf, SIZE_t n) nogil: # <<<<<<<<<<<<<<
* # Median of three pivot selection, after Bentley and McIlroy (1993).
* # Engineering a sort function. SP&E. Requires 8/3 comparisons on average.
*/
static CYTHON_INLINE __pyx_t_7sklearn_4tree_5_tree_DTYPE_t __pyx_f_7sklearn_4tree_5_tree_median3(__pyx_t_7sklearn_4tree_5_tree_DTYPE_t *__pyx_v_Xf, __pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_v_n) {
__pyx_t_7sklearn_4tree_5_tree_DTYPE_t __pyx_v_a;
__pyx_t_7sklearn_4tree_5_tree_DTYPE_t __pyx_v_b;
__pyx_t_7sklearn_4tree_5_tree_DTYPE_t __pyx_v_c;
__pyx_t_7sklearn_4tree_5_tree_DTYPE_t __pyx_r;
int __pyx_t_1;
/* "sklearn/tree/_tree.pyx":1267
* # Median of three pivot selection, after Bentley and McIlroy (1993).
* # Engineering a sort function. SP&E. Requires 8/3 comparisons on average.
* cdef DTYPE_t a = Xf[0], b = Xf[n / 2], c = Xf[n - 1] # <<<<<<<<<<<<<<
* if a < b:
* if b < c:
*/
__pyx_v_a = (__pyx_v_Xf[0]);
__pyx_v_b = (__pyx_v_Xf[(__pyx_v_n / 2)]);
__pyx_v_c = (__pyx_v_Xf[(__pyx_v_n - 1)]);
/* "sklearn/tree/_tree.pyx":1268
* # Engineering a sort function. SP&E. Requires 8/3 comparisons on average.
* cdef DTYPE_t a = Xf[0], b = Xf[n / 2], c = Xf[n - 1]
* if a < b: # <<<<<<<<<<<<<<
* if b < c:
* return b
*/
__pyx_t_1 = ((__pyx_v_a < __pyx_v_b) != 0);
if (__pyx_t_1) {
/* "sklearn/tree/_tree.pyx":1269
* cdef DTYPE_t a = Xf[0], b = Xf[n / 2], c = Xf[n - 1]
* if a < b:
* if b < c: # <<<<<<<<<<<<<<
* return b
* elif a < c:
*/
__pyx_t_1 = ((__pyx_v_b < __pyx_v_c) != 0);
if (__pyx_t_1) {
/* "sklearn/tree/_tree.pyx":1270
* if a < b:
* if b < c:
* return b # <<<<<<<<<<<<<<
* elif a < c:
* return c
*/
__pyx_r = __pyx_v_b;
goto __pyx_L0;
}
/* "sklearn/tree/_tree.pyx":1271
* if b < c:
* return b
* elif a < c: # <<<<<<<<<<<<<<
* return c
* else:
*/
__pyx_t_1 = ((__pyx_v_a < __pyx_v_c) != 0);
if (__pyx_t_1) {
/* "sklearn/tree/_tree.pyx":1272
* return b
* elif a < c:
* return c # <<<<<<<<<<<<<<
* else:
* return a
*/
__pyx_r = __pyx_v_c;
goto __pyx_L0;
}
/*else*/ {
/* "sklearn/tree/_tree.pyx":1274
* return c
* else:
* return a # <<<<<<<<<<<<<<
* elif b < c:
* if a < c:
*/
__pyx_r = __pyx_v_a;
goto __pyx_L0;
}
}
/* "sklearn/tree/_tree.pyx":1275
* else:
* return a
* elif b < c: # <<<<<<<<<<<<<<
* if a < c:
* return a
*/
__pyx_t_1 = ((__pyx_v_b < __pyx_v_c) != 0);
if (__pyx_t_1) {
/* "sklearn/tree/_tree.pyx":1276
* return a
* elif b < c:
* if a < c: # <<<<<<<<<<<<<<
* return a
* else:
*/
__pyx_t_1 = ((__pyx_v_a < __pyx_v_c) != 0);
if (__pyx_t_1) {
/* "sklearn/tree/_tree.pyx":1277
* elif b < c:
* if a < c:
* return a # <<<<<<<<<<<<<<
* else:
* return c
*/
__pyx_r = __pyx_v_a;
goto __pyx_L0;
}
/*else*/ {
/* "sklearn/tree/_tree.pyx":1279
* return a
* else:
* return c # <<<<<<<<<<<<<<
* else:
* return b
*/
__pyx_r = __pyx_v_c;
goto __pyx_L0;
}
}
/*else*/ {
/* "sklearn/tree/_tree.pyx":1281
* return c
* else:
* return b # <<<<<<<<<<<<<<
*
*
*/
__pyx_r = __pyx_v_b;
goto __pyx_L0;
}
/* "sklearn/tree/_tree.pyx":1264
*
*
* cdef inline DTYPE_t median3(DTYPE_t* Xf, SIZE_t n) nogil: # <<<<<<<<<<<<<<
* # Median of three pivot selection, after Bentley and McIlroy (1993).
* # Engineering a sort function. SP&E. Requires 8/3 comparisons on average.
*/
/* function exit code */
__pyx_L0:;
return __pyx_r;
}
/* "sklearn/tree/_tree.pyx":1286
* # Introsort with median of 3 pivot selection and 3-way partition function
* # (robust to repeated elements, e.g. lots of zero features).
* cdef void introsort(DTYPE_t* Xf, SIZE_t *samples, SIZE_t n, int maxd) nogil: # <<<<<<<<<<<<<<
* cdef DTYPE_t pivot
* cdef SIZE_t i, l, r
*/
static void __pyx_f_7sklearn_4tree_5_tree_introsort(__pyx_t_7sklearn_4tree_5_tree_DTYPE_t *__pyx_v_Xf, __pyx_t_7sklearn_4tree_5_tree_SIZE_t *__pyx_v_samples, __pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_v_n, int __pyx_v_maxd) {
__pyx_t_7sklearn_4tree_5_tree_DTYPE_t __pyx_v_pivot;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_v_i;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_v_l;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_v_r;
int __pyx_t_1;
/* "sklearn/tree/_tree.pyx":1290
* cdef SIZE_t i, l, r
*
* while n > 1: # <<<<<<<<<<<<<<
* if maxd <= 0: # max depth limit exceeded ("gone quadratic")
* heapsort(Xf, samples, n)
*/
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__pyx_t_1 = ((__pyx_v_n > 1) != 0);
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/* "sklearn/tree/_tree.pyx":1291
*
* while n > 1:
* if maxd <= 0: # max depth limit exceeded ("gone quadratic") # <<<<<<<<<<<<<<
* heapsort(Xf, samples, n)
* return
*/
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/* "sklearn/tree/_tree.pyx":1292
* while n > 1:
* if maxd <= 0: # max depth limit exceeded ("gone quadratic")
* heapsort(Xf, samples, n) # <<<<<<<<<<<<<<
* return
* maxd -= 1
*/
__pyx_f_7sklearn_4tree_5_tree_heapsort(__pyx_v_Xf, __pyx_v_samples, __pyx_v_n);
/* "sklearn/tree/_tree.pyx":1293
* if maxd <= 0: # max depth limit exceeded ("gone quadratic")
* heapsort(Xf, samples, n)
* return # <<<<<<<<<<<<<<
* maxd -= 1
*
*/
goto __pyx_L0;
}
/* "sklearn/tree/_tree.pyx":1294
* heapsort(Xf, samples, n)
* return
* maxd -= 1 # <<<<<<<<<<<<<<
*
* pivot = median3(Xf, n)
*/
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/* "sklearn/tree/_tree.pyx":1296
* maxd -= 1
*
* pivot = median3(Xf, n) # <<<<<<<<<<<<<<
*
* # Three-way partition.
*/
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/* "sklearn/tree/_tree.pyx":1299
*
* # Three-way partition.
* i = l = 0 # <<<<<<<<<<<<<<
* r = n
* while i < r:
*/
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__pyx_v_l = 0;
/* "sklearn/tree/_tree.pyx":1300
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* while i < r:
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*/
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/* "sklearn/tree/_tree.pyx":1301
* i = l = 0
* r = n
* while i < r: # <<<<<<<<<<<<<<
* if Xf[i] < pivot:
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*/
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/* "sklearn/tree/_tree.pyx":1302
* r = n
* while i < r:
* if Xf[i] < pivot: # <<<<<<<<<<<<<<
* swap(Xf, samples, i, l)
* i += 1
*/
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/* "sklearn/tree/_tree.pyx":1303
* while i < r:
* if Xf[i] < pivot:
* swap(Xf, samples, i, l) # <<<<<<<<<<<<<<
* i += 1
* l += 1
*/
__pyx_f_7sklearn_4tree_5_tree_swap(__pyx_v_Xf, __pyx_v_samples, __pyx_v_i, __pyx_v_l);
/* "sklearn/tree/_tree.pyx":1304
* if Xf[i] < pivot:
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* i += 1 # <<<<<<<<<<<<<<
* l += 1
* elif Xf[i] > pivot:
*/
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/* "sklearn/tree/_tree.pyx":1305
* swap(Xf, samples, i, l)
* i += 1
* l += 1 # <<<<<<<<<<<<<<
* elif Xf[i] > pivot:
* r -= 1
*/
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goto __pyx_L8;
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/* "sklearn/tree/_tree.pyx":1306
* i += 1
* l += 1
* elif Xf[i] > pivot: # <<<<<<<<<<<<<<
* r -= 1
* swap(Xf, samples, i, r)
*/
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/* "sklearn/tree/_tree.pyx":1307
* l += 1
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* swap(Xf, samples, i, r)
* else:
*/
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* elif Xf[i] > pivot:
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* else:
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*/
__pyx_f_7sklearn_4tree_5_tree_swap(__pyx_v_Xf, __pyx_v_samples, __pyx_v_i, __pyx_v_r);
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/* "sklearn/tree/_tree.pyx":1310
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*
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*
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/* "sklearn/tree/_tree.pyx":1318
*
*
* cdef inline void sift_down(DTYPE_t* Xf, SIZE_t* samples, # <<<<<<<<<<<<<<
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int __pyx_t_1;
int __pyx_t_2;
int __pyx_t_3;
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*
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/* "sklearn/tree/_tree.pyx":1324
*
* root = start
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*
*/
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*
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*
*
* cdef inline void sift_down(DTYPE_t* Xf, SIZE_t* samples, # <<<<<<<<<<<<<<
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/* "sklearn/tree/_tree.pyx":1341
*
*
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/* "sklearn/tree/_tree.pyx":1348
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/* "sklearn/tree/_tree.pyx":1349
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*
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/* "sklearn/tree/_tree.pyx":1357
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__pyx_f_7sklearn_4tree_5_tree_sift_down(__pyx_v_Xf, __pyx_v_samples, 0, __pyx_v_end);
/* "sklearn/tree/_tree.pyx":1358
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*
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/* function exit code */
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* def __reduce__(self):
* return (RandomSplitter, (self.criterion, # <<<<<<<<<<<<<<
* self.max_features,
* self.min_samples_leaf,
*/
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/* "sklearn/tree/_tree.pyx":1363
* cdef class RandomSplitter(BaseDenseSplitter):
* """Splitter for finding the best random split."""
* def __reduce__(self): # <<<<<<<<<<<<<<
* return (RandomSplitter, (self.criterion,
* self.max_features,
*/
/* function exit code */
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/* "sklearn/tree/_tree.pyx":1370
* self.random_state), self.__getstate__())
*
* cdef void node_split(self, double impurity, SplitRecord* split, # <<<<<<<<<<<<<<
* SIZE_t* n_constant_features) nogil:
* """Find the best random split on node samples[start:end]."""
*/
static void __pyx_f_7sklearn_4tree_5_tree_14RandomSplitter_node_split(struct __pyx_obj_7sklearn_4tree_5_tree_RandomSplitter *__pyx_v_self, double __pyx_v_impurity, struct __pyx_t_7sklearn_4tree_5_tree_SplitRecord *__pyx_v_split, __pyx_t_7sklearn_4tree_5_tree_SIZE_t *__pyx_v_n_constant_features) {
__pyx_t_7sklearn_4tree_5_tree_SIZE_t *__pyx_v_samples;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_v_start;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_v_end;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t *__pyx_v_features;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t *__pyx_v_constant_features;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_v_n_features;
__pyx_t_7sklearn_4tree_5_tree_DTYPE_t *__pyx_v_X;
__pyx_t_7sklearn_4tree_5_tree_DTYPE_t *__pyx_v_Xf;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_v_X_sample_stride;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_v_X_fx_stride;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_v_max_features;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_v_min_samples_leaf;
double __pyx_v_min_weight_leaf;
__pyx_t_7sklearn_4tree_5_tree_UINT32_t *__pyx_v_random_state;
struct __pyx_t_7sklearn_4tree_5_tree_SplitRecord __pyx_v_best;
struct __pyx_t_7sklearn_4tree_5_tree_SplitRecord __pyx_v_current;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_v_f_i;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_v_f_j;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_v_p;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_v_tmp;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_v_n_found_constants;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_v_n_drawn_constants;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_v_n_known_constants;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_v_n_total_constants;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_v_n_visited_features;
__pyx_t_7sklearn_4tree_5_tree_DTYPE_t __pyx_v_min_feature_value;
__pyx_t_7sklearn_4tree_5_tree_DTYPE_t __pyx_v_max_feature_value;
__pyx_t_7sklearn_4tree_5_tree_DTYPE_t __pyx_v_current_feature_value;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_v_partition_end;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t *__pyx_t_1;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_t_2;
__pyx_t_7sklearn_4tree_5_tree_DTYPE_t *__pyx_t_3;
double __pyx_t_4;
int __pyx_t_5;
int __pyx_t_6;
int __pyx_t_7;
int __pyx_t_8;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_t_9;
/* "sklearn/tree/_tree.pyx":1374
* """Find the best random split on node samples[start:end]."""
* # Draw random splits and pick the best
* cdef SIZE_t* samples = self.samples # <<<<<<<<<<<<<<
* cdef SIZE_t start = self.start
* cdef SIZE_t end = self.end
*/
__pyx_t_1 = __pyx_v_self->__pyx_base.__pyx_base.samples;
__pyx_v_samples = __pyx_t_1;
/* "sklearn/tree/_tree.pyx":1375
* # Draw random splits and pick the best
* cdef SIZE_t* samples = self.samples
* cdef SIZE_t start = self.start # <<<<<<<<<<<<<<
* cdef SIZE_t end = self.end
*
*/
__pyx_t_2 = __pyx_v_self->__pyx_base.__pyx_base.start;
__pyx_v_start = __pyx_t_2;
/* "sklearn/tree/_tree.pyx":1376
* cdef SIZE_t* samples = self.samples
* cdef SIZE_t start = self.start
* cdef SIZE_t end = self.end # <<<<<<<<<<<<<<
*
* cdef SIZE_t* features = self.features
*/
__pyx_t_2 = __pyx_v_self->__pyx_base.__pyx_base.end;
__pyx_v_end = __pyx_t_2;
/* "sklearn/tree/_tree.pyx":1378
* cdef SIZE_t end = self.end
*
* cdef SIZE_t* features = self.features # <<<<<<<<<<<<<<
* cdef SIZE_t* constant_features = self.constant_features
* cdef SIZE_t n_features = self.n_features
*/
__pyx_t_1 = __pyx_v_self->__pyx_base.__pyx_base.features;
__pyx_v_features = __pyx_t_1;
/* "sklearn/tree/_tree.pyx":1379
*
* cdef SIZE_t* features = self.features
* cdef SIZE_t* constant_features = self.constant_features # <<<<<<<<<<<<<<
* cdef SIZE_t n_features = self.n_features
*
*/
__pyx_t_1 = __pyx_v_self->__pyx_base.__pyx_base.constant_features;
__pyx_v_constant_features = __pyx_t_1;
/* "sklearn/tree/_tree.pyx":1380
* cdef SIZE_t* features = self.features
* cdef SIZE_t* constant_features = self.constant_features
* cdef SIZE_t n_features = self.n_features # <<<<<<<<<<<<<<
*
* cdef DTYPE_t* X = self.X
*/
__pyx_t_2 = __pyx_v_self->__pyx_base.__pyx_base.n_features;
__pyx_v_n_features = __pyx_t_2;
/* "sklearn/tree/_tree.pyx":1382
* cdef SIZE_t n_features = self.n_features
*
* cdef DTYPE_t* X = self.X # <<<<<<<<<<<<<<
* cdef DTYPE_t* Xf = self.feature_values
* cdef SIZE_t X_sample_stride = self.X_sample_stride
*/
__pyx_t_3 = __pyx_v_self->__pyx_base.X;
__pyx_v_X = __pyx_t_3;
/* "sklearn/tree/_tree.pyx":1383
*
* cdef DTYPE_t* X = self.X
* cdef DTYPE_t* Xf = self.feature_values # <<<<<<<<<<<<<<
* cdef SIZE_t X_sample_stride = self.X_sample_stride
* cdef SIZE_t X_fx_stride = self.X_fx_stride
*/
__pyx_t_3 = __pyx_v_self->__pyx_base.__pyx_base.feature_values;
__pyx_v_Xf = __pyx_t_3;
/* "sklearn/tree/_tree.pyx":1384
* cdef DTYPE_t* X = self.X
* cdef DTYPE_t* Xf = self.feature_values
* cdef SIZE_t X_sample_stride = self.X_sample_stride # <<<<<<<<<<<<<<
* cdef SIZE_t X_fx_stride = self.X_fx_stride
* cdef SIZE_t max_features = self.max_features
*/
__pyx_t_2 = __pyx_v_self->__pyx_base.X_sample_stride;
__pyx_v_X_sample_stride = __pyx_t_2;
/* "sklearn/tree/_tree.pyx":1385
* cdef DTYPE_t* Xf = self.feature_values
* cdef SIZE_t X_sample_stride = self.X_sample_stride
* cdef SIZE_t X_fx_stride = self.X_fx_stride # <<<<<<<<<<<<<<
* cdef SIZE_t max_features = self.max_features
* cdef SIZE_t min_samples_leaf = self.min_samples_leaf
*/
__pyx_t_2 = __pyx_v_self->__pyx_base.X_fx_stride;
__pyx_v_X_fx_stride = __pyx_t_2;
/* "sklearn/tree/_tree.pyx":1386
* cdef SIZE_t X_sample_stride = self.X_sample_stride
* cdef SIZE_t X_fx_stride = self.X_fx_stride
* cdef SIZE_t max_features = self.max_features # <<<<<<<<<<<<<<
* cdef SIZE_t min_samples_leaf = self.min_samples_leaf
* cdef double min_weight_leaf = self.min_weight_leaf
*/
__pyx_t_2 = __pyx_v_self->__pyx_base.__pyx_base.max_features;
__pyx_v_max_features = __pyx_t_2;
/* "sklearn/tree/_tree.pyx":1387
* cdef SIZE_t X_fx_stride = self.X_fx_stride
* cdef SIZE_t max_features = self.max_features
* cdef SIZE_t min_samples_leaf = self.min_samples_leaf # <<<<<<<<<<<<<<
* cdef double min_weight_leaf = self.min_weight_leaf
* cdef UINT32_t* random_state = &self.rand_r_state
*/
__pyx_t_2 = __pyx_v_self->__pyx_base.__pyx_base.min_samples_leaf;
__pyx_v_min_samples_leaf = __pyx_t_2;
/* "sklearn/tree/_tree.pyx":1388
* cdef SIZE_t max_features = self.max_features
* cdef SIZE_t min_samples_leaf = self.min_samples_leaf
* cdef double min_weight_leaf = self.min_weight_leaf # <<<<<<<<<<<<<<
* cdef UINT32_t* random_state = &self.rand_r_state
*
*/
__pyx_t_4 = __pyx_v_self->__pyx_base.__pyx_base.min_weight_leaf;
__pyx_v_min_weight_leaf = __pyx_t_4;
/* "sklearn/tree/_tree.pyx":1389
* cdef SIZE_t min_samples_leaf = self.min_samples_leaf
* cdef double min_weight_leaf = self.min_weight_leaf
* cdef UINT32_t* random_state = &self.rand_r_state # <<<<<<<<<<<<<<
*
* cdef SplitRecord best, current
*/
__pyx_v_random_state = (&__pyx_v_self->__pyx_base.__pyx_base.rand_r_state);
/* "sklearn/tree/_tree.pyx":1393
* cdef SplitRecord best, current
*
* cdef SIZE_t f_i = n_features # <<<<<<<<<<<<<<
* cdef SIZE_t f_j, p, tmp
* # Number of features discovered to be constant during the split search
*/
__pyx_v_f_i = __pyx_v_n_features;
/* "sklearn/tree/_tree.pyx":1396
* cdef SIZE_t f_j, p, tmp
* # Number of features discovered to be constant during the split search
* cdef SIZE_t n_found_constants = 0 # <<<<<<<<<<<<<<
* # Number of features known to be constant and drawn without replacement
* cdef SIZE_t n_drawn_constants = 0
*/
__pyx_v_n_found_constants = 0;
/* "sklearn/tree/_tree.pyx":1398
* cdef SIZE_t n_found_constants = 0
* # Number of features known to be constant and drawn without replacement
* cdef SIZE_t n_drawn_constants = 0 # <<<<<<<<<<<<<<
* cdef SIZE_t n_known_constants = n_constant_features[0]
* # n_total_constants = n_known_constants + n_found_constants
*/
__pyx_v_n_drawn_constants = 0;
/* "sklearn/tree/_tree.pyx":1399
* # Number of features known to be constant and drawn without replacement
* cdef SIZE_t n_drawn_constants = 0
* cdef SIZE_t n_known_constants = n_constant_features[0] # <<<<<<<<<<<<<<
* # n_total_constants = n_known_constants + n_found_constants
* cdef SIZE_t n_total_constants = n_known_constants
*/
__pyx_v_n_known_constants = (__pyx_v_n_constant_features[0]);
/* "sklearn/tree/_tree.pyx":1401
* cdef SIZE_t n_known_constants = n_constant_features[0]
* # n_total_constants = n_known_constants + n_found_constants
* cdef SIZE_t n_total_constants = n_known_constants # <<<<<<<<<<<<<<
* cdef SIZE_t n_visited_features = 0
* cdef DTYPE_t min_feature_value
*/
__pyx_v_n_total_constants = __pyx_v_n_known_constants;
/* "sklearn/tree/_tree.pyx":1402
* # n_total_constants = n_known_constants + n_found_constants
* cdef SIZE_t n_total_constants = n_known_constants
* cdef SIZE_t n_visited_features = 0 # <<<<<<<<<<<<<<
* cdef DTYPE_t min_feature_value
* cdef DTYPE_t max_feature_value
*/
__pyx_v_n_visited_features = 0;
/* "sklearn/tree/_tree.pyx":1408
* cdef SIZE_t partition_end
*
* _init_split(&best, end) # <<<<<<<<<<<<<<
*
* # Sample up to max_features without replacement using a
*/
__pyx_f_7sklearn_4tree_5_tree__init_split((&__pyx_v_best), __pyx_v_end);
/* "sklearn/tree/_tree.pyx":1419
* # newly discovered constant features to spare computation on descendant
* # nodes.
* while (f_i > n_total_constants and # Stop early if remaining features # <<<<<<<<<<<<<<
* # are constant
* (n_visited_features < max_features or
*/
while (1) {
__pyx_t_5 = ((__pyx_v_f_i > __pyx_v_n_total_constants) != 0);
if (__pyx_t_5) {
/* "sklearn/tree/_tree.pyx":1421
* while (f_i > n_total_constants and # Stop early if remaining features
* # are constant
* (n_visited_features < max_features or # <<<<<<<<<<<<<<
* # At least one drawn features must be non constant
* n_visited_features <= n_found_constants + n_drawn_constants)):
*/
__pyx_t_6 = ((__pyx_v_n_visited_features < __pyx_v_max_features) != 0);
if (!__pyx_t_6) {
/* "sklearn/tree/_tree.pyx":1423
* (n_visited_features < max_features or
* # At least one drawn features must be non constant
* n_visited_features <= n_found_constants + n_drawn_constants)): # <<<<<<<<<<<<<<
* n_visited_features += 1
*
*/
__pyx_t_7 = ((__pyx_v_n_visited_features <= (__pyx_v_n_found_constants + __pyx_v_n_drawn_constants)) != 0);
__pyx_t_8 = __pyx_t_7;
} else {
__pyx_t_8 = __pyx_t_6;
}
__pyx_t_6 = __pyx_t_8;
} else {
__pyx_t_6 = __pyx_t_5;
}
if (!__pyx_t_6) break;
/* "sklearn/tree/_tree.pyx":1424
* # At least one drawn features must be non constant
* n_visited_features <= n_found_constants + n_drawn_constants)):
* n_visited_features += 1 # <<<<<<<<<<<<<<
*
* # Loop invariant: elements of features in
*/
__pyx_v_n_visited_features = (__pyx_v_n_visited_features + 1);
/* "sklearn/tree/_tree.pyx":1438
*
* # Draw a feature at random
* f_j = rand_int(n_drawn_constants, f_i - n_found_constants, # <<<<<<<<<<<<<<
* random_state)
*
*/
__pyx_v_f_j = __pyx_f_7sklearn_4tree_5_tree_rand_int(__pyx_v_n_drawn_constants, (__pyx_v_f_i - __pyx_v_n_found_constants), __pyx_v_random_state);
/* "sklearn/tree/_tree.pyx":1441
* random_state)
*
* if f_j < n_known_constants: # <<<<<<<<<<<<<<
* # f_j in the interval [n_drawn_constants, n_known_constants[
* tmp = features[f_j]
*/
__pyx_t_6 = ((__pyx_v_f_j < __pyx_v_n_known_constants) != 0);
if (__pyx_t_6) {
/* "sklearn/tree/_tree.pyx":1443
* if f_j < n_known_constants:
* # f_j in the interval [n_drawn_constants, n_known_constants[
* tmp = features[f_j] # <<<<<<<<<<<<<<
* features[f_j] = features[n_drawn_constants]
* features[n_drawn_constants] = tmp
*/
__pyx_v_tmp = (__pyx_v_features[__pyx_v_f_j]);
/* "sklearn/tree/_tree.pyx":1444
* # f_j in the interval [n_drawn_constants, n_known_constants[
* tmp = features[f_j]
* features[f_j] = features[n_drawn_constants] # <<<<<<<<<<<<<<
* features[n_drawn_constants] = tmp
*
*/
(__pyx_v_features[__pyx_v_f_j]) = (__pyx_v_features[__pyx_v_n_drawn_constants]);
/* "sklearn/tree/_tree.pyx":1445
* tmp = features[f_j]
* features[f_j] = features[n_drawn_constants]
* features[n_drawn_constants] = tmp # <<<<<<<<<<<<<<
*
* n_drawn_constants += 1
*/
(__pyx_v_features[__pyx_v_n_drawn_constants]) = __pyx_v_tmp;
/* "sklearn/tree/_tree.pyx":1447
* features[n_drawn_constants] = tmp
*
* n_drawn_constants += 1 # <<<<<<<<<<<<<<
*
* else:
*/
__pyx_v_n_drawn_constants = (__pyx_v_n_drawn_constants + 1);
goto __pyx_L5;
}
/*else*/ {
/* "sklearn/tree/_tree.pyx":1451
* else:
* # f_j in the interval [n_known_constants, f_i - n_found_constants[
* f_j += n_found_constants # <<<<<<<<<<<<<<
* # f_j in the interval [n_total_constants, f_i[
*
*/
__pyx_v_f_j = (__pyx_v_f_j + __pyx_v_n_found_constants);
/* "sklearn/tree/_tree.pyx":1454
* # f_j in the interval [n_total_constants, f_i[
*
* current.feature = features[f_j] # <<<<<<<<<<<<<<
*
* # Find min, max
*/
__pyx_v_current.feature = (__pyx_v_features[__pyx_v_f_j]);
/* "sklearn/tree/_tree.pyx":1457
*
* # Find min, max
* min_feature_value = X[X_sample_stride * samples[start] + # <<<<<<<<<<<<<<
* X_fx_stride * current.feature]
* max_feature_value = min_feature_value
*/
__pyx_v_min_feature_value = (__pyx_v_X[((__pyx_v_X_sample_stride * (__pyx_v_samples[__pyx_v_start])) + (__pyx_v_X_fx_stride * __pyx_v_current.feature))]);
/* "sklearn/tree/_tree.pyx":1459
* min_feature_value = X[X_sample_stride * samples[start] +
* X_fx_stride * current.feature]
* max_feature_value = min_feature_value # <<<<<<<<<<<<<<
* Xf[start] = min_feature_value
*
*/
__pyx_v_max_feature_value = __pyx_v_min_feature_value;
/* "sklearn/tree/_tree.pyx":1460
* X_fx_stride * current.feature]
* max_feature_value = min_feature_value
* Xf[start] = min_feature_value # <<<<<<<<<<<<<<
*
* for p in range(start + 1, end):
*/
(__pyx_v_Xf[__pyx_v_start]) = __pyx_v_min_feature_value;
/* "sklearn/tree/_tree.pyx":1462
* Xf[start] = min_feature_value
*
* for p in range(start + 1, end): # <<<<<<<<<<<<<<
* current_feature_value = X[X_sample_stride * samples[p] +
* X_fx_stride * current.feature]
*/
__pyx_t_2 = __pyx_v_end;
for (__pyx_t_9 = (__pyx_v_start + 1); __pyx_t_9 < __pyx_t_2; __pyx_t_9+=1) {
__pyx_v_p = __pyx_t_9;
/* "sklearn/tree/_tree.pyx":1463
*
* for p in range(start + 1, end):
* current_feature_value = X[X_sample_stride * samples[p] + # <<<<<<<<<<<<<<
* X_fx_stride * current.feature]
* Xf[p] = current_feature_value
*/
__pyx_v_current_feature_value = (__pyx_v_X[((__pyx_v_X_sample_stride * (__pyx_v_samples[__pyx_v_p])) + (__pyx_v_X_fx_stride * __pyx_v_current.feature))]);
/* "sklearn/tree/_tree.pyx":1465
* current_feature_value = X[X_sample_stride * samples[p] +
* X_fx_stride * current.feature]
* Xf[p] = current_feature_value # <<<<<<<<<<<<<<
*
* if current_feature_value < min_feature_value:
*/
(__pyx_v_Xf[__pyx_v_p]) = __pyx_v_current_feature_value;
/* "sklearn/tree/_tree.pyx":1467
* Xf[p] = current_feature_value
*
* if current_feature_value < min_feature_value: # <<<<<<<<<<<<<<
* min_feature_value = current_feature_value
* elif current_feature_value > max_feature_value:
*/
__pyx_t_6 = ((__pyx_v_current_feature_value < __pyx_v_min_feature_value) != 0);
if (__pyx_t_6) {
/* "sklearn/tree/_tree.pyx":1468
*
* if current_feature_value < min_feature_value:
* min_feature_value = current_feature_value # <<<<<<<<<<<<<<
* elif current_feature_value > max_feature_value:
* max_feature_value = current_feature_value
*/
__pyx_v_min_feature_value = __pyx_v_current_feature_value;
goto __pyx_L8;
}
/* "sklearn/tree/_tree.pyx":1469
* if current_feature_value < min_feature_value:
* min_feature_value = current_feature_value
* elif current_feature_value > max_feature_value: # <<<<<<<<<<<<<<
* max_feature_value = current_feature_value
*
*/
__pyx_t_6 = ((__pyx_v_current_feature_value > __pyx_v_max_feature_value) != 0);
if (__pyx_t_6) {
/* "sklearn/tree/_tree.pyx":1470
* min_feature_value = current_feature_value
* elif current_feature_value > max_feature_value:
* max_feature_value = current_feature_value # <<<<<<<<<<<<<<
*
* if max_feature_value <= min_feature_value + FEATURE_THRESHOLD:
*/
__pyx_v_max_feature_value = __pyx_v_current_feature_value;
goto __pyx_L8;
}
__pyx_L8:;
}
/* "sklearn/tree/_tree.pyx":1472
* max_feature_value = current_feature_value
*
* if max_feature_value <= min_feature_value + FEATURE_THRESHOLD: # <<<<<<<<<<<<<<
* features[f_j] = features[n_total_constants]
* features[n_total_constants] = current.feature
*/
__pyx_t_6 = ((__pyx_v_max_feature_value <= (__pyx_v_min_feature_value + __pyx_v_7sklearn_4tree_5_tree_FEATURE_THRESHOLD)) != 0);
if (__pyx_t_6) {
/* "sklearn/tree/_tree.pyx":1473
*
* if max_feature_value <= min_feature_value + FEATURE_THRESHOLD:
* features[f_j] = features[n_total_constants] # <<<<<<<<<<<<<<
* features[n_total_constants] = current.feature
*
*/
(__pyx_v_features[__pyx_v_f_j]) = (__pyx_v_features[__pyx_v_n_total_constants]);
/* "sklearn/tree/_tree.pyx":1474
* if max_feature_value <= min_feature_value + FEATURE_THRESHOLD:
* features[f_j] = features[n_total_constants]
* features[n_total_constants] = current.feature # <<<<<<<<<<<<<<
*
* n_found_constants += 1
*/
__pyx_t_2 = __pyx_v_current.feature;
(__pyx_v_features[__pyx_v_n_total_constants]) = __pyx_t_2;
/* "sklearn/tree/_tree.pyx":1476
* features[n_total_constants] = current.feature
*
* n_found_constants += 1 # <<<<<<<<<<<<<<
* n_total_constants += 1
*
*/
__pyx_v_n_found_constants = (__pyx_v_n_found_constants + 1);
/* "sklearn/tree/_tree.pyx":1477
*
* n_found_constants += 1
* n_total_constants += 1 # <<<<<<<<<<<<<<
*
* else:
*/
__pyx_v_n_total_constants = (__pyx_v_n_total_constants + 1);
goto __pyx_L9;
}
/*else*/ {
/* "sklearn/tree/_tree.pyx":1480
*
* else:
* f_i -= 1 # <<<<<<<<<<<<<<
* features[f_i], features[f_j] = features[f_j], features[f_i]
*
*/
__pyx_v_f_i = (__pyx_v_f_i - 1);
/* "sklearn/tree/_tree.pyx":1481
* else:
* f_i -= 1
* features[f_i], features[f_j] = features[f_j], features[f_i] # <<<<<<<<<<<<<<
*
* # Draw a random threshold
*/
__pyx_t_2 = (__pyx_v_features[__pyx_v_f_j]);
__pyx_t_9 = (__pyx_v_features[__pyx_v_f_i]);
(__pyx_v_features[__pyx_v_f_i]) = __pyx_t_2;
(__pyx_v_features[__pyx_v_f_j]) = __pyx_t_9;
/* "sklearn/tree/_tree.pyx":1484
*
* # Draw a random threshold
* current.threshold = rand_uniform(min_feature_value, # <<<<<<<<<<<<<<
* max_feature_value,
* random_state)
*/
__pyx_v_current.threshold = __pyx_f_7sklearn_4tree_5_tree_rand_uniform(__pyx_v_min_feature_value, __pyx_v_max_feature_value, __pyx_v_random_state);
/* "sklearn/tree/_tree.pyx":1488
* random_state)
*
* if current.threshold == max_feature_value: # <<<<<<<<<<<<<<
* current.threshold = min_feature_value
*
*/
__pyx_t_6 = ((__pyx_v_current.threshold == __pyx_v_max_feature_value) != 0);
if (__pyx_t_6) {
/* "sklearn/tree/_tree.pyx":1489
*
* if current.threshold == max_feature_value:
* current.threshold = min_feature_value # <<<<<<<<<<<<<<
*
* # Partition
*/
__pyx_v_current.threshold = __pyx_v_min_feature_value;
goto __pyx_L10;
}
__pyx_L10:;
/* "sklearn/tree/_tree.pyx":1492
*
* # Partition
* partition_end = end # <<<<<<<<<<<<<<
* p = start
* while p < partition_end:
*/
__pyx_v_partition_end = __pyx_v_end;
/* "sklearn/tree/_tree.pyx":1493
* # Partition
* partition_end = end
* p = start # <<<<<<<<<<<<<<
* while p < partition_end:
* current_feature_value = Xf[p]
*/
__pyx_v_p = __pyx_v_start;
/* "sklearn/tree/_tree.pyx":1494
* partition_end = end
* p = start
* while p < partition_end: # <<<<<<<<<<<<<<
* current_feature_value = Xf[p]
* if current_feature_value <= current.threshold:
*/
while (1) {
__pyx_t_6 = ((__pyx_v_p < __pyx_v_partition_end) != 0);
if (!__pyx_t_6) break;
/* "sklearn/tree/_tree.pyx":1495
* p = start
* while p < partition_end:
* current_feature_value = Xf[p] # <<<<<<<<<<<<<<
* if current_feature_value <= current.threshold:
* p += 1
*/
__pyx_v_current_feature_value = (__pyx_v_Xf[__pyx_v_p]);
/* "sklearn/tree/_tree.pyx":1496
* while p < partition_end:
* current_feature_value = Xf[p]
* if current_feature_value <= current.threshold: # <<<<<<<<<<<<<<
* p += 1
* else:
*/
__pyx_t_6 = ((__pyx_v_current_feature_value <= __pyx_v_current.threshold) != 0);
if (__pyx_t_6) {
/* "sklearn/tree/_tree.pyx":1497
* current_feature_value = Xf[p]
* if current_feature_value <= current.threshold:
* p += 1 # <<<<<<<<<<<<<<
* else:
* partition_end -= 1
*/
__pyx_v_p = (__pyx_v_p + 1);
goto __pyx_L13;
}
/*else*/ {
/* "sklearn/tree/_tree.pyx":1499
* p += 1
* else:
* partition_end -= 1 # <<<<<<<<<<<<<<
*
* Xf[p] = Xf[partition_end]
*/
__pyx_v_partition_end = (__pyx_v_partition_end - 1);
/* "sklearn/tree/_tree.pyx":1501
* partition_end -= 1
*
* Xf[p] = Xf[partition_end] # <<<<<<<<<<<<<<
* Xf[partition_end] = current_feature_value
*
*/
(__pyx_v_Xf[__pyx_v_p]) = (__pyx_v_Xf[__pyx_v_partition_end]);
/* "sklearn/tree/_tree.pyx":1502
*
* Xf[p] = Xf[partition_end]
* Xf[partition_end] = current_feature_value # <<<<<<<<<<<<<<
*
* tmp = samples[partition_end]
*/
(__pyx_v_Xf[__pyx_v_partition_end]) = __pyx_v_current_feature_value;
/* "sklearn/tree/_tree.pyx":1504
* Xf[partition_end] = current_feature_value
*
* tmp = samples[partition_end] # <<<<<<<<<<<<<<
* samples[partition_end] = samples[p]
* samples[p] = tmp
*/
__pyx_v_tmp = (__pyx_v_samples[__pyx_v_partition_end]);
/* "sklearn/tree/_tree.pyx":1505
*
* tmp = samples[partition_end]
* samples[partition_end] = samples[p] # <<<<<<<<<<<<<<
* samples[p] = tmp
*
*/
(__pyx_v_samples[__pyx_v_partition_end]) = (__pyx_v_samples[__pyx_v_p]);
/* "sklearn/tree/_tree.pyx":1506
* tmp = samples[partition_end]
* samples[partition_end] = samples[p]
* samples[p] = tmp # <<<<<<<<<<<<<<
*
* current.pos = partition_end
*/
(__pyx_v_samples[__pyx_v_p]) = __pyx_v_tmp;
}
__pyx_L13:;
}
/* "sklearn/tree/_tree.pyx":1508
* samples[p] = tmp
*
* current.pos = partition_end # <<<<<<<<<<<<<<
*
* # Reject if min_samples_leaf is not guaranteed
*/
__pyx_v_current.pos = __pyx_v_partition_end;
/* "sklearn/tree/_tree.pyx":1511
*
* # Reject if min_samples_leaf is not guaranteed
* if (((current.pos - start) < min_samples_leaf) or # <<<<<<<<<<<<<<
* ((end - current.pos) < min_samples_leaf)):
* continue
*/
__pyx_t_6 = (((__pyx_v_current.pos - __pyx_v_start) < __pyx_v_min_samples_leaf) != 0);
if (!__pyx_t_6) {
/* "sklearn/tree/_tree.pyx":1512
* # Reject if min_samples_leaf is not guaranteed
* if (((current.pos - start) < min_samples_leaf) or
* ((end - current.pos) < min_samples_leaf)): # <<<<<<<<<<<<<<
* continue
*
*/
__pyx_t_5 = (((__pyx_v_end - __pyx_v_current.pos) < __pyx_v_min_samples_leaf) != 0);
__pyx_t_8 = __pyx_t_5;
} else {
__pyx_t_8 = __pyx_t_6;
}
if (__pyx_t_8) {
/* "sklearn/tree/_tree.pyx":1513
* if (((current.pos - start) < min_samples_leaf) or
* ((end - current.pos) < min_samples_leaf)):
* continue # <<<<<<<<<<<<<<
*
* # Evaluate split
*/
goto __pyx_L3_continue;
}
/* "sklearn/tree/_tree.pyx":1516
*
* # Evaluate split
* self.criterion.reset() # <<<<<<<<<<<<<<
* self.criterion.update(current.pos)
*
*/
((struct __pyx_vtabstruct_7sklearn_4tree_5_tree_Criterion *)__pyx_v_self->__pyx_base.__pyx_base.criterion->__pyx_vtab)->reset(__pyx_v_self->__pyx_base.__pyx_base.criterion);
/* "sklearn/tree/_tree.pyx":1517
* # Evaluate split
* self.criterion.reset()
* self.criterion.update(current.pos) # <<<<<<<<<<<<<<
*
* # Reject if min_weight_leaf is not satisfied
*/
((struct __pyx_vtabstruct_7sklearn_4tree_5_tree_Criterion *)__pyx_v_self->__pyx_base.__pyx_base.criterion->__pyx_vtab)->update(__pyx_v_self->__pyx_base.__pyx_base.criterion, __pyx_v_current.pos);
/* "sklearn/tree/_tree.pyx":1520
*
* # Reject if min_weight_leaf is not satisfied
* if ((self.criterion.weighted_n_left < min_weight_leaf) or # <<<<<<<<<<<<<<
* (self.criterion.weighted_n_right < min_weight_leaf)):
* continue
*/
__pyx_t_8 = ((__pyx_v_self->__pyx_base.__pyx_base.criterion->weighted_n_left < __pyx_v_min_weight_leaf) != 0);
if (!__pyx_t_8) {
/* "sklearn/tree/_tree.pyx":1521
* # Reject if min_weight_leaf is not satisfied
* if ((self.criterion.weighted_n_left < min_weight_leaf) or
* (self.criterion.weighted_n_right < min_weight_leaf)): # <<<<<<<<<<<<<<
* continue
*
*/
__pyx_t_6 = ((__pyx_v_self->__pyx_base.__pyx_base.criterion->weighted_n_right < __pyx_v_min_weight_leaf) != 0);
__pyx_t_5 = __pyx_t_6;
} else {
__pyx_t_5 = __pyx_t_8;
}
if (__pyx_t_5) {
/* "sklearn/tree/_tree.pyx":1522
* if ((self.criterion.weighted_n_left < min_weight_leaf) or
* (self.criterion.weighted_n_right < min_weight_leaf)):
* continue # <<<<<<<<<<<<<<
*
* current.improvement = self.criterion.impurity_improvement(impurity)
*/
goto __pyx_L3_continue;
}
/* "sklearn/tree/_tree.pyx":1524
* continue
*
* current.improvement = self.criterion.impurity_improvement(impurity) # <<<<<<<<<<<<<<
*
* if current.improvement > best.improvement:
*/
__pyx_v_current.improvement = ((struct __pyx_vtabstruct_7sklearn_4tree_5_tree_Criterion *)__pyx_v_self->__pyx_base.__pyx_base.criterion->__pyx_vtab)->impurity_improvement(__pyx_v_self->__pyx_base.__pyx_base.criterion, __pyx_v_impurity);
/* "sklearn/tree/_tree.pyx":1526
* current.improvement = self.criterion.impurity_improvement(impurity)
*
* if current.improvement > best.improvement: # <<<<<<<<<<<<<<
* self.criterion.children_impurity(&current.impurity_left,
* &current.impurity_right)
*/
__pyx_t_5 = ((__pyx_v_current.improvement > __pyx_v_best.improvement) != 0);
if (__pyx_t_5) {
/* "sklearn/tree/_tree.pyx":1527
*
* if current.improvement > best.improvement:
* self.criterion.children_impurity(&current.impurity_left, # <<<<<<<<<<<<<<
* &current.impurity_right)
* best = current # copy
*/
((struct __pyx_vtabstruct_7sklearn_4tree_5_tree_Criterion *)__pyx_v_self->__pyx_base.__pyx_base.criterion->__pyx_vtab)->children_impurity(__pyx_v_self->__pyx_base.__pyx_base.criterion, (&__pyx_v_current.impurity_left), (&__pyx_v_current.impurity_right));
/* "sklearn/tree/_tree.pyx":1529
* self.criterion.children_impurity(&current.impurity_left,
* &current.impurity_right)
* best = current # copy # <<<<<<<<<<<<<<
*
* # Reorganize into samples[start:best.pos] + samples[best.pos:end]
*/
__pyx_v_best = __pyx_v_current;
goto __pyx_L16;
}
__pyx_L16:;
}
__pyx_L9:;
}
__pyx_L5:;
__pyx_L3_continue:;
}
/* "sklearn/tree/_tree.pyx":1532
*
* # Reorganize into samples[start:best.pos] + samples[best.pos:end]
* if best.pos < end and current.feature != best.feature: # <<<<<<<<<<<<<<
* partition_end = end
* p = start
*/
__pyx_t_5 = ((__pyx_v_best.pos < __pyx_v_end) != 0);
if (__pyx_t_5) {
__pyx_t_8 = ((__pyx_v_current.feature != __pyx_v_best.feature) != 0);
__pyx_t_6 = __pyx_t_8;
} else {
__pyx_t_6 = __pyx_t_5;
}
if (__pyx_t_6) {
/* "sklearn/tree/_tree.pyx":1533
* # Reorganize into samples[start:best.pos] + samples[best.pos:end]
* if best.pos < end and current.feature != best.feature:
* partition_end = end # <<<<<<<<<<<<<<
* p = start
*
*/
__pyx_v_partition_end = __pyx_v_end;
/* "sklearn/tree/_tree.pyx":1534
* if best.pos < end and current.feature != best.feature:
* partition_end = end
* p = start # <<<<<<<<<<<<<<
*
* while p < partition_end:
*/
__pyx_v_p = __pyx_v_start;
/* "sklearn/tree/_tree.pyx":1536
* p = start
*
* while p < partition_end: # <<<<<<<<<<<<<<
* if X[X_sample_stride * samples[p] +
* X_fx_stride * best.feature] <= best.threshold:
*/
while (1) {
__pyx_t_6 = ((__pyx_v_p < __pyx_v_partition_end) != 0);
if (!__pyx_t_6) break;
/* "sklearn/tree/_tree.pyx":1538
* while p < partition_end:
* if X[X_sample_stride * samples[p] +
* X_fx_stride * best.feature] <= best.threshold: # <<<<<<<<<<<<<<
* p += 1
*
*/
__pyx_t_6 = (((__pyx_v_X[((__pyx_v_X_sample_stride * (__pyx_v_samples[__pyx_v_p])) + (__pyx_v_X_fx_stride * __pyx_v_best.feature))]) <= __pyx_v_best.threshold) != 0);
if (__pyx_t_6) {
/* "sklearn/tree/_tree.pyx":1539
* if X[X_sample_stride * samples[p] +
* X_fx_stride * best.feature] <= best.threshold:
* p += 1 # <<<<<<<<<<<<<<
*
* else:
*/
__pyx_v_p = (__pyx_v_p + 1);
goto __pyx_L20;
}
/*else*/ {
/* "sklearn/tree/_tree.pyx":1542
*
* else:
* partition_end -= 1 # <<<<<<<<<<<<<<
*
* tmp = samples[partition_end]
*/
__pyx_v_partition_end = (__pyx_v_partition_end - 1);
/* "sklearn/tree/_tree.pyx":1544
* partition_end -= 1
*
* tmp = samples[partition_end] # <<<<<<<<<<<<<<
* samples[partition_end] = samples[p]
* samples[p] = tmp
*/
__pyx_v_tmp = (__pyx_v_samples[__pyx_v_partition_end]);
/* "sklearn/tree/_tree.pyx":1545
*
* tmp = samples[partition_end]
* samples[partition_end] = samples[p] # <<<<<<<<<<<<<<
* samples[p] = tmp
*
*/
(__pyx_v_samples[__pyx_v_partition_end]) = (__pyx_v_samples[__pyx_v_p]);
/* "sklearn/tree/_tree.pyx":1546
* tmp = samples[partition_end]
* samples[partition_end] = samples[p]
* samples[p] = tmp # <<<<<<<<<<<<<<
*
* # Respect invariant for constant features: the original order of
*/
(__pyx_v_samples[__pyx_v_p]) = __pyx_v_tmp;
}
__pyx_L20:;
}
goto __pyx_L17;
}
__pyx_L17:;
/* "sklearn/tree/_tree.pyx":1551
* # element in features[:n_known_constants] must be preserved for sibling
* # and child nodes
* memcpy(features, constant_features, sizeof(SIZE_t) * n_known_constants) # <<<<<<<<<<<<<<
*
* # Copy newly found constant features
*/
memcpy(__pyx_v_features, __pyx_v_constant_features, ((sizeof(__pyx_t_7sklearn_4tree_5_tree_SIZE_t)) * __pyx_v_n_known_constants));
/* "sklearn/tree/_tree.pyx":1554
*
* # Copy newly found constant features
* memcpy(constant_features + n_known_constants, # <<<<<<<<<<<<<<
* features + n_known_constants,
* sizeof(SIZE_t) * n_found_constants)
*/
memcpy((__pyx_v_constant_features + __pyx_v_n_known_constants), (__pyx_v_features + __pyx_v_n_known_constants), ((sizeof(__pyx_t_7sklearn_4tree_5_tree_SIZE_t)) * __pyx_v_n_found_constants));
/* "sklearn/tree/_tree.pyx":1559
*
* # Return values
* split[0] = best # <<<<<<<<<<<<<<
* n_constant_features[0] = n_total_constants
*
*/
(__pyx_v_split[0]) = __pyx_v_best;
/* "sklearn/tree/_tree.pyx":1560
* # Return values
* split[0] = best
* n_constant_features[0] = n_total_constants # <<<<<<<<<<<<<<
*
*
*/
(__pyx_v_n_constant_features[0]) = __pyx_v_n_total_constants;
/* "sklearn/tree/_tree.pyx":1370
* self.random_state), self.__getstate__())
*
* cdef void node_split(self, double impurity, SplitRecord* split, # <<<<<<<<<<<<<<
* SIZE_t* n_constant_features) nogil:
* """Find the best random split on node samples[start:end]."""
*/
/* function exit code */
}
/* "sklearn/tree/_tree.pyx":1573
* cdef unsigned char* sample_mask
*
* def __cinit__(self, Criterion criterion, SIZE_t max_features, # <<<<<<<<<<<<<<
* SIZE_t min_samples_leaf,
* double min_weight_leaf,
*/
/* Python wrapper */
static int __pyx_pw_7sklearn_4tree_5_tree_19PresortBestSplitter_1__cinit__(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
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CYTHON_UNUSED __pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_v_min_samples_leaf;
CYTHON_UNUSED double __pyx_v_min_weight_leaf;
CYTHON_UNUSED PyObject *__pyx_v_random_state = 0;
int __pyx_lineno = 0;
const char *__pyx_filename = NULL;
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int __pyx_r;
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if (likely((values[3] = PyDict_GetItem(__pyx_kwds, __pyx_n_s_min_weight_leaf)) != 0)) kw_args--;
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__Pyx_RaiseArgtupleInvalid("__cinit__", 1, 5, 5, 3); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1573; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
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__Pyx_RaiseArgtupleInvalid("__cinit__", 1, 5, 5, 4); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1573; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
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* self.n_total_samples = X.shape[0]
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__pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_v_X_fx_stride;
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double __pyx_v_min_weight_leaf;
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__pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_v_f_j;
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unsigned char *__pyx_t_5;
double __pyx_t_6;
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int __pyx_t_8;
int __pyx_t_9;
int __pyx_t_10;
int __pyx_t_11;
/* "sklearn/tree/_tree.pyx":1622
* """Find the best split on node samples[start:end]."""
* # Find the best split
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* cdef SIZE_t start = self.start
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/* "sklearn/tree/_tree.pyx":1623
* # Find the best split
* cdef SIZE_t* samples = self.samples
* cdef SIZE_t start = self.start # <<<<<<<<<<<<<<
* cdef SIZE_t end = self.end
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*/
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/* "sklearn/tree/_tree.pyx":1624
* cdef SIZE_t* samples = self.samples
* cdef SIZE_t start = self.start
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*/
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/* "sklearn/tree/_tree.pyx":1626
* cdef SIZE_t end = self.end
*
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* cdef SIZE_t* constant_features = self.constant_features
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/* "sklearn/tree/_tree.pyx":1627
*
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*/
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* cdef SIZE_t* features = self.features
* cdef SIZE_t* constant_features = self.constant_features
* cdef SIZE_t n_features = self.n_features # <<<<<<<<<<<<<<
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*/
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/* "sklearn/tree/_tree.pyx":1630
* cdef SIZE_t n_features = self.n_features
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/* "sklearn/tree/_tree.pyx":1631
*
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* cdef SIZE_t X_sample_stride = self.X_sample_stride
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/* "sklearn/tree/_tree.pyx":1632
* cdef DTYPE_t* X = self.X
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* cdef SIZE_t X_sample_stride = self.X_sample_stride
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* cdef SIZE_t X_fx_stride = self.X_fx_stride
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* cdef SIZE_t X_argsorted_stride = self.X_argsorted_stride # <<<<<<<<<<<<<<
* cdef SIZE_t n_total_samples = self.n_total_samples
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/* "sklearn/tree/_tree.pyx":1636
* cdef INT32_t* X_argsorted = self.X_argsorted_ptr
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* cdef SIZE_t n_total_samples = self.n_total_samples # <<<<<<<<<<<<<<
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/* "sklearn/tree/_tree.pyx":1637
* cdef SIZE_t X_argsorted_stride = self.X_argsorted_stride
* cdef SIZE_t n_total_samples = self.n_total_samples
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*/
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/* "sklearn/tree/_tree.pyx":1639
* cdef unsigned char* sample_mask = self.sample_mask
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* cdef SIZE_t max_features = self.max_features # <<<<<<<<<<<<<<
* cdef SIZE_t min_samples_leaf = self.min_samples_leaf
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/* "sklearn/tree/_tree.pyx":1640
*
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* cdef SIZE_t min_samples_leaf = self.min_samples_leaf # <<<<<<<<<<<<<<
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* cdef SIZE_t max_features = self.max_features
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* cdef UINT32_t* random_state = &self.rand_r_state
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*/
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/* "sklearn/tree/_tree.pyx":1642
* cdef SIZE_t min_samples_leaf = self.min_samples_leaf
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* cdef SplitRecord best, current
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* cdef SIZE_t f_j, p
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/* "sklearn/tree/_tree.pyx":1651
* cdef SIZE_t n_found_constants = 0
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* cdef SIZE_t n_drawn_constants = 0 # <<<<<<<<<<<<<<
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* # Number of features known to be constant and drawn without replacement
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* cdef SIZE_t n_known_constants = n_constant_features[0] # <<<<<<<<<<<<<<
* # n_total_constants = n_known_constants + n_found_constants
* cdef SIZE_t n_total_constants = n_known_constants
*/
__pyx_v_n_known_constants = (__pyx_v_n_constant_features[0]);
/* "sklearn/tree/_tree.pyx":1654
* cdef SIZE_t n_known_constants = n_constant_features[0]
* # n_total_constants = n_known_constants + n_found_constants
* cdef SIZE_t n_total_constants = n_known_constants # <<<<<<<<<<<<<<
* cdef SIZE_t n_visited_features = 0
* cdef SIZE_t partition_end
*/
__pyx_v_n_total_constants = __pyx_v_n_known_constants;
/* "sklearn/tree/_tree.pyx":1655
* # n_total_constants = n_known_constants + n_found_constants
* cdef SIZE_t n_total_constants = n_known_constants
* cdef SIZE_t n_visited_features = 0 # <<<<<<<<<<<<<<
* cdef SIZE_t partition_end
* cdef SIZE_t i, j
*/
__pyx_v_n_visited_features = 0;
/* "sklearn/tree/_tree.pyx":1659
* cdef SIZE_t i, j
*
* _init_split(&best, end) # <<<<<<<<<<<<<<
*
* # Set sample mask
*/
__pyx_f_7sklearn_4tree_5_tree__init_split((&__pyx_v_best), __pyx_v_end);
/* "sklearn/tree/_tree.pyx":1662
*
* # Set sample mask
* for p in range(start, end): # <<<<<<<<<<<<<<
* sample_mask[samples[p]] = 1
*
*/
__pyx_t_2 = __pyx_v_end;
for (__pyx_t_7 = __pyx_v_start; __pyx_t_7 < __pyx_t_2; __pyx_t_7+=1) {
__pyx_v_p = __pyx_t_7;
/* "sklearn/tree/_tree.pyx":1663
* # Set sample mask
* for p in range(start, end):
* sample_mask[samples[p]] = 1 # <<<<<<<<<<<<<<
*
* # Sample up to max_features without replacement using a
*/
(__pyx_v_sample_mask[(__pyx_v_samples[__pyx_v_p])]) = 1;
}
/* "sklearn/tree/_tree.pyx":1674
* # newly discovered constant features to spare computation on descendant
* # nodes.
* while (f_i > n_total_constants and # Stop early if remaining features # <<<<<<<<<<<<<<
* # are constant
* (n_visited_features < max_features or
*/
while (1) {
__pyx_t_8 = ((__pyx_v_f_i > __pyx_v_n_total_constants) != 0);
if (__pyx_t_8) {
/* "sklearn/tree/_tree.pyx":1676
* while (f_i > n_total_constants and # Stop early if remaining features
* # are constant
* (n_visited_features < max_features or # <<<<<<<<<<<<<<
* # At least one drawn features must be non constant
* n_visited_features <= n_found_constants + n_drawn_constants)):
*/
__pyx_t_9 = ((__pyx_v_n_visited_features < __pyx_v_max_features) != 0);
if (!__pyx_t_9) {
/* "sklearn/tree/_tree.pyx":1678
* (n_visited_features < max_features or
* # At least one drawn features must be non constant
* n_visited_features <= n_found_constants + n_drawn_constants)): # <<<<<<<<<<<<<<
* n_visited_features += 1
*
*/
__pyx_t_10 = ((__pyx_v_n_visited_features <= (__pyx_v_n_found_constants + __pyx_v_n_drawn_constants)) != 0);
__pyx_t_11 = __pyx_t_10;
} else {
__pyx_t_11 = __pyx_t_9;
}
__pyx_t_9 = __pyx_t_11;
} else {
__pyx_t_9 = __pyx_t_8;
}
if (!__pyx_t_9) break;
/* "sklearn/tree/_tree.pyx":1679
* # At least one drawn features must be non constant
* n_visited_features <= n_found_constants + n_drawn_constants)):
* n_visited_features += 1 # <<<<<<<<<<<<<<
*
* # Loop invariant: elements of features in
*/
__pyx_v_n_visited_features = (__pyx_v_n_visited_features + 1);
/* "sklearn/tree/_tree.pyx":1693
*
* # Draw a feature at random
* f_j = rand_int(n_drawn_constants, f_i - n_found_constants, # <<<<<<<<<<<<<<
* random_state)
*
*/
__pyx_v_f_j = __pyx_f_7sklearn_4tree_5_tree_rand_int(__pyx_v_n_drawn_constants, (__pyx_v_f_i - __pyx_v_n_found_constants), __pyx_v_random_state);
/* "sklearn/tree/_tree.pyx":1696
* random_state)
*
* if f_j < n_known_constants: # <<<<<<<<<<<<<<
* # f_j is in [n_drawn_constants, n_known_constants[
* tmp = features[f_j]
*/
__pyx_t_9 = ((__pyx_v_f_j < __pyx_v_n_known_constants) != 0);
if (__pyx_t_9) {
/* "sklearn/tree/_tree.pyx":1698
* if f_j < n_known_constants:
* # f_j is in [n_drawn_constants, n_known_constants[
* tmp = features[f_j] # <<<<<<<<<<<<<<
* features[f_j] = features[n_drawn_constants]
* features[n_drawn_constants] = tmp
*/
__pyx_v_tmp = (__pyx_v_features[__pyx_v_f_j]);
/* "sklearn/tree/_tree.pyx":1699
* # f_j is in [n_drawn_constants, n_known_constants[
* tmp = features[f_j]
* features[f_j] = features[n_drawn_constants] # <<<<<<<<<<<<<<
* features[n_drawn_constants] = tmp
*
*/
(__pyx_v_features[__pyx_v_f_j]) = (__pyx_v_features[__pyx_v_n_drawn_constants]);
/* "sklearn/tree/_tree.pyx":1700
* tmp = features[f_j]
* features[f_j] = features[n_drawn_constants]
* features[n_drawn_constants] = tmp # <<<<<<<<<<<<<<
*
* n_drawn_constants += 1
*/
(__pyx_v_features[__pyx_v_n_drawn_constants]) = __pyx_v_tmp;
/* "sklearn/tree/_tree.pyx":1702
* features[n_drawn_constants] = tmp
*
* n_drawn_constants += 1 # <<<<<<<<<<<<<<
*
* else:
*/
__pyx_v_n_drawn_constants = (__pyx_v_n_drawn_constants + 1);
goto __pyx_L7;
}
/*else*/ {
/* "sklearn/tree/_tree.pyx":1706
* else:
* # f_j in the interval [n_known_constants, f_i - n_found_constants[
* f_j += n_found_constants # <<<<<<<<<<<<<<
* # f_j in the interval [n_total_constants, f_i[
*
*/
__pyx_v_f_j = (__pyx_v_f_j + __pyx_v_n_found_constants);
/* "sklearn/tree/_tree.pyx":1709
* # f_j in the interval [n_total_constants, f_i[
*
* current.feature = features[f_j] # <<<<<<<<<<<<<<
*
* # Extract ordering from X_argsorted
*/
__pyx_v_current.feature = (__pyx_v_features[__pyx_v_f_j]);
/* "sklearn/tree/_tree.pyx":1712
*
* # Extract ordering from X_argsorted
* p = start # <<<<<<<<<<<<<<
*
* for i in range(n_total_samples):
*/
__pyx_v_p = __pyx_v_start;
/* "sklearn/tree/_tree.pyx":1714
* p = start
*
* for i in range(n_total_samples): # <<<<<<<<<<<<<<
* j = X_argsorted[X_argsorted_stride * current.feature + i]
* if sample_mask[j] == 1:
*/
__pyx_t_2 = __pyx_v_n_total_samples;
for (__pyx_t_7 = 0; __pyx_t_7 < __pyx_t_2; __pyx_t_7+=1) {
__pyx_v_i = __pyx_t_7;
/* "sklearn/tree/_tree.pyx":1715
*
* for i in range(n_total_samples):
* j = X_argsorted[X_argsorted_stride * current.feature + i] # <<<<<<<<<<<<<<
* if sample_mask[j] == 1:
* samples[p] = j
*/
__pyx_v_j = (__pyx_v_X_argsorted[((__pyx_v_X_argsorted_stride * __pyx_v_current.feature) + __pyx_v_i)]);
/* "sklearn/tree/_tree.pyx":1716
* for i in range(n_total_samples):
* j = X_argsorted[X_argsorted_stride * current.feature + i]
* if sample_mask[j] == 1: # <<<<<<<<<<<<<<
* samples[p] = j
* Xf[p] = X[X_sample_stride * j +
*/
__pyx_t_9 = (((__pyx_v_sample_mask[__pyx_v_j]) == 1) != 0);
if (__pyx_t_9) {
/* "sklearn/tree/_tree.pyx":1717
* j = X_argsorted[X_argsorted_stride * current.feature + i]
* if sample_mask[j] == 1:
* samples[p] = j # <<<<<<<<<<<<<<
* Xf[p] = X[X_sample_stride * j +
* X_fx_stride * current.feature]
*/
(__pyx_v_samples[__pyx_v_p]) = __pyx_v_j;
/* "sklearn/tree/_tree.pyx":1718
* if sample_mask[j] == 1:
* samples[p] = j
* Xf[p] = X[X_sample_stride * j + # <<<<<<<<<<<<<<
* X_fx_stride * current.feature]
* p += 1
*/
(__pyx_v_Xf[__pyx_v_p]) = (__pyx_v_X[((__pyx_v_X_sample_stride * __pyx_v_j) + (__pyx_v_X_fx_stride * __pyx_v_current.feature))]);
/* "sklearn/tree/_tree.pyx":1720
* Xf[p] = X[X_sample_stride * j +
* X_fx_stride * current.feature]
* p += 1 # <<<<<<<<<<<<<<
*
* # Evaluate all splits
*/
__pyx_v_p = (__pyx_v_p + 1);
goto __pyx_L10;
}
__pyx_L10:;
}
/* "sklearn/tree/_tree.pyx":1723
*
* # Evaluate all splits
* if Xf[end - 1] <= Xf[start] + FEATURE_THRESHOLD: # <<<<<<<<<<<<<<
* features[f_j] = features[n_total_constants]
* features[n_total_constants] = current.feature
*/
__pyx_t_9 = (((__pyx_v_Xf[(__pyx_v_end - 1)]) <= ((__pyx_v_Xf[__pyx_v_start]) + __pyx_v_7sklearn_4tree_5_tree_FEATURE_THRESHOLD)) != 0);
if (__pyx_t_9) {
/* "sklearn/tree/_tree.pyx":1724
* # Evaluate all splits
* if Xf[end - 1] <= Xf[start] + FEATURE_THRESHOLD:
* features[f_j] = features[n_total_constants] # <<<<<<<<<<<<<<
* features[n_total_constants] = current.feature
*
*/
(__pyx_v_features[__pyx_v_f_j]) = (__pyx_v_features[__pyx_v_n_total_constants]);
/* "sklearn/tree/_tree.pyx":1725
* if Xf[end - 1] <= Xf[start] + FEATURE_THRESHOLD:
* features[f_j] = features[n_total_constants]
* features[n_total_constants] = current.feature # <<<<<<<<<<<<<<
*
* n_found_constants += 1
*/
__pyx_t_2 = __pyx_v_current.feature;
(__pyx_v_features[__pyx_v_n_total_constants]) = __pyx_t_2;
/* "sklearn/tree/_tree.pyx":1727
* features[n_total_constants] = current.feature
*
* n_found_constants += 1 # <<<<<<<<<<<<<<
* n_total_constants += 1
*
*/
__pyx_v_n_found_constants = (__pyx_v_n_found_constants + 1);
/* "sklearn/tree/_tree.pyx":1728
*
* n_found_constants += 1
* n_total_constants += 1 # <<<<<<<<<<<<<<
*
* else:
*/
__pyx_v_n_total_constants = (__pyx_v_n_total_constants + 1);
goto __pyx_L11;
}
/*else*/ {
/* "sklearn/tree/_tree.pyx":1731
*
* else:
* f_i -= 1 # <<<<<<<<<<<<<<
* features[f_i], features[f_j] = features[f_j], features[f_i]
*
*/
__pyx_v_f_i = (__pyx_v_f_i - 1);
/* "sklearn/tree/_tree.pyx":1732
* else:
* f_i -= 1
* features[f_i], features[f_j] = features[f_j], features[f_i] # <<<<<<<<<<<<<<
*
* self.criterion.reset()
*/
__pyx_t_2 = (__pyx_v_features[__pyx_v_f_j]);
__pyx_t_7 = (__pyx_v_features[__pyx_v_f_i]);
(__pyx_v_features[__pyx_v_f_i]) = __pyx_t_2;
(__pyx_v_features[__pyx_v_f_j]) = __pyx_t_7;
/* "sklearn/tree/_tree.pyx":1734
* features[f_i], features[f_j] = features[f_j], features[f_i]
*
* self.criterion.reset() # <<<<<<<<<<<<<<
* p = start
*
*/
((struct __pyx_vtabstruct_7sklearn_4tree_5_tree_Criterion *)__pyx_v_self->__pyx_base.__pyx_base.criterion->__pyx_vtab)->reset(__pyx_v_self->__pyx_base.__pyx_base.criterion);
/* "sklearn/tree/_tree.pyx":1735
*
* self.criterion.reset()
* p = start # <<<<<<<<<<<<<<
*
* while p < end:
*/
__pyx_v_p = __pyx_v_start;
/* "sklearn/tree/_tree.pyx":1737
* p = start
*
* while p < end: # <<<<<<<<<<<<<<
* while (p + 1 < end and
* Xf[p + 1] <= Xf[p] + FEATURE_THRESHOLD):
*/
while (1) {
__pyx_t_9 = ((__pyx_v_p < __pyx_v_end) != 0);
if (!__pyx_t_9) break;
/* "sklearn/tree/_tree.pyx":1738
*
* while p < end:
* while (p + 1 < end and # <<<<<<<<<<<<<<
* Xf[p + 1] <= Xf[p] + FEATURE_THRESHOLD):
* p += 1
*/
while (1) {
__pyx_t_9 = (((__pyx_v_p + 1) < __pyx_v_end) != 0);
if (__pyx_t_9) {
/* "sklearn/tree/_tree.pyx":1739
* while p < end:
* while (p + 1 < end and
* Xf[p + 1] <= Xf[p] + FEATURE_THRESHOLD): # <<<<<<<<<<<<<<
* p += 1
*
*/
__pyx_t_8 = (((__pyx_v_Xf[(__pyx_v_p + 1)]) <= ((__pyx_v_Xf[__pyx_v_p]) + __pyx_v_7sklearn_4tree_5_tree_FEATURE_THRESHOLD)) != 0);
__pyx_t_11 = __pyx_t_8;
} else {
__pyx_t_11 = __pyx_t_9;
}
if (!__pyx_t_11) break;
/* "sklearn/tree/_tree.pyx":1740
* while (p + 1 < end and
* Xf[p + 1] <= Xf[p] + FEATURE_THRESHOLD):
* p += 1 # <<<<<<<<<<<<<<
*
* # (p + 1 >= end) or (X[samples[p + 1], current.feature] >
*/
__pyx_v_p = (__pyx_v_p + 1);
}
/* "sklearn/tree/_tree.pyx":1744
* # (p + 1 >= end) or (X[samples[p + 1], current.feature] >
* # X[samples[p], current.feature])
* p += 1 # <<<<<<<<<<<<<<
* # (p >= end) or (X[samples[p], current.feature] >
* # X[samples[p - 1], current.feature])
*/
__pyx_v_p = (__pyx_v_p + 1);
/* "sklearn/tree/_tree.pyx":1748
* # X[samples[p - 1], current.feature])
*
* if p < end: # <<<<<<<<<<<<<<
* current.pos = p
*
*/
__pyx_t_11 = ((__pyx_v_p < __pyx_v_end) != 0);
if (__pyx_t_11) {
/* "sklearn/tree/_tree.pyx":1749
*
* if p < end:
* current.pos = p # <<<<<<<<<<<<<<
*
* # Reject if min_samples_leaf is not guaranteed
*/
__pyx_v_current.pos = __pyx_v_p;
/* "sklearn/tree/_tree.pyx":1752
*
* # Reject if min_samples_leaf is not guaranteed
* if (((current.pos - start) < min_samples_leaf) or # <<<<<<<<<<<<<<
* ((end - current.pos) < min_samples_leaf)):
* continue
*/
__pyx_t_11 = (((__pyx_v_current.pos - __pyx_v_start) < __pyx_v_min_samples_leaf) != 0);
if (!__pyx_t_11) {
/* "sklearn/tree/_tree.pyx":1753
* # Reject if min_samples_leaf is not guaranteed
* if (((current.pos - start) < min_samples_leaf) or
* ((end - current.pos) < min_samples_leaf)): # <<<<<<<<<<<<<<
* continue
*
*/
__pyx_t_9 = (((__pyx_v_end - __pyx_v_current.pos) < __pyx_v_min_samples_leaf) != 0);
__pyx_t_8 = __pyx_t_9;
} else {
__pyx_t_8 = __pyx_t_11;
}
if (__pyx_t_8) {
/* "sklearn/tree/_tree.pyx":1754
* if (((current.pos - start) < min_samples_leaf) or
* ((end - current.pos) < min_samples_leaf)):
* continue # <<<<<<<<<<<<<<
*
* self.criterion.update(current.pos)
*/
goto __pyx_L12_continue;
}
/* "sklearn/tree/_tree.pyx":1756
* continue
*
* self.criterion.update(current.pos) # <<<<<<<<<<<<<<
*
* # Reject if min_weight_leaf is not satisfied
*/
((struct __pyx_vtabstruct_7sklearn_4tree_5_tree_Criterion *)__pyx_v_self->__pyx_base.__pyx_base.criterion->__pyx_vtab)->update(__pyx_v_self->__pyx_base.__pyx_base.criterion, __pyx_v_current.pos);
/* "sklearn/tree/_tree.pyx":1759
*
* # Reject if min_weight_leaf is not satisfied
* if ((self.criterion.weighted_n_left < min_weight_leaf) or # <<<<<<<<<<<<<<
* (self.criterion.weighted_n_right < min_weight_leaf)):
* continue
*/
__pyx_t_8 = ((__pyx_v_self->__pyx_base.__pyx_base.criterion->weighted_n_left < __pyx_v_min_weight_leaf) != 0);
if (!__pyx_t_8) {
/* "sklearn/tree/_tree.pyx":1760
* # Reject if min_weight_leaf is not satisfied
* if ((self.criterion.weighted_n_left < min_weight_leaf) or
* (self.criterion.weighted_n_right < min_weight_leaf)): # <<<<<<<<<<<<<<
* continue
*
*/
__pyx_t_11 = ((__pyx_v_self->__pyx_base.__pyx_base.criterion->weighted_n_right < __pyx_v_min_weight_leaf) != 0);
__pyx_t_9 = __pyx_t_11;
} else {
__pyx_t_9 = __pyx_t_8;
}
if (__pyx_t_9) {
/* "sklearn/tree/_tree.pyx":1761
* if ((self.criterion.weighted_n_left < min_weight_leaf) or
* (self.criterion.weighted_n_right < min_weight_leaf)):
* continue # <<<<<<<<<<<<<<
*
* current.improvement = self.criterion.impurity_improvement(impurity)
*/
goto __pyx_L12_continue;
}
/* "sklearn/tree/_tree.pyx":1763
* continue
*
* current.improvement = self.criterion.impurity_improvement(impurity) # <<<<<<<<<<<<<<
*
* if current.improvement > best.improvement:
*/
__pyx_v_current.improvement = ((struct __pyx_vtabstruct_7sklearn_4tree_5_tree_Criterion *)__pyx_v_self->__pyx_base.__pyx_base.criterion->__pyx_vtab)->impurity_improvement(__pyx_v_self->__pyx_base.__pyx_base.criterion, __pyx_v_impurity);
/* "sklearn/tree/_tree.pyx":1765
* current.improvement = self.criterion.impurity_improvement(impurity)
*
* if current.improvement > best.improvement: # <<<<<<<<<<<<<<
* self.criterion.children_impurity(&current.impurity_left,
* &current.impurity_right)
*/
__pyx_t_9 = ((__pyx_v_current.improvement > __pyx_v_best.improvement) != 0);
if (__pyx_t_9) {
/* "sklearn/tree/_tree.pyx":1766
*
* if current.improvement > best.improvement:
* self.criterion.children_impurity(&current.impurity_left, # <<<<<<<<<<<<<<
* &current.impurity_right)
*
*/
((struct __pyx_vtabstruct_7sklearn_4tree_5_tree_Criterion *)__pyx_v_self->__pyx_base.__pyx_base.criterion->__pyx_vtab)->children_impurity(__pyx_v_self->__pyx_base.__pyx_base.criterion, (&__pyx_v_current.impurity_left), (&__pyx_v_current.impurity_right));
/* "sklearn/tree/_tree.pyx":1769
* &current.impurity_right)
*
* current.threshold = (Xf[p - 1] + Xf[p]) / 2.0 # <<<<<<<<<<<<<<
* if current.threshold == Xf[p]:
* current.threshold = Xf[p - 1]
*/
__pyx_v_current.threshold = (((__pyx_v_Xf[(__pyx_v_p - 1)]) + (__pyx_v_Xf[__pyx_v_p])) / 2.0);
/* "sklearn/tree/_tree.pyx":1770
*
* current.threshold = (Xf[p - 1] + Xf[p]) / 2.0
* if current.threshold == Xf[p]: # <<<<<<<<<<<<<<
* current.threshold = Xf[p - 1]
*
*/
__pyx_t_9 = ((__pyx_v_current.threshold == (__pyx_v_Xf[__pyx_v_p])) != 0);
if (__pyx_t_9) {
/* "sklearn/tree/_tree.pyx":1771
* current.threshold = (Xf[p - 1] + Xf[p]) / 2.0
* if current.threshold == Xf[p]:
* current.threshold = Xf[p - 1] # <<<<<<<<<<<<<<
*
* best = current # copy
*/
__pyx_v_current.threshold = (__pyx_v_Xf[(__pyx_v_p - 1)]);
goto __pyx_L20;
}
__pyx_L20:;
/* "sklearn/tree/_tree.pyx":1773
* current.threshold = Xf[p - 1]
*
* best = current # copy # <<<<<<<<<<<<<<
*
* # Reorganize into samples[start:best.pos] + samples[best.pos:end]
*/
__pyx_v_best = __pyx_v_current;
goto __pyx_L19;
}
__pyx_L19:;
goto __pyx_L16;
}
__pyx_L16:;
__pyx_L12_continue:;
}
}
__pyx_L11:;
}
__pyx_L7:;
}
/* "sklearn/tree/_tree.pyx":1776
*
* # Reorganize into samples[start:best.pos] + samples[best.pos:end]
* if best.pos < end: # <<<<<<<<<<<<<<
* partition_end = end
* p = start
*/
__pyx_t_9 = ((__pyx_v_best.pos < __pyx_v_end) != 0);
if (__pyx_t_9) {
/* "sklearn/tree/_tree.pyx":1777
* # Reorganize into samples[start:best.pos] + samples[best.pos:end]
* if best.pos < end:
* partition_end = end # <<<<<<<<<<<<<<
* p = start
*
*/
__pyx_v_partition_end = __pyx_v_end;
/* "sklearn/tree/_tree.pyx":1778
* if best.pos < end:
* partition_end = end
* p = start # <<<<<<<<<<<<<<
*
* while p < partition_end:
*/
__pyx_v_p = __pyx_v_start;
/* "sklearn/tree/_tree.pyx":1780
* p = start
*
* while p < partition_end: # <<<<<<<<<<<<<<
* if X[X_sample_stride * samples[p] +
* X_fx_stride * best.feature] <= best.threshold:
*/
while (1) {
__pyx_t_9 = ((__pyx_v_p < __pyx_v_partition_end) != 0);
if (!__pyx_t_9) break;
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/* "sklearn/tree/_tree.pyx":1863
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/* "sklearn/tree/_tree.pyx":1873
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/* "sklearn/tree/_tree.pyx":1874
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/* "sklearn/tree/_tree.pyx":1889
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/* "sklearn/tree/_tree.pyx":1890
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/* "sklearn/tree/_tree.pyx":1892
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/* "sklearn/tree/_tree.pyx":1893
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/* "sklearn/tree/_tree.pyx":1894
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/* "sklearn/tree/_tree.pyx":1895
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/* "sklearn/tree/_tree.pyx":1896
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/* "sklearn/tree/_tree.pyx":1903
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/* "sklearn/tree/_tree.pyx":1879
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* SIZE_t* end_negative, SIZE_t* start_positive,
* bint* is_samples_sorted) nogil:
*/
static CYTHON_INLINE void __pyx_f_7sklearn_4tree_5_tree_18BaseSparseSplitter_extract_nnz(struct __pyx_obj_7sklearn_4tree_5_tree_BaseSparseSplitter *__pyx_v_self, __pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_v_feature, __pyx_t_7sklearn_4tree_5_tree_SIZE_t *__pyx_v_end_negative, __pyx_t_7sklearn_4tree_5_tree_SIZE_t *__pyx_v_start_positive, int *__pyx_v_is_samples_sorted) {
__pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_v_indptr_start;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_v_indptr_end;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_v_n_indices;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_v_n_samples;
int __pyx_t_1;
/* "sklearn/tree/_tree.pyx":1949
*
* """
* cdef SIZE_t indptr_start = self.X_indptr[feature], # <<<<<<<<<<<<<<
* cdef SIZE_t indptr_end = self.X_indptr[feature + 1]
* cdef SIZE_t n_indices = <SIZE_t>(indptr_end - indptr_start)
*/
__pyx_v_indptr_start = (__pyx_v_self->X_indptr[__pyx_v_feature]);
/* "sklearn/tree/_tree.pyx":1950
* """
* cdef SIZE_t indptr_start = self.X_indptr[feature],
* cdef SIZE_t indptr_end = self.X_indptr[feature + 1] # <<<<<<<<<<<<<<
* cdef SIZE_t n_indices = <SIZE_t>(indptr_end - indptr_start)
* cdef SIZE_t n_samples = self.end - self.start
*/
__pyx_v_indptr_end = (__pyx_v_self->X_indptr[(__pyx_v_feature + 1)]);
/* "sklearn/tree/_tree.pyx":1951
* cdef SIZE_t indptr_start = self.X_indptr[feature],
* cdef SIZE_t indptr_end = self.X_indptr[feature + 1]
* cdef SIZE_t n_indices = <SIZE_t>(indptr_end - indptr_start) # <<<<<<<<<<<<<<
* cdef SIZE_t n_samples = self.end - self.start
*
*/
__pyx_v_n_indices = ((__pyx_t_7sklearn_4tree_5_tree_SIZE_t)(__pyx_v_indptr_end - __pyx_v_indptr_start));
/* "sklearn/tree/_tree.pyx":1952
* cdef SIZE_t indptr_end = self.X_indptr[feature + 1]
* cdef SIZE_t n_indices = <SIZE_t>(indptr_end - indptr_start)
* cdef SIZE_t n_samples = self.end - self.start # <<<<<<<<<<<<<<
*
* # Use binary search if n_samples * log(n_indices) <
*/
__pyx_v_n_samples = (__pyx_v_self->__pyx_base.end - __pyx_v_self->__pyx_base.start);
/* "sklearn/tree/_tree.pyx":1960
* # approach.
* if ((1 - is_samples_sorted[0]) * n_samples * log(n_samples) +
* n_samples * log(n_indices) < EXTRACT_NNZ_SWITCH * n_indices): # <<<<<<<<<<<<<<
* extract_nnz_binary_search(self.X_indices, self.X_data,
* indptr_start, indptr_end,
*/
__pyx_t_1 = ((((((1 - (__pyx_v_is_samples_sorted[0])) * __pyx_v_n_samples) * __pyx_f_7sklearn_4tree_5_tree_log(__pyx_v_n_samples)) + (__pyx_v_n_samples * __pyx_f_7sklearn_4tree_5_tree_log(__pyx_v_n_indices))) < (__pyx_v_7sklearn_4tree_5_tree_EXTRACT_NNZ_SWITCH * __pyx_v_n_indices)) != 0);
if (__pyx_t_1) {
/* "sklearn/tree/_tree.pyx":1961
* if ((1 - is_samples_sorted[0]) * n_samples * log(n_samples) +
* n_samples * log(n_indices) < EXTRACT_NNZ_SWITCH * n_indices):
* extract_nnz_binary_search(self.X_indices, self.X_data, # <<<<<<<<<<<<<<
* indptr_start, indptr_end,
* self.samples, self.start, self.end,
*/
__pyx_f_7sklearn_4tree_5_tree_extract_nnz_binary_search(__pyx_v_self->X_indices, __pyx_v_self->X_data, __pyx_v_indptr_start, __pyx_v_indptr_end, __pyx_v_self->__pyx_base.samples, __pyx_v_self->__pyx_base.start, __pyx_v_self->__pyx_base.end, __pyx_v_self->index_to_samples, __pyx_v_self->__pyx_base.feature_values, __pyx_v_end_negative, __pyx_v_start_positive, __pyx_v_self->sorted_samples, __pyx_v_is_samples_sorted);
goto __pyx_L3;
}
/*else*/ {
/* "sklearn/tree/_tree.pyx":1972
* # index_to_samples is a mapping from X_indices to samples
* else:
* extract_nnz_index_to_samples(self.X_indices, self.X_data, # <<<<<<<<<<<<<<
* indptr_start, indptr_end,
* self.samples, self.start, self.end,
*/
__pyx_f_7sklearn_4tree_5_tree_extract_nnz_index_to_samples(__pyx_v_self->X_indices, __pyx_v_self->X_data, __pyx_v_indptr_start, __pyx_v_indptr_end, __pyx_v_self->__pyx_base.samples, __pyx_v_self->__pyx_base.start, __pyx_v_self->__pyx_base.end, __pyx_v_self->index_to_samples, __pyx_v_self->__pyx_base.feature_values, __pyx_v_end_negative, __pyx_v_start_positive);
}
__pyx_L3:;
/* "sklearn/tree/_tree.pyx":1917
* return partition_end
*
* cdef inline void extract_nnz(self, SIZE_t feature, # <<<<<<<<<<<<<<
* SIZE_t* end_negative, SIZE_t* start_positive,
* bint* is_samples_sorted) nogil:
*/
/* function exit code */
}
/* "sklearn/tree/_tree.pyx":1980
*
*
* cdef int compare_SIZE_t(const void* a, const void* b) nogil: # <<<<<<<<<<<<<<
* """Comparison function for sort"""
* return <int>((<SIZE_t*>a)[0] - (<SIZE_t*>b)[0])
*/
static int __pyx_f_7sklearn_4tree_5_tree_compare_SIZE_t(void const *__pyx_v_a, void const *__pyx_v_b) {
int __pyx_r;
/* "sklearn/tree/_tree.pyx":1982
* cdef int compare_SIZE_t(const void* a, const void* b) nogil:
* """Comparison function for sort"""
* return <int>((<SIZE_t*>a)[0] - (<SIZE_t*>b)[0]) # <<<<<<<<<<<<<<
*
*
*/
__pyx_r = ((int)((((__pyx_t_7sklearn_4tree_5_tree_SIZE_t *)__pyx_v_a)[0]) - (((__pyx_t_7sklearn_4tree_5_tree_SIZE_t *)__pyx_v_b)[0])));
goto __pyx_L0;
/* "sklearn/tree/_tree.pyx":1980
*
*
* cdef int compare_SIZE_t(const void* a, const void* b) nogil: # <<<<<<<<<<<<<<
* """Comparison function for sort"""
* return <int>((<SIZE_t*>a)[0] - (<SIZE_t*>b)[0])
*/
/* function exit code */
__pyx_L0:;
return __pyx_r;
}
/* "sklearn/tree/_tree.pyx":1985
*
*
* cdef inline void binary_search(INT32_t* sorted_array, # <<<<<<<<<<<<<<
* INT32_t start, INT32_t end,
* SIZE_t value, SIZE_t* index,
*/
static CYTHON_INLINE void __pyx_f_7sklearn_4tree_5_tree_binary_search(__pyx_t_7sklearn_4tree_5_tree_INT32_t *__pyx_v_sorted_array, __pyx_t_7sklearn_4tree_5_tree_INT32_t __pyx_v_start, __pyx_t_7sklearn_4tree_5_tree_INT32_t __pyx_v_end, __pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_v_value, __pyx_t_7sklearn_4tree_5_tree_SIZE_t *__pyx_v_index, __pyx_t_7sklearn_4tree_5_tree_INT32_t *__pyx_v_new_start) {
__pyx_t_7sklearn_4tree_5_tree_INT32_t __pyx_v_pivot;
int __pyx_t_1;
/* "sklearn/tree/_tree.pyx":1994
* """
* cdef INT32_t pivot
* index[0] = -1 # <<<<<<<<<<<<<<
* while start < end:
* pivot = start + (end - start) / 2
*/
(__pyx_v_index[0]) = -1;
/* "sklearn/tree/_tree.pyx":1995
* cdef INT32_t pivot
* index[0] = -1
* while start < end: # <<<<<<<<<<<<<<
* pivot = start + (end - start) / 2
*
*/
while (1) {
__pyx_t_1 = ((__pyx_v_start < __pyx_v_end) != 0);
if (!__pyx_t_1) break;
/* "sklearn/tree/_tree.pyx":1996
* index[0] = -1
* while start < end:
* pivot = start + (end - start) / 2 # <<<<<<<<<<<<<<
*
* if sorted_array[pivot] == value:
*/
__pyx_v_pivot = (__pyx_v_start + ((__pyx_v_end - __pyx_v_start) / 2));
/* "sklearn/tree/_tree.pyx":1998
* pivot = start + (end - start) / 2
*
* if sorted_array[pivot] == value: # <<<<<<<<<<<<<<
* index[0] = pivot
* start = pivot + 1
*/
__pyx_t_1 = (((__pyx_v_sorted_array[__pyx_v_pivot]) == __pyx_v_value) != 0);
if (__pyx_t_1) {
/* "sklearn/tree/_tree.pyx":1999
*
* if sorted_array[pivot] == value:
* index[0] = pivot # <<<<<<<<<<<<<<
* start = pivot + 1
* break
*/
(__pyx_v_index[0]) = __pyx_v_pivot;
/* "sklearn/tree/_tree.pyx":2000
* if sorted_array[pivot] == value:
* index[0] = pivot
* start = pivot + 1 # <<<<<<<<<<<<<<
* break
*
*/
__pyx_v_start = (__pyx_v_pivot + 1);
/* "sklearn/tree/_tree.pyx":2001
* index[0] = pivot
* start = pivot + 1
* break # <<<<<<<<<<<<<<
*
* if sorted_array[pivot] < value:
*/
goto __pyx_L4_break;
}
/* "sklearn/tree/_tree.pyx":2003
* break
*
* if sorted_array[pivot] < value: # <<<<<<<<<<<<<<
* start = pivot + 1
* else:
*/
__pyx_t_1 = (((__pyx_v_sorted_array[__pyx_v_pivot]) < __pyx_v_value) != 0);
if (__pyx_t_1) {
/* "sklearn/tree/_tree.pyx":2004
*
* if sorted_array[pivot] < value:
* start = pivot + 1 # <<<<<<<<<<<<<<
* else:
* end = pivot
*/
__pyx_v_start = (__pyx_v_pivot + 1);
goto __pyx_L6;
}
/*else*/ {
/* "sklearn/tree/_tree.pyx":2006
* start = pivot + 1
* else:
* end = pivot # <<<<<<<<<<<<<<
* new_start[0] = start
*
*/
__pyx_v_end = __pyx_v_pivot;
}
__pyx_L6:;
}
__pyx_L4_break:;
/* "sklearn/tree/_tree.pyx":2007
* else:
* end = pivot
* new_start[0] = start # <<<<<<<<<<<<<<
*
*
*/
(__pyx_v_new_start[0]) = __pyx_v_start;
/* "sklearn/tree/_tree.pyx":1985
*
*
* cdef inline void binary_search(INT32_t* sorted_array, # <<<<<<<<<<<<<<
* INT32_t start, INT32_t end,
* SIZE_t value, SIZE_t* index,
*/
/* function exit code */
}
/* "sklearn/tree/_tree.pyx":2010
*
*
* cdef inline void extract_nnz_index_to_samples(INT32_t* X_indices, # <<<<<<<<<<<<<<
* DTYPE_t* X_data,
* INT32_t indptr_start,
*/
static CYTHON_INLINE void __pyx_f_7sklearn_4tree_5_tree_extract_nnz_index_to_samples(__pyx_t_7sklearn_4tree_5_tree_INT32_t *__pyx_v_X_indices, __pyx_t_7sklearn_4tree_5_tree_DTYPE_t *__pyx_v_X_data, __pyx_t_7sklearn_4tree_5_tree_INT32_t __pyx_v_indptr_start, __pyx_t_7sklearn_4tree_5_tree_INT32_t __pyx_v_indptr_end, __pyx_t_7sklearn_4tree_5_tree_SIZE_t *__pyx_v_samples, __pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_v_start, __pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_v_end, __pyx_t_7sklearn_4tree_5_tree_SIZE_t *__pyx_v_index_to_samples, __pyx_t_7sklearn_4tree_5_tree_DTYPE_t *__pyx_v_Xf, __pyx_t_7sklearn_4tree_5_tree_SIZE_t *__pyx_v_end_negative, __pyx_t_7sklearn_4tree_5_tree_SIZE_t *__pyx_v_start_positive) {
__pyx_t_7sklearn_4tree_5_tree_INT32_t __pyx_v_k;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_v_index;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_v_end_negative_;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_v_start_positive_;
__pyx_t_7sklearn_4tree_5_tree_INT32_t __pyx_t_1;
__pyx_t_7sklearn_4tree_5_tree_INT32_t __pyx_t_2;
int __pyx_t_3;
int __pyx_t_4;
/* "sklearn/tree/_tree.pyx":2027
* cdef INT32_t k
* cdef SIZE_t index
* cdef SIZE_t end_negative_ = start # <<<<<<<<<<<<<<
* cdef SIZE_t start_positive_ = end
*
*/
__pyx_v_end_negative_ = __pyx_v_start;
/* "sklearn/tree/_tree.pyx":2028
* cdef SIZE_t index
* cdef SIZE_t end_negative_ = start
* cdef SIZE_t start_positive_ = end # <<<<<<<<<<<<<<
*
* for k in range(indptr_start, indptr_end):
*/
__pyx_v_start_positive_ = __pyx_v_end;
/* "sklearn/tree/_tree.pyx":2030
* cdef SIZE_t start_positive_ = end
*
* for k in range(indptr_start, indptr_end): # <<<<<<<<<<<<<<
* if start <= index_to_samples[X_indices[k]] < end:
* if X_data[k] > 0:
*/
__pyx_t_1 = __pyx_v_indptr_end;
for (__pyx_t_2 = __pyx_v_indptr_start; __pyx_t_2 < __pyx_t_1; __pyx_t_2+=1) {
__pyx_v_k = __pyx_t_2;
/* "sklearn/tree/_tree.pyx":2031
*
* for k in range(indptr_start, indptr_end):
* if start <= index_to_samples[X_indices[k]] < end: # <<<<<<<<<<<<<<
* if X_data[k] > 0:
* start_positive_ -= 1
*/
__pyx_t_3 = (__pyx_v_start <= (__pyx_v_index_to_samples[(__pyx_v_X_indices[__pyx_v_k])]));
if (__pyx_t_3) {
__pyx_t_3 = ((__pyx_v_index_to_samples[(__pyx_v_X_indices[__pyx_v_k])]) < __pyx_v_end);
}
__pyx_t_4 = (__pyx_t_3 != 0);
if (__pyx_t_4) {
/* "sklearn/tree/_tree.pyx":2032
* for k in range(indptr_start, indptr_end):
* if start <= index_to_samples[X_indices[k]] < end:
* if X_data[k] > 0: # <<<<<<<<<<<<<<
* start_positive_ -= 1
* Xf[start_positive_] = X_data[k]
*/
__pyx_t_4 = (((__pyx_v_X_data[__pyx_v_k]) > 0.0) != 0);
if (__pyx_t_4) {
/* "sklearn/tree/_tree.pyx":2033
* if start <= index_to_samples[X_indices[k]] < end:
* if X_data[k] > 0:
* start_positive_ -= 1 # <<<<<<<<<<<<<<
* Xf[start_positive_] = X_data[k]
* index = index_to_samples[X_indices[k]]
*/
__pyx_v_start_positive_ = (__pyx_v_start_positive_ - 1);
/* "sklearn/tree/_tree.pyx":2034
* if X_data[k] > 0:
* start_positive_ -= 1
* Xf[start_positive_] = X_data[k] # <<<<<<<<<<<<<<
* index = index_to_samples[X_indices[k]]
* sparse_swap(index_to_samples, samples, index, start_positive_)
*/
(__pyx_v_Xf[__pyx_v_start_positive_]) = (__pyx_v_X_data[__pyx_v_k]);
/* "sklearn/tree/_tree.pyx":2035
* start_positive_ -= 1
* Xf[start_positive_] = X_data[k]
* index = index_to_samples[X_indices[k]] # <<<<<<<<<<<<<<
* sparse_swap(index_to_samples, samples, index, start_positive_)
*
*/
__pyx_v_index = (__pyx_v_index_to_samples[(__pyx_v_X_indices[__pyx_v_k])]);
/* "sklearn/tree/_tree.pyx":2036
* Xf[start_positive_] = X_data[k]
* index = index_to_samples[X_indices[k]]
* sparse_swap(index_to_samples, samples, index, start_positive_) # <<<<<<<<<<<<<<
*
*
*/
__pyx_f_7sklearn_4tree_5_tree_sparse_swap(__pyx_v_index_to_samples, __pyx_v_samples, __pyx_v_index, __pyx_v_start_positive_);
goto __pyx_L6;
}
/* "sklearn/tree/_tree.pyx":2039
*
*
* elif X_data[k] < 0: # <<<<<<<<<<<<<<
* Xf[end_negative_] = X_data[k]
* index = index_to_samples[X_indices[k]]
*/
__pyx_t_4 = (((__pyx_v_X_data[__pyx_v_k]) < 0.0) != 0);
if (__pyx_t_4) {
/* "sklearn/tree/_tree.pyx":2040
*
* elif X_data[k] < 0:
* Xf[end_negative_] = X_data[k] # <<<<<<<<<<<<<<
* index = index_to_samples[X_indices[k]]
* sparse_swap(index_to_samples, samples, index, end_negative_)
*/
(__pyx_v_Xf[__pyx_v_end_negative_]) = (__pyx_v_X_data[__pyx_v_k]);
/* "sklearn/tree/_tree.pyx":2041
* elif X_data[k] < 0:
* Xf[end_negative_] = X_data[k]
* index = index_to_samples[X_indices[k]] # <<<<<<<<<<<<<<
* sparse_swap(index_to_samples, samples, index, end_negative_)
* end_negative_ += 1
*/
__pyx_v_index = (__pyx_v_index_to_samples[(__pyx_v_X_indices[__pyx_v_k])]);
/* "sklearn/tree/_tree.pyx":2042
* Xf[end_negative_] = X_data[k]
* index = index_to_samples[X_indices[k]]
* sparse_swap(index_to_samples, samples, index, end_negative_) # <<<<<<<<<<<<<<
* end_negative_ += 1
*
*/
__pyx_f_7sklearn_4tree_5_tree_sparse_swap(__pyx_v_index_to_samples, __pyx_v_samples, __pyx_v_index, __pyx_v_end_negative_);
/* "sklearn/tree/_tree.pyx":2043
* index = index_to_samples[X_indices[k]]
* sparse_swap(index_to_samples, samples, index, end_negative_)
* end_negative_ += 1 # <<<<<<<<<<<<<<
*
* # Returned values
*/
__pyx_v_end_negative_ = (__pyx_v_end_negative_ + 1);
goto __pyx_L6;
}
__pyx_L6:;
goto __pyx_L5;
}
__pyx_L5:;
}
/* "sklearn/tree/_tree.pyx":2046
*
* # Returned values
* end_negative[0] = end_negative_ # <<<<<<<<<<<<<<
* start_positive[0] = start_positive_
*
*/
(__pyx_v_end_negative[0]) = __pyx_v_end_negative_;
/* "sklearn/tree/_tree.pyx":2047
* # Returned values
* end_negative[0] = end_negative_
* start_positive[0] = start_positive_ # <<<<<<<<<<<<<<
*
*
*/
(__pyx_v_start_positive[0]) = __pyx_v_start_positive_;
/* "sklearn/tree/_tree.pyx":2010
*
*
* cdef inline void extract_nnz_index_to_samples(INT32_t* X_indices, # <<<<<<<<<<<<<<
* DTYPE_t* X_data,
* INT32_t indptr_start,
*/
/* function exit code */
}
/* "sklearn/tree/_tree.pyx":2050
*
*
* cdef inline void extract_nnz_binary_search(INT32_t* X_indices, # <<<<<<<<<<<<<<
* DTYPE_t* X_data,
* INT32_t indptr_start,
*/
static CYTHON_INLINE void __pyx_f_7sklearn_4tree_5_tree_extract_nnz_binary_search(__pyx_t_7sklearn_4tree_5_tree_INT32_t *__pyx_v_X_indices, __pyx_t_7sklearn_4tree_5_tree_DTYPE_t *__pyx_v_X_data, __pyx_t_7sklearn_4tree_5_tree_INT32_t __pyx_v_indptr_start, __pyx_t_7sklearn_4tree_5_tree_INT32_t __pyx_v_indptr_end, __pyx_t_7sklearn_4tree_5_tree_SIZE_t *__pyx_v_samples, __pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_v_start, __pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_v_end, __pyx_t_7sklearn_4tree_5_tree_SIZE_t *__pyx_v_index_to_samples, __pyx_t_7sklearn_4tree_5_tree_DTYPE_t *__pyx_v_Xf, __pyx_t_7sklearn_4tree_5_tree_SIZE_t *__pyx_v_end_negative, __pyx_t_7sklearn_4tree_5_tree_SIZE_t *__pyx_v_start_positive, __pyx_t_7sklearn_4tree_5_tree_SIZE_t *__pyx_v_sorted_samples, int *__pyx_v_is_samples_sorted) {
__pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_v_n_samples;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_v_p;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_v_index;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_v_k;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_v_end_negative_;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_v_start_positive_;
int __pyx_t_1;
int __pyx_t_2;
int __pyx_t_3;
/* "sklearn/tree/_tree.pyx":2073
* cdef SIZE_t n_samples
*
* if not is_samples_sorted[0]: # <<<<<<<<<<<<<<
* n_samples = end - start
* memcpy(sorted_samples + start, samples + start,
*/
__pyx_t_1 = ((!((__pyx_v_is_samples_sorted[0]) != 0)) != 0);
if (__pyx_t_1) {
/* "sklearn/tree/_tree.pyx":2074
*
* if not is_samples_sorted[0]:
* n_samples = end - start # <<<<<<<<<<<<<<
* memcpy(sorted_samples + start, samples + start,
* n_samples * sizeof(SIZE_t))
*/
__pyx_v_n_samples = (__pyx_v_end - __pyx_v_start);
/* "sklearn/tree/_tree.pyx":2075
* if not is_samples_sorted[0]:
* n_samples = end - start
* memcpy(sorted_samples + start, samples + start, # <<<<<<<<<<<<<<
* n_samples * sizeof(SIZE_t))
* qsort(sorted_samples + start, n_samples, sizeof(SIZE_t),
*/
memcpy((__pyx_v_sorted_samples + __pyx_v_start), (__pyx_v_samples + __pyx_v_start), (__pyx_v_n_samples * (sizeof(__pyx_t_7sklearn_4tree_5_tree_SIZE_t))));
/* "sklearn/tree/_tree.pyx":2077
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__pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_v_end;
CYTHON_UNUSED __pyx_t_7sklearn_4tree_5_tree_INT32_t *__pyx_v_X_indices;
CYTHON_UNUSED __pyx_t_7sklearn_4tree_5_tree_INT32_t *__pyx_v_X_indptr;
CYTHON_UNUSED __pyx_t_7sklearn_4tree_5_tree_DTYPE_t *__pyx_v_X_data;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t *__pyx_v_features;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t *__pyx_v_constant_features;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_v_n_features;
__pyx_t_7sklearn_4tree_5_tree_DTYPE_t *__pyx_v_Xf;
CYTHON_UNUSED __pyx_t_7sklearn_4tree_5_tree_SIZE_t *__pyx_v_sorted_samples;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t *__pyx_v_index_to_samples;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_v_max_features;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_v_min_samples_leaf;
double __pyx_v_min_weight_leaf;
__pyx_t_7sklearn_4tree_5_tree_UINT32_t *__pyx_v_random_state;
struct __pyx_t_7sklearn_4tree_5_tree_SplitRecord __pyx_v_best;
struct __pyx_t_7sklearn_4tree_5_tree_SplitRecord __pyx_v_current;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_v_f_i;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_v_f_j;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_v_p;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_v_tmp;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_v_n_visited_features;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_v_n_found_constants;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_v_n_drawn_constants;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_v_n_known_constants;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_v_n_total_constants;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_v_p_next;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_v_p_prev;
int __pyx_v_is_samples_sorted;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_v_start_positive;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_v_end_negative;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t *__pyx_t_1;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_t_2;
__pyx_t_7sklearn_4tree_5_tree_INT32_t *__pyx_t_3;
__pyx_t_7sklearn_4tree_5_tree_DTYPE_t *__pyx_t_4;
double __pyx_t_5;
int __pyx_t_6;
int __pyx_t_7;
int __pyx_t_8;
int __pyx_t_9;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_t_10;
/* "sklearn/tree/_tree.pyx":2146
* """
* # Find the best split
* cdef SIZE_t* samples = self.samples # <<<<<<<<<<<<<<
* cdef SIZE_t start = self.start
* cdef SIZE_t end = self.end
*/
__pyx_t_1 = __pyx_v_self->__pyx_base.__pyx_base.samples;
__pyx_v_samples = __pyx_t_1;
/* "sklearn/tree/_tree.pyx":2147
* # Find the best split
* cdef SIZE_t* samples = self.samples
* cdef SIZE_t start = self.start # <<<<<<<<<<<<<<
* cdef SIZE_t end = self.end
*
*/
__pyx_t_2 = __pyx_v_self->__pyx_base.__pyx_base.start;
__pyx_v_start = __pyx_t_2;
/* "sklearn/tree/_tree.pyx":2148
* cdef SIZE_t* samples = self.samples
* cdef SIZE_t start = self.start
* cdef SIZE_t end = self.end # <<<<<<<<<<<<<<
*
* cdef INT32_t* X_indices = self.X_indices
*/
__pyx_t_2 = __pyx_v_self->__pyx_base.__pyx_base.end;
__pyx_v_end = __pyx_t_2;
/* "sklearn/tree/_tree.pyx":2150
* cdef SIZE_t end = self.end
*
* cdef INT32_t* X_indices = self.X_indices # <<<<<<<<<<<<<<
* cdef INT32_t* X_indptr = self.X_indptr
* cdef DTYPE_t* X_data = self.X_data
*/
__pyx_t_3 = __pyx_v_self->__pyx_base.X_indices;
__pyx_v_X_indices = __pyx_t_3;
/* "sklearn/tree/_tree.pyx":2151
*
* cdef INT32_t* X_indices = self.X_indices
* cdef INT32_t* X_indptr = self.X_indptr # <<<<<<<<<<<<<<
* cdef DTYPE_t* X_data = self.X_data
*
*/
__pyx_t_3 = __pyx_v_self->__pyx_base.X_indptr;
__pyx_v_X_indptr = __pyx_t_3;
/* "sklearn/tree/_tree.pyx":2152
* cdef INT32_t* X_indices = self.X_indices
* cdef INT32_t* X_indptr = self.X_indptr
* cdef DTYPE_t* X_data = self.X_data # <<<<<<<<<<<<<<
*
* cdef SIZE_t* features = self.features
*/
__pyx_t_4 = __pyx_v_self->__pyx_base.X_data;
__pyx_v_X_data = __pyx_t_4;
/* "sklearn/tree/_tree.pyx":2154
* cdef DTYPE_t* X_data = self.X_data
*
* cdef SIZE_t* features = self.features # <<<<<<<<<<<<<<
* cdef SIZE_t* constant_features = self.constant_features
* cdef SIZE_t n_features = self.n_features
*/
__pyx_t_1 = __pyx_v_self->__pyx_base.__pyx_base.features;
__pyx_v_features = __pyx_t_1;
/* "sklearn/tree/_tree.pyx":2155
*
* cdef SIZE_t* features = self.features
* cdef SIZE_t* constant_features = self.constant_features # <<<<<<<<<<<<<<
* cdef SIZE_t n_features = self.n_features
*
*/
__pyx_t_1 = __pyx_v_self->__pyx_base.__pyx_base.constant_features;
__pyx_v_constant_features = __pyx_t_1;
/* "sklearn/tree/_tree.pyx":2156
* cdef SIZE_t* features = self.features
* cdef SIZE_t* constant_features = self.constant_features
* cdef SIZE_t n_features = self.n_features # <<<<<<<<<<<<<<
*
* cdef DTYPE_t* Xf = self.feature_values
*/
__pyx_t_2 = __pyx_v_self->__pyx_base.__pyx_base.n_features;
__pyx_v_n_features = __pyx_t_2;
/* "sklearn/tree/_tree.pyx":2158
* cdef SIZE_t n_features = self.n_features
*
* cdef DTYPE_t* Xf = self.feature_values # <<<<<<<<<<<<<<
* cdef SIZE_t* sorted_samples = self.sorted_samples
* cdef SIZE_t* index_to_samples = self.index_to_samples
*/
__pyx_t_4 = __pyx_v_self->__pyx_base.__pyx_base.feature_values;
__pyx_v_Xf = __pyx_t_4;
/* "sklearn/tree/_tree.pyx":2159
*
* cdef DTYPE_t* Xf = self.feature_values
* cdef SIZE_t* sorted_samples = self.sorted_samples # <<<<<<<<<<<<<<
* cdef SIZE_t* index_to_samples = self.index_to_samples
* cdef SIZE_t max_features = self.max_features
*/
__pyx_t_1 = __pyx_v_self->__pyx_base.sorted_samples;
__pyx_v_sorted_samples = __pyx_t_1;
/* "sklearn/tree/_tree.pyx":2160
* cdef DTYPE_t* Xf = self.feature_values
* cdef SIZE_t* sorted_samples = self.sorted_samples
* cdef SIZE_t* index_to_samples = self.index_to_samples # <<<<<<<<<<<<<<
* cdef SIZE_t max_features = self.max_features
* cdef SIZE_t min_samples_leaf = self.min_samples_leaf
*/
__pyx_t_1 = __pyx_v_self->__pyx_base.index_to_samples;
__pyx_v_index_to_samples = __pyx_t_1;
/* "sklearn/tree/_tree.pyx":2161
* cdef SIZE_t* sorted_samples = self.sorted_samples
* cdef SIZE_t* index_to_samples = self.index_to_samples
* cdef SIZE_t max_features = self.max_features # <<<<<<<<<<<<<<
* cdef SIZE_t min_samples_leaf = self.min_samples_leaf
* cdef double min_weight_leaf = self.min_weight_leaf
*/
__pyx_t_2 = __pyx_v_self->__pyx_base.__pyx_base.max_features;
__pyx_v_max_features = __pyx_t_2;
/* "sklearn/tree/_tree.pyx":2162
* cdef SIZE_t* index_to_samples = self.index_to_samples
* cdef SIZE_t max_features = self.max_features
* cdef SIZE_t min_samples_leaf = self.min_samples_leaf # <<<<<<<<<<<<<<
* cdef double min_weight_leaf = self.min_weight_leaf
* cdef UINT32_t* random_state = &self.rand_r_state
*/
__pyx_t_2 = __pyx_v_self->__pyx_base.__pyx_base.min_samples_leaf;
__pyx_v_min_samples_leaf = __pyx_t_2;
/* "sklearn/tree/_tree.pyx":2163
* cdef SIZE_t max_features = self.max_features
* cdef SIZE_t min_samples_leaf = self.min_samples_leaf
* cdef double min_weight_leaf = self.min_weight_leaf # <<<<<<<<<<<<<<
* cdef UINT32_t* random_state = &self.rand_r_state
*
*/
__pyx_t_5 = __pyx_v_self->__pyx_base.__pyx_base.min_weight_leaf;
__pyx_v_min_weight_leaf = __pyx_t_5;
/* "sklearn/tree/_tree.pyx":2164
* cdef SIZE_t min_samples_leaf = self.min_samples_leaf
* cdef double min_weight_leaf = self.min_weight_leaf
* cdef UINT32_t* random_state = &self.rand_r_state # <<<<<<<<<<<<<<
*
* cdef SplitRecord best, current
*/
__pyx_v_random_state = (&__pyx_v_self->__pyx_base.__pyx_base.rand_r_state);
/* "sklearn/tree/_tree.pyx":2167
*
* cdef SplitRecord best, current
* _init_split(&best, end) # <<<<<<<<<<<<<<
*
* cdef SIZE_t f_i = n_features
*/
__pyx_f_7sklearn_4tree_5_tree__init_split((&__pyx_v_best), __pyx_v_end);
/* "sklearn/tree/_tree.pyx":2169
* _init_split(&best, end)
*
* cdef SIZE_t f_i = n_features # <<<<<<<<<<<<<<
* cdef SIZE_t f_j, p, tmp
* cdef SIZE_t n_visited_features = 0
*/
__pyx_v_f_i = __pyx_v_n_features;
/* "sklearn/tree/_tree.pyx":2171
* cdef SIZE_t f_i = n_features
* cdef SIZE_t f_j, p, tmp
* cdef SIZE_t n_visited_features = 0 # <<<<<<<<<<<<<<
* # Number of features discovered to be constant during the split search
* cdef SIZE_t n_found_constants = 0
*/
__pyx_v_n_visited_features = 0;
/* "sklearn/tree/_tree.pyx":2173
* cdef SIZE_t n_visited_features = 0
* # Number of features discovered to be constant during the split search
* cdef SIZE_t n_found_constants = 0 # <<<<<<<<<<<<<<
* # Number of features known to be constant and drawn without replacement
* cdef SIZE_t n_drawn_constants = 0
*/
__pyx_v_n_found_constants = 0;
/* "sklearn/tree/_tree.pyx":2175
* cdef SIZE_t n_found_constants = 0
* # Number of features known to be constant and drawn without replacement
* cdef SIZE_t n_drawn_constants = 0 # <<<<<<<<<<<<<<
* cdef SIZE_t n_known_constants = n_constant_features[0]
* # n_total_constants = n_known_constants + n_found_constants
*/
__pyx_v_n_drawn_constants = 0;
/* "sklearn/tree/_tree.pyx":2176
* # Number of features known to be constant and drawn without replacement
* cdef SIZE_t n_drawn_constants = 0
* cdef SIZE_t n_known_constants = n_constant_features[0] # <<<<<<<<<<<<<<
* # n_total_constants = n_known_constants + n_found_constants
* cdef SIZE_t n_total_constants = n_known_constants
*/
__pyx_v_n_known_constants = (__pyx_v_n_constant_features[0]);
/* "sklearn/tree/_tree.pyx":2178
* cdef SIZE_t n_known_constants = n_constant_features[0]
* # n_total_constants = n_known_constants + n_found_constants
* cdef SIZE_t n_total_constants = n_known_constants # <<<<<<<<<<<<<<
* cdef DTYPE_t current_feature_value
*
*/
__pyx_v_n_total_constants = __pyx_v_n_known_constants;
/* "sklearn/tree/_tree.pyx":2183
* cdef SIZE_t p_next
* cdef SIZE_t p_prev
* cdef bint is_samples_sorted = 0 # indicate is sorted_samples is # <<<<<<<<<<<<<<
* # inititialized
*
*/
__pyx_v_is_samples_sorted = 0;
/* "sklearn/tree/_tree.pyx":2200
* # newly discovered constant features to spare computation on descendant
* # nodes.
* while (f_i > n_total_constants and # Stop early if remaining features # <<<<<<<<<<<<<<
* # are constant
* (n_visited_features < max_features or
*/
while (1) {
__pyx_t_6 = ((__pyx_v_f_i > __pyx_v_n_total_constants) != 0);
if (__pyx_t_6) {
/* "sklearn/tree/_tree.pyx":2202
* while (f_i > n_total_constants and # Stop early if remaining features
* # are constant
* (n_visited_features < max_features or # <<<<<<<<<<<<<<
* # At least one drawn features must be non constant
* n_visited_features <= n_found_constants + n_drawn_constants)):
*/
__pyx_t_7 = ((__pyx_v_n_visited_features < __pyx_v_max_features) != 0);
if (!__pyx_t_7) {
/* "sklearn/tree/_tree.pyx":2204
* (n_visited_features < max_features or
* # At least one drawn features must be non constant
* n_visited_features <= n_found_constants + n_drawn_constants)): # <<<<<<<<<<<<<<
*
* n_visited_features += 1
*/
__pyx_t_8 = ((__pyx_v_n_visited_features <= (__pyx_v_n_found_constants + __pyx_v_n_drawn_constants)) != 0);
__pyx_t_9 = __pyx_t_8;
} else {
__pyx_t_9 = __pyx_t_7;
}
__pyx_t_7 = __pyx_t_9;
} else {
__pyx_t_7 = __pyx_t_6;
}
if (!__pyx_t_7) break;
/* "sklearn/tree/_tree.pyx":2206
* n_visited_features <= n_found_constants + n_drawn_constants)):
*
* n_visited_features += 1 # <<<<<<<<<<<<<<
*
* # Loop invariant: elements of features in
*/
__pyx_v_n_visited_features = (__pyx_v_n_visited_features + 1);
/* "sklearn/tree/_tree.pyx":2220
*
* # Draw a feature at random
* f_j = rand_int(n_drawn_constants, f_i - n_found_constants, # <<<<<<<<<<<<<<
* random_state)
*
*/
__pyx_v_f_j = __pyx_f_7sklearn_4tree_5_tree_rand_int(__pyx_v_n_drawn_constants, (__pyx_v_f_i - __pyx_v_n_found_constants), __pyx_v_random_state);
/* "sklearn/tree/_tree.pyx":2223
* random_state)
*
* if f_j < n_known_constants: # <<<<<<<<<<<<<<
* # f_j in the interval [n_drawn_constants, n_known_constants[
* tmp = features[f_j]
*/
__pyx_t_7 = ((__pyx_v_f_j < __pyx_v_n_known_constants) != 0);
if (__pyx_t_7) {
/* "sklearn/tree/_tree.pyx":2225
* if f_j < n_known_constants:
* # f_j in the interval [n_drawn_constants, n_known_constants[
* tmp = features[f_j] # <<<<<<<<<<<<<<
* features[f_j] = features[n_drawn_constants]
* features[n_drawn_constants] = tmp
*/
__pyx_v_tmp = (__pyx_v_features[__pyx_v_f_j]);
/* "sklearn/tree/_tree.pyx":2226
* # f_j in the interval [n_drawn_constants, n_known_constants[
* tmp = features[f_j]
* features[f_j] = features[n_drawn_constants] # <<<<<<<<<<<<<<
* features[n_drawn_constants] = tmp
*
*/
(__pyx_v_features[__pyx_v_f_j]) = (__pyx_v_features[__pyx_v_n_drawn_constants]);
/* "sklearn/tree/_tree.pyx":2227
* tmp = features[f_j]
* features[f_j] = features[n_drawn_constants]
* features[n_drawn_constants] = tmp # <<<<<<<<<<<<<<
*
* n_drawn_constants += 1
*/
(__pyx_v_features[__pyx_v_n_drawn_constants]) = __pyx_v_tmp;
/* "sklearn/tree/_tree.pyx":2229
* features[n_drawn_constants] = tmp
*
* n_drawn_constants += 1 # <<<<<<<<<<<<<<
*
* else:
*/
__pyx_v_n_drawn_constants = (__pyx_v_n_drawn_constants + 1);
goto __pyx_L5;
}
/*else*/ {
/* "sklearn/tree/_tree.pyx":2233
* else:
* # f_j in the interval [n_known_constants, f_i - n_found_constants[
* f_j += n_found_constants # <<<<<<<<<<<<<<
* # f_j in the interval [n_total_constants, f_i[
*
*/
__pyx_v_f_j = (__pyx_v_f_j + __pyx_v_n_found_constants);
/* "sklearn/tree/_tree.pyx":2236
* # f_j in the interval [n_total_constants, f_i[
*
* current.feature = features[f_j] # <<<<<<<<<<<<<<
* self.extract_nnz(current.feature,
* &end_negative, &start_positive,
*/
__pyx_v_current.feature = (__pyx_v_features[__pyx_v_f_j]);
/* "sklearn/tree/_tree.pyx":2237
*
* current.feature = features[f_j]
* self.extract_nnz(current.feature, # <<<<<<<<<<<<<<
* &end_negative, &start_positive,
* &is_samples_sorted)
*/
__pyx_f_7sklearn_4tree_5_tree_18BaseSparseSplitter_extract_nnz(((struct __pyx_obj_7sklearn_4tree_5_tree_BaseSparseSplitter *)__pyx_v_self), __pyx_v_current.feature, (&__pyx_v_end_negative), (&__pyx_v_start_positive), (&__pyx_v_is_samples_sorted));
/* "sklearn/tree/_tree.pyx":2242
*
* # Sort the positive and negative parts of `Xf`
* sort(Xf + start, samples + start, end_negative - start) # <<<<<<<<<<<<<<
* sort(Xf + start_positive, samples + start_positive,
* end - start_positive)
*/
__pyx_f_7sklearn_4tree_5_tree_sort((__pyx_v_Xf + __pyx_v_start), (__pyx_v_samples + __pyx_v_start), (__pyx_v_end_negative - __pyx_v_start));
/* "sklearn/tree/_tree.pyx":2243
* # Sort the positive and negative parts of `Xf`
* sort(Xf + start, samples + start, end_negative - start)
* sort(Xf + start_positive, samples + start_positive, # <<<<<<<<<<<<<<
* end - start_positive)
*
*/
__pyx_f_7sklearn_4tree_5_tree_sort((__pyx_v_Xf + __pyx_v_start_positive), (__pyx_v_samples + __pyx_v_start_positive), (__pyx_v_end - __pyx_v_start_positive));
/* "sklearn/tree/_tree.pyx":2247
*
* # Update index_to_samples to take into account the sort
* for p in range(start, end_negative): # <<<<<<<<<<<<<<
* index_to_samples[samples[p]] = p
* for p in range(start_positive, end):
*/
__pyx_t_2 = __pyx_v_end_negative;
for (__pyx_t_10 = __pyx_v_start; __pyx_t_10 < __pyx_t_2; __pyx_t_10+=1) {
__pyx_v_p = __pyx_t_10;
/* "sklearn/tree/_tree.pyx":2248
* # Update index_to_samples to take into account the sort
* for p in range(start, end_negative):
* index_to_samples[samples[p]] = p # <<<<<<<<<<<<<<
* for p in range(start_positive, end):
* index_to_samples[samples[p]] = p
*/
(__pyx_v_index_to_samples[(__pyx_v_samples[__pyx_v_p])]) = __pyx_v_p;
}
/* "sklearn/tree/_tree.pyx":2249
* for p in range(start, end_negative):
* index_to_samples[samples[p]] = p
* for p in range(start_positive, end): # <<<<<<<<<<<<<<
* index_to_samples[samples[p]] = p
*
*/
__pyx_t_2 = __pyx_v_end;
for (__pyx_t_10 = __pyx_v_start_positive; __pyx_t_10 < __pyx_t_2; __pyx_t_10+=1) {
__pyx_v_p = __pyx_t_10;
/* "sklearn/tree/_tree.pyx":2250
* index_to_samples[samples[p]] = p
* for p in range(start_positive, end):
* index_to_samples[samples[p]] = p # <<<<<<<<<<<<<<
*
* # Add one or two zeros in Xf, if there is any
*/
(__pyx_v_index_to_samples[(__pyx_v_samples[__pyx_v_p])]) = __pyx_v_p;
}
/* "sklearn/tree/_tree.pyx":2253
*
* # Add one or two zeros in Xf, if there is any
* if end_negative < start_positive: # <<<<<<<<<<<<<<
* start_positive -= 1
* Xf[start_positive] = 0.
*/
__pyx_t_7 = ((__pyx_v_end_negative < __pyx_v_start_positive) != 0);
if (__pyx_t_7) {
/* "sklearn/tree/_tree.pyx":2254
* # Add one or two zeros in Xf, if there is any
* if end_negative < start_positive:
* start_positive -= 1 # <<<<<<<<<<<<<<
* Xf[start_positive] = 0.
*
*/
__pyx_v_start_positive = (__pyx_v_start_positive - 1);
/* "sklearn/tree/_tree.pyx":2255
* if end_negative < start_positive:
* start_positive -= 1
* Xf[start_positive] = 0. # <<<<<<<<<<<<<<
*
* if end_negative != start_positive:
*/
(__pyx_v_Xf[__pyx_v_start_positive]) = 0.;
/* "sklearn/tree/_tree.pyx":2257
* Xf[start_positive] = 0.
*
* if end_negative != start_positive: # <<<<<<<<<<<<<<
* Xf[end_negative] = 0.
* end_negative += 1
*/
__pyx_t_7 = ((__pyx_v_end_negative != __pyx_v_start_positive) != 0);
if (__pyx_t_7) {
/* "sklearn/tree/_tree.pyx":2258
*
* if end_negative != start_positive:
* Xf[end_negative] = 0. # <<<<<<<<<<<<<<
* end_negative += 1
*
*/
(__pyx_v_Xf[__pyx_v_end_negative]) = 0.;
/* "sklearn/tree/_tree.pyx":2259
* if end_negative != start_positive:
* Xf[end_negative] = 0.
* end_negative += 1 # <<<<<<<<<<<<<<
*
* if Xf[end - 1] <= Xf[start] + FEATURE_THRESHOLD:
*/
__pyx_v_end_negative = (__pyx_v_end_negative + 1);
goto __pyx_L11;
}
__pyx_L11:;
goto __pyx_L10;
}
__pyx_L10:;
/* "sklearn/tree/_tree.pyx":2261
* end_negative += 1
*
* if Xf[end - 1] <= Xf[start] + FEATURE_THRESHOLD: # <<<<<<<<<<<<<<
* features[f_j] = features[n_total_constants]
* features[n_total_constants] = current.feature
*/
__pyx_t_7 = (((__pyx_v_Xf[(__pyx_v_end - 1)]) <= ((__pyx_v_Xf[__pyx_v_start]) + __pyx_v_7sklearn_4tree_5_tree_FEATURE_THRESHOLD)) != 0);
if (__pyx_t_7) {
/* "sklearn/tree/_tree.pyx":2262
*
* if Xf[end - 1] <= Xf[start] + FEATURE_THRESHOLD:
* features[f_j] = features[n_total_constants] # <<<<<<<<<<<<<<
* features[n_total_constants] = current.feature
*
*/
(__pyx_v_features[__pyx_v_f_j]) = (__pyx_v_features[__pyx_v_n_total_constants]);
/* "sklearn/tree/_tree.pyx":2263
* if Xf[end - 1] <= Xf[start] + FEATURE_THRESHOLD:
* features[f_j] = features[n_total_constants]
* features[n_total_constants] = current.feature # <<<<<<<<<<<<<<
*
* n_found_constants += 1
*/
__pyx_t_2 = __pyx_v_current.feature;
(__pyx_v_features[__pyx_v_n_total_constants]) = __pyx_t_2;
/* "sklearn/tree/_tree.pyx":2265
* features[n_total_constants] = current.feature
*
* n_found_constants += 1 # <<<<<<<<<<<<<<
* n_total_constants += 1
*
*/
__pyx_v_n_found_constants = (__pyx_v_n_found_constants + 1);
/* "sklearn/tree/_tree.pyx":2266
*
* n_found_constants += 1
* n_total_constants += 1 # <<<<<<<<<<<<<<
*
* else:
*/
__pyx_v_n_total_constants = (__pyx_v_n_total_constants + 1);
goto __pyx_L12;
}
/*else*/ {
/* "sklearn/tree/_tree.pyx":2269
*
* else:
* f_i -= 1 # <<<<<<<<<<<<<<
* features[f_i], features[f_j] = features[f_j], features[f_i]
*
*/
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/* "sklearn/tree/_tree.pyx":2270
* else:
* f_i -= 1
* features[f_i], features[f_j] = features[f_j], features[f_i] # <<<<<<<<<<<<<<
*
* # Evaluate all splits
*/
__pyx_t_2 = (__pyx_v_features[__pyx_v_f_j]);
__pyx_t_10 = (__pyx_v_features[__pyx_v_f_i]);
(__pyx_v_features[__pyx_v_f_i]) = __pyx_t_2;
(__pyx_v_features[__pyx_v_f_j]) = __pyx_t_10;
/* "sklearn/tree/_tree.pyx":2273
*
* # Evaluate all splits
* self.criterion.reset() # <<<<<<<<<<<<<<
* p = start
*
*/
((struct __pyx_vtabstruct_7sklearn_4tree_5_tree_Criterion *)__pyx_v_self->__pyx_base.__pyx_base.criterion->__pyx_vtab)->reset(__pyx_v_self->__pyx_base.__pyx_base.criterion);
/* "sklearn/tree/_tree.pyx":2274
* # Evaluate all splits
* self.criterion.reset()
* p = start # <<<<<<<<<<<<<<
*
* while p < end:
*/
__pyx_v_p = __pyx_v_start;
/* "sklearn/tree/_tree.pyx":2276
* p = start
*
* while p < end: # <<<<<<<<<<<<<<
* if p + 1 != end_negative:
* p_next = p + 1
*/
while (1) {
__pyx_t_7 = ((__pyx_v_p < __pyx_v_end) != 0);
if (!__pyx_t_7) break;
/* "sklearn/tree/_tree.pyx":2277
*
* while p < end:
* if p + 1 != end_negative: # <<<<<<<<<<<<<<
* p_next = p + 1
* else:
*/
__pyx_t_7 = (((__pyx_v_p + 1) != __pyx_v_end_negative) != 0);
if (__pyx_t_7) {
/* "sklearn/tree/_tree.pyx":2278
* while p < end:
* if p + 1 != end_negative:
* p_next = p + 1 # <<<<<<<<<<<<<<
* else:
* p_next = start_positive
*/
__pyx_v_p_next = (__pyx_v_p + 1);
goto __pyx_L15;
}
/*else*/ {
/* "sklearn/tree/_tree.pyx":2280
* p_next = p + 1
* else:
* p_next = start_positive # <<<<<<<<<<<<<<
*
* while (p_next < end and
*/
__pyx_v_p_next = __pyx_v_start_positive;
}
__pyx_L15:;
/* "sklearn/tree/_tree.pyx":2282
* p_next = start_positive
*
* while (p_next < end and # <<<<<<<<<<<<<<
* Xf[p_next] <= Xf[p] + FEATURE_THRESHOLD):
* p = p_next
*/
while (1) {
__pyx_t_7 = ((__pyx_v_p_next < __pyx_v_end) != 0);
if (__pyx_t_7) {
/* "sklearn/tree/_tree.pyx":2283
*
* while (p_next < end and
* Xf[p_next] <= Xf[p] + FEATURE_THRESHOLD): # <<<<<<<<<<<<<<
* p = p_next
* if p + 1 != end_negative:
*/
__pyx_t_6 = (((__pyx_v_Xf[__pyx_v_p_next]) <= ((__pyx_v_Xf[__pyx_v_p]) + __pyx_v_7sklearn_4tree_5_tree_FEATURE_THRESHOLD)) != 0);
__pyx_t_9 = __pyx_t_6;
} else {
__pyx_t_9 = __pyx_t_7;
}
if (!__pyx_t_9) break;
/* "sklearn/tree/_tree.pyx":2284
* while (p_next < end and
* Xf[p_next] <= Xf[p] + FEATURE_THRESHOLD):
* p = p_next # <<<<<<<<<<<<<<
* if p + 1 != end_negative:
* p_next = p + 1
*/
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/* "sklearn/tree/_tree.pyx":2285
* Xf[p_next] <= Xf[p] + FEATURE_THRESHOLD):
* p = p_next
* if p + 1 != end_negative: # <<<<<<<<<<<<<<
* p_next = p + 1
* else:
*/
__pyx_t_9 = (((__pyx_v_p + 1) != __pyx_v_end_negative) != 0);
if (__pyx_t_9) {
/* "sklearn/tree/_tree.pyx":2286
* p = p_next
* if p + 1 != end_negative:
* p_next = p + 1 # <<<<<<<<<<<<<<
* else:
* p_next = start_positive
*/
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goto __pyx_L18;
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/*else*/ {
/* "sklearn/tree/_tree.pyx":2288
* p_next = p + 1
* else:
* p_next = start_positive # <<<<<<<<<<<<<<
*
*
*/
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__pyx_L18:;
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* # (p_next >= end) or (X[samples[p_next], current.feature] >
* # X[samples[p], current.feature])
* p_prev = p # <<<<<<<<<<<<<<
* p = p_next
* # (p >= end) or (X[samples[p], current.feature] >
*/
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/* "sklearn/tree/_tree.pyx":2294
* # X[samples[p], current.feature])
* p_prev = p
* p = p_next # <<<<<<<<<<<<<<
* # (p >= end) or (X[samples[p], current.feature] >
* # X[samples[p_prev], current.feature])
*/
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/* "sklearn/tree/_tree.pyx":2299
*
*
* if p < end: # <<<<<<<<<<<<<<
* current.pos = p
*
*/
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/* "sklearn/tree/_tree.pyx":2300
*
* if p < end:
* current.pos = p # <<<<<<<<<<<<<<
*
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*/
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*
* # Reject if min_samples_leaf is not guaranteed
* if (((current.pos - start) < min_samples_leaf) or # <<<<<<<<<<<<<<
* ((end - current.pos) < min_samples_leaf)):
* continue
*/
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/* "sklearn/tree/_tree.pyx":2304
* # Reject if min_samples_leaf is not guaranteed
* if (((current.pos - start) < min_samples_leaf) or
* ((end - current.pos) < min_samples_leaf)): # <<<<<<<<<<<<<<
* continue
*
*/
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}
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/* "sklearn/tree/_tree.pyx":2305
* if (((current.pos - start) < min_samples_leaf) or
* ((end - current.pos) < min_samples_leaf)):
* continue # <<<<<<<<<<<<<<
*
* self.criterion.update(current.pos)
*/
goto __pyx_L13_continue;
}
/* "sklearn/tree/_tree.pyx":2307
* continue
*
* self.criterion.update(current.pos) # <<<<<<<<<<<<<<
*
* # Reject if min_weight_leaf is not satisfied
*/
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/* "sklearn/tree/_tree.pyx":2310
*
* # Reject if min_weight_leaf is not satisfied
* if ((self.criterion.weighted_n_left < min_weight_leaf) or # <<<<<<<<<<<<<<
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* continue
*/
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/* "sklearn/tree/_tree.pyx":2311
* # Reject if min_weight_leaf is not satisfied
* if ((self.criterion.weighted_n_left < min_weight_leaf) or
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*/
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/* "sklearn/tree/_tree.pyx":2312
* if ((self.criterion.weighted_n_left < min_weight_leaf) or
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goto __pyx_L13_continue;
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/* "sklearn/tree/_tree.pyx":2314
* continue
*
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*/
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/* "sklearn/tree/_tree.pyx":2315
*
* current.improvement = self.criterion.impurity_improvement(impurity)
* if current.improvement > best.improvement: # <<<<<<<<<<<<<<
* self.criterion.children_impurity(&current.impurity_left,
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*/
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/* "sklearn/tree/_tree.pyx":2316
* current.improvement = self.criterion.impurity_improvement(impurity)
* if current.improvement > best.improvement:
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* &current.impurity_right)
*
*/
((struct __pyx_vtabstruct_7sklearn_4tree_5_tree_Criterion *)__pyx_v_self->__pyx_base.__pyx_base.criterion->__pyx_vtab)->children_impurity(__pyx_v_self->__pyx_base.__pyx_base.criterion, (&__pyx_v_current.impurity_left), (&__pyx_v_current.impurity_right));
/* "sklearn/tree/_tree.pyx":2319
* &current.impurity_right)
*
* current.threshold = (Xf[p_prev] + Xf[p]) / 2.0 # <<<<<<<<<<<<<<
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/* "sklearn/tree/_tree.pyx":2320
*
* current.threshold = (Xf[p_prev] + Xf[p]) / 2.0
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*
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/* "sklearn/tree/_tree.pyx":2321
* current.threshold = (Xf[p_prev] + Xf[p]) / 2.0
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*
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__pyx_L23:;
/* "sklearn/tree/_tree.pyx":2323
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*
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__pyx_L22:;
goto __pyx_L19;
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__pyx_L19:;
__pyx_L13_continue:;
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__pyx_L12:;
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__pyx_L5:;
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*
* # Reorganize into samples[start:best.pos] + samples[best.pos:end]
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/* "sklearn/tree/_tree.pyx":2327
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/* "sklearn/tree/_tree.pyx":2330
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*
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*
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__pyx_L24:;
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* # element in features[:n_known_constants] must be preserved for sibling
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*
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*
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* cdef void node_split(self, double impurity, SplitRecord* split,
*/
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/* "sklearn/tree/_tree.pyx":2352
*
* def __reduce__(self):
* return (RandomSparseSplitter, (self.criterion, # <<<<<<<<<<<<<<
* self.max_features,
* self.min_samples_leaf,
*/
__pyx_t_3 = PyTuple_New(3); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2352; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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/* "sklearn/tree/_tree.pyx":2351
* """Splitter for finding a random split, using the sparse data."""
*
* def __reduce__(self): # <<<<<<<<<<<<<<
* return (RandomSparseSplitter, (self.criterion,
* self.max_features,
*/
/* function exit code */
__pyx_L1_error:;
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/* "sklearn/tree/_tree.pyx":2358
* self.random_state), self.__getstate__())
*
* cdef void node_split(self, double impurity, SplitRecord* split, # <<<<<<<<<<<<<<
* SIZE_t* n_constant_features) nogil:
* """Find a random split on node samples[start:end], using sparse
*/
static void __pyx_f_7sklearn_4tree_5_tree_20RandomSparseSplitter_node_split(struct __pyx_obj_7sklearn_4tree_5_tree_RandomSparseSplitter *__pyx_v_self, double __pyx_v_impurity, struct __pyx_t_7sklearn_4tree_5_tree_SplitRecord *__pyx_v_split, __pyx_t_7sklearn_4tree_5_tree_SIZE_t *__pyx_v_n_constant_features) {
CYTHON_UNUSED __pyx_t_7sklearn_4tree_5_tree_SIZE_t *__pyx_v_samples;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_v_start;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_v_end;
CYTHON_UNUSED __pyx_t_7sklearn_4tree_5_tree_INT32_t *__pyx_v_X_indices;
CYTHON_UNUSED __pyx_t_7sklearn_4tree_5_tree_INT32_t *__pyx_v_X_indptr;
CYTHON_UNUSED __pyx_t_7sklearn_4tree_5_tree_DTYPE_t *__pyx_v_X_data;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t *__pyx_v_features;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t *__pyx_v_constant_features;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_v_n_features;
__pyx_t_7sklearn_4tree_5_tree_DTYPE_t *__pyx_v_Xf;
CYTHON_UNUSED __pyx_t_7sklearn_4tree_5_tree_SIZE_t *__pyx_v_sorted_samples;
CYTHON_UNUSED __pyx_t_7sklearn_4tree_5_tree_SIZE_t *__pyx_v_index_to_samples;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_v_max_features;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_v_min_samples_leaf;
double __pyx_v_min_weight_leaf;
__pyx_t_7sklearn_4tree_5_tree_UINT32_t *__pyx_v_random_state;
struct __pyx_t_7sklearn_4tree_5_tree_SplitRecord __pyx_v_best;
struct __pyx_t_7sklearn_4tree_5_tree_SplitRecord __pyx_v_current;
__pyx_t_7sklearn_4tree_5_tree_DTYPE_t __pyx_v_current_feature_value;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_v_f_i;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_v_f_j;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_v_p;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_v_tmp;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_v_n_visited_features;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_v_n_found_constants;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_v_n_drawn_constants;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_v_n_known_constants;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_v_n_total_constants;
__pyx_t_7sklearn_4tree_5_tree_DTYPE_t __pyx_v_min_feature_value;
__pyx_t_7sklearn_4tree_5_tree_DTYPE_t __pyx_v_max_feature_value;
int __pyx_v_is_samples_sorted;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_v_start_positive;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_v_end_negative;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t *__pyx_t_1;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_t_2;
__pyx_t_7sklearn_4tree_5_tree_INT32_t *__pyx_t_3;
__pyx_t_7sklearn_4tree_5_tree_DTYPE_t *__pyx_t_4;
double __pyx_t_5;
int __pyx_t_6;
int __pyx_t_7;
int __pyx_t_8;
int __pyx_t_9;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_t_10;
/* "sklearn/tree/_tree.pyx":2364
* """
* # Find the best split
* cdef SIZE_t* samples = self.samples # <<<<<<<<<<<<<<
* cdef SIZE_t start = self.start
* cdef SIZE_t end = self.end
*/
__pyx_t_1 = __pyx_v_self->__pyx_base.__pyx_base.samples;
__pyx_v_samples = __pyx_t_1;
/* "sklearn/tree/_tree.pyx":2365
* # Find the best split
* cdef SIZE_t* samples = self.samples
* cdef SIZE_t start = self.start # <<<<<<<<<<<<<<
* cdef SIZE_t end = self.end
*
*/
__pyx_t_2 = __pyx_v_self->__pyx_base.__pyx_base.start;
__pyx_v_start = __pyx_t_2;
/* "sklearn/tree/_tree.pyx":2366
* cdef SIZE_t* samples = self.samples
* cdef SIZE_t start = self.start
* cdef SIZE_t end = self.end # <<<<<<<<<<<<<<
*
* cdef INT32_t* X_indices = self.X_indices
*/
__pyx_t_2 = __pyx_v_self->__pyx_base.__pyx_base.end;
__pyx_v_end = __pyx_t_2;
/* "sklearn/tree/_tree.pyx":2368
* cdef SIZE_t end = self.end
*
* cdef INT32_t* X_indices = self.X_indices # <<<<<<<<<<<<<<
* cdef INT32_t* X_indptr = self.X_indptr
* cdef DTYPE_t* X_data = self.X_data
*/
__pyx_t_3 = __pyx_v_self->__pyx_base.X_indices;
__pyx_v_X_indices = __pyx_t_3;
/* "sklearn/tree/_tree.pyx":2369
*
* cdef INT32_t* X_indices = self.X_indices
* cdef INT32_t* X_indptr = self.X_indptr # <<<<<<<<<<<<<<
* cdef DTYPE_t* X_data = self.X_data
*
*/
__pyx_t_3 = __pyx_v_self->__pyx_base.X_indptr;
__pyx_v_X_indptr = __pyx_t_3;
/* "sklearn/tree/_tree.pyx":2370
* cdef INT32_t* X_indices = self.X_indices
* cdef INT32_t* X_indptr = self.X_indptr
* cdef DTYPE_t* X_data = self.X_data # <<<<<<<<<<<<<<
*
* cdef SIZE_t* features = self.features
*/
__pyx_t_4 = __pyx_v_self->__pyx_base.X_data;
__pyx_v_X_data = __pyx_t_4;
/* "sklearn/tree/_tree.pyx":2372
* cdef DTYPE_t* X_data = self.X_data
*
* cdef SIZE_t* features = self.features # <<<<<<<<<<<<<<
* cdef SIZE_t* constant_features = self.constant_features
* cdef SIZE_t n_features = self.n_features
*/
__pyx_t_1 = __pyx_v_self->__pyx_base.__pyx_base.features;
__pyx_v_features = __pyx_t_1;
/* "sklearn/tree/_tree.pyx":2373
*
* cdef SIZE_t* features = self.features
* cdef SIZE_t* constant_features = self.constant_features # <<<<<<<<<<<<<<
* cdef SIZE_t n_features = self.n_features
*
*/
__pyx_t_1 = __pyx_v_self->__pyx_base.__pyx_base.constant_features;
__pyx_v_constant_features = __pyx_t_1;
/* "sklearn/tree/_tree.pyx":2374
* cdef SIZE_t* features = self.features
* cdef SIZE_t* constant_features = self.constant_features
* cdef SIZE_t n_features = self.n_features # <<<<<<<<<<<<<<
*
* cdef DTYPE_t* Xf = self.feature_values
*/
__pyx_t_2 = __pyx_v_self->__pyx_base.__pyx_base.n_features;
__pyx_v_n_features = __pyx_t_2;
/* "sklearn/tree/_tree.pyx":2376
* cdef SIZE_t n_features = self.n_features
*
* cdef DTYPE_t* Xf = self.feature_values # <<<<<<<<<<<<<<
* cdef SIZE_t* sorted_samples = self.sorted_samples
* cdef SIZE_t* index_to_samples = self.index_to_samples
*/
__pyx_t_4 = __pyx_v_self->__pyx_base.__pyx_base.feature_values;
__pyx_v_Xf = __pyx_t_4;
/* "sklearn/tree/_tree.pyx":2377
*
* cdef DTYPE_t* Xf = self.feature_values
* cdef SIZE_t* sorted_samples = self.sorted_samples # <<<<<<<<<<<<<<
* cdef SIZE_t* index_to_samples = self.index_to_samples
* cdef SIZE_t max_features = self.max_features
*/
__pyx_t_1 = __pyx_v_self->__pyx_base.sorted_samples;
__pyx_v_sorted_samples = __pyx_t_1;
/* "sklearn/tree/_tree.pyx":2378
* cdef DTYPE_t* Xf = self.feature_values
* cdef SIZE_t* sorted_samples = self.sorted_samples
* cdef SIZE_t* index_to_samples = self.index_to_samples # <<<<<<<<<<<<<<
* cdef SIZE_t max_features = self.max_features
* cdef SIZE_t min_samples_leaf = self.min_samples_leaf
*/
__pyx_t_1 = __pyx_v_self->__pyx_base.index_to_samples;
__pyx_v_index_to_samples = __pyx_t_1;
/* "sklearn/tree/_tree.pyx":2379
* cdef SIZE_t* sorted_samples = self.sorted_samples
* cdef SIZE_t* index_to_samples = self.index_to_samples
* cdef SIZE_t max_features = self.max_features # <<<<<<<<<<<<<<
* cdef SIZE_t min_samples_leaf = self.min_samples_leaf
* cdef double min_weight_leaf = self.min_weight_leaf
*/
__pyx_t_2 = __pyx_v_self->__pyx_base.__pyx_base.max_features;
__pyx_v_max_features = __pyx_t_2;
/* "sklearn/tree/_tree.pyx":2380
* cdef SIZE_t* index_to_samples = self.index_to_samples
* cdef SIZE_t max_features = self.max_features
* cdef SIZE_t min_samples_leaf = self.min_samples_leaf # <<<<<<<<<<<<<<
* cdef double min_weight_leaf = self.min_weight_leaf
* cdef UINT32_t* random_state = &self.rand_r_state
*/
__pyx_t_2 = __pyx_v_self->__pyx_base.__pyx_base.min_samples_leaf;
__pyx_v_min_samples_leaf = __pyx_t_2;
/* "sklearn/tree/_tree.pyx":2381
* cdef SIZE_t max_features = self.max_features
* cdef SIZE_t min_samples_leaf = self.min_samples_leaf
* cdef double min_weight_leaf = self.min_weight_leaf # <<<<<<<<<<<<<<
* cdef UINT32_t* random_state = &self.rand_r_state
*
*/
__pyx_t_5 = __pyx_v_self->__pyx_base.__pyx_base.min_weight_leaf;
__pyx_v_min_weight_leaf = __pyx_t_5;
/* "sklearn/tree/_tree.pyx":2382
* cdef SIZE_t min_samples_leaf = self.min_samples_leaf
* cdef double min_weight_leaf = self.min_weight_leaf
* cdef UINT32_t* random_state = &self.rand_r_state # <<<<<<<<<<<<<<
*
* cdef SplitRecord best, current
*/
__pyx_v_random_state = (&__pyx_v_self->__pyx_base.__pyx_base.rand_r_state);
/* "sklearn/tree/_tree.pyx":2385
*
* cdef SplitRecord best, current
* _init_split(&best, end) # <<<<<<<<<<<<<<
*
* cdef DTYPE_t current_feature_value
*/
__pyx_f_7sklearn_4tree_5_tree__init_split((&__pyx_v_best), __pyx_v_end);
/* "sklearn/tree/_tree.pyx":2389
* cdef DTYPE_t current_feature_value
*
* cdef SIZE_t f_i = n_features # <<<<<<<<<<<<<<
* cdef SIZE_t f_j, p, tmp
* cdef SIZE_t n_visited_features = 0
*/
__pyx_v_f_i = __pyx_v_n_features;
/* "sklearn/tree/_tree.pyx":2391
* cdef SIZE_t f_i = n_features
* cdef SIZE_t f_j, p, tmp
* cdef SIZE_t n_visited_features = 0 # <<<<<<<<<<<<<<
* # Number of features discovered to be constant during the split search
* cdef SIZE_t n_found_constants = 0
*/
__pyx_v_n_visited_features = 0;
/* "sklearn/tree/_tree.pyx":2393
* cdef SIZE_t n_visited_features = 0
* # Number of features discovered to be constant during the split search
* cdef SIZE_t n_found_constants = 0 # <<<<<<<<<<<<<<
* # Number of features known to be constant and drawn without replacement
* cdef SIZE_t n_drawn_constants = 0
*/
__pyx_v_n_found_constants = 0;
/* "sklearn/tree/_tree.pyx":2395
* cdef SIZE_t n_found_constants = 0
* # Number of features known to be constant and drawn without replacement
* cdef SIZE_t n_drawn_constants = 0 # <<<<<<<<<<<<<<
* cdef SIZE_t n_known_constants = n_constant_features[0]
* # n_total_constants = n_known_constants + n_found_constants
*/
__pyx_v_n_drawn_constants = 0;
/* "sklearn/tree/_tree.pyx":2396
* # Number of features known to be constant and drawn without replacement
* cdef SIZE_t n_drawn_constants = 0
* cdef SIZE_t n_known_constants = n_constant_features[0] # <<<<<<<<<<<<<<
* # n_total_constants = n_known_constants + n_found_constants
* cdef SIZE_t n_total_constants = n_known_constants
*/
__pyx_v_n_known_constants = (__pyx_v_n_constant_features[0]);
/* "sklearn/tree/_tree.pyx":2398
* cdef SIZE_t n_known_constants = n_constant_features[0]
* # n_total_constants = n_known_constants + n_found_constants
* cdef SIZE_t n_total_constants = n_known_constants # <<<<<<<<<<<<<<
* cdef SIZE_t partition_end
*
*/
__pyx_v_n_total_constants = __pyx_v_n_known_constants;
/* "sklearn/tree/_tree.pyx":2404
* cdef DTYPE_t max_feature_value
*
* cdef bint is_samples_sorted = 0 # indicate that sorted_samples is # <<<<<<<<<<<<<<
* # inititialized
*
*/
__pyx_v_is_samples_sorted = 0;
/* "sklearn/tree/_tree.pyx":2421
* # newly discovered constant features to spare computation on descendant
* # nodes.
* while (f_i > n_total_constants and # Stop early if remaining features # <<<<<<<<<<<<<<
* # are constant
* (n_visited_features < max_features or
*/
while (1) {
__pyx_t_6 = ((__pyx_v_f_i > __pyx_v_n_total_constants) != 0);
if (__pyx_t_6) {
/* "sklearn/tree/_tree.pyx":2423
* while (f_i > n_total_constants and # Stop early if remaining features
* # are constant
* (n_visited_features < max_features or # <<<<<<<<<<<<<<
* # At least one drawn features must be non constant
* n_visited_features <= n_found_constants + n_drawn_constants)):
*/
__pyx_t_7 = ((__pyx_v_n_visited_features < __pyx_v_max_features) != 0);
if (!__pyx_t_7) {
/* "sklearn/tree/_tree.pyx":2425
* (n_visited_features < max_features or
* # At least one drawn features must be non constant
* n_visited_features <= n_found_constants + n_drawn_constants)): # <<<<<<<<<<<<<<
*
* n_visited_features += 1
*/
__pyx_t_8 = ((__pyx_v_n_visited_features <= (__pyx_v_n_found_constants + __pyx_v_n_drawn_constants)) != 0);
__pyx_t_9 = __pyx_t_8;
} else {
__pyx_t_9 = __pyx_t_7;
}
__pyx_t_7 = __pyx_t_9;
} else {
__pyx_t_7 = __pyx_t_6;
}
if (!__pyx_t_7) break;
/* "sklearn/tree/_tree.pyx":2427
* n_visited_features <= n_found_constants + n_drawn_constants)):
*
* n_visited_features += 1 # <<<<<<<<<<<<<<
*
* # Loop invariant: elements of features in
*/
__pyx_v_n_visited_features = (__pyx_v_n_visited_features + 1);
/* "sklearn/tree/_tree.pyx":2441
*
* # Draw a feature at random
* f_j = rand_int(n_drawn_constants, f_i - n_found_constants, # <<<<<<<<<<<<<<
* random_state)
*
*/
__pyx_v_f_j = __pyx_f_7sklearn_4tree_5_tree_rand_int(__pyx_v_n_drawn_constants, (__pyx_v_f_i - __pyx_v_n_found_constants), __pyx_v_random_state);
/* "sklearn/tree/_tree.pyx":2444
* random_state)
*
* if f_j < n_known_constants: # <<<<<<<<<<<<<<
* # f_j in the interval [n_drawn_constants, n_known_constants[
* tmp = features[f_j]
*/
__pyx_t_7 = ((__pyx_v_f_j < __pyx_v_n_known_constants) != 0);
if (__pyx_t_7) {
/* "sklearn/tree/_tree.pyx":2446
* if f_j < n_known_constants:
* # f_j in the interval [n_drawn_constants, n_known_constants[
* tmp = features[f_j] # <<<<<<<<<<<<<<
* features[f_j] = features[n_drawn_constants]
* features[n_drawn_constants] = tmp
*/
__pyx_v_tmp = (__pyx_v_features[__pyx_v_f_j]);
/* "sklearn/tree/_tree.pyx":2447
* # f_j in the interval [n_drawn_constants, n_known_constants[
* tmp = features[f_j]
* features[f_j] = features[n_drawn_constants] # <<<<<<<<<<<<<<
* features[n_drawn_constants] = tmp
*
*/
(__pyx_v_features[__pyx_v_f_j]) = (__pyx_v_features[__pyx_v_n_drawn_constants]);
/* "sklearn/tree/_tree.pyx":2448
* tmp = features[f_j]
* features[f_j] = features[n_drawn_constants]
* features[n_drawn_constants] = tmp # <<<<<<<<<<<<<<
*
* n_drawn_constants += 1
*/
(__pyx_v_features[__pyx_v_n_drawn_constants]) = __pyx_v_tmp;
/* "sklearn/tree/_tree.pyx":2450
* features[n_drawn_constants] = tmp
*
* n_drawn_constants += 1 # <<<<<<<<<<<<<<
*
* else:
*/
__pyx_v_n_drawn_constants = (__pyx_v_n_drawn_constants + 1);
goto __pyx_L5;
}
/*else*/ {
/* "sklearn/tree/_tree.pyx":2454
* else:
* # f_j in the interval [n_known_constants, f_i - n_found_constants[
* f_j += n_found_constants # <<<<<<<<<<<<<<
* # f_j in the interval [n_total_constants, f_i[
*
*/
__pyx_v_f_j = (__pyx_v_f_j + __pyx_v_n_found_constants);
/* "sklearn/tree/_tree.pyx":2457
* # f_j in the interval [n_total_constants, f_i[
*
* current.feature = features[f_j] # <<<<<<<<<<<<<<
*
* self.extract_nnz(current.feature,
*/
__pyx_v_current.feature = (__pyx_v_features[__pyx_v_f_j]);
/* "sklearn/tree/_tree.pyx":2459
* current.feature = features[f_j]
*
* self.extract_nnz(current.feature, # <<<<<<<<<<<<<<
* &end_negative, &start_positive,
* &is_samples_sorted)
*/
__pyx_f_7sklearn_4tree_5_tree_18BaseSparseSplitter_extract_nnz(((struct __pyx_obj_7sklearn_4tree_5_tree_BaseSparseSplitter *)__pyx_v_self), __pyx_v_current.feature, (&__pyx_v_end_negative), (&__pyx_v_start_positive), (&__pyx_v_is_samples_sorted));
/* "sklearn/tree/_tree.pyx":2464
*
* # Add one or two zeros in Xf, if there is any
* if end_negative < start_positive: # <<<<<<<<<<<<<<
* start_positive -= 1
* Xf[start_positive] = 0.
*/
__pyx_t_7 = ((__pyx_v_end_negative < __pyx_v_start_positive) != 0);
if (__pyx_t_7) {
/* "sklearn/tree/_tree.pyx":2465
* # Add one or two zeros in Xf, if there is any
* if end_negative < start_positive:
* start_positive -= 1 # <<<<<<<<<<<<<<
* Xf[start_positive] = 0.
*
*/
__pyx_v_start_positive = (__pyx_v_start_positive - 1);
/* "sklearn/tree/_tree.pyx":2466
* if end_negative < start_positive:
* start_positive -= 1
* Xf[start_positive] = 0. # <<<<<<<<<<<<<<
*
* if end_negative != start_positive:
*/
(__pyx_v_Xf[__pyx_v_start_positive]) = 0.;
/* "sklearn/tree/_tree.pyx":2468
* Xf[start_positive] = 0.
*
* if end_negative != start_positive: # <<<<<<<<<<<<<<
* Xf[end_negative] = 0.
* end_negative += 1
*/
__pyx_t_7 = ((__pyx_v_end_negative != __pyx_v_start_positive) != 0);
if (__pyx_t_7) {
/* "sklearn/tree/_tree.pyx":2469
*
* if end_negative != start_positive:
* Xf[end_negative] = 0. # <<<<<<<<<<<<<<
* end_negative += 1
*
*/
(__pyx_v_Xf[__pyx_v_end_negative]) = 0.;
/* "sklearn/tree/_tree.pyx":2470
* if end_negative != start_positive:
* Xf[end_negative] = 0.
* end_negative += 1 # <<<<<<<<<<<<<<
*
* # Find min, max in Xf[start:end_negative]
*/
__pyx_v_end_negative = (__pyx_v_end_negative + 1);
goto __pyx_L7;
}
__pyx_L7:;
goto __pyx_L6;
}
__pyx_L6:;
/* "sklearn/tree/_tree.pyx":2473
*
* # Find min, max in Xf[start:end_negative]
* min_feature_value = Xf[start] # <<<<<<<<<<<<<<
* max_feature_value = min_feature_value
*
*/
__pyx_v_min_feature_value = (__pyx_v_Xf[__pyx_v_start]);
/* "sklearn/tree/_tree.pyx":2474
* # Find min, max in Xf[start:end_negative]
* min_feature_value = Xf[start]
* max_feature_value = min_feature_value # <<<<<<<<<<<<<<
*
* for p in range(start, end_negative):
*/
__pyx_v_max_feature_value = __pyx_v_min_feature_value;
/* "sklearn/tree/_tree.pyx":2476
* max_feature_value = min_feature_value
*
* for p in range(start, end_negative): # <<<<<<<<<<<<<<
* current_feature_value = Xf[p]
*
*/
__pyx_t_2 = __pyx_v_end_negative;
for (__pyx_t_10 = __pyx_v_start; __pyx_t_10 < __pyx_t_2; __pyx_t_10+=1) {
__pyx_v_p = __pyx_t_10;
/* "sklearn/tree/_tree.pyx":2477
*
* for p in range(start, end_negative):
* current_feature_value = Xf[p] # <<<<<<<<<<<<<<
*
* if current_feature_value < min_feature_value:
*/
__pyx_v_current_feature_value = (__pyx_v_Xf[__pyx_v_p]);
/* "sklearn/tree/_tree.pyx":2479
* current_feature_value = Xf[p]
*
* if current_feature_value < min_feature_value: # <<<<<<<<<<<<<<
* min_feature_value = current_feature_value
* elif current_feature_value > max_feature_value:
*/
__pyx_t_7 = ((__pyx_v_current_feature_value < __pyx_v_min_feature_value) != 0);
if (__pyx_t_7) {
/* "sklearn/tree/_tree.pyx":2480
*
* if current_feature_value < min_feature_value:
* min_feature_value = current_feature_value # <<<<<<<<<<<<<<
* elif current_feature_value > max_feature_value:
* max_feature_value = current_feature_value
*/
__pyx_v_min_feature_value = __pyx_v_current_feature_value;
goto __pyx_L10;
}
/* "sklearn/tree/_tree.pyx":2481
* if current_feature_value < min_feature_value:
* min_feature_value = current_feature_value
* elif current_feature_value > max_feature_value: # <<<<<<<<<<<<<<
* max_feature_value = current_feature_value
*
*/
__pyx_t_7 = ((__pyx_v_current_feature_value > __pyx_v_max_feature_value) != 0);
if (__pyx_t_7) {
/* "sklearn/tree/_tree.pyx":2482
* min_feature_value = current_feature_value
* elif current_feature_value > max_feature_value:
* max_feature_value = current_feature_value # <<<<<<<<<<<<<<
*
* # Update min, max given Xf[start_positive:end]
*/
__pyx_v_max_feature_value = __pyx_v_current_feature_value;
goto __pyx_L10;
}
__pyx_L10:;
}
/* "sklearn/tree/_tree.pyx":2485
*
* # Update min, max given Xf[start_positive:end]
* for p in range(start_positive, end): # <<<<<<<<<<<<<<
* current_feature_value = Xf[p]
*
*/
__pyx_t_2 = __pyx_v_end;
for (__pyx_t_10 = __pyx_v_start_positive; __pyx_t_10 < __pyx_t_2; __pyx_t_10+=1) {
__pyx_v_p = __pyx_t_10;
/* "sklearn/tree/_tree.pyx":2486
* # Update min, max given Xf[start_positive:end]
* for p in range(start_positive, end):
* current_feature_value = Xf[p] # <<<<<<<<<<<<<<
*
* if current_feature_value < min_feature_value:
*/
__pyx_v_current_feature_value = (__pyx_v_Xf[__pyx_v_p]);
/* "sklearn/tree/_tree.pyx":2488
* current_feature_value = Xf[p]
*
* if current_feature_value < min_feature_value: # <<<<<<<<<<<<<<
* min_feature_value = current_feature_value
* elif current_feature_value > max_feature_value:
*/
__pyx_t_7 = ((__pyx_v_current_feature_value < __pyx_v_min_feature_value) != 0);
if (__pyx_t_7) {
/* "sklearn/tree/_tree.pyx":2489
*
* if current_feature_value < min_feature_value:
* min_feature_value = current_feature_value # <<<<<<<<<<<<<<
* elif current_feature_value > max_feature_value:
* max_feature_value = current_feature_value
*/
__pyx_v_min_feature_value = __pyx_v_current_feature_value;
goto __pyx_L13;
}
/* "sklearn/tree/_tree.pyx":2490
* if current_feature_value < min_feature_value:
* min_feature_value = current_feature_value
* elif current_feature_value > max_feature_value: # <<<<<<<<<<<<<<
* max_feature_value = current_feature_value
*
*/
__pyx_t_7 = ((__pyx_v_current_feature_value > __pyx_v_max_feature_value) != 0);
if (__pyx_t_7) {
/* "sklearn/tree/_tree.pyx":2491
* min_feature_value = current_feature_value
* elif current_feature_value > max_feature_value:
* max_feature_value = current_feature_value # <<<<<<<<<<<<<<
*
* if max_feature_value <= min_feature_value + FEATURE_THRESHOLD:
*/
__pyx_v_max_feature_value = __pyx_v_current_feature_value;
goto __pyx_L13;
}
__pyx_L13:;
}
/* "sklearn/tree/_tree.pyx":2493
* max_feature_value = current_feature_value
*
* if max_feature_value <= min_feature_value + FEATURE_THRESHOLD: # <<<<<<<<<<<<<<
* features[f_j] = features[n_total_constants]
* features[n_total_constants] = current.feature
*/
__pyx_t_7 = ((__pyx_v_max_feature_value <= (__pyx_v_min_feature_value + __pyx_v_7sklearn_4tree_5_tree_FEATURE_THRESHOLD)) != 0);
if (__pyx_t_7) {
/* "sklearn/tree/_tree.pyx":2494
*
* if max_feature_value <= min_feature_value + FEATURE_THRESHOLD:
* features[f_j] = features[n_total_constants] # <<<<<<<<<<<<<<
* features[n_total_constants] = current.feature
*
*/
(__pyx_v_features[__pyx_v_f_j]) = (__pyx_v_features[__pyx_v_n_total_constants]);
/* "sklearn/tree/_tree.pyx":2495
* if max_feature_value <= min_feature_value + FEATURE_THRESHOLD:
* features[f_j] = features[n_total_constants]
* features[n_total_constants] = current.feature # <<<<<<<<<<<<<<
*
* n_found_constants += 1
*/
__pyx_t_2 = __pyx_v_current.feature;
(__pyx_v_features[__pyx_v_n_total_constants]) = __pyx_t_2;
/* "sklearn/tree/_tree.pyx":2497
* features[n_total_constants] = current.feature
*
* n_found_constants += 1 # <<<<<<<<<<<<<<
* n_total_constants += 1
*
*/
__pyx_v_n_found_constants = (__pyx_v_n_found_constants + 1);
/* "sklearn/tree/_tree.pyx":2498
*
* n_found_constants += 1
* n_total_constants += 1 # <<<<<<<<<<<<<<
*
* else:
*/
__pyx_v_n_total_constants = (__pyx_v_n_total_constants + 1);
goto __pyx_L14;
}
/*else*/ {
/* "sklearn/tree/_tree.pyx":2501
*
* else:
* f_i -= 1 # <<<<<<<<<<<<<<
* features[f_i], features[f_j] = features[f_j], features[f_i]
*
*/
__pyx_v_f_i = (__pyx_v_f_i - 1);
/* "sklearn/tree/_tree.pyx":2502
* else:
* f_i -= 1
* features[f_i], features[f_j] = features[f_j], features[f_i] # <<<<<<<<<<<<<<
*
* # Draw a random threshold
*/
__pyx_t_2 = (__pyx_v_features[__pyx_v_f_j]);
__pyx_t_10 = (__pyx_v_features[__pyx_v_f_i]);
(__pyx_v_features[__pyx_v_f_i]) = __pyx_t_2;
(__pyx_v_features[__pyx_v_f_j]) = __pyx_t_10;
/* "sklearn/tree/_tree.pyx":2505
*
* # Draw a random threshold
* current.threshold = rand_uniform(min_feature_value, # <<<<<<<<<<<<<<
* max_feature_value,
* random_state)
*/
__pyx_v_current.threshold = __pyx_f_7sklearn_4tree_5_tree_rand_uniform(__pyx_v_min_feature_value, __pyx_v_max_feature_value, __pyx_v_random_state);
/* "sklearn/tree/_tree.pyx":2509
* random_state)
*
* if current.threshold == max_feature_value: # <<<<<<<<<<<<<<
* current.threshold = min_feature_value
*
*/
__pyx_t_7 = ((__pyx_v_current.threshold == __pyx_v_max_feature_value) != 0);
if (__pyx_t_7) {
/* "sklearn/tree/_tree.pyx":2510
*
* if current.threshold == max_feature_value:
* current.threshold = min_feature_value # <<<<<<<<<<<<<<
*
* # Partition
*/
__pyx_v_current.threshold = __pyx_v_min_feature_value;
goto __pyx_L15;
}
__pyx_L15:;
/* "sklearn/tree/_tree.pyx":2513
*
* # Partition
* current.pos = self._partition(current.threshold, # <<<<<<<<<<<<<<
* end_negative,
* start_positive,
*/
__pyx_v_current.pos = __pyx_f_7sklearn_4tree_5_tree_18BaseSparseSplitter__partition(((struct __pyx_obj_7sklearn_4tree_5_tree_BaseSparseSplitter *)__pyx_v_self), __pyx_v_current.threshold, __pyx_v_end_negative, __pyx_v_start_positive, (__pyx_v_start_positive + ((__pyx_v_Xf[__pyx_v_start_positive]) == 0.)));
/* "sklearn/tree/_tree.pyx":2520
*
* # Reject if min_samples_leaf is not guaranteed
* if (((current.pos - start) < min_samples_leaf) or # <<<<<<<<<<<<<<
* ((end - current.pos) < min_samples_leaf)):
* continue
*/
__pyx_t_7 = (((__pyx_v_current.pos - __pyx_v_start) < __pyx_v_min_samples_leaf) != 0);
if (!__pyx_t_7) {
/* "sklearn/tree/_tree.pyx":2521
* # Reject if min_samples_leaf is not guaranteed
* if (((current.pos - start) < min_samples_leaf) or
* ((end - current.pos) < min_samples_leaf)): # <<<<<<<<<<<<<<
* continue
*
*/
__pyx_t_6 = (((__pyx_v_end - __pyx_v_current.pos) < __pyx_v_min_samples_leaf) != 0);
__pyx_t_9 = __pyx_t_6;
} else {
__pyx_t_9 = __pyx_t_7;
}
if (__pyx_t_9) {
/* "sklearn/tree/_tree.pyx":2522
* if (((current.pos - start) < min_samples_leaf) or
* ((end - current.pos) < min_samples_leaf)):
* continue # <<<<<<<<<<<<<<
*
* # Evaluate split
*/
goto __pyx_L3_continue;
}
/* "sklearn/tree/_tree.pyx":2525
*
* # Evaluate split
* self.criterion.reset() # <<<<<<<<<<<<<<
* self.criterion.update(current.pos)
*
*/
((struct __pyx_vtabstruct_7sklearn_4tree_5_tree_Criterion *)__pyx_v_self->__pyx_base.__pyx_base.criterion->__pyx_vtab)->reset(__pyx_v_self->__pyx_base.__pyx_base.criterion);
/* "sklearn/tree/_tree.pyx":2526
* # Evaluate split
* self.criterion.reset()
* self.criterion.update(current.pos) # <<<<<<<<<<<<<<
*
* # Reject if min_weight_leaf is not satisfied
*/
((struct __pyx_vtabstruct_7sklearn_4tree_5_tree_Criterion *)__pyx_v_self->__pyx_base.__pyx_base.criterion->__pyx_vtab)->update(__pyx_v_self->__pyx_base.__pyx_base.criterion, __pyx_v_current.pos);
/* "sklearn/tree/_tree.pyx":2529
*
* # Reject if min_weight_leaf is not satisfied
* if ((self.criterion.weighted_n_left < min_weight_leaf) or # <<<<<<<<<<<<<<
* (self.criterion.weighted_n_right < min_weight_leaf)):
* continue
*/
__pyx_t_9 = ((__pyx_v_self->__pyx_base.__pyx_base.criterion->weighted_n_left < __pyx_v_min_weight_leaf) != 0);
if (!__pyx_t_9) {
/* "sklearn/tree/_tree.pyx":2530
* # Reject if min_weight_leaf is not satisfied
* if ((self.criterion.weighted_n_left < min_weight_leaf) or
* (self.criterion.weighted_n_right < min_weight_leaf)): # <<<<<<<<<<<<<<
* continue
*
*/
__pyx_t_7 = ((__pyx_v_self->__pyx_base.__pyx_base.criterion->weighted_n_right < __pyx_v_min_weight_leaf) != 0);
__pyx_t_6 = __pyx_t_7;
} else {
__pyx_t_6 = __pyx_t_9;
}
if (__pyx_t_6) {
/* "sklearn/tree/_tree.pyx":2531
* if ((self.criterion.weighted_n_left < min_weight_leaf) or
* (self.criterion.weighted_n_right < min_weight_leaf)):
* continue # <<<<<<<<<<<<<<
*
* current.improvement = self.criterion.impurity_improvement(impurity)
*/
goto __pyx_L3_continue;
}
/* "sklearn/tree/_tree.pyx":2533
* continue
*
* current.improvement = self.criterion.impurity_improvement(impurity) # <<<<<<<<<<<<<<
*
* if current.improvement > best.improvement:
*/
__pyx_v_current.improvement = ((struct __pyx_vtabstruct_7sklearn_4tree_5_tree_Criterion *)__pyx_v_self->__pyx_base.__pyx_base.criterion->__pyx_vtab)->impurity_improvement(__pyx_v_self->__pyx_base.__pyx_base.criterion, __pyx_v_impurity);
/* "sklearn/tree/_tree.pyx":2535
* current.improvement = self.criterion.impurity_improvement(impurity)
*
* if current.improvement > best.improvement: # <<<<<<<<<<<<<<
* self.criterion.children_impurity(&current.impurity_left,
* &current.impurity_right)
*/
__pyx_t_6 = ((__pyx_v_current.improvement > __pyx_v_best.improvement) != 0);
if (__pyx_t_6) {
/* "sklearn/tree/_tree.pyx":2536
*
* if current.improvement > best.improvement:
* self.criterion.children_impurity(&current.impurity_left, # <<<<<<<<<<<<<<
* &current.impurity_right)
* best = current
*/
((struct __pyx_vtabstruct_7sklearn_4tree_5_tree_Criterion *)__pyx_v_self->__pyx_base.__pyx_base.criterion->__pyx_vtab)->children_impurity(__pyx_v_self->__pyx_base.__pyx_base.criterion, (&__pyx_v_current.impurity_left), (&__pyx_v_current.impurity_right));
/* "sklearn/tree/_tree.pyx":2538
* self.criterion.children_impurity(&current.impurity_left,
* &current.impurity_right)
* best = current # <<<<<<<<<<<<<<
*
* # Reorganize into samples[start:best.pos] + samples[best.pos:end]
*/
__pyx_v_best = __pyx_v_current;
goto __pyx_L18;
}
__pyx_L18:;
}
__pyx_L14:;
}
__pyx_L5:;
__pyx_L3_continue:;
}
/* "sklearn/tree/_tree.pyx":2541
*
* # Reorganize into samples[start:best.pos] + samples[best.pos:end]
* if best.pos < end and current.feature != best.feature: # <<<<<<<<<<<<<<
* self.extract_nnz(best.feature, &end_negative, &start_positive,
* &is_samples_sorted)
*/
__pyx_t_6 = ((__pyx_v_best.pos < __pyx_v_end) != 0);
if (__pyx_t_6) {
__pyx_t_9 = ((__pyx_v_current.feature != __pyx_v_best.feature) != 0);
__pyx_t_7 = __pyx_t_9;
} else {
__pyx_t_7 = __pyx_t_6;
}
if (__pyx_t_7) {
/* "sklearn/tree/_tree.pyx":2542
* # Reorganize into samples[start:best.pos] + samples[best.pos:end]
* if best.pos < end and current.feature != best.feature:
* self.extract_nnz(best.feature, &end_negative, &start_positive, # <<<<<<<<<<<<<<
* &is_samples_sorted)
*
*/
__pyx_f_7sklearn_4tree_5_tree_18BaseSparseSplitter_extract_nnz(((struct __pyx_obj_7sklearn_4tree_5_tree_BaseSparseSplitter *)__pyx_v_self), __pyx_v_best.feature, (&__pyx_v_end_negative), (&__pyx_v_start_positive), (&__pyx_v_is_samples_sorted));
/* "sklearn/tree/_tree.pyx":2545
* &is_samples_sorted)
*
* self._partition(best.threshold, end_negative, start_positive, # <<<<<<<<<<<<<<
* best.pos)
*
*/
__pyx_f_7sklearn_4tree_5_tree_18BaseSparseSplitter__partition(((struct __pyx_obj_7sklearn_4tree_5_tree_BaseSparseSplitter *)__pyx_v_self), __pyx_v_best.threshold, __pyx_v_end_negative, __pyx_v_start_positive, __pyx_v_best.pos);
goto __pyx_L19;
}
__pyx_L19:;
/* "sklearn/tree/_tree.pyx":2551
* # element in features[:n_known_constants] must be preserved for sibling
* # and child nodes
* memcpy(features, constant_features, sizeof(SIZE_t) * n_known_constants) # <<<<<<<<<<<<<<
*
* # Copy newly found constant features
*/
memcpy(__pyx_v_features, __pyx_v_constant_features, ((sizeof(__pyx_t_7sklearn_4tree_5_tree_SIZE_t)) * __pyx_v_n_known_constants));
/* "sklearn/tree/_tree.pyx":2554
*
* # Copy newly found constant features
* memcpy(constant_features + n_known_constants, # <<<<<<<<<<<<<<
* features + n_known_constants,
* sizeof(SIZE_t) * n_found_constants)
*/
memcpy((__pyx_v_constant_features + __pyx_v_n_known_constants), (__pyx_v_features + __pyx_v_n_known_constants), ((sizeof(__pyx_t_7sklearn_4tree_5_tree_SIZE_t)) * __pyx_v_n_found_constants));
/* "sklearn/tree/_tree.pyx":2559
*
* # Return values
* split[0] = best # <<<<<<<<<<<<<<
* n_constant_features[0] = n_total_constants
*
*/
(__pyx_v_split[0]) = __pyx_v_best;
/* "sklearn/tree/_tree.pyx":2560
* # Return values
* split[0] = best
* n_constant_features[0] = n_total_constants # <<<<<<<<<<<<<<
*
*
*/
(__pyx_v_n_constant_features[0]) = __pyx_v_n_total_constants;
/* "sklearn/tree/_tree.pyx":2358
* self.random_state), self.__getstate__())
*
* cdef void node_split(self, double impurity, SplitRecord* split, # <<<<<<<<<<<<<<
* SIZE_t* n_constant_features) nogil:
* """Find a random split on node samples[start:end], using sparse
*/
/* function exit code */
}
/* "sklearn/tree/_tree.pyx":2569
* """Interface for different tree building strategies. """
*
* cpdef build(self, Tree tree, object X, np.ndarray y, # <<<<<<<<<<<<<<
* np.ndarray sample_weight=None):
* """Build a decision tree from the training set (X, y)."""
*/
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/* "sklearn/tree/_tree.pyx":2570
*
* cpdef build(self, Tree tree, object X, np.ndarray y,
* np.ndarray sample_weight=None): # <<<<<<<<<<<<<<
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/* "sklearn/tree/_tree.pyx":2569
* """Interface for different tree building strategies. """
*
* cpdef build(self, Tree tree, object X, np.ndarray y, # <<<<<<<<<<<<<<
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* """Build a decision tree from the training set (X, y)."""
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/* "sklearn/tree/_tree.pyx":2624
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*
* cdef DOUBLE_t* sample_weight_ptr = NULL # <<<<<<<<<<<<<<
* if sample_weight is not None:
* sample_weight_ptr = <DOUBLE_t*> sample_weight.data
*/
__pyx_v_sample_weight_ptr = NULL;
/* "sklearn/tree/_tree.pyx":2625
*
* cdef DOUBLE_t* sample_weight_ptr = NULL
* if sample_weight is not None: # <<<<<<<<<<<<<<
* sample_weight_ptr = <DOUBLE_t*> sample_weight.data
*
*/
__pyx_t_7 = (((PyObject *)__pyx_v_sample_weight) != Py_None);
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/* "sklearn/tree/_tree.pyx":2626
* cdef DOUBLE_t* sample_weight_ptr = NULL
* if sample_weight is not None:
* sample_weight_ptr = <DOUBLE_t*> sample_weight.data # <<<<<<<<<<<<<<
*
* # Initial capacity
*/
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goto __pyx_L5;
}
__pyx_L5:;
/* "sklearn/tree/_tree.pyx":2631
* cdef int init_capacity
*
* if tree.max_depth <= 10: # <<<<<<<<<<<<<<
* init_capacity = (2 ** (tree.max_depth + 1)) - 1
* else:
*/
__pyx_t_8 = ((__pyx_v_tree->max_depth <= 10) != 0);
if (__pyx_t_8) {
/* "sklearn/tree/_tree.pyx":2632
*
* if tree.max_depth <= 10:
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* else:
* init_capacity = 2047
*/
__pyx_v_init_capacity = (__Pyx_pow_long(2, (__pyx_v_tree->max_depth + 1)) - 1);
goto __pyx_L6;
}
/*else*/ {
/* "sklearn/tree/_tree.pyx":2634
* init_capacity = (2 ** (tree.max_depth + 1)) - 1
* else:
* init_capacity = 2047 # <<<<<<<<<<<<<<
*
* tree._resize(init_capacity)
*/
__pyx_v_init_capacity = 2047;
}
__pyx_L6:;
/* "sklearn/tree/_tree.pyx":2636
* init_capacity = 2047
*
* tree._resize(init_capacity) # <<<<<<<<<<<<<<
*
* # Parameters
*/
((struct __pyx_vtabstruct_7sklearn_4tree_5_tree_Tree *)__pyx_v_tree->__pyx_vtab)->_resize(__pyx_v_tree, __pyx_v_init_capacity); if (unlikely(PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2636; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
/* "sklearn/tree/_tree.pyx":2639
*
* # Parameters
* cdef Splitter splitter = self.splitter # <<<<<<<<<<<<<<
* cdef SIZE_t max_depth = self.max_depth
* cdef SIZE_t min_samples_leaf = self.min_samples_leaf
*/
__pyx_t_1 = ((PyObject *)__pyx_v_self->__pyx_base.splitter);
__Pyx_INCREF(__pyx_t_1);
__pyx_v_splitter = ((struct __pyx_obj_7sklearn_4tree_5_tree_Splitter *)__pyx_t_1);
__pyx_t_1 = 0;
/* "sklearn/tree/_tree.pyx":2640
* # Parameters
* cdef Splitter splitter = self.splitter
* cdef SIZE_t max_depth = self.max_depth # <<<<<<<<<<<<<<
* cdef SIZE_t min_samples_leaf = self.min_samples_leaf
* cdef double min_weight_leaf = self.min_weight_leaf
*/
__pyx_t_9 = __pyx_v_self->__pyx_base.max_depth;
__pyx_v_max_depth = __pyx_t_9;
/* "sklearn/tree/_tree.pyx":2641
* cdef Splitter splitter = self.splitter
* cdef SIZE_t max_depth = self.max_depth
* cdef SIZE_t min_samples_leaf = self.min_samples_leaf # <<<<<<<<<<<<<<
* cdef double min_weight_leaf = self.min_weight_leaf
* cdef SIZE_t min_samples_split = self.min_samples_split
*/
__pyx_t_9 = __pyx_v_self->__pyx_base.min_samples_leaf;
__pyx_v_min_samples_leaf = __pyx_t_9;
/* "sklearn/tree/_tree.pyx":2642
* cdef SIZE_t max_depth = self.max_depth
* cdef SIZE_t min_samples_leaf = self.min_samples_leaf
* cdef double min_weight_leaf = self.min_weight_leaf # <<<<<<<<<<<<<<
* cdef SIZE_t min_samples_split = self.min_samples_split
*
*/
__pyx_t_10 = __pyx_v_self->__pyx_base.min_weight_leaf;
__pyx_v_min_weight_leaf = __pyx_t_10;
/* "sklearn/tree/_tree.pyx":2643
* cdef SIZE_t min_samples_leaf = self.min_samples_leaf
* cdef double min_weight_leaf = self.min_weight_leaf
* cdef SIZE_t min_samples_split = self.min_samples_split # <<<<<<<<<<<<<<
*
* # Recursive partition (without actual recursion)
*/
__pyx_t_9 = __pyx_v_self->__pyx_base.min_samples_split;
__pyx_v_min_samples_split = __pyx_t_9;
/* "sklearn/tree/_tree.pyx":2646
*
* # Recursive partition (without actual recursion)
* splitter.init(X, y, sample_weight_ptr) # <<<<<<<<<<<<<<
*
* cdef SIZE_t start
*/
((struct __pyx_vtabstruct_7sklearn_4tree_5_tree_Splitter *)__pyx_v_splitter->__pyx_vtab)->init(__pyx_v_splitter, __pyx_v_X, __pyx_v_y, __pyx_v_sample_weight_ptr);
/* "sklearn/tree/_tree.pyx":2653
* cdef SIZE_t parent
* cdef bint is_left
* cdef SIZE_t n_node_samples = splitter.n_samples # <<<<<<<<<<<<<<
* cdef double weighted_n_samples = splitter.weighted_n_samples
* cdef double weighted_n_node_samples
*/
__pyx_t_9 = __pyx_v_splitter->n_samples;
__pyx_v_n_node_samples = __pyx_t_9;
/* "sklearn/tree/_tree.pyx":2654
* cdef bint is_left
* cdef SIZE_t n_node_samples = splitter.n_samples
* cdef double weighted_n_samples = splitter.weighted_n_samples # <<<<<<<<<<<<<<
* cdef double weighted_n_node_samples
* cdef SplitRecord split
*/
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__pyx_v_weighted_n_samples = __pyx_t_10;
/* "sklearn/tree/_tree.pyx":2660
*
* cdef double threshold
* cdef double impurity = INFINITY # <<<<<<<<<<<<<<
* cdef SIZE_t n_constant_features
* cdef bint is_leaf
*/
__pyx_v_impurity = __pyx_v_7sklearn_4tree_5_tree_INFINITY;
/* "sklearn/tree/_tree.pyx":2663
* cdef SIZE_t n_constant_features
* cdef bint is_leaf
* cdef bint first = 1 # <<<<<<<<<<<<<<
* cdef SIZE_t max_depth_seen = -1
* cdef int rc = 0
*/
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/* "sklearn/tree/_tree.pyx":2664
* cdef bint is_leaf
* cdef bint first = 1
* cdef SIZE_t max_depth_seen = -1 # <<<<<<<<<<<<<<
* cdef int rc = 0
*
*/
__pyx_v_max_depth_seen = -1;
/* "sklearn/tree/_tree.pyx":2665
* cdef bint first = 1
* cdef SIZE_t max_depth_seen = -1
* cdef int rc = 0 # <<<<<<<<<<<<<<
*
* cdef Stack stack = Stack(INITIAL_STACK_SIZE)
*/
__pyx_v_rc = 0;
/* "sklearn/tree/_tree.pyx":2667
* cdef int rc = 0
*
* cdef Stack stack = Stack(INITIAL_STACK_SIZE) # <<<<<<<<<<<<<<
* cdef StackRecord stack_record
*
*/
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PyTuple_SET_ITEM(__pyx_t_4, 0, __pyx_t_1);
__Pyx_GIVEREF(__pyx_t_1);
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__Pyx_GOTREF(__pyx_t_1);
__Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0;
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__pyx_t_1 = 0;
/* "sklearn/tree/_tree.pyx":2671
*
* # push root node onto stack
* rc = stack.push(0, n_node_samples, 0, _TREE_UNDEFINED, 0, INFINITY, 0) # <<<<<<<<<<<<<<
* if rc == -1:
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*/
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/* "sklearn/tree/_tree.pyx":2672
* # push root node onto stack
* rc = stack.push(0, n_node_samples, 0, _TREE_UNDEFINED, 0, INFINITY, 0)
* if rc == -1: # <<<<<<<<<<<<<<
* # got return code -1 - out-of-memory
* raise MemoryError()
*/
__pyx_t_8 = ((__pyx_v_rc == -1) != 0);
if (__pyx_t_8) {
/* "sklearn/tree/_tree.pyx":2674
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* # got return code -1 - out-of-memory
* raise MemoryError() # <<<<<<<<<<<<<<
*
* with nogil:
*/
PyErr_NoMemory(); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2674; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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/* "sklearn/tree/_tree.pyx":2676
* raise MemoryError()
*
* with nogil: # <<<<<<<<<<<<<<
* while not stack.is_empty():
* stack.pop(&stack_record)
*/
{
#ifdef WITH_THREAD
PyThreadState *_save;
Py_UNBLOCK_THREADS
#endif
/*try:*/ {
/* "sklearn/tree/_tree.pyx":2677
*
* with nogil:
* while not stack.is_empty(): # <<<<<<<<<<<<<<
* stack.pop(&stack_record)
*
*/
while (1) {
__pyx_t_8 = ((!(((struct __pyx_vtabstruct_7sklearn_4tree_6_utils_Stack *)__pyx_v_stack->__pyx_vtab)->is_empty(__pyx_v_stack) != 0)) != 0);
if (!__pyx_t_8) break;
/* "sklearn/tree/_tree.pyx":2678
* with nogil:
* while not stack.is_empty():
* stack.pop(&stack_record) # <<<<<<<<<<<<<<
*
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*/
((struct __pyx_vtabstruct_7sklearn_4tree_6_utils_Stack *)__pyx_v_stack->__pyx_vtab)->pop(__pyx_v_stack, (&__pyx_v_stack_record));
/* "sklearn/tree/_tree.pyx":2680
* stack.pop(&stack_record)
*
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* end = stack_record.end
* depth = stack_record.depth
*/
__pyx_t_11 = __pyx_v_stack_record.start;
__pyx_v_start = __pyx_t_11;
/* "sklearn/tree/_tree.pyx":2681
*
* start = stack_record.start
* end = stack_record.end # <<<<<<<<<<<<<<
* depth = stack_record.depth
* parent = stack_record.parent
*/
__pyx_t_11 = __pyx_v_stack_record.end;
__pyx_v_end = __pyx_t_11;
/* "sklearn/tree/_tree.pyx":2682
* start = stack_record.start
* end = stack_record.end
* depth = stack_record.depth # <<<<<<<<<<<<<<
* parent = stack_record.parent
* is_left = stack_record.is_left
*/
__pyx_t_11 = __pyx_v_stack_record.depth;
__pyx_v_depth = __pyx_t_11;
/* "sklearn/tree/_tree.pyx":2683
* end = stack_record.end
* depth = stack_record.depth
* parent = stack_record.parent # <<<<<<<<<<<<<<
* is_left = stack_record.is_left
* impurity = stack_record.impurity
*/
__pyx_t_11 = __pyx_v_stack_record.parent;
__pyx_v_parent = __pyx_t_11;
/* "sklearn/tree/_tree.pyx":2684
* depth = stack_record.depth
* parent = stack_record.parent
* is_left = stack_record.is_left # <<<<<<<<<<<<<<
* impurity = stack_record.impurity
* n_constant_features = stack_record.n_constant_features
*/
__pyx_t_8 = __pyx_v_stack_record.is_left;
__pyx_v_is_left = __pyx_t_8;
/* "sklearn/tree/_tree.pyx":2685
* parent = stack_record.parent
* is_left = stack_record.is_left
* impurity = stack_record.impurity # <<<<<<<<<<<<<<
* n_constant_features = stack_record.n_constant_features
*
*/
__pyx_t_10 = __pyx_v_stack_record.impurity;
__pyx_v_impurity = __pyx_t_10;
/* "sklearn/tree/_tree.pyx":2686
* is_left = stack_record.is_left
* impurity = stack_record.impurity
* n_constant_features = stack_record.n_constant_features # <<<<<<<<<<<<<<
*
* n_node_samples = end - start
*/
__pyx_t_11 = __pyx_v_stack_record.n_constant_features;
__pyx_v_n_constant_features = __pyx_t_11;
/* "sklearn/tree/_tree.pyx":2688
* n_constant_features = stack_record.n_constant_features
*
* n_node_samples = end - start # <<<<<<<<<<<<<<
* splitter.node_reset(start, end, &weighted_n_node_samples)
*
*/
__pyx_v_n_node_samples = (__pyx_v_end - __pyx_v_start);
/* "sklearn/tree/_tree.pyx":2689
*
* n_node_samples = end - start
* splitter.node_reset(start, end, &weighted_n_node_samples) # <<<<<<<<<<<<<<
*
* is_leaf = ((depth >= max_depth) or
*/
((struct __pyx_vtabstruct_7sklearn_4tree_5_tree_Splitter *)__pyx_v_splitter->__pyx_vtab)->node_reset(__pyx_v_splitter, __pyx_v_start, __pyx_v_end, (&__pyx_v_weighted_n_node_samples));
/* "sklearn/tree/_tree.pyx":2691
* splitter.node_reset(start, end, &weighted_n_node_samples)
*
* is_leaf = ((depth >= max_depth) or # <<<<<<<<<<<<<<
* (n_node_samples < min_samples_split) or
* (n_node_samples < 2 * min_samples_leaf) or
*/
__pyx_t_8 = (__pyx_v_depth >= __pyx_v_max_depth);
if (!__pyx_t_8) {
/* "sklearn/tree/_tree.pyx":2692
*
* is_leaf = ((depth >= max_depth) or
* (n_node_samples < min_samples_split) or # <<<<<<<<<<<<<<
* (n_node_samples < 2 * min_samples_leaf) or
* (weighted_n_node_samples < min_weight_leaf))
*/
__pyx_t_7 = (__pyx_v_n_node_samples < __pyx_v_min_samples_split);
if (!__pyx_t_7) {
/* "sklearn/tree/_tree.pyx":2693
* is_leaf = ((depth >= max_depth) or
* (n_node_samples < min_samples_split) or
* (n_node_samples < 2 * min_samples_leaf) or # <<<<<<<<<<<<<<
* (weighted_n_node_samples < min_weight_leaf))
*
*/
__pyx_t_12 = (__pyx_v_n_node_samples < (2 * __pyx_v_min_samples_leaf));
if (!__pyx_t_12) {
/* "sklearn/tree/_tree.pyx":2694
* (n_node_samples < min_samples_split) or
* (n_node_samples < 2 * min_samples_leaf) or
* (weighted_n_node_samples < min_weight_leaf)) # <<<<<<<<<<<<<<
*
* if first:
*/
__pyx_t_13 = (__pyx_v_weighted_n_node_samples < __pyx_v_min_weight_leaf);
__pyx_t_14 = __pyx_t_13;
} else {
__pyx_t_14 = __pyx_t_12;
}
__pyx_t_12 = __pyx_t_14;
} else {
__pyx_t_12 = __pyx_t_7;
}
__pyx_t_7 = __pyx_t_12;
} else {
__pyx_t_7 = __pyx_t_8;
}
__pyx_v_is_leaf = __pyx_t_7;
/* "sklearn/tree/_tree.pyx":2696
* (weighted_n_node_samples < min_weight_leaf))
*
* if first: # <<<<<<<<<<<<<<
* impurity = splitter.node_impurity()
* first = 0
*/
__pyx_t_7 = (__pyx_v_first != 0);
if (__pyx_t_7) {
/* "sklearn/tree/_tree.pyx":2697
*
* if first:
* impurity = splitter.node_impurity() # <<<<<<<<<<<<<<
* first = 0
*
*/
__pyx_v_impurity = ((struct __pyx_vtabstruct_7sklearn_4tree_5_tree_Splitter *)__pyx_v_splitter->__pyx_vtab)->node_impurity(__pyx_v_splitter);
/* "sklearn/tree/_tree.pyx":2698
* if first:
* impurity = splitter.node_impurity()
* first = 0 # <<<<<<<<<<<<<<
*
* is_leaf = is_leaf or (impurity <= MIN_IMPURITY_SPLIT)
*/
__pyx_v_first = 0;
goto __pyx_L13;
}
__pyx_L13:;
/* "sklearn/tree/_tree.pyx":2700
* first = 0
*
* is_leaf = is_leaf or (impurity <= MIN_IMPURITY_SPLIT) # <<<<<<<<<<<<<<
*
* if not is_leaf:
*/
if (!__pyx_v_is_leaf) {
__pyx_t_7 = (__pyx_v_impurity <= __pyx_v_7sklearn_4tree_5_tree_MIN_IMPURITY_SPLIT);
__pyx_t_8 = __pyx_t_7;
} else {
__pyx_t_8 = __pyx_v_is_leaf;
}
__pyx_v_is_leaf = __pyx_t_8;
/* "sklearn/tree/_tree.pyx":2702
* is_leaf = is_leaf or (impurity <= MIN_IMPURITY_SPLIT)
*
* if not is_leaf: # <<<<<<<<<<<<<<
* splitter.node_split(impurity, &split, &n_constant_features)
* is_leaf = is_leaf or (split.pos >= end)
*/
__pyx_t_8 = ((!(__pyx_v_is_leaf != 0)) != 0);
if (__pyx_t_8) {
/* "sklearn/tree/_tree.pyx":2703
*
* if not is_leaf:
* splitter.node_split(impurity, &split, &n_constant_features) # <<<<<<<<<<<<<<
* is_leaf = is_leaf or (split.pos >= end)
*
*/
((struct __pyx_vtabstruct_7sklearn_4tree_5_tree_Splitter *)__pyx_v_splitter->__pyx_vtab)->node_split(__pyx_v_splitter, __pyx_v_impurity, (&__pyx_v_split), (&__pyx_v_n_constant_features));
/* "sklearn/tree/_tree.pyx":2704
* if not is_leaf:
* splitter.node_split(impurity, &split, &n_constant_features)
* is_leaf = is_leaf or (split.pos >= end) # <<<<<<<<<<<<<<
*
* node_id = tree._add_node(parent, is_left, is_leaf, split.feature,
*/
if (!__pyx_v_is_leaf) {
__pyx_t_8 = (__pyx_v_split.pos >= __pyx_v_end);
__pyx_t_7 = __pyx_t_8;
} else {
__pyx_t_7 = __pyx_v_is_leaf;
}
__pyx_v_is_leaf = __pyx_t_7;
goto __pyx_L14;
}
__pyx_L14:;
/* "sklearn/tree/_tree.pyx":2706
* is_leaf = is_leaf or (split.pos >= end)
*
* node_id = tree._add_node(parent, is_left, is_leaf, split.feature, # <<<<<<<<<<<<<<
* split.threshold, impurity, n_node_samples,
* weighted_n_node_samples)
*/
__pyx_v_node_id = ((struct __pyx_vtabstruct_7sklearn_4tree_5_tree_Tree *)__pyx_v_tree->__pyx_vtab)->_add_node(__pyx_v_tree, __pyx_v_parent, __pyx_v_is_left, __pyx_v_is_leaf, __pyx_v_split.feature, __pyx_v_split.threshold, __pyx_v_impurity, __pyx_v_n_node_samples, __pyx_v_weighted_n_node_samples);
/* "sklearn/tree/_tree.pyx":2710
* weighted_n_node_samples)
*
* if is_leaf: # <<<<<<<<<<<<<<
* # Don't store value for internal nodes
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__pyx_v_min_samples_split = __pyx_t_9;
/* "sklearn/tree/_tree.pyx":2790
*
* # Recursive partition (without actual recursion)
* splitter.init(X, y, sample_weight_ptr) # <<<<<<<<<<<<<<
*
* cdef PriorityHeap frontier = PriorityHeap(INITIAL_STACK_SIZE)
*/
((struct __pyx_vtabstruct_7sklearn_4tree_5_tree_Splitter *)__pyx_v_splitter->__pyx_vtab)->init(__pyx_v_splitter, __pyx_v_X, __pyx_v_y, __pyx_v_sample_weight_ptr);
/* "sklearn/tree/_tree.pyx":2792
* splitter.init(X, y, sample_weight_ptr)
*
* cdef PriorityHeap frontier = PriorityHeap(INITIAL_STACK_SIZE) # <<<<<<<<<<<<<<
* cdef PriorityHeapRecord record
* cdef PriorityHeapRecord split_node_left
*/
__pyx_t_1 = __Pyx_PyInt_From_Py_intptr_t(__pyx_v_7sklearn_4tree_5_tree_INITIAL_STACK_SIZE); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2792; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
__Pyx_GOTREF(__pyx_t_1);
__pyx_t_4 = PyTuple_New(1); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2792; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
__Pyx_GOTREF(__pyx_t_4);
PyTuple_SET_ITEM(__pyx_t_4, 0, __pyx_t_1);
__Pyx_GIVEREF(__pyx_t_1);
__pyx_t_1 = 0;
__pyx_t_1 = __Pyx_PyObject_Call(((PyObject *)((PyObject*)__pyx_ptype_7sklearn_4tree_6_utils_PriorityHeap)), __pyx_t_4, NULL); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2792; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
__Pyx_GOTREF(__pyx_t_1);
__Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0;
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__pyx_t_1 = 0;
/* "sklearn/tree/_tree.pyx":2797
* cdef PriorityHeapRecord split_node_right
*
* cdef SIZE_t n_node_samples = splitter.n_samples # <<<<<<<<<<<<<<
* cdef SIZE_t max_split_nodes = max_leaf_nodes - 1
* cdef bint is_leaf
*/
__pyx_t_9 = __pyx_v_splitter->n_samples;
__pyx_v_n_node_samples = __pyx_t_9;
/* "sklearn/tree/_tree.pyx":2798
*
* cdef SIZE_t n_node_samples = splitter.n_samples
* cdef SIZE_t max_split_nodes = max_leaf_nodes - 1 # <<<<<<<<<<<<<<
* cdef bint is_leaf
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*/
__pyx_v_max_split_nodes = (__pyx_v_max_leaf_nodes - 1);
/* "sklearn/tree/_tree.pyx":2800
* cdef SIZE_t max_split_nodes = max_leaf_nodes - 1
* cdef bint is_leaf
* cdef SIZE_t max_depth_seen = -1 # <<<<<<<<<<<<<<
* cdef int rc = 0
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__pyx_v_max_depth_seen = -1;
/* "sklearn/tree/_tree.pyx":2801
* cdef bint is_leaf
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*/
__pyx_v_rc = 0;
/* "sklearn/tree/_tree.pyx":2805
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*/
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/* "sklearn/tree/_tree.pyx":2806
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((struct __pyx_vtabstruct_7sklearn_4tree_5_tree_Tree *)__pyx_v_tree->__pyx_vtab)->_resize(__pyx_v_tree, __pyx_v_init_capacity); if (unlikely(PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2806; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
/* "sklearn/tree/_tree.pyx":2808
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/* "sklearn/tree/_tree.pyx":2814
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goto __pyx_L9;
}
__pyx_L9:;
}
/* "sklearn/tree/_tree.pyx":2808
* tree._resize(init_capacity)
*
* with nogil: # <<<<<<<<<<<<<<
* # add root to frontier
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/*finally:*/ {
/*normal exit:*/{
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Py_BLOCK_THREADS
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__pyx_L8:;
}
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* raise MemoryError()
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__pyx_t_8 = ((__pyx_v_rc == -1) != 0);
if (__pyx_t_8) {
/* "sklearn/tree/_tree.pyx":2816
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PyErr_NoMemory(); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2816; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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/* "sklearn/tree/_tree.pyx":2818
* raise MemoryError()
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* with nogil: # <<<<<<<<<<<<<<
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{
#ifdef WITH_THREAD
PyThreadState *_save;
Py_UNBLOCK_THREADS
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/*try:*/ {
/* "sklearn/tree/_tree.pyx":2819
*
* with nogil:
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* frontier.pop(&record)
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__pyx_t_8 = ((!(((struct __pyx_vtabstruct_7sklearn_4tree_6_utils_PriorityHeap *)__pyx_v_frontier->__pyx_vtab)->is_empty(__pyx_v_frontier) != 0)) != 0);
if (!__pyx_t_8) break;
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* with nogil:
* while not frontier.is_empty():
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((struct __pyx_vtabstruct_7sklearn_4tree_6_utils_PriorityHeap *)__pyx_v_frontier->__pyx_vtab)->pop(__pyx_v_frontier, (&__pyx_v_record));
/* "sklearn/tree/_tree.pyx":2822
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/* "sklearn/tree/_tree.pyx":2823
*
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*
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__pyx_t_8 = (__pyx_v_max_split_nodes <= 0);
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}
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/* "sklearn/tree/_tree.pyx":2825
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*
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*/
__pyx_t_7 = (__pyx_v_is_leaf != 0);
if (__pyx_t_7) {
/* "sklearn/tree/_tree.pyx":2827
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*/
__pyx_v_node->left_child = __pyx_v_7sklearn_4tree_5_tree__TREE_LEAF;
/* "sklearn/tree/_tree.pyx":2828
* # Node is not expandable; set node as leaf
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* node.right_child = _TREE_LEAF # <<<<<<<<<<<<<<
* node.feature = _TREE_UNDEFINED
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*/
__pyx_v_node->right_child = __pyx_v_7sklearn_4tree_5_tree__TREE_LEAF;
/* "sklearn/tree/_tree.pyx":2829
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* node.feature = _TREE_UNDEFINED # <<<<<<<<<<<<<<
* node.threshold = _TREE_UNDEFINED
*
*/
__pyx_v_node->feature = __pyx_v_7sklearn_4tree_5_tree__TREE_UNDEFINED;
/* "sklearn/tree/_tree.pyx":2830
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* node.threshold = _TREE_UNDEFINED # <<<<<<<<<<<<<<
*
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*/
__pyx_v_node->threshold = __pyx_v_7sklearn_4tree_5_tree__TREE_UNDEFINED;
goto __pyx_L16;
}
/*else*/ {
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*
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*/
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/* "sklearn/tree/_tree.pyx":2839
*
* # Compute left split node
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* record.start, record.pos,
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*/
__pyx_v_rc = __pyx_f_7sklearn_4tree_5_tree_20BestFirstTreeBuilder__add_split_node(__pyx_v_self, __pyx_v_splitter, __pyx_v_tree, __pyx_v_record.start, __pyx_v_record.pos, __pyx_v_record.impurity_left, __pyx_v_7sklearn_4tree_5_tree_IS_NOT_FIRST, __pyx_v_7sklearn_4tree_5_tree_IS_LEFT, __pyx_v_node, (__pyx_v_record.depth + 1), (&__pyx_v_split_node_left));
/* "sklearn/tree/_tree.pyx":2845
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/* "sklearn/tree/_tree.pyx":2846
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*/
goto __pyx_L15_break;
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/* "sklearn/tree/_tree.pyx":2849
*
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/* "sklearn/tree/_tree.pyx":2852
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/* "sklearn/tree/_tree.pyx":2864
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goto __pyx_L15_break;
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/* "sklearn/tree/_tree.pyx":2867
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/* "sklearn/tree/_tree.pyx":2868
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goto __pyx_L15_break;
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/* "sklearn/tree/_tree.pyx":2871
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__pyx_L21:;
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/* "sklearn/tree/_tree.pyx":2874
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/* "sklearn/tree/_tree.pyx":2876
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/* "sklearn/tree/_tree.pyx":2877
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/* "sklearn/tree/_tree.pyx":2771
* self.max_leaf_nodes = max_leaf_nodes
*
* cpdef build(self, Tree tree, object X, np.ndarray y, # <<<<<<<<<<<<<<
* np.ndarray sample_weight=None):
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/* function exit code */
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__pyx_L0:;
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static PyObject *__pyx_pf_7sklearn_4tree_5_tree_20BestFirstTreeBuilder_2build(struct __pyx_obj_7sklearn_4tree_5_tree_BestFirstTreeBuilder *__pyx_v_self, struct __pyx_obj_7sklearn_4tree_5_tree_Tree *__pyx_v_tree, PyObject *__pyx_v_X, PyArrayObject *__pyx_v_y, PyArrayObject *__pyx_v_sample_weight) {
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__Pyx_GOTREF(__pyx_t_1);
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__pyx_t_1 = 0;
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/* function exit code */
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__Pyx_XDECREF(__pyx_t_1);
__Pyx_AddTraceback("sklearn.tree._tree.BestFirstTreeBuilder.build", __pyx_clineno, __pyx_lineno, __pyx_filename);
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/* "sklearn/tree/_tree.pyx":2882
* raise MemoryError()
*
* cdef inline int _add_split_node(self, Splitter splitter, Tree tree, # <<<<<<<<<<<<<<
* SIZE_t start, SIZE_t end, double impurity,
* bint is_first, bint is_left, Node* parent,
*/
static CYTHON_INLINE int __pyx_f_7sklearn_4tree_5_tree_20BestFirstTreeBuilder__add_split_node(struct __pyx_obj_7sklearn_4tree_5_tree_BestFirstTreeBuilder *__pyx_v_self, struct __pyx_obj_7sklearn_4tree_5_tree_Splitter *__pyx_v_splitter, struct __pyx_obj_7sklearn_4tree_5_tree_Tree *__pyx_v_tree, __pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_v_start, __pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_v_end, double __pyx_v_impurity, int __pyx_v_is_first, int __pyx_v_is_left, struct __pyx_t_7sklearn_4tree_5_tree_Node *__pyx_v_parent, __pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_v_depth, struct __pyx_t_7sklearn_4tree_6_utils_PriorityHeapRecord *__pyx_v_res) {
struct __pyx_t_7sklearn_4tree_5_tree_SplitRecord __pyx_v_split;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_v_node_id;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_v_n_node_samples;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_v_n_constant_features;
CYTHON_UNUSED double __pyx_v_weighted_n_samples;
double __pyx_v_weighted_n_node_samples;
int __pyx_v_is_leaf;
int __pyx_r;
double __pyx_t_1;
int __pyx_t_2;
int __pyx_t_3;
int __pyx_t_4;
int __pyx_t_5;
int __pyx_t_6;
int __pyx_t_7;
ptrdiff_t __pyx_t_8;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_t_9;
/* "sklearn/tree/_tree.pyx":2891
* cdef SIZE_t node_id
* cdef SIZE_t n_node_samples
* cdef SIZE_t n_constant_features = 0 # <<<<<<<<<<<<<<
* cdef double weighted_n_samples = splitter.weighted_n_samples
* cdef double weighted_n_node_samples
*/
__pyx_v_n_constant_features = 0;
/* "sklearn/tree/_tree.pyx":2892
* cdef SIZE_t n_node_samples
* cdef SIZE_t n_constant_features = 0
* cdef double weighted_n_samples = splitter.weighted_n_samples # <<<<<<<<<<<<<<
* cdef double weighted_n_node_samples
* cdef bint is_leaf
*/
__pyx_t_1 = __pyx_v_splitter->weighted_n_samples;
__pyx_v_weighted_n_samples = __pyx_t_1;
/* "sklearn/tree/_tree.pyx":2898
* cdef double imp_diff
*
* splitter.node_reset(start, end, &weighted_n_node_samples) # <<<<<<<<<<<<<<
*
* if is_first:
*/
((struct __pyx_vtabstruct_7sklearn_4tree_5_tree_Splitter *)__pyx_v_splitter->__pyx_vtab)->node_reset(__pyx_v_splitter, __pyx_v_start, __pyx_v_end, (&__pyx_v_weighted_n_node_samples));
/* "sklearn/tree/_tree.pyx":2900
* splitter.node_reset(start, end, &weighted_n_node_samples)
*
* if is_first: # <<<<<<<<<<<<<<
* impurity = splitter.node_impurity()
*
*/
__pyx_t_2 = (__pyx_v_is_first != 0);
if (__pyx_t_2) {
/* "sklearn/tree/_tree.pyx":2901
*
* if is_first:
* impurity = splitter.node_impurity() # <<<<<<<<<<<<<<
*
* n_node_samples = end - start
*/
__pyx_v_impurity = ((struct __pyx_vtabstruct_7sklearn_4tree_5_tree_Splitter *)__pyx_v_splitter->__pyx_vtab)->node_impurity(__pyx_v_splitter);
goto __pyx_L3;
}
__pyx_L3:;
/* "sklearn/tree/_tree.pyx":2903
* impurity = splitter.node_impurity()
*
* n_node_samples = end - start # <<<<<<<<<<<<<<
* is_leaf = ((depth > self.max_depth) or
* (n_node_samples < self.min_samples_split) or
*/
__pyx_v_n_node_samples = (__pyx_v_end - __pyx_v_start);
/* "sklearn/tree/_tree.pyx":2904
*
* n_node_samples = end - start
* is_leaf = ((depth > self.max_depth) or # <<<<<<<<<<<<<<
* (n_node_samples < self.min_samples_split) or
* (n_node_samples < 2 * self.min_samples_leaf) or
*/
__pyx_t_2 = (__pyx_v_depth > __pyx_v_self->__pyx_base.max_depth);
if (!__pyx_t_2) {
/* "sklearn/tree/_tree.pyx":2905
* n_node_samples = end - start
* is_leaf = ((depth > self.max_depth) or
* (n_node_samples < self.min_samples_split) or # <<<<<<<<<<<<<<
* (n_node_samples < 2 * self.min_samples_leaf) or
* (weighted_n_node_samples < self.min_weight_leaf) or
*/
__pyx_t_3 = (__pyx_v_n_node_samples < __pyx_v_self->__pyx_base.min_samples_split);
if (!__pyx_t_3) {
/* "sklearn/tree/_tree.pyx":2906
* is_leaf = ((depth > self.max_depth) or
* (n_node_samples < self.min_samples_split) or
* (n_node_samples < 2 * self.min_samples_leaf) or # <<<<<<<<<<<<<<
* (weighted_n_node_samples < self.min_weight_leaf) or
* (impurity <= MIN_IMPURITY_SPLIT))
*/
__pyx_t_4 = (__pyx_v_n_node_samples < (2 * __pyx_v_self->__pyx_base.min_samples_leaf));
if (!__pyx_t_4) {
/* "sklearn/tree/_tree.pyx":2907
* (n_node_samples < self.min_samples_split) or
* (n_node_samples < 2 * self.min_samples_leaf) or
* (weighted_n_node_samples < self.min_weight_leaf) or # <<<<<<<<<<<<<<
* (impurity <= MIN_IMPURITY_SPLIT))
*
*/
__pyx_t_5 = (__pyx_v_weighted_n_node_samples < __pyx_v_self->__pyx_base.min_weight_leaf);
if (!__pyx_t_5) {
/* "sklearn/tree/_tree.pyx":2908
* (n_node_samples < 2 * self.min_samples_leaf) or
* (weighted_n_node_samples < self.min_weight_leaf) or
* (impurity <= MIN_IMPURITY_SPLIT)) # <<<<<<<<<<<<<<
*
* if not is_leaf:
*/
__pyx_t_6 = (__pyx_v_impurity <= __pyx_v_7sklearn_4tree_5_tree_MIN_IMPURITY_SPLIT);
__pyx_t_7 = __pyx_t_6;
} else {
__pyx_t_7 = __pyx_t_5;
}
__pyx_t_5 = __pyx_t_7;
} else {
__pyx_t_5 = __pyx_t_4;
}
__pyx_t_4 = __pyx_t_5;
} else {
__pyx_t_4 = __pyx_t_3;
}
__pyx_t_3 = __pyx_t_4;
} else {
__pyx_t_3 = __pyx_t_2;
}
__pyx_v_is_leaf = __pyx_t_3;
/* "sklearn/tree/_tree.pyx":2910
* (impurity <= MIN_IMPURITY_SPLIT))
*
* if not is_leaf: # <<<<<<<<<<<<<<
* splitter.node_split(impurity, &split, &n_constant_features)
* is_leaf = is_leaf or (split.pos >= end)
*/
__pyx_t_3 = ((!(__pyx_v_is_leaf != 0)) != 0);
if (__pyx_t_3) {
/* "sklearn/tree/_tree.pyx":2911
*
* if not is_leaf:
* splitter.node_split(impurity, &split, &n_constant_features) # <<<<<<<<<<<<<<
* is_leaf = is_leaf or (split.pos >= end)
*
*/
((struct __pyx_vtabstruct_7sklearn_4tree_5_tree_Splitter *)__pyx_v_splitter->__pyx_vtab)->node_split(__pyx_v_splitter, __pyx_v_impurity, (&__pyx_v_split), (&__pyx_v_n_constant_features));
/* "sklearn/tree/_tree.pyx":2912
* if not is_leaf:
* splitter.node_split(impurity, &split, &n_constant_features)
* is_leaf = is_leaf or (split.pos >= end) # <<<<<<<<<<<<<<
*
* node_id = tree._add_node(parent - tree.nodes
*/
if (!__pyx_v_is_leaf) {
__pyx_t_3 = (__pyx_v_split.pos >= __pyx_v_end);
__pyx_t_2 = __pyx_t_3;
} else {
__pyx_t_2 = __pyx_v_is_leaf;
}
__pyx_v_is_leaf = __pyx_t_2;
goto __pyx_L4;
}
__pyx_L4:;
/* "sklearn/tree/_tree.pyx":2915
*
* node_id = tree._add_node(parent - tree.nodes
* if parent != NULL # <<<<<<<<<<<<<<
* else _TREE_UNDEFINED,
* is_left, is_leaf,
*/
if (((__pyx_v_parent != NULL) != 0)) {
/* "sklearn/tree/_tree.pyx":2914
* is_leaf = is_leaf or (split.pos >= end)
*
* node_id = tree._add_node(parent - tree.nodes # <<<<<<<<<<<<<<
* if parent != NULL
* else _TREE_UNDEFINED,
*/
__pyx_t_8 = (__pyx_v_parent - __pyx_v_tree->nodes);
} else {
/* "sklearn/tree/_tree.pyx":2916
* node_id = tree._add_node(parent - tree.nodes
* if parent != NULL
* else _TREE_UNDEFINED, # <<<<<<<<<<<<<<
* is_left, is_leaf,
* split.feature, split.threshold, impurity, n_node_samples,
*/
__pyx_t_8 = __pyx_v_7sklearn_4tree_5_tree__TREE_UNDEFINED;
}
/* "sklearn/tree/_tree.pyx":2914
* is_leaf = is_leaf or (split.pos >= end)
*
* node_id = tree._add_node(parent - tree.nodes # <<<<<<<<<<<<<<
* if parent != NULL
* else _TREE_UNDEFINED,
*/
__pyx_v_node_id = ((struct __pyx_vtabstruct_7sklearn_4tree_5_tree_Tree *)__pyx_v_tree->__pyx_vtab)->_add_node(__pyx_v_tree, __pyx_t_8, __pyx_v_is_left, __pyx_v_is_leaf, __pyx_v_split.feature, __pyx_v_split.threshold, __pyx_v_impurity, __pyx_v_n_node_samples, __pyx_v_weighted_n_node_samples);
/* "sklearn/tree/_tree.pyx":2920
* split.feature, split.threshold, impurity, n_node_samples,
* weighted_n_node_samples)
* if node_id == <SIZE_t>(-1): # <<<<<<<<<<<<<<
* return -1
*
*/
__pyx_t_2 = ((__pyx_v_node_id == ((__pyx_t_7sklearn_4tree_5_tree_SIZE_t)-1)) != 0);
if (__pyx_t_2) {
/* "sklearn/tree/_tree.pyx":2921
* weighted_n_node_samples)
* if node_id == <SIZE_t>(-1):
* return -1 # <<<<<<<<<<<<<<
*
* # compute values also for split nodes (might become leafs later).
*/
__pyx_r = -1;
goto __pyx_L0;
}
/* "sklearn/tree/_tree.pyx":2924
*
* # compute values also for split nodes (might become leafs later).
* splitter.node_value(tree.value + node_id * tree.value_stride) # <<<<<<<<<<<<<<
*
* res.node_id = node_id
*/
((struct __pyx_vtabstruct_7sklearn_4tree_5_tree_Splitter *)__pyx_v_splitter->__pyx_vtab)->node_value(__pyx_v_splitter, (__pyx_v_tree->value + (__pyx_v_node_id * __pyx_v_tree->value_stride)));
/* "sklearn/tree/_tree.pyx":2926
* splitter.node_value(tree.value + node_id * tree.value_stride)
*
* res.node_id = node_id # <<<<<<<<<<<<<<
* res.start = start
* res.end = end
*/
__pyx_v_res->node_id = __pyx_v_node_id;
/* "sklearn/tree/_tree.pyx":2927
*
* res.node_id = node_id
* res.start = start # <<<<<<<<<<<<<<
* res.end = end
* res.depth = depth
*/
__pyx_v_res->start = __pyx_v_start;
/* "sklearn/tree/_tree.pyx":2928
* res.node_id = node_id
* res.start = start
* res.end = end # <<<<<<<<<<<<<<
* res.depth = depth
* res.impurity = impurity
*/
__pyx_v_res->end = __pyx_v_end;
/* "sklearn/tree/_tree.pyx":2929
* res.start = start
* res.end = end
* res.depth = depth # <<<<<<<<<<<<<<
* res.impurity = impurity
*
*/
__pyx_v_res->depth = __pyx_v_depth;
/* "sklearn/tree/_tree.pyx":2930
* res.end = end
* res.depth = depth
* res.impurity = impurity # <<<<<<<<<<<<<<
*
* if not is_leaf:
*/
__pyx_v_res->impurity = __pyx_v_impurity;
/* "sklearn/tree/_tree.pyx":2932
* res.impurity = impurity
*
* if not is_leaf: # <<<<<<<<<<<<<<
* # is split node
* res.pos = split.pos
*/
__pyx_t_2 = ((!(__pyx_v_is_leaf != 0)) != 0);
if (__pyx_t_2) {
/* "sklearn/tree/_tree.pyx":2934
* if not is_leaf:
* # is split node
* res.pos = split.pos # <<<<<<<<<<<<<<
* res.is_leaf = 0
* res.improvement = split.improvement
*/
__pyx_t_9 = __pyx_v_split.pos;
__pyx_v_res->pos = __pyx_t_9;
/* "sklearn/tree/_tree.pyx":2935
* # is split node
* res.pos = split.pos
* res.is_leaf = 0 # <<<<<<<<<<<<<<
* res.improvement = split.improvement
* res.impurity_left = split.impurity_left
*/
__pyx_v_res->is_leaf = 0;
/* "sklearn/tree/_tree.pyx":2936
* res.pos = split.pos
* res.is_leaf = 0
* res.improvement = split.improvement # <<<<<<<<<<<<<<
* res.impurity_left = split.impurity_left
* res.impurity_right = split.impurity_right
*/
__pyx_t_1 = __pyx_v_split.improvement;
__pyx_v_res->improvement = __pyx_t_1;
/* "sklearn/tree/_tree.pyx":2937
* res.is_leaf = 0
* res.improvement = split.improvement
* res.impurity_left = split.impurity_left # <<<<<<<<<<<<<<
* res.impurity_right = split.impurity_right
*
*/
__pyx_t_1 = __pyx_v_split.impurity_left;
__pyx_v_res->impurity_left = __pyx_t_1;
/* "sklearn/tree/_tree.pyx":2938
* res.improvement = split.improvement
* res.impurity_left = split.impurity_left
* res.impurity_right = split.impurity_right # <<<<<<<<<<<<<<
*
* else:
*/
__pyx_t_1 = __pyx_v_split.impurity_right;
__pyx_v_res->impurity_right = __pyx_t_1;
goto __pyx_L6;
}
/*else*/ {
/* "sklearn/tree/_tree.pyx":2942
* else:
* # is leaf => 0 improvement
* res.pos = end # <<<<<<<<<<<<<<
* res.is_leaf = 1
* res.improvement = 0.0
*/
__pyx_v_res->pos = __pyx_v_end;
/* "sklearn/tree/_tree.pyx":2943
* # is leaf => 0 improvement
* res.pos = end
* res.is_leaf = 1 # <<<<<<<<<<<<<<
* res.improvement = 0.0
* res.impurity_left = impurity
*/
__pyx_v_res->is_leaf = 1;
/* "sklearn/tree/_tree.pyx":2944
* res.pos = end
* res.is_leaf = 1
* res.improvement = 0.0 # <<<<<<<<<<<<<<
* res.impurity_left = impurity
* res.impurity_right = impurity
*/
__pyx_v_res->improvement = 0.0;
/* "sklearn/tree/_tree.pyx":2945
* res.is_leaf = 1
* res.improvement = 0.0
* res.impurity_left = impurity # <<<<<<<<<<<<<<
* res.impurity_right = impurity
*
*/
__pyx_v_res->impurity_left = __pyx_v_impurity;
/* "sklearn/tree/_tree.pyx":2946
* res.improvement = 0.0
* res.impurity_left = impurity
* res.impurity_right = impurity # <<<<<<<<<<<<<<
*
* return 0
*/
__pyx_v_res->impurity_right = __pyx_v_impurity;
}
__pyx_L6:;
/* "sklearn/tree/_tree.pyx":2948
* res.impurity_right = impurity
*
* return 0 # <<<<<<<<<<<<<<
*
*
*/
__pyx_r = 0;
goto __pyx_L0;
/* "sklearn/tree/_tree.pyx":2882
* raise MemoryError()
*
* cdef inline int _add_split_node(self, Splitter splitter, Tree tree, # <<<<<<<<<<<<<<
* SIZE_t start, SIZE_t end, double impurity,
* bint is_first, bint is_left, Node* parent,
*/
/* function exit code */
__pyx_L0:;
return __pyx_r;
}
/* "sklearn/tree/_tree.pyx":3014
* # (i.e. through `_resize` or `__setstate__`)
* property n_classes:
* def __get__(self): # <<<<<<<<<<<<<<
* # it's small; copy for memory safety
* return sizet_ptr_to_ndarray(self.n_classes, self.n_outputs).copy()
*/
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__Pyx_RefNannyFinishContext();
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/* "sklearn/tree/_tree.pyx":3131
* # XXX using (size_t)(-1) is ugly, but SIZE_MAX is not available in C89
* # (i.e., older MSVC).
* cdef int _resize_c(self, SIZE_t capacity=<SIZE_t>(-1)) nogil: # <<<<<<<<<<<<<<
* """Guts of _resize. Returns 0 for success, -1 for error."""
* if capacity == self.capacity and self.nodes != NULL:
*/
static int __pyx_f_7sklearn_4tree_5_tree_4Tree__resize_c(struct __pyx_obj_7sklearn_4tree_5_tree_Tree *__pyx_v_self, struct __pyx_opt_args_7sklearn_4tree_5_tree_4Tree__resize_c *__pyx_optional_args) {
__pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_v_capacity = __pyx_k__5;
void *__pyx_v_ptr;
int __pyx_r;
int __pyx_t_1;
int __pyx_t_2;
int __pyx_t_3;
if (__pyx_optional_args) {
if (__pyx_optional_args->__pyx_n > 0) {
__pyx_v_capacity = __pyx_optional_args->capacity;
}
}
/* "sklearn/tree/_tree.pyx":3133
* cdef int _resize_c(self, SIZE_t capacity=<SIZE_t>(-1)) nogil:
* """Guts of _resize. Returns 0 for success, -1 for error."""
* if capacity == self.capacity and self.nodes != NULL: # <<<<<<<<<<<<<<
* return 0
*
*/
__pyx_t_1 = ((__pyx_v_capacity == __pyx_v_self->capacity) != 0);
if (__pyx_t_1) {
__pyx_t_2 = ((__pyx_v_self->nodes != NULL) != 0);
__pyx_t_3 = __pyx_t_2;
} else {
__pyx_t_3 = __pyx_t_1;
}
if (__pyx_t_3) {
/* "sklearn/tree/_tree.pyx":3134
* """Guts of _resize. Returns 0 for success, -1 for error."""
* if capacity == self.capacity and self.nodes != NULL:
* return 0 # <<<<<<<<<<<<<<
*
* if capacity == <SIZE_t>(-1):
*/
__pyx_r = 0;
goto __pyx_L0;
}
/* "sklearn/tree/_tree.pyx":3136
* return 0
*
* if capacity == <SIZE_t>(-1): # <<<<<<<<<<<<<<
* if self.capacity == 0:
* capacity = 3 # default initial value
*/
__pyx_t_3 = ((__pyx_v_capacity == ((__pyx_t_7sklearn_4tree_5_tree_SIZE_t)-1)) != 0);
if (__pyx_t_3) {
/* "sklearn/tree/_tree.pyx":3137
*
* if capacity == <SIZE_t>(-1):
* if self.capacity == 0: # <<<<<<<<<<<<<<
* capacity = 3 # default initial value
* else:
*/
__pyx_t_3 = ((__pyx_v_self->capacity == 0) != 0);
if (__pyx_t_3) {
/* "sklearn/tree/_tree.pyx":3138
* if capacity == <SIZE_t>(-1):
* if self.capacity == 0:
* capacity = 3 # default initial value # <<<<<<<<<<<<<<
* else:
* capacity = 2 * self.capacity
*/
__pyx_v_capacity = 3;
goto __pyx_L5;
}
/*else*/ {
/* "sklearn/tree/_tree.pyx":3140
* capacity = 3 # default initial value
* else:
* capacity = 2 * self.capacity # <<<<<<<<<<<<<<
*
* # XXX no safe_realloc here because we need to grab the GIL
*/
__pyx_v_capacity = (2 * __pyx_v_self->capacity);
}
__pyx_L5:;
goto __pyx_L4;
}
__pyx_L4:;
/* "sklearn/tree/_tree.pyx":3143
*
* # XXX no safe_realloc here because we need to grab the GIL
* cdef void* ptr = realloc(self.nodes, capacity * sizeof(Node)) # <<<<<<<<<<<<<<
* if ptr == NULL:
* return -1
*/
__pyx_v_ptr = realloc(__pyx_v_self->nodes, (__pyx_v_capacity * (sizeof(struct __pyx_t_7sklearn_4tree_5_tree_Node))));
/* "sklearn/tree/_tree.pyx":3144
* # XXX no safe_realloc here because we need to grab the GIL
* cdef void* ptr = realloc(self.nodes, capacity * sizeof(Node))
* if ptr == NULL: # <<<<<<<<<<<<<<
* return -1
* self.nodes = <Node*> ptr
*/
__pyx_t_3 = ((__pyx_v_ptr == NULL) != 0);
if (__pyx_t_3) {
/* "sklearn/tree/_tree.pyx":3145
* cdef void* ptr = realloc(self.nodes, capacity * sizeof(Node))
* if ptr == NULL:
* return -1 # <<<<<<<<<<<<<<
* self.nodes = <Node*> ptr
* ptr = realloc(self.value,
*/
__pyx_r = -1;
goto __pyx_L0;
}
/* "sklearn/tree/_tree.pyx":3146
* if ptr == NULL:
* return -1
* self.nodes = <Node*> ptr # <<<<<<<<<<<<<<
* ptr = realloc(self.value,
* capacity * self.value_stride * sizeof(double))
*/
__pyx_v_self->nodes = ((struct __pyx_t_7sklearn_4tree_5_tree_Node *)__pyx_v_ptr);
/* "sklearn/tree/_tree.pyx":3147
* return -1
* self.nodes = <Node*> ptr
* ptr = realloc(self.value, # <<<<<<<<<<<<<<
* capacity * self.value_stride * sizeof(double))
* if ptr == NULL:
*/
__pyx_v_ptr = realloc(__pyx_v_self->value, ((__pyx_v_capacity * __pyx_v_self->value_stride) * (sizeof(double))));
/* "sklearn/tree/_tree.pyx":3149
* ptr = realloc(self.value,
* capacity * self.value_stride * sizeof(double))
* if ptr == NULL: # <<<<<<<<<<<<<<
* return -1
* self.value = <double*> ptr
*/
__pyx_t_3 = ((__pyx_v_ptr == NULL) != 0);
if (__pyx_t_3) {
/* "sklearn/tree/_tree.pyx":3150
* capacity * self.value_stride * sizeof(double))
* if ptr == NULL:
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* self.value = <double*> ptr
*
*/
__pyx_r = -1;
goto __pyx_L0;
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/* "sklearn/tree/_tree.pyx":3151
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*
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*/
__pyx_v_self->value = ((double *)__pyx_v_ptr);
/* "sklearn/tree/_tree.pyx":3154
*
* # value memory is initialised to 0 to enable classifier argmax
* if capacity > self.capacity: # <<<<<<<<<<<<<<
* memset(<void*>(self.value + self.capacity * self.value_stride), 0,
* (capacity - self.capacity) * self.value_stride *
*/
__pyx_t_3 = ((__pyx_v_capacity > __pyx_v_self->capacity) != 0);
if (__pyx_t_3) {
/* "sklearn/tree/_tree.pyx":3155
* # value memory is initialised to 0 to enable classifier argmax
* if capacity > self.capacity:
* memset(<void*>(self.value + self.capacity * self.value_stride), 0, # <<<<<<<<<<<<<<
* (capacity - self.capacity) * self.value_stride *
* sizeof(double))
*/
memset(((void *)(__pyx_v_self->value + (__pyx_v_self->capacity * __pyx_v_self->value_stride))), 0, (((__pyx_v_capacity - __pyx_v_self->capacity) * __pyx_v_self->value_stride) * (sizeof(double))));
goto __pyx_L8;
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__pyx_L8:;
/* "sklearn/tree/_tree.pyx":3160
*
* # if capacity smaller than node_count, adjust the counter
* if capacity < self.node_count: # <<<<<<<<<<<<<<
* self.node_count = capacity
*
*/
__pyx_t_3 = ((__pyx_v_capacity < __pyx_v_self->node_count) != 0);
if (__pyx_t_3) {
/* "sklearn/tree/_tree.pyx":3161
* # if capacity smaller than node_count, adjust the counter
* if capacity < self.node_count:
* self.node_count = capacity # <<<<<<<<<<<<<<
*
* self.capacity = capacity
*/
__pyx_v_self->node_count = __pyx_v_capacity;
goto __pyx_L9;
}
__pyx_L9:;
/* "sklearn/tree/_tree.pyx":3163
* self.node_count = capacity
*
* self.capacity = capacity # <<<<<<<<<<<<<<
* return 0
*
*/
__pyx_v_self->capacity = __pyx_v_capacity;
/* "sklearn/tree/_tree.pyx":3164
*
* self.capacity = capacity
* return 0 # <<<<<<<<<<<<<<
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*/
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goto __pyx_L0;
/* "sklearn/tree/_tree.pyx":3131
* # XXX using (size_t)(-1) is ugly, but SIZE_MAX is not available in C89
* # (i.e., older MSVC).
* cdef int _resize_c(self, SIZE_t capacity=<SIZE_t>(-1)) nogil: # <<<<<<<<<<<<<<
* """Guts of _resize. Returns 0 for success, -1 for error."""
* if capacity == self.capacity and self.nodes != NULL:
*/
/* function exit code */
__pyx_L0:;
return __pyx_r;
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/* "sklearn/tree/_tree.pyx":3166
* return 0
*
* cdef SIZE_t _add_node(self, SIZE_t parent, bint is_left, bint is_leaf, # <<<<<<<<<<<<<<
* SIZE_t feature, double threshold, double impurity,
* SIZE_t n_node_samples, double weighted_n_node_samples) nogil:
*/
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__pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_v_node_id;
struct __pyx_t_7sklearn_4tree_5_tree_Node *__pyx_v_node;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_r;
__pyx_t_7sklearn_4tree_5_tree_SIZE_t __pyx_t_1;
int __pyx_t_2;
/* "sklearn/tree/_tree.pyx":3175
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*
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*/
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/* "sklearn/tree/_tree.pyx":3177
* cdef SIZE_t node_id = self.node_count
*
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* if self._resize_c() != 0:
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*/
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/* "sklearn/tree/_tree.pyx":3178
*
* if node_id >= self.capacity:
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*
*/
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if (__pyx_t_2) {
/* "sklearn/tree/_tree.pyx":3179
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goto __pyx_L3;
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__pyx_L3:;
/* "sklearn/tree/_tree.pyx":3181
* return <SIZE_t>(-1)
*
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*/
__pyx_v_node = (&(__pyx_v_self->nodes[__pyx_v_node_id]));
/* "sklearn/tree/_tree.pyx":3182
*
* cdef Node* node = &self.nodes[node_id]
* node.impurity = impurity # <<<<<<<<<<<<<<
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*/
__pyx_v_node->impurity = __pyx_v_impurity;
/* "sklearn/tree/_tree.pyx":3183
* cdef Node* node = &self.nodes[node_id]
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*/
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/* "sklearn/tree/_tree.pyx":3184
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/* "sklearn/tree/_tree.pyx":3186
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*
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/* "sklearn/tree/_tree.pyx":3187
*
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/* "sklearn/tree/_tree.pyx":3188
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/* "sklearn/tree/_tree.pyx":3190
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__pyx_L6:;
goto __pyx_L5;
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static PyObject *__pyx_getprop_7sklearn_4tree_5_tree_4Tree_n_node_samples(PyObject *o, CYTHON_UNUSED void *x) {
return __pyx_pw_7sklearn_4tree_5_tree_4Tree_14n_node_samples_1__get__(o);
}
static PyObject *__pyx_getprop_7sklearn_4tree_5_tree_4Tree_weighted_n_node_samples(PyObject *o, CYTHON_UNUSED void *x) {
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static PyObject *__pyx_getprop_7sklearn_4tree_5_tree_4Tree_value(PyObject *o, CYTHON_UNUSED void *x) {
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static PyObject *__pyx_getprop_7sklearn_4tree_5_tree_4Tree_n_features(PyObject *o, CYTHON_UNUSED void *x) {
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static int __pyx_setprop_7sklearn_4tree_5_tree_4Tree_n_features(PyObject *o, PyObject *v, CYTHON_UNUSED void *x) {
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return __pyx_pw_7sklearn_4tree_5_tree_4Tree_10n_features_3__set__(o, v);
}
else {
PyErr_SetString(PyExc_NotImplementedError, "__del__");
return -1;
}
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static PyObject *__pyx_getprop_7sklearn_4tree_5_tree_4Tree_n_outputs(PyObject *o, CYTHON_UNUSED void *x) {
return __pyx_pw_7sklearn_4tree_5_tree_4Tree_9n_outputs_1__get__(o);
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static int __pyx_setprop_7sklearn_4tree_5_tree_4Tree_n_outputs(PyObject *o, PyObject *v, CYTHON_UNUSED void *x) {
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PyErr_SetString(PyExc_NotImplementedError, "__del__");
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static PyObject *__pyx_getprop_7sklearn_4tree_5_tree_4Tree_max_n_classes(PyObject *o, CYTHON_UNUSED void *x) {
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PyObject *o = __pyx_tp_new_7sklearn_4tree_5_tree_ClassificationCriterion(t, a, k);
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p = ((struct __pyx_obj_7sklearn_4tree_5_tree_Entropy *)o);
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static struct __pyx_vtabstruct_7sklearn_4tree_5_tree_Gini __pyx_vtable_7sklearn_4tree_5_tree_Gini;
static PyObject *__pyx_tp_new_7sklearn_4tree_5_tree_Gini(PyTypeObject *t, PyObject *a, PyObject *k) {
struct __pyx_obj_7sklearn_4tree_5_tree_Gini *p;
PyObject *o = __pyx_tp_new_7sklearn_4tree_5_tree_ClassificationCriterion(t, a, k);
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static struct __pyx_vtabstruct_7sklearn_4tree_5_tree_FriedmanMSE __pyx_vtable_7sklearn_4tree_5_tree_FriedmanMSE;
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static struct __pyx_vtabstruct_7sklearn_4tree_5_tree_RandomSplitter __pyx_vtable_7sklearn_4tree_5_tree_RandomSplitter;
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struct __pyx_obj_7sklearn_4tree_5_tree_RandomSplitter *p;
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static PyObject *__pyx_tp_new_7sklearn_4tree_5_tree_PresortBestSplitter(PyTypeObject *t, PyObject *a, PyObject *k) {
struct __pyx_obj_7sklearn_4tree_5_tree_PresortBestSplitter *p;
PyObject *o = __pyx_tp_new_7sklearn_4tree_5_tree_BaseDenseSplitter(t, a, k);
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Py_DECREF(o); o = 0;
}
return o;
}
static void __pyx_tp_dealloc_7sklearn_4tree_5_tree_PresortBestSplitter(PyObject *o) {
struct __pyx_obj_7sklearn_4tree_5_tree_PresortBestSplitter *p = (struct __pyx_obj_7sklearn_4tree_5_tree_PresortBestSplitter *)o;
#if PY_VERSION_HEX >= 0x030400a1
if (unlikely(Py_TYPE(o)->tp_finalize) && !_PyGC_FINALIZED(o)) {
if (PyObject_CallFinalizerFromDealloc(o)) return;
}
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PyObject_GC_UnTrack(o);
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PyErr_Fetch(&etype, &eval, &etb);
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__pyx_pw_7sklearn_4tree_5_tree_19PresortBestSplitter_3__dealloc__(o);
--Py_REFCNT(o);
PyErr_Restore(etype, eval, etb);
}
Py_CLEAR(p->X_argsorted);
PyObject_GC_Track(o);
__pyx_tp_dealloc_7sklearn_4tree_5_tree_Splitter(o);
}
static int __pyx_tp_traverse_7sklearn_4tree_5_tree_PresortBestSplitter(PyObject *o, visitproc v, void *a) {
int e;
struct __pyx_obj_7sklearn_4tree_5_tree_PresortBestSplitter *p = (struct __pyx_obj_7sklearn_4tree_5_tree_PresortBestSplitter *)o;
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if (p->X_argsorted) {
e = (*v)(((PyObject*)p->X_argsorted), a); if (e) return e;
}
return 0;
}
static int __pyx_tp_clear_7sklearn_4tree_5_tree_PresortBestSplitter(PyObject *o) {
PyObject* tmp;
struct __pyx_obj_7sklearn_4tree_5_tree_PresortBestSplitter *p = (struct __pyx_obj_7sklearn_4tree_5_tree_PresortBestSplitter *)o;
__pyx_tp_clear_7sklearn_4tree_5_tree_Splitter(o);
tmp = ((PyObject*)p->X_argsorted);
p->X_argsorted = ((PyArrayObject *)Py_None); Py_INCREF(Py_None);
Py_XDECREF(tmp);
return 0;
}
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struct __pyx_obj_7sklearn_4tree_5_tree_BaseSparseSplitter *p;
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PyObject_GC_Track(o);
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struct __pyx_obj_7sklearn_4tree_5_tree_BestSparseSplitter *p;
PyObject *o = __pyx_tp_new_7sklearn_4tree_5_tree_BaseSparseSplitter(t, a, k);
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static struct __pyx_vtabstruct_7sklearn_4tree_5_tree_RandomSparseSplitter __pyx_vtable_7sklearn_4tree_5_tree_RandomSparseSplitter;
static PyObject *__pyx_tp_new_7sklearn_4tree_5_tree_RandomSparseSplitter(PyTypeObject *t, PyObject *a, PyObject *k) {
struct __pyx_obj_7sklearn_4tree_5_tree_RandomSparseSplitter *p;
PyObject *o = __pyx_tp_new_7sklearn_4tree_5_tree_BaseSparseSplitter(t, a, k);
if (unlikely(!o)) return 0;
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struct __pyx_obj_7sklearn_4tree_5_tree_DepthFirstTreeBuilder *p;
PyObject *o = __pyx_tp_new_7sklearn_4tree_5_tree_TreeBuilder(t, a, k);
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p->__pyx_base.__pyx_vtab = (struct __pyx_vtabstruct_7sklearn_4tree_5_tree_TreeBuilder*)__pyx_vtabptr_7sklearn_4tree_5_tree_DepthFirstTreeBuilder;
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Py_DECREF(o); o = 0;
}
return o;
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static PyObject *__pyx_tp_new_7sklearn_4tree_5_tree_BestFirstTreeBuilder(PyTypeObject *t, PyObject *a, PyObject *k) {
struct __pyx_obj_7sklearn_4tree_5_tree_BestFirstTreeBuilder *p;
PyObject *o = __pyx_tp_new_7sklearn_4tree_5_tree_TreeBuilder(t, a, k);
if (unlikely(!o)) return 0;
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__pyx_vtable_7sklearn_4tree_5_tree_TreeBuilder.build = (PyObject *(*)(struct __pyx_obj_7sklearn_4tree_5_tree_TreeBuilder *, struct __pyx_obj_7sklearn_4tree_5_tree_Tree *, PyObject *, PyArrayObject *, int __pyx_skip_dispatch, struct __pyx_opt_args_7sklearn_4tree_5_tree_11TreeBuilder_build *__pyx_optional_args))__pyx_f_7sklearn_4tree_5_tree_11TreeBuilder_build;
__pyx_vtable_7sklearn_4tree_5_tree_TreeBuilder._check_input = (PyObject *(*)(struct __pyx_obj_7sklearn_4tree_5_tree_TreeBuilder *, PyObject *, PyArrayObject *, PyArrayObject *))__pyx_f_7sklearn_4tree_5_tree_11TreeBuilder__check_input;
if (PyType_Ready(&__pyx_type_7sklearn_4tree_5_tree_TreeBuilder) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2566; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
__pyx_type_7sklearn_4tree_5_tree_TreeBuilder.tp_print = 0;
if (__Pyx_SetVtable(__pyx_type_7sklearn_4tree_5_tree_TreeBuilder.tp_dict, __pyx_vtabptr_7sklearn_4tree_5_tree_TreeBuilder) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2566; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
if (__Pyx_SetAttrString(__pyx_m, "TreeBuilder", (PyObject *)&__pyx_type_7sklearn_4tree_5_tree_TreeBuilder) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2566; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
__pyx_ptype_7sklearn_4tree_5_tree_TreeBuilder = &__pyx_type_7sklearn_4tree_5_tree_TreeBuilder;
__pyx_vtabptr_7sklearn_4tree_5_tree_ClassificationCriterion = &__pyx_vtable_7sklearn_4tree_5_tree_ClassificationCriterion;
__pyx_vtable_7sklearn_4tree_5_tree_ClassificationCriterion.__pyx_base = *__pyx_vtabptr_7sklearn_4tree_5_tree_Criterion;
__pyx_vtable_7sklearn_4tree_5_tree_ClassificationCriterion.__pyx_base.init = (void (*)(struct __pyx_obj_7sklearn_4tree_5_tree_Criterion *, __pyx_t_7sklearn_4tree_5_tree_DOUBLE_t *, __pyx_t_7sklearn_4tree_5_tree_SIZE_t, __pyx_t_7sklearn_4tree_5_tree_DOUBLE_t *, double, __pyx_t_7sklearn_4tree_5_tree_SIZE_t *, __pyx_t_7sklearn_4tree_5_tree_SIZE_t, __pyx_t_7sklearn_4tree_5_tree_SIZE_t))__pyx_f_7sklearn_4tree_5_tree_23ClassificationCriterion_init;
__pyx_vtable_7sklearn_4tree_5_tree_ClassificationCriterion.__pyx_base.reset = (void (*)(struct __pyx_obj_7sklearn_4tree_5_tree_Criterion *))__pyx_f_7sklearn_4tree_5_tree_23ClassificationCriterion_reset;
__pyx_vtable_7sklearn_4tree_5_tree_ClassificationCriterion.__pyx_base.update = (void (*)(struct __pyx_obj_7sklearn_4tree_5_tree_Criterion *, __pyx_t_7sklearn_4tree_5_tree_SIZE_t))__pyx_f_7sklearn_4tree_5_tree_23ClassificationCriterion_update;
__pyx_vtable_7sklearn_4tree_5_tree_ClassificationCriterion.__pyx_base.node_impurity = (double (*)(struct __pyx_obj_7sklearn_4tree_5_tree_Criterion *))__pyx_f_7sklearn_4tree_5_tree_23ClassificationCriterion_node_impurity;
__pyx_vtable_7sklearn_4tree_5_tree_ClassificationCriterion.__pyx_base.children_impurity = (void (*)(struct __pyx_obj_7sklearn_4tree_5_tree_Criterion *, double *, double *))__pyx_f_7sklearn_4tree_5_tree_23ClassificationCriterion_children_impurity;
__pyx_vtable_7sklearn_4tree_5_tree_ClassificationCriterion.__pyx_base.node_value = (void (*)(struct __pyx_obj_7sklearn_4tree_5_tree_Criterion *, double *))__pyx_f_7sklearn_4tree_5_tree_23ClassificationCriterion_node_value;
__pyx_type_7sklearn_4tree_5_tree_ClassificationCriterion.tp_base = __pyx_ptype_7sklearn_4tree_5_tree_Criterion;
if (PyType_Ready(&__pyx_type_7sklearn_4tree_5_tree_ClassificationCriterion) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 150; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
__pyx_type_7sklearn_4tree_5_tree_ClassificationCriterion.tp_print = 0;
if (__Pyx_SetVtable(__pyx_type_7sklearn_4tree_5_tree_ClassificationCriterion.tp_dict, __pyx_vtabptr_7sklearn_4tree_5_tree_ClassificationCriterion) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 150; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
if (__Pyx_SetAttrString(__pyx_m, "ClassificationCriterion", (PyObject *)&__pyx_type_7sklearn_4tree_5_tree_ClassificationCriterion) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 150; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
__pyx_ptype_7sklearn_4tree_5_tree_ClassificationCriterion = &__pyx_type_7sklearn_4tree_5_tree_ClassificationCriterion;
__pyx_vtabptr_7sklearn_4tree_5_tree_Entropy = &__pyx_vtable_7sklearn_4tree_5_tree_Entropy;
__pyx_vtable_7sklearn_4tree_5_tree_Entropy.__pyx_base = *__pyx_vtabptr_7sklearn_4tree_5_tree_ClassificationCriterion;
__pyx_vtable_7sklearn_4tree_5_tree_Entropy.__pyx_base.__pyx_base.node_impurity = (double (*)(struct __pyx_obj_7sklearn_4tree_5_tree_Criterion *))__pyx_f_7sklearn_4tree_5_tree_7Entropy_node_impurity;
__pyx_vtable_7sklearn_4tree_5_tree_Entropy.__pyx_base.__pyx_base.children_impurity = (void (*)(struct __pyx_obj_7sklearn_4tree_5_tree_Criterion *, double *, double *))__pyx_f_7sklearn_4tree_5_tree_7Entropy_children_impurity;
__pyx_type_7sklearn_4tree_5_tree_Entropy.tp_base = __pyx_ptype_7sklearn_4tree_5_tree_ClassificationCriterion;
if (PyType_Ready(&__pyx_type_7sklearn_4tree_5_tree_Entropy) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 368; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
__pyx_type_7sklearn_4tree_5_tree_Entropy.tp_print = 0;
if (__Pyx_SetVtable(__pyx_type_7sklearn_4tree_5_tree_Entropy.tp_dict, __pyx_vtabptr_7sklearn_4tree_5_tree_Entropy) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 368; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
if (__Pyx_SetAttrString(__pyx_m, "Entropy", (PyObject *)&__pyx_type_7sklearn_4tree_5_tree_Entropy) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 368; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
__pyx_ptype_7sklearn_4tree_5_tree_Entropy = &__pyx_type_7sklearn_4tree_5_tree_Entropy;
__pyx_vtabptr_7sklearn_4tree_5_tree_Gini = &__pyx_vtable_7sklearn_4tree_5_tree_Gini;
__pyx_vtable_7sklearn_4tree_5_tree_Gini.__pyx_base = *__pyx_vtabptr_7sklearn_4tree_5_tree_ClassificationCriterion;
__pyx_vtable_7sklearn_4tree_5_tree_Gini.__pyx_base.__pyx_base.node_impurity = (double (*)(struct __pyx_obj_7sklearn_4tree_5_tree_Criterion *))__pyx_f_7sklearn_4tree_5_tree_4Gini_node_impurity;
__pyx_vtable_7sklearn_4tree_5_tree_Gini.__pyx_base.__pyx_base.children_impurity = (void (*)(struct __pyx_obj_7sklearn_4tree_5_tree_Criterion *, double *, double *))__pyx_f_7sklearn_4tree_5_tree_4Gini_children_impurity;
__pyx_type_7sklearn_4tree_5_tree_Gini.tp_base = __pyx_ptype_7sklearn_4tree_5_tree_ClassificationCriterion;
if (PyType_Ready(&__pyx_type_7sklearn_4tree_5_tree_Gini) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 459; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
__pyx_type_7sklearn_4tree_5_tree_Gini.tp_print = 0;
if (__Pyx_SetVtable(__pyx_type_7sklearn_4tree_5_tree_Gini.tp_dict, __pyx_vtabptr_7sklearn_4tree_5_tree_Gini) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 459; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
if (__Pyx_SetAttrString(__pyx_m, "Gini", (PyObject *)&__pyx_type_7sklearn_4tree_5_tree_Gini) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 459; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
__pyx_ptype_7sklearn_4tree_5_tree_Gini = &__pyx_type_7sklearn_4tree_5_tree_Gini;
__pyx_vtabptr_7sklearn_4tree_5_tree_RegressionCriterion = &__pyx_vtable_7sklearn_4tree_5_tree_RegressionCriterion;
__pyx_vtable_7sklearn_4tree_5_tree_RegressionCriterion.__pyx_base = *__pyx_vtabptr_7sklearn_4tree_5_tree_Criterion;
__pyx_vtable_7sklearn_4tree_5_tree_RegressionCriterion.__pyx_base.init = (void (*)(struct __pyx_obj_7sklearn_4tree_5_tree_Criterion *, __pyx_t_7sklearn_4tree_5_tree_DOUBLE_t *, __pyx_t_7sklearn_4tree_5_tree_SIZE_t, __pyx_t_7sklearn_4tree_5_tree_DOUBLE_t *, double, __pyx_t_7sklearn_4tree_5_tree_SIZE_t *, __pyx_t_7sklearn_4tree_5_tree_SIZE_t, __pyx_t_7sklearn_4tree_5_tree_SIZE_t))__pyx_f_7sklearn_4tree_5_tree_19RegressionCriterion_init;
__pyx_vtable_7sklearn_4tree_5_tree_RegressionCriterion.__pyx_base.reset = (void (*)(struct __pyx_obj_7sklearn_4tree_5_tree_Criterion *))__pyx_f_7sklearn_4tree_5_tree_19RegressionCriterion_reset;
__pyx_vtable_7sklearn_4tree_5_tree_RegressionCriterion.__pyx_base.update = (void (*)(struct __pyx_obj_7sklearn_4tree_5_tree_Criterion *, __pyx_t_7sklearn_4tree_5_tree_SIZE_t))__pyx_f_7sklearn_4tree_5_tree_19RegressionCriterion_update;
__pyx_vtable_7sklearn_4tree_5_tree_RegressionCriterion.__pyx_base.node_impurity = (double (*)(struct __pyx_obj_7sklearn_4tree_5_tree_Criterion *))__pyx_f_7sklearn_4tree_5_tree_19RegressionCriterion_node_impurity;
__pyx_vtable_7sklearn_4tree_5_tree_RegressionCriterion.__pyx_base.children_impurity = (void (*)(struct __pyx_obj_7sklearn_4tree_5_tree_Criterion *, double *, double *))__pyx_f_7sklearn_4tree_5_tree_19RegressionCriterion_children_impurity;
__pyx_vtable_7sklearn_4tree_5_tree_RegressionCriterion.__pyx_base.node_value = (void (*)(struct __pyx_obj_7sklearn_4tree_5_tree_Criterion *, double *))__pyx_f_7sklearn_4tree_5_tree_19RegressionCriterion_node_value;
__pyx_type_7sklearn_4tree_5_tree_RegressionCriterion.tp_base = __pyx_ptype_7sklearn_4tree_5_tree_Criterion;
if (PyType_Ready(&__pyx_type_7sklearn_4tree_5_tree_RegressionCriterion) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 553; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
__pyx_type_7sklearn_4tree_5_tree_RegressionCriterion.tp_print = 0;
if (__Pyx_SetVtable(__pyx_type_7sklearn_4tree_5_tree_RegressionCriterion.tp_dict, __pyx_vtabptr_7sklearn_4tree_5_tree_RegressionCriterion) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 553; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
if (__Pyx_SetAttrString(__pyx_m, "RegressionCriterion", (PyObject *)&__pyx_type_7sklearn_4tree_5_tree_RegressionCriterion) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 553; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
__pyx_ptype_7sklearn_4tree_5_tree_RegressionCriterion = &__pyx_type_7sklearn_4tree_5_tree_RegressionCriterion;
__pyx_vtabptr_7sklearn_4tree_5_tree_MSE = &__pyx_vtable_7sklearn_4tree_5_tree_MSE;
__pyx_vtable_7sklearn_4tree_5_tree_MSE.__pyx_base = *__pyx_vtabptr_7sklearn_4tree_5_tree_RegressionCriterion;
__pyx_vtable_7sklearn_4tree_5_tree_MSE.__pyx_base.__pyx_base.node_impurity = (double (*)(struct __pyx_obj_7sklearn_4tree_5_tree_Criterion *))__pyx_f_7sklearn_4tree_5_tree_3MSE_node_impurity;
__pyx_vtable_7sklearn_4tree_5_tree_MSE.__pyx_base.__pyx_base.children_impurity = (void (*)(struct __pyx_obj_7sklearn_4tree_5_tree_Criterion *, double *, double *))__pyx_f_7sklearn_4tree_5_tree_3MSE_children_impurity;
__pyx_type_7sklearn_4tree_5_tree_MSE.tp_base = __pyx_ptype_7sklearn_4tree_5_tree_RegressionCriterion;
if (PyType_Ready(&__pyx_type_7sklearn_4tree_5_tree_MSE) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 836; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
__pyx_type_7sklearn_4tree_5_tree_MSE.tp_print = 0;
if (__Pyx_SetVtable(__pyx_type_7sklearn_4tree_5_tree_MSE.tp_dict, __pyx_vtabptr_7sklearn_4tree_5_tree_MSE) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 836; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
if (__Pyx_SetAttrString(__pyx_m, "MSE", (PyObject *)&__pyx_type_7sklearn_4tree_5_tree_MSE) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 836; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
__pyx_ptype_7sklearn_4tree_5_tree_MSE = &__pyx_type_7sklearn_4tree_5_tree_MSE;
__pyx_vtabptr_7sklearn_4tree_5_tree_FriedmanMSE = &__pyx_vtable_7sklearn_4tree_5_tree_FriedmanMSE;
__pyx_vtable_7sklearn_4tree_5_tree_FriedmanMSE.__pyx_base = *__pyx_vtabptr_7sklearn_4tree_5_tree_MSE;
__pyx_vtable_7sklearn_4tree_5_tree_FriedmanMSE.__pyx_base.__pyx_base.__pyx_base.impurity_improvement = (double (*)(struct __pyx_obj_7sklearn_4tree_5_tree_Criterion *, double))__pyx_f_7sklearn_4tree_5_tree_11FriedmanMSE_impurity_improvement;
__pyx_type_7sklearn_4tree_5_tree_FriedmanMSE.tp_base = __pyx_ptype_7sklearn_4tree_5_tree_MSE;
if (PyType_Ready(&__pyx_type_7sklearn_4tree_5_tree_FriedmanMSE) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 877; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
__pyx_type_7sklearn_4tree_5_tree_FriedmanMSE.tp_print = 0;
if (__Pyx_SetVtable(__pyx_type_7sklearn_4tree_5_tree_FriedmanMSE.tp_dict, __pyx_vtabptr_7sklearn_4tree_5_tree_FriedmanMSE) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 877; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
if (__Pyx_SetAttrString(__pyx_m, "FriedmanMSE", (PyObject *)&__pyx_type_7sklearn_4tree_5_tree_FriedmanMSE) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 877; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
__pyx_ptype_7sklearn_4tree_5_tree_FriedmanMSE = &__pyx_type_7sklearn_4tree_5_tree_FriedmanMSE;
__pyx_vtabptr_7sklearn_4tree_5_tree_BaseDenseSplitter = &__pyx_vtable_7sklearn_4tree_5_tree_BaseDenseSplitter;
__pyx_vtable_7sklearn_4tree_5_tree_BaseDenseSplitter.__pyx_base = *__pyx_vtabptr_7sklearn_4tree_5_tree_Splitter;
__pyx_vtable_7sklearn_4tree_5_tree_BaseDenseSplitter.__pyx_base.init = (void (*)(struct __pyx_obj_7sklearn_4tree_5_tree_Splitter *, PyObject *, PyArrayObject *, __pyx_t_7sklearn_4tree_5_tree_DOUBLE_t *))__pyx_f_7sklearn_4tree_5_tree_17BaseDenseSplitter_init;
__pyx_type_7sklearn_4tree_5_tree_BaseDenseSplitter.tp_base = __pyx_ptype_7sklearn_4tree_5_tree_Splitter;
if (PyType_Ready(&__pyx_type_7sklearn_4tree_5_tree_BaseDenseSplitter) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1031; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
__pyx_type_7sklearn_4tree_5_tree_BaseDenseSplitter.tp_print = 0;
if (__Pyx_SetVtable(__pyx_type_7sklearn_4tree_5_tree_BaseDenseSplitter.tp_dict, __pyx_vtabptr_7sklearn_4tree_5_tree_BaseDenseSplitter) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1031; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
if (__Pyx_SetAttrString(__pyx_m, "BaseDenseSplitter", (PyObject *)&__pyx_type_7sklearn_4tree_5_tree_BaseDenseSplitter) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1031; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
__pyx_ptype_7sklearn_4tree_5_tree_BaseDenseSplitter = &__pyx_type_7sklearn_4tree_5_tree_BaseDenseSplitter;
__pyx_vtabptr_7sklearn_4tree_5_tree_BestSplitter = &__pyx_vtable_7sklearn_4tree_5_tree_BestSplitter;
__pyx_vtable_7sklearn_4tree_5_tree_BestSplitter.__pyx_base = *__pyx_vtabptr_7sklearn_4tree_5_tree_BaseDenseSplitter;
__pyx_vtable_7sklearn_4tree_5_tree_BestSplitter.__pyx_base.__pyx_base.node_split = (void (*)(struct __pyx_obj_7sklearn_4tree_5_tree_Splitter *, double, struct __pyx_t_7sklearn_4tree_5_tree_SplitRecord *, __pyx_t_7sklearn_4tree_5_tree_SIZE_t *))__pyx_f_7sklearn_4tree_5_tree_12BestSplitter_node_split;
__pyx_type_7sklearn_4tree_5_tree_BestSplitter.tp_base = __pyx_ptype_7sklearn_4tree_5_tree_BaseDenseSplitter;
if (PyType_Ready(&__pyx_type_7sklearn_4tree_5_tree_BestSplitter) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1062; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
__pyx_type_7sklearn_4tree_5_tree_BestSplitter.tp_print = 0;
if (__Pyx_SetVtable(__pyx_type_7sklearn_4tree_5_tree_BestSplitter.tp_dict, __pyx_vtabptr_7sklearn_4tree_5_tree_BestSplitter) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1062; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
if (__Pyx_SetAttrString(__pyx_m, "BestSplitter", (PyObject *)&__pyx_type_7sklearn_4tree_5_tree_BestSplitter) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1062; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
__pyx_ptype_7sklearn_4tree_5_tree_BestSplitter = &__pyx_type_7sklearn_4tree_5_tree_BestSplitter;
__pyx_vtabptr_7sklearn_4tree_5_tree_RandomSplitter = &__pyx_vtable_7sklearn_4tree_5_tree_RandomSplitter;
__pyx_vtable_7sklearn_4tree_5_tree_RandomSplitter.__pyx_base = *__pyx_vtabptr_7sklearn_4tree_5_tree_BaseDenseSplitter;
__pyx_vtable_7sklearn_4tree_5_tree_RandomSplitter.__pyx_base.__pyx_base.node_split = (void (*)(struct __pyx_obj_7sklearn_4tree_5_tree_Splitter *, double, struct __pyx_t_7sklearn_4tree_5_tree_SplitRecord *, __pyx_t_7sklearn_4tree_5_tree_SIZE_t *))__pyx_f_7sklearn_4tree_5_tree_14RandomSplitter_node_split;
__pyx_type_7sklearn_4tree_5_tree_RandomSplitter.tp_base = __pyx_ptype_7sklearn_4tree_5_tree_BaseDenseSplitter;
if (PyType_Ready(&__pyx_type_7sklearn_4tree_5_tree_RandomSplitter) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1361; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
__pyx_type_7sklearn_4tree_5_tree_RandomSplitter.tp_print = 0;
if (__Pyx_SetVtable(__pyx_type_7sklearn_4tree_5_tree_RandomSplitter.tp_dict, __pyx_vtabptr_7sklearn_4tree_5_tree_RandomSplitter) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1361; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
if (__Pyx_SetAttrString(__pyx_m, "RandomSplitter", (PyObject *)&__pyx_type_7sklearn_4tree_5_tree_RandomSplitter) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1361; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
__pyx_ptype_7sklearn_4tree_5_tree_RandomSplitter = &__pyx_type_7sklearn_4tree_5_tree_RandomSplitter;
__pyx_vtabptr_7sklearn_4tree_5_tree_PresortBestSplitter = &__pyx_vtable_7sklearn_4tree_5_tree_PresortBestSplitter;
__pyx_vtable_7sklearn_4tree_5_tree_PresortBestSplitter.__pyx_base = *__pyx_vtabptr_7sklearn_4tree_5_tree_BaseDenseSplitter;
__pyx_vtable_7sklearn_4tree_5_tree_PresortBestSplitter.__pyx_base.__pyx_base.init = (void (*)(struct __pyx_obj_7sklearn_4tree_5_tree_Splitter *, PyObject *, PyArrayObject *, __pyx_t_7sklearn_4tree_5_tree_DOUBLE_t *))__pyx_f_7sklearn_4tree_5_tree_19PresortBestSplitter_init;
__pyx_vtable_7sklearn_4tree_5_tree_PresortBestSplitter.__pyx_base.__pyx_base.node_split = (void (*)(struct __pyx_obj_7sklearn_4tree_5_tree_Splitter *, double, struct __pyx_t_7sklearn_4tree_5_tree_SplitRecord *, __pyx_t_7sklearn_4tree_5_tree_SIZE_t *))__pyx_f_7sklearn_4tree_5_tree_19PresortBestSplitter_node_split;
__pyx_type_7sklearn_4tree_5_tree_PresortBestSplitter.tp_base = __pyx_ptype_7sklearn_4tree_5_tree_BaseDenseSplitter;
if (PyType_Ready(&__pyx_type_7sklearn_4tree_5_tree_PresortBestSplitter) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1563; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
__pyx_type_7sklearn_4tree_5_tree_PresortBestSplitter.tp_print = 0;
if (__Pyx_SetVtable(__pyx_type_7sklearn_4tree_5_tree_PresortBestSplitter.tp_dict, __pyx_vtabptr_7sklearn_4tree_5_tree_PresortBestSplitter) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1563; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
if (__Pyx_SetAttrString(__pyx_m, "PresortBestSplitter", (PyObject *)&__pyx_type_7sklearn_4tree_5_tree_PresortBestSplitter) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1563; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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__pyx_type_7sklearn_4tree_5_tree_DepthFirstTreeBuilder.tp_base = __pyx_ptype_7sklearn_4tree_5_tree_TreeBuilder;
if (PyType_Ready(&__pyx_type_7sklearn_4tree_5_tree_DepthFirstTreeBuilder) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2605; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
__pyx_type_7sklearn_4tree_5_tree_DepthFirstTreeBuilder.tp_print = 0;
if (__Pyx_SetVtable(__pyx_type_7sklearn_4tree_5_tree_DepthFirstTreeBuilder.tp_dict, __pyx_vtabptr_7sklearn_4tree_5_tree_DepthFirstTreeBuilder) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2605; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
if (__Pyx_SetAttrString(__pyx_m, "DepthFirstTreeBuilder", (PyObject *)&__pyx_type_7sklearn_4tree_5_tree_DepthFirstTreeBuilder) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2605; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
__pyx_ptype_7sklearn_4tree_5_tree_DepthFirstTreeBuilder = &__pyx_type_7sklearn_4tree_5_tree_DepthFirstTreeBuilder;
__pyx_vtabptr_7sklearn_4tree_5_tree_BestFirstTreeBuilder = &__pyx_vtable_7sklearn_4tree_5_tree_BestFirstTreeBuilder;
__pyx_vtable_7sklearn_4tree_5_tree_BestFirstTreeBuilder.__pyx_base = *__pyx_vtabptr_7sklearn_4tree_5_tree_TreeBuilder;
__pyx_vtable_7sklearn_4tree_5_tree_BestFirstTreeBuilder.__pyx_base.build = (PyObject *(*)(struct __pyx_obj_7sklearn_4tree_5_tree_TreeBuilder *, struct __pyx_obj_7sklearn_4tree_5_tree_Tree *, PyObject *, PyArrayObject *, int __pyx_skip_dispatch, struct __pyx_opt_args_7sklearn_4tree_5_tree_11TreeBuilder_build *__pyx_optional_args))__pyx_f_7sklearn_4tree_5_tree_20BestFirstTreeBuilder_build;
__pyx_vtable_7sklearn_4tree_5_tree_BestFirstTreeBuilder._add_split_node = (int (*)(struct __pyx_obj_7sklearn_4tree_5_tree_BestFirstTreeBuilder *, struct __pyx_obj_7sklearn_4tree_5_tree_Splitter *, struct __pyx_obj_7sklearn_4tree_5_tree_Tree *, __pyx_t_7sklearn_4tree_5_tree_SIZE_t, __pyx_t_7sklearn_4tree_5_tree_SIZE_t, double, int, int, struct __pyx_t_7sklearn_4tree_5_tree_Node *, __pyx_t_7sklearn_4tree_5_tree_SIZE_t, struct __pyx_t_7sklearn_4tree_6_utils_PriorityHeapRecord *))__pyx_f_7sklearn_4tree_5_tree_20BestFirstTreeBuilder__add_split_node;
__pyx_type_7sklearn_4tree_5_tree_BestFirstTreeBuilder.tp_base = __pyx_ptype_7sklearn_4tree_5_tree_TreeBuilder;
if (PyType_Ready(&__pyx_type_7sklearn_4tree_5_tree_BestFirstTreeBuilder) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2751; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
__pyx_type_7sklearn_4tree_5_tree_BestFirstTreeBuilder.tp_print = 0;
if (__Pyx_SetVtable(__pyx_type_7sklearn_4tree_5_tree_BestFirstTreeBuilder.tp_dict, __pyx_vtabptr_7sklearn_4tree_5_tree_BestFirstTreeBuilder) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2751; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
if (__Pyx_SetAttrString(__pyx_m, "BestFirstTreeBuilder", (PyObject *)&__pyx_type_7sklearn_4tree_5_tree_BestFirstTreeBuilder) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2751; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
__pyx_ptype_7sklearn_4tree_5_tree_BestFirstTreeBuilder = &__pyx_type_7sklearn_4tree_5_tree_BestFirstTreeBuilder;
/*--- Type import code ---*/
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#if CYTHON_COMPILING_IN_PYPY
sizeof(PyTypeObject),
#else
sizeof(PyHeapTypeObject),
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0); if (unlikely(!__pyx_ptype_7cpython_4type_type)) {__pyx_filename = __pyx_f[3]; __pyx_lineno = 9; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
__pyx_ptype_5numpy_dtype = __Pyx_ImportType("numpy", "dtype", sizeof(PyArray_Descr), 0); if (unlikely(!__pyx_ptype_5numpy_dtype)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 155; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
__pyx_ptype_5numpy_flatiter = __Pyx_ImportType("numpy", "flatiter", sizeof(PyArrayIterObject), 0); if (unlikely(!__pyx_ptype_5numpy_flatiter)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 165; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
__pyx_ptype_5numpy_broadcast = __Pyx_ImportType("numpy", "broadcast", sizeof(PyArrayMultiIterObject), 0); if (unlikely(!__pyx_ptype_5numpy_broadcast)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 169; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
__pyx_ptype_5numpy_ndarray = __Pyx_ImportType("numpy", "ndarray", sizeof(PyArrayObject), 0); if (unlikely(!__pyx_ptype_5numpy_ndarray)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 178; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
__pyx_ptype_5numpy_ufunc = __Pyx_ImportType("numpy", "ufunc", sizeof(PyUFuncObject), 0); if (unlikely(!__pyx_ptype_5numpy_ufunc)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 861; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
__pyx_ptype_7cpython_4bool_bool = __Pyx_ImportType(__Pyx_BUILTIN_MODULE_NAME, "bool", sizeof(PyBoolObject), 0); if (unlikely(!__pyx_ptype_7cpython_4bool_bool)) {__pyx_filename = __pyx_f[4]; __pyx_lineno = 8; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
__pyx_ptype_7cpython_7complex_complex = __Pyx_ImportType(__Pyx_BUILTIN_MODULE_NAME, "complex", sizeof(PyComplexObject), 0); if (unlikely(!__pyx_ptype_7cpython_7complex_complex)) {__pyx_filename = __pyx_f[5]; __pyx_lineno = 15; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
__pyx_ptype_7sklearn_4tree_6_utils_Stack = __Pyx_ImportType("sklearn.tree._utils", "Stack", sizeof(struct __pyx_obj_7sklearn_4tree_6_utils_Stack), 1); if (unlikely(!__pyx_ptype_7sklearn_4tree_6_utils_Stack)) {__pyx_filename = __pyx_f[6]; __pyx_lineno = 29; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
__pyx_vtabptr_7sklearn_4tree_6_utils_Stack = (struct __pyx_vtabstruct_7sklearn_4tree_6_utils_Stack*)__Pyx_GetVtable(__pyx_ptype_7sklearn_4tree_6_utils_Stack->tp_dict); if (unlikely(!__pyx_vtabptr_7sklearn_4tree_6_utils_Stack)) {__pyx_filename = __pyx_f[6]; __pyx_lineno = 29; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
__pyx_ptype_7sklearn_4tree_6_utils_PriorityHeap = __Pyx_ImportType("sklearn.tree._utils", "PriorityHeap", sizeof(struct __pyx_obj_7sklearn_4tree_6_utils_PriorityHeap), 1); if (unlikely(!__pyx_ptype_7sklearn_4tree_6_utils_PriorityHeap)) {__pyx_filename = __pyx_f[6]; __pyx_lineno = 58; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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/*--- Variable import code ---*/
/*--- Function import code ---*/
/*--- Execution code ---*/
/* "sklearn/tree/_tree.pyx":23
* from cpython cimport Py_INCREF, PyObject
*
* import numpy as np # <<<<<<<<<<<<<<
* cimport numpy as np
* np.import_array()
*/
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__Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0;
/* "sklearn/tree/_tree.pyx":25
* import numpy as np
* cimport numpy as np
* np.import_array() # <<<<<<<<<<<<<<
*
* from scipy.sparse import issparse, csc_matrix, csr_matrix
*/
import_array();
/* "sklearn/tree/_tree.pyx":27
* np.import_array()
*
* from scipy.sparse import issparse, csc_matrix, csr_matrix # <<<<<<<<<<<<<<
*
* from sklearn.tree._utils cimport Stack, StackRecord
*/
__pyx_t_1 = PyList_New(3); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 27; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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/* "sklearn/tree/_tree.pyx":44
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*
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if (!ctx->is_valid_array) {
PyErr_Format(PyExc_ValueError, "Expected %d dimensions, got %d",
ctx->head->field->type->ndim, ndim);
return -1;
}
for (i = 0; i < ctx->head->field->type->ndim; i++) {
arraysize *= ctx->head->field->type->arraysize[i];
}
ctx->is_valid_array = 0;
ctx->enc_count = 1;
}
group = __Pyx_BufFmt_TypeCharToGroup(ctx->enc_type, ctx->is_complex);
do {
__Pyx_StructField* field = ctx->head->field;
__Pyx_TypeInfo* type = field->type;
if (ctx->enc_packmode == '@' || ctx->enc_packmode == '^') {
size = __Pyx_BufFmt_TypeCharToNativeSize(ctx->enc_type, ctx->is_complex);
} else {
size = __Pyx_BufFmt_TypeCharToStandardSize(ctx->enc_type, ctx->is_complex);
}
if (ctx->enc_packmode == '@') {
size_t align_at = __Pyx_BufFmt_TypeCharToAlignment(ctx->enc_type, ctx->is_complex);
size_t align_mod_offset;
if (align_at == 0) return -1;
align_mod_offset = ctx->fmt_offset % align_at;
if (align_mod_offset > 0) ctx->fmt_offset += align_at - align_mod_offset;
if (ctx->struct_alignment == 0)
ctx->struct_alignment = __Pyx_BufFmt_TypeCharToPadding(ctx->enc_type,
ctx->is_complex);
}
if (type->size != size || type->typegroup != group) {
if (type->typegroup == 'C' && type->fields != NULL) {
size_t parent_offset = ctx->head->parent_offset + field->offset;
++ctx->head;
ctx->head->field = type->fields;
ctx->head->parent_offset = parent_offset;
continue;
}
if ((type->typegroup == 'H' || group == 'H') && type->size == size) {
} else {
__Pyx_BufFmt_RaiseExpected(ctx);
return -1;
}
}
offset = ctx->head->parent_offset + field->offset;
if (ctx->fmt_offset != offset) {
PyErr_Format(PyExc_ValueError,
"Buffer dtype mismatch; next field is at offset %" CYTHON_FORMAT_SSIZE_T "d but %" CYTHON_FORMAT_SSIZE_T "d expected",
(Py_ssize_t)ctx->fmt_offset, (Py_ssize_t)offset);
return -1;
}
ctx->fmt_offset += size;
if (arraysize)
ctx->fmt_offset += (arraysize - 1) * size;
--ctx->enc_count; /* Consume from buffer string */
while (1) {
if (field == &ctx->root) {
ctx->head = NULL;
if (ctx->enc_count != 0) {
__Pyx_BufFmt_RaiseExpected(ctx);
return -1;
}
break; /* breaks both loops as ctx->enc_count == 0 */
}
ctx->head->field = ++field;
if (field->type == NULL) {
--ctx->head;
field = ctx->head->field;
continue;
} else if (field->type->typegroup == 'S') {
size_t parent_offset = ctx->head->parent_offset + field->offset;
if (field->type->fields->type == NULL) continue; /* empty struct */
field = field->type->fields;
++ctx->head;
ctx->head->field = field;
ctx->head->parent_offset = parent_offset;
break;
} else {
break;
}
}
} while (ctx->enc_count);
ctx->enc_type = 0;
ctx->is_complex = 0;
return 0;
}
static CYTHON_INLINE PyObject *
__pyx_buffmt_parse_array(__Pyx_BufFmt_Context* ctx, const char** tsp)
{
const char *ts = *tsp;
int i = 0, number;
int ndim = ctx->head->field->type->ndim;
;
++ts;
if (ctx->new_count != 1) {
PyErr_SetString(PyExc_ValueError,
"Cannot handle repeated arrays in format string");
return NULL;
}
if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL;
while (*ts && *ts != ')') {
switch (*ts) {
case ' ': case '\f': case '\r': case '\n': case '\t': case '\v': continue;
default: break; /* not a 'break' in the loop */
}
number = __Pyx_BufFmt_ExpectNumber(&ts);
if (number == -1) return NULL;
if (i < ndim && (size_t) number != ctx->head->field->type->arraysize[i])
return PyErr_Format(PyExc_ValueError,
"Expected a dimension of size %zu, got %d",
ctx->head->field->type->arraysize[i], number);
if (*ts != ',' && *ts != ')')
return PyErr_Format(PyExc_ValueError,
"Expected a comma in format string, got '%c'", *ts);
if (*ts == ',') ts++;
i++;
}
if (i != ndim)
return PyErr_Format(PyExc_ValueError, "Expected %d dimension(s), got %d",
ctx->head->field->type->ndim, i);
if (!*ts) {
PyErr_SetString(PyExc_ValueError,
"Unexpected end of format string, expected ')'");
return NULL;
}
ctx->is_valid_array = 1;
ctx->new_count = 1;
*tsp = ++ts;
return Py_None;
}
static const char* __Pyx_BufFmt_CheckString(__Pyx_BufFmt_Context* ctx, const char* ts) {
int got_Z = 0;
while (1) {
switch(*ts) {
case 0:
if (ctx->enc_type != 0 && ctx->head == NULL) {
__Pyx_BufFmt_RaiseExpected(ctx);
return NULL;
}
if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL;
if (ctx->head != NULL) {
__Pyx_BufFmt_RaiseExpected(ctx);
return NULL;
}
return ts;
case ' ':
case '\r':
case '\n':
++ts;
break;
case '<':
if (!__Pyx_IsLittleEndian()) {
PyErr_SetString(PyExc_ValueError, "Little-endian buffer not supported on big-endian compiler");
return NULL;
}
ctx->new_packmode = '=';
++ts;
break;
case '>':
case '!':
if (__Pyx_IsLittleEndian()) {
PyErr_SetString(PyExc_ValueError, "Big-endian buffer not supported on little-endian compiler");
return NULL;
}
ctx->new_packmode = '=';
++ts;
break;
case '=':
case '@':
case '^':
ctx->new_packmode = *ts++;
break;
case 'T': /* substruct */
{
const char* ts_after_sub;
size_t i, struct_count = ctx->new_count;
size_t struct_alignment = ctx->struct_alignment;
ctx->new_count = 1;
++ts;
if (*ts != '{') {
PyErr_SetString(PyExc_ValueError, "Buffer acquisition: Expected '{' after 'T'");
return NULL;
}
if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL;
ctx->enc_type = 0; /* Erase processed last struct element */
ctx->enc_count = 0;
ctx->struct_alignment = 0;
++ts;
ts_after_sub = ts;
for (i = 0; i != struct_count; ++i) {
ts_after_sub = __Pyx_BufFmt_CheckString(ctx, ts);
if (!ts_after_sub) return NULL;
}
ts = ts_after_sub;
if (struct_alignment) ctx->struct_alignment = struct_alignment;
}
break;
case '}': /* end of substruct; either repeat or move on */
{
size_t alignment = ctx->struct_alignment;
++ts;
if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL;
ctx->enc_type = 0; /* Erase processed last struct element */
if (alignment && ctx->fmt_offset % alignment) {
ctx->fmt_offset += alignment - (ctx->fmt_offset % alignment);
}
}
return ts;
case 'x':
if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL;
ctx->fmt_offset += ctx->new_count;
ctx->new_count = 1;
ctx->enc_count = 0;
ctx->enc_type = 0;
ctx->enc_packmode = ctx->new_packmode;
++ts;
break;
case 'Z':
got_Z = 1;
++ts;
if (*ts != 'f' && *ts != 'd' && *ts != 'g') {
__Pyx_BufFmt_RaiseUnexpectedChar('Z');
return NULL;
}
case 'c': case 'b': case 'B': case 'h': case 'H': case 'i': case 'I':
case 'l': case 'L': case 'q': case 'Q':
case 'f': case 'd': case 'g':
case 'O': case 'p':
if (ctx->enc_type == *ts && got_Z == ctx->is_complex &&
ctx->enc_packmode == ctx->new_packmode) {
ctx->enc_count += ctx->new_count;
ctx->new_count = 1;
got_Z = 0;
++ts;
break;
}
case 's':
if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL;
ctx->enc_count = ctx->new_count;
ctx->enc_packmode = ctx->new_packmode;
ctx->enc_type = *ts;
ctx->is_complex = got_Z;
++ts;
ctx->new_count = 1;
got_Z = 0;
break;
case ':':
++ts;
while(*ts != ':') ++ts;
++ts;
break;
case '(':
if (!__pyx_buffmt_parse_array(ctx, &ts)) return NULL;
break;
default:
{
int number = __Pyx_BufFmt_ExpectNumber(&ts);
if (number == -1) return NULL;
ctx->new_count = (size_t)number;
}
}
}
}
static CYTHON_INLINE void __Pyx_ZeroBuffer(Py_buffer* buf) {
buf->buf = NULL;
buf->obj = NULL;
buf->strides = __Pyx_zeros;
buf->shape = __Pyx_zeros;
buf->suboffsets = __Pyx_minusones;
}
static CYTHON_INLINE int __Pyx_GetBufferAndValidate(
Py_buffer* buf, PyObject* obj, __Pyx_TypeInfo* dtype, int flags,
int nd, int cast, __Pyx_BufFmt_StackElem* stack)
{
if (obj == Py_None || obj == NULL) {
__Pyx_ZeroBuffer(buf);
return 0;
}
buf->buf = NULL;
if (__Pyx_GetBuffer(obj, buf, flags) == -1) goto fail;
if (buf->ndim != nd) {
PyErr_Format(PyExc_ValueError,
"Buffer has wrong number of dimensions (expected %d, got %d)",
nd, buf->ndim);
goto fail;
}
if (!cast) {
__Pyx_BufFmt_Context ctx;
__Pyx_BufFmt_Init(&ctx, stack, dtype);
if (!__Pyx_BufFmt_CheckString(&ctx, buf->format)) goto fail;
}
if ((unsigned)buf->itemsize != dtype->size) {
PyErr_Format(PyExc_ValueError,
"Item size of buffer (%" CYTHON_FORMAT_SSIZE_T "d byte%s) does not match size of '%s' (%" CYTHON_FORMAT_SSIZE_T "d byte%s)",
buf->itemsize, (buf->itemsize > 1) ? "s" : "",
dtype->name, (Py_ssize_t)dtype->size, (dtype->size > 1) ? "s" : "");
goto fail;
}
if (buf->suboffsets == NULL) buf->suboffsets = __Pyx_minusones;
return 0;
fail:;
__Pyx_ZeroBuffer(buf);
return -1;
}
static CYTHON_INLINE void __Pyx_SafeReleaseBuffer(Py_buffer* info) {
if (info->buf == NULL) return;
if (info->suboffsets == __Pyx_minusones) info->suboffsets = NULL;
__Pyx_ReleaseBuffer(info);
}
static CYTHON_INLINE void __Pyx_ErrRestore(PyObject *type, PyObject *value, PyObject *tb) {
#if CYTHON_COMPILING_IN_CPYTHON
PyObject *tmp_type, *tmp_value, *tmp_tb;
PyThreadState *tstate = PyThreadState_GET();
tmp_type = tstate->curexc_type;
tmp_value = tstate->curexc_value;
tmp_tb = tstate->curexc_traceback;
tstate->curexc_type = type;
tstate->curexc_value = value;
tstate->curexc_traceback = tb;
Py_XDECREF(tmp_type);
Py_XDECREF(tmp_value);
Py_XDECREF(tmp_tb);
#else
PyErr_Restore(type, value, tb);
#endif
}
static CYTHON_INLINE void __Pyx_ErrFetch(PyObject **type, PyObject **value, PyObject **tb) {
#if CYTHON_COMPILING_IN_CPYTHON
PyThreadState *tstate = PyThreadState_GET();
*type = tstate->curexc_type;
*value = tstate->curexc_value;
*tb = tstate->curexc_traceback;
tstate->curexc_type = 0;
tstate->curexc_value = 0;
tstate->curexc_traceback = 0;
#else
PyErr_Fetch(type, value, tb);
#endif
}
#if CYTHON_COMPILING_IN_CPYTHON
static CYTHON_INLINE PyObject* __Pyx_PyObject_Call(PyObject *func, PyObject *arg, PyObject *kw) {
PyObject *result;
ternaryfunc call = func->ob_type->tp_call;
if (unlikely(!call))
return PyObject_Call(func, arg, kw);
#if PY_VERSION_HEX >= 0x02060000
if (unlikely(Py_EnterRecursiveCall((char*)" while calling a Python object")))
return NULL;
#endif
result = (*call)(func, arg, kw);
#if PY_VERSION_HEX >= 0x02060000
Py_LeaveRecursiveCall();
#endif
if (unlikely(!result) && unlikely(!PyErr_Occurred())) {
PyErr_SetString(
PyExc_SystemError,
"NULL result without error in PyObject_Call");
}
return result;
}
#endif
static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Generic(PyObject *o, PyObject* j) {
PyObject *r;
if (!j) return NULL;
r = PyObject_GetItem(o, j);
Py_DECREF(j);
return r;
}
static CYTHON_INLINE PyObject *__Pyx_GetItemInt_List_Fast(PyObject *o, Py_ssize_t i,
int wraparound, int boundscheck) {
#if CYTHON_COMPILING_IN_CPYTHON
if (wraparound & unlikely(i < 0)) i += PyList_GET_SIZE(o);
if ((!boundscheck) || likely((0 <= i) & (i < PyList_GET_SIZE(o)))) {
PyObject *r = PyList_GET_ITEM(o, i);
Py_INCREF(r);
return r;
}
return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i));
#else
return PySequence_GetItem(o, i);
#endif
}
static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Tuple_Fast(PyObject *o, Py_ssize_t i,
int wraparound, int boundscheck) {
#if CYTHON_COMPILING_IN_CPYTHON
if (wraparound & unlikely(i < 0)) i += PyTuple_GET_SIZE(o);
if ((!boundscheck) || likely((0 <= i) & (i < PyTuple_GET_SIZE(o)))) {
PyObject *r = PyTuple_GET_ITEM(o, i);
Py_INCREF(r);
return r;
}
return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i));
#else
return PySequence_GetItem(o, i);
#endif
}
static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Fast(PyObject *o, Py_ssize_t i,
int is_list, int wraparound, int boundscheck) {
#if CYTHON_COMPILING_IN_CPYTHON
if (is_list || PyList_CheckExact(o)) {
Py_ssize_t n = ((!wraparound) | likely(i >= 0)) ? i : i + PyList_GET_SIZE(o);
if ((!boundscheck) || (likely((n >= 0) & (n < PyList_GET_SIZE(o))))) {
PyObject *r = PyList_GET_ITEM(o, n);
Py_INCREF(r);
return r;
}
}
else if (PyTuple_CheckExact(o)) {
Py_ssize_t n = ((!wraparound) | likely(i >= 0)) ? i : i + PyTuple_GET_SIZE(o);
if ((!boundscheck) || likely((n >= 0) & (n < PyTuple_GET_SIZE(o)))) {
PyObject *r = PyTuple_GET_ITEM(o, n);
Py_INCREF(r);
return r;
}
} else {
PySequenceMethods *m = Py_TYPE(o)->tp_as_sequence;
if (likely(m && m->sq_item)) {
if (wraparound && unlikely(i < 0) && likely(m->sq_length)) {
Py_ssize_t l = m->sq_length(o);
if (likely(l >= 0)) {
i += l;
} else {
if (PyErr_ExceptionMatches(PyExc_OverflowError))
PyErr_Clear();
else
return NULL;
}
}
return m->sq_item(o, i);
}
}
#else
if (is_list || PySequence_Check(o)) {
return PySequence_GetItem(o, i);
}
#endif
return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i));
}
static void __Pyx_WriteUnraisable(const char *name, CYTHON_UNUSED int clineno,
CYTHON_UNUSED int lineno, CYTHON_UNUSED const char *filename,
int full_traceback) {
PyObject *old_exc, *old_val, *old_tb;
PyObject *ctx;
__Pyx_ErrFetch(&old_exc, &old_val, &old_tb);
if (full_traceback) {
Py_XINCREF(old_exc);
Py_XINCREF(old_val);
Py_XINCREF(old_tb);
__Pyx_ErrRestore(old_exc, old_val, old_tb);
PyErr_PrintEx(1);
}
#if PY_MAJOR_VERSION < 3
ctx = PyString_FromString(name);
#else
ctx = PyUnicode_FromString(name);
#endif
__Pyx_ErrRestore(old_exc, old_val, old_tb);
if (!ctx) {
PyErr_WriteUnraisable(Py_None);
} else {
PyErr_WriteUnraisable(ctx);
Py_DECREF(ctx);
}
}
static CYTHON_INLINE int __Pyx_TypeTest(PyObject *obj, PyTypeObject *type) {
if (unlikely(!type)) {
PyErr_SetString(PyExc_SystemError, "Missing type object");
return 0;
}
if (likely(PyObject_TypeCheck(obj, type)))
return 1;
PyErr_Format(PyExc_TypeError, "Cannot convert %.200s to %.200s",
Py_TYPE(obj)->tp_name, type->tp_name);
return 0;
}
static CYTHON_INLINE PyObject *__Pyx_GetModuleGlobalName(PyObject *name) {
PyObject *result;
#if CYTHON_COMPILING_IN_CPYTHON
result = PyDict_GetItem(__pyx_d, name);
if (result) {
Py_INCREF(result);
} else {
#else
result = PyObject_GetItem(__pyx_d, name);
if (!result) {
PyErr_Clear();
#endif
result = __Pyx_GetBuiltinName(name);
}
return result;
}
#if PY_MAJOR_VERSION < 3
static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb,
CYTHON_UNUSED PyObject *cause) {
Py_XINCREF(type);
if (!value || value == Py_None)
value = NULL;
else
Py_INCREF(value);
if (!tb || tb == Py_None)
tb = NULL;
else {
Py_INCREF(tb);
if (!PyTraceBack_Check(tb)) {
PyErr_SetString(PyExc_TypeError,
"raise: arg 3 must be a traceback or None");
goto raise_error;
}
}
#if PY_VERSION_HEX < 0x02050000
if (PyClass_Check(type)) {
#else
if (PyType_Check(type)) {
#endif
#if CYTHON_COMPILING_IN_PYPY
if (!value) {
Py_INCREF(Py_None);
value = Py_None;
}
#endif
PyErr_NormalizeException(&type, &value, &tb);
} else {
if (value) {
PyErr_SetString(PyExc_TypeError,
"instance exception may not have a separate value");
goto raise_error;
}
value = type;
#if PY_VERSION_HEX < 0x02050000
if (PyInstance_Check(type)) {
type = (PyObject*) ((PyInstanceObject*)type)->in_class;
Py_INCREF(type);
} else {
type = 0;
PyErr_SetString(PyExc_TypeError,
"raise: exception must be an old-style class or instance");
goto raise_error;
}
#else
type = (PyObject*) Py_TYPE(type);
Py_INCREF(type);
if (!PyType_IsSubtype((PyTypeObject *)type, (PyTypeObject *)PyExc_BaseException)) {
PyErr_SetString(PyExc_TypeError,
"raise: exception class must be a subclass of BaseException");
goto raise_error;
}
#endif
}
__Pyx_ErrRestore(type, value, tb);
return;
raise_error:
Py_XDECREF(value);
Py_XDECREF(type);
Py_XDECREF(tb);
return;
}
#else /* Python 3+ */
static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, PyObject *cause) {
PyObject* owned_instance = NULL;
if (tb == Py_None) {
tb = 0;
} else if (tb && !PyTraceBack_Check(tb)) {
PyErr_SetString(PyExc_TypeError,
"raise: arg 3 must be a traceback or None");
goto bad;
}
if (value == Py_None)
value = 0;
if (PyExceptionInstance_Check(type)) {
if (value) {
PyErr_SetString(PyExc_TypeError,
"instance exception may not have a separate value");
goto bad;
}
value = type;
type = (PyObject*) Py_TYPE(value);
} else if (PyExceptionClass_Check(type)) {
PyObject *instance_class = NULL;
if (value && PyExceptionInstance_Check(value)) {
instance_class = (PyObject*) Py_TYPE(value);
if (instance_class != type) {
if (PyObject_IsSubclass(instance_class, type)) {
type = instance_class;
} else {
instance_class = NULL;
}
}
}
if (!instance_class) {
PyObject *args;
if (!value)
args = PyTuple_New(0);
else if (PyTuple_Check(value)) {
Py_INCREF(value);
args = value;
} else
args = PyTuple_Pack(1, value);
if (!args)
goto bad;
owned_instance = PyObject_Call(type, args, NULL);
Py_DECREF(args);
if (!owned_instance)
goto bad;
value = owned_instance;
if (!PyExceptionInstance_Check(value)) {
PyErr_Format(PyExc_TypeError,
"calling %R should have returned an instance of "
"BaseException, not %R",
type, Py_TYPE(value));
goto bad;
}
}
} else {
PyErr_SetString(PyExc_TypeError,
"raise: exception class must be a subclass of BaseException");
goto bad;
}
#if PY_VERSION_HEX >= 0x03030000
if (cause) {
#else
if (cause && cause != Py_None) {
#endif
PyObject *fixed_cause;
if (cause == Py_None) {
fixed_cause = NULL;
} else if (PyExceptionClass_Check(cause)) {
fixed_cause = PyObject_CallObject(cause, NULL);
if (fixed_cause == NULL)
goto bad;
} else if (PyExceptionInstance_Check(cause)) {
fixed_cause = cause;
Py_INCREF(fixed_cause);
} else {
PyErr_SetString(PyExc_TypeError,
"exception causes must derive from "
"BaseException");
goto bad;
}
PyException_SetCause(value, fixed_cause);
}
PyErr_SetObject(type, value);
if (tb) {
PyThreadState *tstate = PyThreadState_GET();
PyObject* tmp_tb = tstate->curexc_traceback;
if (tb != tmp_tb) {
Py_INCREF(tb);
tstate->curexc_traceback = tb;
Py_XDECREF(tmp_tb);
}
}
bad:
Py_XDECREF(owned_instance);
return;
}
#endif
static CYTHON_INLINE void __Pyx_RaiseTooManyValuesError(Py_ssize_t expected) {
PyErr_Format(PyExc_ValueError,
"too many values to unpack (expected %" CYTHON_FORMAT_SSIZE_T "d)", expected);
}
static CYTHON_INLINE void __Pyx_RaiseNeedMoreValuesError(Py_ssize_t index) {
PyErr_Format(PyExc_ValueError,
"need more than %" CYTHON_FORMAT_SSIZE_T "d value%.1s to unpack",
index, (index == 1) ? "" : "s");
}
static CYTHON_INLINE int __Pyx_IterFinish(void) {
#if CYTHON_COMPILING_IN_CPYTHON
PyThreadState *tstate = PyThreadState_GET();
PyObject* exc_type = tstate->curexc_type;
if (unlikely(exc_type)) {
if (likely(exc_type == PyExc_StopIteration) || PyErr_GivenExceptionMatches(exc_type, PyExc_StopIteration)) {
PyObject *exc_value, *exc_tb;
exc_value = tstate->curexc_value;
exc_tb = tstate->curexc_traceback;
tstate->curexc_type = 0;
tstate->curexc_value = 0;
tstate->curexc_traceback = 0;
Py_DECREF(exc_type);
Py_XDECREF(exc_value);
Py_XDECREF(exc_tb);
return 0;
} else {
return -1;
}
}
return 0;
#else
if (unlikely(PyErr_Occurred())) {
if (likely(PyErr_ExceptionMatches(PyExc_StopIteration))) {
PyErr_Clear();
return 0;
} else {
return -1;
}
}
return 0;
#endif
}
static int __Pyx_IternextUnpackEndCheck(PyObject *retval, Py_ssize_t expected) {
if (unlikely(retval)) {
Py_DECREF(retval);
__Pyx_RaiseTooManyValuesError(expected);
return -1;
} else {
return __Pyx_IterFinish();
}
return 0;
}
static CYTHON_INLINE PyObject* __Pyx_PyObject_GetSlice(
PyObject* obj, Py_ssize_t cstart, Py_ssize_t cstop,
PyObject** _py_start, PyObject** _py_stop, PyObject** _py_slice,
int has_cstart, int has_cstop, CYTHON_UNUSED int wraparound) {
#if CYTHON_COMPILING_IN_CPYTHON
PyMappingMethods* mp;
#if PY_MAJOR_VERSION < 3
PySequenceMethods* ms = Py_TYPE(obj)->tp_as_sequence;
if (likely(ms && ms->sq_slice)) {
if (!has_cstart) {
if (_py_start && (*_py_start != Py_None)) {
cstart = __Pyx_PyIndex_AsSsize_t(*_py_start);
if ((cstart == (Py_ssize_t)-1) && PyErr_Occurred()) goto bad;
} else
cstart = 0;
}
if (!has_cstop) {
if (_py_stop && (*_py_stop != Py_None)) {
cstop = __Pyx_PyIndex_AsSsize_t(*_py_stop);
if ((cstop == (Py_ssize_t)-1) && PyErr_Occurred()) goto bad;
} else
cstop = PY_SSIZE_T_MAX;
}
if (wraparound && unlikely((cstart < 0) | (cstop < 0)) && likely(ms->sq_length)) {
Py_ssize_t l = ms->sq_length(obj);
if (likely(l >= 0)) {
if (cstop < 0) {
cstop += l;
if (cstop < 0) cstop = 0;
}
if (cstart < 0) {
cstart += l;
if (cstart < 0) cstart = 0;
}
} else {
if (PyErr_ExceptionMatches(PyExc_OverflowError))
PyErr_Clear();
else
goto bad;
}
}
return ms->sq_slice(obj, cstart, cstop);
}
#endif
mp = Py_TYPE(obj)->tp_as_mapping;
if (likely(mp && mp->mp_subscript))
#endif
{
PyObject* result;
PyObject *py_slice, *py_start, *py_stop;
if (_py_slice) {
py_slice = *_py_slice;
} else {
PyObject* owned_start = NULL;
PyObject* owned_stop = NULL;
if (_py_start) {
py_start = *_py_start;
} else {
if (has_cstart) {
owned_start = py_start = PyInt_FromSsize_t(cstart);
if (unlikely(!py_start)) goto bad;
} else
py_start = Py_None;
}
if (_py_stop) {
py_stop = *_py_stop;
} else {
if (has_cstop) {
owned_stop = py_stop = PyInt_FromSsize_t(cstop);
if (unlikely(!py_stop)) {
Py_XDECREF(owned_start);
goto bad;
}
} else
py_stop = Py_None;
}
py_slice = PySlice_New(py_start, py_stop, Py_None);
Py_XDECREF(owned_start);
Py_XDECREF(owned_stop);
if (unlikely(!py_slice)) goto bad;
}
#if CYTHON_COMPILING_IN_CPYTHON
result = mp->mp_subscript(obj, py_slice);
#else
result = PyObject_GetItem(obj, py_slice);
#endif
if (!_py_slice) {
Py_DECREF(py_slice);
}
return result;
}
PyErr_Format(PyExc_TypeError,
"'%.200s' object is unsliceable", Py_TYPE(obj)->tp_name);
bad:
return NULL;
}
static void __Pyx_RaiseBufferFallbackError(void) {
PyErr_SetString(PyExc_ValueError,
"Buffer acquisition failed on assignment; and then reacquiring the old buffer failed too!");
}
static CYTHON_INLINE void __Pyx_RaiseNoneNotIterableError(void) {
PyErr_SetString(PyExc_TypeError, "'NoneType' object is not iterable");
}
static int __Pyx_SetVtable(PyObject *dict, void *vtable) {
#if PY_VERSION_HEX >= 0x02070000 && !(PY_MAJOR_VERSION==3&&PY_MINOR_VERSION==0)
PyObject *ob = PyCapsule_New(vtable, 0, 0);
#else
PyObject *ob = PyCObject_FromVoidPtr(vtable, 0);
#endif
if (!ob)
goto bad;
if (PyDict_SetItem(dict, __pyx_n_s_pyx_vtable, ob) < 0)
goto bad;
Py_DECREF(ob);
return 0;
bad:
Py_XDECREF(ob);
return -1;
}
static void* __Pyx_GetVtable(PyObject *dict) {
void* ptr;
PyObject *ob = PyObject_GetItem(dict, __pyx_n_s_pyx_vtable);
if (!ob)
goto bad;
#if PY_VERSION_HEX >= 0x02070000 && !(PY_MAJOR_VERSION==3&&PY_MINOR_VERSION==0)
ptr = PyCapsule_GetPointer(ob, 0);
#else
ptr = PyCObject_AsVoidPtr(ob);
#endif
if (!ptr && !PyErr_Occurred())
PyErr_SetString(PyExc_RuntimeError, "invalid vtable found for imported type");
Py_DECREF(ob);
return ptr;
bad:
Py_XDECREF(ob);
return NULL;
}
static PyObject* __Pyx_ImportFrom(PyObject* module, PyObject* name) {
PyObject* value = __Pyx_PyObject_GetAttrStr(module, name);
if (unlikely(!value) && PyErr_ExceptionMatches(PyExc_AttributeError)) {
PyErr_Format(PyExc_ImportError,
#if PY_MAJOR_VERSION < 3
"cannot import name %.230s", PyString_AS_STRING(name));
#else
"cannot import name %S", name);
#endif
}
return value;
}
static CYTHON_INLINE PyObject* __Pyx_PyInt_From_Py_intptr_t(Py_intptr_t value) {
const Py_intptr_t neg_one = (Py_intptr_t) -1, const_zero = 0;
const int is_unsigned = neg_one > const_zero;
if (is_unsigned) {
if (sizeof(Py_intptr_t) < sizeof(long)) {
return PyInt_FromLong((long) value);
} else if (sizeof(Py_intptr_t) <= sizeof(unsigned long)) {
return PyLong_FromUnsignedLong((unsigned long) value);
} else if (sizeof(Py_intptr_t) <= sizeof(unsigned long long)) {
return PyLong_FromUnsignedLongLong((unsigned long long) value);
}
} else {
if (sizeof(Py_intptr_t) <= sizeof(long)) {
return PyInt_FromLong((long) value);
} else if (sizeof(Py_intptr_t) <= sizeof(long long)) {
return PyLong_FromLongLong((long long) value);
}
}
{
int one = 1; int little = (int)*(unsigned char *)&one;
unsigned char *bytes = (unsigned char *)&value;
return _PyLong_FromByteArray(bytes, sizeof(Py_intptr_t),
little, !is_unsigned);
}
}
#define __PYX_VERIFY_RETURN_INT(target_type, func_type, func) \
{ \
func_type value = func(x); \
if (sizeof(target_type) < sizeof(func_type)) { \
if (unlikely(value != (func_type) (target_type) value)) { \
func_type zero = 0; \
PyErr_SetString(PyExc_OverflowError, \
(is_unsigned && unlikely(value < zero)) ? \
"can't convert negative value to " #target_type : \
"value too large to convert to " #target_type); \
return (target_type) -1; \
} \
} \
return (target_type) value; \
}
#if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3
#if CYTHON_USE_PYLONG_INTERNALS
#include "longintrepr.h"
#endif
#endif
static CYTHON_INLINE Py_intptr_t __Pyx_PyInt_As_Py_intptr_t(PyObject *x) {
const Py_intptr_t neg_one = (Py_intptr_t) -1, const_zero = 0;
const int is_unsigned = neg_one > const_zero;
#if PY_MAJOR_VERSION < 3
if (likely(PyInt_Check(x))) {
if (sizeof(Py_intptr_t) < sizeof(long)) {
__PYX_VERIFY_RETURN_INT(Py_intptr_t, long, PyInt_AS_LONG)
} else {
long val = PyInt_AS_LONG(x);
if (is_unsigned && unlikely(val < 0)) {
PyErr_SetString(PyExc_OverflowError,
"can't convert negative value to Py_intptr_t");
return (Py_intptr_t) -1;
}
return (Py_intptr_t) val;
}
} else
#endif
if (likely(PyLong_Check(x))) {
if (is_unsigned) {
#if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3
#if CYTHON_USE_PYLONG_INTERNALS
if (sizeof(digit) <= sizeof(Py_intptr_t)) {
switch (Py_SIZE(x)) {
case 0: return 0;
case 1: return (Py_intptr_t) ((PyLongObject*)x)->ob_digit[0];
}
}
#endif
#endif
if (unlikely(Py_SIZE(x) < 0)) {
PyErr_SetString(PyExc_OverflowError,
"can't convert negative value to Py_intptr_t");
return (Py_intptr_t) -1;
}
if (sizeof(Py_intptr_t) <= sizeof(unsigned long)) {
__PYX_VERIFY_RETURN_INT(Py_intptr_t, unsigned long, PyLong_AsUnsignedLong)
} else if (sizeof(Py_intptr_t) <= sizeof(unsigned long long)) {
__PYX_VERIFY_RETURN_INT(Py_intptr_t, unsigned long long, PyLong_AsUnsignedLongLong)
}
} else {
#if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3
#if CYTHON_USE_PYLONG_INTERNALS
if (sizeof(digit) <= sizeof(Py_intptr_t)) {
switch (Py_SIZE(x)) {
case 0: return 0;
case 1: return +(Py_intptr_t) ((PyLongObject*)x)->ob_digit[0];
case -1: return -(Py_intptr_t) ((PyLongObject*)x)->ob_digit[0];
}
}
#endif
#endif
if (sizeof(Py_intptr_t) <= sizeof(long)) {
__PYX_VERIFY_RETURN_INT(Py_intptr_t, long, PyLong_AsLong)
} else if (sizeof(Py_intptr_t) <= sizeof(long long)) {
__PYX_VERIFY_RETURN_INT(Py_intptr_t, long long, PyLong_AsLongLong)
}
}
{
#if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray)
PyErr_SetString(PyExc_RuntimeError,
"_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers");
#else
Py_intptr_t val;
PyObject *v = __Pyx_PyNumber_Int(x);
#if PY_MAJOR_VERSION < 3
if (likely(v) && !PyLong_Check(v)) {
PyObject *tmp = v;
v = PyNumber_Long(tmp);
Py_DECREF(tmp);
}
#endif
if (likely(v)) {
int one = 1; int is_little = (int)*(unsigned char *)&one;
unsigned char *bytes = (unsigned char *)&val;
int ret = _PyLong_AsByteArray((PyLongObject *)v,
bytes, sizeof(val),
is_little, !is_unsigned);
Py_DECREF(v);
if (likely(!ret))
return val;
}
#endif
return (Py_intptr_t) -1;
}
} else {
Py_intptr_t val;
PyObject *tmp = __Pyx_PyNumber_Int(x);
if (!tmp) return (Py_intptr_t) -1;
val = __Pyx_PyInt_As_Py_intptr_t(tmp);
Py_DECREF(tmp);
return val;
}
}
static PyObject *__Pyx_Import(PyObject *name, PyObject *from_list, int level) {
PyObject *empty_list = 0;
PyObject *module = 0;
PyObject *global_dict = 0;
PyObject *empty_dict = 0;
PyObject *list;
#if PY_VERSION_HEX < 0x03030000
PyObject *py_import;
py_import = __Pyx_PyObject_GetAttrStr(__pyx_b, __pyx_n_s_import);
if (!py_import)
goto bad;
#endif
if (from_list)
list = from_list;
else {
empty_list = PyList_New(0);
if (!empty_list)
goto bad;
list = empty_list;
}
global_dict = PyModule_GetDict(__pyx_m);
if (!global_dict)
goto bad;
empty_dict = PyDict_New();
if (!empty_dict)
goto bad;
#if PY_VERSION_HEX >= 0x02050000
{
#if PY_MAJOR_VERSION >= 3
if (level == -1) {
if (strchr(__Pyx_MODULE_NAME, '.')) {
#if PY_VERSION_HEX < 0x03030000
PyObject *py_level = PyInt_FromLong(1);
if (!py_level)
goto bad;
module = PyObject_CallFunctionObjArgs(py_import,
name, global_dict, empty_dict, list, py_level, NULL);
Py_DECREF(py_level);
#else
module = PyImport_ImportModuleLevelObject(
name, global_dict, empty_dict, list, 1);
#endif
if (!module) {
if (!PyErr_ExceptionMatches(PyExc_ImportError))
goto bad;
PyErr_Clear();
}
}
level = 0; /* try absolute import on failure */
}
#endif
if (!module) {
#if PY_VERSION_HEX < 0x03030000
PyObject *py_level = PyInt_FromLong(level);
if (!py_level)
goto bad;
module = PyObject_CallFunctionObjArgs(py_import,
name, global_dict, empty_dict, list, py_level, NULL);
Py_DECREF(py_level);
#else
module = PyImport_ImportModuleLevelObject(
name, global_dict, empty_dict, list, level);
#endif
}
}
#else
if (level>0) {
PyErr_SetString(PyExc_RuntimeError, "Relative import is not supported for Python <=2.4.");
goto bad;
}
module = PyObject_CallFunctionObjArgs(py_import,
name, global_dict, empty_dict, list, NULL);
#endif
bad:
#if PY_VERSION_HEX < 0x03030000
Py_XDECREF(py_import);
#endif
Py_XDECREF(empty_list);
Py_XDECREF(empty_dict);
return module;
}
#if PY_MAJOR_VERSION < 3
static int __Pyx_GetBuffer(PyObject *obj, Py_buffer *view, int flags) {
#if PY_VERSION_HEX >= 0x02060000
if (PyObject_CheckBuffer(obj)) return PyObject_GetBuffer(obj, view, flags);
#endif
if (PyObject_TypeCheck(obj, __pyx_ptype_5numpy_ndarray)) return __pyx_pw_5numpy_7ndarray_1__getbuffer__(obj, view, flags);
#if PY_VERSION_HEX < 0x02060000
if (obj->ob_type->tp_dict) {
PyObject *getbuffer_cobj = PyObject_GetItem(
obj->ob_type->tp_dict, __pyx_n_s_pyx_getbuffer);
if (getbuffer_cobj) {
getbufferproc func = (getbufferproc) PyCObject_AsVoidPtr(getbuffer_cobj);
Py_DECREF(getbuffer_cobj);
if (!func)
goto fail;
return func(obj, view, flags);
} else {
PyErr_Clear();
}
}
#endif
PyErr_Format(PyExc_TypeError, "'%.200s' does not have the buffer interface", Py_TYPE(obj)->tp_name);
#if PY_VERSION_HEX < 0x02060000
fail:
#endif
return -1;
}
static void __Pyx_ReleaseBuffer(Py_buffer *view) {
PyObject *obj = view->obj;
if (!obj) return;
#if PY_VERSION_HEX >= 0x02060000
if (PyObject_CheckBuffer(obj)) {
PyBuffer_Release(view);
return;
}
#endif
if (PyObject_TypeCheck(obj, __pyx_ptype_5numpy_ndarray)) { __pyx_pw_5numpy_7ndarray_3__releasebuffer__(obj, view); return; }
#if PY_VERSION_HEX < 0x02060000
if (obj->ob_type->tp_dict) {
PyObject *releasebuffer_cobj = PyObject_GetItem(
obj->ob_type->tp_dict, __pyx_n_s_pyx_releasebuffer);
if (releasebuffer_cobj) {
releasebufferproc func = (releasebufferproc) PyCObject_AsVoidPtr(releasebuffer_cobj);
Py_DECREF(releasebuffer_cobj);
if (!func)
goto fail;
func(obj, view);
return;
} else {
PyErr_Clear();
}
}
#endif
goto nofail;
#if PY_VERSION_HEX < 0x02060000
fail:
#endif
PyErr_WriteUnraisable(obj);
nofail:
Py_DECREF(obj);
view->obj = NULL;
}
#endif /* PY_MAJOR_VERSION < 3 */
#if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3
#if CYTHON_USE_PYLONG_INTERNALS
#include "longintrepr.h"
#endif
#endif
static CYTHON_INLINE int __Pyx_PyInt_As_int(PyObject *x) {
const int neg_one = (int) -1, const_zero = 0;
const int is_unsigned = neg_one > const_zero;
#if PY_MAJOR_VERSION < 3
if (likely(PyInt_Check(x))) {
if (sizeof(int) < sizeof(long)) {
__PYX_VERIFY_RETURN_INT(int, long, PyInt_AS_LONG)
} else {
long val = PyInt_AS_LONG(x);
if (is_unsigned && unlikely(val < 0)) {
PyErr_SetString(PyExc_OverflowError,
"can't convert negative value to int");
return (int) -1;
}
return (int) val;
}
} else
#endif
if (likely(PyLong_Check(x))) {
if (is_unsigned) {
#if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3
#if CYTHON_USE_PYLONG_INTERNALS
if (sizeof(digit) <= sizeof(int)) {
switch (Py_SIZE(x)) {
case 0: return 0;
case 1: return (int) ((PyLongObject*)x)->ob_digit[0];
}
}
#endif
#endif
if (unlikely(Py_SIZE(x) < 0)) {
PyErr_SetString(PyExc_OverflowError,
"can't convert negative value to int");
return (int) -1;
}
if (sizeof(int) <= sizeof(unsigned long)) {
__PYX_VERIFY_RETURN_INT(int, unsigned long, PyLong_AsUnsignedLong)
} else if (sizeof(int) <= sizeof(unsigned long long)) {
__PYX_VERIFY_RETURN_INT(int, unsigned long long, PyLong_AsUnsignedLongLong)
}
} else {
#if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3
#if CYTHON_USE_PYLONG_INTERNALS
if (sizeof(digit) <= sizeof(int)) {
switch (Py_SIZE(x)) {
case 0: return 0;
case 1: return +(int) ((PyLongObject*)x)->ob_digit[0];
case -1: return -(int) ((PyLongObject*)x)->ob_digit[0];
}
}
#endif
#endif
if (sizeof(int) <= sizeof(long)) {
__PYX_VERIFY_RETURN_INT(int, long, PyLong_AsLong)
} else if (sizeof(int) <= sizeof(long long)) {
__PYX_VERIFY_RETURN_INT(int, long long, PyLong_AsLongLong)
}
}
{
#if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray)
PyErr_SetString(PyExc_RuntimeError,
"_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers");
#else
int val;
PyObject *v = __Pyx_PyNumber_Int(x);
#if PY_MAJOR_VERSION < 3
if (likely(v) && !PyLong_Check(v)) {
PyObject *tmp = v;
v = PyNumber_Long(tmp);
Py_DECREF(tmp);
}
#endif
if (likely(v)) {
int one = 1; int is_little = (int)*(unsigned char *)&one;
unsigned char *bytes = (unsigned char *)&val;
int ret = _PyLong_AsByteArray((PyLongObject *)v,
bytes, sizeof(val),
is_little, !is_unsigned);
Py_DECREF(v);
if (likely(!ret))
return val;
}
#endif
return (int) -1;
}
} else {
int val;
PyObject *tmp = __Pyx_PyNumber_Int(x);
if (!tmp) return (int) -1;
val = __Pyx_PyInt_As_int(tmp);
Py_DECREF(tmp);
return val;
}
}
static CYTHON_INLINE PyObject* __Pyx_PyInt_From_int(int value) {
const int neg_one = (int) -1, const_zero = 0;
const int is_unsigned = neg_one > const_zero;
if (is_unsigned) {
if (sizeof(int) < sizeof(long)) {
return PyInt_FromLong((long) value);
} else if (sizeof(int) <= sizeof(unsigned long)) {
return PyLong_FromUnsignedLong((unsigned long) value);
} else if (sizeof(int) <= sizeof(unsigned long long)) {
return PyLong_FromUnsignedLongLong((unsigned long long) value);
}
} else {
if (sizeof(int) <= sizeof(long)) {
return PyInt_FromLong((long) value);
} else if (sizeof(int) <= sizeof(long long)) {
return PyLong_FromLongLong((long long) value);
}
}
{
int one = 1; int little = (int)*(unsigned char *)&one;
unsigned char *bytes = (unsigned char *)&value;
return _PyLong_FromByteArray(bytes, sizeof(int),
little, !is_unsigned);
}
}
#if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3
#if CYTHON_USE_PYLONG_INTERNALS
#include "longintrepr.h"
#endif
#endif
static CYTHON_INLINE npy_uint32 __Pyx_PyInt_As_npy_uint32(PyObject *x) {
const npy_uint32 neg_one = (npy_uint32) -1, const_zero = 0;
const int is_unsigned = neg_one > const_zero;
#if PY_MAJOR_VERSION < 3
if (likely(PyInt_Check(x))) {
if (sizeof(npy_uint32) < sizeof(long)) {
__PYX_VERIFY_RETURN_INT(npy_uint32, long, PyInt_AS_LONG)
} else {
long val = PyInt_AS_LONG(x);
if (is_unsigned && unlikely(val < 0)) {
PyErr_SetString(PyExc_OverflowError,
"can't convert negative value to npy_uint32");
return (npy_uint32) -1;
}
return (npy_uint32) val;
}
} else
#endif
if (likely(PyLong_Check(x))) {
if (is_unsigned) {
#if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3
#if CYTHON_USE_PYLONG_INTERNALS
if (sizeof(digit) <= sizeof(npy_uint32)) {
switch (Py_SIZE(x)) {
case 0: return 0;
case 1: return (npy_uint32) ((PyLongObject*)x)->ob_digit[0];
}
}
#endif
#endif
if (unlikely(Py_SIZE(x) < 0)) {
PyErr_SetString(PyExc_OverflowError,
"can't convert negative value to npy_uint32");
return (npy_uint32) -1;
}
if (sizeof(npy_uint32) <= sizeof(unsigned long)) {
__PYX_VERIFY_RETURN_INT(npy_uint32, unsigned long, PyLong_AsUnsignedLong)
} else if (sizeof(npy_uint32) <= sizeof(unsigned long long)) {
__PYX_VERIFY_RETURN_INT(npy_uint32, unsigned long long, PyLong_AsUnsignedLongLong)
}
} else {
#if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3
#if CYTHON_USE_PYLONG_INTERNALS
if (sizeof(digit) <= sizeof(npy_uint32)) {
switch (Py_SIZE(x)) {
case 0: return 0;
case 1: return +(npy_uint32) ((PyLongObject*)x)->ob_digit[0];
case -1: return -(npy_uint32) ((PyLongObject*)x)->ob_digit[0];
}
}
#endif
#endif
if (sizeof(npy_uint32) <= sizeof(long)) {
__PYX_VERIFY_RETURN_INT(npy_uint32, long, PyLong_AsLong)
} else if (sizeof(npy_uint32) <= sizeof(long long)) {
__PYX_VERIFY_RETURN_INT(npy_uint32, long long, PyLong_AsLongLong)
}
}
{
#if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray)
PyErr_SetString(PyExc_RuntimeError,
"_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers");
#else
npy_uint32 val;
PyObject *v = __Pyx_PyNumber_Int(x);
#if PY_MAJOR_VERSION < 3
if (likely(v) && !PyLong_Check(v)) {
PyObject *tmp = v;
v = PyNumber_Long(tmp);
Py_DECREF(tmp);
}
#endif
if (likely(v)) {
int one = 1; int is_little = (int)*(unsigned char *)&one;
unsigned char *bytes = (unsigned char *)&val;
int ret = _PyLong_AsByteArray((PyLongObject *)v,
bytes, sizeof(val),
is_little, !is_unsigned);
Py_DECREF(v);
if (likely(!ret))
return val;
}
#endif
return (npy_uint32) -1;
}
} else {
npy_uint32 val;
PyObject *tmp = __Pyx_PyNumber_Int(x);
if (!tmp) return (npy_uint32) -1;
val = __Pyx_PyInt_As_npy_uint32(tmp);
Py_DECREF(tmp);
return val;
}
}
static CYTHON_INLINE PyObject* __Pyx_PyInt_From_long(long value) {
const long neg_one = (long) -1, const_zero = 0;
const int is_unsigned = neg_one > const_zero;
if (is_unsigned) {
if (sizeof(long) < sizeof(long)) {
return PyInt_FromLong((long) value);
} else if (sizeof(long) <= sizeof(unsigned long)) {
return PyLong_FromUnsignedLong((unsigned long) value);
} else if (sizeof(long) <= sizeof(unsigned long long)) {
return PyLong_FromUnsignedLongLong((unsigned long long) value);
}
} else {
if (sizeof(long) <= sizeof(long)) {
return PyInt_FromLong((long) value);
} else if (sizeof(long) <= sizeof(long long)) {
return PyLong_FromLongLong((long long) value);
}
}
{
int one = 1; int little = (int)*(unsigned char *)&one;
unsigned char *bytes = (unsigned char *)&value;
return _PyLong_FromByteArray(bytes, sizeof(long),
little, !is_unsigned);
}
}
static CYTHON_INLINE PyObject* __Pyx_PyInt_From_npy_int32(npy_int32 value) {
const npy_int32 neg_one = (npy_int32) -1, const_zero = 0;
const int is_unsigned = neg_one > const_zero;
if (is_unsigned) {
if (sizeof(npy_int32) < sizeof(long)) {
return PyInt_FromLong((long) value);
} else if (sizeof(npy_int32) <= sizeof(unsigned long)) {
return PyLong_FromUnsignedLong((unsigned long) value);
} else if (sizeof(npy_int32) <= sizeof(unsigned long long)) {
return PyLong_FromUnsignedLongLong((unsigned long long) value);
}
} else {
if (sizeof(npy_int32) <= sizeof(long)) {
return PyInt_FromLong((long) value);
} else if (sizeof(npy_int32) <= sizeof(long long)) {
return PyLong_FromLongLong((long long) value);
}
}
{
int one = 1; int little = (int)*(unsigned char *)&one;
unsigned char *bytes = (unsigned char *)&value;
return _PyLong_FromByteArray(bytes, sizeof(npy_int32),
little, !is_unsigned);
}
}
#if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3
#if CYTHON_USE_PYLONG_INTERNALS
#include "longintrepr.h"
#endif
#endif
static CYTHON_INLINE npy_int32 __Pyx_PyInt_As_npy_int32(PyObject *x) {
const npy_int32 neg_one = (npy_int32) -1, const_zero = 0;
const int is_unsigned = neg_one > const_zero;
#if PY_MAJOR_VERSION < 3
if (likely(PyInt_Check(x))) {
if (sizeof(npy_int32) < sizeof(long)) {
__PYX_VERIFY_RETURN_INT(npy_int32, long, PyInt_AS_LONG)
} else {
long val = PyInt_AS_LONG(x);
if (is_unsigned && unlikely(val < 0)) {
PyErr_SetString(PyExc_OverflowError,
"can't convert negative value to npy_int32");
return (npy_int32) -1;
}
return (npy_int32) val;
}
} else
#endif
if (likely(PyLong_Check(x))) {
if (is_unsigned) {
#if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3
#if CYTHON_USE_PYLONG_INTERNALS
if (sizeof(digit) <= sizeof(npy_int32)) {
switch (Py_SIZE(x)) {
case 0: return 0;
case 1: return (npy_int32) ((PyLongObject*)x)->ob_digit[0];
}
}
#endif
#endif
if (unlikely(Py_SIZE(x) < 0)) {
PyErr_SetString(PyExc_OverflowError,
"can't convert negative value to npy_int32");
return (npy_int32) -1;
}
if (sizeof(npy_int32) <= sizeof(unsigned long)) {
__PYX_VERIFY_RETURN_INT(npy_int32, unsigned long, PyLong_AsUnsignedLong)
} else if (sizeof(npy_int32) <= sizeof(unsigned long long)) {
__PYX_VERIFY_RETURN_INT(npy_int32, unsigned long long, PyLong_AsUnsignedLongLong)
}
} else {
#if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3
#if CYTHON_USE_PYLONG_INTERNALS
if (sizeof(digit) <= sizeof(npy_int32)) {
switch (Py_SIZE(x)) {
case 0: return 0;
case 1: return +(npy_int32) ((PyLongObject*)x)->ob_digit[0];
case -1: return -(npy_int32) ((PyLongObject*)x)->ob_digit[0];
}
}
#endif
#endif
if (sizeof(npy_int32) <= sizeof(long)) {
__PYX_VERIFY_RETURN_INT(npy_int32, long, PyLong_AsLong)
} else if (sizeof(npy_int32) <= sizeof(long long)) {
__PYX_VERIFY_RETURN_INT(npy_int32, long long, PyLong_AsLongLong)
}
}
{
#if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray)
PyErr_SetString(PyExc_RuntimeError,
"_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers");
#else
npy_int32 val;
PyObject *v = __Pyx_PyNumber_Int(x);
#if PY_MAJOR_VERSION < 3
if (likely(v) && !PyLong_Check(v)) {
PyObject *tmp = v;
v = PyNumber_Long(tmp);
Py_DECREF(tmp);
}
#endif
if (likely(v)) {
int one = 1; int is_little = (int)*(unsigned char *)&one;
unsigned char *bytes = (unsigned char *)&val;
int ret = _PyLong_AsByteArray((PyLongObject *)v,
bytes, sizeof(val),
is_little, !is_unsigned);
Py_DECREF(v);
if (likely(!ret))
return val;
}
#endif
return (npy_int32) -1;
}
} else {
npy_int32 val;
PyObject *tmp = __Pyx_PyNumber_Int(x);
if (!tmp) return (npy_int32) -1;
val = __Pyx_PyInt_As_npy_int32(tmp);
Py_DECREF(tmp);
return val;
}
}
static CYTHON_INLINE long __Pyx_pow_long(long b, long e) {
long t = b;
switch (e) {
case 3:
t *= b;
case 2:
t *= b;
case 1:
return t;
case 0:
return 1;
}
#if 1
if (unlikely(e<0)) return 0;
#endif
t = 1;
while (likely(e)) {
t *= (b * (e&1)) | ((~e)&1); /* 1 or b */
b *= b;
e >>= 1;
}
return t;
}
#if CYTHON_CCOMPLEX
#ifdef __cplusplus
static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float x, float y) {
return ::std::complex< float >(x, y);
}
#else
static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float x, float y) {
return x + y*(__pyx_t_float_complex)_Complex_I;
}
#endif
#else
static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float x, float y) {
__pyx_t_float_complex z;
z.real = x;
z.imag = y;
return z;
}
#endif
#if CYTHON_CCOMPLEX
#else
static CYTHON_INLINE int __Pyx_c_eqf(__pyx_t_float_complex a, __pyx_t_float_complex b) {
return (a.real == b.real) && (a.imag == b.imag);
}
static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_sumf(__pyx_t_float_complex a, __pyx_t_float_complex b) {
__pyx_t_float_complex z;
z.real = a.real + b.real;
z.imag = a.imag + b.imag;
return z;
}
static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_difff(__pyx_t_float_complex a, __pyx_t_float_complex b) {
__pyx_t_float_complex z;
z.real = a.real - b.real;
z.imag = a.imag - b.imag;
return z;
}
static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_prodf(__pyx_t_float_complex a, __pyx_t_float_complex b) {
__pyx_t_float_complex z;
z.real = a.real * b.real - a.imag * b.imag;
z.imag = a.real * b.imag + a.imag * b.real;
return z;
}
static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_quotf(__pyx_t_float_complex a, __pyx_t_float_complex b) {
__pyx_t_float_complex z;
float denom = b.real * b.real + b.imag * b.imag;
z.real = (a.real * b.real + a.imag * b.imag) / denom;
z.imag = (a.imag * b.real - a.real * b.imag) / denom;
return z;
}
static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_negf(__pyx_t_float_complex a) {
__pyx_t_float_complex z;
z.real = -a.real;
z.imag = -a.imag;
return z;
}
static CYTHON_INLINE int __Pyx_c_is_zerof(__pyx_t_float_complex a) {
return (a.real == 0) && (a.imag == 0);
}
static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_conjf(__pyx_t_float_complex a) {
__pyx_t_float_complex z;
z.real = a.real;
z.imag = -a.imag;
return z;
}
#if 1
static CYTHON_INLINE float __Pyx_c_absf(__pyx_t_float_complex z) {
#if !defined(HAVE_HYPOT) || defined(_MSC_VER)
return sqrtf(z.real*z.real + z.imag*z.imag);
#else
return hypotf(z.real, z.imag);
#endif
}
static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_powf(__pyx_t_float_complex a, __pyx_t_float_complex b) {
__pyx_t_float_complex z;
float r, lnr, theta, z_r, z_theta;
if (b.imag == 0 && b.real == (int)b.real) {
if (b.real < 0) {
float denom = a.real * a.real + a.imag * a.imag;
a.real = a.real / denom;
a.imag = -a.imag / denom;
b.real = -b.real;
}
switch ((int)b.real) {
case 0:
z.real = 1;
z.imag = 0;
return z;
case 1:
return a;
case 2:
z = __Pyx_c_prodf(a, a);
return __Pyx_c_prodf(a, a);
case 3:
z = __Pyx_c_prodf(a, a);
return __Pyx_c_prodf(z, a);
case 4:
z = __Pyx_c_prodf(a, a);
return __Pyx_c_prodf(z, z);
}
}
if (a.imag == 0) {
if (a.real == 0) {
return a;
}
r = a.real;
theta = 0;
} else {
r = __Pyx_c_absf(a);
theta = atan2f(a.imag, a.real);
}
lnr = logf(r);
z_r = expf(lnr * b.real - theta * b.imag);
z_theta = theta * b.real + lnr * b.imag;
z.real = z_r * cosf(z_theta);
z.imag = z_r * sinf(z_theta);
return z;
}
#endif
#endif
#if CYTHON_CCOMPLEX
#ifdef __cplusplus
static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double x, double y) {
return ::std::complex< double >(x, y);
}
#else
static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double x, double y) {
return x + y*(__pyx_t_double_complex)_Complex_I;
}
#endif
#else
static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double x, double y) {
__pyx_t_double_complex z;
z.real = x;
z.imag = y;
return z;
}
#endif
#if CYTHON_CCOMPLEX
#else
static CYTHON_INLINE int __Pyx_c_eq(__pyx_t_double_complex a, __pyx_t_double_complex b) {
return (a.real == b.real) && (a.imag == b.imag);
}
static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_sum(__pyx_t_double_complex a, __pyx_t_double_complex b) {
__pyx_t_double_complex z;
z.real = a.real + b.real;
z.imag = a.imag + b.imag;
return z;
}
static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_diff(__pyx_t_double_complex a, __pyx_t_double_complex b) {
__pyx_t_double_complex z;
z.real = a.real - b.real;
z.imag = a.imag - b.imag;
return z;
}
static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_prod(__pyx_t_double_complex a, __pyx_t_double_complex b) {
__pyx_t_double_complex z;
z.real = a.real * b.real - a.imag * b.imag;
z.imag = a.real * b.imag + a.imag * b.real;
return z;
}
static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_quot(__pyx_t_double_complex a, __pyx_t_double_complex b) {
__pyx_t_double_complex z;
double denom = b.real * b.real + b.imag * b.imag;
z.real = (a.real * b.real + a.imag * b.imag) / denom;
z.imag = (a.imag * b.real - a.real * b.imag) / denom;
return z;
}
static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_neg(__pyx_t_double_complex a) {
__pyx_t_double_complex z;
z.real = -a.real;
z.imag = -a.imag;
return z;
}
static CYTHON_INLINE int __Pyx_c_is_zero(__pyx_t_double_complex a) {
return (a.real == 0) && (a.imag == 0);
}
static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_conj(__pyx_t_double_complex a) {
__pyx_t_double_complex z;
z.real = a.real;
z.imag = -a.imag;
return z;
}
#if 1
static CYTHON_INLINE double __Pyx_c_abs(__pyx_t_double_complex z) {
#if !defined(HAVE_HYPOT) || defined(_MSC_VER)
return sqrt(z.real*z.real + z.imag*z.imag);
#else
return hypot(z.real, z.imag);
#endif
}
static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_pow(__pyx_t_double_complex a, __pyx_t_double_complex b) {
__pyx_t_double_complex z;
double r, lnr, theta, z_r, z_theta;
if (b.imag == 0 && b.real == (int)b.real) {
if (b.real < 0) {
double denom = a.real * a.real + a.imag * a.imag;
a.real = a.real / denom;
a.imag = -a.imag / denom;
b.real = -b.real;
}
switch ((int)b.real) {
case 0:
z.real = 1;
z.imag = 0;
return z;
case 1:
return a;
case 2:
z = __Pyx_c_prod(a, a);
return __Pyx_c_prod(a, a);
case 3:
z = __Pyx_c_prod(a, a);
return __Pyx_c_prod(z, a);
case 4:
z = __Pyx_c_prod(a, a);
return __Pyx_c_prod(z, z);
}
}
if (a.imag == 0) {
if (a.real == 0) {
return a;
}
r = a.real;
theta = 0;
} else {
r = __Pyx_c_abs(a);
theta = atan2(a.imag, a.real);
}
lnr = log(r);
z_r = exp(lnr * b.real - theta * b.imag);
z_theta = theta * b.real + lnr * b.imag;
z.real = z_r * cos(z_theta);
z.imag = z_r * sin(z_theta);
return z;
}
#endif
#endif
#if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3
#if CYTHON_USE_PYLONG_INTERNALS
#include "longintrepr.h"
#endif
#endif
static CYTHON_INLINE long __Pyx_PyInt_As_long(PyObject *x) {
const long neg_one = (long) -1, const_zero = 0;
const int is_unsigned = neg_one > const_zero;
#if PY_MAJOR_VERSION < 3
if (likely(PyInt_Check(x))) {
if (sizeof(long) < sizeof(long)) {
__PYX_VERIFY_RETURN_INT(long, long, PyInt_AS_LONG)
} else {
long val = PyInt_AS_LONG(x);
if (is_unsigned && unlikely(val < 0)) {
PyErr_SetString(PyExc_OverflowError,
"can't convert negative value to long");
return (long) -1;
}
return (long) val;
}
} else
#endif
if (likely(PyLong_Check(x))) {
if (is_unsigned) {
#if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3
#if CYTHON_USE_PYLONG_INTERNALS
if (sizeof(digit) <= sizeof(long)) {
switch (Py_SIZE(x)) {
case 0: return 0;
case 1: return (long) ((PyLongObject*)x)->ob_digit[0];
}
}
#endif
#endif
if (unlikely(Py_SIZE(x) < 0)) {
PyErr_SetString(PyExc_OverflowError,
"can't convert negative value to long");
return (long) -1;
}
if (sizeof(long) <= sizeof(unsigned long)) {
__PYX_VERIFY_RETURN_INT(long, unsigned long, PyLong_AsUnsignedLong)
} else if (sizeof(long) <= sizeof(unsigned long long)) {
__PYX_VERIFY_RETURN_INT(long, unsigned long long, PyLong_AsUnsignedLongLong)
}
} else {
#if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3
#if CYTHON_USE_PYLONG_INTERNALS
if (sizeof(digit) <= sizeof(long)) {
switch (Py_SIZE(x)) {
case 0: return 0;
case 1: return +(long) ((PyLongObject*)x)->ob_digit[0];
case -1: return -(long) ((PyLongObject*)x)->ob_digit[0];
}
}
#endif
#endif
if (sizeof(long) <= sizeof(long)) {
__PYX_VERIFY_RETURN_INT(long, long, PyLong_AsLong)
} else if (sizeof(long) <= sizeof(long long)) {
__PYX_VERIFY_RETURN_INT(long, long long, PyLong_AsLongLong)
}
}
{
#if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray)
PyErr_SetString(PyExc_RuntimeError,
"_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers");
#else
long val;
PyObject *v = __Pyx_PyNumber_Int(x);
#if PY_MAJOR_VERSION < 3
if (likely(v) && !PyLong_Check(v)) {
PyObject *tmp = v;
v = PyNumber_Long(tmp);
Py_DECREF(tmp);
}
#endif
if (likely(v)) {
int one = 1; int is_little = (int)*(unsigned char *)&one;
unsigned char *bytes = (unsigned char *)&val;
int ret = _PyLong_AsByteArray((PyLongObject *)v,
bytes, sizeof(val),
is_little, !is_unsigned);
Py_DECREF(v);
if (likely(!ret))
return val;
}
#endif
return (long) -1;
}
} else {
long val;
PyObject *tmp = __Pyx_PyNumber_Int(x);
if (!tmp) return (long) -1;
val = __Pyx_PyInt_As_long(tmp);
Py_DECREF(tmp);
return val;
}
}
static int __Pyx_check_binary_version(void) {
char ctversion[4], rtversion[4];
PyOS_snprintf(ctversion, 4, "%d.%d", PY_MAJOR_VERSION, PY_MINOR_VERSION);
PyOS_snprintf(rtversion, 4, "%s", Py_GetVersion());
if (ctversion[0] != rtversion[0] || ctversion[2] != rtversion[2]) {
char message[200];
PyOS_snprintf(message, sizeof(message),
"compiletime version %s of module '%.100s' "
"does not match runtime version %s",
ctversion, __Pyx_MODULE_NAME, rtversion);
#if PY_VERSION_HEX < 0x02050000
return PyErr_Warn(NULL, message);
#else
return PyErr_WarnEx(NULL, message, 1);
#endif
}
return 0;
}
#ifndef __PYX_HAVE_RT_ImportModule
#define __PYX_HAVE_RT_ImportModule
static PyObject *__Pyx_ImportModule(const char *name) {
PyObject *py_name = 0;
PyObject *py_module = 0;
py_name = __Pyx_PyIdentifier_FromString(name);
if (!py_name)
goto bad;
py_module = PyImport_Import(py_name);
Py_DECREF(py_name);
return py_module;
bad:
Py_XDECREF(py_name);
return 0;
}
#endif
#ifndef __PYX_HAVE_RT_ImportType
#define __PYX_HAVE_RT_ImportType
static PyTypeObject *__Pyx_ImportType(const char *module_name, const char *class_name,
size_t size, int strict)
{
PyObject *py_module = 0;
PyObject *result = 0;
PyObject *py_name = 0;
char warning[200];
Py_ssize_t basicsize;
#ifdef Py_LIMITED_API
PyObject *py_basicsize;
#endif
py_module = __Pyx_ImportModule(module_name);
if (!py_module)
goto bad;
py_name = __Pyx_PyIdentifier_FromString(class_name);
if (!py_name)
goto bad;
result = PyObject_GetAttr(py_module, py_name);
Py_DECREF(py_name);
py_name = 0;
Py_DECREF(py_module);
py_module = 0;
if (!result)
goto bad;
if (!PyType_Check(result)) {
PyErr_Format(PyExc_TypeError,
"%.200s.%.200s is not a type object",
module_name, class_name);
goto bad;
}
#ifndef Py_LIMITED_API
basicsize = ((PyTypeObject *)result)->tp_basicsize;
#else
py_basicsize = PyObject_GetAttrString(result, "__basicsize__");
if (!py_basicsize)
goto bad;
basicsize = PyLong_AsSsize_t(py_basicsize);
Py_DECREF(py_basicsize);
py_basicsize = 0;
if (basicsize == (Py_ssize_t)-1 && PyErr_Occurred())
goto bad;
#endif
if (!strict && (size_t)basicsize > size) {
PyOS_snprintf(warning, sizeof(warning),
"%s.%s size changed, may indicate binary incompatibility",
module_name, class_name);
#if PY_VERSION_HEX < 0x02050000
if (PyErr_Warn(NULL, warning) < 0) goto bad;
#else
if (PyErr_WarnEx(NULL, warning, 0) < 0) goto bad;
#endif
}
else if ((size_t)basicsize != size) {
PyErr_Format(PyExc_ValueError,
"%.200s.%.200s has the wrong size, try recompiling",
module_name, class_name);
goto bad;
}
return (PyTypeObject *)result;
bad:
Py_XDECREF(py_module);
Py_XDECREF(result);
return NULL;
}
#endif
static int __pyx_bisect_code_objects(__Pyx_CodeObjectCacheEntry* entries, int count, int code_line) {
int start = 0, mid = 0, end = count - 1;
if (end >= 0 && code_line > entries[end].code_line) {
return count;
}
while (start < end) {
mid = (start + end) / 2;
if (code_line < entries[mid].code_line) {
end = mid;
} else if (code_line > entries[mid].code_line) {
start = mid + 1;
} else {
return mid;
}
}
if (code_line <= entries[mid].code_line) {
return mid;
} else {
return mid + 1;
}
}
static PyCodeObject *__pyx_find_code_object(int code_line) {
PyCodeObject* code_object;
int pos;
if (unlikely(!code_line) || unlikely(!__pyx_code_cache.entries)) {
return NULL;
}
pos = __pyx_bisect_code_objects(__pyx_code_cache.entries, __pyx_code_cache.count, code_line);
if (unlikely(pos >= __pyx_code_cache.count) || unlikely(__pyx_code_cache.entries[pos].code_line != code_line)) {
return NULL;
}
code_object = __pyx_code_cache.entries[pos].code_object;
Py_INCREF(code_object);
return code_object;
}
static void __pyx_insert_code_object(int code_line, PyCodeObject* code_object) {
int pos, i;
__Pyx_CodeObjectCacheEntry* entries = __pyx_code_cache.entries;
if (unlikely(!code_line)) {
return;
}
if (unlikely(!entries)) {
entries = (__Pyx_CodeObjectCacheEntry*)PyMem_Malloc(64*sizeof(__Pyx_CodeObjectCacheEntry));
if (likely(entries)) {
__pyx_code_cache.entries = entries;
__pyx_code_cache.max_count = 64;
__pyx_code_cache.count = 1;
entries[0].code_line = code_line;
entries[0].code_object = code_object;
Py_INCREF(code_object);
}
return;
}
pos = __pyx_bisect_code_objects(__pyx_code_cache.entries, __pyx_code_cache.count, code_line);
if ((pos < __pyx_code_cache.count) && unlikely(__pyx_code_cache.entries[pos].code_line == code_line)) {
PyCodeObject* tmp = entries[pos].code_object;
entries[pos].code_object = code_object;
Py_DECREF(tmp);
return;
}
if (__pyx_code_cache.count == __pyx_code_cache.max_count) {
int new_max = __pyx_code_cache.max_count + 64;
entries = (__Pyx_CodeObjectCacheEntry*)PyMem_Realloc(
__pyx_code_cache.entries, (size_t)new_max*sizeof(__Pyx_CodeObjectCacheEntry));
if (unlikely(!entries)) {
return;
}
__pyx_code_cache.entries = entries;
__pyx_code_cache.max_count = new_max;
}
for (i=__pyx_code_cache.count; i>pos; i--) {
entries[i] = entries[i-1];
}
entries[pos].code_line = code_line;
entries[pos].code_object = code_object;
__pyx_code_cache.count++;
Py_INCREF(code_object);
}
#include "compile.h"
#include "frameobject.h"
#include "traceback.h"
static PyCodeObject* __Pyx_CreateCodeObjectForTraceback(
const char *funcname, int c_line,
int py_line, const char *filename) {
PyCodeObject *py_code = 0;
PyObject *py_srcfile = 0;
PyObject *py_funcname = 0;
#if PY_MAJOR_VERSION < 3
py_srcfile = PyString_FromString(filename);
#else
py_srcfile = PyUnicode_FromString(filename);
#endif
if (!py_srcfile) goto bad;
if (c_line) {
#if PY_MAJOR_VERSION < 3
py_funcname = PyString_FromFormat( "%s (%s:%d)", funcname, __pyx_cfilenm, c_line);
#else
py_funcname = PyUnicode_FromFormat( "%s (%s:%d)", funcname, __pyx_cfilenm, c_line);
#endif
}
else {
#if PY_MAJOR_VERSION < 3
py_funcname = PyString_FromString(funcname);
#else
py_funcname = PyUnicode_FromString(funcname);
#endif
}
if (!py_funcname) goto bad;
py_code = __Pyx_PyCode_New(
0, /*int argcount,*/
0, /*int kwonlyargcount,*/
0, /*int nlocals,*/
0, /*int stacksize,*/
0, /*int flags,*/
__pyx_empty_bytes, /*PyObject *code,*/
__pyx_empty_tuple, /*PyObject *consts,*/
__pyx_empty_tuple, /*PyObject *names,*/
__pyx_empty_tuple, /*PyObject *varnames,*/
__pyx_empty_tuple, /*PyObject *freevars,*/
__pyx_empty_tuple, /*PyObject *cellvars,*/
py_srcfile, /*PyObject *filename,*/
py_funcname, /*PyObject *name,*/
py_line, /*int firstlineno,*/
__pyx_empty_bytes /*PyObject *lnotab*/
);
Py_DECREF(py_srcfile);
Py_DECREF(py_funcname);
return py_code;
bad:
Py_XDECREF(py_srcfile);
Py_XDECREF(py_funcname);
return NULL;
}
static void __Pyx_AddTraceback(const char *funcname, int c_line,
int py_line, const char *filename) {
PyCodeObject *py_code = 0;
PyObject *py_globals = 0;
PyFrameObject *py_frame = 0;
py_code = __pyx_find_code_object(c_line ? c_line : py_line);
if (!py_code) {
py_code = __Pyx_CreateCodeObjectForTraceback(
funcname, c_line, py_line, filename);
if (!py_code) goto bad;
__pyx_insert_code_object(c_line ? c_line : py_line, py_code);
}
py_globals = PyModule_GetDict(__pyx_m);
if (!py_globals) goto bad;
py_frame = PyFrame_New(
PyThreadState_GET(), /*PyThreadState *tstate,*/
py_code, /*PyCodeObject *code,*/
py_globals, /*PyObject *globals,*/
0 /*PyObject *locals*/
);
if (!py_frame) goto bad;
py_frame->f_lineno = py_line;
PyTraceBack_Here(py_frame);
bad:
Py_XDECREF(py_code);
Py_XDECREF(py_frame);
}
static int __Pyx_InitStrings(__Pyx_StringTabEntry *t) {
while (t->p) {
#if PY_MAJOR_VERSION < 3
if (t->is_unicode) {
*t->p = PyUnicode_DecodeUTF8(t->s, t->n - 1, NULL);
} else if (t->intern) {
*t->p = PyString_InternFromString(t->s);
} else {
*t->p = PyString_FromStringAndSize(t->s, t->n - 1);
}
#else /* Python 3+ has unicode identifiers */
if (t->is_unicode | t->is_str) {
if (t->intern) {
*t->p = PyUnicode_InternFromString(t->s);
} else if (t->encoding) {
*t->p = PyUnicode_Decode(t->s, t->n - 1, t->encoding, NULL);
} else {
*t->p = PyUnicode_FromStringAndSize(t->s, t->n - 1);
}
} else {
*t->p = PyBytes_FromStringAndSize(t->s, t->n - 1);
}
#endif
if (!*t->p)
return -1;
++t;
}
return 0;
}
static CYTHON_INLINE PyObject* __Pyx_PyUnicode_FromString(const char* c_str) {
return __Pyx_PyUnicode_FromStringAndSize(c_str, (Py_ssize_t)strlen(c_str));
}
static CYTHON_INLINE char* __Pyx_PyObject_AsString(PyObject* o) {
Py_ssize_t ignore;
return __Pyx_PyObject_AsStringAndSize(o, &ignore);
}
static CYTHON_INLINE char* __Pyx_PyObject_AsStringAndSize(PyObject* o, Py_ssize_t *length) {
#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII || __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT
if (
#if PY_MAJOR_VERSION < 3 && __PYX_DEFAULT_STRING_ENCODING_IS_ASCII
__Pyx_sys_getdefaultencoding_not_ascii &&
#endif
PyUnicode_Check(o)) {
#if PY_VERSION_HEX < 0x03030000
char* defenc_c;
PyObject* defenc = _PyUnicode_AsDefaultEncodedString(o, NULL);
if (!defenc) return NULL;
defenc_c = PyBytes_AS_STRING(defenc);
#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII
{
char* end = defenc_c + PyBytes_GET_SIZE(defenc);
char* c;
for (c = defenc_c; c < end; c++) {
if ((unsigned char) (*c) >= 128) {
PyUnicode_AsASCIIString(o);
return NULL;
}
}
}
#endif /*__PYX_DEFAULT_STRING_ENCODING_IS_ASCII*/
*length = PyBytes_GET_SIZE(defenc);
return defenc_c;
#else /* PY_VERSION_HEX < 0x03030000 */
if (PyUnicode_READY(o) == -1) return NULL;
#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII
if (PyUnicode_IS_ASCII(o)) {
*length = PyUnicode_GET_LENGTH(o);
return PyUnicode_AsUTF8(o);
} else {
PyUnicode_AsASCIIString(o);
return NULL;
}
#else /* __PYX_DEFAULT_STRING_ENCODING_IS_ASCII */
return PyUnicode_AsUTF8AndSize(o, length);
#endif /* __PYX_DEFAULT_STRING_ENCODING_IS_ASCII */
#endif /* PY_VERSION_HEX < 0x03030000 */
} else
#endif /* __PYX_DEFAULT_STRING_ENCODING_IS_ASCII || __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT */
#if !CYTHON_COMPILING_IN_PYPY
#if PY_VERSION_HEX >= 0x02060000
if (PyByteArray_Check(o)) {
*length = PyByteArray_GET_SIZE(o);
return PyByteArray_AS_STRING(o);
} else
#endif
#endif
{
char* result;
int r = PyBytes_AsStringAndSize(o, &result, length);
if (unlikely(r < 0)) {
return NULL;
} else {
return result;
}
}
}
static CYTHON_INLINE int __Pyx_PyObject_IsTrue(PyObject* x) {
int is_true = x == Py_True;
if (is_true | (x == Py_False) | (x == Py_None)) return is_true;
else return PyObject_IsTrue(x);
}
static CYTHON_INLINE PyObject* __Pyx_PyNumber_Int(PyObject* x) {
PyNumberMethods *m;
const char *name = NULL;
PyObject *res = NULL;
#if PY_MAJOR_VERSION < 3
if (PyInt_Check(x) || PyLong_Check(x))
#else
if (PyLong_Check(x))
#endif
return Py_INCREF(x), x;
m = Py_TYPE(x)->tp_as_number;
#if PY_MAJOR_VERSION < 3
if (m && m->nb_int) {
name = "int";
res = PyNumber_Int(x);
}
else if (m && m->nb_long) {
name = "long";
res = PyNumber_Long(x);
}
#else
if (m && m->nb_int) {
name = "int";
res = PyNumber_Long(x);
}
#endif
if (res) {
#if PY_MAJOR_VERSION < 3
if (!PyInt_Check(res) && !PyLong_Check(res)) {
#else
if (!PyLong_Check(res)) {
#endif
PyErr_Format(PyExc_TypeError,
"__%.4s__ returned non-%.4s (type %.200s)",
name, name, Py_TYPE(res)->tp_name);
Py_DECREF(res);
return NULL;
}
}
else if (!PyErr_Occurred()) {
PyErr_SetString(PyExc_TypeError,
"an integer is required");
}
return res;
}
#if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3
#if CYTHON_USE_PYLONG_INTERNALS
#include "longintrepr.h"
#endif
#endif
static CYTHON_INLINE Py_ssize_t __Pyx_PyIndex_AsSsize_t(PyObject* b) {
Py_ssize_t ival;
PyObject *x;
#if PY_MAJOR_VERSION < 3
if (likely(PyInt_CheckExact(b)))
return PyInt_AS_LONG(b);
#endif
if (likely(PyLong_CheckExact(b))) {
#if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3
#if CYTHON_USE_PYLONG_INTERNALS
switch (Py_SIZE(b)) {
case -1: return -(sdigit)((PyLongObject*)b)->ob_digit[0];
case 0: return 0;
case 1: return ((PyLongObject*)b)->ob_digit[0];
}
#endif
#endif
#if PY_VERSION_HEX < 0x02060000
return PyInt_AsSsize_t(b);
#else
return PyLong_AsSsize_t(b);
#endif
}
x = PyNumber_Index(b);
if (!x) return -1;
ival = PyInt_AsSsize_t(x);
Py_DECREF(x);
return ival;
}
static CYTHON_INLINE PyObject * __Pyx_PyInt_FromSize_t(size_t ival) {
#if PY_VERSION_HEX < 0x02050000
if (ival <= LONG_MAX)
return PyInt_FromLong((long)ival);
else {
unsigned char *bytes = (unsigned char *) &ival;
int one = 1; int little = (int)*(unsigned char*)&one;
return _PyLong_FromByteArray(bytes, sizeof(size_t), little, 0);
}
#else
return PyInt_FromSize_t(ival);
#endif
}
#endif /* Py_PYTHON_H */
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