127 lines
3.8 KiB
Cython
127 lines
3.8 KiB
Cython
# encoding: utf-8
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# cython: cdivision=True
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# cython: boundscheck=False
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# cython: wraparound=False
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#
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# Author: Peter Prettenhofer <peter.prettenhofer@gmail.com>
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#
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# License: BSD Style.
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import numpy as np
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cimport numpy as np
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cimport cython
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np.import_array()
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cdef class WeightVector(object):
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"""Dense vector represented by a scalar and a numpy array.
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The class provides methods to ``add`` a sparse vector
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and scale the vector.
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Representing a vector explicitly as a scalar times a
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vector allows for efficient scaling operations.
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Attributes
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----------
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w : ndarray, dtype=np.float64, order='C'
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The numpy array which backs the weight vector.
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w_data_ptr : np.float64*
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A pointer to the data of the numpy array.
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wscale : double
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The scale of the vector.
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n_features : int
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The number of features (= dimensionality of ``w``).
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sq_norm : double
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The squared norm of ``w``.
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"""
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def __cinit__(self, np.ndarray[DOUBLE, ndim=1, mode='c'] w):
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self.w = w
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self.w_data_ptr = <DOUBLE *>w.data
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self.wscale = 1.0
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self.n_features = w.shape[0]
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self.sq_norm = np.dot(w, w)
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cdef void add(self, DOUBLE *x_data_ptr, INTEGER *x_ind_ptr,
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int xnnz, double c):
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"""Scales example x by constant c and adds it to the weight vector.
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This operation updates ``sq_norm``.
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Parameters
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----------
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x_data_ptr : double*
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The array which holds the feature values of ``x``.
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x_ind_ptr : np.int32*
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The array which holds the feature indices of ``x``.
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xnnz : int
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The number of non-zero features of ``x``.
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c : double
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The scaling constant for the example.
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"""
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cdef int j
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cdef int idx
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cdef double val
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cdef double innerprod = 0.0
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cdef double xsqnorm = 0.0
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# the next two lines save a factor of 2!
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cdef double wscale = self.wscale
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cdef DOUBLE* w_data_ptr = self.w_data_ptr
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for j in range(xnnz):
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idx = x_ind_ptr[j]
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val = x_data_ptr[j]
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innerprod += (w_data_ptr[idx] * val)
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xsqnorm += (val * val)
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w_data_ptr[idx] += val * (c / wscale)
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self.sq_norm += (xsqnorm * c * c) + (2.0 * innerprod * wscale * c)
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cdef double dot(self, DOUBLE *x_data_ptr, INTEGER *x_ind_ptr, int xnnz):
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"""Computes the dot product of a sample x and the weight vector.
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Parameters
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----------
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x_data_ptr : double*
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The array which holds the feature values of ``x``.
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x_ind_ptr : np.int32*
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The array which holds the feature indices of ``x``.
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xnnz : int
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The number of non-zero features of ``x``.
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Returns
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-------
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innerprod : double
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The inner product of ``x`` and ``w``.
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"""
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cdef int j
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cdef int idx
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cdef double innerprod = 0.0
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cdef DOUBLE* w_data_ptr = self.w_data_ptr
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for j in range(xnnz):
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idx = x_ind_ptr[j]
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innerprod += w_data_ptr[idx] * x_data_ptr[j]
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innerprod *= self.wscale
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return innerprod
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cdef void scale(self, double c):
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"""Scales the weight vector by a constant ``c``.
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It updates ``wscale`` and ``sq_norm``. If ``wscale`` gets too
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small we call ``reset_swcale``."""
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self.wscale *= c
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self.sq_norm *= (c * c)
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if self.wscale < 1e-9:
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self.reset_wscale()
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cdef void reset_wscale(self):
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"""Scales each coef of ``w`` by ``wscale`` and resets it to 1. """
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self.w *= self.wscale
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self.wscale = 1.0
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cdef double norm(self):
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"""The L2 norm of the weight vector. """
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return sqrt(self.sq_norm)
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