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scikit-learn/sklearn/tree/_splitter.c

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/* Generated by Cython 0.22.1 */
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#define __PYX_NAN() ((float) NAN)
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float value;
memset(&value, 0xFF, sizeof(value));
return value;
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void __Pyx_call_destructor(T* x) {
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__Pyx_FakeReference() : ptr(NULL) { }
__Pyx_FakeReference(T& ref) : ptr(&ref) { }
T *operator->() { return ptr; }
operator T&() { return *ptr; }
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T *ptr;
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#define __PYX_EXTERN_C extern
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#endif
#if defined(WIN32) || defined(MS_WINDOWS)
#define _USE_MATH_DEFINES
#endif
#include <math.h>
#define __PYX_HAVE__sklearn__tree___splitter
#define __PYX_HAVE_API__sklearn__tree___splitter
#include "string.h"
#include "stdio.h"
#include "stdlib.h"
#include "numpy/arrayobject.h"
#include "numpy/ufuncobject.h"
#ifdef _OPENMP
#include <omp.h>
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#ifdef PYREX_WITHOUT_ASSERTIONS
#define CYTHON_WITHOUT_ASSERTIONS
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#ifndef CYTHON_UNUSED
# if defined(__GNUC__)
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# elif defined(__ICC) || (defined(__INTEL_COMPILER) && !defined(_MSC_VER))
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# if CYTHON_COMPILING_IN_CPYTHON
# define CYTHON_NCP_UNUSED
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# define CYTHON_NCP_UNUSED CYTHON_UNUSED
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const char is_unicode; const char is_str; const char intern; } __Pyx_StringTabEntry;
#define __PYX_DEFAULT_STRING_ENCODING_IS_ASCII 0
#define __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT 0
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#define __Pyx_PyObject_FromStringAndSize __Pyx_PyBytes_FromStringAndSize
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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*);
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#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
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#define __Pyx_PyStr_FromString __Pyx_PyUnicode_FromString
#define __Pyx_PyStr_FromStringAndSize __Pyx_PyUnicode_FromStringAndSize
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#define __Pyx_PyStr_FromCString(s) __Pyx_PyStr_FromString((const char*)s)
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#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);
}
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#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
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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))
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#define __pyx_PyFloat_AsDouble(x) PyFloat_AsDouble(x)
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#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*) "getdefaultencoding", NULL);
Py_DECREF(sys);
if (!default_encoding) goto bad;
default_encoding_c = PyBytes_AsString(default_encoding);
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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);
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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;
}
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#if __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT && PY_MAJOR_VERSION >= 3
#define __Pyx_PyUnicode_FromStringAndSize(c_str, size) PyUnicode_DecodeUTF8(c_str, size, NULL)
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#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 */
#if defined(__GNUC__) && (__GNUC__ > 2 || (__GNUC__ == 2 && (__GNUC_MINOR__ > 95)))
#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;
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static int __pyx_lineno;
static int __pyx_clineno = 0;
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[] = {
"sklearn/tree/_splitter.pyx",
"sklearn/tree/_splitter.pxd",
"__init__.pxd",
"type.pxd",
"sklearn/tree/_criterion.pxd",
"sklearn/tree/_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;
struct __Pyx_StructField_* fields;
size_t size;
size_t arraysize[8];
int ndim;
char typegroup;
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;
size_t parent_offset;
} __Pyx_BufFmt_StackElem;
typedef struct {
__Pyx_StructField root;
__Pyx_BufFmt_StackElem* head;
size_t fmt_offset;
size_t new_count, enc_count;
size_t struct_alignment;
int is_complex;
char enc_type;
char new_packmode;
char enc_packmode;
char is_valid_array;
} __Pyx_BufFmt_Context;
/* "../../anaconda/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":726
* # 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;
/* "../../anaconda/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":727
*
* 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;
/* "../../anaconda/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":728
* 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;
/* "../../anaconda/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":729
* 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;
/* "../../anaconda/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":733
* #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;
/* "../../anaconda/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":734
*
* 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;
/* "../../anaconda/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":735
* 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;
/* "../../anaconda/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":736
* 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;
/* "../../anaconda/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":740
* #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;
/* "../../anaconda/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":741
*
* 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;
/* "../../anaconda/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":750
* # 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;
/* "../../anaconda/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":751
* # 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;
/* "../../anaconda/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":752
* 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;
/* "../../anaconda/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":754
* 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;
/* "../../anaconda/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":755
*
* ctypedef npy_ulong uint_t
* ctypedef npy_ulonglong ulong_t # <<<<<<<<<<<<<<
* ctypedef npy_ulonglong ulonglong_t
*
*/
typedef npy_ulonglong __pyx_t_5numpy_ulong_t;
/* "../../anaconda/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":756
* 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;
/* "../../anaconda/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":758
* ctypedef npy_ulonglong ulonglong_t
*
* ctypedef npy_intp intp_t # <<<<<<<<<<<<<<
* ctypedef npy_uintp uintp_t
*
*/
typedef npy_intp __pyx_t_5numpy_intp_t;
/* "../../anaconda/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":759
*
* ctypedef npy_intp intp_t
* ctypedef npy_uintp uintp_t # <<<<<<<<<<<<<<
*
* ctypedef npy_double float_t
*/
typedef npy_uintp __pyx_t_5numpy_uintp_t;
/* "../../anaconda/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":761
* 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;
/* "../../anaconda/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":762
*
* ctypedef npy_double float_t
* ctypedef npy_double double_t # <<<<<<<<<<<<<<
* ctypedef npy_longdouble longdouble_t
*
*/
typedef npy_double __pyx_t_5numpy_double_t;
/* "../../anaconda/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":763
* 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/_criterion.pxd":15
* 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_10_criterion_DTYPE_t;
/* "sklearn/tree/_criterion.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
*/
typedef npy_float64 __pyx_t_7sklearn_4tree_10_criterion_DOUBLE_t;
/* "sklearn/tree/_criterion.pxd":17
* 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_10_criterion_SIZE_t;
/* "sklearn/tree/_criterion.pxd":18
* 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_10_criterion_INT32_t;
/* "sklearn/tree/_criterion.pxd":19
* 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 # <<<<<<<<<<<<<<
*
* cdef class Criterion:
*/
typedef npy_uint32 __pyx_t_7sklearn_4tree_10_criterion_UINT32_t;
/* "sklearn/tree/_utils.pxd":13
* 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_6_utils_DTYPE_t;
/* "sklearn/tree/_utils.pxd":14
*
* 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_6_utils_DOUBLE_t;
/* "sklearn/tree/_utils.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
* ctypedef np.npy_uint32 UINT32_t # Unsigned 32 bit integer
*/
typedef npy_intp __pyx_t_7sklearn_4tree_6_utils_SIZE_t;
/* "sklearn/tree/_utils.pxd":16
* 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_6_utils_INT32_t;
/* "sklearn/tree/_utils.pxd":17
* 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 # <<<<<<<<<<<<<<
*
* cdef enum:
*/
typedef npy_uint32 __pyx_t_7sklearn_4tree_6_utils_UINT32_t;
typedef npy_float32 __pyx_t_7sklearn_4tree_9_splitter_DTYPE_t;
/* "sklearn/tree/_splitter.pxd":18
*
* 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_9_splitter_DOUBLE_t;
/* "sklearn/tree/_splitter.pxd":19
* 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_9_splitter_SIZE_t;
/* "sklearn/tree/_splitter.pxd":20
* 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_9_splitter_INT32_t;
/* "sklearn/tree/_splitter.pxd":21
* 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 # <<<<<<<<<<<<<<
*
* cdef struct SplitRecord:
*/
typedef npy_uint32 __pyx_t_7sklearn_4tree_9_splitter_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_10_criterion_Criterion;
struct __pyx_obj_7sklearn_4tree_6_utils_Stack;
struct __pyx_obj_7sklearn_4tree_6_utils_PriorityHeap;
struct __pyx_obj_7sklearn_4tree_9_splitter_Splitter;
struct __pyx_obj_7sklearn_4tree_9_splitter_BaseDenseSplitter;
struct __pyx_obj_7sklearn_4tree_9_splitter_BestSplitter;
struct __pyx_obj_7sklearn_4tree_9_splitter_RandomSplitter;
struct __pyx_obj_7sklearn_4tree_9_splitter_BaseSparseSplitter;
struct __pyx_obj_7sklearn_4tree_9_splitter_BestSparseSplitter;
struct __pyx_obj_7sklearn_4tree_9_splitter_RandomSparseSplitter;
/* "../../anaconda/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":765
* 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;
/* "../../anaconda/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":766
*
* ctypedef npy_cfloat cfloat_t
* ctypedef npy_cdouble cdouble_t # <<<<<<<<<<<<<<
* ctypedef npy_clongdouble clongdouble_t
*
*/
typedef npy_cdouble __pyx_t_5numpy_cdouble_t;
/* "../../anaconda/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":767
* 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;
/* "../../anaconda/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":769
* 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":19
* ctypedef np.npy_uint32 UINT32_t # Unsigned 32 bit integer
*
* 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_6_utils_RAND_R_MAX = 0x7FFFFFFF
};
/* "sklearn/tree/_utils.pxd":58
*
* # 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":84
*
* # 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_9_splitter_SplitRecord;
struct __pyx_opt_args_7sklearn_4tree_9_splitter_8Splitter_init;
struct __pyx_opt_args_7sklearn_4tree_9_splitter_17BaseDenseSplitter_init;
struct __pyx_opt_args_7sklearn_4tree_9_splitter_18BaseSparseSplitter_init;
/* "sklearn/tree/_splitter.pxd":23
* ctypedef np.npy_uint32 UINT32_t # Unsigned 32 bit integer
*
* cdef struct SplitRecord: # <<<<<<<<<<<<<<
* # Data to track sample split
* SIZE_t feature # Which feature to split on.
*/
struct __pyx_t_7sklearn_4tree_9_splitter_SplitRecord {
__pyx_t_7sklearn_4tree_9_splitter_SIZE_t feature;
__pyx_t_7sklearn_4tree_9_splitter_SIZE_t pos;
double threshold;
double improvement;
double impurity_left;
double impurity_right;
};
/* "sklearn/tree/_splitter.pxd":84
*
* # Methods
* cdef void init(self, object X, np.ndarray y, # <<<<<<<<<<<<<<
* DOUBLE_t* sample_weight,
* np.ndarray X_idx_sorted=*) except *
*/
struct __pyx_opt_args_7sklearn_4tree_9_splitter_8Splitter_init {
int __pyx_n;
PyArrayObject *X_idx_sorted;
};
/* "sklearn/tree/_splitter.pyx":260
* free(self.sample_mask)
*
* cdef void init(self, # <<<<<<<<<<<<<<
* object X,
* np.ndarray[DOUBLE_t, ndim=2, mode="c"] y,
*/
struct __pyx_opt_args_7sklearn_4tree_9_splitter_17BaseDenseSplitter_init {
int __pyx_n;
PyArrayObject *X_idx_sorted;
};
/* "sklearn/tree/_splitter.pyx":868
* free(self.sorted_samples)
*
* cdef void init(self, # <<<<<<<<<<<<<<
* object X,
* np.ndarray[DOUBLE_t, ndim=2, mode="c"] y,
*/
struct __pyx_opt_args_7sklearn_4tree_9_splitter_18BaseSparseSplitter_init {
int __pyx_n;
PyArrayObject *X_idx_sorted;
};
/* "sklearn/tree/_criterion.pxd":21
* ctypedef np.npy_uint32 UINT32_t # Unsigned 32 bit integer
*
* 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_10_criterion_Criterion {
PyObject_HEAD
struct __pyx_vtabstruct_7sklearn_4tree_10_criterion_Criterion *__pyx_vtab;
__pyx_t_7sklearn_4tree_10_criterion_DOUBLE_t *y;
__pyx_t_7sklearn_4tree_10_criterion_SIZE_t y_stride;
__pyx_t_7sklearn_4tree_10_criterion_DOUBLE_t *sample_weight;
__pyx_t_7sklearn_4tree_10_criterion_SIZE_t *samples;
__pyx_t_7sklearn_4tree_10_criterion_SIZE_t start;
__pyx_t_7sklearn_4tree_10_criterion_SIZE_t pos;
__pyx_t_7sklearn_4tree_10_criterion_SIZE_t end;
__pyx_t_7sklearn_4tree_10_criterion_SIZE_t n_outputs;
__pyx_t_7sklearn_4tree_10_criterion_SIZE_t n_node_samples;
double weighted_n_samples;
double weighted_n_node_samples;
double weighted_n_left;
double weighted_n_right;
double *sum_total;
double *sum_left;
double *sum_right;
};
/* "sklearn/tree/_utils.pxd":67
* 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":96
* 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/_splitter.pxd":34
* double impurity_right # Impurity of the right split.
*
* 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_9_splitter_Splitter {
PyObject_HEAD
struct __pyx_vtabstruct_7sklearn_4tree_9_splitter_Splitter *__pyx_vtab;
struct __pyx_obj_7sklearn_4tree_10_criterion_Criterion *criterion;
__pyx_t_7sklearn_4tree_9_splitter_SIZE_t max_features;
__pyx_t_7sklearn_4tree_9_splitter_SIZE_t min_samples_leaf;
double min_weight_leaf;
PyObject *random_state;
__pyx_t_7sklearn_4tree_9_splitter_UINT32_t rand_r_state;
__pyx_t_7sklearn_4tree_9_splitter_SIZE_t *samples;
__pyx_t_7sklearn_4tree_9_splitter_SIZE_t n_samples;
double weighted_n_samples;
__pyx_t_7sklearn_4tree_9_splitter_SIZE_t *features;
__pyx_t_7sklearn_4tree_9_splitter_SIZE_t *constant_features;
__pyx_t_7sklearn_4tree_9_splitter_SIZE_t n_features;
__pyx_t_7sklearn_4tree_9_splitter_DTYPE_t *feature_values;
__pyx_t_7sklearn_4tree_9_splitter_SIZE_t start;
__pyx_t_7sklearn_4tree_9_splitter_SIZE_t end;
int presort;
__pyx_t_7sklearn_4tree_9_splitter_DOUBLE_t *y;
__pyx_t_7sklearn_4tree_9_splitter_SIZE_t y_stride;
__pyx_t_7sklearn_4tree_9_splitter_DOUBLE_t *sample_weight;
};
/* "sklearn/tree/_splitter.pyx":232
*
*
* cdef class BaseDenseSplitter(Splitter): # <<<<<<<<<<<<<<
* cdef DTYPE_t* X
* cdef SIZE_t X_sample_stride
*/
struct __pyx_obj_7sklearn_4tree_9_splitter_BaseDenseSplitter {
struct __pyx_obj_7sklearn_4tree_9_splitter_Splitter __pyx_base;
__pyx_t_7sklearn_4tree_9_splitter_DTYPE_t *X;
__pyx_t_7sklearn_4tree_9_splitter_SIZE_t X_sample_stride;
__pyx_t_7sklearn_4tree_9_splitter_SIZE_t X_feature_stride;
PyArrayObject *X_idx_sorted;
__pyx_t_7sklearn_4tree_9_splitter_INT32_t *X_idx_sorted_ptr;
__pyx_t_7sklearn_4tree_9_splitter_SIZE_t X_idx_sorted_stride;
__pyx_t_7sklearn_4tree_9_splitter_SIZE_t n_total_samples;
__pyx_t_7sklearn_4tree_9_splitter_SIZE_t *sample_mask;
};
/* "sklearn/tree/_splitter.pyx":288
*
*
* cdef class BestSplitter(BaseDenseSplitter): # <<<<<<<<<<<<<<
* """Splitter for finding the best split."""
* def __reduce__(self):
*/
struct __pyx_obj_7sklearn_4tree_9_splitter_BestSplitter {
struct __pyx_obj_7sklearn_4tree_9_splitter_BaseDenseSplitter __pyx_base;
};
/* "sklearn/tree/_splitter.pyx":624
*
*
* cdef class RandomSplitter(BaseDenseSplitter): # <<<<<<<<<<<<<<
* """Splitter for finding the best random split."""
* def __reduce__(self):
*/
struct __pyx_obj_7sklearn_4tree_9_splitter_RandomSplitter {
struct __pyx_obj_7sklearn_4tree_9_splitter_BaseDenseSplitter __pyx_base;
};
/* "sklearn/tree/_splitter.pyx":838
*
*
* cdef class BaseSparseSplitter(Splitter): # <<<<<<<<<<<<<<
* # The sparse splitter works only with csc sparse matrix format
* cdef DTYPE_t* X_data
*/
struct __pyx_obj_7sklearn_4tree_9_splitter_BaseSparseSplitter {
struct __pyx_obj_7sklearn_4tree_9_splitter_Splitter __pyx_base;
__pyx_t_7sklearn_4tree_9_splitter_DTYPE_t *X_data;
__pyx_t_7sklearn_4tree_9_splitter_INT32_t *X_indices;
__pyx_t_7sklearn_4tree_9_splitter_INT32_t *X_indptr;
__pyx_t_7sklearn_4tree_9_splitter_SIZE_t n_total_samples;
__pyx_t_7sklearn_4tree_9_splitter_SIZE_t *index_to_samples;
__pyx_t_7sklearn_4tree_9_splitter_SIZE_t *sorted_samples;
};
/* "sklearn/tree/_splitter.pyx":1158
*
*
* cdef class BestSparseSplitter(BaseSparseSplitter): # <<<<<<<<<<<<<<
* """Splitter for finding the best split, using the sparse data."""
*
*/
struct __pyx_obj_7sklearn_4tree_9_splitter_BestSparseSplitter {
struct __pyx_obj_7sklearn_4tree_9_splitter_BaseSparseSplitter __pyx_base;
};
/* "sklearn/tree/_splitter.pyx":1385
*
*
* cdef class RandomSparseSplitter(BaseSparseSplitter): # <<<<<<<<<<<<<<
* """Splitter for finding a random split, using the sparse data."""
*
*/
struct __pyx_obj_7sklearn_4tree_9_splitter_RandomSparseSplitter {
struct __pyx_obj_7sklearn_4tree_9_splitter_BaseSparseSplitter __pyx_base;
};
/* "sklearn/tree/_criterion.pxd":21
* ctypedef np.npy_uint32 UINT32_t # Unsigned 32 bit integer
*
* 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_vtabstruct_7sklearn_4tree_10_criterion_Criterion {
void (*init)(struct __pyx_obj_7sklearn_4tree_10_criterion_Criterion *, __pyx_t_7sklearn_4tree_10_criterion_DOUBLE_t *, __pyx_t_7sklearn_4tree_10_criterion_SIZE_t, __pyx_t_7sklearn_4tree_10_criterion_DOUBLE_t *, double, __pyx_t_7sklearn_4tree_10_criterion_SIZE_t *, __pyx_t_7sklearn_4tree_10_criterion_SIZE_t, __pyx_t_7sklearn_4tree_10_criterion_SIZE_t);
void (*reset)(struct __pyx_obj_7sklearn_4tree_10_criterion_Criterion *);
void (*reverse_reset)(struct __pyx_obj_7sklearn_4tree_10_criterion_Criterion *);
void (*update)(struct __pyx_obj_7sklearn_4tree_10_criterion_Criterion *, __pyx_t_7sklearn_4tree_10_criterion_SIZE_t);
double (*node_impurity)(struct __pyx_obj_7sklearn_4tree_10_criterion_Criterion *);
void (*children_impurity)(struct __pyx_obj_7sklearn_4tree_10_criterion_Criterion *, double *, double *);
void (*node_value)(struct __pyx_obj_7sklearn_4tree_10_criterion_Criterion *, double *);
double (*impurity_improvement)(struct __pyx_obj_7sklearn_4tree_10_criterion_Criterion *, double);
double (*proxy_impurity_improvement)(struct __pyx_obj_7sklearn_4tree_10_criterion_Criterion *);
};
static struct __pyx_vtabstruct_7sklearn_4tree_10_criterion_Criterion *__pyx_vtabptr_7sklearn_4tree_10_criterion_Criterion;
/* "sklearn/tree/_utils.pxd":67
* 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":96
* 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/_splitter.pyx":54
* self.improvement = -INFINITY
*
* cdef class Splitter: # <<<<<<<<<<<<<<
* """Abstract splitter class.
*
*/
struct __pyx_vtabstruct_7sklearn_4tree_9_splitter_Splitter {
void (*init)(struct __pyx_obj_7sklearn_4tree_9_splitter_Splitter *, PyObject *, PyArrayObject *, __pyx_t_7sklearn_4tree_9_splitter_DOUBLE_t *, struct __pyx_opt_args_7sklearn_4tree_9_splitter_8Splitter_init *__pyx_optional_args);
void (*node_reset)(struct __pyx_obj_7sklearn_4tree_9_splitter_Splitter *, __pyx_t_7sklearn_4tree_9_splitter_SIZE_t, __pyx_t_7sklearn_4tree_9_splitter_SIZE_t, double *);
void (*node_split)(struct __pyx_obj_7sklearn_4tree_9_splitter_Splitter *, double, struct __pyx_t_7sklearn_4tree_9_splitter_SplitRecord *, __pyx_t_7sklearn_4tree_9_splitter_SIZE_t *);
void (*node_value)(struct __pyx_obj_7sklearn_4tree_9_splitter_Splitter *, double *);
double (*node_impurity)(struct __pyx_obj_7sklearn_4tree_9_splitter_Splitter *);
};
static struct __pyx_vtabstruct_7sklearn_4tree_9_splitter_Splitter *__pyx_vtabptr_7sklearn_4tree_9_splitter_Splitter;
/* "sklearn/tree/_splitter.pyx":232
*
*
* cdef class BaseDenseSplitter(Splitter): # <<<<<<<<<<<<<<
* cdef DTYPE_t* X
* cdef SIZE_t X_sample_stride
*/
struct __pyx_vtabstruct_7sklearn_4tree_9_splitter_BaseDenseSplitter {
struct __pyx_vtabstruct_7sklearn_4tree_9_splitter_Splitter __pyx_base;
};
static struct __pyx_vtabstruct_7sklearn_4tree_9_splitter_BaseDenseSplitter *__pyx_vtabptr_7sklearn_4tree_9_splitter_BaseDenseSplitter;
/* "sklearn/tree/_splitter.pyx":288
*
*
* cdef class BestSplitter(BaseDenseSplitter): # <<<<<<<<<<<<<<
* """Splitter for finding the best split."""
* def __reduce__(self):
*/
struct __pyx_vtabstruct_7sklearn_4tree_9_splitter_BestSplitter {
struct __pyx_vtabstruct_7sklearn_4tree_9_splitter_BaseDenseSplitter __pyx_base;
};
static struct __pyx_vtabstruct_7sklearn_4tree_9_splitter_BestSplitter *__pyx_vtabptr_7sklearn_4tree_9_splitter_BestSplitter;
/* "sklearn/tree/_splitter.pyx":624
*
*
* cdef class RandomSplitter(BaseDenseSplitter): # <<<<<<<<<<<<<<
* """Splitter for finding the best random split."""
* def __reduce__(self):
*/
struct __pyx_vtabstruct_7sklearn_4tree_9_splitter_RandomSplitter {
struct __pyx_vtabstruct_7sklearn_4tree_9_splitter_BaseDenseSplitter __pyx_base;
};
static struct __pyx_vtabstruct_7sklearn_4tree_9_splitter_RandomSplitter *__pyx_vtabptr_7sklearn_4tree_9_splitter_RandomSplitter;
/* "sklearn/tree/_splitter.pyx":838
*
*
* cdef class BaseSparseSplitter(Splitter): # <<<<<<<<<<<<<<
* # The sparse splitter works only with csc sparse matrix format
* cdef DTYPE_t* X_data
*/
struct __pyx_vtabstruct_7sklearn_4tree_9_splitter_BaseSparseSplitter {
struct __pyx_vtabstruct_7sklearn_4tree_9_splitter_Splitter __pyx_base;
__pyx_t_7sklearn_4tree_9_splitter_SIZE_t (*_partition)(struct __pyx_obj_7sklearn_4tree_9_splitter_BaseSparseSplitter *, double, __pyx_t_7sklearn_4tree_9_splitter_SIZE_t, __pyx_t_7sklearn_4tree_9_splitter_SIZE_t, __pyx_t_7sklearn_4tree_9_splitter_SIZE_t);
void (*extract_nnz)(struct __pyx_obj_7sklearn_4tree_9_splitter_BaseSparseSplitter *, __pyx_t_7sklearn_4tree_9_splitter_SIZE_t, __pyx_t_7sklearn_4tree_9_splitter_SIZE_t *, __pyx_t_7sklearn_4tree_9_splitter_SIZE_t *, int *);
};
static struct __pyx_vtabstruct_7sklearn_4tree_9_splitter_BaseSparseSplitter *__pyx_vtabptr_7sklearn_4tree_9_splitter_BaseSparseSplitter;
static CYTHON_INLINE __pyx_t_7sklearn_4tree_9_splitter_SIZE_t __pyx_f_7sklearn_4tree_9_splitter_18BaseSparseSplitter__partition(struct __pyx_obj_7sklearn_4tree_9_splitter_BaseSparseSplitter *, double, __pyx_t_7sklearn_4tree_9_splitter_SIZE_t, __pyx_t_7sklearn_4tree_9_splitter_SIZE_t, __pyx_t_7sklearn_4tree_9_splitter_SIZE_t);
static CYTHON_INLINE void __pyx_f_7sklearn_4tree_9_splitter_18BaseSparseSplitter_extract_nnz(struct __pyx_obj_7sklearn_4tree_9_splitter_BaseSparseSplitter *, __pyx_t_7sklearn_4tree_9_splitter_SIZE_t, __pyx_t_7sklearn_4tree_9_splitter_SIZE_t *, __pyx_t_7sklearn_4tree_9_splitter_SIZE_t *, int *);
/* "sklearn/tree/_splitter.pyx":1158
*
*
* cdef class BestSparseSplitter(BaseSparseSplitter): # <<<<<<<<<<<<<<
* """Splitter for finding the best split, using the sparse data."""
*
*/
struct __pyx_vtabstruct_7sklearn_4tree_9_splitter_BestSparseSplitter {
struct __pyx_vtabstruct_7sklearn_4tree_9_splitter_BaseSparseSplitter __pyx_base;
};
static struct __pyx_vtabstruct_7sklearn_4tree_9_splitter_BestSparseSplitter *__pyx_vtabptr_7sklearn_4tree_9_splitter_BestSparseSplitter;
/* "sklearn/tree/_splitter.pyx":1385
*
*
* cdef class RandomSparseSplitter(BaseSparseSplitter): # <<<<<<<<<<<<<<
* """Splitter for finding a random split, using the sparse data."""
*
*/
struct __pyx_vtabstruct_7sklearn_4tree_9_splitter_RandomSparseSplitter {
struct __pyx_vtabstruct_7sklearn_4tree_9_splitter_BaseSparseSplitter __pyx_base;
};
static struct __pyx_vtabstruct_7sklearn_4tree_9_splitter_RandomSparseSplitter *__pyx_vtabptr_7sklearn_4tree_9_splitter_RandomSparseSplitter;
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static CYTHON_INLINE PyObject* __Pyx_PyInt_From_npy_int32(npy_int32 value);
static CYTHON_INLINE npy_int32 __Pyx_PyInt_As_npy_int32(PyObject *);
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#define __Pyx_CREAL(z) ((z).real())
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#define __Pyx_SET_CIMAG(z,y) __Pyx_CIMAG(z) = (y)
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static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float, float);
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#define __Pyx_c_eqf(a, b) ((a)==(b))
#define __Pyx_c_sumf(a, b) ((a)+(b))
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#define __Pyx_c_is_zerof(z) ((z)==(float)0)
#define __Pyx_c_conjf(z) (::std::conj(z))
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static CYTHON_INLINE double __Pyx_c_abs(__pyx_t_double_complex);
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static CYTHON_INLINE int __Pyx_PyInt_As_int(PyObject *);
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static PyObject *__Pyx_ImportModule(const char *name);
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static int __Pyx_ImportFunction(PyObject *module, const char *funcname, void (**f)(void), const char *sig);
static int __Pyx_InitStrings(__Pyx_StringTabEntry *t);
static void __pyx_f_7sklearn_4tree_9_splitter_8Splitter_init(struct __pyx_obj_7sklearn_4tree_9_splitter_Splitter *__pyx_v_self, PyObject *__pyx_v_X, PyArrayObject *__pyx_v_y, __pyx_t_7sklearn_4tree_9_splitter_DOUBLE_t *__pyx_v_sample_weight, struct __pyx_opt_args_7sklearn_4tree_9_splitter_8Splitter_init *__pyx_optional_args); /* proto*/
static void __pyx_f_7sklearn_4tree_9_splitter_8Splitter_node_reset(struct __pyx_obj_7sklearn_4tree_9_splitter_Splitter *__pyx_v_self, __pyx_t_7sklearn_4tree_9_splitter_SIZE_t __pyx_v_start, __pyx_t_7sklearn_4tree_9_splitter_SIZE_t __pyx_v_end, double *__pyx_v_weighted_n_node_samples); /* proto*/
static void __pyx_f_7sklearn_4tree_9_splitter_8Splitter_node_split(CYTHON_UNUSED struct __pyx_obj_7sklearn_4tree_9_splitter_Splitter *__pyx_v_self, CYTHON_UNUSED double __pyx_v_impurity, CYTHON_UNUSED struct __pyx_t_7sklearn_4tree_9_splitter_SplitRecord *__pyx_v_split, CYTHON_UNUSED __pyx_t_7sklearn_4tree_9_splitter_SIZE_t *__pyx_v_n_constant_features); /* proto*/
static void __pyx_f_7sklearn_4tree_9_splitter_8Splitter_node_value(struct __pyx_obj_7sklearn_4tree_9_splitter_Splitter *__pyx_v_self, double *__pyx_v_dest); /* proto*/
static double __pyx_f_7sklearn_4tree_9_splitter_8Splitter_node_impurity(struct __pyx_obj_7sklearn_4tree_9_splitter_Splitter *__pyx_v_self); /* proto*/
static void __pyx_f_7sklearn_4tree_9_splitter_17BaseDenseSplitter_init(struct __pyx_obj_7sklearn_4tree_9_splitter_BaseDenseSplitter *__pyx_v_self, PyObject *__pyx_v_X, PyArrayObject *__pyx_v_y, __pyx_t_7sklearn_4tree_9_splitter_DOUBLE_t *__pyx_v_sample_weight, struct __pyx_opt_args_7sklearn_4tree_9_splitter_17BaseDenseSplitter_init *__pyx_optional_args); /* proto*/
static void __pyx_f_7sklearn_4tree_9_splitter_12BestSplitter_node_split(struct __pyx_obj_7sklearn_4tree_9_splitter_BestSplitter *__pyx_v_self, double __pyx_v_impurity, struct __pyx_t_7sklearn_4tree_9_splitter_SplitRecord *__pyx_v_split, __pyx_t_7sklearn_4tree_9_splitter_SIZE_t *__pyx_v_n_constant_features); /* proto*/
static void __pyx_f_7sklearn_4tree_9_splitter_14RandomSplitter_node_split(struct __pyx_obj_7sklearn_4tree_9_splitter_RandomSplitter *__pyx_v_self, double __pyx_v_impurity, struct __pyx_t_7sklearn_4tree_9_splitter_SplitRecord *__pyx_v_split, __pyx_t_7sklearn_4tree_9_splitter_SIZE_t *__pyx_v_n_constant_features); /* proto*/
static void __pyx_f_7sklearn_4tree_9_splitter_18BaseSparseSplitter_init(struct __pyx_obj_7sklearn_4tree_9_splitter_BaseSparseSplitter *__pyx_v_self, PyObject *__pyx_v_X, PyArrayObject *__pyx_v_y, __pyx_t_7sklearn_4tree_9_splitter_DOUBLE_t *__pyx_v_sample_weight, struct __pyx_opt_args_7sklearn_4tree_9_splitter_18BaseSparseSplitter_init *__pyx_optional_args); /* proto*/
static CYTHON_INLINE __pyx_t_7sklearn_4tree_9_splitter_SIZE_t __pyx_f_7sklearn_4tree_9_splitter_18BaseSparseSplitter__partition(struct __pyx_obj_7sklearn_4tree_9_splitter_BaseSparseSplitter *__pyx_v_self, double __pyx_v_threshold, __pyx_t_7sklearn_4tree_9_splitter_SIZE_t __pyx_v_end_negative, __pyx_t_7sklearn_4tree_9_splitter_SIZE_t __pyx_v_start_positive, __pyx_t_7sklearn_4tree_9_splitter_SIZE_t __pyx_v_zero_pos); /* proto*/
static CYTHON_INLINE void __pyx_f_7sklearn_4tree_9_splitter_18BaseSparseSplitter_extract_nnz(struct __pyx_obj_7sklearn_4tree_9_splitter_BaseSparseSplitter *__pyx_v_self, __pyx_t_7sklearn_4tree_9_splitter_SIZE_t __pyx_v_feature, __pyx_t_7sklearn_4tree_9_splitter_SIZE_t *__pyx_v_end_negative, __pyx_t_7sklearn_4tree_9_splitter_SIZE_t *__pyx_v_start_positive, int *__pyx_v_is_samples_sorted); /* proto*/
static void __pyx_f_7sklearn_4tree_9_splitter_18BestSparseSplitter_node_split(struct __pyx_obj_7sklearn_4tree_9_splitter_BestSparseSplitter *__pyx_v_self, double __pyx_v_impurity, struct __pyx_t_7sklearn_4tree_9_splitter_SplitRecord *__pyx_v_split, __pyx_t_7sklearn_4tree_9_splitter_SIZE_t *__pyx_v_n_constant_features); /* proto*/
static void __pyx_f_7sklearn_4tree_9_splitter_20RandomSparseSplitter_node_split(struct __pyx_obj_7sklearn_4tree_9_splitter_RandomSparseSplitter *__pyx_v_self, double __pyx_v_impurity, struct __pyx_t_7sklearn_4tree_9_splitter_SplitRecord *__pyx_v_split, __pyx_t_7sklearn_4tree_9_splitter_SIZE_t *__pyx_v_n_constant_features); /* proto*/
/* Module declarations from 'cpython.buffer' */
/* Module declarations from 'cpython.ref' */
/* Module declarations from 'libc.string' */
/* Module declarations from 'libc.stdio' */
/* Module declarations from 'cpython.object' */
/* Module declarations from '__builtin__' */
/* Module declarations from 'cpython.type' */
static PyTypeObject *__pyx_ptype_7cpython_4type_type = 0;
/* Module declarations from 'libc.stdlib' */
/* Module declarations from 'numpy' */
/* Module declarations from 'numpy' */
static PyTypeObject *__pyx_ptype_5numpy_dtype = 0;
static PyTypeObject *__pyx_ptype_5numpy_flatiter = 0;
static PyTypeObject *__pyx_ptype_5numpy_broadcast = 0;
static PyTypeObject *__pyx_ptype_5numpy_ndarray = 0;
static PyTypeObject *__pyx_ptype_5numpy_ufunc = 0;
static CYTHON_INLINE char *__pyx_f_5numpy__util_dtypestring(PyArray_Descr *, char *, char *, int *); /*proto*/
/* Module declarations from 'sklearn.tree._criterion' */
static PyTypeObject *__pyx_ptype_7sklearn_4tree_10_criterion_Criterion = 0;
/* Module declarations from 'sklearn.tree._utils' */
static PyTypeObject *__pyx_ptype_7sklearn_4tree_6_utils_Stack = 0;
static PyTypeObject *__pyx_ptype_7sklearn_4tree_6_utils_PriorityHeap = 0;
static __pyx_t_7sklearn_4tree_6_utils_SIZE_t (*__pyx_f_7sklearn_4tree_6_utils_rand_int)(__pyx_t_7sklearn_4tree_6_utils_SIZE_t, __pyx_t_7sklearn_4tree_6_utils_SIZE_t, __pyx_t_7sklearn_4tree_6_utils_UINT32_t *); /*proto*/
static double (*__pyx_f_7sklearn_4tree_6_utils_rand_uniform)(double, double, __pyx_t_7sklearn_4tree_6_utils_UINT32_t *); /*proto*/
static double (*__pyx_f_7sklearn_4tree_6_utils_log)(double); /*proto*/
static __pyx_t_7sklearn_4tree_6_utils_DTYPE_t *(*__pyx_fuse_0__pyx_f_7sklearn_4tree_6_utils_safe_realloc)(__pyx_t_7sklearn_4tree_6_utils_DTYPE_t **, size_t); /*proto*/
static __pyx_t_7sklearn_4tree_6_utils_SIZE_t *(*__pyx_fuse_1__pyx_f_7sklearn_4tree_6_utils_safe_realloc)(__pyx_t_7sklearn_4tree_6_utils_SIZE_t **, size_t); /*proto*/
static unsigned char *(*__pyx_fuse_2__pyx_f_7sklearn_4tree_6_utils_safe_realloc)(unsigned char **, size_t); /*proto*/
/* Module declarations from 'sklearn.tree._splitter' */
static PyTypeObject *__pyx_ptype_7sklearn_4tree_9_splitter_Splitter = 0;
static PyTypeObject *__pyx_ptype_7sklearn_4tree_9_splitter_BaseDenseSplitter = 0;
static PyTypeObject *__pyx_ptype_7sklearn_4tree_9_splitter_BestSplitter = 0;
static PyTypeObject *__pyx_ptype_7sklearn_4tree_9_splitter_RandomSplitter = 0;
static PyTypeObject *__pyx_ptype_7sklearn_4tree_9_splitter_BaseSparseSplitter = 0;
static PyTypeObject *__pyx_ptype_7sklearn_4tree_9_splitter_BestSparseSplitter = 0;
static PyTypeObject *__pyx_ptype_7sklearn_4tree_9_splitter_RandomSparseSplitter = 0;
static double __pyx_v_7sklearn_4tree_9_splitter_INFINITY;
static __pyx_t_7sklearn_4tree_9_splitter_DTYPE_t __pyx_v_7sklearn_4tree_9_splitter_FEATURE_THRESHOLD;
static __pyx_t_7sklearn_4tree_9_splitter_DTYPE_t __pyx_v_7sklearn_4tree_9_splitter_EXTRACT_NNZ_SWITCH;
static CYTHON_INLINE void __pyx_f_7sklearn_4tree_9_splitter__init_split(struct __pyx_t_7sklearn_4tree_9_splitter_SplitRecord *, __pyx_t_7sklearn_4tree_9_splitter_SIZE_t); /*proto*/
static CYTHON_INLINE void __pyx_f_7sklearn_4tree_9_splitter_sort(__pyx_t_7sklearn_4tree_9_splitter_DTYPE_t *, __pyx_t_7sklearn_4tree_9_splitter_SIZE_t *, __pyx_t_7sklearn_4tree_9_splitter_SIZE_t); /*proto*/
static CYTHON_INLINE void __pyx_f_7sklearn_4tree_9_splitter_swap(__pyx_t_7sklearn_4tree_9_splitter_DTYPE_t *, __pyx_t_7sklearn_4tree_9_splitter_SIZE_t *, __pyx_t_7sklearn_4tree_9_splitter_SIZE_t, __pyx_t_7sklearn_4tree_9_splitter_SIZE_t); /*proto*/
static CYTHON_INLINE __pyx_t_7sklearn_4tree_9_splitter_DTYPE_t __pyx_f_7sklearn_4tree_9_splitter_median3(__pyx_t_7sklearn_4tree_9_splitter_DTYPE_t *, __pyx_t_7sklearn_4tree_9_splitter_SIZE_t); /*proto*/
static void __pyx_f_7sklearn_4tree_9_splitter_introsort(__pyx_t_7sklearn_4tree_9_splitter_DTYPE_t *, __pyx_t_7sklearn_4tree_9_splitter_SIZE_t *, __pyx_t_7sklearn_4tree_9_splitter_SIZE_t, int); /*proto*/
static CYTHON_INLINE void __pyx_f_7sklearn_4tree_9_splitter_sift_down(__pyx_t_7sklearn_4tree_9_splitter_DTYPE_t *, __pyx_t_7sklearn_4tree_9_splitter_SIZE_t *, __pyx_t_7sklearn_4tree_9_splitter_SIZE_t, __pyx_t_7sklearn_4tree_9_splitter_SIZE_t); /*proto*/
static void __pyx_f_7sklearn_4tree_9_splitter_heapsort(__pyx_t_7sklearn_4tree_9_splitter_DTYPE_t *, __pyx_t_7sklearn_4tree_9_splitter_SIZE_t *, __pyx_t_7sklearn_4tree_9_splitter_SIZE_t); /*proto*/
static int __pyx_f_7sklearn_4tree_9_splitter_compare_SIZE_t(void const *, void const *); /*proto*/
static CYTHON_INLINE void __pyx_f_7sklearn_4tree_9_splitter_binary_search(__pyx_t_7sklearn_4tree_9_splitter_INT32_t *, __pyx_t_7sklearn_4tree_9_splitter_INT32_t, __pyx_t_7sklearn_4tree_9_splitter_INT32_t, __pyx_t_7sklearn_4tree_9_splitter_SIZE_t, __pyx_t_7sklearn_4tree_9_splitter_SIZE_t *, __pyx_t_7sklearn_4tree_9_splitter_INT32_t *); /*proto*/
static CYTHON_INLINE void __pyx_f_7sklearn_4tree_9_splitter_extract_nnz_index_to_samples(__pyx_t_7sklearn_4tree_9_splitter_INT32_t *, __pyx_t_7sklearn_4tree_9_splitter_DTYPE_t *, __pyx_t_7sklearn_4tree_9_splitter_INT32_t, __pyx_t_7sklearn_4tree_9_splitter_INT32_t, __pyx_t_7sklearn_4tree_9_splitter_SIZE_t *, __pyx_t_7sklearn_4tree_9_splitter_SIZE_t, __pyx_t_7sklearn_4tree_9_splitter_SIZE_t, __pyx_t_7sklearn_4tree_9_splitter_SIZE_t *, __pyx_t_7sklearn_4tree_9_splitter_DTYPE_t *, __pyx_t_7sklearn_4tree_9_splitter_SIZE_t *, __pyx_t_7sklearn_4tree_9_splitter_SIZE_t *); /*proto*/
static CYTHON_INLINE void __pyx_f_7sklearn_4tree_9_splitter_extract_nnz_binary_search(__pyx_t_7sklearn_4tree_9_splitter_INT32_t *, __pyx_t_7sklearn_4tree_9_splitter_DTYPE_t *, __pyx_t_7sklearn_4tree_9_splitter_INT32_t, __pyx_t_7sklearn_4tree_9_splitter_INT32_t, __pyx_t_7sklearn_4tree_9_splitter_SIZE_t *, __pyx_t_7sklearn_4tree_9_splitter_SIZE_t, __pyx_t_7sklearn_4tree_9_splitter_SIZE_t, __pyx_t_7sklearn_4tree_9_splitter_SIZE_t *, __pyx_t_7sklearn_4tree_9_splitter_DTYPE_t *, __pyx_t_7sklearn_4tree_9_splitter_SIZE_t *, __pyx_t_7sklearn_4tree_9_splitter_SIZE_t *, __pyx_t_7sklearn_4tree_9_splitter_SIZE_t *, int *); /*proto*/
static CYTHON_INLINE void __pyx_f_7sklearn_4tree_9_splitter_sparse_swap(__pyx_t_7sklearn_4tree_9_splitter_SIZE_t *, __pyx_t_7sklearn_4tree_9_splitter_SIZE_t *, __pyx_t_7sklearn_4tree_9_splitter_SIZE_t, __pyx_t_7sklearn_4tree_9_splitter_SIZE_t); /*proto*/
static __Pyx_TypeInfo __Pyx_TypeInfo_nn___pyx_t_7sklearn_4tree_9_splitter_DOUBLE_t = { "DOUBLE_t", NULL, sizeof(__pyx_t_7sklearn_4tree_9_splitter_DOUBLE_t), { 0 }, 0, 'R', 0, 0 };
static __Pyx_TypeInfo __Pyx_TypeInfo_nn___pyx_t_7sklearn_4tree_9_splitter_DTYPE_t = { "DTYPE_t", NULL, sizeof(__pyx_t_7sklearn_4tree_9_splitter_DTYPE_t), { 0 }, 0, 'R', 0, 0 };
static __Pyx_TypeInfo __Pyx_TypeInfo_nn___pyx_t_7sklearn_4tree_9_splitter_INT32_t = { "INT32_t", NULL, sizeof(__pyx_t_7sklearn_4tree_9_splitter_INT32_t), { 0 }, 0, IS_UNSIGNED(__pyx_t_7sklearn_4tree_9_splitter_INT32_t) ? 'U' : 'I', IS_UNSIGNED(__pyx_t_7sklearn_4tree_9_splitter_INT32_t), 0 };
#define __Pyx_MODULE_NAME "sklearn.tree._splitter"
int __pyx_module_is_main_sklearn__tree___splitter = 0;
/* Implementation of 'sklearn.tree._splitter' */
static PyObject *__pyx_builtin_range;
static PyObject *__pyx_builtin_ValueError;
static PyObject *__pyx_builtin_RuntimeError;
static int __pyx_pf_7sklearn_4tree_9_splitter_8Splitter___cinit__(struct __pyx_obj_7sklearn_4tree_9_splitter_Splitter *__pyx_v_self, struct __pyx_obj_7sklearn_4tree_10_criterion_Criterion *__pyx_v_criterion, __pyx_t_7sklearn_4tree_9_splitter_SIZE_t __pyx_v_max_features, __pyx_t_7sklearn_4tree_9_splitter_SIZE_t __pyx_v_min_samples_leaf, double __pyx_v_min_weight_leaf, PyObject *__pyx_v_random_state, int __pyx_v_presort); /* proto */
static void __pyx_pf_7sklearn_4tree_9_splitter_8Splitter_2__dealloc__(struct __pyx_obj_7sklearn_4tree_9_splitter_Splitter *__pyx_v_self); /* proto */
static PyObject *__pyx_pf_7sklearn_4tree_9_splitter_8Splitter_4__getstate__(CYTHON_UNUSED struct __pyx_obj_7sklearn_4tree_9_splitter_Splitter *__pyx_v_self); /* proto */
static PyObject *__pyx_pf_7sklearn_4tree_9_splitter_8Splitter_6__setstate__(CYTHON_UNUSED struct __pyx_obj_7sklearn_4tree_9_splitter_Splitter *__pyx_v_self, CYTHON_UNUSED PyObject *__pyx_v_d); /* proto */
static PyObject *__pyx_pf_7sklearn_4tree_9_splitter_8Splitter_9criterion___get__(struct __pyx_obj_7sklearn_4tree_9_splitter_Splitter *__pyx_v_self); /* proto */
static int __pyx_pf_7sklearn_4tree_9_splitter_8Splitter_9criterion_2__set__(struct __pyx_obj_7sklearn_4tree_9_splitter_Splitter *__pyx_v_self, PyObject *__pyx_v_value); /* proto */
static int __pyx_pf_7sklearn_4tree_9_splitter_8Splitter_9criterion_4__del__(struct __pyx_obj_7sklearn_4tree_9_splitter_Splitter *__pyx_v_self); /* proto */
static PyObject *__pyx_pf_7sklearn_4tree_9_splitter_8Splitter_12max_features___get__(struct __pyx_obj_7sklearn_4tree_9_splitter_Splitter *__pyx_v_self); /* proto */
static int __pyx_pf_7sklearn_4tree_9_splitter_8Splitter_12max_features_2__set__(struct __pyx_obj_7sklearn_4tree_9_splitter_Splitter *__pyx_v_self, PyObject *__pyx_v_value); /* proto */
static PyObject *__pyx_pf_7sklearn_4tree_9_splitter_8Splitter_16min_samples_leaf___get__(struct __pyx_obj_7sklearn_4tree_9_splitter_Splitter *__pyx_v_self); /* proto */
static int __pyx_pf_7sklearn_4tree_9_splitter_8Splitter_16min_samples_leaf_2__set__(struct __pyx_obj_7sklearn_4tree_9_splitter_Splitter *__pyx_v_self, PyObject *__pyx_v_value); /* proto */
static PyObject *__pyx_pf_7sklearn_4tree_9_splitter_8Splitter_15min_weight_leaf___get__(struct __pyx_obj_7sklearn_4tree_9_splitter_Splitter *__pyx_v_self); /* proto */
static int __pyx_pf_7sklearn_4tree_9_splitter_8Splitter_15min_weight_leaf_2__set__(struct __pyx_obj_7sklearn_4tree_9_splitter_Splitter *__pyx_v_self, PyObject *__pyx_v_value); /* proto */
static int __pyx_pf_7sklearn_4tree_9_splitter_17BaseDenseSplitter___cinit__(struct __pyx_obj_7sklearn_4tree_9_splitter_BaseDenseSplitter *__pyx_v_self, CYTHON_UNUSED struct __pyx_obj_7sklearn_4tree_10_criterion_Criterion *__pyx_v_criterion, CYTHON_UNUSED __pyx_t_7sklearn_4tree_9_splitter_SIZE_t __pyx_v_max_features, CYTHON_UNUSED __pyx_t_7sklearn_4tree_9_splitter_SIZE_t __pyx_v_min_samples_leaf, CYTHON_UNUSED double __pyx_v_min_weight_leaf, CYTHON_UNUSED PyObject *__pyx_v_random_state, int __pyx_v_presort); /* proto */
static void __pyx_pf_7sklearn_4tree_9_splitter_17BaseDenseSplitter_2__dealloc__(struct __pyx_obj_7sklearn_4tree_9_splitter_BaseDenseSplitter *__pyx_v_self); /* proto */
static PyObject *__pyx_pf_7sklearn_4tree_9_splitter_12BestSplitter___reduce__(struct __pyx_obj_7sklearn_4tree_9_splitter_BestSplitter *__pyx_v_self); /* proto */
static PyObject *__pyx_pf_7sklearn_4tree_9_splitter_14RandomSplitter___reduce__(struct __pyx_obj_7sklearn_4tree_9_splitter_RandomSplitter *__pyx_v_self); /* proto */
static int __pyx_pf_7sklearn_4tree_9_splitter_18BaseSparseSplitter___cinit__(struct __pyx_obj_7sklearn_4tree_9_splitter_BaseSparseSplitter *__pyx_v_self, CYTHON_UNUSED struct __pyx_obj_7sklearn_4tree_10_criterion_Criterion *__pyx_v_criterion, CYTHON_UNUSED __pyx_t_7sklearn_4tree_9_splitter_SIZE_t __pyx_v_max_features, CYTHON_UNUSED __pyx_t_7sklearn_4tree_9_splitter_SIZE_t __pyx_v_min_samples_leaf, CYTHON_UNUSED double __pyx_v_min_weight_leaf, CYTHON_UNUSED PyObject *__pyx_v_random_state, CYTHON_UNUSED int __pyx_v_presort); /* proto */
static void __pyx_pf_7sklearn_4tree_9_splitter_18BaseSparseSplitter_2__dealloc__(struct __pyx_obj_7sklearn_4tree_9_splitter_BaseSparseSplitter *__pyx_v_self); /* proto */
static PyObject *__pyx_pf_7sklearn_4tree_9_splitter_18BestSparseSplitter___reduce__(struct __pyx_obj_7sklearn_4tree_9_splitter_BestSparseSplitter *__pyx_v_self); /* proto */
static PyObject *__pyx_pf_7sklearn_4tree_9_splitter_20RandomSparseSplitter___reduce__(struct __pyx_obj_7sklearn_4tree_9_splitter_RandomSparseSplitter *__pyx_v_self); /* proto */
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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_9_splitter_Splitter(PyTypeObject *t, PyObject *a, PyObject *k); /*proto*/
static PyObject *__pyx_tp_new_7sklearn_4tree_9_splitter_BaseDenseSplitter(PyTypeObject *t, PyObject *a, PyObject *k); /*proto*/
static PyObject *__pyx_tp_new_7sklearn_4tree_9_splitter_BestSplitter(PyTypeObject *t, PyObject *a, PyObject *k); /*proto*/
static PyObject *__pyx_tp_new_7sklearn_4tree_9_splitter_RandomSplitter(PyTypeObject *t, PyObject *a, PyObject *k); /*proto*/
static PyObject *__pyx_tp_new_7sklearn_4tree_9_splitter_BaseSparseSplitter(PyTypeObject *t, PyObject *a, PyObject *k); /*proto*/
static PyObject *__pyx_tp_new_7sklearn_4tree_9_splitter_BestSparseSplitter(PyTypeObject *t, PyObject *a, PyObject *k); /*proto*/
static PyObject *__pyx_tp_new_7sklearn_4tree_9_splitter_RandomSparseSplitter(PyTypeObject *t, PyObject *a, PyObject *k); /*proto*/
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static PyObject *__pyx_kp_u_Format_string_allocated_too_shor_2;
static PyObject *__pyx_kp_u_Non_native_byte_order_not_suppor;
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static PyObject *__pyx_kp_s_X_should_be_in_csc_format;
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static PyObject *__pyx_n_s_csc_matrix;
static PyObject *__pyx_n_s_data;
static PyObject *__pyx_n_s_getstate;
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static PyObject *__pyx_n_s_indptr;
static PyObject *__pyx_n_s_inf;
static PyObject *__pyx_n_s_itemsize;
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static PyObject *__pyx_n_s_max_features;
static PyObject *__pyx_n_s_min_samples_leaf;
static PyObject *__pyx_n_s_min_weight_leaf;
static PyObject *__pyx_kp_u_ndarray_is_not_C_contiguous;
static PyObject *__pyx_kp_u_ndarray_is_not_Fortran_contiguou;
static PyObject *__pyx_n_s_np;
static PyObject *__pyx_n_s_numpy;
static PyObject *__pyx_n_s_presort;
static PyObject *__pyx_n_s_pyx_vtable;
static PyObject *__pyx_n_s_randint;
static PyObject *__pyx_n_s_random_state;
static PyObject *__pyx_n_s_range;
static PyObject *__pyx_n_s_scipy_sparse;
static PyObject *__pyx_n_s_shape;
static PyObject *__pyx_n_s_strides;
static PyObject *__pyx_n_s_test;
static PyObject *__pyx_kp_u_unknown_dtype_code_in_numpy_pxd;
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static PyObject *__pyx_tuple_;
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/* "sklearn/tree/_splitter.pyx":46
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*
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* self.impurity_right = INFINITY
*/
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/* "sklearn/tree/_splitter.pyx":47
*
* cdef inline void _init_split(SplitRecord* self, SIZE_t start_pos) nogil:
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* self.impurity_right = INFINITY
* self.pos = start_pos
*/
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/* "sklearn/tree/_splitter.pyx":48
* cdef inline void _init_split(SplitRecord* self, SIZE_t start_pos) nogil:
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* self.impurity_right = INFINITY # <<<<<<<<<<<<<<
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*/
__pyx_v_self->impurity_right = __pyx_v_7sklearn_4tree_9_splitter_INFINITY;
/* "sklearn/tree/_splitter.pyx":49
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* self.impurity_right = INFINITY
* self.pos = start_pos # <<<<<<<<<<<<<<
* self.feature = 0
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*/
__pyx_v_self->pos = __pyx_v_start_pos;
/* "sklearn/tree/_splitter.pyx":50
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* self.threshold = 0.
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*/
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/* "sklearn/tree/_splitter.pyx":51
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/* "sklearn/tree/_splitter.pyx":52
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/* "sklearn/tree/_splitter.pyx":46
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*
* cdef inline void _init_split(SplitRecord* self, SIZE_t start_pos) nogil: # <<<<<<<<<<<<<<
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/* function exit code */
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/* "sklearn/tree/_splitter.pyx":61
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/* Python wrapper */
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__pyx_t_7sklearn_4tree_9_splitter_SIZE_t __pyx_v_min_samples_leaf;
double __pyx_v_min_weight_leaf;
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int __pyx_v_presort;
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/* "sklearn/tree/_splitter.pyx":87
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/* "sklearn/tree/_splitter.pyx":89
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/* "sklearn/tree/_splitter.pyx":91
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/* "sklearn/tree/_splitter.pyx":92
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/* "sklearn/tree/_splitter.pyx":93
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/* "sklearn/tree/_splitter.pyx":290
* cdef class BestSplitter(BaseDenseSplitter):
* """Splitter for finding the best split."""
* def __reduce__(self): # <<<<<<<<<<<<<<
* return (BestSplitter, (self.criterion,
* self.max_features,
*/
/* function exit code */
__pyx_L1_error:;
__Pyx_XDECREF(__pyx_t_1);
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}
/* "sklearn/tree/_splitter.pyx":298
* self.presort), 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]."""
*/
static void __pyx_f_7sklearn_4tree_9_splitter_12BestSplitter_node_split(struct __pyx_obj_7sklearn_4tree_9_splitter_BestSplitter *__pyx_v_self, double __pyx_v_impurity, struct __pyx_t_7sklearn_4tree_9_splitter_SplitRecord *__pyx_v_split, __pyx_t_7sklearn_4tree_9_splitter_SIZE_t *__pyx_v_n_constant_features) {
__pyx_t_7sklearn_4tree_9_splitter_SIZE_t *__pyx_v_samples;
__pyx_t_7sklearn_4tree_9_splitter_SIZE_t __pyx_v_start;
__pyx_t_7sklearn_4tree_9_splitter_SIZE_t __pyx_v_end;
__pyx_t_7sklearn_4tree_9_splitter_SIZE_t *__pyx_v_features;
__pyx_t_7sklearn_4tree_9_splitter_SIZE_t *__pyx_v_constant_features;
__pyx_t_7sklearn_4tree_9_splitter_SIZE_t __pyx_v_n_features;
__pyx_t_7sklearn_4tree_9_splitter_DTYPE_t *__pyx_v_X;
__pyx_t_7sklearn_4tree_9_splitter_DTYPE_t *__pyx_v_Xf;
__pyx_t_7sklearn_4tree_9_splitter_SIZE_t __pyx_v_X_sample_stride;
__pyx_t_7sklearn_4tree_9_splitter_SIZE_t __pyx_v_X_feature_stride;
__pyx_t_7sklearn_4tree_9_splitter_SIZE_t __pyx_v_max_features;
__pyx_t_7sklearn_4tree_9_splitter_SIZE_t __pyx_v_min_samples_leaf;
double __pyx_v_min_weight_leaf;
__pyx_t_7sklearn_4tree_9_splitter_UINT32_t *__pyx_v_random_state;
__pyx_t_7sklearn_4tree_9_splitter_INT32_t *__pyx_v_X_idx_sorted;
__pyx_t_7sklearn_4tree_9_splitter_SIZE_t *__pyx_v_sample_mask;
struct __pyx_t_7sklearn_4tree_9_splitter_SplitRecord __pyx_v_best;
struct __pyx_t_7sklearn_4tree_9_splitter_SplitRecord __pyx_v_current;
double __pyx_v_current_proxy_improvement;
double __pyx_v_best_proxy_improvement;
__pyx_t_7sklearn_4tree_9_splitter_SIZE_t __pyx_v_f_i;
__pyx_t_7sklearn_4tree_9_splitter_SIZE_t __pyx_v_f_j;
__pyx_t_7sklearn_4tree_9_splitter_SIZE_t __pyx_v_tmp;
__pyx_t_7sklearn_4tree_9_splitter_SIZE_t __pyx_v_p;
__pyx_t_7sklearn_4tree_9_splitter_SIZE_t __pyx_v_feature_idx_offset;
__pyx_t_7sklearn_4tree_9_splitter_SIZE_t __pyx_v_feature_offset;
__pyx_t_7sklearn_4tree_9_splitter_SIZE_t __pyx_v_i;
__pyx_t_7sklearn_4tree_9_splitter_SIZE_t __pyx_v_j;
__pyx_t_7sklearn_4tree_9_splitter_SIZE_t __pyx_v_n_visited_features;
__pyx_t_7sklearn_4tree_9_splitter_SIZE_t __pyx_v_n_found_constants;
__pyx_t_7sklearn_4tree_9_splitter_SIZE_t __pyx_v_n_drawn_constants;
__pyx_t_7sklearn_4tree_9_splitter_SIZE_t __pyx_v_n_known_constants;
__pyx_t_7sklearn_4tree_9_splitter_SIZE_t __pyx_v_n_total_constants;
__pyx_t_7sklearn_4tree_9_splitter_SIZE_t __pyx_v_partition_end;
__pyx_t_7sklearn_4tree_9_splitter_SIZE_t *__pyx_t_1;
__pyx_t_7sklearn_4tree_9_splitter_SIZE_t __pyx_t_2;
__pyx_t_7sklearn_4tree_9_splitter_DTYPE_t *__pyx_t_3;
double __pyx_t_4;
__pyx_t_7sklearn_4tree_9_splitter_INT32_t *__pyx_t_5;
int __pyx_t_6;
__pyx_t_7sklearn_4tree_9_splitter_SIZE_t __pyx_t_7;
int __pyx_t_8;
/* "sklearn/tree/_splitter.pyx":302
* """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/_splitter.pyx":303
* # 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/_splitter.pyx":304
* 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/_splitter.pyx":306
* 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/_splitter.pyx":307
*
* 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/_splitter.pyx":308
* 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/_splitter.pyx":310
* 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/_splitter.pyx":311
*
* 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_feature_stride = self.X_feature_stride
*/
__pyx_t_3 = __pyx_v_self->__pyx_base.__pyx_base.feature_values;
__pyx_v_Xf = __pyx_t_3;
/* "sklearn/tree/_splitter.pyx":312
* 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_feature_stride = self.X_feature_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/_splitter.pyx":313
* cdef DTYPE_t* Xf = self.feature_values
* cdef SIZE_t X_sample_stride = self.X_sample_stride
* cdef SIZE_t X_feature_stride = self.X_feature_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_feature_stride;
__pyx_v_X_feature_stride = __pyx_t_2;
/* "sklearn/tree/_splitter.pyx":314
* cdef SIZE_t X_sample_stride = self.X_sample_stride
* cdef SIZE_t X_feature_stride = self.X_feature_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/_splitter.pyx":315
* cdef SIZE_t X_feature_stride = self.X_feature_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/_splitter.pyx":316
* 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/_splitter.pyx":317
* 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 INT32_t* X_idx_sorted = self.X_idx_sorted_ptr
*/
__pyx_v_random_state = (&__pyx_v_self->__pyx_base.__pyx_base.rand_r_state);
/* "sklearn/tree/_splitter.pyx":319
* cdef UINT32_t* random_state = &self.rand_r_state
*
* cdef INT32_t* X_idx_sorted = self.X_idx_sorted_ptr # <<<<<<<<<<<<<<
* cdef SIZE_t* sample_mask = self.sample_mask
*
*/
__pyx_t_5 = __pyx_v_self->__pyx_base.X_idx_sorted_ptr;
__pyx_v_X_idx_sorted = __pyx_t_5;
/* "sklearn/tree/_splitter.pyx":320
*
* cdef INT32_t* X_idx_sorted = self.X_idx_sorted_ptr
* cdef SIZE_t* sample_mask = self.sample_mask # <<<<<<<<<<<<<<
*
* cdef SplitRecord best, current
*/
__pyx_t_1 = __pyx_v_self->__pyx_base.sample_mask;
__pyx_v_sample_mask = __pyx_t_1;
/* "sklearn/tree/_splitter.pyx":323
*
* cdef SplitRecord best, current
* cdef double current_proxy_improvement = -INFINITY # <<<<<<<<<<<<<<
* cdef double best_proxy_improvement = -INFINITY
*
*/
__pyx_v_current_proxy_improvement = (-__pyx_v_7sklearn_4tree_9_splitter_INFINITY);
/* "sklearn/tree/_splitter.pyx":324
* cdef SplitRecord best, current
* cdef double current_proxy_improvement = -INFINITY
* cdef double best_proxy_improvement = -INFINITY # <<<<<<<<<<<<<<
*
* cdef SIZE_t f_i = n_features
*/
__pyx_v_best_proxy_improvement = (-__pyx_v_7sklearn_4tree_9_splitter_INFINITY);
/* "sklearn/tree/_splitter.pyx":326
* cdef double best_proxy_improvement = -INFINITY
*
* cdef SIZE_t f_i = n_features # <<<<<<<<<<<<<<
* cdef SIZE_t f_j
* cdef SIZE_t tmp
*/
__pyx_v_f_i = __pyx_v_n_features;
/* "sklearn/tree/_splitter.pyx":335
* cdef SIZE_t j
*
* 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/_splitter.pyx":337
* 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/_splitter.pyx":339
* 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/_splitter.pyx":340
* # 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/_splitter.pyx":342
* 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/_splitter.pyx":346
* cdef SIZE_t partition_end
*
* _init_split(&best, end) # <<<<<<<<<<<<<<
*
* if self.presort == 1:
*/
__pyx_f_7sklearn_4tree_9_splitter__init_split((&__pyx_v_best), __pyx_v_end);
/* "sklearn/tree/_splitter.pyx":348
* _init_split(&best, end)
*
* if self.presort == 1: # <<<<<<<<<<<<<<
* for p in range(start, end):
* sample_mask[samples[p]] = 1
*/
__pyx_t_6 = ((__pyx_v_self->__pyx_base.__pyx_base.presort == 1) != 0);
if (__pyx_t_6) {
/* "sklearn/tree/_splitter.pyx":349
*
* if self.presort == 1:
* 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/_splitter.pyx":350
* if self.presort == 1:
* 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;
}
goto __pyx_L3;
}
__pyx_L3:;
/* "sklearn/tree/_splitter.pyx":361
* # 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) {
} else {
__pyx_t_6 = __pyx_t_8;
goto __pyx_L8_bool_binop_done;
}
/* "sklearn/tree/_splitter.pyx":363
* 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_8 = ((__pyx_v_n_visited_features < __pyx_v_max_features) != 0);
if (!__pyx_t_8) {
} else {
__pyx_t_6 = __pyx_t_8;
goto __pyx_L8_bool_binop_done;
}
/* "sklearn/tree/_splitter.pyx":365
* (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_6 = __pyx_t_8;
__pyx_L8_bool_binop_done:;
if (!__pyx_t_6) break;
/* "sklearn/tree/_splitter.pyx":367
* 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/_splitter.pyx":381
*
* # 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_6_utils_rand_int(__pyx_v_n_drawn_constants, (__pyx_v_f_i - __pyx_v_n_found_constants), __pyx_v_random_state);
/* "sklearn/tree/_splitter.pyx":384
* 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/_splitter.pyx":386
* 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/_splitter.pyx":387
* # 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/_splitter.pyx":388
* 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/_splitter.pyx":390
* features[n_drawn_constants] = tmp
*
* n_drawn_constants += 1 # <<<<<<<<<<<<<<
*
* else:
*/
__pyx_v_n_drawn_constants = (__pyx_v_n_drawn_constants + 1);
goto __pyx_L11;
}
/*else*/ {
/* "sklearn/tree/_splitter.pyx":394
* 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[
* current.feature = features[f_j]
*/
__pyx_v_f_j = (__pyx_v_f_j + __pyx_v_n_found_constants);
/* "sklearn/tree/_splitter.pyx":396
* f_j += n_found_constants
* # f_j in the interval [n_total_constants, f_i[
* current.feature = features[f_j] # <<<<<<<<<<<<<<
* feature_offset = self.X_feature_stride * current.feature
*
*/
__pyx_v_current.feature = (__pyx_v_features[__pyx_v_f_j]);
/* "sklearn/tree/_splitter.pyx":397
* # f_j in the interval [n_total_constants, f_i[
* current.feature = features[f_j]
* feature_offset = self.X_feature_stride * current.feature # <<<<<<<<<<<<<<
*
* # Sort samples along that feature; either by utilizing
*/
__pyx_v_feature_offset = (__pyx_v_self->__pyx_base.X_feature_stride * __pyx_v_current.feature);
/* "sklearn/tree/_splitter.pyx":403
* # sorting the array in a manner which utilizes the cache more
* # effectively.
* if self.presort == 1: # <<<<<<<<<<<<<<
* p = start
* feature_idx_offset = self.X_idx_sorted_stride * current.feature
*/
__pyx_t_6 = ((__pyx_v_self->__pyx_base.__pyx_base.presort == 1) != 0);
if (__pyx_t_6) {
/* "sklearn/tree/_splitter.pyx":404
* # effectively.
* if self.presort == 1:
* p = start # <<<<<<<<<<<<<<
* feature_idx_offset = self.X_idx_sorted_stride * current.feature
*
*/
__pyx_v_p = __pyx_v_start;
/* "sklearn/tree/_splitter.pyx":405
* if self.presort == 1:
* p = start
* feature_idx_offset = self.X_idx_sorted_stride * current.feature # <<<<<<<<<<<<<<
*
* for i in range(self.n_total_samples):
*/
__pyx_v_feature_idx_offset = (__pyx_v_self->__pyx_base.X_idx_sorted_stride * __pyx_v_current.feature);
/* "sklearn/tree/_splitter.pyx":407
* feature_idx_offset = self.X_idx_sorted_stride * current.feature
*
* for i in range(self.n_total_samples): # <<<<<<<<<<<<<<
* j = X_idx_sorted[i + feature_idx_offset]
* if sample_mask[j] == 1:
*/
__pyx_t_2 = __pyx_v_self->__pyx_base.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/_splitter.pyx":408
*
* for i in range(self.n_total_samples):
* j = X_idx_sorted[i + feature_idx_offset] # <<<<<<<<<<<<<<
* if sample_mask[j] == 1:
* samples[p] = j
*/
__pyx_v_j = (__pyx_v_X_idx_sorted[(__pyx_v_i + __pyx_v_feature_idx_offset)]);
/* "sklearn/tree/_splitter.pyx":409
* for i in range(self.n_total_samples):
* j = X_idx_sorted[i + feature_idx_offset]
* if sample_mask[j] == 1: # <<<<<<<<<<<<<<
* samples[p] = j
* Xf[p] = X[self.X_sample_stride * j + feature_offset]
*/
__pyx_t_6 = (((__pyx_v_sample_mask[__pyx_v_j]) == 1) != 0);
if (__pyx_t_6) {
/* "sklearn/tree/_splitter.pyx":410
* j = X_idx_sorted[i + feature_idx_offset]
* if sample_mask[j] == 1:
* samples[p] = j # <<<<<<<<<<<<<<
* Xf[p] = X[self.X_sample_stride * j + feature_offset]
* p += 1
*/
(__pyx_v_samples[__pyx_v_p]) = __pyx_v_j;
/* "sklearn/tree/_splitter.pyx":411
* if sample_mask[j] == 1:
* samples[p] = j
* Xf[p] = X[self.X_sample_stride * j + feature_offset] # <<<<<<<<<<<<<<
* p += 1
* else:
*/
(__pyx_v_Xf[__pyx_v_p]) = (__pyx_v_X[((__pyx_v_self->__pyx_base.X_sample_stride * __pyx_v_j) + __pyx_v_feature_offset)]);
/* "sklearn/tree/_splitter.pyx":412
* samples[p] = j
* Xf[p] = X[self.X_sample_stride * j + feature_offset]
* p += 1 # <<<<<<<<<<<<<<
* else:
* for i in range(start, end):
*/
__pyx_v_p = (__pyx_v_p + 1);
goto __pyx_L15;
}
__pyx_L15:;
}
goto __pyx_L12;
}
/*else*/ {
/* "sklearn/tree/_splitter.pyx":414
* p += 1
* else:
* for i in range(start, end): # <<<<<<<<<<<<<<
* Xf[i] = X[self.X_sample_stride * samples[i] + feature_offset]
*
*/
__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_i = __pyx_t_7;
/* "sklearn/tree/_splitter.pyx":415
* else:
* for i in range(start, end):
* Xf[i] = X[self.X_sample_stride * samples[i] + feature_offset] # <<<<<<<<<<<<<<
*
* sort(Xf + start, samples + start, end - start)
*/
(__pyx_v_Xf[__pyx_v_i]) = (__pyx_v_X[((__pyx_v_self->__pyx_base.X_sample_stride * (__pyx_v_samples[__pyx_v_i])) + __pyx_v_feature_offset)]);
}
/* "sklearn/tree/_splitter.pyx":417
* Xf[i] = X[self.X_sample_stride * samples[i] + feature_offset]
*
* sort(Xf + start, samples + start, end - start) # <<<<<<<<<<<<<<
*
* if Xf[end - 1] <= Xf[start] + FEATURE_THRESHOLD:
*/
__pyx_f_7sklearn_4tree_9_splitter_sort((__pyx_v_Xf + __pyx_v_start), (__pyx_v_samples + __pyx_v_start), (__pyx_v_end - __pyx_v_start));
}
__pyx_L12:;
/* "sklearn/tree/_splitter.pyx":419
* 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_9_splitter_FEATURE_THRESHOLD)) != 0);
if (__pyx_t_6) {
/* "sklearn/tree/_splitter.pyx":420
*
* 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/_splitter.pyx":421
* 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/_splitter.pyx":423
* 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/_splitter.pyx":424
*
* n_found_constants += 1
* n_total_constants += 1 # <<<<<<<<<<<<<<
*
* else:
*/
__pyx_v_n_total_constants = (__pyx_v_n_total_constants + 1);
goto __pyx_L18;
}
/*else*/ {
/* "sklearn/tree/_splitter.pyx":427
*
* 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/_splitter.pyx":428
* 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_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/_splitter.pyx":431
*
* # Evaluate all splits
* self.criterion.reset() # <<<<<<<<<<<<<<
* p = start
*
*/
((struct __pyx_vtabstruct_7sklearn_4tree_10_criterion_Criterion *)__pyx_v_self->__pyx_base.__pyx_base.criterion->__pyx_vtab)->reset(__pyx_v_self->__pyx_base.__pyx_base.criterion);
/* "sklearn/tree/_splitter.pyx":432
* # Evaluate all splits
* self.criterion.reset()
* p = start # <<<<<<<<<<<<<<
*
* while p < end:
*/
__pyx_v_p = __pyx_v_start;
/* "sklearn/tree/_splitter.pyx":434
* 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/_splitter.pyx":435
*
* while p < end:
* while (p + 1 < end and # <<<<<<<<<<<<<<
* Xf[p + 1] <= Xf[p] + FEATURE_THRESHOLD):
* p += 1
*/
while (1) {
__pyx_t_8 = (((__pyx_v_p + 1) < __pyx_v_end) != 0);
if (__pyx_t_8) {
} else {
__pyx_t_6 = __pyx_t_8;
goto __pyx_L23_bool_binop_done;
}
/* "sklearn/tree/_splitter.pyx":436
* 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_9_splitter_FEATURE_THRESHOLD)) != 0);
__pyx_t_6 = __pyx_t_8;
__pyx_L23_bool_binop_done:;
if (!__pyx_t_6) break;
/* "sklearn/tree/_splitter.pyx":437
* 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/_splitter.pyx":441
* # (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/_splitter.pyx":445
* # X[samples[p - 1], current.feature])
*
* if p < end: # <<<<<<<<<<<<<<
* current.pos = p
*
*/
__pyx_t_6 = ((__pyx_v_p < __pyx_v_end) != 0);
if (__pyx_t_6) {
/* "sklearn/tree/_splitter.pyx":446
*
* if p < end:
* current.pos = p # <<<<<<<<<<<<<<
*
* # Reject if min_samples_leaf is not guaranteed
*/
__pyx_v_current.pos = __pyx_v_p;
/* "sklearn/tree/_splitter.pyx":449
*
* # 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) {
} else {
__pyx_t_6 = __pyx_t_8;
goto __pyx_L27_bool_binop_done;
}
/* "sklearn/tree/_splitter.pyx":450
* # 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_end - __pyx_v_current.pos) < __pyx_v_min_samples_leaf) != 0);
__pyx_t_6 = __pyx_t_8;
__pyx_L27_bool_binop_done:;
if (__pyx_t_6) {
/* "sklearn/tree/_splitter.pyx":451
* if (((current.pos - start) < min_samples_leaf) or
* ((end - current.pos) < min_samples_leaf)):
* continue # <<<<<<<<<<<<<<
*
* self.criterion.update(current.pos)
*/
goto __pyx_L19_continue;
}
/* "sklearn/tree/_splitter.pyx":453
* continue
*
* self.criterion.update(current.pos) # <<<<<<<<<<<<<<
*
* # Reject if min_weight_leaf is not satisfied
*/
((struct __pyx_vtabstruct_7sklearn_4tree_10_criterion_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/_splitter.pyx":456
*
* # 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) {
} else {
__pyx_t_6 = __pyx_t_8;
goto __pyx_L30_bool_binop_done;
}
/* "sklearn/tree/_splitter.pyx":457
* # 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;
__pyx_L30_bool_binop_done:;
if (__pyx_t_6) {
/* "sklearn/tree/_splitter.pyx":458
* if ((self.criterion.weighted_n_left < min_weight_leaf) or
* (self.criterion.weighted_n_right < min_weight_leaf)):
* continue # <<<<<<<<<<<<<<
*
* current_proxy_improvement = self.criterion.proxy_impurity_improvement()
*/
goto __pyx_L19_continue;
}
/* "sklearn/tree/_splitter.pyx":460
* continue
*
* current_proxy_improvement = self.criterion.proxy_impurity_improvement() # <<<<<<<<<<<<<<
*
* if current_proxy_improvement > best_proxy_improvement:
*/
__pyx_v_current_proxy_improvement = ((struct __pyx_vtabstruct_7sklearn_4tree_10_criterion_Criterion *)__pyx_v_self->__pyx_base.__pyx_base.criterion->__pyx_vtab)->proxy_impurity_improvement(__pyx_v_self->__pyx_base.__pyx_base.criterion);
/* "sklearn/tree/_splitter.pyx":462
* current_proxy_improvement = self.criterion.proxy_impurity_improvement()
*
* if current_proxy_improvement > best_proxy_improvement: # <<<<<<<<<<<<<<
* best_proxy_improvement = current_proxy_improvement
* current.threshold = (Xf[p - 1] + Xf[p]) / 2.0
*/
__pyx_t_6 = ((__pyx_v_current_proxy_improvement > __pyx_v_best_proxy_improvement) != 0);
if (__pyx_t_6) {
/* "sklearn/tree/_splitter.pyx":463
*
* if current_proxy_improvement > best_proxy_improvement:
* best_proxy_improvement = current_proxy_improvement # <<<<<<<<<<<<<<
* current.threshold = (Xf[p - 1] + Xf[p]) / 2.0
*
*/
__pyx_v_best_proxy_improvement = __pyx_v_current_proxy_improvement;
/* "sklearn/tree/_splitter.pyx":464
* if current_proxy_improvement > best_proxy_improvement:
* best_proxy_improvement = current_proxy_improvement
* 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/_splitter.pyx":466
* 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/_splitter.pyx":467
*
* 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_L33;
}
__pyx_L33:;
/* "sklearn/tree/_splitter.pyx":469
* 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_L32;
}
__pyx_L32:;
goto __pyx_L25;
}
__pyx_L25:;
__pyx_L19_continue:;
}
}
__pyx_L18:;
}
__pyx_L11:;
}
/* "sklearn/tree/_splitter.pyx":472
*
* # Reorganize into samples[start:best.pos] + samples[best.pos:end]
* if best.pos < end: # <<<<<<<<<<<<<<
* feature_offset = X_feature_stride * best.feature
* partition_end = end
*/
__pyx_t_6 = ((__pyx_v_best.pos < __pyx_v_end) != 0);
if (__pyx_t_6) {
/* "sklearn/tree/_splitter.pyx":473
* # Reorganize into samples[start:best.pos] + samples[best.pos:end]
* if best.pos < end:
* feature_offset = X_feature_stride * best.feature # <<<<<<<<<<<<<<
* partition_end = end
* p = start
*/
__pyx_v_feature_offset = (__pyx_v_X_feature_stride * __pyx_v_best.feature);
/* "sklearn/tree/_splitter.pyx":474
* if best.pos < end:
* feature_offset = X_feature_stride * best.feature
* partition_end = end # <<<<<<<<<<<<<<
* p = start
*
*/
__pyx_v_partition_end = __pyx_v_end;
/* "sklearn/tree/_splitter.pyx":475
* feature_offset = X_feature_stride * best.feature
* partition_end = end
* p = start # <<<<<<<<<<<<<<
*
* while p < partition_end:
*/
__pyx_v_p = __pyx_v_start;
/* "sklearn/tree/_splitter.pyx":477
* p = start
*
* while p < partition_end: # <<<<<<<<<<<<<<
* if X[X_sample_stride * samples[p] + feature_offset] <= best.threshold:
* p += 1
*/
while (1) {
__pyx_t_6 = ((__pyx_v_p < __pyx_v_partition_end) != 0);
if (!__pyx_t_6) break;
/* "sklearn/tree/_splitter.pyx":478
*
* while p < partition_end:
* if X[X_sample_stride * samples[p] + feature_offset] <= best.threshold: # <<<<<<<<<<<<<<
* p += 1
*
*/
__pyx_t_6 = (((__pyx_v_X[((__pyx_v_X_sample_stride * (__pyx_v_samples[__pyx_v_p])) + __pyx_v_feature_offset)]) <= __pyx_v_best.threshold) != 0);
if (__pyx_t_6) {
/* "sklearn/tree/_splitter.pyx":479
* while p < partition_end:
* if X[X_sample_stride * samples[p] + feature_offset] <= best.threshold:
* p += 1 # <<<<<<<<<<<<<<
*
* else:
*/
__pyx_v_p = (__pyx_v_p + 1);
goto __pyx_L37;
}
/*else*/ {
/* "sklearn/tree/_splitter.pyx":482
*
* else:
* partition_end -= 1 # <<<<<<<<<<<<<<
*
* tmp = samples[partition_end]
*/
__pyx_v_partition_end = (__pyx_v_partition_end - 1);
/* "sklearn/tree/_splitter.pyx":484
* 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/_splitter.pyx":485
*
* 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/_splitter.pyx":486
* tmp = samples[partition_end]
* samples[partition_end] = samples[p]
* samples[p] = tmp # <<<<<<<<<<<<<<
*
* self.criterion.reset()
*/
(__pyx_v_samples[__pyx_v_p]) = __pyx_v_tmp;
}
__pyx_L37:;
}
/* "sklearn/tree/_splitter.pyx":488
* samples[p] = tmp
*
* self.criterion.reset() # <<<<<<<<<<<<<<
* self.criterion.update(best.pos)
* best.improvement = self.criterion.impurity_improvement(impurity)
*/
((struct __pyx_vtabstruct_7sklearn_4tree_10_criterion_Criterion *)__pyx_v_self->__pyx_base.__pyx_base.criterion->__pyx_vtab)->reset(__pyx_v_self->__pyx_base.__pyx_base.criterion);
/* "sklearn/tree/_splitter.pyx":489
*
* self.criterion.reset()
* self.criterion.update(best.pos) # <<<<<<<<<<<<<<
* best.improvement = self.criterion.impurity_improvement(impurity)
* self.criterion.children_impurity(&best.impurity_left,
*/
((struct __pyx_vtabstruct_7sklearn_4tree_10_criterion_Criterion *)__pyx_v_self->__pyx_base.__pyx_base.criterion->__pyx_vtab)->update(__pyx_v_self->__pyx_base.__pyx_base.criterion, __pyx_v_best.pos);
/* "sklearn/tree/_splitter.pyx":490
* self.criterion.reset()
* self.criterion.update(best.pos)
* best.improvement = self.criterion.impurity_improvement(impurity) # <<<<<<<<<<<<<<
* self.criterion.children_impurity(&best.impurity_left,
* &best.impurity_right)
*/
__pyx_v_best.improvement = ((struct __pyx_vtabstruct_7sklearn_4tree_10_criterion_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/_splitter.pyx":491
* self.criterion.update(best.pos)
* best.improvement = self.criterion.impurity_improvement(impurity)
* self.criterion.children_impurity(&best.impurity_left, # <<<<<<<<<<<<<<
* &best.impurity_right)
*
*/
((struct __pyx_vtabstruct_7sklearn_4tree_10_criterion_Criterion *)__pyx_v_self->__pyx_base.__pyx_base.criterion->__pyx_vtab)->children_impurity(__pyx_v_self->__pyx_base.__pyx_base.criterion, (&__pyx_v_best.impurity_left), (&__pyx_v_best.impurity_right));
goto __pyx_L34;
}
__pyx_L34:;
/* "sklearn/tree/_splitter.pyx":495
*
* # Reset sample mask
* if self.presort == 1: # <<<<<<<<<<<<<<
* for p in range(start, end):
* sample_mask[samples[p]] = 0
*/
__pyx_t_6 = ((__pyx_v_self->__pyx_base.__pyx_base.presort == 1) != 0);
if (__pyx_t_6) {
/* "sklearn/tree/_splitter.pyx":496
* # Reset sample mask
* if self.presort == 1:
* for p in range(start, end): # <<<<<<<<<<<<<<
* sample_mask[samples[p]] = 0
*
*/
__pyx_t_7 = __pyx_v_end;
for (__pyx_t_2 = __pyx_v_start; __pyx_t_2 < __pyx_t_7; __pyx_t_2+=1) {
__pyx_v_p = __pyx_t_2;
/* "sklearn/tree/_splitter.pyx":497
* if self.presort == 1:
* for p in range(start, end):
* sample_mask[samples[p]] = 0 # <<<<<<<<<<<<<<
*
* # Respect invariant for constant features: the original order of
*/
(__pyx_v_sample_mask[(__pyx_v_samples[__pyx_v_p])]) = 0;
}
goto __pyx_L38;
}
__pyx_L38:;
/* "sklearn/tree/_splitter.pyx":502
* # 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_9_splitter_SIZE_t)) * __pyx_v_n_known_constants));
/* "sklearn/tree/_splitter.pyx":505
*
* # 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_9_splitter_SIZE_t)) * __pyx_v_n_found_constants));
/* "sklearn/tree/_splitter.pyx":510
*
* # Return values
* split[0] = best # <<<<<<<<<<<<<<
* n_constant_features[0] = n_total_constants
*
*/
(__pyx_v_split[0]) = __pyx_v_best;
/* "sklearn/tree/_splitter.pyx":511
* # 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/_splitter.pyx":298
* self.presort), 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/_splitter.pyx":516
* # 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_9_splitter_sort(__pyx_t_7sklearn_4tree_9_splitter_DTYPE_t *__pyx_v_Xf, __pyx_t_7sklearn_4tree_9_splitter_SIZE_t *__pyx_v_samples, __pyx_t_7sklearn_4tree_9_splitter_SIZE_t __pyx_v_n) {
int __pyx_v_maxd;
/* "sklearn/tree/_splitter.pyx":517
* # 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_6_utils_log(__pyx_v_n)));
/* "sklearn/tree/_splitter.pyx":518
* 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_9_splitter_introsort(__pyx_v_Xf, __pyx_v_samples, __pyx_v_n, __pyx_v_maxd);
/* "sklearn/tree/_splitter.pyx":516
* # 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/_splitter.pyx":521
*
*
* 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_9_splitter_swap(__pyx_t_7sklearn_4tree_9_splitter_DTYPE_t *__pyx_v_Xf, __pyx_t_7sklearn_4tree_9_splitter_SIZE_t *__pyx_v_samples, __pyx_t_7sklearn_4tree_9_splitter_SIZE_t __pyx_v_i, __pyx_t_7sklearn_4tree_9_splitter_SIZE_t __pyx_v_j) {
__pyx_t_7sklearn_4tree_9_splitter_DTYPE_t __pyx_t_1;
__pyx_t_7sklearn_4tree_9_splitter_DTYPE_t __pyx_t_2;
__pyx_t_7sklearn_4tree_9_splitter_SIZE_t __pyx_t_3;
__pyx_t_7sklearn_4tree_9_splitter_SIZE_t __pyx_t_4;
/* "sklearn/tree/_splitter.pyx":523
* 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/_splitter.pyx":524
* # 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/_splitter.pyx":521
*
*
* 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/_splitter.pyx":527
*
*
* 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_9_splitter_DTYPE_t __pyx_f_7sklearn_4tree_9_splitter_median3(__pyx_t_7sklearn_4tree_9_splitter_DTYPE_t *__pyx_v_Xf, __pyx_t_7sklearn_4tree_9_splitter_SIZE_t __pyx_v_n) {
__pyx_t_7sklearn_4tree_9_splitter_DTYPE_t __pyx_v_a;
__pyx_t_7sklearn_4tree_9_splitter_DTYPE_t __pyx_v_b;
__pyx_t_7sklearn_4tree_9_splitter_DTYPE_t __pyx_v_c;
__pyx_t_7sklearn_4tree_9_splitter_DTYPE_t __pyx_r;
int __pyx_t_1;
/* "sklearn/tree/_splitter.pyx":530
* # 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/_splitter.pyx":531
* # 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/_splitter.pyx":532
* 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/_splitter.pyx":533
* if a < b:
* if b < c:
* return b # <<<<<<<<<<<<<<
* elif a < c:
* return c
*/
__pyx_r = __pyx_v_b;
goto __pyx_L0;
}
/* "sklearn/tree/_splitter.pyx":534
* 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/_splitter.pyx":535
* return b
* elif a < c:
* return c # <<<<<<<<<<<<<<
* else:
* return a
*/
__pyx_r = __pyx_v_c;
goto __pyx_L0;
}
/*else*/ {
/* "sklearn/tree/_splitter.pyx":537
* return c
* else:
* return a # <<<<<<<<<<<<<<
* elif b < c:
* if a < c:
*/
__pyx_r = __pyx_v_a;
goto __pyx_L0;
}
}
/* "sklearn/tree/_splitter.pyx":538
* 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/_splitter.pyx":539
* 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/_splitter.pyx":540
* elif b < c:
* if a < c:
* return a # <<<<<<<<<<<<<<
* else:
* return c
*/
__pyx_r = __pyx_v_a;
goto __pyx_L0;
}
/*else*/ {
/* "sklearn/tree/_splitter.pyx":542
* return a
* else:
* return c # <<<<<<<<<<<<<<
* else:
* return b
*/
__pyx_r = __pyx_v_c;
goto __pyx_L0;
}
}
/*else*/ {
/* "sklearn/tree/_splitter.pyx":544
* return c
* else:
* return b # <<<<<<<<<<<<<<
*
*
*/
__pyx_r = __pyx_v_b;
goto __pyx_L0;
}
/* "sklearn/tree/_splitter.pyx":527
*
*
* 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/_splitter.pyx":549
* # 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_9_splitter_introsort(__pyx_t_7sklearn_4tree_9_splitter_DTYPE_t *__pyx_v_Xf, __pyx_t_7sklearn_4tree_9_splitter_SIZE_t *__pyx_v_samples, __pyx_t_7sklearn_4tree_9_splitter_SIZE_t __pyx_v_n, int __pyx_v_maxd) {
__pyx_t_7sklearn_4tree_9_splitter_DTYPE_t __pyx_v_pivot;
__pyx_t_7sklearn_4tree_9_splitter_SIZE_t __pyx_v_i;
__pyx_t_7sklearn_4tree_9_splitter_SIZE_t __pyx_v_l;
__pyx_t_7sklearn_4tree_9_splitter_SIZE_t __pyx_v_r;
int __pyx_t_1;
/* "sklearn/tree/_splitter.pyx":553
* cdef SIZE_t i, l, r
*
* while n > 1: # <<<<<<<<<<<<<<
* if maxd <= 0: # max depth limit exceeded ("gone quadratic")
* heapsort(Xf, samples, n)
*/
while (1) {
__pyx_t_1 = ((__pyx_v_n > 1) != 0);
if (!__pyx_t_1) break;
/* "sklearn/tree/_splitter.pyx":554
*
* while n > 1:
* if maxd <= 0: # max depth limit exceeded ("gone quadratic") # <<<<<<<<<<<<<<
* heapsort(Xf, samples, n)
* return
*/
__pyx_t_1 = ((__pyx_v_maxd <= 0) != 0);
if (__pyx_t_1) {
/* "sklearn/tree/_splitter.pyx":555
* while n > 1:
* if maxd <= 0: # max depth limit exceeded ("gone quadratic")
* heapsort(Xf, samples, n) # <<<<<<<<<<<<<<
* return
* maxd -= 1
*/
__pyx_f_7sklearn_4tree_9_splitter_heapsort(__pyx_v_Xf, __pyx_v_samples, __pyx_v_n);
/* "sklearn/tree/_splitter.pyx":556
* if maxd <= 0: # max depth limit exceeded ("gone quadratic")
* heapsort(Xf, samples, n)
* return # <<<<<<<<<<<<<<
* maxd -= 1
*
*/
goto __pyx_L0;
}
/* "sklearn/tree/_splitter.pyx":557
* heapsort(Xf, samples, n)
* return
* maxd -= 1 # <<<<<<<<<<<<<<
*
* pivot = median3(Xf, n)
*/
__pyx_v_maxd = (__pyx_v_maxd - 1);
/* "sklearn/tree/_splitter.pyx":559
* maxd -= 1
*
* pivot = median3(Xf, n) # <<<<<<<<<<<<<<
*
* # Three-way partition.
*/
__pyx_v_pivot = __pyx_f_7sklearn_4tree_9_splitter_median3(__pyx_v_Xf, __pyx_v_n);
/* "sklearn/tree/_splitter.pyx":562
*
* # Three-way partition.
* i = l = 0 # <<<<<<<<<<<<<<
* r = n
* while i < r:
*/
__pyx_v_i = 0;
__pyx_v_l = 0;
/* "sklearn/tree/_splitter.pyx":563
* # Three-way partition.
* i = l = 0
* r = n # <<<<<<<<<<<<<<
* while i < r:
* if Xf[i] < pivot:
*/
__pyx_v_r = __pyx_v_n;
/* "sklearn/tree/_splitter.pyx":564
* i = l = 0
* r = n
* while i < r: # <<<<<<<<<<<<<<
* if Xf[i] < pivot:
* swap(Xf, samples, i, l)
*/
while (1) {
__pyx_t_1 = ((__pyx_v_i < __pyx_v_r) != 0);
if (!__pyx_t_1) break;
/* "sklearn/tree/_splitter.pyx":565
* r = n
* while i < r:
* if Xf[i] < pivot: # <<<<<<<<<<<<<<
* swap(Xf, samples, i, l)
* i += 1
*/
__pyx_t_1 = (((__pyx_v_Xf[__pyx_v_i]) < __pyx_v_pivot) != 0);
if (__pyx_t_1) {
/* "sklearn/tree/_splitter.pyx":566
* while i < r:
* if Xf[i] < pivot:
* swap(Xf, samples, i, l) # <<<<<<<<<<<<<<
* i += 1
* l += 1
*/
__pyx_f_7sklearn_4tree_9_splitter_swap(__pyx_v_Xf, __pyx_v_samples, __pyx_v_i, __pyx_v_l);
/* "sklearn/tree/_splitter.pyx":567
* if Xf[i] < pivot:
* swap(Xf, samples, i, l)
* i += 1 # <<<<<<<<<<<<<<
* l += 1
* elif Xf[i] > pivot:
*/
__pyx_v_i = (__pyx_v_i + 1);
/* "sklearn/tree/_splitter.pyx":568
* swap(Xf, samples, i, l)
* i += 1
* l += 1 # <<<<<<<<<<<<<<
* elif Xf[i] > pivot:
* r -= 1
*/
__pyx_v_l = (__pyx_v_l + 1);
goto __pyx_L8;
}
/* "sklearn/tree/_splitter.pyx":569
* i += 1
* l += 1
* elif Xf[i] > pivot: # <<<<<<<<<<<<<<
* r -= 1
* swap(Xf, samples, i, r)
*/
__pyx_t_1 = (((__pyx_v_Xf[__pyx_v_i]) > __pyx_v_pivot) != 0);
if (__pyx_t_1) {
/* "sklearn/tree/_splitter.pyx":570
* l += 1
* elif Xf[i] > pivot:
* r -= 1 # <<<<<<<<<<<<<<
* swap(Xf, samples, i, r)
* else:
*/
__pyx_v_r = (__pyx_v_r - 1);
/* "sklearn/tree/_splitter.pyx":571
* elif Xf[i] > pivot:
* r -= 1
* swap(Xf, samples, i, r) # <<<<<<<<<<<<<<
* else:
* i += 1
*/
__pyx_f_7sklearn_4tree_9_splitter_swap(__pyx_v_Xf, __pyx_v_samples, __pyx_v_i, __pyx_v_r);
goto __pyx_L8;
}
/*else*/ {
/* "sklearn/tree/_splitter.pyx":573
* swap(Xf, samples, i, r)
* else:
* i += 1 # <<<<<<<<<<<<<<
*
* introsort(Xf, samples, l, maxd)
*/
__pyx_v_i = (__pyx_v_i + 1);
}
__pyx_L8:;
}
/* "sklearn/tree/_splitter.pyx":575
* i += 1
*
* introsort(Xf, samples, l, maxd) # <<<<<<<<<<<<<<
* Xf += r
* samples += r
*/
__pyx_f_7sklearn_4tree_9_splitter_introsort(__pyx_v_Xf, __pyx_v_samples, __pyx_v_l, __pyx_v_maxd);
/* "sklearn/tree/_splitter.pyx":576
*
* introsort(Xf, samples, l, maxd)
* Xf += r # <<<<<<<<<<<<<<
* samples += r
* n -= r
*/
__pyx_v_Xf = (__pyx_v_Xf + __pyx_v_r);
/* "sklearn/tree/_splitter.pyx":577
* introsort(Xf, samples, l, maxd)
* Xf += r
* samples += r # <<<<<<<<<<<<<<
* n -= r
*
*/
__pyx_v_samples = (__pyx_v_samples + __pyx_v_r);
/* "sklearn/tree/_splitter.pyx":578
* Xf += r
* samples += r
* n -= r # <<<<<<<<<<<<<<
*
*
*/
__pyx_v_n = (__pyx_v_n - __pyx_v_r);
}
/* "sklearn/tree/_splitter.pyx":549
* # 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
*/
/* function exit code */
__pyx_L0:;
}
/* "sklearn/tree/_splitter.pyx":581
*
*
* cdef inline void sift_down(DTYPE_t* Xf, SIZE_t* samples, # <<<<<<<<<<<<<<
* SIZE_t start, SIZE_t end) nogil:
* # Restore heap order in Xf[start:end] by moving the max element to start.
*/
static CYTHON_INLINE void __pyx_f_7sklearn_4tree_9_splitter_sift_down(__pyx_t_7sklearn_4tree_9_splitter_DTYPE_t *__pyx_v_Xf, __pyx_t_7sklearn_4tree_9_splitter_SIZE_t *__pyx_v_samples, __pyx_t_7sklearn_4tree_9_splitter_SIZE_t __pyx_v_start, __pyx_t_7sklearn_4tree_9_splitter_SIZE_t __pyx_v_end) {
__pyx_t_7sklearn_4tree_9_splitter_SIZE_t __pyx_v_child;
__pyx_t_7sklearn_4tree_9_splitter_SIZE_t __pyx_v_maxind;
__pyx_t_7sklearn_4tree_9_splitter_SIZE_t __pyx_v_root;
int __pyx_t_1;
int __pyx_t_2;
/* "sklearn/tree/_splitter.pyx":586
* cdef SIZE_t child, maxind, root
*
* root = start # <<<<<<<<<<<<<<
* while True:
* child = root * 2 + 1
*/
__pyx_v_root = __pyx_v_start;
/* "sklearn/tree/_splitter.pyx":587
*
* root = start
* while True: # <<<<<<<<<<<<<<
* child = root * 2 + 1
*
*/
while (1) {
/* "sklearn/tree/_splitter.pyx":588
* root = start
* while True:
* child = root * 2 + 1 # <<<<<<<<<<<<<<
*
* # find max of root, left child, right child
*/
__pyx_v_child = ((__pyx_v_root * 2) + 1);
/* "sklearn/tree/_splitter.pyx":591
*
* # find max of root, left child, right child
* maxind = root # <<<<<<<<<<<<<<
* if child < end and Xf[maxind] < Xf[child]:
* maxind = child
*/
__pyx_v_maxind = __pyx_v_root;
/* "sklearn/tree/_splitter.pyx":592
* # find max of root, left child, right child
* maxind = root
* if child < end and Xf[maxind] < Xf[child]: # <<<<<<<<<<<<<<
* maxind = child
* if child + 1 < end and Xf[maxind] < Xf[child + 1]:
*/
__pyx_t_2 = ((__pyx_v_child < __pyx_v_end) != 0);
if (__pyx_t_2) {
} else {
__pyx_t_1 = __pyx_t_2;
goto __pyx_L6_bool_binop_done;
}
__pyx_t_2 = (((__pyx_v_Xf[__pyx_v_maxind]) < (__pyx_v_Xf[__pyx_v_child])) != 0);
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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
*/
__pyx_t_1 = __pyx_v_self->__pyx_base.__pyx_base.features;
__pyx_v_features = __pyx_t_1;
/* "sklearn/tree/_splitter.pyx":643
*
* 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/_splitter.pyx":644
* 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/_splitter.pyx":646
* 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/_splitter.pyx":647
*
* 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_feature_stride = self.X_feature_stride
*/
__pyx_t_3 = __pyx_v_self->__pyx_base.__pyx_base.feature_values;
__pyx_v_Xf = __pyx_t_3;
/* "sklearn/tree/_splitter.pyx":648
* 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_feature_stride = self.X_feature_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/_splitter.pyx":649
* cdef DTYPE_t* Xf = self.feature_values
* cdef SIZE_t X_sample_stride = self.X_sample_stride
* cdef SIZE_t X_feature_stride = self.X_feature_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_feature_stride;
__pyx_v_X_feature_stride = __pyx_t_2;
/* "sklearn/tree/_splitter.pyx":650
* cdef SIZE_t X_sample_stride = self.X_sample_stride
* cdef SIZE_t X_feature_stride = self.X_feature_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/_splitter.pyx":651
* cdef SIZE_t X_feature_stride = self.X_feature_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/_splitter.pyx":652
* 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/_splitter.pyx":653
* 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/_splitter.pyx":656
*
* cdef SplitRecord best, current
* cdef double current_proxy_improvement = - INFINITY # <<<<<<<<<<<<<<
* cdef double best_proxy_improvement = - INFINITY
*
*/
__pyx_v_current_proxy_improvement = (-__pyx_v_7sklearn_4tree_9_splitter_INFINITY);
/* "sklearn/tree/_splitter.pyx":657
* cdef SplitRecord best, current
* cdef double current_proxy_improvement = - INFINITY
* cdef double best_proxy_improvement = - INFINITY # <<<<<<<<<<<<<<
*
* cdef SIZE_t f_i = n_features
*/
__pyx_v_best_proxy_improvement = (-__pyx_v_7sklearn_4tree_9_splitter_INFINITY);
/* "sklearn/tree/_splitter.pyx":659
* cdef double best_proxy_improvement = - INFINITY
*
* cdef SIZE_t f_i = n_features # <<<<<<<<<<<<<<
* cdef SIZE_t f_j
* cdef SIZE_t p
*/
__pyx_v_f_i = __pyx_v_n_features;
/* "sklearn/tree/_splitter.pyx":665
* cdef SIZE_t feature_stride
* # 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/_splitter.pyx":667
* 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/_splitter.pyx":668
* # 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/_splitter.pyx":670
* 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/_splitter.pyx":671
* # 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/_splitter.pyx":677
* cdef SIZE_t partition_end
*
* _init_split(&best, end) # <<<<<<<<<<<<<<
*
* # Sample up to max_features without replacement using a
*/
__pyx_f_7sklearn_4tree_9_splitter__init_split((&__pyx_v_best), __pyx_v_end);
/* "sklearn/tree/_splitter.pyx":688
* # 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) {
} else {
__pyx_t_5 = __pyx_t_6;
goto __pyx_L5_bool_binop_done;
}
/* "sklearn/tree/_splitter.pyx":690
* 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) {
} else {
__pyx_t_5 = __pyx_t_6;
goto __pyx_L5_bool_binop_done;
}
/* "sklearn/tree/_splitter.pyx":692
* (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_6 = ((__pyx_v_n_visited_features <= (__pyx_v_n_found_constants + __pyx_v_n_drawn_constants)) != 0);
__pyx_t_5 = __pyx_t_6;
__pyx_L5_bool_binop_done:;
if (!__pyx_t_5) break;
/* "sklearn/tree/_splitter.pyx":693
* # 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/_splitter.pyx":707
*
* # 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_6_utils_rand_int(__pyx_v_n_drawn_constants, (__pyx_v_f_i - __pyx_v_n_found_constants), __pyx_v_random_state);
/* "sklearn/tree/_splitter.pyx":710
* 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_5 = ((__pyx_v_f_j < __pyx_v_n_known_constants) != 0);
if (__pyx_t_5) {
/* "sklearn/tree/_splitter.pyx":712
* 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/_splitter.pyx":713
* # 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/_splitter.pyx":714
* 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/_splitter.pyx":716
* features[n_drawn_constants] = tmp
*
* n_drawn_constants += 1 # <<<<<<<<<<<<<<
*
* else:
*/
__pyx_v_n_drawn_constants = (__pyx_v_n_drawn_constants + 1);
goto __pyx_L8;
}
/*else*/ {
/* "sklearn/tree/_splitter.pyx":720
* 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/_splitter.pyx":723
* # f_j in the interval [n_total_constants, f_i[
*
* current.feature = features[f_j] # <<<<<<<<<<<<<<
* feature_stride = X_feature_stride * current.feature
*
*/
__pyx_v_current.feature = (__pyx_v_features[__pyx_v_f_j]);
/* "sklearn/tree/_splitter.pyx":724
*
* current.feature = features[f_j]
* feature_stride = X_feature_stride * current.feature # <<<<<<<<<<<<<<
*
* # Find min, max
*/
__pyx_v_feature_stride = (__pyx_v_X_feature_stride * __pyx_v_current.feature);
/* "sklearn/tree/_splitter.pyx":727
*
* # Find min, max
* min_feature_value = X[X_sample_stride * samples[start] + feature_stride] # <<<<<<<<<<<<<<
* max_feature_value = min_feature_value
* Xf[start] = min_feature_value
*/
__pyx_v_min_feature_value = (__pyx_v_X[((__pyx_v_X_sample_stride * (__pyx_v_samples[__pyx_v_start])) + __pyx_v_feature_stride)]);
/* "sklearn/tree/_splitter.pyx":728
* # Find min, max
* min_feature_value = X[X_sample_stride * samples[start] + feature_stride]
* max_feature_value = min_feature_value # <<<<<<<<<<<<<<
* Xf[start] = min_feature_value
*
*/
__pyx_v_max_feature_value = __pyx_v_min_feature_value;
/* "sklearn/tree/_splitter.pyx":729
* min_feature_value = X[X_sample_stride * samples[start] + feature_stride]
* 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/_splitter.pyx":731
* Xf[start] = min_feature_value
*
* for p in range(start + 1, end): # <<<<<<<<<<<<<<
* current_feature_value = X[X_sample_stride * samples[p] + feature_stride]
* Xf[p] = current_feature_value
*/
__pyx_t_2 = __pyx_v_end;
for (__pyx_t_7 = (__pyx_v_start + 1); __pyx_t_7 < __pyx_t_2; __pyx_t_7+=1) {
__pyx_v_p = __pyx_t_7;
/* "sklearn/tree/_splitter.pyx":732
*
* for p in range(start + 1, end):
* current_feature_value = X[X_sample_stride * samples[p] + feature_stride] # <<<<<<<<<<<<<<
* 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_feature_stride)]);
/* "sklearn/tree/_splitter.pyx":733
* for p in range(start + 1, end):
* current_feature_value = X[X_sample_stride * samples[p] + feature_stride]
* 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/_splitter.pyx":735
* 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_5 = ((__pyx_v_current_feature_value < __pyx_v_min_feature_value) != 0);
if (__pyx_t_5) {
/* "sklearn/tree/_splitter.pyx":736
*
* 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_L11;
}
/* "sklearn/tree/_splitter.pyx":737
* 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_5 = ((__pyx_v_current_feature_value > __pyx_v_max_feature_value) != 0);
if (__pyx_t_5) {
/* "sklearn/tree/_splitter.pyx":738
* 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_L11;
}
__pyx_L11:;
}
/* "sklearn/tree/_splitter.pyx":740
* 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_5 = ((__pyx_v_max_feature_value <= (__pyx_v_min_feature_value + __pyx_v_7sklearn_4tree_9_splitter_FEATURE_THRESHOLD)) != 0);
if (__pyx_t_5) {
/* "sklearn/tree/_splitter.pyx":741
*
* 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/_splitter.pyx":742
* 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/_splitter.pyx":744
* 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/_splitter.pyx":745
*
* 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/_splitter.pyx":748
*
* 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/_splitter.pyx":749
* 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_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/_splitter.pyx":752
*
* # Draw a random threshold
* current.threshold = rand_uniform(min_feature_value, # <<<<<<<<<<<<<<
* max_feature_value,
* random_state)
*/
__pyx_v_current.threshold = __pyx_f_7sklearn_4tree_6_utils_rand_uniform(__pyx_v_min_feature_value, __pyx_v_max_feature_value, __pyx_v_random_state);
/* "sklearn/tree/_splitter.pyx":756
* random_state)
*
* if current.threshold == max_feature_value: # <<<<<<<<<<<<<<
* current.threshold = min_feature_value
*
*/
__pyx_t_5 = ((__pyx_v_current.threshold == __pyx_v_max_feature_value) != 0);
if (__pyx_t_5) {
/* "sklearn/tree/_splitter.pyx":757
*
* if current.threshold == max_feature_value:
* current.threshold = min_feature_value # <<<<<<<<<<<<<<
*
* # Partition
*/
__pyx_v_current.threshold = __pyx_v_min_feature_value;
goto __pyx_L13;
}
__pyx_L13:;
/* "sklearn/tree/_splitter.pyx":760
*
* # Partition
* partition_end = end # <<<<<<<<<<<<<<
* p = start
* while p < partition_end:
*/
__pyx_v_partition_end = __pyx_v_end;
/* "sklearn/tree/_splitter.pyx":761
* # Partition
* partition_end = end
* p = start # <<<<<<<<<<<<<<
* while p < partition_end:
* current_feature_value = Xf[p]
*/
__pyx_v_p = __pyx_v_start;
/* "sklearn/tree/_splitter.pyx":762
* partition_end = end
* p = start
* while p < partition_end: # <<<<<<<<<<<<<<
* current_feature_value = Xf[p]
* if current_feature_value <= current.threshold:
*/
while (1) {
__pyx_t_5 = ((__pyx_v_p < __pyx_v_partition_end) != 0);
if (!__pyx_t_5) break;
/* "sklearn/tree/_splitter.pyx":763
* 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/_splitter.pyx":764
* while p < partition_end:
* current_feature_value = Xf[p]
* if current_feature_value <= current.threshold: # <<<<<<<<<<<<<<
* p += 1
* else:
*/
__pyx_t_5 = ((__pyx_v_current_feature_value <= __pyx_v_current.threshold) != 0);
if (__pyx_t_5) {
/* "sklearn/tree/_splitter.pyx":765
* 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_L16;
}
/*else*/ {
/* "sklearn/tree/_splitter.pyx":767
* p += 1
* else:
* partition_end -= 1 # <<<<<<<<<<<<<<
*
* Xf[p] = Xf[partition_end]
*/
__pyx_v_partition_end = (__pyx_v_partition_end - 1);
/* "sklearn/tree/_splitter.pyx":769
* 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/_splitter.pyx":770
*
* 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/_splitter.pyx":772
* 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/_splitter.pyx":773
*
* 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/_splitter.pyx":774
* 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_L16:;
}
/* "sklearn/tree/_splitter.pyx":776
* 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/_splitter.pyx":779
*
* # 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) {
} else {
__pyx_t_5 = __pyx_t_6;
goto __pyx_L18_bool_binop_done;
}
/* "sklearn/tree/_splitter.pyx":780
* # 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;
__pyx_L18_bool_binop_done:;
if (__pyx_t_5) {
/* "sklearn/tree/_splitter.pyx":781
* if (((current.pos - start) < min_samples_leaf) or
* ((end - current.pos) < min_samples_leaf)):
* continue # <<<<<<<<<<<<<<
*
* # Evaluate split
*/
goto __pyx_L3_continue;
}
/* "sklearn/tree/_splitter.pyx":784
*
* # Evaluate split
* self.criterion.reset() # <<<<<<<<<<<<<<
* self.criterion.update(current.pos)
*
*/
((struct __pyx_vtabstruct_7sklearn_4tree_10_criterion_Criterion *)__pyx_v_self->__pyx_base.__pyx_base.criterion->__pyx_vtab)->reset(__pyx_v_self->__pyx_base.__pyx_base.criterion);
/* "sklearn/tree/_splitter.pyx":785
* # Evaluate split
* self.criterion.reset()
* self.criterion.update(current.pos) # <<<<<<<<<<<<<<
*
* # Reject if min_weight_leaf is not satisfied
*/
((struct __pyx_vtabstruct_7sklearn_4tree_10_criterion_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/_splitter.pyx":788
*
* # 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_left < __pyx_v_min_weight_leaf) != 0);
if (!__pyx_t_6) {
} else {
__pyx_t_5 = __pyx_t_6;
goto __pyx_L21_bool_binop_done;
}
/* "sklearn/tree/_splitter.pyx":789
* # 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;
__pyx_L21_bool_binop_done:;
if (__pyx_t_5) {
/* "sklearn/tree/_splitter.pyx":790
* if ((self.criterion.weighted_n_left < min_weight_leaf) or
* (self.criterion.weighted_n_right < min_weight_leaf)):
* continue # <<<<<<<<<<<<<<
*
* current_proxy_improvement = self.criterion.proxy_impurity_improvement()
*/
goto __pyx_L3_continue;
}
/* "sklearn/tree/_splitter.pyx":792
* continue
*
* current_proxy_improvement = self.criterion.proxy_impurity_improvement() # <<<<<<<<<<<<<<
*
* if current_proxy_improvement > best_proxy_improvement:
*/
__pyx_v_current_proxy_improvement = ((struct __pyx_vtabstruct_7sklearn_4tree_10_criterion_Criterion *)__pyx_v_self->__pyx_base.__pyx_base.criterion->__pyx_vtab)->proxy_impurity_improvement(__pyx_v_self->__pyx_base.__pyx_base.criterion);
/* "sklearn/tree/_splitter.pyx":794
* current_proxy_improvement = self.criterion.proxy_impurity_improvement()
*
* if current_proxy_improvement > best_proxy_improvement: # <<<<<<<<<<<<<<
* best_proxy_improvement = current_proxy_improvement
* best = current # copy
*/
__pyx_t_5 = ((__pyx_v_current_proxy_improvement > __pyx_v_best_proxy_improvement) != 0);
if (__pyx_t_5) {
/* "sklearn/tree/_splitter.pyx":795
*
* if current_proxy_improvement > best_proxy_improvement:
* best_proxy_improvement = current_proxy_improvement # <<<<<<<<<<<<<<
* best = current # copy
*
*/
__pyx_v_best_proxy_improvement = __pyx_v_current_proxy_improvement;
/* "sklearn/tree/_splitter.pyx":796
* if current_proxy_improvement > best_proxy_improvement:
* best_proxy_improvement = current_proxy_improvement
* best = current # copy # <<<<<<<<<<<<<<
*
* # Reorganize into samples[start:best.pos] + samples[best.pos:end]
*/
__pyx_v_best = __pyx_v_current;
goto __pyx_L23;
}
__pyx_L23:;
}
__pyx_L12:;
}
__pyx_L8:;
__pyx_L3_continue:;
}
/* "sklearn/tree/_splitter.pyx":799
*
* # Reorganize into samples[start:best.pos] + samples[best.pos:end]
* feature_stride = X_feature_stride * best.feature # <<<<<<<<<<<<<<
* if best.pos < end:
* if current.feature != best.feature:
*/
__pyx_v_feature_stride = (__pyx_v_X_feature_stride * __pyx_v_best.feature);
/* "sklearn/tree/_splitter.pyx":800
* # Reorganize into samples[start:best.pos] + samples[best.pos:end]
* feature_stride = X_feature_stride * best.feature
* if best.pos < end: # <<<<<<<<<<<<<<
* if current.feature != best.feature:
* partition_end = end
*/
__pyx_t_5 = ((__pyx_v_best.pos < __pyx_v_end) != 0);
if (__pyx_t_5) {
/* "sklearn/tree/_splitter.pyx":801
* feature_stride = X_feature_stride * best.feature
* if best.pos < end:
* if current.feature != best.feature: # <<<<<<<<<<<<<<
* partition_end = end
* p = start
*/
__pyx_t_5 = ((__pyx_v_current.feature != __pyx_v_best.feature) != 0);
if (__pyx_t_5) {
/* "sklearn/tree/_splitter.pyx":802
* if best.pos < end:
* if current.feature != best.feature:
* partition_end = end # <<<<<<<<<<<<<<
* p = start
*
*/
__pyx_v_partition_end = __pyx_v_end;
/* "sklearn/tree/_splitter.pyx":803
* if current.feature != best.feature:
* partition_end = end
* p = start # <<<<<<<<<<<<<<
*
* while p < partition_end:
*/
__pyx_v_p = __pyx_v_start;
/* "sklearn/tree/_splitter.pyx":805
* p = start
*
* while p < partition_end: # <<<<<<<<<<<<<<
* if X[X_sample_stride * samples[p] + feature_stride] <= best.threshold:
* p += 1
*/
while (1) {
__pyx_t_5 = ((__pyx_v_p < __pyx_v_partition_end) != 0);
if (!__pyx_t_5) break;
/* "sklearn/tree/_splitter.pyx":806
*
* while p < partition_end:
* if X[X_sample_stride * samples[p] + feature_stride] <= best.threshold: # <<<<<<<<<<<<<<
* p += 1
*
*/
__pyx_t_5 = (((__pyx_v_X[((__pyx_v_X_sample_stride * (__pyx_v_samples[__pyx_v_p])) + __pyx_v_feature_stride)]) <= __pyx_v_best.threshold) != 0);
if (__pyx_t_5) {
/* "sklearn/tree/_splitter.pyx":807
* while p < partition_end:
* if X[X_sample_stride * samples[p] + feature_stride] <= best.threshold:
* p += 1 # <<<<<<<<<<<<<<
*
* else:
*/
__pyx_v_p = (__pyx_v_p + 1);
goto __pyx_L28;
}
/*else*/ {
/* "sklearn/tree/_splitter.pyx":810
*
* else:
* partition_end -= 1 # <<<<<<<<<<<<<<
*
* tmp = samples[partition_end]
*/
__pyx_v_partition_end = (__pyx_v_partition_end - 1);
/* "sklearn/tree/_splitter.pyx":812
* 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/_splitter.pyx":813
*
* 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/_splitter.pyx":814
* tmp = samples[partition_end]
* samples[partition_end] = samples[p]
* samples[p] = tmp # <<<<<<<<<<<<<<
*
*
*/
(__pyx_v_samples[__pyx_v_p]) = __pyx_v_tmp;
}
__pyx_L28:;
}
goto __pyx_L25;
}
__pyx_L25:;
/* "sklearn/tree/_splitter.pyx":817
*
*
* self.criterion.reset() # <<<<<<<<<<<<<<
* self.criterion.update(best.pos)
* best.improvement = self.criterion.impurity_improvement(impurity)
*/
((struct __pyx_vtabstruct_7sklearn_4tree_10_criterion_Criterion *)__pyx_v_self->__pyx_base.__pyx_base.criterion->__pyx_vtab)->reset(__pyx_v_self->__pyx_base.__pyx_base.criterion);
/* "sklearn/tree/_splitter.pyx":818
*
* self.criterion.reset()
* self.criterion.update(best.pos) # <<<<<<<<<<<<<<
* best.improvement = self.criterion.impurity_improvement(impurity)
* self.criterion.children_impurity(&best.impurity_left,
*/
((struct __pyx_vtabstruct_7sklearn_4tree_10_criterion_Criterion *)__pyx_v_self->__pyx_base.__pyx_base.criterion->__pyx_vtab)->update(__pyx_v_self->__pyx_base.__pyx_base.criterion, __pyx_v_best.pos);
/* "sklearn/tree/_splitter.pyx":819
* self.criterion.reset()
* self.criterion.update(best.pos)
* best.improvement = self.criterion.impurity_improvement(impurity) # <<<<<<<<<<<<<<
* self.criterion.children_impurity(&best.impurity_left,
* &best.impurity_right)
*/
__pyx_v_best.improvement = ((struct __pyx_vtabstruct_7sklearn_4tree_10_criterion_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/_splitter.pyx":820
* self.criterion.update(best.pos)
* best.improvement = self.criterion.impurity_improvement(impurity)
* self.criterion.children_impurity(&best.impurity_left, # <<<<<<<<<<<<<<
* &best.impurity_right)
*
*/
((struct __pyx_vtabstruct_7sklearn_4tree_10_criterion_Criterion *)__pyx_v_self->__pyx_base.__pyx_base.criterion->__pyx_vtab)->children_impurity(__pyx_v_self->__pyx_base.__pyx_base.criterion, (&__pyx_v_best.impurity_left), (&__pyx_v_best.impurity_right));
goto __pyx_L24;
}
__pyx_L24:;
/* "sklearn/tree/_splitter.pyx":826
* # 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_9_splitter_SIZE_t)) * __pyx_v_n_known_constants));
/* "sklearn/tree/_splitter.pyx":829
*
* # 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_9_splitter_SIZE_t)) * __pyx_v_n_found_constants));
/* "sklearn/tree/_splitter.pyx":834
*
* # Return values
* split[0] = best # <<<<<<<<<<<<<<
* n_constant_features[0] = n_total_constants
*
*/
(__pyx_v_split[0]) = __pyx_v_best;
/* "sklearn/tree/_splitter.pyx":835
* # 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/_splitter.pyx":634
* self.presort), 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/_splitter.pyx":849
* cdef SIZE_t* sorted_samples
*
* def __cinit__(self, Criterion criterion, SIZE_t max_features, # <<<<<<<<<<<<<<
* SIZE_t min_samples_leaf, double min_weight_leaf,
* object random_state, bint presort):
*/
/* Python wrapper */
static int __pyx_pw_7sklearn_4tree_9_splitter_18BaseSparseSplitter_1__cinit__(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
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CYTHON_UNUSED __pyx_t_7sklearn_4tree_9_splitter_SIZE_t __pyx_v_min_samples_leaf;
CYTHON_UNUSED double __pyx_v_min_weight_leaf;
CYTHON_UNUSED PyObject *__pyx_v_random_state = 0;
CYTHON_UNUSED int __pyx_v_presort;
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if (unlikely(__pyx_kwds)) {
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case 3:
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__Pyx_RaiseArgtupleInvalid("__cinit__", 1, 6, 6, 3); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 849; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
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__Pyx_RaiseArgtupleInvalid("__cinit__", 1, 6, 6, 4); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 849; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
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__Pyx_RaiseArgtupleInvalid("__cinit__", 1, 6, 6, 5); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 849; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
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__pyx_v_min_samples_leaf = __Pyx_PyInt_As_Py_intptr_t(values[2]); if (unlikely((__pyx_v_min_samples_leaf == (npy_intp)-1) && PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 850; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
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__pyx_v_random_state = values[4];
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goto __pyx_L4_argument_unpacking_done;
__pyx_L5_argtuple_error:;
__Pyx_RaiseArgtupleInvalid("__cinit__", 1, 6, 6, PyTuple_GET_SIZE(__pyx_args)); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 849; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
__pyx_L3_error:;
__Pyx_AddTraceback("sklearn.tree._splitter.BaseSparseSplitter.__cinit__", __pyx_clineno, __pyx_lineno, __pyx_filename);
__Pyx_RefNannyFinishContext();
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__pyx_L4_argument_unpacking_done:;
if (unlikely(!__Pyx_ArgTypeTest(((PyObject *)__pyx_v_criterion), __pyx_ptype_7sklearn_4tree_10_criterion_Criterion, 1, "criterion", 0))) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 849; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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__pyx_v_index_to_samples = __pyx_t_4;
/* "sklearn/tree/_splitter.pyx":901
* cdef SIZE_t* index_to_samples = self.index_to_samples
* cdef SIZE_t p
* for p in range(n_total_samples): # <<<<<<<<<<<<<<
* index_to_samples[p] = -1
*
*/
__pyx_t_5 = __pyx_v_n_total_samples;
for (__pyx_t_10 = 0; __pyx_t_10 < __pyx_t_5; __pyx_t_10+=1) {
__pyx_v_p = __pyx_t_10;
/* "sklearn/tree/_splitter.pyx":902
* cdef SIZE_t p
* for p in range(n_total_samples):
* index_to_samples[p] = -1 # <<<<<<<<<<<<<<
*
* for p in range(n_samples):
*/
(__pyx_v_index_to_samples[__pyx_v_p]) = -1;
}
/* "sklearn/tree/_splitter.pyx":904
* index_to_samples[p] = -1
*
* for p in range(n_samples): # <<<<<<<<<<<<<<
* index_to_samples[samples[p]] = p
*
*/
__pyx_t_5 = __pyx_v_n_samples;
for (__pyx_t_10 = 0; __pyx_t_10 < __pyx_t_5; __pyx_t_10+=1) {
__pyx_v_p = __pyx_t_10;
/* "sklearn/tree/_splitter.pyx":905
*
* for p in range(n_samples):
* index_to_samples[samples[p]] = p # <<<<<<<<<<<<<<
*
* cdef inline SIZE_t _partition(self, double threshold,
*/
(__pyx_v_index_to_samples[(__pyx_v_samples[__pyx_v_p])]) = __pyx_v_p;
}
/* "sklearn/tree/_splitter.pyx":868
* free(self.sorted_samples)
*
* cdef void init(self, # <<<<<<<<<<<<<<
* object X,
* np.ndarray[DOUBLE_t, ndim=2, mode="c"] y,
*/
/* function exit code */
goto __pyx_L0;
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__Pyx_SafeReleaseBuffer(&__pyx_pybuffernd_data.rcbuffer->pybuffer);
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/* "sklearn/tree/_splitter.pyx":907
* index_to_samples[samples[p]] = p
*
* cdef inline SIZE_t _partition(self, double threshold, # <<<<<<<<<<<<<<
* SIZE_t end_negative, SIZE_t start_positive,
* SIZE_t zero_pos) nogil:
*/
static CYTHON_INLINE __pyx_t_7sklearn_4tree_9_splitter_SIZE_t __pyx_f_7sklearn_4tree_9_splitter_18BaseSparseSplitter__partition(struct __pyx_obj_7sklearn_4tree_9_splitter_BaseSparseSplitter *__pyx_v_self, double __pyx_v_threshold, __pyx_t_7sklearn_4tree_9_splitter_SIZE_t __pyx_v_end_negative, __pyx_t_7sklearn_4tree_9_splitter_SIZE_t __pyx_v_start_positive, __pyx_t_7sklearn_4tree_9_splitter_SIZE_t __pyx_v_zero_pos) {
double __pyx_v_value;
__pyx_t_7sklearn_4tree_9_splitter_SIZE_t __pyx_v_partition_end;
__pyx_t_7sklearn_4tree_9_splitter_SIZE_t __pyx_v_p;
__pyx_t_7sklearn_4tree_9_splitter_DTYPE_t *__pyx_v_Xf;
__pyx_t_7sklearn_4tree_9_splitter_SIZE_t *__pyx_v_samples;
__pyx_t_7sklearn_4tree_9_splitter_SIZE_t *__pyx_v_index_to_samples;
__pyx_t_7sklearn_4tree_9_splitter_SIZE_t __pyx_r;
__pyx_t_7sklearn_4tree_9_splitter_DTYPE_t *__pyx_t_1;
__pyx_t_7sklearn_4tree_9_splitter_SIZE_t *__pyx_t_2;
int __pyx_t_3;
__pyx_t_7sklearn_4tree_9_splitter_SIZE_t __pyx_t_4;
/* "sklearn/tree/_splitter.pyx":916
* cdef SIZE_t p
*
* cdef DTYPE_t* Xf = self.feature_values # <<<<<<<<<<<<<<
* cdef SIZE_t* samples = self.samples
* cdef SIZE_t* index_to_samples = self.index_to_samples
*/
__pyx_t_1 = __pyx_v_self->__pyx_base.feature_values;
__pyx_v_Xf = __pyx_t_1;
/* "sklearn/tree/_splitter.pyx":917
*
* cdef DTYPE_t* Xf = self.feature_values
* cdef SIZE_t* samples = self.samples # <<<<<<<<<<<<<<
* cdef SIZE_t* index_to_samples = self.index_to_samples
*
*/
__pyx_t_2 = __pyx_v_self->__pyx_base.samples;
__pyx_v_samples = __pyx_t_2;
/* "sklearn/tree/_splitter.pyx":918
* cdef DTYPE_t* Xf = self.feature_values
* cdef SIZE_t* samples = self.samples
* cdef SIZE_t* index_to_samples = self.index_to_samples # <<<<<<<<<<<<<<
*
* if threshold < 0.:
*/
__pyx_t_2 = __pyx_v_self->index_to_samples;
__pyx_v_index_to_samples = __pyx_t_2;
/* "sklearn/tree/_splitter.pyx":920
* cdef SIZE_t* index_to_samples = self.index_to_samples
*
* if threshold < 0.: # <<<<<<<<<<<<<<
* p = self.start
* partition_end = end_negative
*/
__pyx_t_3 = ((__pyx_v_threshold < 0.) != 0);
if (__pyx_t_3) {
/* "sklearn/tree/_splitter.pyx":921
*
* if threshold < 0.:
* p = self.start # <<<<<<<<<<<<<<
* partition_end = end_negative
* elif threshold > 0.:
*/
__pyx_t_4 = __pyx_v_self->__pyx_base.start;
__pyx_v_p = __pyx_t_4;
/* "sklearn/tree/_splitter.pyx":922
* if threshold < 0.:
* p = self.start
* partition_end = end_negative # <<<<<<<<<<<<<<
* elif threshold > 0.:
* p = start_positive
*/
__pyx_v_partition_end = __pyx_v_end_negative;
goto __pyx_L3;
}
/* "sklearn/tree/_splitter.pyx":923
* p = self.start
* partition_end = end_negative
* elif threshold > 0.: # <<<<<<<<<<<<<<
* p = start_positive
* partition_end = self.end
*/
__pyx_t_3 = ((__pyx_v_threshold > 0.) != 0);
if (__pyx_t_3) {
/* "sklearn/tree/_splitter.pyx":924
* partition_end = end_negative
* elif threshold > 0.:
* p = start_positive # <<<<<<<<<<<<<<
* partition_end = self.end
* else:
*/
__pyx_v_p = __pyx_v_start_positive;
/* "sklearn/tree/_splitter.pyx":925
* elif threshold > 0.:
* p = start_positive
* partition_end = self.end # <<<<<<<<<<<<<<
* else:
* # Data are already split
*/
__pyx_t_4 = __pyx_v_self->__pyx_base.end;
__pyx_v_partition_end = __pyx_t_4;
goto __pyx_L3;
}
/*else*/ {
/* "sklearn/tree/_splitter.pyx":928
* else:
* # Data are already split
* return zero_pos # <<<<<<<<<<<<<<
*
* while p < partition_end:
*/
__pyx_r = __pyx_v_zero_pos;
goto __pyx_L0;
}
__pyx_L3:;
/* "sklearn/tree/_splitter.pyx":930
* return zero_pos
*
* while p < partition_end: # <<<<<<<<<<<<<<
* value = Xf[p]
*
*/
while (1) {
__pyx_t_3 = ((__pyx_v_p < __pyx_v_partition_end) != 0);
if (!__pyx_t_3) break;
/* "sklearn/tree/_splitter.pyx":931
*
* while p < partition_end:
* value = Xf[p] # <<<<<<<<<<<<<<
*
* if value <= threshold:
*/
__pyx_v_value = (__pyx_v_Xf[__pyx_v_p]);
/* "sklearn/tree/_splitter.pyx":933
* value = Xf[p]
*
* if value <= threshold: # <<<<<<<<<<<<<<
* p += 1
*
*/
__pyx_t_3 = ((__pyx_v_value <= __pyx_v_threshold) != 0);
if (__pyx_t_3) {
/* "sklearn/tree/_splitter.pyx":934
*
* if value <= threshold:
* p += 1 # <<<<<<<<<<<<<<
*
* else:
*/
__pyx_v_p = (__pyx_v_p + 1);
goto __pyx_L6;
}
/*else*/ {
/* "sklearn/tree/_splitter.pyx":937
*
* else:
* partition_end -= 1 # <<<<<<<<<<<<<<
*
* Xf[p] = Xf[partition_end]
*/
__pyx_v_partition_end = (__pyx_v_partition_end - 1);
/* "sklearn/tree/_splitter.pyx":939
* partition_end -= 1
*
* Xf[p] = Xf[partition_end] # <<<<<<<<<<<<<<
* Xf[partition_end] = value
* sparse_swap(index_to_samples, samples, p, partition_end)
*/
(__pyx_v_Xf[__pyx_v_p]) = (__pyx_v_Xf[__pyx_v_partition_end]);
/* "sklearn/tree/_splitter.pyx":940
*
* Xf[p] = Xf[partition_end]
* Xf[partition_end] = value # <<<<<<<<<<<<<<
* sparse_swap(index_to_samples, samples, p, partition_end)
*
*/
(__pyx_v_Xf[__pyx_v_partition_end]) = __pyx_v_value;
/* "sklearn/tree/_splitter.pyx":941
* Xf[p] = Xf[partition_end]
* Xf[partition_end] = value
* sparse_swap(index_to_samples, samples, p, partition_end) # <<<<<<<<<<<<<<
*
* return partition_end
*/
__pyx_f_7sklearn_4tree_9_splitter_sparse_swap(__pyx_v_index_to_samples, __pyx_v_samples, __pyx_v_p, __pyx_v_partition_end);
}
__pyx_L6:;
}
/* "sklearn/tree/_splitter.pyx":943
* sparse_swap(index_to_samples, samples, p, partition_end)
*
* return partition_end # <<<<<<<<<<<<<<
*
* cdef inline void extract_nnz(self, SIZE_t feature,
*/
__pyx_r = __pyx_v_partition_end;
goto __pyx_L0;
/* "sklearn/tree/_splitter.pyx":907
* index_to_samples[samples[p]] = p
*
* cdef inline SIZE_t _partition(self, double threshold, # <<<<<<<<<<<<<<
* SIZE_t end_negative, SIZE_t start_positive,
* SIZE_t zero_pos) nogil:
*/
/* function exit code */
__pyx_L0:;
return __pyx_r;
}
/* "sklearn/tree/_splitter.pyx":945
* 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:
*/
static CYTHON_INLINE void __pyx_f_7sklearn_4tree_9_splitter_18BaseSparseSplitter_extract_nnz(struct __pyx_obj_7sklearn_4tree_9_splitter_BaseSparseSplitter *__pyx_v_self, __pyx_t_7sklearn_4tree_9_splitter_SIZE_t __pyx_v_feature, __pyx_t_7sklearn_4tree_9_splitter_SIZE_t *__pyx_v_end_negative, __pyx_t_7sklearn_4tree_9_splitter_SIZE_t *__pyx_v_start_positive, int *__pyx_v_is_samples_sorted) {
__pyx_t_7sklearn_4tree_9_splitter_SIZE_t __pyx_v_indptr_start;
__pyx_t_7sklearn_4tree_9_splitter_SIZE_t __pyx_v_indptr_end;
__pyx_t_7sklearn_4tree_9_splitter_SIZE_t __pyx_v_n_indices;
__pyx_t_7sklearn_4tree_9_splitter_SIZE_t __pyx_v_n_samples;
int __pyx_t_1;
/* "sklearn/tree/_splitter.pyx":977
*
* """
* 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/_splitter.pyx":978
* """
* 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/_splitter.pyx":979
* 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_9_splitter_SIZE_t)(__pyx_v_indptr_end - __pyx_v_indptr_start));
/* "sklearn/tree/_splitter.pyx":980
* 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/_splitter.pyx":988
* # 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_6_utils_log(__pyx_v_n_samples)) + (__pyx_v_n_samples * __pyx_f_7sklearn_4tree_6_utils_log(__pyx_v_n_indices))) < (__pyx_v_7sklearn_4tree_9_splitter_EXTRACT_NNZ_SWITCH * __pyx_v_n_indices)) != 0);
if (__pyx_t_1) {
/* "sklearn/tree/_splitter.pyx":989
* 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_9_splitter_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/_splitter.pyx":1000
* # 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_9_splitter_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/_splitter.pyx":945
* 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/_splitter.pyx":1008
*
*
* 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_9_splitter_compare_SIZE_t(void const *__pyx_v_a, void const *__pyx_v_b) {
int __pyx_r;
/* "sklearn/tree/_splitter.pyx":1010
* 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_9_splitter_SIZE_t *)__pyx_v_a)[0]) - (((__pyx_t_7sklearn_4tree_9_splitter_SIZE_t *)__pyx_v_b)[0])));
goto __pyx_L0;
/* "sklearn/tree/_splitter.pyx":1008
*
*
* 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/_splitter.pyx":1013
*
*
* 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_9_splitter_binary_search(__pyx_t_7sklearn_4tree_9_splitter_INT32_t *__pyx_v_sorted_array, __pyx_t_7sklearn_4tree_9_splitter_INT32_t __pyx_v_start, __pyx_t_7sklearn_4tree_9_splitter_INT32_t __pyx_v_end, __pyx_t_7sklearn_4tree_9_splitter_SIZE_t __pyx_v_value, __pyx_t_7sklearn_4tree_9_splitter_SIZE_t *__pyx_v_index, __pyx_t_7sklearn_4tree_9_splitter_INT32_t *__pyx_v_new_start) {
__pyx_t_7sklearn_4tree_9_splitter_INT32_t __pyx_v_pivot;
int __pyx_t_1;
/* "sklearn/tree/_splitter.pyx":1022
* """
* cdef INT32_t pivot
* index[0] = -1 # <<<<<<<<<<<<<<
* while start < end:
* pivot = start + (end - start) / 2
*/
(__pyx_v_index[0]) = -1;
/* "sklearn/tree/_splitter.pyx":1023
* 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/_splitter.pyx":1024
* 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/_splitter.pyx":1026
* 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/_splitter.pyx":1027
*
* if sorted_array[pivot] == value:
* index[0] = pivot # <<<<<<<<<<<<<<
* start = pivot + 1
* break
*/
(__pyx_v_index[0]) = __pyx_v_pivot;
/* "sklearn/tree/_splitter.pyx":1028
* if sorted_array[pivot] == value:
* index[0] = pivot
* start = pivot + 1 # <<<<<<<<<<<<<<
* break
*
*/
__pyx_v_start = (__pyx_v_pivot + 1);
/* "sklearn/tree/_splitter.pyx":1029
* index[0] = pivot
* start = pivot + 1
* break # <<<<<<<<<<<<<<
*
* if sorted_array[pivot] < value:
*/
goto __pyx_L4_break;
}
/* "sklearn/tree/_splitter.pyx":1031
* 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/_splitter.pyx":1032
*
* if sorted_array[pivot] < value:
* start = pivot + 1 # <<<<<<<<<<<<<<
* else:
* end = pivot
*/
__pyx_v_start = (__pyx_v_pivot + 1);
goto __pyx_L6;
}
/*else*/ {
/* "sklearn/tree/_splitter.pyx":1034
* start = pivot + 1
* else:
* end = pivot # <<<<<<<<<<<<<<
* new_start[0] = start
*
*/
__pyx_v_end = __pyx_v_pivot;
}
__pyx_L6:;
}
__pyx_L4_break:;
/* "sklearn/tree/_splitter.pyx":1035
* else:
* end = pivot
* new_start[0] = start # <<<<<<<<<<<<<<
*
*
*/
(__pyx_v_new_start[0]) = __pyx_v_start;
/* "sklearn/tree/_splitter.pyx":1013
*
*
* 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/_splitter.pyx":1038
*
*
* 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_9_splitter_extract_nnz_index_to_samples(__pyx_t_7sklearn_4tree_9_splitter_INT32_t *__pyx_v_X_indices, __pyx_t_7sklearn_4tree_9_splitter_DTYPE_t *__pyx_v_X_data, __pyx_t_7sklearn_4tree_9_splitter_INT32_t __pyx_v_indptr_start, __pyx_t_7sklearn_4tree_9_splitter_INT32_t __pyx_v_indptr_end, __pyx_t_7sklearn_4tree_9_splitter_SIZE_t *__pyx_v_samples, __pyx_t_7sklearn_4tree_9_splitter_SIZE_t __pyx_v_start, __pyx_t_7sklearn_4tree_9_splitter_SIZE_t __pyx_v_end, __pyx_t_7sklearn_4tree_9_splitter_SIZE_t *__pyx_v_index_to_samples, __pyx_t_7sklearn_4tree_9_splitter_DTYPE_t *__pyx_v_Xf, __pyx_t_7sklearn_4tree_9_splitter_SIZE_t *__pyx_v_end_negative, __pyx_t_7sklearn_4tree_9_splitter_SIZE_t *__pyx_v_start_positive) {
__pyx_t_7sklearn_4tree_9_splitter_INT32_t __pyx_v_k;
__pyx_t_7sklearn_4tree_9_splitter_SIZE_t __pyx_v_index;
__pyx_t_7sklearn_4tree_9_splitter_SIZE_t __pyx_v_end_negative_;
__pyx_t_7sklearn_4tree_9_splitter_SIZE_t __pyx_v_start_positive_;
__pyx_t_7sklearn_4tree_9_splitter_INT32_t __pyx_t_1;
__pyx_t_7sklearn_4tree_9_splitter_INT32_t __pyx_t_2;
int __pyx_t_3;
int __pyx_t_4;
/* "sklearn/tree/_splitter.pyx":1055
* 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/_splitter.pyx":1056
* 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/_splitter.pyx":1058
* 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/_splitter.pyx":1059
*
* 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/_splitter.pyx":1060
* 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/_splitter.pyx":1061
* 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/_splitter.pyx":1062
* 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/_splitter.pyx":1063
* 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/_splitter.pyx":1064
* 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_9_splitter_sparse_swap(__pyx_v_index_to_samples, __pyx_v_samples, __pyx_v_index, __pyx_v_start_positive_);
goto __pyx_L6;
}
/* "sklearn/tree/_splitter.pyx":1067
*
*
* 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/_splitter.pyx":1068
*
* 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/_splitter.pyx":1069
* 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/_splitter.pyx":1070
* 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_9_splitter_sparse_swap(__pyx_v_index_to_samples, __pyx_v_samples, __pyx_v_index, __pyx_v_end_negative_);
/* "sklearn/tree/_splitter.pyx":1071
* 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/_splitter.pyx":1074
*
* # Returned values
* end_negative[0] = end_negative_ # <<<<<<<<<<<<<<
* start_positive[0] = start_positive_
*
*/
(__pyx_v_end_negative[0]) = __pyx_v_end_negative_;
/* "sklearn/tree/_splitter.pyx":1075
* # Returned values
* end_negative[0] = end_negative_
* start_positive[0] = start_positive_ # <<<<<<<<<<<<<<
*
*
*/
(__pyx_v_start_positive[0]) = __pyx_v_start_positive_;
/* "sklearn/tree/_splitter.pyx":1038
*
*
* 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/_splitter.pyx":1078
*
*
* 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_9_splitter_extract_nnz_binary_search(__pyx_t_7sklearn_4tree_9_splitter_INT32_t *__pyx_v_X_indices, __pyx_t_7sklearn_4tree_9_splitter_DTYPE_t *__pyx_v_X_data, __pyx_t_7sklearn_4tree_9_splitter_INT32_t __pyx_v_indptr_start, __pyx_t_7sklearn_4tree_9_splitter_INT32_t __pyx_v_indptr_end, __pyx_t_7sklearn_4tree_9_splitter_SIZE_t *__pyx_v_samples, __pyx_t_7sklearn_4tree_9_splitter_SIZE_t __pyx_v_start, __pyx_t_7sklearn_4tree_9_splitter_SIZE_t __pyx_v_end, __pyx_t_7sklearn_4tree_9_splitter_SIZE_t *__pyx_v_index_to_samples, __pyx_t_7sklearn_4tree_9_splitter_DTYPE_t *__pyx_v_Xf, __pyx_t_7sklearn_4tree_9_splitter_SIZE_t *__pyx_v_end_negative, __pyx_t_7sklearn_4tree_9_splitter_SIZE_t *__pyx_v_start_positive, __pyx_t_7sklearn_4tree_9_splitter_SIZE_t *__pyx_v_sorted_samples, int *__pyx_v_is_samples_sorted) {
__pyx_t_7sklearn_4tree_9_splitter_SIZE_t __pyx_v_n_samples;
__pyx_t_7sklearn_4tree_9_splitter_SIZE_t __pyx_v_p;
__pyx_t_7sklearn_4tree_9_splitter_SIZE_t __pyx_v_index;
__pyx_t_7sklearn_4tree_9_splitter_SIZE_t __pyx_v_k;
__pyx_t_7sklearn_4tree_9_splitter_SIZE_t __pyx_v_end_negative_;
__pyx_t_7sklearn_4tree_9_splitter_SIZE_t __pyx_v_start_positive_;
int __pyx_t_1;
int __pyx_t_2;
/* "sklearn/tree/_splitter.pyx":1101
* 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/_splitter.pyx":1102
*
* 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/_splitter.pyx":1103
* 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_9_splitter_SIZE_t))));
/* "sklearn/tree/_splitter.pyx":1105
* memcpy(sorted_samples + start, samples + start,
* n_samples * sizeof(SIZE_t))
* qsort(sorted_samples + start, n_samples, sizeof(SIZE_t), # <<<<<<<<<<<<<<
* compare_SIZE_t)
* is_samples_sorted[0] = 1
*/
qsort((__pyx_v_sorted_samples + __pyx_v_start), __pyx_v_n_samples, (sizeof(__pyx_t_7sklearn_4tree_9_splitter_SIZE_t)), __pyx_f_7sklearn_4tree_9_splitter_compare_SIZE_t);
/* "sklearn/tree/_splitter.pyx":1107
* qsort(sorted_samples + start, n_samples, sizeof(SIZE_t),
* compare_SIZE_t)
* is_samples_sorted[0] = 1 # <<<<<<<<<<<<<<
*
* while (indptr_start < indptr_end and
*/
(__pyx_v_is_samples_sorted[0]) = 1;
goto __pyx_L3;
}
__pyx_L3:;
/* "sklearn/tree/_splitter.pyx":1109
* is_samples_sorted[0] = 1
*
* while (indptr_start < indptr_end and # <<<<<<<<<<<<<<
* sorted_samples[start] > X_indices[indptr_start]):
* indptr_start += 1
*/
while (1) {
__pyx_t_2 = ((__pyx_v_indptr_start < __pyx_v_indptr_end) != 0);
if (__pyx_t_2) {
} else {
__pyx_t_1 = __pyx_t_2;
goto __pyx_L6_bool_binop_done;
}
/* "sklearn/tree/_splitter.pyx":1110
*
* while (indptr_start < indptr_end and
* sorted_samples[start] > X_indices[indptr_start]): # <<<<<<<<<<<<<<
* indptr_start += 1
*
*/
__pyx_t_2 = (((__pyx_v_sorted_samples[__pyx_v_start]) > (__pyx_v_X_indices[__pyx_v_indptr_start])) != 0);
__pyx_t_1 = __pyx_t_2;
__pyx_L6_bool_binop_done:;
if (!__pyx_t_1) break;
/* "sklearn/tree/_splitter.pyx":1111
* while (indptr_start < indptr_end and
* sorted_samples[start] > X_indices[indptr_start]):
* indptr_start += 1 # <<<<<<<<<<<<<<
*
* while (indptr_start < indptr_end and
*/
__pyx_v_indptr_start = (__pyx_v_indptr_start + 1);
}
/* "sklearn/tree/_splitter.pyx":1113
* indptr_start += 1
*
* while (indptr_start < indptr_end and # <<<<<<<<<<<<<<
* sorted_samples[end - 1] < X_indices[indptr_end - 1]):
* indptr_end -= 1
*/
while (1) {
__pyx_t_2 = ((__pyx_v_indptr_start < __pyx_v_indptr_end) != 0);
if (__pyx_t_2) {
} else {
__pyx_t_1 = __pyx_t_2;
goto __pyx_L10_bool_binop_done;
}
/* "sklearn/tree/_splitter.pyx":1114
*
* while (indptr_start < indptr_end and
* sorted_samples[end - 1] < X_indices[indptr_end - 1]): # <<<<<<<<<<<<<<
* indptr_end -= 1
*
*/
__pyx_t_2 = (((__pyx_v_sorted_samples[(__pyx_v_end - 1)]) < (__pyx_v_X_indices[(__pyx_v_indptr_end - 1)])) != 0);
__pyx_t_1 = __pyx_t_2;
__pyx_L10_bool_binop_done:;
if (!__pyx_t_1) break;
/* "sklearn/tree/_splitter.pyx":1115
* while (indptr_start < indptr_end and
* sorted_samples[end - 1] < X_indices[indptr_end - 1]):
* indptr_end -= 1 # <<<<<<<<<<<<<<
*
* cdef SIZE_t p = start
*/
__pyx_v_indptr_end = (__pyx_v_indptr_end - 1);
}
/* "sklearn/tree/_splitter.pyx":1117
* indptr_end -= 1
*
* cdef SIZE_t p = start # <<<<<<<<<<<<<<
* cdef SIZE_t index
* cdef SIZE_t k
*/
__pyx_v_p = __pyx_v_start;
/* "sklearn/tree/_splitter.pyx":1120
* cdef SIZE_t index
* cdef SIZE_t k
* cdef SIZE_t end_negative_ = start # <<<<<<<<<<<<<<
* cdef SIZE_t start_positive_ = end
*
*/
__pyx_v_end_negative_ = __pyx_v_start;
/* "sklearn/tree/_splitter.pyx":1121
* cdef SIZE_t k
* cdef SIZE_t end_negative_ = start
* cdef SIZE_t start_positive_ = end # <<<<<<<<<<<<<<
*
* while (p < end and indptr_start < indptr_end):
*/
__pyx_v_start_positive_ = __pyx_v_end;
/* "sklearn/tree/_splitter.pyx":1123
* cdef SIZE_t start_positive_ = end
*
* while (p < end and indptr_start < indptr_end): # <<<<<<<<<<<<<<
* # Find index of sorted_samples[p] in X_indices
* binary_search(X_indices, indptr_start, indptr_end,
*/
while (1) {
__pyx_t_2 = ((__pyx_v_p < __pyx_v_end) != 0);
if (__pyx_t_2) {
} else {
__pyx_t_1 = __pyx_t_2;
goto __pyx_L14_bool_binop_done;
}
__pyx_t_2 = ((__pyx_v_indptr_start < __pyx_v_indptr_end) != 0);
__pyx_t_1 = __pyx_t_2;
__pyx_L14_bool_binop_done:;
if (!__pyx_t_1) break;
/* "sklearn/tree/_splitter.pyx":1125
* while (p < end and indptr_start < indptr_end):
* # Find index of sorted_samples[p] in X_indices
* binary_search(X_indices, indptr_start, indptr_end, # <<<<<<<<<<<<<<
* sorted_samples[p], &k, &indptr_start)
*
*/
__pyx_f_7sklearn_4tree_9_splitter_binary_search(__pyx_v_X_indices, __pyx_v_indptr_start, __pyx_v_indptr_end, (__pyx_v_sorted_samples[__pyx_v_p]), (&__pyx_v_k), (&__pyx_v_indptr_start));
/* "sklearn/tree/_splitter.pyx":1128
* sorted_samples[p], &k, &indptr_start)
*
* if k != -1: # <<<<<<<<<<<<<<
* # If k != -1, we have found a non zero value
*
*/
__pyx_t_1 = ((__pyx_v_k != -1) != 0);
if (__pyx_t_1) {
/* "sklearn/tree/_splitter.pyx":1131
* # If k != -1, we have found a non zero value
*
* if X_data[k] > 0: # <<<<<<<<<<<<<<
* start_positive_ -= 1
* Xf[start_positive_] = X_data[k]
*/
__pyx_t_1 = (((__pyx_v_X_data[__pyx_v_k]) > 0.0) != 0);
if (__pyx_t_1) {
/* "sklearn/tree/_splitter.pyx":1132
*
* 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/_splitter.pyx":1133
* 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/_splitter.pyx":1134
* 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/_splitter.pyx":1135
* 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_9_splitter_sparse_swap(__pyx_v_index_to_samples, __pyx_v_samples, __pyx_v_index, __pyx_v_start_positive_);
goto __pyx_L17;
}
/* "sklearn/tree/_splitter.pyx":1138
*
*
* elif X_data[k] < 0: # <<<<<<<<<<<<<<
* Xf[end_negative_] = X_data[k]
* index = index_to_samples[X_indices[k]]
*/
__pyx_t_1 = (((__pyx_v_X_data[__pyx_v_k]) < 0.0) != 0);
if (__pyx_t_1) {
/* "sklearn/tree/_splitter.pyx":1139
*
* 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/_splitter.pyx":1140
* 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/_splitter.pyx":1141
* 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
* p += 1
*/
__pyx_f_7sklearn_4tree_9_splitter_sparse_swap(__pyx_v_index_to_samples, __pyx_v_samples, __pyx_v_index, __pyx_v_end_negative_);
/* "sklearn/tree/_splitter.pyx":1142
* index = index_to_samples[X_indices[k]]
* sparse_swap(index_to_samples, samples, index, end_negative_)
* end_negative_ += 1 # <<<<<<<<<<<<<<
* p += 1
*
*/
__pyx_v_end_negative_ = (__pyx_v_end_negative_ + 1);
goto __pyx_L17;
}
__pyx_L17:;
goto __pyx_L16;
}
__pyx_L16:;
/* "sklearn/tree/_splitter.pyx":1143
* sparse_swap(index_to_samples, samples, index, end_negative_)
* end_negative_ += 1
* p += 1 # <<<<<<<<<<<<<<
*
* # Returned values
*/
__pyx_v_p = (__pyx_v_p + 1);
}
/* "sklearn/tree/_splitter.pyx":1146
*
* # Returned values
* end_negative[0] = end_negative_ # <<<<<<<<<<<<<<
* start_positive[0] = start_positive_
*
*/
(__pyx_v_end_negative[0]) = __pyx_v_end_negative_;
/* "sklearn/tree/_splitter.pyx":1147
* # Returned values
* end_negative[0] = end_negative_
* start_positive[0] = start_positive_ # <<<<<<<<<<<<<<
*
*
*/
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* 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/_splitter.pyx":1192
* 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/_splitter.pyx":1193
* 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/_splitter.pyx":1196
*
* cdef SplitRecord best, current
* _init_split(&best, end) # <<<<<<<<<<<<<<
* cdef double current_proxy_improvement = - INFINITY
* cdef double best_proxy_improvement = - INFINITY
*/
__pyx_f_7sklearn_4tree_9_splitter__init_split((&__pyx_v_best), __pyx_v_end);
/* "sklearn/tree/_splitter.pyx":1197
* cdef SplitRecord best, current
* _init_split(&best, end)
* cdef double current_proxy_improvement = - INFINITY # <<<<<<<<<<<<<<
* cdef double best_proxy_improvement = - INFINITY
*
*/
__pyx_v_current_proxy_improvement = (-__pyx_v_7sklearn_4tree_9_splitter_INFINITY);
/* "sklearn/tree/_splitter.pyx":1198
* _init_split(&best, end)
* cdef double current_proxy_improvement = - INFINITY
* cdef double best_proxy_improvement = - INFINITY # <<<<<<<<<<<<<<
*
* cdef SIZE_t f_i = n_features
*/
__pyx_v_best_proxy_improvement = (-__pyx_v_7sklearn_4tree_9_splitter_INFINITY);
/* "sklearn/tree/_splitter.pyx":1200
* cdef double best_proxy_improvement = - INFINITY
*
* 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/_splitter.pyx":1202
* 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/_splitter.pyx":1204
* 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/_splitter.pyx":1206
* 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/_splitter.pyx":1207
* # 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/_splitter.pyx":1209
* 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/_splitter.pyx":1214
* 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/_splitter.pyx":1231
* # 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_7 = ((__pyx_v_f_i > __pyx_v_n_total_constants) != 0);
if (__pyx_t_7) {
} else {
__pyx_t_6 = __pyx_t_7;
goto __pyx_L5_bool_binop_done;
}
/* "sklearn/tree/_splitter.pyx":1233
* 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) {
} else {
__pyx_t_6 = __pyx_t_7;
goto __pyx_L5_bool_binop_done;
}
/* "sklearn/tree/_splitter.pyx":1235
* (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_6 = __pyx_t_7;
__pyx_L5_bool_binop_done:;
if (!__pyx_t_6) break;
/* "sklearn/tree/_splitter.pyx":1237
* 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/_splitter.pyx":1251
*
* # 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_6_utils_rand_int(__pyx_v_n_drawn_constants, (__pyx_v_f_i - __pyx_v_n_found_constants), __pyx_v_random_state);
/* "sklearn/tree/_splitter.pyx":1254
* 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/_splitter.pyx":1256
* 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/_splitter.pyx":1257
* # 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/_splitter.pyx":1258
* 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/_splitter.pyx":1260
* features[n_drawn_constants] = tmp
*
* n_drawn_constants += 1 # <<<<<<<<<<<<<<
*
* else:
*/
__pyx_v_n_drawn_constants = (__pyx_v_n_drawn_constants + 1);
goto __pyx_L8;
}
/*else*/ {
/* "sklearn/tree/_splitter.pyx":1264
* 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/_splitter.pyx":1267
* # 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/_splitter.pyx":1268
*
* current.feature = features[f_j]
* self.extract_nnz(current.feature, # <<<<<<<<<<<<<<
* &end_negative, &start_positive,
* &is_samples_sorted)
*/
__pyx_f_7sklearn_4tree_9_splitter_18BaseSparseSplitter_extract_nnz(((struct __pyx_obj_7sklearn_4tree_9_splitter_BaseSparseSplitter *)__pyx_v_self), __pyx_v_current.feature, (&__pyx_v_end_negative), (&__pyx_v_start_positive), (&__pyx_v_is_samples_sorted));
/* "sklearn/tree/_splitter.pyx":1273
*
* # 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_9_splitter_sort((__pyx_v_Xf + __pyx_v_start), (__pyx_v_samples + __pyx_v_start), (__pyx_v_end_negative - __pyx_v_start));
/* "sklearn/tree/_splitter.pyx":1274
* # 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_9_splitter_sort((__pyx_v_Xf + __pyx_v_start_positive), (__pyx_v_samples + __pyx_v_start_positive), (__pyx_v_end - __pyx_v_start_positive));
/* "sklearn/tree/_splitter.pyx":1278
*
* # 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_8 = __pyx_v_start; __pyx_t_8 < __pyx_t_2; __pyx_t_8+=1) {
__pyx_v_p = __pyx_t_8;
/* "sklearn/tree/_splitter.pyx":1279
* # 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/_splitter.pyx":1280
* 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_8 = __pyx_v_start_positive; __pyx_t_8 < __pyx_t_2; __pyx_t_8+=1) {
__pyx_v_p = __pyx_t_8;
/* "sklearn/tree/_splitter.pyx":1281
* 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/_splitter.pyx":1284
*
* # 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_6 = ((__pyx_v_end_negative < __pyx_v_start_positive) != 0);
if (__pyx_t_6) {
/* "sklearn/tree/_splitter.pyx":1285
* # 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/_splitter.pyx":1286
* 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/_splitter.pyx":1288
* Xf[start_positive] = 0.
*
* if end_negative != start_positive: # <<<<<<<<<<<<<<
* Xf[end_negative] = 0.
* end_negative += 1
*/
__pyx_t_6 = ((__pyx_v_end_negative != __pyx_v_start_positive) != 0);
if (__pyx_t_6) {
/* "sklearn/tree/_splitter.pyx":1289
*
* if end_negative != start_positive:
* Xf[end_negative] = 0. # <<<<<<<<<<<<<<
* end_negative += 1
*
*/
(__pyx_v_Xf[__pyx_v_end_negative]) = 0.;
/* "sklearn/tree/_splitter.pyx":1290
* 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_L14;
}
__pyx_L14:;
goto __pyx_L13;
}
__pyx_L13:;
/* "sklearn/tree/_splitter.pyx":1292
* 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_6 = (((__pyx_v_Xf[(__pyx_v_end - 1)]) <= ((__pyx_v_Xf[__pyx_v_start]) + __pyx_v_7sklearn_4tree_9_splitter_FEATURE_THRESHOLD)) != 0);
if (__pyx_t_6) {
/* "sklearn/tree/_splitter.pyx":1293
*
* 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/_splitter.pyx":1294
* 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/_splitter.pyx":1296
* 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/_splitter.pyx":1297
*
* n_found_constants += 1
* n_total_constants += 1 # <<<<<<<<<<<<<<
*
* else:
*/
__pyx_v_n_total_constants = (__pyx_v_n_total_constants + 1);
goto __pyx_L15;
}
/*else*/ {
/* "sklearn/tree/_splitter.pyx":1300
*
* 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/_splitter.pyx":1301
* 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_8 = (__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_8;
/* "sklearn/tree/_splitter.pyx":1304
*
* # Evaluate all splits
* self.criterion.reset() # <<<<<<<<<<<<<<
* p = start
*
*/
((struct __pyx_vtabstruct_7sklearn_4tree_10_criterion_Criterion *)__pyx_v_self->__pyx_base.__pyx_base.criterion->__pyx_vtab)->reset(__pyx_v_self->__pyx_base.__pyx_base.criterion);
/* "sklearn/tree/_splitter.pyx":1305
* # Evaluate all splits
* self.criterion.reset()
* p = start # <<<<<<<<<<<<<<
*
* while p < end:
*/
__pyx_v_p = __pyx_v_start;
/* "sklearn/tree/_splitter.pyx":1307
* p = start
*
* while p < end: # <<<<<<<<<<<<<<
* if p + 1 != end_negative:
* p_next = p + 1
*/
while (1) {
__pyx_t_6 = ((__pyx_v_p < __pyx_v_end) != 0);
if (!__pyx_t_6) break;
/* "sklearn/tree/_splitter.pyx":1308
*
* while p < end:
* if p + 1 != end_negative: # <<<<<<<<<<<<<<
* p_next = p + 1
* else:
*/
__pyx_t_6 = (((__pyx_v_p + 1) != __pyx_v_end_negative) != 0);
if (__pyx_t_6) {
/* "sklearn/tree/_splitter.pyx":1309
* 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_L18;
}
/*else*/ {
/* "sklearn/tree/_splitter.pyx":1311
* p_next = p + 1
* else:
* p_next = start_positive # <<<<<<<<<<<<<<
*
* while (p_next < end and
*/
__pyx_v_p_next = __pyx_v_start_positive;
}
__pyx_L18:;
/* "sklearn/tree/_splitter.pyx":1313
* 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) {
} else {
__pyx_t_6 = __pyx_t_7;
goto __pyx_L21_bool_binop_done;
}
/* "sklearn/tree/_splitter.pyx":1314
*
* while (p_next < end and
* Xf[p_next] <= Xf[p] + FEATURE_THRESHOLD): # <<<<<<<<<<<<<<
* p = p_next
* if p + 1 != end_negative:
*/
__pyx_t_7 = (((__pyx_v_Xf[__pyx_v_p_next]) <= ((__pyx_v_Xf[__pyx_v_p]) + __pyx_v_7sklearn_4tree_9_splitter_FEATURE_THRESHOLD)) != 0);
__pyx_t_6 = __pyx_t_7;
__pyx_L21_bool_binop_done:;
if (!__pyx_t_6) break;
/* "sklearn/tree/_splitter.pyx":1315
* while (p_next < end and
* Xf[p_next] <= Xf[p] + FEATURE_THRESHOLD):
* p = p_next # <<<<<<<<<<<<<<
* if p + 1 != end_negative:
* p_next = p + 1
*/
__pyx_v_p = __pyx_v_p_next;
/* "sklearn/tree/_splitter.pyx":1316
* Xf[p_next] <= Xf[p] + FEATURE_THRESHOLD):
* p = p_next
* if p + 1 != end_negative: # <<<<<<<<<<<<<<
* p_next = p + 1
* else:
*/
__pyx_t_6 = (((__pyx_v_p + 1) != __pyx_v_end_negative) != 0);
if (__pyx_t_6) {
/* "sklearn/tree/_splitter.pyx":1317
* p = p_next
* if p + 1 != end_negative:
* p_next = p + 1 # <<<<<<<<<<<<<<
* else:
* p_next = start_positive
*/
__pyx_v_p_next = (__pyx_v_p + 1);
goto __pyx_L23;
}
/*else*/ {
/* "sklearn/tree/_splitter.pyx":1319
* p_next = p + 1
* else:
* p_next = start_positive # <<<<<<<<<<<<<<
*
*
*/
__pyx_v_p_next = __pyx_v_start_positive;
}
__pyx_L23:;
}
/* "sklearn/tree/_splitter.pyx":1324
* # (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] >
*/
__pyx_v_p_prev = __pyx_v_p;
/* "sklearn/tree/_splitter.pyx":1325
* # 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])
*/
__pyx_v_p = __pyx_v_p_next;
/* "sklearn/tree/_splitter.pyx":1330
*
*
* if p < end: # <<<<<<<<<<<<<<
* current.pos = p
*
*/
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__pyx_t_7sklearn_4tree_9_splitter_SIZE_t __pyx_v_end;
CYTHON_UNUSED __pyx_t_7sklearn_4tree_9_splitter_INT32_t *__pyx_v_X_indices;
CYTHON_UNUSED __pyx_t_7sklearn_4tree_9_splitter_INT32_t *__pyx_v_X_indptr;
CYTHON_UNUSED __pyx_t_7sklearn_4tree_9_splitter_DTYPE_t *__pyx_v_X_data;
__pyx_t_7sklearn_4tree_9_splitter_SIZE_t *__pyx_v_features;
__pyx_t_7sklearn_4tree_9_splitter_SIZE_t *__pyx_v_constant_features;
__pyx_t_7sklearn_4tree_9_splitter_SIZE_t __pyx_v_n_features;
__pyx_t_7sklearn_4tree_9_splitter_DTYPE_t *__pyx_v_Xf;
CYTHON_UNUSED __pyx_t_7sklearn_4tree_9_splitter_SIZE_t *__pyx_v_sorted_samples;
CYTHON_UNUSED __pyx_t_7sklearn_4tree_9_splitter_SIZE_t *__pyx_v_index_to_samples;
__pyx_t_7sklearn_4tree_9_splitter_SIZE_t __pyx_v_max_features;
__pyx_t_7sklearn_4tree_9_splitter_SIZE_t __pyx_v_min_samples_leaf;
double __pyx_v_min_weight_leaf;
__pyx_t_7sklearn_4tree_9_splitter_UINT32_t *__pyx_v_random_state;
struct __pyx_t_7sklearn_4tree_9_splitter_SplitRecord __pyx_v_best;
struct __pyx_t_7sklearn_4tree_9_splitter_SplitRecord __pyx_v_current;
double __pyx_v_current_proxy_improvement;
double __pyx_v_best_proxy_improvement;
__pyx_t_7sklearn_4tree_9_splitter_DTYPE_t __pyx_v_current_feature_value;
__pyx_t_7sklearn_4tree_9_splitter_SIZE_t __pyx_v_f_i;
__pyx_t_7sklearn_4tree_9_splitter_SIZE_t __pyx_v_f_j;
__pyx_t_7sklearn_4tree_9_splitter_SIZE_t __pyx_v_p;
__pyx_t_7sklearn_4tree_9_splitter_SIZE_t __pyx_v_tmp;
__pyx_t_7sklearn_4tree_9_splitter_SIZE_t __pyx_v_n_visited_features;
__pyx_t_7sklearn_4tree_9_splitter_SIZE_t __pyx_v_n_found_constants;
__pyx_t_7sklearn_4tree_9_splitter_SIZE_t __pyx_v_n_drawn_constants;
__pyx_t_7sklearn_4tree_9_splitter_SIZE_t __pyx_v_n_known_constants;
__pyx_t_7sklearn_4tree_9_splitter_SIZE_t __pyx_v_n_total_constants;
__pyx_t_7sklearn_4tree_9_splitter_DTYPE_t __pyx_v_min_feature_value;
__pyx_t_7sklearn_4tree_9_splitter_DTYPE_t __pyx_v_max_feature_value;
int __pyx_v_is_samples_sorted;
__pyx_t_7sklearn_4tree_9_splitter_SIZE_t __pyx_v_start_positive;
__pyx_t_7sklearn_4tree_9_splitter_SIZE_t __pyx_v_end_negative;
__pyx_t_7sklearn_4tree_9_splitter_SIZE_t *__pyx_t_1;
__pyx_t_7sklearn_4tree_9_splitter_SIZE_t __pyx_t_2;
__pyx_t_7sklearn_4tree_9_splitter_INT32_t *__pyx_t_3;
__pyx_t_7sklearn_4tree_9_splitter_DTYPE_t *__pyx_t_4;
double __pyx_t_5;
int __pyx_t_6;
int __pyx_t_7;
__pyx_t_7sklearn_4tree_9_splitter_SIZE_t __pyx_t_8;
/* "sklearn/tree/_splitter.pyx":1402
* """
* # 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/_splitter.pyx":1403
* # 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/_splitter.pyx":1404
* 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/_splitter.pyx":1406
* 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/_splitter.pyx":1407
*
* 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/_splitter.pyx":1408
* 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/_splitter.pyx":1410
* 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/_splitter.pyx":1411
*
* 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/_splitter.pyx":1412
* 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/_splitter.pyx":1414
* 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/_splitter.pyx":1415
*
* 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/_splitter.pyx":1416
* 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/_splitter.pyx":1417
* 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/_splitter.pyx":1418
* 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/_splitter.pyx":1419
* 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/_splitter.pyx":1420
* 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/_splitter.pyx":1423
*
* cdef SplitRecord best, current
* _init_split(&best, end) # <<<<<<<<<<<<<<
* cdef double current_proxy_improvement = - INFINITY
* cdef double best_proxy_improvement = - INFINITY
*/
__pyx_f_7sklearn_4tree_9_splitter__init_split((&__pyx_v_best), __pyx_v_end);
/* "sklearn/tree/_splitter.pyx":1424
* cdef SplitRecord best, current
* _init_split(&best, end)
* cdef double current_proxy_improvement = - INFINITY # <<<<<<<<<<<<<<
* cdef double best_proxy_improvement = - INFINITY
*
*/
__pyx_v_current_proxy_improvement = (-__pyx_v_7sklearn_4tree_9_splitter_INFINITY);
/* "sklearn/tree/_splitter.pyx":1425
* _init_split(&best, end)
* cdef double current_proxy_improvement = - INFINITY
* cdef double best_proxy_improvement = - INFINITY # <<<<<<<<<<<<<<
*
* cdef DTYPE_t current_feature_value
*/
__pyx_v_best_proxy_improvement = (-__pyx_v_7sklearn_4tree_9_splitter_INFINITY);
/* "sklearn/tree/_splitter.pyx":1429
* 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/_splitter.pyx":1431
* 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/_splitter.pyx":1433
* 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/_splitter.pyx":1435
* 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/_splitter.pyx":1436
* # 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/_splitter.pyx":1438
* 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/_splitter.pyx":1444
* 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/_splitter.pyx":1461
* # 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_7 = ((__pyx_v_f_i > __pyx_v_n_total_constants) != 0);
if (__pyx_t_7) {
} else {
__pyx_t_6 = __pyx_t_7;
goto __pyx_L5_bool_binop_done;
}
/* "sklearn/tree/_splitter.pyx":1463
* 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) {
} else {
__pyx_t_6 = __pyx_t_7;
goto __pyx_L5_bool_binop_done;
}
/* "sklearn/tree/_splitter.pyx":1465
* (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_6 = __pyx_t_7;
__pyx_L5_bool_binop_done:;
if (!__pyx_t_6) break;
/* "sklearn/tree/_splitter.pyx":1467
* 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/_splitter.pyx":1481
*
* # 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_6_utils_rand_int(__pyx_v_n_drawn_constants, (__pyx_v_f_i - __pyx_v_n_found_constants), __pyx_v_random_state);
/* "sklearn/tree/_splitter.pyx":1484
* 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/_splitter.pyx":1486
* 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/_splitter.pyx":1487
* # 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/_splitter.pyx":1488
* 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/_splitter.pyx":1490
* features[n_drawn_constants] = tmp
*
* n_drawn_constants += 1 # <<<<<<<<<<<<<<
*
* else:
*/
__pyx_v_n_drawn_constants = (__pyx_v_n_drawn_constants + 1);
goto __pyx_L8;
}
/*else*/ {
/* "sklearn/tree/_splitter.pyx":1494
* 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/_splitter.pyx":1497
* # 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/_splitter.pyx":1499
* current.feature = features[f_j]
*
* self.extract_nnz(current.feature, # <<<<<<<<<<<<<<
* &end_negative, &start_positive,
* &is_samples_sorted)
*/
__pyx_f_7sklearn_4tree_9_splitter_18BaseSparseSplitter_extract_nnz(((struct __pyx_obj_7sklearn_4tree_9_splitter_BaseSparseSplitter *)__pyx_v_self), __pyx_v_current.feature, (&__pyx_v_end_negative), (&__pyx_v_start_positive), (&__pyx_v_is_samples_sorted));
/* "sklearn/tree/_splitter.pyx":1504
*
* # 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_6 = ((__pyx_v_end_negative < __pyx_v_start_positive) != 0);
if (__pyx_t_6) {
/* "sklearn/tree/_splitter.pyx":1505
* # 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/_splitter.pyx":1506
* 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/_splitter.pyx":1508
* Xf[start_positive] = 0.
*
* if end_negative != start_positive: # <<<<<<<<<<<<<<
* Xf[end_negative] = 0.
* end_negative += 1
*/
__pyx_t_6 = ((__pyx_v_end_negative != __pyx_v_start_positive) != 0);
if (__pyx_t_6) {
/* "sklearn/tree/_splitter.pyx":1509
*
* if end_negative != start_positive:
* Xf[end_negative] = 0. # <<<<<<<<<<<<<<
* end_negative += 1
*
*/
(__pyx_v_Xf[__pyx_v_end_negative]) = 0.;
/* "sklearn/tree/_splitter.pyx":1510
* 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_L10;
}
__pyx_L10:;
goto __pyx_L9;
}
__pyx_L9:;
/* "sklearn/tree/_splitter.pyx":1513
*
* # 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/_splitter.pyx":1514
* # 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/_splitter.pyx":1516
* 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_8 = __pyx_v_start; __pyx_t_8 < __pyx_t_2; __pyx_t_8+=1) {
__pyx_v_p = __pyx_t_8;
/* "sklearn/tree/_splitter.pyx":1517
*
* 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/_splitter.pyx":1519
* 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_6 = ((__pyx_v_current_feature_value < __pyx_v_min_feature_value) != 0);
if (__pyx_t_6) {
/* "sklearn/tree/_splitter.pyx":1520
*
* 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/_splitter.pyx":1521
* 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/_splitter.pyx":1522
* 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_L13;
}
__pyx_L13:;
}
/* "sklearn/tree/_splitter.pyx":1525
*
* # 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_8 = __pyx_v_start_positive; __pyx_t_8 < __pyx_t_2; __pyx_t_8+=1) {
__pyx_v_p = __pyx_t_8;
/* "sklearn/tree/_splitter.pyx":1526
* # 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/_splitter.pyx":1528
* 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_6 = ((__pyx_v_current_feature_value < __pyx_v_min_feature_value) != 0);
if (__pyx_t_6) {
/* "sklearn/tree/_splitter.pyx":1529
*
* 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_L16;
}
/* "sklearn/tree/_splitter.pyx":1530
* 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/_splitter.pyx":1531
* 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_L16;
}
__pyx_L16:;
}
/* "sklearn/tree/_splitter.pyx":1533
* 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_9_splitter_FEATURE_THRESHOLD)) != 0);
if (__pyx_t_6) {
/* "sklearn/tree/_splitter.pyx":1534
*
* 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/_splitter.pyx":1535
* 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/_splitter.pyx":1537
* 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/_splitter.pyx":1538
*
* n_found_constants += 1
* n_total_constants += 1 # <<<<<<<<<<<<<<
*
* else:
*/
__pyx_v_n_total_constants = (__pyx_v_n_total_constants + 1);
goto __pyx_L17;
}
/*else*/ {
/* "sklearn/tree/_splitter.pyx":1541
*
* 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/_splitter.pyx":1542
* 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_8 = (__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_8;
/* "sklearn/tree/_splitter.pyx":1545
*
* # Draw a random threshold
* current.threshold = rand_uniform(min_feature_value, # <<<<<<<<<<<<<<
* max_feature_value,
* random_state)
*/
__pyx_v_current.threshold = __pyx_f_7sklearn_4tree_6_utils_rand_uniform(__pyx_v_min_feature_value, __pyx_v_max_feature_value, __pyx_v_random_state);
/* "sklearn/tree/_splitter.pyx":1549
* 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/_splitter.pyx":1550
*
* if current.threshold == max_feature_value:
* current.threshold = min_feature_value # <<<<<<<<<<<<<<
*
* # Partition
*/
__pyx_v_current.threshold = __pyx_v_min_feature_value;
goto __pyx_L18;
}
__pyx_L18:;
/* "sklearn/tree/_splitter.pyx":1553
*
* # Partition
* current.pos = self._partition(current.threshold, # <<<<<<<<<<<<<<
* end_negative,
* start_positive,
*/
__pyx_v_current.pos = __pyx_f_7sklearn_4tree_9_splitter_18BaseSparseSplitter__partition(((struct __pyx_obj_7sklearn_4tree_9_splitter_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/_splitter.pyx":1560
*
* # 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) {
} else {
__pyx_t_6 = __pyx_t_7;
goto __pyx_L20_bool_binop_done;
}
/* "sklearn/tree/_splitter.pyx":1561
* # 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_end - __pyx_v_current.pos) < __pyx_v_min_samples_leaf) != 0);
__pyx_t_6 = __pyx_t_7;
__pyx_L20_bool_binop_done:;
if (__pyx_t_6) {
/* "sklearn/tree/_splitter.pyx":1562
* if (((current.pos - start) < min_samples_leaf) or
* ((end - current.pos) < min_samples_leaf)):
* continue # <<<<<<<<<<<<<<
*
* # Evaluate split
*/
goto __pyx_L3_continue;
}
/* "sklearn/tree/_splitter.pyx":1565
*
* # Evaluate split
* self.criterion.reset() # <<<<<<<<<<<<<<
* self.criterion.update(current.pos)
*
*/
((struct __pyx_vtabstruct_7sklearn_4tree_10_criterion_Criterion *)__pyx_v_self->__pyx_base.__pyx_base.criterion->__pyx_vtab)->reset(__pyx_v_self->__pyx_base.__pyx_base.criterion);
/* "sklearn/tree/_splitter.pyx":1566
* # Evaluate split
* self.criterion.reset()
* self.criterion.update(current.pos) # <<<<<<<<<<<<<<
*
* # Reject if min_weight_leaf is not satisfied
*/
((struct __pyx_vtabstruct_7sklearn_4tree_10_criterion_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/_splitter.pyx":1569
*
* # 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_left < __pyx_v_min_weight_leaf) != 0);
if (!__pyx_t_7) {
} else {
__pyx_t_6 = __pyx_t_7;
goto __pyx_L23_bool_binop_done;
}
/* "sklearn/tree/_splitter.pyx":1570
* # 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;
__pyx_L23_bool_binop_done:;
if (__pyx_t_6) {
/* "sklearn/tree/_splitter.pyx":1571
* if ((self.criterion.weighted_n_left < min_weight_leaf) or
* (self.criterion.weighted_n_right < min_weight_leaf)):
* continue # <<<<<<<<<<<<<<
*
* current_proxy_improvement = self.criterion.proxy_impurity_improvement()
*/
goto __pyx_L3_continue;
}
/* "sklearn/tree/_splitter.pyx":1573
* continue
*
* current_proxy_improvement = self.criterion.proxy_impurity_improvement() # <<<<<<<<<<<<<<
*
* if current_proxy_improvement > best_proxy_improvement:
*/
__pyx_v_current_proxy_improvement = ((struct __pyx_vtabstruct_7sklearn_4tree_10_criterion_Criterion *)__pyx_v_self->__pyx_base.__pyx_base.criterion->__pyx_vtab)->proxy_impurity_improvement(__pyx_v_self->__pyx_base.__pyx_base.criterion);
/* "sklearn/tree/_splitter.pyx":1575
* current_proxy_improvement = self.criterion.proxy_impurity_improvement()
*
* if current_proxy_improvement > best_proxy_improvement: # <<<<<<<<<<<<<<
* best_proxy_improvement = current_proxy_improvement
* current.improvement = self.criterion.impurity_improvement(impurity)
*/
__pyx_t_6 = ((__pyx_v_current_proxy_improvement > __pyx_v_best_proxy_improvement) != 0);
if (__pyx_t_6) {
/* "sklearn/tree/_splitter.pyx":1576
*
* if current_proxy_improvement > best_proxy_improvement:
* best_proxy_improvement = current_proxy_improvement # <<<<<<<<<<<<<<
* current.improvement = self.criterion.impurity_improvement(impurity)
*
*/
__pyx_v_best_proxy_improvement = __pyx_v_current_proxy_improvement;
/* "sklearn/tree/_splitter.pyx":1577
* if current_proxy_improvement > best_proxy_improvement:
* best_proxy_improvement = current_proxy_improvement
* current.improvement = self.criterion.impurity_improvement(impurity) # <<<<<<<<<<<<<<
*
* self.criterion.children_impurity(&current.impurity_left,
*/
__pyx_v_current.improvement = ((struct __pyx_vtabstruct_7sklearn_4tree_10_criterion_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/_splitter.pyx":1579
* current.improvement = self.criterion.impurity_improvement(impurity)
*
* self.criterion.children_impurity(&current.impurity_left, # <<<<<<<<<<<<<<
* &current.impurity_right)
* best = current
*/
((struct __pyx_vtabstruct_7sklearn_4tree_10_criterion_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/_splitter.pyx":1581
* 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_L25;
}
__pyx_L25:;
}
__pyx_L17:;
}
__pyx_L8:;
__pyx_L3_continue:;
}
/* "sklearn/tree/_splitter.pyx":1584
*
* # Reorganize into samples[start:best.pos] + samples[best.pos:end]
* if best.pos < end: # <<<<<<<<<<<<<<
* if current.feature != best.feature:
* self.extract_nnz(best.feature, &end_negative, &start_positive,
*/
__pyx_t_6 = ((__pyx_v_best.pos < __pyx_v_end) != 0);
if (__pyx_t_6) {
/* "sklearn/tree/_splitter.pyx":1585
* # Reorganize into samples[start:best.pos] + samples[best.pos:end]
* if best.pos < end:
* if current.feature != best.feature: # <<<<<<<<<<<<<<
* self.extract_nnz(best.feature, &end_negative, &start_positive,
* &is_samples_sorted)
*/
__pyx_t_6 = ((__pyx_v_current.feature != __pyx_v_best.feature) != 0);
if (__pyx_t_6) {
/* "sklearn/tree/_splitter.pyx":1586
* if best.pos < end:
* if current.feature != best.feature:
* self.extract_nnz(best.feature, &end_negative, &start_positive, # <<<<<<<<<<<<<<
* &is_samples_sorted)
*
*/
__pyx_f_7sklearn_4tree_9_splitter_18BaseSparseSplitter_extract_nnz(((struct __pyx_obj_7sklearn_4tree_9_splitter_BaseSparseSplitter *)__pyx_v_self), __pyx_v_best.feature, (&__pyx_v_end_negative), (&__pyx_v_start_positive), (&__pyx_v_is_samples_sorted));
/* "sklearn/tree/_splitter.pyx":1589
* &is_samples_sorted)
*
* self._partition(best.threshold, end_negative, start_positive, # <<<<<<<<<<<<<<
* best.pos)
*
*/
__pyx_f_7sklearn_4tree_9_splitter_18BaseSparseSplitter__partition(((struct __pyx_obj_7sklearn_4tree_9_splitter_BaseSparseSplitter *)__pyx_v_self), __pyx_v_best.threshold, __pyx_v_end_negative, __pyx_v_start_positive, __pyx_v_best.pos);
goto __pyx_L27;
}
__pyx_L27:;
/* "sklearn/tree/_splitter.pyx":1592
* best.pos)
*
* self.criterion.reset() # <<<<<<<<<<<<<<
* self.criterion.update(best.pos)
* best.improvement = self.criterion.impurity_improvement(impurity)
*/
((struct __pyx_vtabstruct_7sklearn_4tree_10_criterion_Criterion *)__pyx_v_self->__pyx_base.__pyx_base.criterion->__pyx_vtab)->reset(__pyx_v_self->__pyx_base.__pyx_base.criterion);
/* "sklearn/tree/_splitter.pyx":1593
*
* self.criterion.reset()
* self.criterion.update(best.pos) # <<<<<<<<<<<<<<
* best.improvement = self.criterion.impurity_improvement(impurity)
* self.criterion.children_impurity(&best.impurity_left,
*/
((struct __pyx_vtabstruct_7sklearn_4tree_10_criterion_Criterion *)__pyx_v_self->__pyx_base.__pyx_base.criterion->__pyx_vtab)->update(__pyx_v_self->__pyx_base.__pyx_base.criterion, __pyx_v_best.pos);
/* "sklearn/tree/_splitter.pyx":1594
* self.criterion.reset()
* self.criterion.update(best.pos)
* best.improvement = self.criterion.impurity_improvement(impurity) # <<<<<<<<<<<<<<
* self.criterion.children_impurity(&best.impurity_left,
* &best.impurity_right)
*/
__pyx_v_best.improvement = ((struct __pyx_vtabstruct_7sklearn_4tree_10_criterion_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/_splitter.pyx":1595
* self.criterion.update(best.pos)
* best.improvement = self.criterion.impurity_improvement(impurity)
* self.criterion.children_impurity(&best.impurity_left, # <<<<<<<<<<<<<<
* &best.impurity_right)
*
*/
((struct __pyx_vtabstruct_7sklearn_4tree_10_criterion_Criterion *)__pyx_v_self->__pyx_base.__pyx_base.criterion->__pyx_vtab)->children_impurity(__pyx_v_self->__pyx_base.__pyx_base.criterion, (&__pyx_v_best.impurity_left), (&__pyx_v_best.impurity_right));
goto __pyx_L26;
}
__pyx_L26:;
/* "sklearn/tree/_splitter.pyx":1601
* # 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_9_splitter_SIZE_t)) * __pyx_v_n_known_constants));
/* "sklearn/tree/_splitter.pyx":1604
*
* # 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_9_splitter_SIZE_t)) * __pyx_v_n_found_constants));
/* "sklearn/tree/_splitter.pyx":1609
*
* # Return values
* split[0] = best # <<<<<<<<<<<<<<
* n_constant_features[0] = n_total_constants
*/
(__pyx_v_split[0]) = __pyx_v_best;
/* "sklearn/tree/_splitter.pyx":1610
* # 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/_splitter.pyx":1396
* self.presort), 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 */
}
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p = ((struct __pyx_obj_7sklearn_4tree_9_splitter_BaseDenseSplitter *)o);
p->__pyx_base.__pyx_vtab = (struct __pyx_vtabstruct_7sklearn_4tree_9_splitter_Splitter*)__pyx_vtabptr_7sklearn_4tree_9_splitter_BaseDenseSplitter;
p->X_idx_sorted = ((PyArrayObject *)Py_None); Py_INCREF(Py_None);
if (unlikely(__pyx_pw_7sklearn_4tree_9_splitter_17BaseDenseSplitter_1__cinit__(o, a, k) < 0)) {
Py_DECREF(o); o = 0;
}
return o;
}
static void __pyx_tp_dealloc_7sklearn_4tree_9_splitter_BaseDenseSplitter(PyObject *o) {
struct __pyx_obj_7sklearn_4tree_9_splitter_BaseDenseSplitter *p = (struct __pyx_obj_7sklearn_4tree_9_splitter_BaseDenseSplitter *)o;
#if PY_VERSION_HEX >= 0x030400a1
if (unlikely(Py_TYPE(o)->tp_finalize) && !_PyGC_FINALIZED(o)) {
if (PyObject_CallFinalizerFromDealloc(o)) return;
}
#endif
PyObject_GC_UnTrack(o);
{
PyObject *etype, *eval, *etb;
PyErr_Fetch(&etype, &eval, &etb);
++Py_REFCNT(o);
__pyx_pw_7sklearn_4tree_9_splitter_17BaseDenseSplitter_3__dealloc__(o);
--Py_REFCNT(o);
PyErr_Restore(etype, eval, etb);
}
Py_CLEAR(p->X_idx_sorted);
PyObject_GC_Track(o);
__pyx_tp_dealloc_7sklearn_4tree_9_splitter_Splitter(o);
}
static int __pyx_tp_traverse_7sklearn_4tree_9_splitter_BaseDenseSplitter(PyObject *o, visitproc v, void *a) {
int e;
struct __pyx_obj_7sklearn_4tree_9_splitter_BaseDenseSplitter *p = (struct __pyx_obj_7sklearn_4tree_9_splitter_BaseDenseSplitter *)o;
e = __pyx_tp_traverse_7sklearn_4tree_9_splitter_Splitter(o, v, a); if (e) return e;
if (p->X_idx_sorted) {
e = (*v)(((PyObject*)p->X_idx_sorted), a); if (e) return e;
}
return 0;
}
static int __pyx_tp_clear_7sklearn_4tree_9_splitter_BaseDenseSplitter(PyObject *o) {
PyObject* tmp;
struct __pyx_obj_7sklearn_4tree_9_splitter_BaseDenseSplitter *p = (struct __pyx_obj_7sklearn_4tree_9_splitter_BaseDenseSplitter *)o;
__pyx_tp_clear_7sklearn_4tree_9_splitter_Splitter(o);
tmp = ((PyObject*)p->X_idx_sorted);
p->X_idx_sorted = ((PyArrayObject *)Py_None); Py_INCREF(Py_None);
Py_XDECREF(tmp);
return 0;
}
static PyMethodDef __pyx_methods_7sklearn_4tree_9_splitter_BaseDenseSplitter[] = {
{0, 0, 0, 0}
};
static PyTypeObject __pyx_type_7sklearn_4tree_9_splitter_BaseDenseSplitter = {
PyVarObject_HEAD_INIT(0, 0)
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__pyx_tp_dealloc_7sklearn_4tree_9_splitter_BaseDenseSplitter, /*tp_dealloc*/
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#if PY_MAJOR_VERSION < 3
0, /*tp_compare*/
#else
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0, /*tp_doc*/
__pyx_tp_traverse_7sklearn_4tree_9_splitter_BaseDenseSplitter, /*tp_traverse*/
__pyx_tp_clear_7sklearn_4tree_9_splitter_BaseDenseSplitter, /*tp_clear*/
0, /*tp_richcompare*/
0, /*tp_weaklistoffset*/
0, /*tp_iter*/
0, /*tp_iternext*/
__pyx_methods_7sklearn_4tree_9_splitter_BaseDenseSplitter, /*tp_methods*/
0, /*tp_members*/
0, /*tp_getset*/
0, /*tp_base*/
0, /*tp_dict*/
0, /*tp_descr_get*/
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0, /*tp_init*/
0, /*tp_alloc*/
__pyx_tp_new_7sklearn_4tree_9_splitter_BaseDenseSplitter, /*tp_new*/
0, /*tp_free*/
0, /*tp_is_gc*/
0, /*tp_bases*/
0, /*tp_mro*/
0, /*tp_cache*/
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0, /*tp_weaklist*/
0, /*tp_del*/
0, /*tp_version_tag*/
#if PY_VERSION_HEX >= 0x030400a1
0, /*tp_finalize*/
#endif
};
static struct __pyx_vtabstruct_7sklearn_4tree_9_splitter_BestSplitter __pyx_vtable_7sklearn_4tree_9_splitter_BestSplitter;
static PyObject *__pyx_tp_new_7sklearn_4tree_9_splitter_BestSplitter(PyTypeObject *t, PyObject *a, PyObject *k) {
struct __pyx_obj_7sklearn_4tree_9_splitter_BestSplitter *p;
PyObject *o = __pyx_tp_new_7sklearn_4tree_9_splitter_BaseDenseSplitter(t, a, k);
if (unlikely(!o)) return 0;
p = ((struct __pyx_obj_7sklearn_4tree_9_splitter_BestSplitter *)o);
p->__pyx_base.__pyx_base.__pyx_vtab = (struct __pyx_vtabstruct_7sklearn_4tree_9_splitter_Splitter*)__pyx_vtabptr_7sklearn_4tree_9_splitter_BestSplitter;
return o;
}
static PyMethodDef __pyx_methods_7sklearn_4tree_9_splitter_BestSplitter[] = {
{"__reduce__", (PyCFunction)__pyx_pw_7sklearn_4tree_9_splitter_12BestSplitter_1__reduce__, METH_NOARGS, 0},
{0, 0, 0, 0}
};
static PyTypeObject __pyx_type_7sklearn_4tree_9_splitter_BestSplitter = {
PyVarObject_HEAD_INIT(0, 0)
"sklearn.tree._splitter.BestSplitter", /*tp_name*/
sizeof(struct __pyx_obj_7sklearn_4tree_9_splitter_BestSplitter), /*tp_basicsize*/
0, /*tp_itemsize*/
__pyx_tp_dealloc_7sklearn_4tree_9_splitter_BaseDenseSplitter, /*tp_dealloc*/
0, /*tp_print*/
0, /*tp_getattr*/
0, /*tp_setattr*/
#if PY_MAJOR_VERSION < 3
0, /*tp_compare*/
#else
0, /*reserved*/
#endif
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0, /*tp_as_sequence*/
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0, /*tp_setattro*/
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Py_TPFLAGS_DEFAULT|Py_TPFLAGS_HAVE_VERSION_TAG|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_HAVE_NEWBUFFER|Py_TPFLAGS_BASETYPE|Py_TPFLAGS_HAVE_GC, /*tp_flags*/
"Splitter for finding the best split.", /*tp_doc*/
__pyx_tp_traverse_7sklearn_4tree_9_splitter_BaseDenseSplitter, /*tp_traverse*/
__pyx_tp_clear_7sklearn_4tree_9_splitter_BaseDenseSplitter, /*tp_clear*/
0, /*tp_richcompare*/
0, /*tp_weaklistoffset*/
0, /*tp_iter*/
0, /*tp_iternext*/
__pyx_methods_7sklearn_4tree_9_splitter_BestSplitter, /*tp_methods*/
0, /*tp_members*/
0, /*tp_getset*/
0, /*tp_base*/
0, /*tp_dict*/
0, /*tp_descr_get*/
0, /*tp_descr_set*/
0, /*tp_dictoffset*/
0, /*tp_init*/
0, /*tp_alloc*/
__pyx_tp_new_7sklearn_4tree_9_splitter_BestSplitter, /*tp_new*/
0, /*tp_free*/
0, /*tp_is_gc*/
0, /*tp_bases*/
0, /*tp_mro*/
0, /*tp_cache*/
0, /*tp_subclasses*/
0, /*tp_weaklist*/
0, /*tp_del*/
0, /*tp_version_tag*/
#if PY_VERSION_HEX >= 0x030400a1
0, /*tp_finalize*/
#endif
};
static struct __pyx_vtabstruct_7sklearn_4tree_9_splitter_RandomSplitter __pyx_vtable_7sklearn_4tree_9_splitter_RandomSplitter;
static PyObject *__pyx_tp_new_7sklearn_4tree_9_splitter_RandomSplitter(PyTypeObject *t, PyObject *a, PyObject *k) {
struct __pyx_obj_7sklearn_4tree_9_splitter_RandomSplitter *p;
PyObject *o = __pyx_tp_new_7sklearn_4tree_9_splitter_BaseDenseSplitter(t, a, k);
if (unlikely(!o)) return 0;
p = ((struct __pyx_obj_7sklearn_4tree_9_splitter_RandomSplitter *)o);
p->__pyx_base.__pyx_base.__pyx_vtab = (struct __pyx_vtabstruct_7sklearn_4tree_9_splitter_Splitter*)__pyx_vtabptr_7sklearn_4tree_9_splitter_RandomSplitter;
return o;
}
static PyMethodDef __pyx_methods_7sklearn_4tree_9_splitter_RandomSplitter[] = {
{"__reduce__", (PyCFunction)__pyx_pw_7sklearn_4tree_9_splitter_14RandomSplitter_1__reduce__, METH_NOARGS, 0},
{0, 0, 0, 0}
};
static PyTypeObject __pyx_type_7sklearn_4tree_9_splitter_RandomSplitter = {
PyVarObject_HEAD_INIT(0, 0)
"sklearn.tree._splitter.RandomSplitter", /*tp_name*/
sizeof(struct __pyx_obj_7sklearn_4tree_9_splitter_RandomSplitter), /*tp_basicsize*/
0, /*tp_itemsize*/
__pyx_tp_dealloc_7sklearn_4tree_9_splitter_BaseDenseSplitter, /*tp_dealloc*/
0, /*tp_print*/
0, /*tp_getattr*/
0, /*tp_setattr*/
#if PY_MAJOR_VERSION < 3
0, /*tp_compare*/
#else
0, /*reserved*/
#endif
0, /*tp_repr*/
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Py_TPFLAGS_DEFAULT|Py_TPFLAGS_HAVE_VERSION_TAG|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_HAVE_NEWBUFFER|Py_TPFLAGS_BASETYPE|Py_TPFLAGS_HAVE_GC, /*tp_flags*/
"Splitter for finding the best random split.", /*tp_doc*/
__pyx_tp_traverse_7sklearn_4tree_9_splitter_BaseDenseSplitter, /*tp_traverse*/
__pyx_tp_clear_7sklearn_4tree_9_splitter_BaseDenseSplitter, /*tp_clear*/
0, /*tp_richcompare*/
0, /*tp_weaklistoffset*/
0, /*tp_iter*/
0, /*tp_iternext*/
__pyx_methods_7sklearn_4tree_9_splitter_RandomSplitter, /*tp_methods*/
0, /*tp_members*/
0, /*tp_getset*/
0, /*tp_base*/
0, /*tp_dict*/
0, /*tp_descr_get*/
0, /*tp_descr_set*/
0, /*tp_dictoffset*/
0, /*tp_init*/
0, /*tp_alloc*/
__pyx_tp_new_7sklearn_4tree_9_splitter_RandomSplitter, /*tp_new*/
0, /*tp_free*/
0, /*tp_is_gc*/
0, /*tp_bases*/
0, /*tp_mro*/
0, /*tp_cache*/
0, /*tp_subclasses*/
0, /*tp_weaklist*/
0, /*tp_del*/
0, /*tp_version_tag*/
#if PY_VERSION_HEX >= 0x030400a1
0, /*tp_finalize*/
#endif
};
static struct __pyx_vtabstruct_7sklearn_4tree_9_splitter_BaseSparseSplitter __pyx_vtable_7sklearn_4tree_9_splitter_BaseSparseSplitter;
static PyObject *__pyx_tp_new_7sklearn_4tree_9_splitter_BaseSparseSplitter(PyTypeObject *t, PyObject *a, PyObject *k) {
struct __pyx_obj_7sklearn_4tree_9_splitter_BaseSparseSplitter *p;
PyObject *o = __pyx_tp_new_7sklearn_4tree_9_splitter_Splitter(t, a, k);
if (unlikely(!o)) return 0;
p = ((struct __pyx_obj_7sklearn_4tree_9_splitter_BaseSparseSplitter *)o);
p->__pyx_base.__pyx_vtab = (struct __pyx_vtabstruct_7sklearn_4tree_9_splitter_Splitter*)__pyx_vtabptr_7sklearn_4tree_9_splitter_BaseSparseSplitter;
if (unlikely(__pyx_pw_7sklearn_4tree_9_splitter_18BaseSparseSplitter_1__cinit__(o, a, k) < 0)) {
Py_DECREF(o); o = 0;
}
return o;
}
static void __pyx_tp_dealloc_7sklearn_4tree_9_splitter_BaseSparseSplitter(PyObject *o) {
#if PY_VERSION_HEX >= 0x030400a1
if (unlikely(Py_TYPE(o)->tp_finalize) && !_PyGC_FINALIZED(o)) {
if (PyObject_CallFinalizerFromDealloc(o)) return;
}
#endif
PyObject_GC_UnTrack(o);
{
PyObject *etype, *eval, *etb;
PyErr_Fetch(&etype, &eval, &etb);
++Py_REFCNT(o);
__pyx_pw_7sklearn_4tree_9_splitter_18BaseSparseSplitter_3__dealloc__(o);
--Py_REFCNT(o);
PyErr_Restore(etype, eval, etb);
}
PyObject_GC_Track(o);
__pyx_tp_dealloc_7sklearn_4tree_9_splitter_Splitter(o);
}
static PyMethodDef __pyx_methods_7sklearn_4tree_9_splitter_BaseSparseSplitter[] = {
{0, 0, 0, 0}
};
static PyTypeObject __pyx_type_7sklearn_4tree_9_splitter_BaseSparseSplitter = {
PyVarObject_HEAD_INIT(0, 0)
"sklearn.tree._splitter.BaseSparseSplitter", /*tp_name*/
sizeof(struct __pyx_obj_7sklearn_4tree_9_splitter_BaseSparseSplitter), /*tp_basicsize*/
0, /*tp_itemsize*/
__pyx_tp_dealloc_7sklearn_4tree_9_splitter_BaseSparseSplitter, /*tp_dealloc*/
0, /*tp_print*/
0, /*tp_getattr*/
0, /*tp_setattr*/
#if PY_MAJOR_VERSION < 3
0, /*tp_compare*/
#else
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Py_TPFLAGS_DEFAULT|Py_TPFLAGS_HAVE_VERSION_TAG|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_HAVE_NEWBUFFER|Py_TPFLAGS_BASETYPE|Py_TPFLAGS_HAVE_GC, /*tp_flags*/
0, /*tp_doc*/
__pyx_tp_traverse_7sklearn_4tree_9_splitter_Splitter, /*tp_traverse*/
__pyx_tp_clear_7sklearn_4tree_9_splitter_Splitter, /*tp_clear*/
0, /*tp_richcompare*/
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0, /*tp_iter*/
0, /*tp_iternext*/
__pyx_methods_7sklearn_4tree_9_splitter_BaseSparseSplitter, /*tp_methods*/
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__pyx_tp_new_7sklearn_4tree_9_splitter_BaseSparseSplitter, /*tp_new*/
0, /*tp_free*/
0, /*tp_is_gc*/
0, /*tp_bases*/
0, /*tp_mro*/
0, /*tp_cache*/
0, /*tp_subclasses*/
0, /*tp_weaklist*/
0, /*tp_del*/
0, /*tp_version_tag*/
#if PY_VERSION_HEX >= 0x030400a1
0, /*tp_finalize*/
#endif
};
static struct __pyx_vtabstruct_7sklearn_4tree_9_splitter_BestSparseSplitter __pyx_vtable_7sklearn_4tree_9_splitter_BestSparseSplitter;
static PyObject *__pyx_tp_new_7sklearn_4tree_9_splitter_BestSparseSplitter(PyTypeObject *t, PyObject *a, PyObject *k) {
struct __pyx_obj_7sklearn_4tree_9_splitter_BestSparseSplitter *p;
PyObject *o = __pyx_tp_new_7sklearn_4tree_9_splitter_BaseSparseSplitter(t, a, k);
if (unlikely(!o)) return 0;
p = ((struct __pyx_obj_7sklearn_4tree_9_splitter_BestSparseSplitter *)o);
p->__pyx_base.__pyx_base.__pyx_vtab = (struct __pyx_vtabstruct_7sklearn_4tree_9_splitter_Splitter*)__pyx_vtabptr_7sklearn_4tree_9_splitter_BestSparseSplitter;
return o;
}
static PyMethodDef __pyx_methods_7sklearn_4tree_9_splitter_BestSparseSplitter[] = {
{"__reduce__", (PyCFunction)__pyx_pw_7sklearn_4tree_9_splitter_18BestSparseSplitter_1__reduce__, METH_NOARGS, 0},
{0, 0, 0, 0}
};
static PyTypeObject __pyx_type_7sklearn_4tree_9_splitter_BestSparseSplitter = {
PyVarObject_HEAD_INIT(0, 0)
"sklearn.tree._splitter.BestSparseSplitter", /*tp_name*/
sizeof(struct __pyx_obj_7sklearn_4tree_9_splitter_BestSparseSplitter), /*tp_basicsize*/
0, /*tp_itemsize*/
__pyx_tp_dealloc_7sklearn_4tree_9_splitter_BaseSparseSplitter, /*tp_dealloc*/
0, /*tp_print*/
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Py_TPFLAGS_DEFAULT|Py_TPFLAGS_HAVE_VERSION_TAG|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_HAVE_NEWBUFFER|Py_TPFLAGS_BASETYPE|Py_TPFLAGS_HAVE_GC, /*tp_flags*/
"Splitter for finding the best split, using the sparse data.", /*tp_doc*/
__pyx_tp_traverse_7sklearn_4tree_9_splitter_Splitter, /*tp_traverse*/
__pyx_tp_clear_7sklearn_4tree_9_splitter_Splitter, /*tp_clear*/
0, /*tp_richcompare*/
0, /*tp_weaklistoffset*/
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0, /*tp_iternext*/
__pyx_methods_7sklearn_4tree_9_splitter_BestSparseSplitter, /*tp_methods*/
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0, /*tp_alloc*/
__pyx_tp_new_7sklearn_4tree_9_splitter_BestSparseSplitter, /*tp_new*/
0, /*tp_free*/
0, /*tp_is_gc*/
0, /*tp_bases*/
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0, /*tp_cache*/
0, /*tp_subclasses*/
0, /*tp_weaklist*/
0, /*tp_del*/
0, /*tp_version_tag*/
#if PY_VERSION_HEX >= 0x030400a1
0, /*tp_finalize*/
#endif
};
static struct __pyx_vtabstruct_7sklearn_4tree_9_splitter_RandomSparseSplitter __pyx_vtable_7sklearn_4tree_9_splitter_RandomSparseSplitter;
static PyObject *__pyx_tp_new_7sklearn_4tree_9_splitter_RandomSparseSplitter(PyTypeObject *t, PyObject *a, PyObject *k) {
struct __pyx_obj_7sklearn_4tree_9_splitter_RandomSparseSplitter *p;
PyObject *o = __pyx_tp_new_7sklearn_4tree_9_splitter_BaseSparseSplitter(t, a, k);
if (unlikely(!o)) return 0;
p = ((struct __pyx_obj_7sklearn_4tree_9_splitter_RandomSparseSplitter *)o);
p->__pyx_base.__pyx_base.__pyx_vtab = (struct __pyx_vtabstruct_7sklearn_4tree_9_splitter_Splitter*)__pyx_vtabptr_7sklearn_4tree_9_splitter_RandomSparseSplitter;
return o;
}
static PyMethodDef __pyx_methods_7sklearn_4tree_9_splitter_RandomSparseSplitter[] = {
{"__reduce__", (PyCFunction)__pyx_pw_7sklearn_4tree_9_splitter_20RandomSparseSplitter_1__reduce__, METH_NOARGS, 0},
{0, 0, 0, 0}
};
static PyTypeObject __pyx_type_7sklearn_4tree_9_splitter_RandomSparseSplitter = {
PyVarObject_HEAD_INIT(0, 0)
"sklearn.tree._splitter.RandomSparseSplitter", /*tp_name*/
sizeof(struct __pyx_obj_7sklearn_4tree_9_splitter_RandomSparseSplitter), /*tp_basicsize*/
0, /*tp_itemsize*/
__pyx_tp_dealloc_7sklearn_4tree_9_splitter_BaseSparseSplitter, /*tp_dealloc*/
0, /*tp_print*/
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#if PY_MAJOR_VERSION < 3
0, /*tp_compare*/
#else
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Py_TPFLAGS_DEFAULT|Py_TPFLAGS_HAVE_VERSION_TAG|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_HAVE_NEWBUFFER|Py_TPFLAGS_BASETYPE|Py_TPFLAGS_HAVE_GC, /*tp_flags*/
"Splitter for finding a random split, using the sparse data.", /*tp_doc*/
__pyx_tp_traverse_7sklearn_4tree_9_splitter_Splitter, /*tp_traverse*/
__pyx_tp_clear_7sklearn_4tree_9_splitter_Splitter, /*tp_clear*/
0, /*tp_richcompare*/
0, /*tp_weaklistoffset*/
0, /*tp_iter*/
0, /*tp_iternext*/
__pyx_methods_7sklearn_4tree_9_splitter_RandomSparseSplitter, /*tp_methods*/
0, /*tp_members*/
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__pyx_tp_new_7sklearn_4tree_9_splitter_RandomSparseSplitter, /*tp_new*/
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0, /*tp_del*/
0, /*tp_version_tag*/
#if PY_VERSION_HEX >= 0x030400a1
0, /*tp_finalize*/
#endif
};
static PyMethodDef __pyx_methods[] = {
{0, 0, 0, 0}
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#if PY_MAJOR_VERSION >= 3
static struct PyModuleDef __pyx_moduledef = {
#if PY_VERSION_HEX < 0x03020000
{ PyObject_HEAD_INIT(NULL) NULL, 0, NULL },
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PyModuleDef_HEAD_INIT,
#endif
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-1, /* m_size */
__pyx_methods /* m_methods */,
NULL, /* m_reload */
NULL, /* m_traverse */
NULL, /* m_clear */
NULL /* m_free */
};
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if (PyObject_SetAttrString(__pyx_m, "BestSplitter", (PyObject *)&__pyx_type_7sklearn_4tree_9_splitter_BestSplitter) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 288; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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/*--- Function import code ---*/
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if (__pyx_d) {
__Pyx_AddTraceback("init sklearn.tree._splitter", __pyx_clineno, __pyx_lineno, __pyx_filename);
}
Py_DECREF(__pyx_m); __pyx_m = 0;
} else if (!PyErr_Occurred()) {
PyErr_SetString(PyExc_ImportError, "init sklearn.tree._splitter");
}
__pyx_L0:;
__Pyx_RefNannyFinishContext();
#if PY_MAJOR_VERSION < 3
return;
#else
return __pyx_m;
#endif
}
/* --- Runtime support code --- */
#if CYTHON_REFNANNY
static __Pyx_RefNannyAPIStruct *__Pyx_RefNannyImportAPI(const char *modname) {
PyObject *m = NULL, *p = NULL;
void *r = NULL;
m = PyImport_ImportModule((char *)modname);
if (!m) goto end;
p = PyObject_GetAttrString(m, (char *)"RefNannyAPI");
if (!p) goto end;
r = PyLong_AsVoidPtr(p);
end:
Py_XDECREF(p);
Py_XDECREF(m);
return (__Pyx_RefNannyAPIStruct *)r;
}
#endif
static PyObject *__Pyx_GetBuiltinName(PyObject *name) {
PyObject* result = __Pyx_PyObject_GetAttrStr(__pyx_b, name);
if (unlikely(!result)) {
PyErr_Format(PyExc_NameError,
#if PY_MAJOR_VERSION >= 3
"name '%U' is not defined", name);
#else
"name '%.200s' is not defined", PyString_AS_STRING(name));
#endif
}
return result;
}
static void __Pyx_RaiseArgtupleInvalid(
const char* func_name,
int exact,
Py_ssize_t num_min,
Py_ssize_t num_max,
Py_ssize_t num_found)
{
Py_ssize_t num_expected;
const char *more_or_less;
if (num_found < num_min) {
num_expected = num_min;
more_or_less = "at least";
} else {
num_expected = num_max;
more_or_less = "at most";
}
if (exact) {
more_or_less = "exactly";
}
PyErr_Format(PyExc_TypeError,
"%.200s() takes %.8s %" CYTHON_FORMAT_SSIZE_T "d positional argument%.1s (%" CYTHON_FORMAT_SSIZE_T "d given)",
func_name, more_or_less, num_expected,
(num_expected == 1) ? "" : "s", num_found);
}
static void __Pyx_RaiseDoubleKeywordsError(
const char* func_name,
PyObject* kw_name)
{
PyErr_Format(PyExc_TypeError,
#if PY_MAJOR_VERSION >= 3
"%s() got multiple values for keyword argument '%U'", func_name, kw_name);
#else
"%s() got multiple values for keyword argument '%s'", func_name,
PyString_AsString(kw_name));
#endif
}
static int __Pyx_ParseOptionalKeywords(
PyObject *kwds,
PyObject **argnames[],
PyObject *kwds2,
PyObject *values[],
Py_ssize_t num_pos_args,
const char* function_name)
{
PyObject *key = 0, *value = 0;
Py_ssize_t pos = 0;
PyObject*** name;
PyObject*** first_kw_arg = argnames + num_pos_args;
while (PyDict_Next(kwds, &pos, &key, &value)) {
name = first_kw_arg;
while (*name && (**name != key)) name++;
if (*name) {
values[name-argnames] = value;
continue;
}
name = first_kw_arg;
#if PY_MAJOR_VERSION < 3
if (likely(PyString_CheckExact(key)) || likely(PyString_Check(key))) {
while (*name) {
if ((CYTHON_COMPILING_IN_PYPY || PyString_GET_SIZE(**name) == PyString_GET_SIZE(key))
&& _PyString_Eq(**name, key)) {
values[name-argnames] = value;
break;
}
name++;
}
if (*name) continue;
else {
PyObject*** argname = argnames;
while (argname != first_kw_arg) {
if ((**argname == key) || (
(CYTHON_COMPILING_IN_PYPY || PyString_GET_SIZE(**argname) == PyString_GET_SIZE(key))
&& _PyString_Eq(**argname, key))) {
goto arg_passed_twice;
}
argname++;
}
}
} else
#endif
if (likely(PyUnicode_Check(key))) {
while (*name) {
int cmp = (**name == key) ? 0 :
#if !CYTHON_COMPILING_IN_PYPY && PY_MAJOR_VERSION >= 3
(PyUnicode_GET_SIZE(**name) != PyUnicode_GET_SIZE(key)) ? 1 :
#endif
PyUnicode_Compare(**name, key);
if (cmp < 0 && unlikely(PyErr_Occurred())) goto bad;
if (cmp == 0) {
values[name-argnames] = value;
break;
}
name++;
}
if (*name) continue;
else {
PyObject*** argname = argnames;
while (argname != first_kw_arg) {
int cmp = (**argname == key) ? 0 :
#if !CYTHON_COMPILING_IN_PYPY && PY_MAJOR_VERSION >= 3
(PyUnicode_GET_SIZE(**argname) != PyUnicode_GET_SIZE(key)) ? 1 :
#endif
PyUnicode_Compare(**argname, key);
if (cmp < 0 && unlikely(PyErr_Occurred())) goto bad;
if (cmp == 0) goto arg_passed_twice;
argname++;
}
}
} else
goto invalid_keyword_type;
if (kwds2) {
if (unlikely(PyDict_SetItem(kwds2, key, value))) goto bad;
} else {
goto invalid_keyword;
}
}
return 0;
arg_passed_twice:
__Pyx_RaiseDoubleKeywordsError(function_name, key);
goto bad;
invalid_keyword_type:
PyErr_Format(PyExc_TypeError,
"%.200s() keywords must be strings", function_name);
goto bad;
invalid_keyword:
PyErr_Format(PyExc_TypeError,
#if PY_MAJOR_VERSION < 3
"%.200s() got an unexpected keyword argument '%.200s'",
function_name, PyString_AsString(key));
#else
"%s() got an unexpected keyword argument '%U'",
function_name, key);
#endif
bad:
return -1;
}
static void __Pyx_RaiseArgumentTypeInvalid(const char* name, PyObject *obj, PyTypeObject *type) {
PyErr_Format(PyExc_TypeError,
"Argument '%.200s' has incorrect type (expected %.200s, got %.200s)",
name, type->tp_name, Py_TYPE(obj)->tp_name);
}
static CYTHON_INLINE int __Pyx_ArgTypeTest(PyObject *obj, PyTypeObject *type, int none_allowed,
const char *name, int exact)
{
if (unlikely(!type)) {
PyErr_SetString(PyExc_SystemError, "Missing type object");
return 0;
}
if (none_allowed && obj == Py_None) return 1;
else if (exact) {
if (likely(Py_TYPE(obj) == type)) return 1;
#if PY_MAJOR_VERSION == 2
else if ((type == &PyBaseString_Type) && likely(__Pyx_PyBaseString_CheckExact(obj))) return 1;
#endif
}
else {
if (likely(PyObject_TypeCheck(obj, type))) return 1;
}
__Pyx_RaiseArgumentTypeInvalid(name, obj, type);
return 0;
}
static CYTHON_INLINE int __Pyx_IsLittleEndian(void) {
unsigned int n = 1;
return *(unsigned char*)(&n) != 0;
}
static void __Pyx_BufFmt_Init(__Pyx_BufFmt_Context* ctx,
__Pyx_BufFmt_StackElem* stack,
__Pyx_TypeInfo* type) {
stack[0].field = &ctx->root;
stack[0].parent_offset = 0;
ctx->root.type = type;
ctx->root.name = "buffer dtype";
ctx->root.offset = 0;
ctx->head = stack;
ctx->head->field = &ctx->root;
ctx->fmt_offset = 0;
ctx->head->parent_offset = 0;
ctx->new_packmode = '@';
ctx->enc_packmode = '@';
ctx->new_count = 1;
ctx->enc_count = 0;
ctx->enc_type = 0;
ctx->is_complex = 0;
ctx->is_valid_array = 0;
ctx->struct_alignment = 0;
while (type->typegroup == 'S') {
++ctx->head;
ctx->head->field = type->fields;
ctx->head->parent_offset = 0;
type = type->fields->type;
}
}
static int __Pyx_BufFmt_ParseNumber(const char** ts) {
int count;
const char* t = *ts;
if (*t < '0' || *t > '9') {
return -1;
} else {
count = *t++ - '0';
while (*t >= '0' && *t < '9') {
count *= 10;
count += *t++ - '0';
}
}
*ts = t;
return count;
}
static int __Pyx_BufFmt_ExpectNumber(const char **ts) {
int number = __Pyx_BufFmt_ParseNumber(ts);
if (number == -1)
PyErr_Format(PyExc_ValueError,\
"Does not understand character buffer dtype format string ('%c')", **ts);
return number;
}
static void __Pyx_BufFmt_RaiseUnexpectedChar(char ch) {
PyErr_Format(PyExc_ValueError,
"Unexpected format string character: '%c'", ch);
}
static const char* __Pyx_BufFmt_DescribeTypeChar(char ch, int is_complex) {
switch (ch) {
case 'c': return "'char'";
case 'b': return "'signed char'";
case 'B': return "'unsigned char'";
case 'h': return "'short'";
case 'H': return "'unsigned short'";
case 'i': return "'int'";
case 'I': return "'unsigned int'";
case 'l': return "'long'";
case 'L': return "'unsigned long'";
case 'q': return "'long long'";
case 'Q': return "'unsigned long long'";
case 'f': return (is_complex ? "'complex float'" : "'float'");
case 'd': return (is_complex ? "'complex double'" : "'double'");
case 'g': return (is_complex ? "'complex long double'" : "'long double'");
case 'T': return "a struct";
case 'O': return "Python object";
case 'P': return "a pointer";
case 's': case 'p': return "a string";
case 0: return "end";
default: return "unparseable format string";
}
}
static size_t __Pyx_BufFmt_TypeCharToStandardSize(char ch, int is_complex) {
switch (ch) {
case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1;
case 'h': case 'H': return 2;
case 'i': case 'I': case 'l': case 'L': return 4;
case 'q': case 'Q': return 8;
case 'f': return (is_complex ? 8 : 4);
case 'd': return (is_complex ? 16 : 8);
case 'g': {
PyErr_SetString(PyExc_ValueError, "Python does not define a standard format string size for long double ('g')..");
return 0;
}
case 'O': case 'P': return sizeof(void*);
default:
__Pyx_BufFmt_RaiseUnexpectedChar(ch);
return 0;
}
}
static size_t __Pyx_BufFmt_TypeCharToNativeSize(char ch, int is_complex) {
switch (ch) {
case 'c': case 'b': case 'B': case 's': case 'p': return 1;
case 'h': case 'H': return sizeof(short);
case 'i': case 'I': return sizeof(int);
case 'l': case 'L': return sizeof(long);
#ifdef HAVE_LONG_LONG
case 'q': case 'Q': return sizeof(PY_LONG_LONG);
#endif
case 'f': return sizeof(float) * (is_complex ? 2 : 1);
case 'd': return sizeof(double) * (is_complex ? 2 : 1);
case 'g': return sizeof(long double) * (is_complex ? 2 : 1);
case 'O': case 'P': return sizeof(void*);
default: {
__Pyx_BufFmt_RaiseUnexpectedChar(ch);
return 0;
}
}
}
typedef struct { char c; short x; } __Pyx_st_short;
typedef struct { char c; int x; } __Pyx_st_int;
typedef struct { char c; long x; } __Pyx_st_long;
typedef struct { char c; float x; } __Pyx_st_float;
typedef struct { char c; double x; } __Pyx_st_double;
typedef struct { char c; long double x; } __Pyx_st_longdouble;
typedef struct { char c; void *x; } __Pyx_st_void_p;
#ifdef HAVE_LONG_LONG
typedef struct { char c; PY_LONG_LONG x; } __Pyx_st_longlong;
#endif
static size_t __Pyx_BufFmt_TypeCharToAlignment(char ch, CYTHON_UNUSED int is_complex) {
switch (ch) {
case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1;
case 'h': case 'H': return sizeof(__Pyx_st_short) - sizeof(short);
case 'i': case 'I': return sizeof(__Pyx_st_int) - sizeof(int);
case 'l': case 'L': return sizeof(__Pyx_st_long) - sizeof(long);
#ifdef HAVE_LONG_LONG
case 'q': case 'Q': return sizeof(__Pyx_st_longlong) - sizeof(PY_LONG_LONG);
#endif
case 'f': return sizeof(__Pyx_st_float) - sizeof(float);
case 'd': return sizeof(__Pyx_st_double) - sizeof(double);
case 'g': return sizeof(__Pyx_st_longdouble) - sizeof(long double);
case 'P': case 'O': return sizeof(__Pyx_st_void_p) - sizeof(void*);
default:
__Pyx_BufFmt_RaiseUnexpectedChar(ch);
return 0;
}
}
/* These are for computing the padding at the end of the struct to align
on the first member of the struct. This will probably the same as above,
but we don't have any guarantees.
*/
typedef struct { short x; char c; } __Pyx_pad_short;
typedef struct { int x; char c; } __Pyx_pad_int;
typedef struct { long x; char c; } __Pyx_pad_long;
typedef struct { float x; char c; } __Pyx_pad_float;
typedef struct { double x; char c; } __Pyx_pad_double;
typedef struct { long double x; char c; } __Pyx_pad_longdouble;
typedef struct { void *x; char c; } __Pyx_pad_void_p;
#ifdef HAVE_LONG_LONG
typedef struct { PY_LONG_LONG x; char c; } __Pyx_pad_longlong;
#endif
static size_t __Pyx_BufFmt_TypeCharToPadding(char ch, CYTHON_UNUSED int is_complex) {
switch (ch) {
case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1;
case 'h': case 'H': return sizeof(__Pyx_pad_short) - sizeof(short);
case 'i': case 'I': return sizeof(__Pyx_pad_int) - sizeof(int);
case 'l': case 'L': return sizeof(__Pyx_pad_long) - sizeof(long);
#ifdef HAVE_LONG_LONG
case 'q': case 'Q': return sizeof(__Pyx_pad_longlong) - sizeof(PY_LONG_LONG);
#endif
case 'f': return sizeof(__Pyx_pad_float) - sizeof(float);
case 'd': return sizeof(__Pyx_pad_double) - sizeof(double);
case 'g': return sizeof(__Pyx_pad_longdouble) - sizeof(long double);
case 'P': case 'O': return sizeof(__Pyx_pad_void_p) - sizeof(void*);
default:
__Pyx_BufFmt_RaiseUnexpectedChar(ch);
return 0;
}
}
static char __Pyx_BufFmt_TypeCharToGroup(char ch, int is_complex) {
switch (ch) {
case 'c':
return 'H';
case 'b': case 'h': case 'i':
case 'l': case 'q': case 's': case 'p':
return 'I';
case 'B': case 'H': case 'I': case 'L': case 'Q':
return 'U';
case 'f': case 'd': case 'g':
return (is_complex ? 'C' : 'R');
case 'O':
return 'O';
case 'P':
return 'P';
default: {
__Pyx_BufFmt_RaiseUnexpectedChar(ch);
return 0;
}
}
}
static void __Pyx_BufFmt_RaiseExpected(__Pyx_BufFmt_Context* ctx) {
if (ctx->head == NULL || ctx->head->field == &ctx->root) {
const char* expected;
const char* quote;
if (ctx->head == NULL) {
expected = "end";
quote = "";
} else {
expected = ctx->head->field->type->name;
quote = "'";
}
PyErr_Format(PyExc_ValueError,
"Buffer dtype mismatch, expected %s%s%s but got %s",
quote, expected, quote,
__Pyx_BufFmt_DescribeTypeChar(ctx->enc_type, ctx->is_complex));
} else {
__Pyx_StructField* field = ctx->head->field;
__Pyx_StructField* parent = (ctx->head - 1)->field;
PyErr_Format(PyExc_ValueError,
"Buffer dtype mismatch, expected '%s' but got %s in '%s.%s'",
field->type->name, __Pyx_BufFmt_DescribeTypeChar(ctx->enc_type, ctx->is_complex),
parent->type->name, field->name);
}
}
static int __Pyx_BufFmt_ProcessTypeChunk(__Pyx_BufFmt_Context* ctx) {
char group;
size_t size, offset, arraysize = 1;
if (ctx->enc_type == 0) return 0;
if (ctx->head->field->type->arraysize[0]) {
int i, ndim = 0;
if (ctx->enc_type == 's' || ctx->enc_type == 'p') {
ctx->is_valid_array = ctx->head->field->type->ndim == 1;
ndim = 1;
if (ctx->enc_count != ctx->head->field->type->arraysize[0]) {
PyErr_Format(PyExc_ValueError,
"Expected a dimension of size %zu, got %zu",
ctx->head->field->type->arraysize[0], ctx->enc_count);
return -1;
}
}
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;
while (1) {
if (field == &ctx->root) {
ctx->head = NULL;
if (ctx->enc_count != 0) {
__Pyx_BufFmt_RaiseExpected(ctx);
return -1;
}
break;
}
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;
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;
}
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':
{
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;
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 '}':
{
size_t alignment = ctx->struct_alignment;
++ts;
if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL;
ctx->enc_type = 0;
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);
}
#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 (unlikely(Py_EnterRecursiveCall((char*)" while calling a Python object")))
return NULL;
result = (*call)(func, arg, kw);
Py_LeaveRecursiveCall();
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,
CYTHON_NCP_UNUSED int wraparound,
CYTHON_NCP_UNUSED 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,
CYTHON_NCP_UNUSED int wraparound,
CYTHON_NCP_UNUSED 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,
CYTHON_NCP_UNUSED int wraparound,
CYTHON_NCP_UNUSED 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 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
}
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;
}
#if CYTHON_COMPILING_IN_CPYTHON
static CYTHON_INLINE PyObject* __Pyx_PyObject_CallMethO(PyObject *func, PyObject *arg) {
PyObject *self, *result;
PyCFunction cfunc;
cfunc = PyCFunction_GET_FUNCTION(func);
self = PyCFunction_GET_SELF(func);
if (unlikely(Py_EnterRecursiveCall((char*)" while calling a Python object")))
return NULL;
result = cfunc(self, arg);
Py_LeaveRecursiveCall();
if (unlikely(!result) && unlikely(!PyErr_Occurred())) {
PyErr_SetString(
PyExc_SystemError,
"NULL result without error in PyObject_Call");
}
return result;
}
#endif
#if CYTHON_COMPILING_IN_CPYTHON
static PyObject* __Pyx__PyObject_CallOneArg(PyObject *func, PyObject *arg) {
PyObject *result;
PyObject *args = PyTuple_New(1);
if (unlikely(!args)) return NULL;
Py_INCREF(arg);
PyTuple_SET_ITEM(args, 0, arg);
result = __Pyx_PyObject_Call(func, args, NULL);
Py_DECREF(args);
return result;
}
static CYTHON_INLINE PyObject* __Pyx_PyObject_CallOneArg(PyObject *func, PyObject *arg) {
#ifdef __Pyx_CyFunction_USED
if (likely(PyCFunction_Check(func) || PyObject_TypeCheck(func, __pyx_CyFunctionType))) {
#else
if (likely(PyCFunction_Check(func))) {
#endif
if (likely(PyCFunction_GET_FLAGS(func) & METH_O)) {
return __Pyx_PyObject_CallMethO(func, arg);
}
}
return __Pyx__PyObject_CallOneArg(func, arg);
}
#else
static CYTHON_INLINE PyObject* __Pyx_PyObject_CallOneArg(PyObject *func, PyObject *arg) {
PyObject* args = PyTuple_Pack(1, arg);
return (likely(args)) ? __Pyx_PyObject_Call(func, args, NULL) : NULL;
}
#endif
#if CYTHON_COMPILING_IN_CPYTHON
static CYTHON_INLINE PyObject* __Pyx_PyObject_CallNoArg(PyObject *func) {
#ifdef __Pyx_CyFunction_USED
if (likely(PyCFunction_Check(func) || PyObject_TypeCheck(func, __pyx_CyFunctionType))) {
#else
if (likely(PyCFunction_Check(func))) {
#endif
if (likely(PyCFunction_GET_FLAGS(func) & METH_NOARGS)) {
return __Pyx_PyObject_CallMethO(func, NULL);
}
}
return __Pyx_PyObject_Call(func, __pyx_empty_tuple, NULL);
}
#endif
static CYTHON_INLINE PyObject *__Pyx_GetModuleGlobalName(PyObject *name) {
PyObject *result;
#if CYTHON_COMPILING_IN_CPYTHON
result = PyDict_GetItem(__pyx_d, name);
if (likely(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 (PyType_Check(type)) {
#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;
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;
}
}
__Pyx_ErrRestore(type, value, tb);
return;
raise_error:
Py_XDECREF(value);
Py_XDECREF(type);
Py_XDECREF(tb);
return;
}
#else
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) {
int is_subclass = PyObject_IsSubclass(instance_class, type);
if (!is_subclass) {
instance_class = NULL;
} else if (unlikely(is_subclass == -1)) {
goto bad;
} else {
type = instance_class;
}
}
}
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) {
#if CYTHON_COMPILING_IN_PYPY
PyObject *tmp_type, *tmp_value, *tmp_tb;
PyErr_Fetch(&tmp_type, &tmp_value, &tmp_tb);
Py_INCREF(tb);
PyErr_Restore(tmp_type, tmp_value, tb);
Py_XDECREF(tmp_tb);
#else
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);
}
#endif
}
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 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
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
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 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,
0,
0,
0,
0,
__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,
__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;
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_frame = PyFrame_New(
PyThreadState_GET(), /*PyThreadState *tstate,*/
py_code, /*PyCodeObject *code,*/
__pyx_d, /*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 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_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;
}
#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
}
}
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 (PyObject_CheckBuffer(obj)) return PyObject_GetBuffer(obj, view, flags);
if (PyObject_TypeCheck(obj, __pyx_ptype_5numpy_ndarray)) return __pyx_pw_5numpy_7ndarray_1__getbuffer__(obj, view, flags);
PyErr_Format(PyExc_TypeError, "'%.200s' does not have the buffer interface", Py_TYPE(obj)->tp_name);
return -1;
}
static void __Pyx_ReleaseBuffer(Py_buffer *view) {
PyObject *obj = view->obj;
if (!obj) return;
if (PyObject_CheckBuffer(obj)) {
PyBuffer_Release(view);
return;
}
if (PyObject_TypeCheck(obj, __pyx_ptype_5numpy_ndarray)) { __pyx_pw_5numpy_7ndarray_3__releasebuffer__(obj, view); return; }
Py_DECREF(obj);
view->obj = NULL;
}
#endif
#define __PYX_VERIFY_RETURN_INT(target_type, func_type, func_value) \
{ \
func_type value = func_value; \
if (sizeof(target_type) < sizeof(func_type)) { \
if (unlikely(value != (func_type) (target_type) value)) { \
func_type zero = 0; \
if (is_unsigned && unlikely(value < zero)) \
goto raise_neg_overflow; \
else \
goto raise_overflow; \
} \
} \
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(x))
} else {
long val = PyInt_AS_LONG(x);
if (is_unsigned && unlikely(val < 0)) {
goto raise_neg_overflow;
}
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
switch (Py_SIZE(x)) {
case 0: return 0;
case 1: __PYX_VERIFY_RETURN_INT(Py_intptr_t, digit, ((PyLongObject*)x)->ob_digit[0]);
}
#endif
#endif
#if CYTHON_COMPILING_IN_CPYTHON
if (unlikely(Py_SIZE(x) < 0)) {
goto raise_neg_overflow;
}
#else
{
int result = PyObject_RichCompareBool(x, Py_False, Py_LT);
if (unlikely(result < 0))
return (Py_intptr_t) -1;
if (unlikely(result == 1))
goto raise_neg_overflow;
}
#endif
if (sizeof(Py_intptr_t) <= sizeof(unsigned long)) {
__PYX_VERIFY_RETURN_INT(Py_intptr_t, unsigned long, PyLong_AsUnsignedLong(x))
} else if (sizeof(Py_intptr_t) <= sizeof(unsigned PY_LONG_LONG)) {
__PYX_VERIFY_RETURN_INT(Py_intptr_t, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x))
}
} else {
#if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3
#if CYTHON_USE_PYLONG_INTERNALS
switch (Py_SIZE(x)) {
case 0: return 0;
case 1: __PYX_VERIFY_RETURN_INT(Py_intptr_t, digit, +(((PyLongObject*)x)->ob_digit[0]));
case -1: __PYX_VERIFY_RETURN_INT(Py_intptr_t, sdigit, -(sdigit) ((PyLongObject*)x)->ob_digit[0]);
}
#endif
#endif
if (sizeof(Py_intptr_t) <= sizeof(long)) {
__PYX_VERIFY_RETURN_INT(Py_intptr_t, long, PyLong_AsLong(x))
} else if (sizeof(Py_intptr_t) <= sizeof(PY_LONG_LONG)) {
__PYX_VERIFY_RETURN_INT(Py_intptr_t, PY_LONG_LONG, PyLong_AsLongLong(x))
}
}
{
#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;
}
raise_overflow:
PyErr_SetString(PyExc_OverflowError,
"value too large to convert to Py_intptr_t");
return (Py_intptr_t) -1;
raise_neg_overflow:
PyErr_SetString(PyExc_OverflowError,
"can't convert negative value to Py_intptr_t");
return (Py_intptr_t) -1;
}
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 PY_LONG_LONG)) {
return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value);
}
} else {
if (sizeof(int) <= sizeof(long)) {
return PyInt_FromLong((long) value);
} else if (sizeof(int) <= sizeof(PY_LONG_LONG)) {
return PyLong_FromLongLong((PY_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);
}
}
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(x))
} else {
long val = PyInt_AS_LONG(x);
if (is_unsigned && unlikely(val < 0)) {
goto raise_neg_overflow;
}
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
switch (Py_SIZE(x)) {
case 0: return 0;
case 1: __PYX_VERIFY_RETURN_INT(npy_uint32, digit, ((PyLongObject*)x)->ob_digit[0]);
}
#endif
#endif
#if CYTHON_COMPILING_IN_CPYTHON
if (unlikely(Py_SIZE(x) < 0)) {
goto raise_neg_overflow;
}
#else
{
int result = PyObject_RichCompareBool(x, Py_False, Py_LT);
if (unlikely(result < 0))
return (npy_uint32) -1;
if (unlikely(result == 1))
goto raise_neg_overflow;
}
#endif
if (sizeof(npy_uint32) <= sizeof(unsigned long)) {
__PYX_VERIFY_RETURN_INT(npy_uint32, unsigned long, PyLong_AsUnsignedLong(x))
} else if (sizeof(npy_uint32) <= sizeof(unsigned PY_LONG_LONG)) {
__PYX_VERIFY_RETURN_INT(npy_uint32, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x))
}
} else {
#if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3
#if CYTHON_USE_PYLONG_INTERNALS
switch (Py_SIZE(x)) {
case 0: return 0;
case 1: __PYX_VERIFY_RETURN_INT(npy_uint32, digit, +(((PyLongObject*)x)->ob_digit[0]));
case -1: __PYX_VERIFY_RETURN_INT(npy_uint32, sdigit, -(sdigit) ((PyLongObject*)x)->ob_digit[0]);
}
#endif
#endif
if (sizeof(npy_uint32) <= sizeof(long)) {
__PYX_VERIFY_RETURN_INT(npy_uint32, long, PyLong_AsLong(x))
} else if (sizeof(npy_uint32) <= sizeof(PY_LONG_LONG)) {
__PYX_VERIFY_RETURN_INT(npy_uint32, PY_LONG_LONG, PyLong_AsLongLong(x))
}
}
{
#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;
}
raise_overflow:
PyErr_SetString(PyExc_OverflowError,
"value too large to convert to npy_uint32");
return (npy_uint32) -1;
raise_neg_overflow:
PyErr_SetString(PyExc_OverflowError,
"can't convert negative value to npy_uint32");
return (npy_uint32) -1;
}
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 PY_LONG_LONG)) {
return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value);
}
} else {
if (sizeof(long) <= sizeof(long)) {
return PyInt_FromLong((long) value);
} else if (sizeof(long) <= sizeof(PY_LONG_LONG)) {
return PyLong_FromLongLong((PY_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_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 PY_LONG_LONG)) {
return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value);
}
} else {
if (sizeof(Py_intptr_t) <= sizeof(long)) {
return PyInt_FromLong((long) value);
} else if (sizeof(Py_intptr_t) <= sizeof(PY_LONG_LONG)) {
return PyLong_FromLongLong((PY_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);
}
}
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 PY_LONG_LONG)) {
return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value);
}
} else {
if (sizeof(npy_int32) <= sizeof(long)) {
return PyInt_FromLong((long) value);
} else if (sizeof(npy_int32) <= sizeof(PY_LONG_LONG)) {
return PyLong_FromLongLong((PY_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);
}
}
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(x))
} else {
long val = PyInt_AS_LONG(x);
if (is_unsigned && unlikely(val < 0)) {
goto raise_neg_overflow;
}
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
switch (Py_SIZE(x)) {
case 0: return 0;
case 1: __PYX_VERIFY_RETURN_INT(npy_int32, digit, ((PyLongObject*)x)->ob_digit[0]);
}
#endif
#endif
#if CYTHON_COMPILING_IN_CPYTHON
if (unlikely(Py_SIZE(x) < 0)) {
goto raise_neg_overflow;
}
#else
{
int result = PyObject_RichCompareBool(x, Py_False, Py_LT);
if (unlikely(result < 0))
return (npy_int32) -1;
if (unlikely(result == 1))
goto raise_neg_overflow;
}
#endif
if (sizeof(npy_int32) <= sizeof(unsigned long)) {
__PYX_VERIFY_RETURN_INT(npy_int32, unsigned long, PyLong_AsUnsignedLong(x))
} else if (sizeof(npy_int32) <= sizeof(unsigned PY_LONG_LONG)) {
__PYX_VERIFY_RETURN_INT(npy_int32, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x))
}
} else {
#if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3
#if CYTHON_USE_PYLONG_INTERNALS
switch (Py_SIZE(x)) {
case 0: return 0;
case 1: __PYX_VERIFY_RETURN_INT(npy_int32, digit, +(((PyLongObject*)x)->ob_digit[0]));
case -1: __PYX_VERIFY_RETURN_INT(npy_int32, sdigit, -(sdigit) ((PyLongObject*)x)->ob_digit[0]);
}
#endif
#endif
if (sizeof(npy_int32) <= sizeof(long)) {
__PYX_VERIFY_RETURN_INT(npy_int32, long, PyLong_AsLong(x))
} else if (sizeof(npy_int32) <= sizeof(PY_LONG_LONG)) {
__PYX_VERIFY_RETURN_INT(npy_int32, PY_LONG_LONG, PyLong_AsLongLong(x))
}
}
{
#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;
}
raise_overflow:
PyErr_SetString(PyExc_OverflowError,
"value too large to convert to npy_int32");
return (npy_int32) -1;
raise_neg_overflow:
PyErr_SetString(PyExc_OverflowError,
"can't convert negative value to npy_int32");
return (npy_int32) -1;
}
#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
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(x))
} else {
long val = PyInt_AS_LONG(x);
if (is_unsigned && unlikely(val < 0)) {
goto raise_neg_overflow;
}
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
switch (Py_SIZE(x)) {
case 0: return 0;
case 1: __PYX_VERIFY_RETURN_INT(int, digit, ((PyLongObject*)x)->ob_digit[0]);
}
#endif
#endif
#if CYTHON_COMPILING_IN_CPYTHON
if (unlikely(Py_SIZE(x) < 0)) {
goto raise_neg_overflow;
}
#else
{
int result = PyObject_RichCompareBool(x, Py_False, Py_LT);
if (unlikely(result < 0))
return (int) -1;
if (unlikely(result == 1))
goto raise_neg_overflow;
}
#endif
if (sizeof(int) <= sizeof(unsigned long)) {
__PYX_VERIFY_RETURN_INT(int, unsigned long, PyLong_AsUnsignedLong(x))
} else if (sizeof(int) <= sizeof(unsigned PY_LONG_LONG)) {
__PYX_VERIFY_RETURN_INT(int, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x))
}
} else {
#if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3
#if CYTHON_USE_PYLONG_INTERNALS
switch (Py_SIZE(x)) {
case 0: return 0;
case 1: __PYX_VERIFY_RETURN_INT(int, digit, +(((PyLongObject*)x)->ob_digit[0]));
case -1: __PYX_VERIFY_RETURN_INT(int, sdigit, -(sdigit) ((PyLongObject*)x)->ob_digit[0]);
}
#endif
#endif
if (sizeof(int) <= sizeof(long)) {
__PYX_VERIFY_RETURN_INT(int, long, PyLong_AsLong(x))
} else if (sizeof(int) <= sizeof(PY_LONG_LONG)) {
__PYX_VERIFY_RETURN_INT(int, PY_LONG_LONG, PyLong_AsLongLong(x))
}
}
{
#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;
}
raise_overflow:
PyErr_SetString(PyExc_OverflowError,
"value too large to convert to int");
return (int) -1;
raise_neg_overflow:
PyErr_SetString(PyExc_OverflowError,
"can't convert negative value to int");
return (int) -1;
}
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(x))
} else {
long val = PyInt_AS_LONG(x);
if (is_unsigned && unlikely(val < 0)) {
goto raise_neg_overflow;
}
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
switch (Py_SIZE(x)) {
case 0: return 0;
case 1: __PYX_VERIFY_RETURN_INT(long, digit, ((PyLongObject*)x)->ob_digit[0]);
}
#endif
#endif
#if CYTHON_COMPILING_IN_CPYTHON
if (unlikely(Py_SIZE(x) < 0)) {
goto raise_neg_overflow;
}
#else
{
int result = PyObject_RichCompareBool(x, Py_False, Py_LT);
if (unlikely(result < 0))
return (long) -1;
if (unlikely(result == 1))
goto raise_neg_overflow;
}
#endif
if (sizeof(long) <= sizeof(unsigned long)) {
__PYX_VERIFY_RETURN_INT(long, unsigned long, PyLong_AsUnsignedLong(x))
} else if (sizeof(long) <= sizeof(unsigned PY_LONG_LONG)) {
__PYX_VERIFY_RETURN_INT(long, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x))
}
} else {
#if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3
#if CYTHON_USE_PYLONG_INTERNALS
switch (Py_SIZE(x)) {
case 0: return 0;
case 1: __PYX_VERIFY_RETURN_INT(long, digit, +(((PyLongObject*)x)->ob_digit[0]));
case -1: __PYX_VERIFY_RETURN_INT(long, sdigit, -(sdigit) ((PyLongObject*)x)->ob_digit[0]);
}
#endif
#endif
if (sizeof(long) <= sizeof(long)) {
__PYX_VERIFY_RETURN_INT(long, long, PyLong_AsLong(x))
} else if (sizeof(long) <= sizeof(PY_LONG_LONG)) {
__PYX_VERIFY_RETURN_INT(long, PY_LONG_LONG, PyLong_AsLongLong(x))
}
}
{
#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;
}
raise_overflow:
PyErr_SetString(PyExc_OverflowError,
"value too large to convert to long");
return (long) -1;
raise_neg_overflow:
PyErr_SetString(PyExc_OverflowError,
"can't convert negative value to long");
return (long) -1;
}
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);
return PyErr_WarnEx(NULL, message, 1);
}
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 (PyErr_WarnEx(NULL, warning, 0) < 0) goto bad;
}
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
#ifndef __PYX_HAVE_RT_ImportFunction
#define __PYX_HAVE_RT_ImportFunction
static int __Pyx_ImportFunction(PyObject *module, const char *funcname, void (**f)(void), const char *sig) {
PyObject *d = 0;
PyObject *cobj = 0;
union {
void (*fp)(void);
void *p;
} tmp;
d = PyObject_GetAttrString(module, (char *)"__pyx_capi__");
if (!d)
goto bad;
cobj = PyDict_GetItemString(d, funcname);
if (!cobj) {
PyErr_Format(PyExc_ImportError,
"%.200s does not export expected C function %.200s",
PyModule_GetName(module), funcname);
goto bad;
}
#if PY_VERSION_HEX >= 0x02070000
if (!PyCapsule_IsValid(cobj, sig)) {
PyErr_Format(PyExc_TypeError,
"C function %.200s.%.200s has wrong signature (expected %.500s, got %.500s)",
PyModule_GetName(module), funcname, sig, PyCapsule_GetName(cobj));
goto bad;
}
tmp.p = PyCapsule_GetPointer(cobj, sig);
#else
{const char *desc, *s1, *s2;
desc = (const char *)PyCObject_GetDesc(cobj);
if (!desc)
goto bad;
s1 = desc; s2 = sig;
while (*s1 != '\0' && *s1 == *s2) { s1++; s2++; }
if (*s1 != *s2) {
PyErr_Format(PyExc_TypeError,
"C function %.200s.%.200s has wrong signature (expected %.500s, got %.500s)",
PyModule_GetName(module), funcname, sig, desc);
goto bad;
}
tmp.p = PyCObject_AsVoidPtr(cobj);}
#endif
*f = tmp.fp;
if (!(*f))
goto bad;
Py_DECREF(d);
return 0;
bad:
Py_XDECREF(d);
return -1;
}
#endif
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
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
*length = PyBytes_GET_SIZE(defenc);
return defenc_c;
#else
if (__Pyx_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
return PyUnicode_AsUTF8AndSize(o, length);
#endif
#endif
} else
#endif
#if !CYTHON_COMPILING_IN_PYPY
if (PyByteArray_Check(o)) {
*length = PyByteArray_GET_SIZE(o);
return PyByteArray_AS_STRING(o);
} else
#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;
}
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
return PyLong_AsSsize_t(b);
}
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) {
return PyInt_FromSize_t(ival);
}
#endif /* Py_PYTHON_H */
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