diff --git a/.gitattributes b/.gitattributes index 8258b95aada50..854c33c948d14 100644 --- a/.gitattributes +++ b/.gitattributes @@ -1,5 +1,4 @@ /sklearn/__check_build/_check_build.c -diff -/sklearn/_hmmc.c -diff /sklearn/_isotonic.c -diff /sklearn/cluster/_dbscan_inner.cpp -diff /sklearn/cluster/_hierarchical.cpp -diff diff --git a/doc/modules/hmm.rst b/doc/modules/hmm.rst deleted file mode 100644 index 609f567b0b3a8..0000000000000 --- a/doc/modules/hmm.rst +++ /dev/null @@ -1,120 +0,0 @@ -.. _hmm: - -==================== -Hidden Markov Models -==================== - -.. currentmodule:: sklearn.hmm - -.. warning:: - - The `sklearn.hmm` module has now been deprecated due to it no longer matching - the scope and the API of the project. It is scheduled for removal in the - 0.17 release of the project. - - This module has been moved to a seperate repository: - https://github.com/hmmlearn/hmmlearn - - -`sklearn.hmm` implements the Hidden Markov Models (HMMs). -The HMM is a generative probabilistic model, in which a sequence of observable -:math:`\mathbf{X}` variable is generated by a sequence of internal hidden -state :math:`\mathbf{Z}`. The hidden states can not be observed directly. -The transitions between hidden states are assumed to have the form of a -(first-order) Markov chain. They can be specified by the start probability -vector :math:`\boldsymbol{\Pi}` and a transition probability matrix -:math:`\mathbf{A}`. -The emission probability of an observable can be any distribution with -parameters :math:`\boldsymbol{{\Theta}_i}` -conditioned on the current hidden state (e.g. multinomial, Gaussian). -The HMM is completely determined by -:math:`\boldsymbol{\Pi, \mathbf{A}}` and :math:`\boldsymbol{{\Theta}_i}`. - -There are three fundamental problems for HMMs: - -* Given the model parameters and observed data, estimate the optimal - sequence of hidden states. - -* Given the model parameters and observed data, calculate the likelihood - of the data. - -* Given just the observed data, estimate the model parameters. - - -The first and the second problem can be solved by the dynamic programming -algorithms known as -the Viterbi algorithm and the Forward-Backward algorithm, respectively. -The last one can be solved by an iterative Expectation-Maximization (EM) -algorithm, known as the Baum-Welch algorithm. - -See the ref listed below for further detailed information. - -.. topic:: References: - - [Rabiner89] `A tutorial on hidden Markov models and selected applications in speech recognition `_ - Lawrence, R. Rabiner, 1989 - - -Using HMM -========= - -Classes in this module include :class:`MultinomialHMM`, :class:`GaussianHMM`, -and :class:`GMMHMM`. They implement HMM with emission probabilities -determined by multimomial distributions, Gaussian distributions -and mixtures of Gaussian distributions. - - -Building HMM and generating samples ------------------------------------- - -You can build an HMM instance by passing the parameters described above to the -constructor. Then, you can generate samples from the HMM by calling `sample`.:: - - >>> import numpy as np - >>> from sklearn import hmm - - >>> startprob = np.array([0.6, 0.3, 0.1]) - >>> transmat = np.array([[0.7, 0.2, 0.1], [0.3, 0.5, 0.2], [0.3, 0.3, 0.4]]) - >>> means = np.array([[0.0, 0.0], [3.0, -3.0], [5.0, 10.0]]) - >>> covars = np.tile(np.identity(2), (3, 1, 1)) - >>> model = hmm.GaussianHMM(3, "full", startprob, transmat) - >>> model.means_ = means - >>> model.covars_ = covars - >>> X, Z = model.sample(100) - - -Training HMM parameters and inferring the hidden states -------------------------------------------------------- - -You can train an HMM by calling the `fit` method. The input is "the list" of -the sequence of observed value. Note, since the EM algorithm is a gradient-based -optimization method, it will generally get stuck in local optima. You should try -to run `fit` with various initializations and select the highest scored model. -The score of the model can be calculated by the `score` method. -The inferred optimal hidden states can be obtained by calling `predict` method. -The `predict` method can be specified with decoder algorithm. -Currently the Viterbi algorithm (`viterbi`), and maximum a posteriori -estimation (`map`) are supported. -This time, the input is a single sequence of observed values. Note, the states -in model2 will have a different order than those in the generating model.:: - - >>> model2 = hmm.GaussianHMM(3, "full") - >>> model2.fit([X]) # doctest: +ELLIPSIS, +NORMALIZE_WHITESPACE - GaussianHMM(algorithm='viterbi',... - >>> Z2 = model2.predict(X) - - -.. topic:: Examples: - - * :ref:`example_applications_plot_hmm_stock_analysis.py` - - -Implementing HMMs with custom emission probabilities ----------------------------------------------------- - -If you want to implement other emission probability (e.g. Poisson), you have to -implement a new HMM class by inheriting the :class:`_BaseHMM` and overriding -the methods `__init__`, `_compute_log_likelihood`, -`_set` and `_get` for additional parameters, -`_initialize_sufficient_statistics`, `_accumulate_sufficient_statistics` and -`_do_mstep`. diff --git a/sklearn/__init__.py b/sklearn/__init__.py index 634129f8082d0..78392bf0fe919 100644 --- a/sklearn/__init__.py +++ b/sklearn/__init__.py @@ -60,7 +60,7 @@ __all__ = ['calibration', 'cluster', 'covariance', 'cross_decomposition', 'cross_validation', 'datasets', 'decomposition', 'dummy', 'ensemble', 'externals', 'feature_extraction', - 'feature_selection', 'gaussian_process', 'grid_search', 'hmm', + 'feature_selection', 'gaussian_process', 'grid_search', 'isotonic', 'kernel_approximation', 'kernel_ridge', 'lda', 'learning_curve', 'linear_model', 'manifold', 'metrics', 'mixture', 'multiclass', diff --git a/sklearn/_hmmc.c b/sklearn/_hmmc.c deleted file mode 100644 index 8e3ccd7ea24d3..0000000000000 --- a/sklearn/_hmmc.c +++ /dev/null @@ -1,20659 +0,0 @@ -/* Generated by Cython 0.19.1 on Sun Jul 28 01:04:40 2013 */ - -#define PY_SSIZE_T_CLEAN -#ifndef CYTHON_USE_PYLONG_INTERNALS -#ifdef PYLONG_BITS_IN_DIGIT -#define CYTHON_USE_PYLONG_INTERNALS 0 -#else -#include "pyconfig.h" -#ifdef PYLONG_BITS_IN_DIGIT -#define CYTHON_USE_PYLONG_INTERNALS 1 -#else -#define CYTHON_USE_PYLONG_INTERNALS 0 -#endif -#endif -#endif -#include "Python.h" -#ifndef Py_PYTHON_H - #error Python headers needed to compile C extensions, please install development version of Python. -#elif PY_VERSION_HEX < 0x02040000 - #error Cython requires Python 2.4+. -#else -#include /* For offsetof */ -#ifndef offsetof -#define offsetof(type, member) ( (size_t) & ((type*)0) -> member ) -#endif -#if !defined(WIN32) && !defined(MS_WINDOWS) - #ifndef __stdcall - #define __stdcall - #endif - #ifndef __cdecl - #define __cdecl - #endif - #ifndef __fastcall - #define __fastcall - #endif -#endif -#ifndef DL_IMPORT - #define DL_IMPORT(t) t -#endif -#ifndef DL_EXPORT - #define DL_EXPORT(t) t -#endif -#ifndef PY_LONG_LONG - #define PY_LONG_LONG LONG_LONG -#endif -#ifndef Py_HUGE_VAL - #define Py_HUGE_VAL HUGE_VAL -#endif -#ifdef PYPY_VERSION -#define CYTHON_COMPILING_IN_PYPY 1 -#define CYTHON_COMPILING_IN_CPYTHON 0 -#else -#define CYTHON_COMPILING_IN_PYPY 0 -#define CYTHON_COMPILING_IN_CPYTHON 1 -#endif -#if PY_VERSION_HEX < 0x02050000 - typedef int Py_ssize_t; - #define PY_SSIZE_T_MAX INT_MAX - #define PY_SSIZE_T_MIN INT_MIN - #define PY_FORMAT_SIZE_T "" - #define CYTHON_FORMAT_SSIZE_T "" - #define PyInt_FromSsize_t(z) PyInt_FromLong(z) - #define PyInt_AsSsize_t(o) __Pyx_PyInt_AsInt(o) - #define PyNumber_Index(o) ((PyNumber_Check(o) && !PyFloat_Check(o)) ? 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* - * if abs_py_ssize_t(c_stride) <= abs_py_ssize_t(f_stride): - */ - goto __pyx_L7_break; - goto __pyx_L8; - } - __pyx_L8:; - } - __pyx_L7_break:; - - /* "View.MemoryView":1092 - * break - * - * if abs_py_ssize_t(c_stride) <= abs_py_ssize_t(f_stride): # <<<<<<<<<<<<<< - * return 'C' - * else: - */ - __pyx_t_2 = ((abs_py_ssize_t(__pyx_v_c_stride) <= abs_py_ssize_t(__pyx_v_f_stride)) != 0); - if (__pyx_t_2) { - - /* "View.MemoryView":1093 - * - * if abs_py_ssize_t(c_stride) <= abs_py_ssize_t(f_stride): - * return 'C' # <<<<<<<<<<<<<< - * else: - * return 'F' - */ - __pyx_r = 'C'; - goto __pyx_L0; - goto __pyx_L9; - } - /*else*/ { - - /* "View.MemoryView":1095 - * return 'C' - * else: - * return 'F' # <<<<<<<<<<<<<< - * - * @cython.cdivision(True) - */ - __pyx_r = 'F'; - goto __pyx_L0; - } - __pyx_L9:; - - __pyx_r = 0; - __pyx_L0:; - return __pyx_r; -} - -/* "View.MemoryView":1098 - * - * @cython.cdivision(True) - * cdef void _copy_strided_to_strided(char *src_data, Py_ssize_t *src_strides, # <<<<<<<<<<<<<< - * char *dst_data, Py_ssize_t *dst_strides, - * Py_ssize_t *src_shape, Py_ssize_t *dst_shape, - */ - -static void _copy_strided_to_strided(char *__pyx_v_src_data, Py_ssize_t *__pyx_v_src_strides, char *__pyx_v_dst_data, Py_ssize_t *__pyx_v_dst_strides, Py_ssize_t *__pyx_v_src_shape, Py_ssize_t *__pyx_v_dst_shape, int __pyx_v_ndim, size_t __pyx_v_itemsize) { - CYTHON_UNUSED Py_ssize_t __pyx_v_i; - CYTHON_UNUSED Py_ssize_t __pyx_v_src_extent; - Py_ssize_t __pyx_v_dst_extent; - Py_ssize_t __pyx_v_src_stride; - Py_ssize_t __pyx_v_dst_stride; - int __pyx_t_1; - int __pyx_t_2; - int __pyx_t_3; - int __pyx_t_4; - Py_ssize_t __pyx_t_5; - Py_ssize_t __pyx_t_6; - - /* "View.MemoryView":1105 - * - * cdef Py_ssize_t i - * cdef Py_ssize_t src_extent = src_shape[0] # <<<<<<<<<<<<<< - * cdef Py_ssize_t dst_extent = dst_shape[0] - * cdef Py_ssize_t src_stride = src_strides[0] - */ - __pyx_v_src_extent = (__pyx_v_src_shape[0]); - - /* "View.MemoryView":1106 - * cdef Py_ssize_t i - * cdef Py_ssize_t src_extent = src_shape[0] - * cdef Py_ssize_t dst_extent = dst_shape[0] # <<<<<<<<<<<<<< - * cdef Py_ssize_t src_stride = src_strides[0] - * cdef Py_ssize_t dst_stride = dst_strides[0] - */ - __pyx_v_dst_extent = (__pyx_v_dst_shape[0]); - - /* "View.MemoryView":1107 - * cdef Py_ssize_t src_extent = src_shape[0] - * cdef Py_ssize_t dst_extent = dst_shape[0] - * cdef Py_ssize_t src_stride = src_strides[0] # <<<<<<<<<<<<<< - * cdef Py_ssize_t dst_stride = dst_strides[0] - * - */ - __pyx_v_src_stride = (__pyx_v_src_strides[0]); - - /* "View.MemoryView":1108 - * cdef Py_ssize_t dst_extent = dst_shape[0] - * cdef Py_ssize_t src_stride = src_strides[0] - * cdef Py_ssize_t dst_stride = dst_strides[0] # <<<<<<<<<<<<<< - * - * if ndim == 1: - */ - __pyx_v_dst_stride = (__pyx_v_dst_strides[0]); - - /* "View.MemoryView":1110 - * cdef Py_ssize_t dst_stride = dst_strides[0] - * - * if ndim == 1: # <<<<<<<<<<<<<< - * if (src_stride > 0 and dst_stride > 0 and - * src_stride == itemsize == dst_stride): - */ - __pyx_t_1 = ((__pyx_v_ndim == 1) != 0); - if (__pyx_t_1) { - - /* "View.MemoryView":1111 - * - * if ndim == 1: - * if (src_stride > 0 and dst_stride > 0 and # <<<<<<<<<<<<<< - * src_stride == itemsize == dst_stride): - * memcpy(dst_data, src_data, itemsize * dst_extent) - */ - __pyx_t_1 = ((__pyx_v_src_stride > 0) != 0); - if (__pyx_t_1) { - __pyx_t_2 = ((__pyx_v_dst_stride > 0) != 0); - if (__pyx_t_2) { - - /* "View.MemoryView":1112 - * if ndim == 1: - * if (src_stride > 0 and dst_stride > 0 and - * src_stride == itemsize == dst_stride): # <<<<<<<<<<<<<< - * memcpy(dst_data, src_data, itemsize * dst_extent) - * else: - */ - __pyx_t_3 = (((size_t)__pyx_v_src_stride) == __pyx_v_itemsize); - if (__pyx_t_3) { - __pyx_t_3 = (__pyx_v_itemsize == ((size_t)__pyx_v_dst_stride)); - } - __pyx_t_4 = (__pyx_t_3 != 0); - } else { - __pyx_t_4 = __pyx_t_2; - } - __pyx_t_2 = __pyx_t_4; - } else { - __pyx_t_2 = __pyx_t_1; - } - if (__pyx_t_2) { - - /* "View.MemoryView":1113 - * if (src_stride > 0 and dst_stride > 0 and - * src_stride == itemsize == dst_stride): - * memcpy(dst_data, src_data, itemsize * dst_extent) # <<<<<<<<<<<<<< - * else: - * for i in range(dst_extent): - */ - memcpy(__pyx_v_dst_data, __pyx_v_src_data, (__pyx_v_itemsize * __pyx_v_dst_extent)); - goto __pyx_L4; - } - /*else*/ { - - /* "View.MemoryView":1115 - * memcpy(dst_data, src_data, itemsize * dst_extent) - * else: - * for i in range(dst_extent): # <<<<<<<<<<<<<< - * memcpy(dst_data, src_data, itemsize) - * src_data += src_stride - */ - __pyx_t_5 = __pyx_v_dst_extent; - for (__pyx_t_6 = 0; __pyx_t_6 < __pyx_t_5; __pyx_t_6+=1) { - __pyx_v_i = __pyx_t_6; - - /* "View.MemoryView":1116 - * else: - * for i in range(dst_extent): - * memcpy(dst_data, src_data, itemsize) # <<<<<<<<<<<<<< - * src_data += src_stride - * dst_data += dst_stride - */ - memcpy(__pyx_v_dst_data, __pyx_v_src_data, __pyx_v_itemsize); - - /* "View.MemoryView":1117 - * for i in range(dst_extent): - * memcpy(dst_data, src_data, itemsize) - * src_data += src_stride # <<<<<<<<<<<<<< - * dst_data += dst_stride - * else: - */ - __pyx_v_src_data = (__pyx_v_src_data + __pyx_v_src_stride); - - /* "View.MemoryView":1118 - * memcpy(dst_data, src_data, itemsize) - * src_data += src_stride - * dst_data += dst_stride # <<<<<<<<<<<<<< - * else: - * for i in range(dst_extent): - */ - __pyx_v_dst_data = (__pyx_v_dst_data + __pyx_v_dst_stride); - } - } - __pyx_L4:; - goto __pyx_L3; - } - /*else*/ { - - /* "View.MemoryView":1120 - * dst_data += dst_stride - * else: - * for i in range(dst_extent): # <<<<<<<<<<<<<< - * _copy_strided_to_strided(src_data, src_strides + 1, - * dst_data, dst_strides + 1, - */ - __pyx_t_5 = __pyx_v_dst_extent; - for (__pyx_t_6 = 0; __pyx_t_6 < __pyx_t_5; __pyx_t_6+=1) { - __pyx_v_i = __pyx_t_6; - - /* "View.MemoryView":1124 - * dst_data, dst_strides + 1, - * src_shape + 1, dst_shape + 1, - * ndim - 1, itemsize) # <<<<<<<<<<<<<< - * src_data += src_stride - * dst_data += dst_stride - */ - _copy_strided_to_strided(__pyx_v_src_data, (__pyx_v_src_strides + 1), __pyx_v_dst_data, (__pyx_v_dst_strides + 1), (__pyx_v_src_shape + 1), (__pyx_v_dst_shape + 1), (__pyx_v_ndim - 1), __pyx_v_itemsize); - - /* "View.MemoryView":1125 - * src_shape + 1, dst_shape + 1, - * ndim - 1, itemsize) - * src_data += src_stride # <<<<<<<<<<<<<< - * dst_data += dst_stride - * - */ - __pyx_v_src_data = (__pyx_v_src_data + __pyx_v_src_stride); - - /* "View.MemoryView":1126 - * ndim - 1, itemsize) - * src_data += src_stride - * dst_data += dst_stride # <<<<<<<<<<<<<< - * - * cdef void copy_strided_to_strided(__Pyx_memviewslice *src, - */ - __pyx_v_dst_data = (__pyx_v_dst_data + __pyx_v_dst_stride); - } - } - __pyx_L3:; - -} - -/* "View.MemoryView":1128 - * dst_data += dst_stride - * - * cdef void copy_strided_to_strided(__Pyx_memviewslice *src, # <<<<<<<<<<<<<< - * __Pyx_memviewslice *dst, - * int ndim, size_t itemsize) nogil: - */ - -static void copy_strided_to_strided(__Pyx_memviewslice *__pyx_v_src, __Pyx_memviewslice *__pyx_v_dst, int __pyx_v_ndim, size_t __pyx_v_itemsize) { - - /* "View.MemoryView":1132 - * int ndim, size_t itemsize) nogil: - * _copy_strided_to_strided(src.data, src.strides, dst.data, dst.strides, - * src.shape, dst.shape, ndim, itemsize) # <<<<<<<<<<<<<< - * - * @cname('__pyx_memoryview_slice_get_size') - */ - _copy_strided_to_strided(__pyx_v_src->data, __pyx_v_src->strides, __pyx_v_dst->data, __pyx_v_dst->strides, __pyx_v_src->shape, __pyx_v_dst->shape, __pyx_v_ndim, __pyx_v_itemsize); - -} - -/* "View.MemoryView":1135 - * - * @cname('__pyx_memoryview_slice_get_size') - * cdef Py_ssize_t slice_get_size(__Pyx_memviewslice *src, int ndim) nogil: # <<<<<<<<<<<<<< - * "Return the size of the memory occupied by the slice in number of bytes" - * cdef int i - */ - -static Py_ssize_t __pyx_memoryview_slice_get_size(__Pyx_memviewslice *__pyx_v_src, int __pyx_v_ndim) { - int __pyx_v_i; - Py_ssize_t __pyx_v_size; - Py_ssize_t __pyx_r; - Py_ssize_t __pyx_t_1; - int __pyx_t_2; - int __pyx_t_3; - - /* "View.MemoryView":1138 - * "Return the size of the memory occupied by the slice in number of bytes" - * cdef int i - * cdef Py_ssize_t size = src.memview.view.itemsize # <<<<<<<<<<<<<< - * - * for i in range(ndim): - */ - __pyx_t_1 = __pyx_v_src->memview->view.itemsize; - __pyx_v_size = __pyx_t_1; - - /* "View.MemoryView":1140 - * cdef Py_ssize_t size = src.memview.view.itemsize - * - * for i in range(ndim): # <<<<<<<<<<<<<< - * size *= src.shape[i] - * - */ - __pyx_t_2 = __pyx_v_ndim; - for (__pyx_t_3 = 0; __pyx_t_3 < __pyx_t_2; __pyx_t_3+=1) { - __pyx_v_i = __pyx_t_3; - - /* "View.MemoryView":1141 - * - * for i in range(ndim): - * size *= src.shape[i] # <<<<<<<<<<<<<< - * - * return size - */ - __pyx_v_size = (__pyx_v_size * (__pyx_v_src->shape[__pyx_v_i])); - } - - /* "View.MemoryView":1143 - * size *= src.shape[i] - * - * return size # <<<<<<<<<<<<<< - * - * @cname('__pyx_fill_contig_strides_array') - */ - __pyx_r = __pyx_v_size; - goto __pyx_L0; - - __pyx_r = 0; - __pyx_L0:; - return __pyx_r; -} - -/* "View.MemoryView":1146 - * - * @cname('__pyx_fill_contig_strides_array') - * cdef Py_ssize_t fill_contig_strides_array( # <<<<<<<<<<<<<< - * Py_ssize_t *shape, Py_ssize_t *strides, Py_ssize_t stride, - * int ndim, char order) nogil: - */ - -static Py_ssize_t __pyx_fill_contig_strides_array(Py_ssize_t *__pyx_v_shape, Py_ssize_t *__pyx_v_strides, Py_ssize_t __pyx_v_stride, int __pyx_v_ndim, char __pyx_v_order) { - int __pyx_v_idx; - Py_ssize_t __pyx_r; - int __pyx_t_1; - int __pyx_t_2; - int __pyx_t_3; - - /* "View.MemoryView":1155 - * cdef int idx - * - * if order == 'F': # <<<<<<<<<<<<<< - * for idx in range(ndim): - * strides[idx] = stride - */ - __pyx_t_1 = ((__pyx_v_order == 'F') != 0); - if (__pyx_t_1) { - - /* "View.MemoryView":1156 - * - * if order == 'F': - * for idx in range(ndim): # <<<<<<<<<<<<<< - * strides[idx] = stride - * stride = stride * shape[idx] - */ - __pyx_t_2 = __pyx_v_ndim; - for (__pyx_t_3 = 0; __pyx_t_3 < __pyx_t_2; __pyx_t_3+=1) { - __pyx_v_idx = __pyx_t_3; - - /* "View.MemoryView":1157 - * if order == 'F': - * for idx in range(ndim): - * strides[idx] = stride # <<<<<<<<<<<<<< - * stride = stride * shape[idx] - * else: - */ - (__pyx_v_strides[__pyx_v_idx]) = __pyx_v_stride; - - /* "View.MemoryView":1158 - * for idx in range(ndim): - * strides[idx] = stride - * stride = stride * shape[idx] # <<<<<<<<<<<<<< - * else: - * for idx in range(ndim - 1, -1, -1): - */ - __pyx_v_stride = (__pyx_v_stride * (__pyx_v_shape[__pyx_v_idx])); - } - goto __pyx_L3; - } - /*else*/ { - - /* "View.MemoryView":1160 - * stride = stride * shape[idx] - * else: - * for idx in range(ndim - 1, -1, -1): # <<<<<<<<<<<<<< - * strides[idx] = stride - * stride = stride * shape[idx] - */ - for (__pyx_t_2 = (__pyx_v_ndim - 1); __pyx_t_2 > -1; __pyx_t_2-=1) { - __pyx_v_idx = __pyx_t_2; - - /* "View.MemoryView":1161 - * else: - * for idx in range(ndim - 1, -1, -1): - * strides[idx] = stride # <<<<<<<<<<<<<< - * stride = stride * shape[idx] - * - */ - (__pyx_v_strides[__pyx_v_idx]) = __pyx_v_stride; - - /* "View.MemoryView":1162 - * for idx in range(ndim - 1, -1, -1): - * strides[idx] = stride - * stride = stride * shape[idx] # <<<<<<<<<<<<<< - * - * return stride - */ - __pyx_v_stride = (__pyx_v_stride * (__pyx_v_shape[__pyx_v_idx])); - } - } - __pyx_L3:; - - /* "View.MemoryView":1164 - * stride = stride * shape[idx] - * - * return stride # <<<<<<<<<<<<<< - * - * @cname('__pyx_memoryview_copy_data_to_temp') - */ - __pyx_r = __pyx_v_stride; - goto __pyx_L0; - - __pyx_r = 0; - __pyx_L0:; - return __pyx_r; -} - -/* "View.MemoryView":1167 - * - * @cname('__pyx_memoryview_copy_data_to_temp') - * cdef void *copy_data_to_temp(__Pyx_memviewslice *src, # <<<<<<<<<<<<<< - * __Pyx_memviewslice *tmpslice, - * char order, - */ - -static void *__pyx_memoryview_copy_data_to_temp(__Pyx_memviewslice *__pyx_v_src, __Pyx_memviewslice *__pyx_v_tmpslice, char __pyx_v_order, int __pyx_v_ndim) { - int __pyx_v_i; - void *__pyx_v_result; - size_t __pyx_v_itemsize; - size_t __pyx_v_size; - void *__pyx_r; - Py_ssize_t __pyx_t_1; - int __pyx_t_2; - int __pyx_t_3; - struct __pyx_memoryview_obj *__pyx_t_4; - int __pyx_t_5; - int __pyx_lineno = 0; - const char *__pyx_filename = NULL; - int __pyx_clineno = 0; - - /* "View.MemoryView":1178 - * cdef void *result - * - * cdef size_t itemsize = src.memview.view.itemsize # <<<<<<<<<<<<<< - * cdef size_t size = slice_get_size(src, ndim) - * - */ - __pyx_t_1 = __pyx_v_src->memview->view.itemsize; - __pyx_v_itemsize = __pyx_t_1; - - /* "View.MemoryView":1179 - * - * cdef size_t itemsize = src.memview.view.itemsize - * cdef size_t size = slice_get_size(src, ndim) # <<<<<<<<<<<<<< - * - * result = malloc(size) - */ - __pyx_v_size = __pyx_memoryview_slice_get_size(__pyx_v_src, __pyx_v_ndim); - - /* "View.MemoryView":1181 - * cdef size_t size = slice_get_size(src, ndim) - * - * result = malloc(size) # <<<<<<<<<<<<<< - * if not result: - * _err(MemoryError, NULL) - */ - __pyx_v_result = malloc(__pyx_v_size); - - /* "View.MemoryView":1182 - * - * result = malloc(size) - * if not result: # <<<<<<<<<<<<<< - * _err(MemoryError, NULL) - * - */ - __pyx_t_2 = ((!(__pyx_v_result != 0)) != 0); - if (__pyx_t_2) { - - /* "View.MemoryView":1183 - * result = malloc(size) - * if not result: - * _err(MemoryError, NULL) # <<<<<<<<<<<<<< - 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__pyx_v_i = __pyx_t_3; - - /* "View.MemoryView":1251 - * - * for i in range(ndim): - * if src.shape[i] != dst.shape[i]: # <<<<<<<<<<<<<< - * if src.shape[i] == 1: - * broadcasting = True - */ - __pyx_t_2 = (((__pyx_v_src.shape[__pyx_v_i]) != (__pyx_v_dst.shape[__pyx_v_i])) != 0); - if (__pyx_t_2) { - - /* "View.MemoryView":1252 - * for i in range(ndim): - * if src.shape[i] != dst.shape[i]: - * if src.shape[i] == 1: # <<<<<<<<<<<<<< - * broadcasting = True - * src.strides[i] = 0 - */ - __pyx_t_2 = (((__pyx_v_src.shape[__pyx_v_i]) == 1) != 0); - if (__pyx_t_2) { - - /* "View.MemoryView":1253 - * if src.shape[i] != dst.shape[i]: - * if src.shape[i] == 1: - * broadcasting = True # <<<<<<<<<<<<<< - * src.strides[i] = 0 - * else: - */ - __pyx_v_broadcasting = 1; - - /* "View.MemoryView":1254 - * if src.shape[i] == 1: - * broadcasting = True - * src.strides[i] = 0 # <<<<<<<<<<<<<< - * else: - * _err_extents(i, dst.shape[i], src.shape[i]) - */ - (__pyx_v_src.strides[__pyx_v_i]) = 0; - goto __pyx_L7; - } - /*else*/ { - - /* "View.MemoryView":1256 - * src.strides[i] = 0 - * else: - * _err_extents(i, dst.shape[i], src.shape[i]) # <<<<<<<<<<<<<< - * - * if src.suboffsets[i] >= 0: - */ - __pyx_t_4 = __pyx_memoryview_err_extents(__pyx_v_i, (__pyx_v_dst.shape[__pyx_v_i]), (__pyx_v_src.shape[__pyx_v_i])); if (unlikely(__pyx_t_4 == -1)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 1256; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - } - __pyx_L7:; - goto __pyx_L6; - } - __pyx_L6:; - - /* "View.MemoryView":1258 - * _err_extents(i, dst.shape[i], src.shape[i]) - * - * if src.suboffsets[i] >= 0: # <<<<<<<<<<<<<< - * _err_dim(ValueError, "Dimension %d is not direct", i) - * - */ - __pyx_t_2 = (((__pyx_v_src.suboffsets[__pyx_v_i]) >= 0) != 0); - if (__pyx_t_2) { - - /* "View.MemoryView":1259 - * - * if src.suboffsets[i] >= 0: - * _err_dim(ValueError, "Dimension %d is not direct", i) # <<<<<<<<<<<<<< - * - * if slices_overlap(&src, &dst, ndim, itemsize): - */ - __pyx_t_4 = __pyx_memoryview_err_dim(__pyx_builtin_ValueError, __pyx_k_48, __pyx_v_i); if (unlikely(__pyx_t_4 == -1)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 1259; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - goto __pyx_L8; - } - __pyx_L8:; - } - - /* "View.MemoryView":1261 - * _err_dim(ValueError, "Dimension %d is not direct", i) - * - * if slices_overlap(&src, &dst, ndim, itemsize): # <<<<<<<<<<<<<< - * - * if not slice_is_contig(&src, order, ndim): - */ - __pyx_t_2 = (__pyx_slices_overlap((&__pyx_v_src), (&__pyx_v_dst), __pyx_v_ndim, __pyx_v_itemsize) != 0); - if (__pyx_t_2) { - - /* "View.MemoryView":1263 - * if slices_overlap(&src, &dst, ndim, itemsize): - * - * if not slice_is_contig(&src, order, ndim): # <<<<<<<<<<<<<< - * order = get_best_order(&dst, ndim) - * - */ - __pyx_t_2 = ((!(__pyx_memviewslice_is_contig((&__pyx_v_src), __pyx_v_order, __pyx_v_ndim) != 0)) != 0); - if (__pyx_t_2) { - - /* "View.MemoryView":1264 - * - * if not slice_is_contig(&src, order, ndim): - * order = get_best_order(&dst, ndim) # <<<<<<<<<<<<<< - * - * tmpdata = copy_data_to_temp(&src, &tmp, order, ndim) - */ - __pyx_v_order = __pyx_get_best_slice_order((&__pyx_v_dst), __pyx_v_ndim); - goto __pyx_L10; - } - __pyx_L10:; - - /* "View.MemoryView":1266 - * order = get_best_order(&dst, ndim) - * - * tmpdata = copy_data_to_temp(&src, &tmp, order, ndim) # <<<<<<<<<<<<<< - * src = tmp - * - */ - __pyx_t_6 = __pyx_memoryview_copy_data_to_temp((&__pyx_v_src), (&__pyx_v_tmp), __pyx_v_order, __pyx_v_ndim); if (unlikely(__pyx_t_6 == NULL)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 1266; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __pyx_v_tmpdata = __pyx_t_6; - - /* "View.MemoryView":1267 - * - * tmpdata = copy_data_to_temp(&src, &tmp, order, ndim) - * src = tmp # <<<<<<<<<<<<<< - * - * if not broadcasting: - */ - __pyx_v_src = __pyx_v_tmp; - goto __pyx_L9; - } - __pyx_L9:; - - /* "View.MemoryView":1269 - * src = tmp - * - * if not broadcasting: # <<<<<<<<<<<<<< - * - * - */ - __pyx_t_2 = ((!(__pyx_v_broadcasting != 0)) != 0); - if (__pyx_t_2) { - - /* "View.MemoryView":1272 - * - * - * if slice_is_contig(&src, 'C', ndim): # <<<<<<<<<<<<<< - * direct_copy = slice_is_contig(&dst, 'C', ndim) - * elif slice_is_contig(&src, 'F', ndim): - */ - __pyx_t_2 = (__pyx_memviewslice_is_contig((&__pyx_v_src), 'C', __pyx_v_ndim) != 0); - if (__pyx_t_2) { - - /* "View.MemoryView":1273 - * - * if slice_is_contig(&src, 'C', ndim): - * direct_copy = slice_is_contig(&dst, 'C', ndim) # <<<<<<<<<<<<<< - * elif slice_is_contig(&src, 'F', ndim): - * direct_copy = slice_is_contig(&dst, 'F', ndim) - */ - __pyx_v_direct_copy = __pyx_memviewslice_is_contig((&__pyx_v_dst), 'C', __pyx_v_ndim); - goto __pyx_L12; - } - - /* "View.MemoryView":1274 - * if slice_is_contig(&src, 'C', ndim): - * direct_copy = slice_is_contig(&dst, 'C', ndim) - * elif slice_is_contig(&src, 'F', ndim): # <<<<<<<<<<<<<< - * direct_copy = slice_is_contig(&dst, 'F', ndim) - * - */ - __pyx_t_2 = (__pyx_memviewslice_is_contig((&__pyx_v_src), 'F', __pyx_v_ndim) != 0); - if (__pyx_t_2) { - - /* "View.MemoryView":1275 - * direct_copy = slice_is_contig(&dst, 'C', ndim) - * elif slice_is_contig(&src, 'F', ndim): - * direct_copy = slice_is_contig(&dst, 'F', ndim) # <<<<<<<<<<<<<< - * - * if direct_copy: - */ - __pyx_v_direct_copy = __pyx_memviewslice_is_contig((&__pyx_v_dst), 'F', __pyx_v_ndim); - goto __pyx_L12; - } - __pyx_L12:; - - /* "View.MemoryView":1277 - * direct_copy = slice_is_contig(&dst, 'F', ndim) - * - * if direct_copy: # <<<<<<<<<<<<<< - * - * refcount_copying(&dst, dtype_is_object, ndim, False) - */ - __pyx_t_2 = (__pyx_v_direct_copy != 0); - if (__pyx_t_2) { - - /* "View.MemoryView":1279 - * if direct_copy: - * - * refcount_copying(&dst, dtype_is_object, ndim, False) # <<<<<<<<<<<<<< - * memcpy(dst.data, src.data, slice_get_size(&src, ndim)) - * refcount_copying(&dst, dtype_is_object, ndim, True) - */ - __pyx_memoryview_refcount_copying((&__pyx_v_dst), __pyx_v_dtype_is_object, __pyx_v_ndim, 0); - - /* "View.MemoryView":1280 - * - * refcount_copying(&dst, dtype_is_object, ndim, False) - * memcpy(dst.data, src.data, slice_get_size(&src, ndim)) # <<<<<<<<<<<<<< - * refcount_copying(&dst, dtype_is_object, ndim, True) - * return 0 - */ - memcpy(__pyx_v_dst.data, __pyx_v_src.data, __pyx_memoryview_slice_get_size((&__pyx_v_src), __pyx_v_ndim)); - - /* "View.MemoryView":1281 - * refcount_copying(&dst, dtype_is_object, ndim, False) - * memcpy(dst.data, src.data, slice_get_size(&src, ndim)) - * refcount_copying(&dst, dtype_is_object, ndim, True) # <<<<<<<<<<<<<< - * return 0 - * - */ - __pyx_memoryview_refcount_copying((&__pyx_v_dst), __pyx_v_dtype_is_object, __pyx_v_ndim, 1); - - /* "View.MemoryView":1282 - * memcpy(dst.data, src.data, slice_get_size(&src, ndim)) - * refcount_copying(&dst, dtype_is_object, ndim, True) - * return 0 # <<<<<<<<<<<<<< - * - * if order == 'F' == get_best_order(&dst, ndim): - */ - __pyx_r = 0; - goto __pyx_L0; - goto __pyx_L13; - } - __pyx_L13:; - goto __pyx_L11; - } - __pyx_L11:; - - /* "View.MemoryView":1284 - * return 0 - * - * if order == 'F' == get_best_order(&dst, ndim): # <<<<<<<<<<<<<< - * - * - */ - __pyx_t_2 = (__pyx_v_order == 'F'); - if (__pyx_t_2) { - __pyx_t_2 = ('F' == __pyx_get_best_slice_order((&__pyx_v_dst), __pyx_v_ndim)); - } - __pyx_t_7 = (__pyx_t_2 != 0); - if (__pyx_t_7) { - - /* "View.MemoryView":1287 - * - * - * transpose_memslice(&src) # <<<<<<<<<<<<<< - * transpose_memslice(&dst) - * - */ - __pyx_t_5 = __pyx_memslice_transpose((&__pyx_v_src)); if (unlikely(__pyx_t_5 == 0)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 1287; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - - /* "View.MemoryView":1288 - * - * transpose_memslice(&src) - * transpose_memslice(&dst) # <<<<<<<<<<<<<< - * - * refcount_copying(&dst, dtype_is_object, ndim, False) - */ - __pyx_t_5 = __pyx_memslice_transpose((&__pyx_v_dst)); if (unlikely(__pyx_t_5 == 0)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 1288; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - goto __pyx_L14; - } - __pyx_L14:; - - /* "View.MemoryView":1290 - * transpose_memslice(&dst) - * - * refcount_copying(&dst, dtype_is_object, ndim, False) # <<<<<<<<<<<<<< - * copy_strided_to_strided(&src, &dst, ndim, itemsize) - * refcount_copying(&dst, dtype_is_object, ndim, True) - */ - __pyx_memoryview_refcount_copying((&__pyx_v_dst), __pyx_v_dtype_is_object, __pyx_v_ndim, 0); - - /* "View.MemoryView":1291 - * - * refcount_copying(&dst, dtype_is_object, ndim, False) - * copy_strided_to_strided(&src, &dst, ndim, itemsize) # <<<<<<<<<<<<<< - * refcount_copying(&dst, dtype_is_object, ndim, True) - * - */ - copy_strided_to_strided((&__pyx_v_src), (&__pyx_v_dst), __pyx_v_ndim, __pyx_v_itemsize); - - /* "View.MemoryView":1292 - * refcount_copying(&dst, dtype_is_object, ndim, False) - * copy_strided_to_strided(&src, &dst, ndim, itemsize) - * refcount_copying(&dst, dtype_is_object, ndim, True) # <<<<<<<<<<<<<< - * - * free(tmpdata) - */ - __pyx_memoryview_refcount_copying((&__pyx_v_dst), __pyx_v_dtype_is_object, __pyx_v_ndim, 1); - - /* "View.MemoryView":1294 - * refcount_copying(&dst, dtype_is_object, ndim, True) - * - * free(tmpdata) # <<<<<<<<<<<<<< - * return 0 - * - */ - free(__pyx_v_tmpdata); - - /* "View.MemoryView":1295 - * - * free(tmpdata) - * return 0 # <<<<<<<<<<<<<< - * - * @cname('__pyx_memoryview_broadcast_leading') - */ - __pyx_r = 0; - goto __pyx_L0; - 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__pyx_v_ndim); - - /* "View.MemoryView":1304 - * cdef int offset = ndim_other - ndim - * - * for i in range(ndim - 1, -1, -1): # <<<<<<<<<<<<<< - * slice.shape[i + offset] = slice.shape[i] - * slice.strides[i + offset] = slice.strides[i] - */ - for (__pyx_t_1 = (__pyx_v_ndim - 1); __pyx_t_1 > -1; __pyx_t_1-=1) { - __pyx_v_i = __pyx_t_1; - - /* "View.MemoryView":1305 - * - * for i in range(ndim - 1, -1, -1): - * slice.shape[i + offset] = slice.shape[i] # <<<<<<<<<<<<<< - * slice.strides[i + offset] = slice.strides[i] - * slice.suboffsets[i + offset] = slice.suboffsets[i] - */ - (__pyx_v_slice->shape[(__pyx_v_i + __pyx_v_offset)]) = (__pyx_v_slice->shape[__pyx_v_i]); - - /* "View.MemoryView":1306 - * for i in range(ndim - 1, -1, -1): - * slice.shape[i + offset] = slice.shape[i] - * slice.strides[i + offset] = slice.strides[i] # <<<<<<<<<<<<<< - * slice.suboffsets[i + offset] = slice.suboffsets[i] - * - */ - (__pyx_v_slice->strides[(__pyx_v_i + __pyx_v_offset)]) = (__pyx_v_slice->strides[__pyx_v_i]); - - /* "View.MemoryView":1307 - * slice.shape[i + offset] = slice.shape[i] - * slice.strides[i + offset] = slice.strides[i] - * slice.suboffsets[i + offset] = slice.suboffsets[i] # <<<<<<<<<<<<<< - * - * for i in range(offset): - */ - (__pyx_v_slice->suboffsets[(__pyx_v_i + __pyx_v_offset)]) = (__pyx_v_slice->suboffsets[__pyx_v_i]); - } - - /* "View.MemoryView":1309 - * slice.suboffsets[i + offset] = slice.suboffsets[i] - * - * for i in range(offset): # <<<<<<<<<<<<<< - * slice.shape[i] = 1 - * slice.strides[i] = slice.strides[0] - */ - __pyx_t_1 = __pyx_v_offset; - for (__pyx_t_2 = 0; __pyx_t_2 < __pyx_t_1; __pyx_t_2+=1) { - __pyx_v_i = __pyx_t_2; - - /* "View.MemoryView":1310 - * - * for i in range(offset): - * slice.shape[i] = 1 # <<<<<<<<<<<<<< - * slice.strides[i] = slice.strides[0] - * slice.suboffsets[i] = -1 - */ - (__pyx_v_slice->shape[__pyx_v_i]) = 1; - - /* "View.MemoryView":1311 - * for i in range(offset): - * slice.shape[i] = 1 - * slice.strides[i] = slice.strides[0] # <<<<<<<<<<<<<< - 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"'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; /* Consume from buffer string */ - while (1) { - if (field == &ctx->root) { - ctx->head = NULL; - if (ctx->enc_count != 0) { - __Pyx_BufFmt_RaiseExpected(ctx); - return -1; - } - break; /* breaks both loops as ctx->enc_count == 0 */ - } - ctx->head->field = ++field; - if (field->type == NULL) { - --ctx->head; - field = ctx->head->field; - continue; - } else if (field->type->typegroup == 'S') { - size_t parent_offset = ctx->head->parent_offset + field->offset; - if (field->type->fields->type == NULL) continue; /* empty struct */ - field = field->type->fields; - ++ctx->head; - ctx->head->field = field; - ctx->head->parent_offset = parent_offset; - break; - } else { - break; - } - } - } while (ctx->enc_count); - ctx->enc_type = 0; - ctx->is_complex = 0; - return 0; -} -static CYTHON_INLINE PyObject * -__pyx_buffmt_parse_array(__Pyx_BufFmt_Context* ctx, const char** tsp) -{ - const char *ts = *tsp; - int i = 0, number; - int ndim = ctx->head->field->type->ndim; -; - ++ts; - if (ctx->new_count != 1) { - PyErr_SetString(PyExc_ValueError, - "Cannot handle repeated arrays in format string"); - return NULL; - } - if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; - while (*ts && *ts != ')') { - if (isspace(*ts)) - continue; - 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 10: - case 13: - ++ts; - break; - case '<': - if (!__Pyx_IsLittleEndian()) { - PyErr_SetString(PyExc_ValueError, "Little-endian buffer not supported on big-endian compiler"); - return NULL; - } - ctx->new_packmode = '='; - ++ts; - break; - case '>': - case '!': - if (__Pyx_IsLittleEndian()) { - PyErr_SetString(PyExc_ValueError, "Big-endian buffer not supported on little-endian compiler"); - return NULL; - } - ctx->new_packmode = '='; - ++ts; - break; - case '=': - case '@': - case '^': - ctx->new_packmode = *ts++; - break; - case 'T': /* substruct */ - { - const char* ts_after_sub; - size_t i, struct_count = ctx->new_count; - size_t struct_alignment = ctx->struct_alignment; - ctx->new_count = 1; - ++ts; - if (*ts != '{') { - PyErr_SetString(PyExc_ValueError, "Buffer acquisition: Expected '{' after 'T'"); - return NULL; - } - if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; - ctx->enc_type = 0; /* Erase processed last struct element */ - ctx->enc_count = 0; - ctx->struct_alignment = 0; - ++ts; - ts_after_sub = ts; - for (i = 0; i != struct_count; ++i) { - ts_after_sub = __Pyx_BufFmt_CheckString(ctx, ts); - if (!ts_after_sub) return NULL; - } - ts = ts_after_sub; - if (struct_alignment) ctx->struct_alignment = struct_alignment; - } - break; - case '}': /* end of substruct; either repeat or move on */ - { - size_t alignment = ctx->struct_alignment; - ++ts; - if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; - ctx->enc_type = 0; /* Erase processed last struct element */ - if (alignment && ctx->fmt_offset % alignment) { - ctx->fmt_offset += alignment - (ctx->fmt_offset % alignment); - } - } - return ts; - case 'x': - if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; - ctx->fmt_offset += ctx->new_count; - ctx->new_count = 1; - ctx->enc_count = 0; - ctx->enc_type = 0; - ctx->enc_packmode = ctx->new_packmode; - ++ts; - break; - case 'Z': - got_Z = 1; - ++ts; - if (*ts != 'f' && *ts != 'd' && *ts != 'g') { - __Pyx_BufFmt_RaiseUnexpectedChar('Z'); - return NULL; - } /* fall through */ - 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 's': case 'p': - if (ctx->enc_type == *ts && got_Z == ctx->is_complex && - ctx->enc_packmode == ctx->new_packmode) { - ctx->enc_count += ctx->new_count; - } else { - if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; - ctx->enc_count = ctx->new_count; - ctx->enc_packmode = ctx->new_packmode; - ctx->enc_type = *ts; - ctx->is_complex = got_Z; - } - ++ts; - ctx->new_count = 1; - got_Z = 0; - break; - case ':': - ++ts; - while(*ts != ':') ++ts; - ++ts; - break; - case '(': - if (!__pyx_buffmt_parse_array(ctx, &ts)) return NULL; - break; - default: - { - int number = __Pyx_BufFmt_ExpectNumber(&ts); - if (number == -1) return NULL; - ctx->new_count = (size_t)number; - } - } - } -} -static CYTHON_INLINE void __Pyx_ZeroBuffer(Py_buffer* buf) { - buf->buf = NULL; - buf->obj = NULL; - buf->strides = __Pyx_zeros; - buf->shape = __Pyx_zeros; - buf->suboffsets = __Pyx_minusones; -} -static CYTHON_INLINE int __Pyx_GetBufferAndValidate( - Py_buffer* buf, PyObject* obj, __Pyx_TypeInfo* dtype, int flags, - int nd, int cast, __Pyx_BufFmt_StackElem* stack) -{ - if (obj == Py_None || obj == NULL) { - __Pyx_ZeroBuffer(buf); - return 0; - } - buf->buf = NULL; - if (__Pyx_GetBuffer(obj, buf, flags) == -1) goto fail; - if (buf->ndim != nd) { - PyErr_Format(PyExc_ValueError, - "Buffer has wrong number of dimensions (expected %d, got %d)", - nd, buf->ndim); - goto fail; - } - if (!cast) { - __Pyx_BufFmt_Context ctx; - __Pyx_BufFmt_Init(&ctx, stack, dtype); - if (!__Pyx_BufFmt_CheckString(&ctx, buf->format)) goto fail; - } - if ((unsigned)buf->itemsize != dtype->size) { - PyErr_Format(PyExc_ValueError, - "Item size of buffer (%" CYTHON_FORMAT_SSIZE_T "d byte%s) does not match size of '%s' (%" CYTHON_FORMAT_SSIZE_T "d byte%s)", - buf->itemsize, (buf->itemsize > 1) ? "s" : "", - dtype->name, (Py_ssize_t)dtype->size, (dtype->size > 1) ? "s" : ""); - goto fail; - } - if (buf->suboffsets == NULL) buf->suboffsets = __Pyx_minusones; - return 0; -fail:; - __Pyx_ZeroBuffer(buf); - return -1; -} -static CYTHON_INLINE void __Pyx_SafeReleaseBuffer(Py_buffer* info) { - if (info->buf == NULL) return; - if (info->suboffsets == __Pyx_minusones) info->suboffsets = NULL; - __Pyx_ReleaseBuffer(info); -} - -static int -__Pyx_init_memviewslice(struct __pyx_memoryview_obj *memview, - int ndim, - __Pyx_memviewslice *memviewslice, - int memview_is_new_reference) -{ - __Pyx_RefNannyDeclarations - int i, retval=-1; - Py_buffer *buf = &memview->view; - __Pyx_RefNannySetupContext("init_memviewslice", 0); - if (!buf) { - PyErr_SetString(PyExc_ValueError, - "buf is NULL."); - goto fail; - } else if (memviewslice->memview || memviewslice->data) { - PyErr_SetString(PyExc_ValueError, - "memviewslice is already initialized!"); - goto fail; - } - if (buf->strides) { - for (i = 0; i < ndim; i++) { - memviewslice->strides[i] = buf->strides[i]; - } - } else { - Py_ssize_t stride = buf->itemsize; - for (i = ndim - 1; i >= 0; i--) { - memviewslice->strides[i] = stride; - stride *= buf->shape[i]; - } - } - for (i = 0; i < ndim; i++) { - memviewslice->shape[i] = buf->shape[i]; - if (buf->suboffsets) { - memviewslice->suboffsets[i] = buf->suboffsets[i]; - } else { - memviewslice->suboffsets[i] = -1; - } - } - memviewslice->memview = memview; - memviewslice->data = (char *)buf->buf; - if (__pyx_add_acquisition_count(memview) == 0 && !memview_is_new_reference) { - Py_INCREF(memview); - } - retval = 0; - goto no_fail; -fail: - memviewslice->memview = 0; - memviewslice->data = 0; - retval = -1; -no_fail: - __Pyx_RefNannyFinishContext(); - return retval; -} -static CYTHON_INLINE void __pyx_fatalerror(const char *fmt, ...) { - va_list vargs; - char msg[200]; - va_start(vargs, fmt); -#ifdef HAVE_STDARG_PROTOTYPES - va_start(vargs, fmt); -#else - va_start(vargs); -#endif - vsnprintf(msg, 200, fmt, vargs); - Py_FatalError(msg); - va_end(vargs); -} -static CYTHON_INLINE int -__pyx_add_acquisition_count_locked(__pyx_atomic_int *acquisition_count, - PyThread_type_lock lock) -{ - int result; - PyThread_acquire_lock(lock, 1); - result = (*acquisition_count)++; - PyThread_release_lock(lock); - return result; -} -static CYTHON_INLINE int -__pyx_sub_acquisition_count_locked(__pyx_atomic_int *acquisition_count, - PyThread_type_lock lock) -{ - int result; - PyThread_acquire_lock(lock, 1); - result = (*acquisition_count)--; - PyThread_release_lock(lock); - return result; -} -static CYTHON_INLINE void -__Pyx_INC_MEMVIEW(__Pyx_memviewslice *memslice, int have_gil, int lineno) -{ - int first_time; - struct __pyx_memoryview_obj *memview = memslice->memview; - if (!memview || (PyObject *) memview == Py_None) - return; /* allow uninitialized memoryview assignment */ - if (__pyx_get_slice_count(memview) < 0) - __pyx_fatalerror("Acquisition count is %d (line %d)", - __pyx_get_slice_count(memview), lineno); - first_time = __pyx_add_acquisition_count(memview) == 0; - if (first_time) { - if (have_gil) { - Py_INCREF((PyObject *) memview); - } else { - PyGILState_STATE _gilstate = PyGILState_Ensure(); - Py_INCREF((PyObject *) memview); - PyGILState_Release(_gilstate); - } - } -} -static CYTHON_INLINE void __Pyx_XDEC_MEMVIEW(__Pyx_memviewslice *memslice, - int have_gil, int lineno) { - int last_time; - struct __pyx_memoryview_obj *memview = memslice->memview; - if (!memview ) { - return; - } else if ((PyObject *) memview == Py_None) { - memslice->memview = NULL; - return; - } - if (__pyx_get_slice_count(memview) <= 0) - __pyx_fatalerror("Acquisition count is %d (line %d)", - __pyx_get_slice_count(memview), lineno); - last_time = __pyx_sub_acquisition_count(memview) == 1; - memslice->data = NULL; - if (last_time) { - if (have_gil) { - Py_CLEAR(memslice->memview); - } else { - PyGILState_STATE _gilstate = PyGILState_Ensure(); - Py_CLEAR(memslice->memview); - PyGILState_Release(_gilstate); - } - } else { - memslice->memview = NULL; - } -} - -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, - "%s() takes %s %" CYTHON_FORMAT_SSIZE_T "d positional argument%s (%" 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, - "%s() keywords must be strings", function_name); - goto bad; -invalid_keyword: - PyErr_Format(PyExc_TypeError, - #if PY_MAJOR_VERSION < 3 - "%s() got an unexpected keyword argument '%s'", - function_name, PyString_AsString(key)); - #else - "%s() got an unexpected keyword argument '%U'", - function_name, key); - #endif -bad: - return -1; -} - -static int __Pyx_ArgTypeTest(PyObject *obj, PyTypeObject *type, int none_allowed, - const char *name, int exact) -{ - if (!type) { - PyErr_Format(PyExc_SystemError, "Missing type object"); - return 0; - } - if (none_allowed && obj == Py_None) return 1; - else if (exact) { - if (Py_TYPE(obj) == type) return 1; - } - else { - if (PyObject_TypeCheck(obj, type)) return 1; - } - PyErr_Format(PyExc_TypeError, - "Argument '%s' has incorrect type (expected %s, got %s)", - name, type->tp_name, Py_TYPE(obj)->tp_name); - return 0; -} - -static CYTHON_INLINE PyObject *__Pyx_GetModuleGlobalName(PyObject *name) { - PyObject *result; -#if CYTHON_COMPILING_IN_CPYTHON - result = PyDict_GetItem(__pyx_d, name); - if (result) { - Py_INCREF(result); - } else { -#else - result = PyObject_GetItem(__pyx_d, name); - if (!result) { - PyErr_Clear(); -#endif - result = __Pyx_GetBuiltinName(name); - } - return result; -} - -static CYTHON_INLINE int __Pyx_TypeTest(PyObject *obj, PyTypeObject *type) { - if (unlikely(!type)) { - PyErr_Format(PyExc_SystemError, "Missing type object"); - return 0; - } - if (likely(PyObject_TypeCheck(obj, type))) - return 1; - PyErr_Format(PyExc_TypeError, "Cannot convert %.200s to %.200s", - Py_TYPE(obj)->tp_name, type->tp_name); - return 0; -} - -static void __Pyx_RaiseBufferFallbackError(void) { - PyErr_Format(PyExc_ValueError, - "Buffer acquisition failed on assignment; and then reacquiring the old buffer failed too!"); -} - -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 PyObject *__Pyx_GetItemInt_Generic(PyObject *o, PyObject* j) { - PyObject *r; - if (!j) return NULL; - r = PyObject_GetItem(o, j); - Py_DECREF(j); - return r; -} -static CYTHON_INLINE PyObject *__Pyx_GetItemInt_List_Fast(PyObject *o, Py_ssize_t i, - int wraparound, int boundscheck) { -#if CYTHON_COMPILING_IN_CPYTHON - if (wraparound & unlikely(i < 0)) i += PyList_GET_SIZE(o); - if ((!boundscheck) || likely((0 <= i) & (i < PyList_GET_SIZE(o)))) { - PyObject *r = PyList_GET_ITEM(o, i); - Py_INCREF(r); - return r; - } - return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i)); -#else - return PySequence_GetItem(o, i); -#endif -} -static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Tuple_Fast(PyObject *o, Py_ssize_t i, - int wraparound, int boundscheck) { -#if CYTHON_COMPILING_IN_CPYTHON - if (wraparound & unlikely(i < 0)) i += PyTuple_GET_SIZE(o); - if ((!boundscheck) || likely((0 <= i) & (i < PyTuple_GET_SIZE(o)))) { - PyObject *r = PyTuple_GET_ITEM(o, i); - Py_INCREF(r); - return r; - } - return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i)); -#else - return PySequence_GetItem(o, i); -#endif -} -static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Fast(PyObject *o, Py_ssize_t i, - int is_list, int wraparound, int boundscheck) { -#if CYTHON_COMPILING_IN_CPYTHON - if (is_list || PyList_CheckExact(o)) { - Py_ssize_t n = ((!wraparound) | likely(i >= 0)) ? i : i + PyList_GET_SIZE(o); - if ((!boundscheck) || (likely((n >= 0) & (n < PyList_GET_SIZE(o))))) { - PyObject *r = PyList_GET_ITEM(o, n); - Py_INCREF(r); - return r; - } - } - else if (PyTuple_CheckExact(o)) { - Py_ssize_t n = ((!wraparound) | likely(i >= 0)) ? i : i + PyTuple_GET_SIZE(o); - if ((!boundscheck) || likely((n >= 0) & (n < PyTuple_GET_SIZE(o)))) { - PyObject *r = PyTuple_GET_ITEM(o, n); - Py_INCREF(r); - return r; - } - } else { - PySequenceMethods *m = Py_TYPE(o)->tp_as_sequence; - if (likely(m && m->sq_item)) { - if (wraparound && unlikely(i < 0) && likely(m->sq_length)) { - Py_ssize_t l = m->sq_length(o); - if (likely(l >= 0)) { - i += l; - } else { - if (PyErr_ExceptionMatches(PyExc_OverflowError)) - PyErr_Clear(); - else - return NULL; - } - } - return m->sq_item(o, i); - } - } -#else - if (is_list || PySequence_Check(o)) { - return PySequence_GetItem(o, i); - } -#endif - return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i)); -} - -static CYTHON_INLINE int __Pyx_SetItemInt_Generic(PyObject *o, PyObject *j, PyObject *v) { - int r; - if (!j) return -1; - r = PyObject_SetItem(o, j, v); - Py_DECREF(j); - return r; -} -static CYTHON_INLINE int __Pyx_SetItemInt_Fast(PyObject *o, Py_ssize_t i, PyObject *v, - int is_list, int wraparound, int boundscheck) { -#if CYTHON_COMPILING_IN_CPYTHON - if (is_list || PyList_CheckExact(o)) { - Py_ssize_t n = (!wraparound) ? i : ((likely(i >= 0)) ? i : i + PyList_GET_SIZE(o)); - if ((!boundscheck) || likely((n >= 0) & (n < PyList_GET_SIZE(o)))) { - PyObject* old = PyList_GET_ITEM(o, n); - Py_INCREF(v); - PyList_SET_ITEM(o, n, v); - Py_DECREF(old); - return 1; - } - } else { - PySequenceMethods *m = Py_TYPE(o)->tp_as_sequence; - if (likely(m && m->sq_ass_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 -1; - } - } - return m->sq_ass_item(o, i, v); - } - } -#else -#if CYTHON_COMPILING_IN_PYPY - if (is_list || (PySequence_Check(o) && !PyDict_Check(o))) { -#else - if (is_list || PySequence_Check(o)) { -#endif - return PySequence_SetItem(o, i, v); - } -#endif - return __Pyx_SetItemInt_Generic(o, PyInt_FromSsize_t(i), v); -} - -#if PY_MAJOR_VERSION < 3 -static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, - CYTHON_UNUSED PyObject *cause) { - Py_XINCREF(type); - if (!value || value == Py_None) - value = NULL; - else - Py_INCREF(value); - if (!tb || tb == Py_None) - tb = NULL; - else { - Py_INCREF(tb); - if (!PyTraceBack_Check(tb)) { - PyErr_SetString(PyExc_TypeError, - "raise: arg 3 must be a traceback or None"); - goto raise_error; - } - } - #if PY_VERSION_HEX < 0x02050000 - if (PyClass_Check(type)) { - #else - if (PyType_Check(type)) { - #endif -#if CYTHON_COMPILING_IN_PYPY - if (!value) { - Py_INCREF(Py_None); - value = Py_None; - } -#endif - PyErr_NormalizeException(&type, &value, &tb); - } else { - if (value) { - PyErr_SetString(PyExc_TypeError, - "instance exception may not have a separate value"); - goto raise_error; - } - value = type; - #if PY_VERSION_HEX < 0x02050000 - if (PyInstance_Check(type)) { - type = (PyObject*) ((PyInstanceObject*)type)->in_class; - Py_INCREF(type); - } else { - type = 0; - PyErr_SetString(PyExc_TypeError, - "raise: exception must be an old-style class or instance"); - goto raise_error; - } - #else - type = (PyObject*) Py_TYPE(type); - Py_INCREF(type); - if (!PyType_IsSubtype((PyTypeObject *)type, (PyTypeObject *)PyExc_BaseException)) { - PyErr_SetString(PyExc_TypeError, - "raise: exception class must be a subclass of BaseException"); - goto raise_error; - } - #endif - } - __Pyx_ErrRestore(type, value, tb); - return; -raise_error: - Py_XDECREF(value); - Py_XDECREF(type); - Py_XDECREF(tb); - return; -} -#else /* Python 3+ */ -static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, PyObject *cause) { - PyObject* owned_instance = NULL; - if (tb == Py_None) { - tb = 0; - } else if (tb && !PyTraceBack_Check(tb)) { - PyErr_SetString(PyExc_TypeError, - "raise: arg 3 must be a traceback or None"); - goto bad; - } - if (value == Py_None) - value = 0; - if (PyExceptionInstance_Check(type)) { - if (value) { - PyErr_SetString(PyExc_TypeError, - "instance exception may not have a separate value"); - goto bad; - } - value = type; - type = (PyObject*) Py_TYPE(value); - } else if (PyExceptionClass_Check(type)) { - PyObject *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 = PyEval_CallObject(type, args); - Py_DECREF(args); - if (!owned_instance) - goto bad; - value = owned_instance; - if (!PyExceptionInstance_Check(value)) { - PyErr_Format(PyExc_TypeError, - "calling %R should have returned an instance of " - "BaseException, not %R", - type, Py_TYPE(value)); - goto bad; - } - } else { - PyErr_SetString(PyExc_TypeError, - "raise: exception class must be a subclass of BaseException"); - goto bad; - } -#if PY_VERSION_HEX >= 0x03030000 - if (cause) { -#else - if (cause && cause != Py_None) { -#endif - PyObject *fixed_cause; - if (cause == Py_None) { - fixed_cause = NULL; - } else if (PyExceptionClass_Check(cause)) { - fixed_cause = PyObject_CallObject(cause, NULL); - if (fixed_cause == NULL) - goto bad; - } else if (PyExceptionInstance_Check(cause)) { - fixed_cause = cause; - Py_INCREF(fixed_cause); - } else { - PyErr_SetString(PyExc_TypeError, - "exception causes must derive from " - "BaseException"); - goto bad; - } - PyException_SetCause(value, fixed_cause); - } - PyErr_SetObject(type, value); - if (tb) { - PyThreadState *tstate = PyThreadState_GET(); - PyObject* tmp_tb = tstate->curexc_traceback; - if (tb != tmp_tb) { - Py_INCREF(tb); - tstate->curexc_traceback = tb; - Py_XDECREF(tmp_tb); - } - } -bad: - Py_XDECREF(owned_instance); - return; -} -#endif - -static CYTHON_INLINE void __Pyx_RaiseTooManyValuesError(Py_ssize_t expected) { - PyErr_Format(PyExc_ValueError, - "too many values to unpack (expected %" CYTHON_FORMAT_SSIZE_T "d)", expected); -} - -static CYTHON_INLINE void __Pyx_RaiseNeedMoreValuesError(Py_ssize_t index) { - PyErr_Format(PyExc_ValueError, - "need more than %" CYTHON_FORMAT_SSIZE_T "d value%s to unpack", - index, (index == 1) ? "" : "s"); -} - -static CYTHON_INLINE void __Pyx_RaiseNoneNotIterableError(void) { - PyErr_SetString(PyExc_TypeError, "'NoneType' object is not iterable"); -} - -static CYTHON_INLINE int __Pyx_IterFinish(void) { -#if CYTHON_COMPILING_IN_CPYTHON - PyThreadState *tstate = PyThreadState_GET(); - PyObject* exc_type = tstate->curexc_type; - if (unlikely(exc_type)) { - if (likely(exc_type == PyExc_StopIteration) || PyErr_GivenExceptionMatches(exc_type, PyExc_StopIteration)) { - PyObject *exc_value, *exc_tb; - exc_value = tstate->curexc_value; - exc_tb = tstate->curexc_traceback; - tstate->curexc_type = 0; - tstate->curexc_value = 0; - tstate->curexc_traceback = 0; - Py_DECREF(exc_type); - Py_XDECREF(exc_value); - Py_XDECREF(exc_tb); - return 0; - } else { - return -1; - } - } - return 0; -#else - if (unlikely(PyErr_Occurred())) { - if (likely(PyErr_ExceptionMatches(PyExc_StopIteration))) { - PyErr_Clear(); - return 0; - } else { - return -1; - } - } - return 0; -#endif -} - -static int __Pyx_IternextUnpackEndCheck(PyObject *retval, Py_ssize_t expected) { - if (unlikely(retval)) { - Py_DECREF(retval); - __Pyx_RaiseTooManyValuesError(expected); - return -1; - } else { - return __Pyx_IterFinish(); - } - return 0; -} - -static CYTHON_INLINE PyObject *__Pyx_GetAttr(PyObject *o, PyObject *n) { -#if CYTHON_COMPILING_IN_CPYTHON -#if PY_MAJOR_VERSION >= 3 - if (likely(PyUnicode_Check(n))) -#else - if (likely(PyString_Check(n))) -#endif - return __Pyx_PyObject_GetAttrStr(o, n); -#endif - return PyObject_GetAttr(o, n); -} - -static CYTHON_INLINE PyObject *__Pyx_GetAttr3(PyObject *o, PyObject *n, PyObject *d) { - PyObject *r = __Pyx_GetAttr(o, n); - if (!r) { - if (!PyErr_ExceptionMatches(PyExc_AttributeError)) - goto bad; - PyErr_Clear(); - r = d; - Py_INCREF(d); - } - return r; -bad: - return NULL; -} - -static CYTHON_INLINE Py_ssize_t __Pyx_div_Py_ssize_t(Py_ssize_t a, Py_ssize_t b) { - Py_ssize_t q = a / b; - Py_ssize_t r = a - q*b; - q -= ((r != 0) & ((r ^ b) < 0)); - return q; -} - -static CYTHON_INLINE int __Pyx_PyUnicode_Equals(PyObject* s1, PyObject* s2, int equals) { -#if CYTHON_COMPILING_IN_PYPY - return PyObject_RichCompareBool(s1, s2, equals); -#else - if (s1 == s2) { - return (equals == Py_EQ); - } else if (PyUnicode_CheckExact(s1) & PyUnicode_CheckExact(s2)) { - #if CYTHON_PEP393_ENABLED - if ((PyUnicode_READY(s1) < 0) || (PyUnicode_READY(s2) < 0)) - return -1; - if (PyUnicode_GET_LENGTH(s1) != PyUnicode_GET_LENGTH(s2)) { - return (equals == Py_NE); - } else if (PyUnicode_GET_LENGTH(s1) == 1) { - Py_UCS4 ch1 = PyUnicode_READ_CHAR(s1, 0); - Py_UCS4 ch2 = PyUnicode_READ_CHAR(s2, 0); - return (equals == Py_EQ) ? (ch1 == ch2) : (ch1 != ch2); - #else - if (PyUnicode_GET_SIZE(s1) != PyUnicode_GET_SIZE(s2)) { - return (equals == Py_NE); - } else if (PyUnicode_GET_SIZE(s1) == 1) { - Py_UNICODE ch1 = PyUnicode_AS_UNICODE(s1)[0]; - Py_UNICODE ch2 = PyUnicode_AS_UNICODE(s2)[0]; - return (equals == Py_EQ) ? (ch1 == ch2) : (ch1 != ch2); - #endif - } else { - int result = PyUnicode_Compare(s1, s2); - if ((result == -1) && unlikely(PyErr_Occurred())) - return -1; - return (equals == Py_EQ) ? (result == 0) : (result != 0); - } - } else if ((s1 == Py_None) & PyUnicode_CheckExact(s2)) { - return (equals == Py_NE); - } else if ((s2 == Py_None) & PyUnicode_CheckExact(s1)) { - return (equals == Py_NE); - } else { - int result; - PyObject* py_result = PyObject_RichCompare(s1, s2, equals); - if (!py_result) - return -1; - result = __Pyx_PyObject_IsTrue(py_result); - Py_DECREF(py_result); - return result; - } -#endif -} - -static CYTHON_INLINE PyObject* __Pyx_decode_c_string( - const char* cstring, Py_ssize_t start, Py_ssize_t stop, - const char* encoding, const char* errors, - PyObject* (*decode_func)(const char *s, Py_ssize_t size, const char *errors)) { - Py_ssize_t length; - if (unlikely((start < 0) | (stop < 0))) { - length = strlen(cstring); - if (start < 0) { - start += length; - if (start < 0) - start = 0; - } - if (stop < 0) - stop += length; - } - length = stop - start; - if (unlikely(length <= 0)) - return PyUnicode_FromUnicode(NULL, 0); - cstring += start; - if (decode_func) { - return decode_func(cstring, length, errors); - } else { - return PyUnicode_Decode(cstring, length, encoding, errors); - } -} - -static int __Pyx_GetException(PyObject **type, PyObject **value, PyObject **tb) { - PyObject *local_type, *local_value, *local_tb; -#if CYTHON_COMPILING_IN_CPYTHON - PyObject *tmp_type, *tmp_value, *tmp_tb; - PyThreadState *tstate = PyThreadState_GET(); - local_type = tstate->curexc_type; - local_value = tstate->curexc_value; - local_tb = tstate->curexc_traceback; - tstate->curexc_type = 0; - tstate->curexc_value = 0; - tstate->curexc_traceback = 0; -#else - PyErr_Fetch(&local_type, &local_value, &local_tb); -#endif - PyErr_NormalizeException(&local_type, &local_value, &local_tb); -#if CYTHON_COMPILING_IN_CPYTHON - if (unlikely(tstate->curexc_type)) -#else - if (unlikely(PyErr_Occurred())) -#endif - goto bad; - #if PY_MAJOR_VERSION >= 3 - if (unlikely(PyException_SetTraceback(local_value, local_tb) < 0)) - goto bad; - #endif - Py_INCREF(local_type); - Py_INCREF(local_value); - Py_INCREF(local_tb); - *type = local_type; - *value = local_value; - *tb = local_tb; -#if CYTHON_COMPILING_IN_CPYTHON - tmp_type = tstate->exc_type; - tmp_value = tstate->exc_value; - tmp_tb = tstate->exc_traceback; - tstate->exc_type = local_type; - tstate->exc_value = local_value; - tstate->exc_traceback = local_tb; - /* Make sure tstate is in a consistent state when we XDECREF - these objects (DECREF may run arbitrary code). */ - Py_XDECREF(tmp_type); - Py_XDECREF(tmp_value); - Py_XDECREF(tmp_tb); -#else - PyErr_SetExcInfo(local_type, local_value, local_tb); -#endif - return 0; -bad: - *type = 0; - *value = 0; - *tb = 0; - Py_XDECREF(local_type); - Py_XDECREF(local_value); - Py_XDECREF(local_tb); - return -1; -} - -static CYTHON_INLINE long __Pyx_div_long(long a, long b) { - long q = a / b; - long r = a - q*b; - q -= ((r != 0) & ((r ^ b) < 0)); - return q; -} - -static void __Pyx_WriteUnraisable(const char *name, CYTHON_UNUSED int clineno, - CYTHON_UNUSED int lineno, CYTHON_UNUSED const char *filename) { - PyObject *old_exc, *old_val, *old_tb; - PyObject *ctx; - __Pyx_ErrFetch(&old_exc, &old_val, &old_tb); - #if PY_MAJOR_VERSION < 3 - ctx = PyString_FromString(name); - #else - ctx = PyUnicode_FromString(name); - #endif - __Pyx_ErrRestore(old_exc, old_val, old_tb); - if (!ctx) { - PyErr_WriteUnraisable(Py_None); - } else { - PyErr_WriteUnraisable(ctx); - Py_DECREF(ctx); - } -} - -static int __Pyx_SetVtable(PyObject *dict, void *vtable) { -#if PY_VERSION_HEX >= 0x02070000 && !(PY_MAJOR_VERSION==3&&PY_MINOR_VERSION==0) - PyObject *ob = PyCapsule_New(vtable, 0, 0); -#else - PyObject *ob = PyCObject_FromVoidPtr(vtable, 0); -#endif - if (!ob) - goto bad; - if (PyDict_SetItem(dict, __pyx_n_s____pyx_vtable__, ob) < 0) - goto bad; - Py_DECREF(ob); - return 0; -bad: - Py_XDECREF(ob); - return -1; -} - -#if PY_MAJOR_VERSION < 3 -static int __Pyx_GetBuffer(PyObject *obj, Py_buffer *view, int flags) { - #if PY_VERSION_HEX >= 0x02060000 - if (PyObject_CheckBuffer(obj)) return PyObject_GetBuffer(obj, view, flags); - #endif - if (PyObject_TypeCheck(obj, __pyx_ptype_5numpy_ndarray)) return __pyx_pw_5numpy_7ndarray_1__getbuffer__(obj, view, flags); - if (PyObject_TypeCheck(obj, __pyx_array_type)) return __pyx_array_getbuffer(obj, view, flags); - if (PyObject_TypeCheck(obj, __pyx_memoryview_type)) return __pyx_memoryview_getbuffer(obj, view, flags); - #if PY_VERSION_HEX < 0x02060000 - if (obj->ob_type->tp_dict) { - PyObject *getbuffer_cobj = PyObject_GetItem( - obj->ob_type->tp_dict, __pyx_n_s____pyx_getbuffer); - if (getbuffer_cobj) { - getbufferproc func = (getbufferproc) PyCObject_AsVoidPtr(getbuffer_cobj); - Py_DECREF(getbuffer_cobj); - if (!func) - goto fail; - return func(obj, view, flags); - } else { - PyErr_Clear(); - } - } - #endif - PyErr_Format(PyExc_TypeError, "'%100s' does not have the buffer interface", Py_TYPE(obj)->tp_name); -#if PY_VERSION_HEX < 0x02060000 -fail: -#endif - return -1; -} -static void __Pyx_ReleaseBuffer(Py_buffer *view) { - PyObject *obj = view->obj; - if (!obj) return; - #if PY_VERSION_HEX >= 0x02060000 - if (PyObject_CheckBuffer(obj)) { - PyBuffer_Release(view); - return; - } - #endif - if (PyObject_TypeCheck(obj, __pyx_ptype_5numpy_ndarray)) { __pyx_pw_5numpy_7ndarray_3__releasebuffer__(obj, view); return; } - #if PY_VERSION_HEX < 0x02060000 - if (obj->ob_type->tp_dict) { - PyObject *releasebuffer_cobj = PyObject_GetItem( - obj->ob_type->tp_dict, __pyx_n_s____pyx_releasebuffer); - if (releasebuffer_cobj) { - releasebufferproc func = (releasebufferproc) PyCObject_AsVoidPtr(releasebuffer_cobj); - Py_DECREF(releasebuffer_cobj); - if (!func) - goto fail; - func(obj, view); - return; - } else { - PyErr_Clear(); - } - } - #endif - goto nofail; -#if PY_VERSION_HEX < 0x02060000 -fail: -#endif - PyErr_WriteUnraisable(obj); -nofail: - Py_DECREF(obj); - view->obj = NULL; -} -#endif /* PY_MAJOR_VERSION < 3 */ - - - static PyObject *__Pyx_Import(PyObject *name, PyObject *from_list, int level) { - PyObject *empty_list = 0; - PyObject *module = 0; - PyObject *global_dict = 0; - PyObject *empty_dict = 0; - PyObject *list; - #if PY_VERSION_HEX < 0x03030000 - PyObject *py_import; - py_import = __Pyx_PyObject_GetAttrStr(__pyx_b, __pyx_n_s____import__); - if (!py_import) - goto bad; - #endif - if (from_list) - list = from_list; - else { - empty_list = PyList_New(0); - if (!empty_list) - goto bad; - list = empty_list; - } - global_dict = PyModule_GetDict(__pyx_m); - if (!global_dict) - goto bad; - empty_dict = PyDict_New(); - if (!empty_dict) - goto bad; - #if PY_VERSION_HEX >= 0x02050000 - { - #if PY_MAJOR_VERSION >= 3 - if (level == -1) { - if (strchr(__Pyx_MODULE_NAME, '.')) { - #if PY_VERSION_HEX < 0x03030000 - PyObject *py_level = PyInt_FromLong(1); - if (!py_level) - goto bad; - module = PyObject_CallFunctionObjArgs(py_import, - name, global_dict, empty_dict, list, py_level, NULL); - Py_DECREF(py_level); - #else - module = PyImport_ImportModuleLevelObject( - name, global_dict, empty_dict, list, 1); - #endif - if (!module) { - if (!PyErr_ExceptionMatches(PyExc_ImportError)) - goto bad; - PyErr_Clear(); - } - } - level = 0; /* try absolute import on failure */ - } - #endif - if (!module) { - #if PY_VERSION_HEX < 0x03030000 - PyObject *py_level = PyInt_FromLong(level); - if (!py_level) - goto bad; - module = PyObject_CallFunctionObjArgs(py_import, - name, global_dict, empty_dict, list, py_level, NULL); - Py_DECREF(py_level); - #else - module = PyImport_ImportModuleLevelObject( - name, global_dict, empty_dict, list, level); - #endif - } - } - #else - if (level>0) { - PyErr_SetString(PyExc_RuntimeError, "Relative import is not supported for Python <=2.4."); - goto bad; - } - module = PyObject_CallFunctionObjArgs(py_import, - name, global_dict, empty_dict, list, NULL); - #endif -bad: - #if PY_VERSION_HEX < 0x03030000 - Py_XDECREF(py_import); - #endif - Py_XDECREF(empty_list); - Py_XDECREF(empty_dict); - return module; -} - -static int -__pyx_typeinfo_cmp(__Pyx_TypeInfo *a, __Pyx_TypeInfo *b) -{ - int i; - if (!a || !b) - return 0; - if (a == b) - return 1; - if (a->size != b->size || a->typegroup != b->typegroup || - a->is_unsigned != b->is_unsigned || a->ndim != b->ndim) { - if (a->typegroup == 'H' || b->typegroup == 'H') { - return a->size == b->size; - } else { - return 0; - } - } - if (a->ndim) { - for (i = 0; i < a->ndim; i++) - if (a->arraysize[i] != b->arraysize[i]) - return 0; - } - if (a->typegroup == 'S') { - if (a->flags != b->flags) - return 0; - if (a->fields || b->fields) { - if (!(a->fields && b->fields)) - return 0; - for (i = 0; a->fields[i].type && b->fields[i].type; i++) { - __Pyx_StructField *field_a = a->fields + i; - __Pyx_StructField *field_b = b->fields + i; - if (field_a->offset != field_b->offset || - !__pyx_typeinfo_cmp(field_a->type, field_b->type)) - return 0; - } - return !a->fields[i].type && !b->fields[i].type; - } - } - return 1; -} - -static int -__pyx_check_strides(Py_buffer *buf, int dim, int ndim, int spec) -{ - if (buf->shape[dim] <= 1) - return 1; - if (buf->strides) { - if (spec & __Pyx_MEMVIEW_CONTIG) { - if (spec & (__Pyx_MEMVIEW_PTR|__Pyx_MEMVIEW_FULL)) { - if (buf->strides[dim] != sizeof(void *)) { - PyErr_Format(PyExc_ValueError, - "Buffer is not indirectly contiguous " - "in dimension %d.", dim); - goto fail; - } - } else if (buf->strides[dim] != buf->itemsize) { - PyErr_SetString(PyExc_ValueError, - "Buffer and memoryview are not contiguous " - "in the same dimension."); - goto fail; - } - } - if (spec & __Pyx_MEMVIEW_FOLLOW) { - Py_ssize_t stride = buf->strides[dim]; - if (stride < 0) - stride = -stride; - if (stride < buf->itemsize) { - PyErr_SetString(PyExc_ValueError, - "Buffer and memoryview are not contiguous " - "in the same dimension."); - goto fail; - } - } - } else { - if (spec & __Pyx_MEMVIEW_CONTIG && dim != ndim - 1) { - PyErr_Format(PyExc_ValueError, - "C-contiguous buffer is not contiguous in " - "dimension %d", dim); - goto fail; - } else if (spec & (__Pyx_MEMVIEW_PTR)) { - PyErr_Format(PyExc_ValueError, - "C-contiguous buffer is not indirect in " - "dimension %d", dim); - goto fail; - } else if (buf->suboffsets) { - PyErr_SetString(PyExc_ValueError, - "Buffer exposes suboffsets but no strides"); - goto fail; - } - } - return 1; -fail: - return 0; -} -static int -__pyx_check_suboffsets(Py_buffer *buf, int dim, CYTHON_UNUSED int ndim, int spec) -{ - if (spec & __Pyx_MEMVIEW_DIRECT) { - if (buf->suboffsets && buf->suboffsets[dim] >= 0) { - PyErr_Format(PyExc_ValueError, - "Buffer not compatible with direct access " - "in dimension %d.", dim); - goto fail; - } - } - if (spec & __Pyx_MEMVIEW_PTR) { - if (!buf->suboffsets || (buf->suboffsets && buf->suboffsets[dim] < 0)) { - PyErr_Format(PyExc_ValueError, - "Buffer is not indirectly accessisble " - "in dimension %d.", dim); - goto fail; - } - } - return 1; -fail: - return 0; -} -static int -__pyx_verify_contig(Py_buffer *buf, int ndim, int c_or_f_flag) -{ - int i; - if (c_or_f_flag & __Pyx_IS_F_CONTIG) { - Py_ssize_t stride = 1; - for (i = 0; i < ndim; i++) { - if (stride * buf->itemsize != buf->strides[i] && - buf->shape[i] > 1) - { - PyErr_SetString(PyExc_ValueError, - "Buffer not fortran contiguous."); - goto fail; - } - stride = stride * buf->shape[i]; - } - } else if (c_or_f_flag & __Pyx_IS_C_CONTIG) { - Py_ssize_t stride = 1; - for (i = ndim - 1; i >- 1; i--) { - if (stride * buf->itemsize != buf->strides[i] && - buf->shape[i] > 1) { - PyErr_SetString(PyExc_ValueError, - "Buffer not C contiguous."); - goto fail; - } - stride = stride * buf->shape[i]; - } - } - return 1; -fail: - return 0; -} -static int __Pyx_ValidateAndInit_memviewslice( - int *axes_specs, - int c_or_f_flag, - int buf_flags, - int ndim, - __Pyx_TypeInfo *dtype, - __Pyx_BufFmt_StackElem stack[], - __Pyx_memviewslice *memviewslice, - PyObject *original_obj) -{ - struct __pyx_memoryview_obj *memview, *new_memview; - __Pyx_RefNannyDeclarations - Py_buffer *buf; - int i, spec = 0, retval = -1; - __Pyx_BufFmt_Context ctx; - int from_memoryview = __pyx_memoryview_check(original_obj); - __Pyx_RefNannySetupContext("ValidateAndInit_memviewslice", 0); - if (from_memoryview && __pyx_typeinfo_cmp(dtype, ((struct __pyx_memoryview_obj *) - original_obj)->typeinfo)) { - memview = (struct __pyx_memoryview_obj *) original_obj; - new_memview = NULL; - } else { - memview = (struct __pyx_memoryview_obj *) __pyx_memoryview_new( - original_obj, buf_flags, 0, dtype); - new_memview = memview; - if (unlikely(!memview)) - goto fail; - } - buf = &memview->view; - if (buf->ndim != ndim) { - PyErr_Format(PyExc_ValueError, - "Buffer has wrong number of dimensions (expected %d, got %d)", - ndim, buf->ndim); - goto fail; - } - if (new_memview) { - __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 "u byte%s) " - "does not match size of '%s' (%" CYTHON_FORMAT_SSIZE_T "u byte%s)", - buf->itemsize, - (buf->itemsize > 1) ? "s" : "", - dtype->name, - dtype->size, - (dtype->size > 1) ? "s" : ""); - goto fail; - } - for (i = 0; i < ndim; i++) { - spec = axes_specs[i]; - if (!__pyx_check_strides(buf, i, ndim, spec)) - goto fail; - if (!__pyx_check_suboffsets(buf, i, ndim, spec)) - goto fail; - } - if (buf->strides && !__pyx_verify_contig(buf, ndim, c_or_f_flag)) - goto fail; - if (unlikely(__Pyx_init_memviewslice(memview, ndim, memviewslice, - new_memview != NULL) == -1)) { - goto fail; - } - retval = 0; - goto no_fail; -fail: - Py_XDECREF(new_memview); - retval = -1; -no_fail: - __Pyx_RefNannyFinishContext(); - return retval; -} - -static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_ds_nn___pyx_t_7sklearn_5_hmmc_dtype_t(PyObject *obj) { - __Pyx_memviewslice result = { 0, 0, { 0 }, { 0 }, { 0 } }; - __Pyx_BufFmt_StackElem stack[1]; - int axes_specs[] = { (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_STRIDED) }; - int retcode; - if (obj == Py_None) { - result.memview = (struct __pyx_memoryview_obj *) Py_None; - return result; - } - retcode = __Pyx_ValidateAndInit_memviewslice(axes_specs, 0, - PyBUF_RECORDS, 1, - &__Pyx_TypeInfo_nn___pyx_t_7sklearn_5_hmmc_dtype_t, stack, - &result, obj); - if (unlikely(retcode == -1)) - goto __pyx_fail; - return result; -__pyx_fail: - result.memview = NULL; - result.data = NULL; - return result; -} - -static CYTHON_INLINE PyObject *__Pyx_PyInt_to_py_npy_long(npy_long val) { - const npy_long neg_one = (npy_long)-1, const_zero = (npy_long)0; - const int is_unsigned = const_zero < neg_one; - if ((sizeof(npy_long) == sizeof(char)) || - (sizeof(npy_long) == sizeof(short))) { - return PyInt_FromLong((long)val); - } else if ((sizeof(npy_long) == sizeof(int)) || - (sizeof(npy_long) == sizeof(long))) { - if (is_unsigned) - return PyLong_FromUnsignedLong((unsigned long)val); - else - return PyInt_FromLong((long)val); - } else if (sizeof(npy_long) == sizeof(PY_LONG_LONG)) { - if (is_unsigned) - return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG)val); - else - return PyLong_FromLongLong((PY_LONG_LONG)val); - } else { - int one = 1; int little = (int)*(unsigned char *)&one; - unsigned char *bytes = (unsigned char *)&val; - return _PyLong_FromByteArray(bytes, sizeof(npy_long), - little, !is_unsigned); - } -} - -#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 int -__pyx_memviewslice_is_contig(const __Pyx_memviewslice *mvs, - char order, int ndim) -{ - int i, index, step, start; - Py_ssize_t itemsize = mvs->memview->view.itemsize; - if (order == 'F') { - step = 1; - start = 0; - } else { - step = -1; - start = ndim - 1; - } - for (i = 0; i < ndim; i++) { - index = start + step * i; - if (mvs->suboffsets[index] >= 0 || mvs->strides[index] != itemsize) - return 0; - itemsize *= mvs->shape[index]; - } - return 1; -} - -static void -__pyx_get_array_memory_extents(__Pyx_memviewslice *slice, - void **out_start, void **out_end, - int ndim, size_t itemsize) -{ - char *start, *end; - int i; - start = end = slice->data; - for (i = 0; i < ndim; i++) { - Py_ssize_t stride = slice->strides[i]; - Py_ssize_t extent = slice->shape[i]; - if (extent == 0) { - *out_start = *out_end = start; - return; - } else { - if (stride > 0) - end += stride * (extent - 1); - else - start += stride * (extent - 1); - } - } - *out_start = start; - *out_end = end + itemsize; -} -static int -__pyx_slices_overlap(__Pyx_memviewslice *slice1, - __Pyx_memviewslice *slice2, - int ndim, size_t itemsize) -{ - void *start1, *end1, *start2, *end2; - __pyx_get_array_memory_extents(slice1, &start1, &end1, ndim, itemsize); - __pyx_get_array_memory_extents(slice2, &start2, &end2, ndim, itemsize); - return (start1 < end2) && (start2 < end1); -} - -static __Pyx_memviewslice -__pyx_memoryview_copy_new_contig(const __Pyx_memviewslice *from_mvs, - const char *mode, int ndim, - size_t sizeof_dtype, int contig_flag, - int dtype_is_object) -{ - __Pyx_RefNannyDeclarations - int i; - __Pyx_memviewslice new_mvs = { 0, 0, { 0 }, { 0 }, { 0 } }; - struct __pyx_memoryview_obj *from_memview = from_mvs->memview; - Py_buffer *buf = &from_memview->view; - PyObject *shape_tuple = NULL; - PyObject *temp_int = NULL; - struct __pyx_array_obj *array_obj = NULL; - struct __pyx_memoryview_obj *memview_obj = NULL; - __Pyx_RefNannySetupContext("__pyx_memoryview_copy_new_contig", 0); - for (i = 0; i < ndim; i++) { - if (from_mvs->suboffsets[i] >= 0) { - PyErr_Format(PyExc_ValueError, "Cannot copy memoryview slice with " - "indirect dimensions (axis %d)", i); - goto fail; - } - } - shape_tuple = PyTuple_New(ndim); - if (unlikely(!shape_tuple)) { - goto fail; - } - __Pyx_GOTREF(shape_tuple); - for(i = 0; i < ndim; i++) { - temp_int = PyInt_FromLong(from_mvs->shape[i]); - if(unlikely(!temp_int)) { - goto fail; - } else { - PyTuple_SET_ITEM(shape_tuple, i, temp_int); - temp_int = NULL; - } - } - array_obj = __pyx_array_new(shape_tuple, sizeof_dtype, buf->format, (char *) mode, NULL); - if (unlikely(!array_obj)) { - goto fail; - } - __Pyx_GOTREF(array_obj); - memview_obj = (struct __pyx_memoryview_obj *) __pyx_memoryview_new( - (PyObject *) array_obj, contig_flag, - dtype_is_object, - from_mvs->memview->typeinfo); - if (unlikely(!memview_obj)) - goto fail; - if (unlikely(__Pyx_init_memviewslice(memview_obj, ndim, &new_mvs, 1) < 0)) - goto fail; - if (unlikely(__pyx_memoryview_copy_contents(*from_mvs, new_mvs, ndim, ndim, - dtype_is_object) < 0)) - goto fail; - goto no_fail; -fail: - __Pyx_XDECREF(new_mvs.memview); - new_mvs.memview = NULL; - new_mvs.data = NULL; -no_fail: - __Pyx_XDECREF(shape_tuple); - __Pyx_XDECREF(temp_int); - __Pyx_XDECREF(array_obj); - __Pyx_RefNannyFinishContext(); - return new_mvs; -} - -static CYTHON_INLINE PyObject * -__pyx_capsule_create(void *p, CYTHON_UNUSED const char *sig) -{ - PyObject *cobj; -#if PY_VERSION_HEX >= 0x02070000 && !(PY_MAJOR_VERSION == 3 && PY_MINOR_VERSION == 0) - cobj = PyCapsule_New(p, sig, NULL); -#else - cobj = PyCObject_FromVoidPtr(p, NULL); -#endif - return cobj; -} - -static CYTHON_INLINE void __Pyx_ExceptionSave(PyObject **type, PyObject **value, PyObject **tb) { -#if CYTHON_COMPILING_IN_CPYTHON - PyThreadState *tstate = PyThreadState_GET(); - *type = tstate->exc_type; - *value = tstate->exc_value; - *tb = tstate->exc_traceback; - Py_XINCREF(*type); - Py_XINCREF(*value); - Py_XINCREF(*tb); -#else - PyErr_GetExcInfo(type, value, tb); -#endif -} -static void __Pyx_ExceptionReset(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->exc_type; - tmp_value = tstate->exc_value; - tmp_tb = tstate->exc_traceback; - tstate->exc_type = type; - tstate->exc_value = value; - tstate->exc_traceback = tb; - Py_XDECREF(tmp_type); - Py_XDECREF(tmp_value); - Py_XDECREF(tmp_tb); -#else - PyErr_SetExcInfo(type, value, tb); -#endif -} - -static CYTHON_INLINE unsigned char __Pyx_PyInt_AsUnsignedChar(PyObject* x) { - const unsigned char neg_one = (unsigned char)-1, const_zero = 0; - const int is_unsigned = neg_one > const_zero; - if (sizeof(unsigned char) < sizeof(long)) { - long val = __Pyx_PyInt_AsLong(x); - if (unlikely(val != (long)(unsigned char)val)) { - if (!unlikely(val == -1 && PyErr_Occurred())) { - PyErr_SetString(PyExc_OverflowError, - (is_unsigned && unlikely(val < 0)) ? - "can't convert negative value to unsigned char" : - "value too large to convert to unsigned char"); - } - return (unsigned char)-1; - } - return (unsigned char)val; - } - return (unsigned char)__Pyx_PyInt_AsUnsignedLong(x); -} - -static CYTHON_INLINE unsigned short __Pyx_PyInt_AsUnsignedShort(PyObject* x) { - const unsigned short neg_one = (unsigned short)-1, const_zero = 0; - const int is_unsigned = neg_one > const_zero; - if (sizeof(unsigned short) < sizeof(long)) { - long val = __Pyx_PyInt_AsLong(x); - if (unlikely(val != (long)(unsigned short)val)) { - if (!unlikely(val == -1 && PyErr_Occurred())) { - PyErr_SetString(PyExc_OverflowError, - (is_unsigned && unlikely(val < 0)) ? - "can't convert negative value to unsigned short" : - "value too large to convert to unsigned short"); - } - return (unsigned short)-1; - } - return (unsigned short)val; - } - return (unsigned short)__Pyx_PyInt_AsUnsignedLong(x); -} - -static CYTHON_INLINE unsigned int __Pyx_PyInt_AsUnsignedInt(PyObject* x) { - const unsigned int neg_one = (unsigned int)-1, const_zero = 0; - const int is_unsigned = neg_one > const_zero; - if (sizeof(unsigned int) < sizeof(long)) { - long val = __Pyx_PyInt_AsLong(x); - if (unlikely(val != (long)(unsigned int)val)) { - if (!unlikely(val == -1 && PyErr_Occurred())) { - PyErr_SetString(PyExc_OverflowError, - (is_unsigned && unlikely(val < 0)) ? - "can't convert negative value to unsigned int" : - "value too large to convert to unsigned int"); - } - return (unsigned int)-1; - } - return (unsigned int)val; - } - return (unsigned int)__Pyx_PyInt_AsUnsignedLong(x); -} - -static CYTHON_INLINE char __Pyx_PyInt_AsChar(PyObject* x) { - const char neg_one = (char)-1, const_zero = 0; - const int is_unsigned = neg_one > const_zero; - if (sizeof(char) < sizeof(long)) { - long val = __Pyx_PyInt_AsLong(x); - if (unlikely(val != (long)(char)val)) { - if (!unlikely(val == -1 && PyErr_Occurred())) { - PyErr_SetString(PyExc_OverflowError, - (is_unsigned && unlikely(val < 0)) ? - "can't convert negative value to char" : - "value too large to convert to char"); - } - return (char)-1; - } - return (char)val; - } - return (char)__Pyx_PyInt_AsLong(x); -} - -static CYTHON_INLINE short __Pyx_PyInt_AsShort(PyObject* x) { - const short neg_one = (short)-1, const_zero = 0; - const int is_unsigned = neg_one > const_zero; - if (sizeof(short) < sizeof(long)) { - long val = __Pyx_PyInt_AsLong(x); - if (unlikely(val != (long)(short)val)) { - if (!unlikely(val == -1 && PyErr_Occurred())) { - PyErr_SetString(PyExc_OverflowError, - (is_unsigned && unlikely(val < 0)) ? - "can't convert negative value to short" : - "value too large to convert to short"); - } - return (short)-1; - } - return (short)val; - } - return (short)__Pyx_PyInt_AsLong(x); -} - -static CYTHON_INLINE int __Pyx_PyInt_AsInt(PyObject* x) { - const int neg_one = (int)-1, const_zero = 0; - const int is_unsigned = neg_one > const_zero; - if (sizeof(int) < sizeof(long)) { - long val = __Pyx_PyInt_AsLong(x); - if (unlikely(val != (long)(int)val)) { - if (!unlikely(val == -1 && PyErr_Occurred())) { - PyErr_SetString(PyExc_OverflowError, - (is_unsigned && unlikely(val < 0)) ? - "can't convert negative value to int" : - "value too large to convert to int"); - } - return (int)-1; - } - return (int)val; - } - return (int)__Pyx_PyInt_AsLong(x); -} - -static CYTHON_INLINE signed char __Pyx_PyInt_AsSignedChar(PyObject* x) { - const signed char neg_one = (signed char)-1, const_zero = 0; - const int is_unsigned = neg_one > const_zero; - if (sizeof(signed char) < sizeof(long)) { - long val = __Pyx_PyInt_AsLong(x); - if (unlikely(val != (long)(signed char)val)) { - if (!unlikely(val == -1 && PyErr_Occurred())) { - PyErr_SetString(PyExc_OverflowError, - (is_unsigned && unlikely(val < 0)) ? - "can't convert negative value to signed char" : - "value too large to convert to signed char"); - } - return (signed char)-1; - } - return (signed char)val; - } - return (signed char)__Pyx_PyInt_AsSignedLong(x); -} - -static CYTHON_INLINE signed short __Pyx_PyInt_AsSignedShort(PyObject* x) { - const signed short neg_one = (signed short)-1, const_zero = 0; - const int is_unsigned = neg_one > const_zero; - if (sizeof(signed short) < sizeof(long)) { - long val = __Pyx_PyInt_AsLong(x); - if (unlikely(val != (long)(signed short)val)) { - if (!unlikely(val == -1 && PyErr_Occurred())) { - PyErr_SetString(PyExc_OverflowError, - (is_unsigned && unlikely(val < 0)) ? - "can't convert negative value to signed short" : - "value too large to convert to signed short"); - } - return (signed short)-1; - } - return (signed short)val; - } - return (signed short)__Pyx_PyInt_AsSignedLong(x); -} - -static CYTHON_INLINE signed int __Pyx_PyInt_AsSignedInt(PyObject* x) { - const signed int neg_one = (signed int)-1, const_zero = 0; - const int is_unsigned = neg_one > const_zero; - if (sizeof(signed int) < sizeof(long)) { - long val = __Pyx_PyInt_AsLong(x); - if (unlikely(val != (long)(signed int)val)) { - if (!unlikely(val == -1 && PyErr_Occurred())) { - PyErr_SetString(PyExc_OverflowError, - (is_unsigned && unlikely(val < 0)) ? - "can't convert negative value to signed int" : - "value too large to convert to signed int"); - } - return (signed int)-1; - } - return (signed int)val; - } - return (signed int)__Pyx_PyInt_AsSignedLong(x); -} - -static CYTHON_INLINE int __Pyx_PyInt_AsLongDouble(PyObject* x) { - const int neg_one = (int)-1, const_zero = 0; - const int is_unsigned = neg_one > const_zero; - if (sizeof(int) < sizeof(long)) { - long val = __Pyx_PyInt_AsLong(x); - if (unlikely(val != (long)(int)val)) { - if (!unlikely(val == -1 && PyErr_Occurred())) { - PyErr_SetString(PyExc_OverflowError, - (is_unsigned && unlikely(val < 0)) ? - "can't convert negative value to int" : - "value too large to convert to int"); - } - return (int)-1; - } - return (int)val; - } - return (int)__Pyx_PyInt_AsLong(x); -} - -#if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3 -#if CYTHON_USE_PYLONG_INTERNALS -#include "longintrepr.h" -#endif -#endif -static CYTHON_INLINE unsigned long __Pyx_PyInt_AsUnsignedLong(PyObject* x) { - const unsigned long neg_one = (unsigned long)-1, const_zero = 0; - const int is_unsigned = neg_one > const_zero; -#if PY_MAJOR_VERSION < 3 - if (likely(PyInt_Check(x))) { - long val = PyInt_AS_LONG(x); - if (is_unsigned && unlikely(val < 0)) { - PyErr_SetString(PyExc_OverflowError, - "can't convert negative value to unsigned long"); - return (unsigned long)-1; - } - return (unsigned long)val; - } else -#endif - if (likely(PyLong_Check(x))) { - if (is_unsigned) { -#if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3 -#if CYTHON_USE_PYLONG_INTERNALS - if (sizeof(digit) <= sizeof(unsigned long)) { - switch (Py_SIZE(x)) { - case 0: return 0; - case 1: return (unsigned long) ((PyLongObject*)x)->ob_digit[0]; - } - } -#endif -#endif - if (unlikely(Py_SIZE(x) < 0)) { - PyErr_SetString(PyExc_OverflowError, - "can't convert negative value to unsigned long"); - return (unsigned long)-1; - } - return (unsigned long)PyLong_AsUnsignedLong(x); - } else { -#if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3 -#if CYTHON_USE_PYLONG_INTERNALS - if (sizeof(digit) <= sizeof(unsigned long)) { - switch (Py_SIZE(x)) { - case 0: return 0; - case 1: return +(unsigned long) ((PyLongObject*)x)->ob_digit[0]; - case -1: return -(unsigned long) ((PyLongObject*)x)->ob_digit[0]; - } - } -#endif -#endif - return (unsigned long)PyLong_AsLong(x); - } - } else { - unsigned long val; - PyObject *tmp = __Pyx_PyNumber_Int(x); - if (!tmp) return (unsigned long)-1; - val = __Pyx_PyInt_AsUnsignedLong(tmp); - Py_DECREF(tmp); - return val; - } -} - -#if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3 -#if CYTHON_USE_PYLONG_INTERNALS -#include "longintrepr.h" -#endif -#endif -static CYTHON_INLINE unsigned PY_LONG_LONG __Pyx_PyInt_AsUnsignedLongLong(PyObject* x) { - const unsigned PY_LONG_LONG neg_one = (unsigned PY_LONG_LONG)-1, const_zero = 0; - const int is_unsigned = neg_one > const_zero; -#if PY_MAJOR_VERSION < 3 - if (likely(PyInt_Check(x))) { - long val = PyInt_AS_LONG(x); - if (is_unsigned && unlikely(val < 0)) { - PyErr_SetString(PyExc_OverflowError, - "can't convert negative value to unsigned PY_LONG_LONG"); - return (unsigned PY_LONG_LONG)-1; - } - return (unsigned PY_LONG_LONG)val; - } else -#endif - if (likely(PyLong_Check(x))) { - if (is_unsigned) { -#if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3 -#if CYTHON_USE_PYLONG_INTERNALS - if (sizeof(digit) <= sizeof(unsigned PY_LONG_LONG)) { - switch (Py_SIZE(x)) { - case 0: return 0; - case 1: return (unsigned PY_LONG_LONG) ((PyLongObject*)x)->ob_digit[0]; - } - } -#endif -#endif - if (unlikely(Py_SIZE(x) < 0)) { - PyErr_SetString(PyExc_OverflowError, - "can't convert negative value to unsigned PY_LONG_LONG"); - return (unsigned PY_LONG_LONG)-1; - } - return (unsigned PY_LONG_LONG)PyLong_AsUnsignedLongLong(x); - } else { -#if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3 -#if CYTHON_USE_PYLONG_INTERNALS - if (sizeof(digit) <= sizeof(unsigned PY_LONG_LONG)) { - switch (Py_SIZE(x)) { - case 0: return 0; - case 1: return +(unsigned PY_LONG_LONG) ((PyLongObject*)x)->ob_digit[0]; - case -1: return -(unsigned PY_LONG_LONG) ((PyLongObject*)x)->ob_digit[0]; - } - } -#endif -#endif - return (unsigned PY_LONG_LONG)PyLong_AsLongLong(x); - } - } else { - unsigned PY_LONG_LONG val; - PyObject *tmp = __Pyx_PyNumber_Int(x); - if (!tmp) return (unsigned PY_LONG_LONG)-1; - val = __Pyx_PyInt_AsUnsignedLongLong(tmp); - Py_DECREF(tmp); - return val; - } -} - -#if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3 -#if CYTHON_USE_PYLONG_INTERNALS -#include "longintrepr.h" -#endif -#endif -static CYTHON_INLINE long __Pyx_PyInt_AsLong(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))) { - long val = PyInt_AS_LONG(x); - if (is_unsigned && unlikely(val < 0)) { - PyErr_SetString(PyExc_OverflowError, - "can't convert negative value to long"); - return (long)-1; - } - return (long)val; - } else -#endif - if (likely(PyLong_Check(x))) { - if (is_unsigned) { -#if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3 -#if CYTHON_USE_PYLONG_INTERNALS - if (sizeof(digit) <= sizeof(long)) { - switch (Py_SIZE(x)) { - case 0: return 0; - case 1: return (long) ((PyLongObject*)x)->ob_digit[0]; - } - } -#endif -#endif - if (unlikely(Py_SIZE(x) < 0)) { - PyErr_SetString(PyExc_OverflowError, - "can't convert negative value to long"); - return (long)-1; - } - return (long)PyLong_AsUnsignedLong(x); - } else { -#if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3 -#if CYTHON_USE_PYLONG_INTERNALS - if (sizeof(digit) <= sizeof(long)) { - switch (Py_SIZE(x)) { - case 0: return 0; - case 1: return +(long) ((PyLongObject*)x)->ob_digit[0]; - case -1: return -(long) ((PyLongObject*)x)->ob_digit[0]; - } - } -#endif -#endif - return (long)PyLong_AsLong(x); - } - } else { - long val; - PyObject *tmp = __Pyx_PyNumber_Int(x); - if (!tmp) return (long)-1; - val = __Pyx_PyInt_AsLong(tmp); - Py_DECREF(tmp); - return val; - } -} - -#if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3 -#if CYTHON_USE_PYLONG_INTERNALS -#include "longintrepr.h" -#endif -#endif -static CYTHON_INLINE PY_LONG_LONG __Pyx_PyInt_AsLongLong(PyObject* x) { - const PY_LONG_LONG neg_one = (PY_LONG_LONG)-1, const_zero = 0; - const int is_unsigned = neg_one > const_zero; -#if PY_MAJOR_VERSION < 3 - if (likely(PyInt_Check(x))) { - long val = PyInt_AS_LONG(x); - if (is_unsigned && unlikely(val < 0)) { - PyErr_SetString(PyExc_OverflowError, - "can't convert negative value to PY_LONG_LONG"); - return (PY_LONG_LONG)-1; - } - return (PY_LONG_LONG)val; - } else -#endif - if (likely(PyLong_Check(x))) { - if (is_unsigned) { -#if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3 -#if CYTHON_USE_PYLONG_INTERNALS - if (sizeof(digit) <= sizeof(PY_LONG_LONG)) { - switch (Py_SIZE(x)) { - case 0: return 0; - case 1: return (PY_LONG_LONG) ((PyLongObject*)x)->ob_digit[0]; - } - } -#endif -#endif - if (unlikely(Py_SIZE(x) < 0)) { - PyErr_SetString(PyExc_OverflowError, - "can't convert negative value to PY_LONG_LONG"); - return (PY_LONG_LONG)-1; - } - return (PY_LONG_LONG)PyLong_AsUnsignedLongLong(x); - } else { -#if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3 -#if CYTHON_USE_PYLONG_INTERNALS - if (sizeof(digit) <= sizeof(PY_LONG_LONG)) { - switch (Py_SIZE(x)) { - case 0: return 0; - case 1: return +(PY_LONG_LONG) ((PyLongObject*)x)->ob_digit[0]; - case -1: return -(PY_LONG_LONG) ((PyLongObject*)x)->ob_digit[0]; - } - } -#endif -#endif - return (PY_LONG_LONG)PyLong_AsLongLong(x); - } - } else { - PY_LONG_LONG val; - PyObject *tmp = __Pyx_PyNumber_Int(x); - if (!tmp) return (PY_LONG_LONG)-1; - val = __Pyx_PyInt_AsLongLong(tmp); - Py_DECREF(tmp); - return val; - } -} - -#if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3 -#if CYTHON_USE_PYLONG_INTERNALS -#include "longintrepr.h" -#endif -#endif -static CYTHON_INLINE signed long __Pyx_PyInt_AsSignedLong(PyObject* x) { - const signed long neg_one = (signed long)-1, const_zero = 0; - const int is_unsigned = neg_one > const_zero; -#if PY_MAJOR_VERSION < 3 - if (likely(PyInt_Check(x))) { - long val = PyInt_AS_LONG(x); - if (is_unsigned && unlikely(val < 0)) { - PyErr_SetString(PyExc_OverflowError, - "can't convert negative value to signed long"); - return (signed long)-1; - } - return (signed long)val; - } else -#endif - if (likely(PyLong_Check(x))) { - if (is_unsigned) { -#if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3 -#if CYTHON_USE_PYLONG_INTERNALS - if (sizeof(digit) <= sizeof(signed long)) { - switch (Py_SIZE(x)) { - case 0: return 0; - case 1: return (signed long) ((PyLongObject*)x)->ob_digit[0]; - } - } -#endif -#endif - if (unlikely(Py_SIZE(x) < 0)) { - PyErr_SetString(PyExc_OverflowError, - "can't convert negative value to signed long"); - return (signed long)-1; - } - return (signed long)PyLong_AsUnsignedLong(x); - } else { -#if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3 -#if CYTHON_USE_PYLONG_INTERNALS - if (sizeof(digit) <= sizeof(signed long)) { - switch (Py_SIZE(x)) { - case 0: return 0; - case 1: return +(signed long) ((PyLongObject*)x)->ob_digit[0]; - case -1: return -(signed long) ((PyLongObject*)x)->ob_digit[0]; - } - } -#endif -#endif - return (signed long)PyLong_AsLong(x); - } - } else { - signed long val; - PyObject *tmp = __Pyx_PyNumber_Int(x); - if (!tmp) return (signed long)-1; - val = __Pyx_PyInt_AsSignedLong(tmp); - Py_DECREF(tmp); - return val; - } -} - -#if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3 -#if CYTHON_USE_PYLONG_INTERNALS -#include "longintrepr.h" -#endif -#endif -static CYTHON_INLINE signed PY_LONG_LONG __Pyx_PyInt_AsSignedLongLong(PyObject* x) { - const signed PY_LONG_LONG neg_one = (signed PY_LONG_LONG)-1, const_zero = 0; - const int is_unsigned = neg_one > const_zero; -#if PY_MAJOR_VERSION < 3 - if (likely(PyInt_Check(x))) { - long val = PyInt_AS_LONG(x); - if (is_unsigned && unlikely(val < 0)) { - PyErr_SetString(PyExc_OverflowError, - "can't convert negative value to signed PY_LONG_LONG"); - return (signed PY_LONG_LONG)-1; - } - return (signed PY_LONG_LONG)val; - } else -#endif - if (likely(PyLong_Check(x))) { - if (is_unsigned) { -#if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3 -#if CYTHON_USE_PYLONG_INTERNALS - if (sizeof(digit) <= sizeof(signed PY_LONG_LONG)) { - switch (Py_SIZE(x)) { - case 0: return 0; - case 1: return (signed PY_LONG_LONG) ((PyLongObject*)x)->ob_digit[0]; - } - } -#endif -#endif - if (unlikely(Py_SIZE(x) < 0)) { - PyErr_SetString(PyExc_OverflowError, - "can't convert negative value to signed PY_LONG_LONG"); - return (signed PY_LONG_LONG)-1; - } - return (signed PY_LONG_LONG)PyLong_AsUnsignedLongLong(x); - } else { -#if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3 -#if CYTHON_USE_PYLONG_INTERNALS - if (sizeof(digit) <= sizeof(signed PY_LONG_LONG)) { - switch (Py_SIZE(x)) { - case 0: return 0; - case 1: return +(signed PY_LONG_LONG) ((PyLongObject*)x)->ob_digit[0]; - case -1: return -(signed PY_LONG_LONG) ((PyLongObject*)x)->ob_digit[0]; - } - } -#endif -#endif - return (signed PY_LONG_LONG)PyLong_AsLongLong(x); - } - } else { - signed PY_LONG_LONG val; - PyObject *tmp = __Pyx_PyNumber_Int(x); - if (!tmp) return (signed PY_LONG_LONG)-1; - val = __Pyx_PyInt_AsSignedLongLong(tmp); - Py_DECREF(tmp); - return val; - } -} - -static int __Pyx_check_binary_version(void) { - char ctversion[4], rtversion[4]; - PyOS_snprintf(ctversion, 4, "%d.%d", PY_MAJOR_VERSION, PY_MINOR_VERSION); - PyOS_snprintf(rtversion, 4, "%s", Py_GetVersion()); - if (ctversion[0] != rtversion[0] || ctversion[2] != rtversion[2]) { - char message[200]; - PyOS_snprintf(message, sizeof(message), - "compiletime version %s of module '%.100s' " - "does not match runtime version %s", - ctversion, __Pyx_MODULE_NAME, rtversion); - #if PY_VERSION_HEX < 0x02050000 - return PyErr_Warn(NULL, message); - #else - return PyErr_WarnEx(NULL, message, 1); - #endif - } - return 0; -} - -#ifndef __PYX_HAVE_RT_ImportModule -#define __PYX_HAVE_RT_ImportModule -static PyObject *__Pyx_ImportModule(const char *name) { - PyObject *py_name = 0; - PyObject *py_module = 0; - py_name = __Pyx_PyIdentifier_FromString(name); - if (!py_name) - goto bad; - py_module = PyImport_Import(py_name); - Py_DECREF(py_name); - return py_module; -bad: - Py_XDECREF(py_name); - return 0; -} -#endif - -#ifndef __PYX_HAVE_RT_ImportType -#define __PYX_HAVE_RT_ImportType -static PyTypeObject *__Pyx_ImportType(const char *module_name, const char *class_name, - size_t size, int strict) -{ - PyObject *py_module = 0; - PyObject *result = 0; - PyObject *py_name = 0; - char warning[200]; - Py_ssize_t basicsize; -#ifdef Py_LIMITED_API - PyObject *py_basicsize; -#endif - py_module = __Pyx_ImportModule(module_name); - if (!py_module) - goto bad; - py_name = __Pyx_PyIdentifier_FromString(class_name); - if (!py_name) - goto bad; - result = PyObject_GetAttr(py_module, py_name); - Py_DECREF(py_name); - py_name = 0; - Py_DECREF(py_module); - py_module = 0; - if (!result) - goto bad; - if (!PyType_Check(result)) { - PyErr_Format(PyExc_TypeError, - "%s.%s is not a type object", - module_name, class_name); - goto bad; - } -#ifndef Py_LIMITED_API - basicsize = ((PyTypeObject *)result)->tp_basicsize; -#else - py_basicsize = PyObject_GetAttrString(result, "__basicsize__"); - if (!py_basicsize) - goto bad; - basicsize = PyLong_AsSsize_t(py_basicsize); - Py_DECREF(py_basicsize); - py_basicsize = 0; - if (basicsize == (Py_ssize_t)-1 && PyErr_Occurred()) - goto bad; -#endif - if (!strict && (size_t)basicsize > size) { - PyOS_snprintf(warning, sizeof(warning), - "%s.%s size changed, may indicate binary incompatibility", - module_name, class_name); - #if PY_VERSION_HEX < 0x02050000 - if (PyErr_Warn(NULL, warning) < 0) goto bad; - 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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, new_max*sizeof(__Pyx_CodeObjectCacheEntry)); - if (unlikely(!entries)) { - return; - } - __pyx_code_cache.entries = entries; - __pyx_code_cache.max_count = new_max; - } - for (i=__pyx_code_cache.count; i>pos; i--) { - entries[i] = entries[i-1]; - } - entries[pos].code_line = code_line; - entries[pos].code_object = code_object; - __pyx_code_cache.count++; - Py_INCREF(code_object); -} - -#include "compile.h" -#include "frameobject.h" -#include "traceback.h" -static PyCodeObject* __Pyx_CreateCodeObjectForTraceback( - const char *funcname, int c_line, - int py_line, const char *filename) { - PyCodeObject *py_code = 0; - PyObject *py_srcfile = 0; - PyObject *py_funcname = 0; - #if PY_MAJOR_VERSION < 3 - py_srcfile = PyString_FromString(filename); - #else - py_srcfile = PyUnicode_FromString(filename); - #endif - if (!py_srcfile) goto bad; - if (c_line) { - #if PY_MAJOR_VERSION < 3 - py_funcname = PyString_FromFormat( "%s (%s:%d)", funcname, __pyx_cfilenm, c_line); - #else - py_funcname = PyUnicode_FromFormat( "%s (%s:%d)", funcname, __pyx_cfilenm, c_line); - #endif - } - else { - #if PY_MAJOR_VERSION < 3 - py_funcname = PyString_FromString(funcname); - #else - py_funcname = PyUnicode_FromString(funcname); - #endif - } - if (!py_funcname) goto bad; - py_code = __Pyx_PyCode_New( - 0, /*int argcount,*/ - 0, /*int kwonlyargcount,*/ - 0, /*int nlocals,*/ - 0, /*int stacksize,*/ - 0, /*int flags,*/ - __pyx_empty_bytes, /*PyObject *code,*/ - __pyx_empty_tuple, /*PyObject *consts,*/ - __pyx_empty_tuple, /*PyObject *names,*/ - __pyx_empty_tuple, /*PyObject *varnames,*/ - __pyx_empty_tuple, /*PyObject *freevars,*/ - __pyx_empty_tuple, /*PyObject *cellvars,*/ - py_srcfile, /*PyObject *filename,*/ - py_funcname, /*PyObject *name,*/ - py_line, /*int firstlineno,*/ - __pyx_empty_bytes /*PyObject *lnotab*/ - ); - Py_DECREF(py_srcfile); - Py_DECREF(py_funcname); - return py_code; -bad: - Py_XDECREF(py_srcfile); - Py_XDECREF(py_funcname); - return NULL; -} -static void __Pyx_AddTraceback(const char *funcname, int c_line, - int py_line, const char *filename) { - PyCodeObject *py_code = 0; - PyObject *py_globals = 0; - PyFrameObject *py_frame = 0; - py_code = __pyx_find_code_object(c_line ? c_line : py_line); - if (!py_code) { - py_code = __Pyx_CreateCodeObjectForTraceback( - funcname, c_line, py_line, filename); - if (!py_code) goto bad; - __pyx_insert_code_object(c_line ? c_line : py_line, py_code); - } - py_globals = PyModule_GetDict(__pyx_m); - if (!py_globals) goto bad; - py_frame = PyFrame_New( - PyThreadState_GET(), /*PyThreadState *tstate,*/ - py_code, /*PyCodeObject *code,*/ - py_globals, /*PyObject *globals,*/ - 0 /*PyObject *locals*/ - ); - if (!py_frame) goto bad; - py_frame->f_lineno = py_line; - PyTraceBack_Here(py_frame); -bad: - Py_XDECREF(py_code); - Py_XDECREF(py_frame); -} - -static int __Pyx_InitStrings(__Pyx_StringTabEntry *t) { - while (t->p) { - #if PY_MAJOR_VERSION < 3 - if (t->is_unicode) { - *t->p = PyUnicode_DecodeUTF8(t->s, t->n - 1, NULL); - } else if (t->intern) { - *t->p = PyString_InternFromString(t->s); - } else { - *t->p = PyString_FromStringAndSize(t->s, t->n - 1); - } - #else /* Python 3+ has unicode identifiers */ - if (t->is_unicode | t->is_str) { - if (t->intern) { - *t->p = PyUnicode_InternFromString(t->s); - } else if (t->encoding) { - *t->p = PyUnicode_Decode(t->s, t->n - 1, t->encoding, NULL); - } else { - *t->p = PyUnicode_FromStringAndSize(t->s, t->n - 1); - } - } else { - *t->p = PyBytes_FromStringAndSize(t->s, t->n - 1); - } - #endif - if (!*t->p) - return -1; - ++t; - } - return 0; -} - -static CYTHON_INLINE PyObject* __Pyx_PyUnicode_FromString(char* c_str) { - return __Pyx_PyUnicode_FromStringAndSize(c_str, strlen(c_str)); -} -static CYTHON_INLINE char* __Pyx_PyObject_AsString(PyObject* o) { - Py_ssize_t ignore; - return __Pyx_PyObject_AsStringAndSize(o, &ignore); -} -static CYTHON_INLINE char* __Pyx_PyObject_AsStringAndSize(PyObject* o, Py_ssize_t *length) { -#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII || __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT - if ( -#if PY_MAJOR_VERSION < 3 && __PYX_DEFAULT_STRING_ENCODING_IS_ASCII - __Pyx_sys_getdefaultencoding_not_ascii && -#endif - PyUnicode_Check(o)) { -#if PY_VERSION_HEX < 0x03030000 - char* defenc_c; - PyObject* defenc = _PyUnicode_AsDefaultEncodedString(o, NULL); - if (!defenc) return NULL; - defenc_c = PyBytes_AS_STRING(defenc); -#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII - { - char* end = defenc_c + PyBytes_GET_SIZE(defenc); - char* c; - for (c = defenc_c; c < end; c++) { - if ((unsigned char) (*c) >= 128) { - PyUnicode_AsASCIIString(o); - return NULL; - } - } - } -#endif /*__PYX_DEFAULT_STRING_ENCODING_IS_ASCII*/ - *length = PyBytes_GET_SIZE(defenc); - return defenc_c; -#else /* PY_VERSION_HEX < 0x03030000 */ - if (PyUnicode_READY(o) == -1) return NULL; -#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII - if (PyUnicode_IS_ASCII(o)) { - *length = PyUnicode_GET_DATA_SIZE(o); - return PyUnicode_AsUTF8(o); - } else { - PyUnicode_AsASCIIString(o); - return NULL; - } -#else /* __PYX_DEFAULT_STRING_ENCODING_IS_ASCII */ - return PyUnicode_AsUTF8AndSize(o, length); -#endif /* __PYX_DEFAULT_STRING_ENCODING_IS_ASCII */ -#endif /* PY_VERSION_HEX < 0x03030000 */ - } else -#endif /* __PYX_DEFAULT_STRING_ENCODING_IS_ASCII || __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT */ - { - char* result; - int r = PyBytes_AsStringAndSize(o, &result, length); - if (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, - "__%s__ returned non-%s (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 = PyNumber_Index(b); - if (!x) return -1; - ival = PyInt_AsSsize_t(x); - Py_DECREF(x); - return ival; -} -static CYTHON_INLINE PyObject * __Pyx_PyInt_FromSize_t(size_t ival) { -#if PY_VERSION_HEX < 0x02050000 - if (ival <= LONG_MAX) - return PyInt_FromLong((long)ival); - else { - unsigned char *bytes = (unsigned char *) &ival; - int one = 1; int little = (int)*(unsigned char*)&one; - return _PyLong_FromByteArray(bytes, sizeof(size_t), little, 0); - } -#else - return PyInt_FromSize_t(ival); -#endif -} -static CYTHON_INLINE size_t __Pyx_PyInt_AsSize_t(PyObject* x) { - unsigned PY_LONG_LONG val = __Pyx_PyInt_AsUnsignedLongLong(x); - if (unlikely(val != (unsigned PY_LONG_LONG)(size_t)val)) { - if ((val != (unsigned PY_LONG_LONG)-1) || !PyErr_Occurred()) - PyErr_SetString(PyExc_OverflowError, - "value too large to convert to size_t"); - return (size_t)-1; - } - return (size_t)val; -} - - -#endif /* Py_PYTHON_H */ diff --git a/sklearn/_hmmc.pyx b/sklearn/_hmmc.pyx deleted file mode 100644 index 4aacc0319c56c..0000000000000 --- a/sklearn/_hmmc.pyx +++ /dev/null @@ -1,128 +0,0 @@ -from libc.math cimport exp, log -import numpy as np -cimport numpy as np -cimport cython - -np.import_array() - -ctypedef np.float64_t dtype_t - -cdef dtype_t _NINF = -np.inf - -@cython.boundscheck(False) -cdef dtype_t _max(dtype_t[:] values): - # find maximum value (builtin 'max' is unrolled for speed) - cdef dtype_t value - cdef dtype_t vmax = _NINF - for i in range(values.shape[0]): - value = values[i] - if value > vmax: - vmax = value - return vmax - -@cython.boundscheck(False) -cpdef dtype_t _logsum(dtype_t[:] X): - cdef dtype_t vmax = _max(X) - cdef dtype_t power_sum = 0 - - for i in range(X.shape[0]): - power_sum += exp(X[i]-vmax) - - return log(power_sum) + vmax - - -@cython.boundscheck(False) -def _forward(int n_observations, int n_components, - np.ndarray[dtype_t, ndim=1] log_startprob, - np.ndarray[dtype_t, ndim=2] log_transmat, - np.ndarray[dtype_t, ndim=2] framelogprob, - np.ndarray[dtype_t, ndim=2] fwdlattice): - - cdef int t, i, j - cdef double logprob - cdef np.ndarray[dtype_t, ndim = 1] work_buffer - work_buffer = np.zeros(n_components) - - for i in range(n_components): - fwdlattice[0, i] = log_startprob[i] + framelogprob[0, i] - - for t in range(1, n_observations): - for j in range(n_components): - for i in range(n_components): - work_buffer[i] = fwdlattice[t - 1, i] + log_transmat[i, j] - fwdlattice[t, j] = _logsum(work_buffer) + framelogprob[t, j] - - -@cython.boundscheck(False) -def _backward(int n_observations, int n_components, - np.ndarray[dtype_t, ndim=1] log_startprob, - np.ndarray[dtype_t, ndim=2] log_transmat, - np.ndarray[dtype_t, ndim=2] framelogprob, - np.ndarray[dtype_t, ndim=2] bwdlattice): - - cdef int t, i, j - cdef double logprob - cdef np.ndarray[dtype_t, ndim = 1] work_buffer - work_buffer = np.zeros(n_components) - - for i in range(n_components): - bwdlattice[n_observations - 1, i] = 0.0 - - for t in range(n_observations - 2, -1, -1): - for i in range(n_components): - for j in range(n_components): - work_buffer[j] = log_transmat[i, j] + framelogprob[t + 1, j] \ - + bwdlattice[t + 1, j] - bwdlattice[t, i] = _logsum(work_buffer) - - -@cython.boundscheck(False) -def _compute_lneta(int n_observations, int n_components, - np.ndarray[dtype_t, ndim=2] fwdlattice, - np.ndarray[dtype_t, ndim=2] log_transmat, - np.ndarray[dtype_t, ndim=2] bwdlattice, - np.ndarray[dtype_t, ndim=2] framelogprob, - double logprob, - np.ndarray[dtype_t, ndim=3] lneta): - - cdef int i, j, t - for t in range(n_observations - 1): - for i in range(n_components): - for j in range(n_components): - lneta[t, i, j] = fwdlattice[t, i] + log_transmat[i, j] \ - + framelogprob[t + 1, j] + bwdlattice[t + 1, j] - logprob - - -@cython.boundscheck(False) -def _viterbi(int n_observations, int n_components, - np.ndarray[dtype_t, ndim=1] log_startprob, - np.ndarray[dtype_t, ndim=2] log_transmat, - np.ndarray[dtype_t, ndim=2] framelogprob): - - cdef int t, max_pos - cdef np.ndarray[dtype_t, ndim = 2] viterbi_lattice - cdef np.ndarray[np.int_t, ndim = 1] state_sequence - cdef dtype_t logprob - cdef np.ndarray[dtype_t, ndim = 2] work_buffer - - # Initialization - state_sequence = np.empty(n_observations, dtype=np.int) - viterbi_lattice = np.zeros((n_observations, n_components)) - viterbi_lattice[0] = log_startprob + framelogprob[0] - - # Induction - for t in range(1, n_observations): - work_buffer = viterbi_lattice[t-1] + log_transmat.T - viterbi_lattice[t] = np.max(work_buffer, axis=1) + framelogprob[t] - - # Observation traceback - max_pos = np.argmax(viterbi_lattice[n_observations - 1, :]) - state_sequence[n_observations - 1] = max_pos - logprob = viterbi_lattice[n_observations - 1, max_pos] - - for t in range(n_observations - 2, -1, -1): - max_pos = np.argmax(viterbi_lattice[t, :] \ - + log_transmat[:, state_sequence[t + 1]]) - state_sequence[t] = max_pos - - return state_sequence, logprob diff --git a/sklearn/cluster/__init__.py b/sklearn/cluster/__init__.py index 16f43d374e510..c9afcd98f23ce 100644 --- a/sklearn/cluster/__init__.py +++ b/sklearn/cluster/__init__.py @@ -7,8 +7,7 @@ from .mean_shift_ import (mean_shift, MeanShift, estimate_bandwidth, get_bin_seeds) from .affinity_propagation_ import affinity_propagation, AffinityPropagation -from .hierarchical import (ward_tree, Ward, WardAgglomeration, - AgglomerativeClustering, linkage_tree, +from .hierarchical import (ward_tree, AgglomerativeClustering, linkage_tree, FeatureAgglomeration) from .k_means_ import k_means, KMeans, MiniBatchKMeans from .dbscan_ import dbscan, DBSCAN @@ -24,8 +23,6 @@ 'MeanShift', 'MiniBatchKMeans', 'SpectralClustering', - 'Ward', - 'WardAgglomeration', 'affinity_propagation', 'dbscan', 'estimate_bandwidth', diff --git a/sklearn/cluster/hierarchical.py b/sklearn/cluster/hierarchical.py index a7ff170d126cf..45c937e75476b 100644 --- a/sklearn/cluster/hierarchical.py +++ b/sklearn/cluster/hierarchical.py @@ -44,7 +44,7 @@ def _fix_connectivity(X, connectivity, n_components=None, """ n_samples = X.shape[0] if (connectivity.shape[0] != n_samples or - connectivity.shape[1] != n_samples): + connectivity.shape[1] != n_samples): raise ValueError('Wrong shape for connectivity matrix: %s ' 'when X is %s' % (connectivity.shape, X.shape)) @@ -387,8 +387,7 @@ def linkage_tree(X, connectivity=None, n_components=None, n_samples, n_features = X.shape linkage_choices = {'complete': _hierarchical.max_merge, - 'average': _hierarchical.average_merge, - } + 'average': _hierarchical.average_merge} try: join_func = linkage_choices[linkage] except KeyError: @@ -534,6 +533,7 @@ def linkage_tree(X, connectivity=None, n_components=None, return children, n_components, n_leaves, parent, distances return children, n_components, n_leaves, parent + # Matching names to tree-building strategies def _complete_linkage(*args, **kwargs): kwargs['linkage'] = 'complete' @@ -548,8 +548,7 @@ def _average_linkage(*args, **kwargs): _TREE_BUILDERS = dict( ward=ward_tree, complete=_complete_linkage, - average=_average_linkage, - ) + average=_average_linkage) ############################################################################### @@ -868,157 +867,3 @@ def fit(self, X, y=None, **params): @property def fit_predict(self): raise AttributeError - -############################################################################### -# Backward compatibility: class for Ward hierarchical clustering - -class Ward(AgglomerativeClustering): - """Ward hierarchical clustering: constructs a tree and cuts it. - - Recursively merges the pair of clusters that minimally increases - within-cluster variance. - - Parameters - ---------- - n_clusters : int or ndarray - The number of clusters to find. - - connectivity : sparse matrix (optional) - Connectivity matrix. Defines for each sample the neighboring - samples following a given structure of the data. - Default is None, i.e, the hierarchical clustering algorithm is - unstructured. - - memory : Instance of joblib.Memory or string (optional) - Used to cache the output of the computation of the tree. - By default, no caching is done. If a string is given, it is the - path to the caching directory. - - n_components : int (optional) - The number of connected components in the graph defined by the - connectivity matrix. If not set, it is estimated. - - compute_full_tree : bool or 'auto' (optional) - Stop early the construction of the tree at n_clusters. This is - useful to decrease computation time if the number of clusters is - not small compared to the number of samples. This option is - useful only when specifying a connectivity matrix. Note also that - when varying the number of clusters and using caching, it may - be advantageous to compute the full tree. - - Attributes - ---------- - labels_ : array [n_features] - cluster labels for each feature - - n_leaves_ : int - Number of leaves in the hierarchical tree. - - n_components_ : int - The estimated number of connected components in the graph. - - children_ : array-like, shape (n_nodes-1, 2) - The children of each non-leaf node. Values less than `n_samples` - refer to leaves of the tree. A greater value `i` indicates a node with - children `children_[i - n_samples]`. - - - See also - -------- - AgglomerativeClustering : agglomerative hierarchical clustering - """ - linkage = 'ward' - - def __init__(self, n_clusters=2, memory=Memory(cachedir=None, verbose=0), - connectivity=None, n_components=None, - compute_full_tree='auto', pooling_func=np.mean): - - warnings.warn("The Ward class is deprecated since 0.14 and will be " - "removed in 0.17. Use the AgglomerativeClustering " - "instead.", DeprecationWarning) - self.n_clusters = n_clusters - self.memory = memory - self.n_components = n_components - self.connectivity = connectivity - self.compute_full_tree = compute_full_tree - self.affinity = "euclidean" - self.pooling_func = pooling_func - - -class WardAgglomeration(AgglomerationTransform, Ward): - """Feature agglomeration based on Ward hierarchical clustering - - Parameters - ---------- - n_clusters : int or ndarray - The number of clusters. - - connectivity : array-like or callable, optional - Connectivity matrix. Defines for each sample the neighboring - samples following a given structure of the data. - This can be a connectivity matrix itself or a callable that transforms - the data into a connectivity matrix, such as derived from - kneighbors_graph. Default is None, i.e, the - hierarchical clustering algorithm is unstructured. - - memory : Instance of joblib.Memory or string, optional - Used to cache the output of the computation of the tree. - By default, no caching is done. If a string is given, it is the - path to the caching directory. - - n_components : int (optional) - The number of connected components in the graph defined by the - connectivity matrix. If not set, it is estimated. - - compute_full_tree : bool or 'auto' (optional) - Stop early the construction of the tree at n_clusters. This is - useful to decrease computation time if the number of clusters is - not small compared to the number of samples. This option is - useful only when specifying a connectivity matrix. Note also that - when varying the number of cluster and using caching, it may - be advantageous to compute the full tree. - - pooling_func : callable, default=np.mean - This combines the values of agglomerated features into a single - value, and should accept an array of shape [M, N] and the keyword - argument `axis=1`, and reduce it to an array of size [M]. - - Attributes - ---------- - children_ : array-like, shape (n_nodes-1, 2) - The children of each non-leaf node. Values less than `n_features` - correspond to leaves of the tree which are the original samples. - A node `i` greater than or equal to `n_features` is a non-leaf - node and has children `children_[i - n_features]`. Alternatively - at the i-th iteration, children[i][0] and children[i][1] - are merged to form node `n_features + i` - - labels_ : array [n_features] - cluster labels for each feature - - n_leaves_ : int - Number of leaves in the hierarchical tree. - - n_components_ : int - The estimated number of connected components in the graph. - - """ - - def fit(self, X, y=None, **params): - """Fit the hierarchical clustering on the data - - Parameters - ---------- - X : array-like, shape = [n_samples, n_features] - The data - - Returns - ------- - self - """ - X = check_array(X) - return Ward.fit(self, X.T, **params) - - @property - def fit_predict(self): - raise AttributeError diff --git a/sklearn/cluster/tests/test_hierarchical.py b/sklearn/cluster/tests/test_hierarchical.py index 7c921377024a5..a3b4140039cea 100644 --- a/sklearn/cluster/tests/test_hierarchical.py +++ b/sklearn/cluster/tests/test_hierarchical.py @@ -20,7 +20,7 @@ from sklearn.utils.testing import assert_array_almost_equal from sklearn.utils.testing import ignore_warnings -from sklearn.cluster import Ward, WardAgglomeration, ward_tree +from sklearn.cluster import ward_tree from sklearn.cluster import AgglomerativeClustering, FeatureAgglomeration from sklearn.cluster.hierarchical import (_hc_cut, _TREE_BUILDERS, linkage_tree) @@ -46,9 +46,6 @@ def test_linkage_misc(): # Smoke test FeatureAgglomeration FeatureAgglomeration().fit(X) - # Deprecation of Ward class - assert_warns(DeprecationWarning, Ward).fit(X) - # test hiearchical clustering on a precomputed distances matrix dis = cosine_distances(X) @@ -221,14 +218,8 @@ def test_ward_agglomeration(): mask = np.ones([10, 10], dtype=np.bool) X = rng.randn(50, 100) connectivity = grid_to_graph(*mask.shape) - assert_warns(DeprecationWarning, WardAgglomeration) - - with ignore_warnings(): - ward = WardAgglomeration(n_clusters=5, connectivity=connectivity) - ward.fit(X) agglo = FeatureAgglomeration(n_clusters=5, connectivity=connectivity) agglo.fit(X) - assert_array_equal(agglo.labels_, ward.labels_) assert_true(np.size(np.unique(agglo.labels_)) == 5) X_red = agglo.transform(X) diff --git a/sklearn/cross_validation.py b/sklearn/cross_validation.py index 441004648620d..92528b4389fe4 100644 --- a/sklearn/cross_validation.py +++ b/sklearn/cross_validation.py @@ -32,8 +32,7 @@ from .metrics.scorer import check_scoring from .utils.fixes import bincount -__all__ = ['Bootstrap', - 'KFold', +__all__ = ['KFold', 'LeaveOneLabelOut', 'LeaveOneOut', 'LeavePLabelOut', @@ -60,34 +59,17 @@ class _PartitionIterator(with_metaclass(ABCMeta)): Total number of elements in dataset. """ - def __init__(self, n, indices=None): - if indices is None: - indices = True - else: - warnings.warn("The indices parameter is deprecated and will be " - "removed (assumed True) in 0.17", DeprecationWarning, - stacklevel=1) + def __init__(self, n): if abs(n - int(n)) >= np.finfo('f').eps: raise ValueError("n must be an integer") self.n = int(n) - self._indices = indices - - @property - def indices(self): - warnings.warn("The indices attribute is deprecated and will be " - "removed (assumed True) in 0.17", DeprecationWarning, - stacklevel=1) - return self._indices def __iter__(self): - indices = self._indices - if indices: - ind = np.arange(self.n) + ind = np.arange(self.n) for test_index in self._iter_test_masks(): train_index = np.logical_not(test_index) - if indices: - train_index = ind[train_index] - test_index = ind[test_index] + train_index = ind[train_index] + test_index = ind[test_index] yield train_index, test_index # Since subclasses must implement either _iter_test_masks or @@ -214,8 +196,8 @@ class LeavePOut(_PartitionIterator): TRAIN: [0 1] TEST: [2 3] """ - def __init__(self, n, p, indices=None): - super(LeavePOut, self).__init__(n, indices) + def __init__(self, n, p): + super(LeavePOut, self).__init__(n) self.p = p def _iter_test_indices(self): @@ -239,8 +221,8 @@ class _BaseKFold(with_metaclass(ABCMeta, _PartitionIterator)): """Base class to validate KFold approaches""" @abstractmethod - def __init__(self, n, n_folds, indices, shuffle, random_state): - super(_BaseKFold, self).__init__(n, indices) + def __init__(self, n, n_folds, shuffle, random_state): + super(_BaseKFold, self).__init__(n) if abs(n_folds - int(n_folds)) >= np.finfo('f').eps: raise ValueError("n_folds must be an integer") @@ -317,9 +299,9 @@ class KFold(_BaseKFold): classification tasks). """ - def __init__(self, n, n_folds=3, indices=None, shuffle=False, + def __init__(self, n, n_folds=3, shuffle=False, random_state=None): - super(KFold, self).__init__(n, n_folds, indices, shuffle, random_state) + super(KFold, self).__init__(n, n_folds, shuffle, random_state) self.idxs = np.arange(n) if shuffle: rng = check_random_state(self.random_state) @@ -400,10 +382,10 @@ class StratifiedKFold(_BaseKFold): """ - def __init__(self, y, n_folds=3, indices=None, shuffle=False, + def __init__(self, y, n_folds=3, shuffle=False, random_state=None): super(StratifiedKFold, self).__init__( - len(y), n_folds, indices, shuffle, random_state) + len(y), n_folds, shuffle, random_state) y = np.asarray(y) n_samples = y.shape[0] unique_labels, y_inversed = np.unique(y, return_inverse=True) @@ -506,8 +488,8 @@ class LeaveOneLabelOut(_PartitionIterator): """ - def __init__(self, labels, indices=None): - super(LeaveOneLabelOut, self).__init__(len(labels), indices) + def __init__(self, labels): + super(LeaveOneLabelOut, self).__init__(len(labels)) # We make a copy of labels to avoid side-effects during iteration self.labels = np.array(labels, copy=True) self.unique_labels = np.unique(labels) @@ -579,9 +561,9 @@ class LeavePLabelOut(_PartitionIterator): [5 6]] [1] [2 1] """ - def __init__(self, labels, p, indices=None): + def __init__(self, labels, p): # We make a copy of labels to avoid side-effects during iteration - super(LeavePLabelOut, self).__init__(len(labels), indices) + super(LeavePLabelOut, self).__init__(len(labels)) self.labels = np.array(labels, copy=True) self.unique_labels = np.unique(labels) self.n_unique_labels = len(self.unique_labels) @@ -610,152 +592,11 @@ def __len__(self): factorial(self.p)) -class Bootstrap(object): - """Random sampling with replacement cross-validation iterator - - Provides train/test indices to split data in train test sets - while resampling the input n_iter times: each time a new - random split of the data is performed and then samples are drawn - (with replacement) on each side of the split to build the training - and test sets. - - Note: contrary to other cross-validation strategies, bootstrapping - will allow some samples to occur several times in each splits. However - a sample that occurs in the train split will never occur in the test - split and vice-versa. - - If you want each sample to occur at most once you should probably - use ShuffleSplit cross validation instead. - - Parameters - ---------- - n : int - Total number of elements in the dataset. - - n_iter : int (default is 3) - Number of bootstrapping iterations - - train_size : int or float (default is 0.5) - If int, number of samples to include in the training split - (should be smaller than the total number of samples passed - in the dataset). - - If float, should be between 0.0 and 1.0 and represent the - proportion of the dataset to include in the train split. - - test_size : int or float or None (default is None) - If int, number of samples to include in the training set - (should be smaller than the total number of samples passed - in the dataset). - - If float, should be between 0.0 and 1.0 and represent the - proportion of the dataset to include in the test split. - - If None, n_test is set as the complement of n_train. - - random_state : int or RandomState - Pseudo number generator state used for random sampling. - - Examples - -------- - >>> from sklearn import cross_validation - >>> bs = cross_validation.Bootstrap(9, random_state=0) - >>> len(bs) - 3 - >>> print(bs) - Bootstrap(9, n_iter=3, train_size=5, test_size=4, random_state=0) - >>> for train_index, test_index in bs: - ... print("TRAIN:", train_index, "TEST:", test_index) - ... - TRAIN: [1 8 7 7 8] TEST: [0 3 0 5] - TRAIN: [5 4 2 4 2] TEST: [6 7 1 0] - TRAIN: [4 7 0 1 1] TEST: [5 3 6 5] - - See also - -------- - ShuffleSplit: cross validation using random permutations. - """ - - # Static marker to be able to introspect the CV type - indices = True - - def __init__(self, n, n_iter=3, train_size=.5, test_size=None, - random_state=None): - # See, e.g., http://youtu.be/BzHz0J9a6k0?t=9m38s for a motivation - # behind this deprecation - warnings.warn("Bootstrap will no longer be supported as a " + - "cross-validation method as of version 0.15 and " + - "will be removed in 0.17", DeprecationWarning) - self.n = n - self.n_iter = n_iter - if isinstance(train_size, numbers.Integral): - self.train_size = train_size - elif (isinstance(train_size, numbers.Real) and train_size >= 0.0 - and train_size <= 1.0): - self.train_size = int(ceil(train_size * n)) - else: - raise ValueError("Invalid value for train_size: %r" % - train_size) - if self.train_size > n: - raise ValueError("train_size=%d should not be larger than n=%d" % - (self.train_size, n)) - - if isinstance(test_size, numbers.Integral): - self.test_size = test_size - elif isinstance(test_size, numbers.Real) and 0.0 <= test_size <= 1.0: - self.test_size = int(ceil(test_size * n)) - elif test_size is None: - self.test_size = self.n - self.train_size - else: - raise ValueError("Invalid value for test_size: %r" % test_size) - if self.test_size > n - self.train_size: - raise ValueError(("test_size + train_size=%d, should not be " + - "larger than n=%d") % - (self.test_size + self.train_size, n)) - - self.random_state = random_state - - def __iter__(self): - rng = check_random_state(self.random_state) - for i in range(self.n_iter): - # random partition - permutation = rng.permutation(self.n) - ind_train = permutation[:self.train_size] - ind_test = permutation[self.train_size:self.train_size - + self.test_size] - - # bootstrap in each split individually - train = rng.randint(0, self.train_size, - size=(self.train_size,)) - test = rng.randint(0, self.test_size, - size=(self.test_size,)) - yield ind_train[train], ind_test[test] - - def __repr__(self): - return ('%s(%d, n_iter=%d, train_size=%d, test_size=%d, ' - 'random_state=%s)' % ( - self.__class__.__name__, - self.n, - self.n_iter, - self.train_size, - self.test_size, - self.random_state, - )) - - def __len__(self): - return self.n_iter - - class BaseShuffleSplit(with_metaclass(ABCMeta)): """Base class for ShuffleSplit and StratifiedShuffleSplit""" def __init__(self, n, n_iter=10, test_size=0.1, train_size=None, - indices=None, random_state=None, n_iterations=None): - if indices is None: - indices = True - else: - warnings.warn("The indices parameter is deprecated and will be " - "removed (assumed True) in 0.17", DeprecationWarning) + random_state=None, n_iterations=None): self.n = n self.n_iter = n_iter if n_iterations is not None: # pragma: no cover @@ -765,29 +606,14 @@ def __init__(self, n, n_iter=10, test_size=0.1, train_size=None, self.test_size = test_size self.train_size = train_size self.random_state = random_state - self._indices = indices self.n_train, self.n_test = _validate_shuffle_split(n, test_size, train_size) - @property - def indices(self): - warnings.warn("The indices attribute is deprecated and will be " - "removed (assumed True) in 0.17", DeprecationWarning, - stacklevel=1) - return self._indices - def __iter__(self): - if self._indices: - for train, test in self._iter_indices(): - yield train, test - return for train, test in self._iter_indices(): - train_m = np.zeros(self.n, dtype=bool) - test_m = np.zeros(self.n, dtype=bool) - train_m[train] = True - test_m[test] = True - yield train_m, test_m + yield train, test + return @abstractmethod def _iter_indices(self): @@ -852,9 +678,6 @@ class ShuffleSplit(BaseShuffleSplit): TRAIN: [2 1] TEST: [0] TRAIN: [0 2] TEST: [3] - See also - -------- - Bootstrap: cross-validation using re-sampling with replacement. """ def _iter_indices(self): @@ -999,10 +822,10 @@ class StratifiedShuffleSplit(BaseShuffleSplit): """ def __init__(self, y, n_iter=10, test_size=0.1, train_size=None, - indices=None, random_state=None, n_iterations=None): + random_state=None, n_iterations=None): super(StratifiedShuffleSplit, self).__init__( - len(y), n_iter, test_size, train_size, indices, random_state, + len(y), n_iter, test_size, train_size, random_state, n_iterations) self.y = np.array(y) self.classes, self.y_indices = np.unique(y, return_inverse=True) @@ -1105,8 +928,8 @@ class PredefinedSplit(_PartitionIterator): TRAIN: [0 2] TEST: [1 3] """ - def __init__(self, test_fold, indices=None): - super(PredefinedSplit, self).__init__(len(test_fold), indices) + def __init__(self, test_fold): + super(PredefinedSplit, self).__init__(len(test_fold)) self.test_fold = np.array(test_fold, dtype=np.int) self.test_fold = column_or_1d(self.test_fold) self.unique_folds = np.unique(self.test_fold) @@ -1586,35 +1409,26 @@ def check_cv(cv, X=None, y=None, classifier=False): The return value is guaranteed to be a cv generator instance, whatever the input type. """ - return _check_cv(cv, X=X, y=y, classifier=classifier, warn_mask=True) + return _check_cv(cv, X=X, y=y, classifier=classifier) -def _check_cv(cv, X=None, y=None, classifier=False, warn_mask=False): +def _check_cv(cv, X=None, y=None, classifier=False): # This exists for internal use while indices is being deprecated. is_sparse = sp.issparse(X) - needs_indices = is_sparse or not hasattr(X, "shape") if cv is None: cv = 3 if isinstance(cv, numbers.Integral): - if warn_mask and not needs_indices: - warnings.warn('check_cv will return indices instead of boolean ' - 'masks from 0.17', DeprecationWarning) - else: - needs_indices = None if classifier: if type_of_target(y) in ['binary', 'multiclass']: - cv = StratifiedKFold(y, cv, indices=needs_indices) + cv = StratifiedKFold(y, cv) else: - cv = KFold(_num_samples(y), cv, indices=needs_indices) + cv = KFold(_num_samples(y), cv) else: if not is_sparse: n_samples = len(X) else: n_samples = X.shape[0] - cv = KFold(n_samples, cv, indices=needs_indices) - if needs_indices and not getattr(cv, "_indices", True): - raise ValueError("Sparse data and lists require indices-based cross" - " validation generator, got: %r", cv) + cv = KFold(n_samples, cv) return cv diff --git a/sklearn/datasets/samples_generator.py b/sklearn/datasets/samples_generator.py index 58e2dfbefccb5..dae2b5ae82361 100644 --- a/sklearn/datasets/samples_generator.py +++ b/sklearn/datasets/samples_generator.py @@ -612,7 +612,7 @@ def make_circles(n_samples=100, shuffle=True, noise=None, random_state=None, if shuffle: X, y = util_shuffle(X, y, random_state=generator) - if not noise is None: + if noise is not None: X += generator.normal(scale=noise, size=X.shape) return X, y @@ -662,7 +662,7 @@ def make_moons(n_samples=100, shuffle=True, noise=None, random_state=None): if shuffle: X, y = util_shuffle(X, y, random_state=generator) - if not noise is None: + if noise is not None: X += generator.normal(scale=noise, size=X.shape) return X, y @@ -1219,7 +1219,7 @@ def make_sparse_spd_matrix(dim=1, alpha=0.95, norm_diag=False, d = np.diag(prec).reshape(1, prec.shape[0]) d = 1. / np.sqrt(d) - prec *= d + prec *= d prec *= d.T return prec diff --git a/sklearn/dummy.py b/sklearn/dummy.py index 2bb4fdb94ca53..196aefb45b0b7 100644 --- a/sklearn/dummy.py +++ b/sklearn/dummy.py @@ -12,7 +12,6 @@ from .utils import check_random_state from .utils.validation import check_array from .utils.validation import check_consistent_length -from .utils import deprecated from .utils.random import random_choice_csc from .utils.stats import _weighted_percentile from .utils.multiclass import class_distribution @@ -360,13 +359,6 @@ def __init__(self, strategy="mean", constant=None, quantile=None): self.constant = constant self.quantile = quantile - @property - @deprecated('This will be removed in version 0.17') - def y_mean_(self): - if self.strategy == 'mean': - return self.constant_ - raise AttributeError - def fit(self, X, y, sample_weight=None): """Fit the random regressor. diff --git a/sklearn/hmm.py b/sklearn/hmm.py deleted file mode 100644 index c05d3ef616054..0000000000000 --- a/sklearn/hmm.py +++ /dev/null @@ -1,1300 +0,0 @@ -# Hidden Markov Models -# -# Author: Ron Weiss -# and Shiqiao Du -# API changes: Jaques Grobler - -""" -The :mod:`sklearn.hmm` module implements hidden Markov models. - -**Warning:** :mod:`sklearn.hmm` is orphaned, undocumented and has known -numerical stability issues. This module will be removed in version 0.17. - -It has been moved to a separate repository: -https://github.com/hmmlearn/hmmlearn -""" - -import string - -import numpy as np - -from .utils import check_random_state, deprecated -from .utils.extmath import logsumexp -from .utils.validation import check_is_fitted -from .base import BaseEstimator -from .mixture import ( - GMM, log_multivariate_normal_density, sample_gaussian, - distribute_covar_matrix_to_match_covariance_type, _validate_covars) -from . import cluster -from . import _hmmc - -__all__ = ['GMMHMM', - 'GaussianHMM', - 'MultinomialHMM', - 'decoder_algorithms', - 'normalize'] - -ZEROLOGPROB = -1e200 -EPS = np.finfo(float).eps -NEGINF = -np.inf -decoder_algorithms = ("viterbi", "map") - - -@deprecated("WARNING: The HMM module and its functions will be removed in 0.17 " - "as it no longer falls within the project's scope and API. " - "It has been moved to a separate repository: " - "https://github.com/hmmlearn/hmmlearn") -def normalize(A, axis=None): - """ Normalize the input array so that it sums to 1. - - WARNING: The HMM module and its functions will be removed in 0.17 - as it no longer falls within the project's scope and API. - - Parameters - ---------- - A: array, shape (n_samples, n_features) - Non-normalized input data - axis: int - dimension along which normalization is performed - - Returns - ------- - normalized_A: array, shape (n_samples, n_features) - A with values normalized (summing to 1) along the prescribed axis - - WARNING: Modifies inplace the array - """ - A += EPS - Asum = A.sum(axis) - if axis and A.ndim > 1: - # Make sure we don't divide by zero. - Asum[Asum == 0] = 1 - shape = list(A.shape) - shape[axis] = 1 - Asum.shape = shape - return A / Asum - - -@deprecated("WARNING: The HMM module and its function will be removed in 0.17" - "as it no longer falls within the project's scope and API. " - "It has been moved to a separate repository: " - "https://github.com/hmmlearn/hmmlearn") -class _BaseHMM(BaseEstimator): - """Hidden Markov Model base class. - - Representation of a hidden Markov model probability distribution. - This class allows for easy evaluation of, sampling from, and - maximum-likelihood estimation of the parameters of a HMM. - - See the instance documentation for details specific to a - particular object. - - .. warning:: - - The HMM module and its functions will be removed in 0.17 - as it no longer falls within the project's scope and API. - - Attributes - ---------- - n_components : int - Number of states in the model. - - transmat : array, shape (`n_components`, `n_components`) - Matrix of transition probabilities between states. - - startprob : array, shape ('n_components`,) - Initial state occupation distribution. - - transmat_prior : array, shape (`n_components`, `n_components`) - Matrix of prior transition probabilities between states. - - startprob_prior : array, shape ('n_components`,) - Initial state occupation prior distribution. - - algorithm : string, one of the decoder_algorithms - decoder algorithm - - random_state: RandomState or an int seed (0 by default) - A random number generator instance - - n_iter : int, optional - Number of iterations to perform. - - thresh : float, optional - Convergence threshold. - - params : string, optional - Controls which parameters are updated in the training - process. Can contain any combination of 's' for startprob, - 't' for transmat, and other characters for subclass-specific - emmission parameters. Defaults to all parameters. - - - init_params : string, optional - Controls which parameters are initialized prior to - training. Can contain any combination of 's' for - startprob, 't' for transmat, and other characters for - subclass-specific emmission parameters. Defaults to all - parameters. - - See Also - -------- - GMM : Gaussian mixture model - """ - - # This class implements the public interface to all HMMs that - # derive from it, including all of the machinery for the - # forward-backward and Viterbi algorithms. Subclasses need only - # implement _generate_sample_from_state(), _compute_log_likelihood(), - # _init(), _initialize_sufficient_statistics(), - # _accumulate_sufficient_statistics(), and _do_mstep(), all of - # which depend on the specific emission distribution. - # - # Subclasses will probably also want to implement properties for - # the emission distribution parameters to expose them publicly. - - def __init__(self, n_components=1, startprob=None, transmat=None, - startprob_prior=None, transmat_prior=None, - algorithm="viterbi", random_state=None, - n_iter=10, thresh=1e-2, params=string.ascii_letters, - init_params=string.ascii_letters): - - self.n_components = n_components - self.n_iter = n_iter - self.thresh = thresh - self.params = params - self.init_params = init_params - self.startprob_ = startprob - self.startprob_prior = startprob_prior - self.transmat_ = transmat - self.transmat_prior = transmat_prior - self._algorithm = algorithm - self.random_state = random_state - - def eval(self, X): - return self.score_samples(X) - - def score_samples(self, obs): - """Compute the log probability under the model and compute posteriors. - - Parameters - ---------- - obs : array_like, shape (n, n_features) - Sequence of n_features-dimensional data points. Each row - corresponds to a single point in the sequence. - - Returns - ------- - logprob : float - Log likelihood of the sequence ``obs``. - - posteriors : array_like, shape (n, n_components) - Posterior probabilities of each state for each - observation - - See Also - -------- - score : Compute the log probability under the model - decode : Find most likely state sequence corresponding to a `obs` - """ - obs = np.asarray(obs) - framelogprob = self._compute_log_likelihood(obs) - logprob, fwdlattice = self._do_forward_pass(framelogprob) - bwdlattice = self._do_backward_pass(framelogprob) - gamma = fwdlattice + bwdlattice - # gamma is guaranteed to be correctly normalized by logprob at - # all frames, unless we do approximate inference using pruning. - # So, we will normalize each frame explicitly in case we - # pruned too aggressively. - posteriors = np.exp(gamma.T - logsumexp(gamma, axis=1)).T - posteriors += np.finfo(np.float32).eps - posteriors /= np.sum(posteriors, axis=1).reshape((-1, 1)) - return logprob, posteriors - - def score(self, obs): - """Compute the log probability under the model. - - Parameters - ---------- - obs : array_like, shape (n, n_features) - Sequence of n_features-dimensional data points. Each row - corresponds to a single data point. - - Returns - ------- - logprob : float - Log likelihood of the ``obs``. - - See Also - -------- - score_samples : Compute the log probability under the model and - posteriors - - decode : Find most likely state sequence corresponding to a `obs` - """ - obs = np.asarray(obs) - framelogprob = self._compute_log_likelihood(obs) - logprob, _ = self._do_forward_pass(framelogprob) - return logprob - - def _decode_viterbi(self, obs): - """Find most likely state sequence corresponding to ``obs``. - - Uses the Viterbi algorithm. - - Parameters - ---------- - obs : array_like, shape (n, n_features) - Sequence of n_features-dimensional data points. Each row - corresponds to a single point in the sequence. - - Returns - ------- - viterbi_logprob : float - Log probability of the maximum likelihood path through the HMM. - - state_sequence : array_like, shape (n,) - Index of the most likely states for each observation. - - See Also - -------- - score_samples : Compute the log probability under the model and - posteriors. - - score : Compute the log probability under the model - """ - obs = np.asarray(obs) - framelogprob = self._compute_log_likelihood(obs) - viterbi_logprob, state_sequence = self._do_viterbi_pass(framelogprob) - return viterbi_logprob, state_sequence - - def _decode_map(self, obs): - """Find most likely state sequence corresponding to `obs`. - - Uses the maximum a posteriori estimation. - - Parameters - ---------- - obs : array_like, shape (n, n_features) - Sequence of n_features-dimensional data points. Each row - corresponds to a single point in the sequence. - - Returns - ------- - map_logprob : float - Log probability of the maximum likelihood path through the HMM - state_sequence : array_like, shape (n,) - Index of the most likely states for each observation - - See Also - -------- - score_samples : Compute the log probability under the model and - posteriors. - score : Compute the log probability under the model. - """ - _, posteriors = self.score_samples(obs) - state_sequence = np.argmax(posteriors, axis=1) - map_logprob = np.max(posteriors, axis=1).sum() - return map_logprob, state_sequence - - def decode(self, obs, algorithm="viterbi"): - """Find most likely state sequence corresponding to ``obs``. - Uses the selected algorithm for decoding. - - Parameters - ---------- - obs : array_like, shape (n, n_features) - Sequence of n_features-dimensional data points. Each row - corresponds to a single point in the sequence. - - algorithm : string, one of the `decoder_algorithms` - decoder algorithm to be used - - Returns - ------- - logprob : float - Log probability of the maximum likelihood path through the HMM - - state_sequence : array_like, shape (n,) - Index of the most likely states for each observation - - See Also - -------- - score_samples : Compute the log probability under the model and - posteriors. - - score : Compute the log probability under the model. - """ - if self._algorithm in decoder_algorithms: - algorithm = self._algorithm - elif algorithm in decoder_algorithms: - algorithm = algorithm - decoder = {"viterbi": self._decode_viterbi, - "map": self._decode_map} - logprob, state_sequence = decoder[algorithm](obs) - return logprob, state_sequence - - def predict(self, obs, algorithm="viterbi"): - """Find most likely state sequence corresponding to `obs`. - - Parameters - ---------- - obs : array_like, shape (n, n_features) - Sequence of n_features-dimensional data points. Each row - corresponds to a single point in the sequence. - - Returns - ------- - state_sequence : array_like, shape (n,) - Index of the most likely states for each observation - """ - _, state_sequence = self.decode(obs, algorithm) - return state_sequence - - def predict_proba(self, obs): - """Compute the posterior probability for each state in the model - - Parameters - ---------- - obs : array_like, shape (n, n_features) - Sequence of n_features-dimensional data points. Each row - corresponds to a single point in the sequence. - - Returns - ------- - T : array-like, shape (n, n_components) - Returns the probability of the sample for each state in the model. - """ - _, posteriors = self.score_samples(obs) - return posteriors - - def sample(self, n=1, random_state=None): - """Generate random samples from the model. - - Parameters - ---------- - n : int - Number of samples to generate. - - random_state: RandomState or an int seed (0 by default) - A random number generator instance. If None is given, the - object's random_state is used - - Returns - ------- - (obs, hidden_states) - obs : array_like, length `n` List of samples - hidden_states : array_like, length `n` List of hidden states - """ - if random_state is None: - random_state = self.random_state - random_state = check_random_state(random_state) - - startprob_pdf = self.startprob_ - startprob_cdf = np.cumsum(startprob_pdf) - transmat_pdf = self.transmat_ - transmat_cdf = np.cumsum(transmat_pdf, 1) - - # Initial state. - rand = random_state.rand() - currstate = (startprob_cdf > rand).argmax() - hidden_states = [currstate] - obs = [self._generate_sample_from_state( - currstate, random_state=random_state)] - - for _ in range(n - 1): - rand = random_state.rand() - currstate = (transmat_cdf[currstate] > rand).argmax() - hidden_states.append(currstate) - obs.append(self._generate_sample_from_state( - currstate, random_state=random_state)) - - return np.array(obs), np.array(hidden_states, dtype=int) - - def fit(self, obs): - """Estimate model parameters. - - An initialization step is performed before entering the EM - algorithm. If you want to avoid this step, pass proper - ``init_params`` keyword argument to estimator's constructor. - - Parameters - ---------- - obs : list - List of array-like observation sequences, each of which - has shape (n_i, n_features), where n_i is the length of - the i_th observation. - - Notes - ----- - In general, `logprob` should be non-decreasing unless - aggressive pruning is used. Decreasing `logprob` is generally - a sign of overfitting (e.g. a covariance parameter getting too - small). You can fix this by getting more training data, - or strengthening the appropriate subclass-specific regularization - parameter. - """ - - if self.algorithm not in decoder_algorithms: - self._algorithm = "viterbi" - - self._init(obs, self.init_params) - - logprob = [] - for i in range(self.n_iter): - # Expectation step - stats = self._initialize_sufficient_statistics() - curr_logprob = 0 - for seq in obs: - framelogprob = self._compute_log_likelihood(seq) - lpr, fwdlattice = self._do_forward_pass(framelogprob) - bwdlattice = self._do_backward_pass(framelogprob) - gamma = fwdlattice + bwdlattice - posteriors = np.exp(gamma.T - logsumexp(gamma, axis=1)).T - curr_logprob += lpr - self._accumulate_sufficient_statistics( - stats, seq, framelogprob, posteriors, fwdlattice, - bwdlattice, self.params) - logprob.append(curr_logprob) - - # Check for convergence. - if i > 0 and abs(logprob[-1] - logprob[-2]) < self.thresh: - break - - # Maximization step - self._do_mstep(stats, self.params) - - return self - - def _get_algorithm(self): - "decoder algorithm" - return self._algorithm - - def _set_algorithm(self, algorithm): - if algorithm not in decoder_algorithms: - raise ValueError("algorithm must be one of the decoder_algorithms") - self._algorithm = algorithm - - algorithm = property(_get_algorithm, _set_algorithm) - - def _get_startprob(self): - """Mixing startprob for each state.""" - return np.exp(self._log_startprob) - - def _set_startprob(self, startprob): - if startprob is None: - startprob = np.tile(1.0 / self.n_components, self.n_components) - else: - startprob = np.asarray(startprob, dtype=np.float) - - # check if there exists a component whose value is exactly zero - # if so, add a small number and re-normalize - if not np.alltrue(startprob): - normalize(startprob) - - if len(startprob) != self.n_components: - raise ValueError('startprob must have length n_components') - if not np.allclose(np.sum(startprob), 1.0): - raise ValueError('startprob must sum to 1.0') - - self._log_startprob = np.log(np.asarray(startprob).copy()) - - startprob_ = property(_get_startprob, _set_startprob) - - def _get_transmat(self): - """Matrix of transition probabilities.""" - return np.exp(self._log_transmat) - - def _set_transmat(self, transmat): - if transmat is None: - transmat = np.tile(1.0 / self.n_components, - (self.n_components, self.n_components)) - - # check if there exists a component whose value is exactly zero - # if so, add a small number and re-normalize - if not np.alltrue(transmat): - normalize(transmat, axis=1) - - if (np.asarray(transmat).shape - != (self.n_components, self.n_components)): - raise ValueError('transmat must have shape ' - '(n_components, n_components)') - if not np.all(np.allclose(np.sum(transmat, axis=1), 1.0)): - raise ValueError('Rows of transmat must sum to 1.0') - - self._log_transmat = np.log(np.asarray(transmat).copy()) - underflow_idx = np.isnan(self._log_transmat) - self._log_transmat[underflow_idx] = NEGINF - - transmat_ = property(_get_transmat, _set_transmat) - - def _do_viterbi_pass(self, framelogprob): - n_observations, n_components = framelogprob.shape - state_sequence, logprob = _hmmc._viterbi( - n_observations, n_components, self._log_startprob, - self._log_transmat, framelogprob) - return logprob, state_sequence - - def _do_forward_pass(self, framelogprob): - - n_observations, n_components = framelogprob.shape - fwdlattice = np.zeros((n_observations, n_components)) - _hmmc._forward(n_observations, n_components, self._log_startprob, - self._log_transmat, framelogprob, fwdlattice) - fwdlattice[fwdlattice <= ZEROLOGPROB] = NEGINF - return logsumexp(fwdlattice[-1]), fwdlattice - - def _do_backward_pass(self, framelogprob): - n_observations, n_components = framelogprob.shape - bwdlattice = np.zeros((n_observations, n_components)) - _hmmc._backward(n_observations, n_components, self._log_startprob, - self._log_transmat, framelogprob, bwdlattice) - - bwdlattice[bwdlattice <= ZEROLOGPROB] = NEGINF - - return bwdlattice - - def _compute_log_likelihood(self, obs): - pass - - def _generate_sample_from_state(self, state, random_state=None): - pass - - def _init(self, obs, params): - if 's' in params: - self.startprob_.fill(1.0 / self.n_components) - if 't' in params: - self.transmat_.fill(1.0 / self.n_components) - - # Methods used by self.fit() - - def _initialize_sufficient_statistics(self): - stats = {'nobs': 0, - 'start': np.zeros(self.n_components), - 'trans': np.zeros((self.n_components, self.n_components))} - return stats - - def _accumulate_sufficient_statistics(self, stats, seq, framelogprob, - posteriors, fwdlattice, bwdlattice, - params): - stats['nobs'] += 1 - if 's' in params: - stats['start'] += posteriors[0] - if 't' in params: - n_observations, n_components = framelogprob.shape - # when the sample is of length 1, it contains no transitions - # so there is no reason to update our trans. matrix estimate - if n_observations > 1: - lneta = np.zeros((n_observations - 1, n_components, n_components)) - lnP = logsumexp(fwdlattice[-1]) - _hmmc._compute_lneta(n_observations, n_components, fwdlattice, - self._log_transmat, bwdlattice, framelogprob, - lnP, lneta) - stats['trans'] += np.exp(np.minimum(logsumexp(lneta, 0), 700)) - - def _do_mstep(self, stats, params): - # Based on Huang, Acero, Hon, "Spoken Language Processing", - # p. 443 - 445 - if self.startprob_prior is None: - self.startprob_prior = 1.0 - if self.transmat_prior is None: - self.transmat_prior = 1.0 - - if 's' in params: - self.startprob_ = normalize( - np.maximum(self.startprob_prior - 1.0 + stats['start'], 1e-20)) - if 't' in params: - transmat_ = normalize( - np.maximum(self.transmat_prior - 1.0 + stats['trans'], 1e-20), - axis=1) - self.transmat_ = transmat_ - - -class GaussianHMM(_BaseHMM): - """Hidden Markov Model with Gaussian emissions - - Representation of a hidden Markov model probability distribution. - This class allows for easy evaluation of, sampling from, and - maximum-likelihood estimation of the parameters of a HMM. - - .. warning:: - - The HMM module and its functions will be removed in 0.17 - as it no longer falls within the project's scope and API. - - Parameters - ---------- - n_components : int - Number of states. - - ``_covariance_type`` : string - String describing the type of covariance parameters to - use. Must be one of 'spherical', 'tied', 'diag', 'full'. - Defaults to 'diag'. - - Attributes - ---------- - ``_covariance_type`` : string - String describing the type of covariance parameters used by - the model. Must be one of 'spherical', 'tied', 'diag', 'full'. - - n_features : int - Dimensionality of the Gaussian emissions. - - n_components : int - Number of states in the model. - - transmat : array, shape (`n_components`, `n_components`) - Matrix of transition probabilities between states. - - startprob : array, shape ('n_components`,) - Initial state occupation distribution. - - means : array, shape (`n_components`, `n_features`) - Mean parameters for each state. - - covars : array - Covariance parameters for each state. The shape depends on - ``_covariance_type``:: - - (`n_components`,) if 'spherical', - (`n_features`, `n_features`) if 'tied', - (`n_components`, `n_features`) if 'diag', - (`n_components`, `n_features`, `n_features`) if 'full' - - random_state: RandomState or an int seed (0 by default) - A random number generator instance - - n_iter : int, optional - Number of iterations to perform. - - thresh : float, optional - Convergence threshold. - - params : string, optional - Controls which parameters are updated in the training - process. Can contain any combination of 's' for startprob, - 't' for transmat, 'm' for means, and 'c' for covars. - Defaults to all parameters. - - init_params : string, optional - Controls which parameters are initialized prior to - training. Can contain any combination of 's' for - startprob, 't' for transmat, 'm' for means, and 'c' for - covars. Defaults to all parameters. - - - Examples - -------- - >>> from sklearn.hmm import GaussianHMM - >>> GaussianHMM(n_components=2) - ... #doctest: +ELLIPSIS +NORMALIZE_WHITESPACE - GaussianHMM(algorithm='viterbi',... - - - See Also - -------- - GMM : Gaussian mixture model - """ - - def __init__(self, n_components=1, covariance_type='diag', startprob=None, - transmat=None, startprob_prior=None, transmat_prior=None, - algorithm="viterbi", means_prior=None, means_weight=0, - covars_prior=1e-2, covars_weight=1, - random_state=None, n_iter=10, thresh=1e-2, - params=string.ascii_letters, - init_params=string.ascii_letters): - _BaseHMM.__init__(self, n_components, startprob, transmat, - startprob_prior=startprob_prior, - transmat_prior=transmat_prior, algorithm=algorithm, - random_state=random_state, n_iter=n_iter, - thresh=thresh, params=params, - init_params=init_params) - - self._covariance_type = covariance_type - if not covariance_type in ['spherical', 'tied', 'diag', 'full']: - raise ValueError('bad covariance_type') - - self.means_prior = means_prior - self.means_weight = means_weight - - self.covars_prior = covars_prior - self.covars_weight = covars_weight - - @property - def covariance_type(self): - """Covariance type of the model. - - Must be one of 'spherical', 'tied', 'diag', 'full'. - """ - return self._covariance_type - - def _get_means(self): - """Mean parameters for each state.""" - return self._means_ - - def _set_means(self, means): - means = np.asarray(means) - if (hasattr(self, 'n_features') - and means.shape != (self.n_components, self.n_features)): - raise ValueError('means must have shape ' - '(n_components, n_features)') - self._means_ = means.copy() - self.n_features = self._means_.shape[1] - - means_ = property(_get_means, _set_means) - - def _get_covars(self): - """Return covars as a full matrix.""" - if self._covariance_type == 'full': - return self._covars_ - elif self._covariance_type == 'diag': - return [np.diag(cov) for cov in self._covars_] - elif self._covariance_type == 'tied': - return [self._covars_] * self.n_components - elif self._covariance_type == 'spherical': - return [np.eye(self.n_features) * f for f in self._covars_] - - def _set_covars(self, covars): - covars = np.asarray(covars) - _validate_covars(covars, self._covariance_type, self.n_components) - self._covars_ = covars.copy() - - covars_ = property(_get_covars, _set_covars) - - def _compute_log_likelihood(self, obs): - check_is_fitted(self, '_means_') - - return log_multivariate_normal_density( - obs, self._means_, self._covars_, self._covariance_type) - - def _generate_sample_from_state(self, state, random_state=None): - if self._covariance_type == 'tied': - cv = self._covars_ - else: - cv = self._covars_[state] - return sample_gaussian(self._means_[state], cv, self._covariance_type, - random_state=random_state) - - def _init(self, obs, params='stmc'): - super(GaussianHMM, self)._init(obs, params=params) - - if (hasattr(self, 'n_features') - and self.n_features != obs[0].shape[1]): - raise ValueError('Unexpected number of dimensions, got %s but ' - 'expected %s' % (obs[0].shape[1], - self.n_features)) - - self.n_features = obs[0].shape[1] - - if 'm' in params: - self._means_ = cluster.KMeans( - n_clusters=self.n_components).fit(obs[0]).cluster_centers_ - if 'c' in params: - cv = np.cov(obs[0].T) - if not cv.shape: - cv.shape = (1, 1) - self._covars_ = distribute_covar_matrix_to_match_covariance_type( - cv, self._covariance_type, self.n_components) - self._covars_[self._covars_ == 0] = 1e-5 - - def _initialize_sufficient_statistics(self): - stats = super(GaussianHMM, self)._initialize_sufficient_statistics() - stats['post'] = np.zeros(self.n_components) - stats['obs'] = np.zeros((self.n_components, self.n_features)) - stats['obs**2'] = np.zeros((self.n_components, self.n_features)) - stats['obs*obs.T'] = np.zeros((self.n_components, self.n_features, - self.n_features)) - return stats - - def _accumulate_sufficient_statistics(self, stats, obs, framelogprob, - posteriors, fwdlattice, bwdlattice, - params): - super(GaussianHMM, self)._accumulate_sufficient_statistics( - stats, obs, framelogprob, posteriors, fwdlattice, bwdlattice, - params) - - if 'm' in params or 'c' in params: - stats['post'] += posteriors.sum(axis=0) - stats['obs'] += np.dot(posteriors.T, obs) - - if 'c' in params: - if self._covariance_type in ('spherical', 'diag'): - stats['obs**2'] += np.dot(posteriors.T, obs ** 2) - elif self._covariance_type in ('tied', 'full'): - for t, o in enumerate(obs): - obsobsT = np.outer(o, o) - for c in range(self.n_components): - stats['obs*obs.T'][c] += posteriors[t, c] * obsobsT - - def _do_mstep(self, stats, params): - super(GaussianHMM, self)._do_mstep(stats, params) - - # Based on Huang, Acero, Hon, "Spoken Language Processing", - # p. 443 - 445 - denom = stats['post'][:, np.newaxis] - if 'm' in params: - prior = self.means_prior - weight = self.means_weight - if prior is None: - weight = 0 - prior = 0 - self._means_ = (weight * prior + stats['obs']) / (weight + denom) - - if 'c' in params: - covars_prior = self.covars_prior - covars_weight = self.covars_weight - if covars_prior is None: - covars_weight = 0 - covars_prior = 0 - - means_prior = self.means_prior - means_weight = self.means_weight - if means_prior is None: - means_weight = 0 - means_prior = 0 - meandiff = self._means_ - means_prior - - if self._covariance_type in ('spherical', 'diag'): - cv_num = (means_weight * (meandiff) ** 2 - + stats['obs**2'] - - 2 * self._means_ * stats['obs'] - + self._means_ ** 2 * denom) - cv_den = max(covars_weight - 1, 0) + denom - self._covars_ = (covars_prior + cv_num) / np.maximum(cv_den, 1e-5) - if self._covariance_type == 'spherical': - self._covars_ = np.tile( - self._covars_.mean(1)[:, np.newaxis], - (1, self._covars_.shape[1])) - elif self._covariance_type in ('tied', 'full'): - cvnum = np.empty((self.n_components, self.n_features, - self.n_features)) - for c in range(self.n_components): - obsmean = np.outer(stats['obs'][c], self._means_[c]) - - cvnum[c] = (means_weight * np.outer(meandiff[c], - meandiff[c]) - + stats['obs*obs.T'][c] - - obsmean - obsmean.T - + np.outer(self._means_[c], self._means_[c]) - * stats['post'][c]) - cvweight = max(covars_weight - self.n_features, 0) - if self._covariance_type == 'tied': - self._covars_ = ((covars_prior + cvnum.sum(axis=0)) / - (cvweight + stats['post'].sum())) - elif self._covariance_type == 'full': - self._covars_ = ((covars_prior + cvnum) / - (cvweight + stats['post'][:, None, None])) - - def fit(self, obs): - """Estimate model parameters. - - An initialization step is performed before entering the EM - algorithm. If you want to avoid this step, pass proper - ``init_params`` keyword argument to estimator's constructor. - - Parameters - ---------- - obs : list - List of array-like observation sequences, each of which - has shape (n_i, n_features), where n_i is the length of - the i_th observation. - - Notes - ----- - In general, `logprob` should be non-decreasing unless - aggressive pruning is used. Decreasing `logprob` is generally - a sign of overfitting (e.g. the covariance parameter on one or - more components becomminging too small). You can fix this by getting - more training data, or increasing covars_prior. - """ - return super(GaussianHMM, self).fit(obs) - -class MultinomialHMM(_BaseHMM): - """Hidden Markov Model with multinomial (discrete) emissions - - .. warning:: - - The HMM module and its functions will be removed in 0.17 - as it no longer falls within the project's scope and API. - - Attributes - ---------- - n_components : int - Number of states in the model. - - n_symbols : int - Number of possible symbols emitted by the model (in the observations). - - transmat : array, shape (`n_components`, `n_components`) - Matrix of transition probabilities between states. - - startprob : array, shape ('n_components`,) - Initial state occupation distribution. - - emissionprob : array, shape ('n_components`, 'n_symbols`) - Probability of emitting a given symbol when in each state. - - random_state: RandomState or an int seed (0 by default) - A random number generator instance - - n_iter : int, optional - Number of iterations to perform. - - thresh : float, optional - Convergence threshold. - - params : string, optional - Controls which parameters are updated in the training - process. Can contain any combination of 's' for startprob, - 't' for transmat, 'e' for emmissionprob. - Defaults to all parameters. - - init_params : string, optional - Controls which parameters are initialized prior to - training. Can contain any combination of 's' for - startprob, 't' for transmat, 'e' for emmissionprob. - Defaults to all parameters. - - Examples - -------- - >>> from sklearn.hmm import MultinomialHMM - >>> MultinomialHMM(n_components=2) - ... #doctest: +ELLIPSIS +NORMALIZE_WHITESPACE - MultinomialHMM(algorithm='viterbi',... - - See Also - -------- - GaussianHMM : HMM with Gaussian emissions - """ - - def __init__(self, n_components=1, startprob=None, transmat=None, - startprob_prior=None, transmat_prior=None, - algorithm="viterbi", random_state=None, - n_iter=10, thresh=1e-2, params=string.ascii_letters, - init_params=string.ascii_letters): - """Create a hidden Markov model with multinomial emissions. - - Parameters - ---------- - n_components : int - Number of states. - """ - _BaseHMM.__init__(self, n_components, startprob, transmat, - startprob_prior=startprob_prior, - transmat_prior=transmat_prior, - algorithm=algorithm, - random_state=random_state, - n_iter=n_iter, - thresh=thresh, - params=params, - init_params=init_params) - - def _get_emissionprob(self): - """Emission probability distribution for each state.""" - return np.exp(self._log_emissionprob) - - def _set_emissionprob(self, emissionprob): - emissionprob = np.asarray(emissionprob) - if hasattr(self, 'n_symbols') and \ - emissionprob.shape != (self.n_components, self.n_symbols): - raise ValueError('emissionprob must have shape ' - '(n_components, n_symbols)') - - # check if there exists a component whose value is exactly zero - # if so, add a small number and re-normalize - if not np.alltrue(emissionprob): - normalize(emissionprob) - - self._log_emissionprob = np.log(emissionprob) - underflow_idx = np.isnan(self._log_emissionprob) - self._log_emissionprob[underflow_idx] = NEGINF - self.n_symbols = self._log_emissionprob.shape[1] - - emissionprob_ = property(_get_emissionprob, _set_emissionprob) - - def _compute_log_likelihood(self, obs): - check_is_fitted(self, 'emissionprob_') - return self._log_emissionprob[:, obs].T - - def _generate_sample_from_state(self, state, random_state=None): - cdf = np.cumsum(self.emissionprob_[state, :]) - random_state = check_random_state(random_state) - rand = random_state.rand() - symbol = (cdf > rand).argmax() - return symbol - - def _init(self, obs, params='ste'): - super(MultinomialHMM, self)._init(obs, params=params) - self.random_state = check_random_state(self.random_state) - - if 'e' in params: - if not hasattr(self, 'n_symbols'): - symbols = set() - for o in obs: - symbols = symbols.union(set(o)) - self.n_symbols = len(symbols) - emissionprob = normalize(self.random_state.rand(self.n_components, - self.n_symbols), 1) - self.emissionprob_ = emissionprob - - def _initialize_sufficient_statistics(self): - stats = super(MultinomialHMM, self)._initialize_sufficient_statistics() - stats['obs'] = np.zeros((self.n_components, self.n_symbols)) - return stats - - def _accumulate_sufficient_statistics(self, stats, obs, framelogprob, - posteriors, fwdlattice, bwdlattice, - params): - super(MultinomialHMM, self)._accumulate_sufficient_statistics( - stats, obs, framelogprob, posteriors, fwdlattice, bwdlattice, - params) - if 'e' in params: - for t, symbol in enumerate(obs): - stats['obs'][:, symbol] += posteriors[t] - - def _do_mstep(self, stats, params): - super(MultinomialHMM, self)._do_mstep(stats, params) - if 'e' in params: - self.emissionprob_ = (stats['obs'] - / stats['obs'].sum(1)[:, np.newaxis]) - - def _check_input_symbols(self, obs): - """check if input can be used for Multinomial.fit input must be both - positive integer array and every element must be continuous. - e.g. x = [0, 0, 2, 1, 3, 1, 1] is OK and y = [0, 0, 3, 5, 10] not - """ - - symbols = np.asarray(obs).flatten() - - if symbols.dtype.kind != 'i': - # input symbols must be integer - return False - - if len(symbols) == 1: - # input too short - return False - - if np.any(symbols < 0): - # input contains negative intiger - return False - - symbols.sort() - if np.any(np.diff(symbols) > 1): - # input is discontinous - return False - - return True - - def fit(self, obs, **kwargs): - """Estimate model parameters. - - An initialization step is performed before entering the EM - algorithm. If you want to avoid this step, pass proper - ``init_params`` keyword argument to estimator's constructor. - - Parameters - ---------- - obs : list - List of array-like observation sequences, each of which - has shape (n_i, n_features), where n_i is the length of - the i_th observation. - """ - err_msg = ("Input must be both positive integer array and " - "every element must be continuous, but %s was given.") - - if not self._check_input_symbols(obs): - raise ValueError(err_msg % obs) - - return _BaseHMM.fit(self, obs, **kwargs) - - -class GMMHMM(_BaseHMM): - """Hidden Markov Model with Gaussin mixture emissions - - .. warning:: - - The HMM module and its functions will be removed in 0.17 - as it no longer falls within the project's scope and API. - - Attributes - ---------- - n_components : int - Number of states in the model. - - transmat : array, shape (`n_components`, `n_components`) - Matrix of transition probabilities between states. - - startprob : array, shape ('n_components`,) - Initial state occupation distribution. - - gmms : array of GMM objects, length `n_components` - GMM emission distributions for each state. - - random_state : RandomState or an int seed (0 by default) - A random number generator instance - - n_iter : int, optional - Number of iterations to perform. - - thresh : float, optional - Convergence threshold. - - init_params : string, optional - Controls which parameters are initialized prior to training. - Can contain any combination of 's' for startprob, 't' for transmat, 'm' - for means, 'c' for covars, and 'w' for GMM mixing weights. Defaults to - all parameters. - - params : string, optional - Controls which parameters are updated in the training process. Can - contain any combination of 's' for startprob, 't' for transmat, - 'm' for means, and 'c' for covars, and 'w' for GMM mixing weights. - Defaults to all parameters. - - Examples - -------- - >>> from sklearn.hmm import GMMHMM - >>> GMMHMM(n_components=2, n_mix=10, covariance_type='diag') - ... # doctest: +ELLIPSIS, +NORMALIZE_WHITESPACE - GMMHMM(algorithm='viterbi', covariance_type='diag',... - - See Also - -------- - GaussianHMM : HMM with Gaussian emissions - """ - - def __init__(self, n_components=1, n_mix=1, startprob=None, transmat=None, - startprob_prior=None, transmat_prior=None, - algorithm="viterbi", gmms=None, covariance_type='diag', - covars_prior=1e-2, random_state=None, n_iter=10, thresh=1e-2, - params=string.ascii_letters, - init_params=string.ascii_letters): - """Create a hidden Markov model with GMM emissions. - - Parameters - ---------- - n_components : int - Number of states. - """ - _BaseHMM.__init__(self, n_components, startprob, transmat, - startprob_prior=startprob_prior, - transmat_prior=transmat_prior, - algorithm=algorithm, - random_state=random_state, - n_iter=n_iter, - thresh=thresh, - params=params, - init_params=init_params) - - # XXX: Hotfit for n_mix that is incompatible with the scikit's - # BaseEstimator API - self.n_mix = n_mix - self._covariance_type = covariance_type - self.covars_prior = covars_prior - self.gmms = gmms - if gmms is None: - gmms = [] - for x in range(self.n_components): - if covariance_type is None: - g = GMM(n_mix) - else: - g = GMM(n_mix, covariance_type=covariance_type) - gmms.append(g) - self.gmms_ = gmms - - # Read-only properties. - @property - def covariance_type(self): - """Covariance type of the model. - - Must be one of 'spherical', 'tied', 'diag', 'full'. - """ - return self._covariance_type - - def _compute_log_likelihood(self, obs): - return np.array([g.score(obs) for g in self.gmms_]).T - - def _generate_sample_from_state(self, state, random_state=None): - return self.gmms_[state].sample(1, random_state=random_state).flatten() - - def _init(self, obs, params='stwmc'): - super(GMMHMM, self)._init(obs, params=params) - - allobs = np.concatenate(obs, 0) - for g in self.gmms_: - g.set_params(init_params=params, n_iter=0) - g.fit(allobs) - - def _initialize_sufficient_statistics(self): - stats = super(GMMHMM, self)._initialize_sufficient_statistics() - stats['norm'] = [np.zeros(g.weights_.shape) for g in self.gmms_] - stats['means'] = [np.zeros(np.shape(g.means_)) for g in self.gmms_] - stats['covars'] = [np.zeros(np.shape(g.covars_)) for g in self.gmms_] - return stats - - def _accumulate_sufficient_statistics(self, stats, obs, framelogprob, - posteriors, fwdlattice, bwdlattice, - params): - super(GMMHMM, self)._accumulate_sufficient_statistics( - stats, obs, framelogprob, posteriors, fwdlattice, bwdlattice, - params) - - for state, g in enumerate(self.gmms_): - _, lgmm_posteriors = g.score_samples(obs) - lgmm_posteriors += np.log(posteriors[:, state][:, np.newaxis] - + np.finfo(np.float).eps) - gmm_posteriors = np.exp(lgmm_posteriors) - tmp_gmm = GMM(g.n_components, covariance_type=g.covariance_type) - n_features = g.means_.shape[1] - tmp_gmm._set_covars( - distribute_covar_matrix_to_match_covariance_type( - np.eye(n_features), g.covariance_type, - g.n_components)) - norm = tmp_gmm._do_mstep(obs, gmm_posteriors, params) - - if np.any(np.isnan(tmp_gmm.covars_)): - raise ValueError - - stats['norm'][state] += norm - if 'm' in params: - stats['means'][state] += tmp_gmm.means_ * norm[:, np.newaxis] - if 'c' in params: - if tmp_gmm.covariance_type == 'tied': - stats['covars'][state] += tmp_gmm.covars_ * norm.sum() - else: - cvnorm = np.copy(norm) - shape = np.ones(tmp_gmm.covars_.ndim) - shape[0] = np.shape(tmp_gmm.covars_)[0] - cvnorm.shape = shape - stats['covars'][state] += tmp_gmm.covars_ * cvnorm - - def _do_mstep(self, stats, params): - super(GMMHMM, self)._do_mstep(stats, params) - # All that is left to do is to apply covars_prior to the - # parameters updated in _accumulate_sufficient_statistics. - for state, g in enumerate(self.gmms_): - n_features = g.means_.shape[1] - norm = stats['norm'][state] - if 'w' in params: - g.weights_ = normalize(norm) - if 'm' in params: - g.means_ = stats['means'][state] / norm[:, np.newaxis] - if 'c' in params: - if g.covariance_type == 'tied': - g.covars_ = ((stats['covars'][state] - + self.covars_prior * np.eye(n_features)) - / norm.sum()) - else: - cvnorm = np.copy(norm) - shape = np.ones(g.covars_.ndim) - shape[0] = np.shape(g.covars_)[0] - cvnorm.shape = shape - if (g.covariance_type in ['spherical', 'diag']): - g.covars_ = (stats['covars'][state] + - self.covars_prior) / cvnorm - elif g.covariance_type == 'full': - eye = np.eye(n_features) - g.covars_ = ((stats['covars'][state] - + self.covars_prior * eye[np.newaxis]) - / cvnorm) diff --git a/sklearn/linear_model/base.py b/sklearn/linear_model/base.py index ca4c36e1098b1..295e76c584135 100644 --- a/sklearn/linear_model/base.py +++ b/sklearn/linear_model/base.py @@ -15,7 +15,6 @@ from __future__ import division from abc import ABCMeta, abstractmethod import numbers -import warnings import numpy as np import scipy.sparse as sp @@ -348,7 +347,7 @@ def __init__(self, fit_intercept=True, normalize=False, copy_X=True, self.copy_X = copy_X self.n_jobs = n_jobs - def fit(self, X, y, n_jobs=1): + def fit(self, X, y): """ Fit linear model. @@ -364,14 +363,7 @@ def fit(self, X, y, n_jobs=1): ------- self : returns an instance of self. """ - if n_jobs != 1: - warnings.warn("The n_jobs parameter in fit is deprecated and will " - "be removed in 0.17. It has been moved from the fit " - "method to the LinearRegression class constructor.", - DeprecationWarning, stacklevel=2) - n_jobs_ = n_jobs - else: - n_jobs_ = self.n_jobs + n_jobs_ = self.n_jobs X, y = check_X_y(X, y, accept_sparse=['csr', 'csc', 'coo'], y_numeric=True, multi_output=True) diff --git a/sklearn/linear_model/ridge.py b/sklearn/linear_model/ridge.py index 9cf6ed43077e0..a5c3056b6d593 100644 --- a/sklearn/linear_model/ridge.py +++ b/sklearn/linear_model/ridge.py @@ -21,7 +21,7 @@ from ..base import RegressorMixin from ..utils.extmath import safe_sparse_dot from ..utils import check_X_y -from ..utils import compute_sample_weight, compute_class_weight +from ..utils import compute_sample_weight from ..utils import column_or_1d from ..preprocessing import LabelBinarizer from ..grid_search import GridSearchCV @@ -185,16 +185,6 @@ def _solve_svd(X, y, alpha): return np.dot(Vt.T, d_UT_y).T -def _deprecate_dense_cholesky(solver): - if solver == 'dense_cholesky': - warnings.warn(DeprecationWarning( - "The name 'dense_cholesky' is deprecated and will " - "be removed in 0.17. Use 'cholesky' instead. ")) - solver = 'cholesky' - - return solver - - def _rescale_data(X, y, sample_weight): """Rescale data so as to support sample_weight""" n_samples = X.shape[0] @@ -292,8 +282,6 @@ def ridge_regression(X, y, alpha, sample_weight=None, solver='auto', has_sw = sample_weight is not None - solver = _deprecate_dense_cholesky(solver) - if solver == 'auto': # cholesky if it's a dense array and cg in # any other case @@ -389,14 +377,12 @@ def fit(self, X, y, sample_weight=None): X, y, self.fit_intercept, self.normalize, self.copy_X, sample_weight=sample_weight) - solver = _deprecate_dense_cholesky(self.solver) - self.coef_ = ridge_regression(X, y, alpha=self.alpha, sample_weight=sample_weight, max_iter=self.max_iter, tol=self.tol, - solver=solver) + solver=self.solver) self._set_intercept(X_mean, y_mean, X_std) return self diff --git a/sklearn/linear_model/stochastic_gradient.py b/sklearn/linear_model/stochastic_gradient.py index 77602c80a7470..3031174a8c83a 100644 --- a/sklearn/linear_model/stochastic_gradient.py +++ b/sklearn/linear_model/stochastic_gradient.py @@ -6,7 +6,6 @@ import numpy as np import scipy.sparse as sp -import warnings from abc import ABCMeta, abstractmethod @@ -525,8 +524,7 @@ def partial_fit(self, X, y, classes=None, sample_weight=None): classes=classes, sample_weight=sample_weight, coef_init=None, intercept_init=None) - def fit(self, X, y, coef_init=None, intercept_init=None, - class_weight=None, sample_weight=None): + def fit(self, X, y, coef_init=None, intercept_init=None, sample_weight=None): """Fit linear model with Stochastic Gradient Descent. Parameters @@ -553,11 +551,6 @@ def fit(self, X, y, coef_init=None, intercept_init=None, ------- self : returns an instance of self. """ - if class_weight is not None: - warnings.warn("You are trying to set class_weight through the fit " - "method, which is deprecated and will be removed in" - "v0.17 of scikit-learn. Pass the class_weight into " - "the constructor instead.", DeprecationWarning) return self._fit(X, y, alpha=self.alpha, C=1.0, loss=self.loss, learning_rate=self.learning_rate, coef_init=coef_init, intercept_init=intercept_init, diff --git a/sklearn/linear_model/tests/test_base.py b/sklearn/linear_model/tests/test_base.py index 0be3b2dec7d5a..cf04c8f205cee 100644 --- a/sklearn/linear_model/tests/test_base.py +++ b/sklearn/linear_model/tests/test_base.py @@ -5,7 +5,6 @@ import numpy as np from scipy import sparse -import warnings from sklearn.utils.testing import assert_array_almost_equal from sklearn.utils.testing import assert_equal @@ -43,19 +42,6 @@ def test_linear_regression(): assert_array_almost_equal(clf.predict(X), [0]) -def test_linear_regression_n_jobs(): - """ - Test for the n_jobs parameter on the fit method and the constructor - """ - X = [[1], [2]] - Y = [1, 2] - clf = LinearRegression() - with warnings.catch_warnings(record=True): - clf_fit = clf.fit(X, Y, 4) - assert_equal(clf_fit.n_jobs, clf.n_jobs) - assert_equal(clf.n_jobs, 1) - - def test_fit_intercept(): """ Test assertions on betas shape. diff --git a/sklearn/linear_model/tests/test_sgd.py b/sklearn/linear_model/tests/test_sgd.py index eeb32ebda06f8..a5fd6b3c03dd7 100644 --- a/sklearn/linear_model/tests/test_sgd.py +++ b/sklearn/linear_model/tests/test_sgd.py @@ -14,7 +14,6 @@ from sklearn.utils.testing import assert_false, assert_true from sklearn.utils.testing import assert_equal from sklearn.utils.testing import assert_raises_regexp -from sklearn.utils.testing import assert_warns_message from sklearn import linear_model, datasets, metrics from sklearn.base import clone @@ -602,20 +601,6 @@ def test_wrong_class_weight_format(self): clf = self.factory(alpha=0.1, n_iter=1000, class_weight=[0.5]) clf.fit(X, Y) - def test_class_weight_warning(self): - """Tests that class_weight passed through fit raises warning. - This test should be removed after deprecating support for this""" - - clf = self.factory() - warning_message = ("You are trying to set class_weight through the fit " - "method, which is deprecated and will be removed in" - "v0.17 of scikit-learn. Pass the class_weight into " - "the constructor instead.") - assert_warns_message(DeprecationWarning, - warning_message, - clf.fit, X4, Y4, - class_weight=1) - def test_weights_multiplied(self): """Tests that class_weight and sample_weight are multiplicative""" class_weights = {1: .6, 2: .3} diff --git a/sklearn/preprocessing/tests/test_label.py b/sklearn/preprocessing/tests/test_label.py index f523870ffd2fd..77629d140f422 100644 --- a/sklearn/preprocessing/tests/test_label.py +++ b/sklearn/preprocessing/tests/test_label.py @@ -12,10 +12,6 @@ from sklearn.utils.testing import assert_array_equal from sklearn.utils.testing import assert_equal from sklearn.utils.testing import assert_raises -from sklearn.utils.testing import assert_true -from sklearn.utils.testing import assert_false -from sklearn.utils.testing import assert_warns -from sklearn.utils.testing import assert_warns_message from sklearn.utils.testing import ignore_warnings from sklearn.preprocessing.label import LabelBinarizer @@ -44,7 +40,6 @@ def test_label_binarizer(): inp = ["pos", "pos", "pos", "pos"] expected = np.array([[0, 0, 0, 0]]).T got = lb.fit_transform(inp) - assert_false(assert_warns(DeprecationWarning, getattr, lb, "multilabel_")) assert_array_equal(lb.classes_, ["pos"]) assert_array_equal(expected, got) assert_array_equal(lb.inverse_transform(got), inp) @@ -53,7 +48,6 @@ def test_label_binarizer(): inp = ["neg", "pos", "pos", "neg"] expected = np.array([[0, 1, 1, 0]]).T got = lb.fit_transform(inp) - assert_false(assert_warns(DeprecationWarning, getattr, lb, "multilabel_")) assert_array_equal(lb.classes_, ["neg", "pos"]) assert_array_equal(expected, got) @@ -72,7 +66,6 @@ def test_label_binarizer(): [1, 0, 0, 0]]) got = lb.fit_transform(inp) assert_array_equal(lb.classes_, ['0', 'eggs', 'ham', 'spam']) - assert_false(assert_warns(DeprecationWarning, getattr, lb, "multilabel_")) assert_array_equal(expected, got) assert_array_equal(lb.inverse_transform(got), inp) @@ -113,13 +106,7 @@ def test_label_binarizer_column_y(): out_2 = lb_2.fit_transform(inp_array) assert_array_equal(out_1, multilabel_indicator) - assert_true(assert_warns(DeprecationWarning, getattr, lb_1, "multilabel_")) - assert_false(assert_warns(DeprecationWarning, getattr, lb_1, - "indicator_matrix_")) - assert_array_equal(out_2, binaryclass_array) - assert_false(assert_warns(DeprecationWarning, getattr, lb_2, - "multilabel_")) # second for multiclass classification vs multi-label with multiple # classes @@ -136,11 +123,7 @@ def test_label_binarizer_column_y(): out_2 = lb_2.fit_transform(inp_array) assert_array_equal(out_1, out_2) - assert_true(assert_warns(DeprecationWarning, getattr, lb_1, "multilabel_")) - assert_array_equal(out_2, indicator) - assert_false(assert_warns(DeprecationWarning, getattr, lb_2, - "multilabel_")) def test_label_binarizer_set_label_encoding(): @@ -150,7 +133,6 @@ def test_label_binarizer_set_label_encoding(): inp = np.array([0, 1, 1, 0]) expected = np.array([[-2, 0, 0, -2]]).T got = lb.fit_transform(inp) - assert_false(assert_warns(DeprecationWarning, getattr, lb, "multilabel_")) assert_array_equal(expected, got) assert_array_equal(lb.inverse_transform(got), inp) @@ -164,7 +146,6 @@ def test_label_binarizer_set_label_encoding(): [-2, -2, +2, -2], [+2, -2, -2, -2]]) got = lb.fit_transform(inp) - assert_false(assert_warns(DeprecationWarning, getattr, lb, "multilabel_")) assert_array_equal(expected, got) assert_array_equal(lb.inverse_transform(got), inp) @@ -174,7 +155,6 @@ def test_label_binarizer_errors(): """Check that invalid arguments yield ValueError""" one_class = np.array([0, 0, 0, 0]) lb = LabelBinarizer().fit(one_class) - assert_false(assert_warns(DeprecationWarning, getattr, lb, "multilabel_")) multi_label = [(2, 3), (0,), (0, 2)] assert_raises(ValueError, lb.transform, multi_label) @@ -183,13 +163,6 @@ def test_label_binarizer_errors(): assert_raises(ValueError, lb.transform, []) assert_raises(ValueError, lb.inverse_transform, []) - y = np.array([[0, 1, 0], [1, 1, 1]]) - classes = np.arange(3) - assert_raises(ValueError, label_binarize, y, classes, multilabel=True, - neg_label=2, pos_label=1) - assert_raises(ValueError, label_binarize, y, classes, multilabel=True, - neg_label=2, pos_label=2) - assert_raises(ValueError, LabelBinarizer, neg_label=2, pos_label=1) assert_raises(ValueError, LabelBinarizer, neg_label=2, pos_label=2) @@ -513,7 +486,6 @@ def test_label_binarize_multiclass(): def test_label_binarize_multilabel(): - y_seq = [(1,), (0, 1, 2), tuple()] y_ind = np.array([[0, 1, 0], [1, 1, 1], [0, 0, 0]]) classes = [0, 1, 2] pos_label = 2 @@ -527,34 +499,10 @@ def test_label_binarize_multilabel(): yield (check_binarized_results, y, classes, pos_label, neg_label, expected) - deprecation_message = ("Direct support for sequence of sequences " + - "multilabel representation will be unavailable " + - "from version 0.17. Use sklearn.preprocessing." + - "MultiLabelBinarizer to convert to a label " + - "indicator representation.") - - assert_warns_message(DeprecationWarning, deprecation_message, - check_binarized_results, y_seq, classes, pos_label, - neg_label, expected) - assert_raises(ValueError, label_binarize, y, classes, neg_label=-1, pos_label=pos_label, sparse_output=True) -def test_deprecation_inverse_binarize_thresholding(): - deprecation_message = ("Direct support for sequence of sequences " + - "multilabel representation will be unavailable " + - "from version 0.17. Use sklearn.preprocessing." + - "MultiLabelBinarizer to convert to a label " + - "indicator representation.") - - assert_warns_message(DeprecationWarning, deprecation_message, - _inverse_binarize_thresholding, - y=csr_matrix([[1, 0], [0, 1]]), - output_type="multilabel-sequences", - classes=[1, 2], threshold=0) - - def test_invalid_input_label_binarize(): assert_raises(ValueError, label_binarize, [0, 2], classes=[0, 2], pos_label=0, neg_label=1) @@ -566,7 +514,3 @@ def test_inverse_binarize_multiclass(): [0, 0, 0]]), np.arange(3)) assert_array_equal(got, np.array([1, 1, 0])) - -if __name__ == "__main__": - import nose - nose.runmodule() diff --git a/sklearn/setup.py b/sklearn/setup.py index 111d9680287e6..6634e20ba33a4 100644 --- a/sklearn/setup.py +++ b/sklearn/setup.py @@ -50,13 +50,7 @@ def configuration(parent_package='', top_path=None): config.add_subpackage('metrics/cluster') config.add_subpackage('metrics/cluster/tests') - # add cython extension module for hmm - config.add_extension( - '_hmmc', - sources=['_hmmc.c'], - include_dirs=[numpy.get_include()], - libraries=libraries, - ) + # add cython extension module for isotonic regression config.add_extension( '_isotonic', sources=['_isotonic.c'], diff --git a/sklearn/tests/test_cross_validation.py b/sklearn/tests/test_cross_validation.py index 5dae0ebd0bc87..94946eff9f4fd 100644 --- a/sklearn/tests/test_cross_validation.py +++ b/sklearn/tests/test_cross_validation.py @@ -16,7 +16,6 @@ from sklearn.utils.testing import assert_not_equal from sklearn.utils.testing import assert_array_almost_equal from sklearn.utils.testing import assert_array_equal -from sklearn.utils.testing import assert_warns from sklearn.utils.testing import assert_warns_message from sklearn.utils.testing import ignore_warnings from sklearn.utils.mocking import CheckingClassifier, MockDataFrame @@ -417,19 +416,6 @@ def test_stratified_shuffle_split_iter(): assert_array_equal(np.lib.arraysetops.intersect1d(train, test), []) -@ignore_warnings -def test_stratified_shuffle_split_iter_no_indices(): - y = np.asarray([0, 1, 2] * 10) - - sss1 = cval.StratifiedShuffleSplit(y, indices=False, random_state=0) - train_mask, test_mask = next(iter(sss1)) - - sss2 = cval.StratifiedShuffleSplit(y, indices=True, random_state=0) - train_indices, test_indices = next(iter(sss2)) - - assert_array_equal(sorted(test_indices), np.where(test_mask)[0]) - - def test_stratified_shuffle_split_even(): # Test the StratifiedShuffleSplit, indices are drawn with a # equal chance @@ -574,11 +560,16 @@ def test_cross_val_score_mask(): svm = SVC(kernel="linear") iris = load_iris() X, y = iris.data, iris.target - with warnings.catch_warnings(record=True): - cv_indices = cval.KFold(len(y), 5, indices=True) + cv_indices = cval.KFold(len(y), 5) scores_indices = cval.cross_val_score(svm, X, y, cv=cv_indices) - with warnings.catch_warnings(record=True): - cv_masks = cval.KFold(len(y), 5, indices=False) + cv_indices = cval.KFold(len(y), 5) + cv_masks = [] + for train, test in cv_indices: + mask_train = np.zeros(len(y), dtype=np.bool) + mask_test = np.zeros(len(y), dtype=np.bool) + mask_train[train] = 1 + mask_test[test] = 1 + cv_masks.append((train, test)) scores_masks = cval.cross_val_score(svm, X, y, cv=cv_masks) assert_array_equal(scores_indices, scores_masks) @@ -830,58 +821,20 @@ def custom_score(y_true, y_pred): assert_greater(pvalue, 0.2) -def test_cross_val_generator_with_mask(): - X = np.array([[1, 2], [3, 4], [5, 6], [7, 8]]) - y = np.array([1, 1, 2, 2]) - labels = np.array([1, 2, 3, 4]) - loo = assert_warns(DeprecationWarning, cval.LeaveOneOut, - 4, indices=False) - lpo = assert_warns(DeprecationWarning, cval.LeavePOut, - 4, 2, indices=False) - kf = assert_warns(DeprecationWarning, cval.KFold, - 4, 2, indices=False) - skf = assert_warns(DeprecationWarning, cval.StratifiedKFold, - y, 2, indices=False) - lolo = assert_warns(DeprecationWarning, cval.LeaveOneLabelOut, - labels, indices=False) - lopo = assert_warns(DeprecationWarning, cval.LeavePLabelOut, - labels, 2, indices=False) - ss = assert_warns(DeprecationWarning, cval.ShuffleSplit, - 4, indices=False) - ps = assert_warns(DeprecationWarning, cval.PredefinedSplit, [1, 1, 2, 2], - indices=False) - for cv in [loo, lpo, kf, skf, lolo, lopo, ss, ps]: - for train, test in cv: - assert_equal(np.asarray(train).dtype.kind, 'b') - assert_equal(np.asarray(train).dtype.kind, 'b') - X[train], X[test] - y[train], y[test] - - def test_cross_val_generator_with_indices(): X = np.array([[1, 2], [3, 4], [5, 6], [7, 8]]) y = np.array([1, 1, 2, 2]) labels = np.array([1, 2, 3, 4]) # explicitly passing indices value is deprecated - loo = assert_warns(DeprecationWarning, cval.LeaveOneOut, - 4, indices=True) - lpo = assert_warns(DeprecationWarning, cval.LeavePOut, - 4, 2, indices=True) - kf = assert_warns(DeprecationWarning, cval.KFold, - 4, 2, indices=True) - skf = assert_warns(DeprecationWarning, cval.StratifiedKFold, - y, 2, indices=True) - lolo = assert_warns(DeprecationWarning, cval.LeaveOneLabelOut, - labels, indices=True) - lopo = assert_warns(DeprecationWarning, cval.LeavePLabelOut, - labels, 2, indices=True) - ps = assert_warns(DeprecationWarning, cval.PredefinedSplit, - [1, 1, 2, 2], indices=True) - # Bootstrap as a cross-validation is deprecated - b = assert_warns(DeprecationWarning, cval.Bootstrap, 2) - ss = assert_warns(DeprecationWarning, cval.ShuffleSplit, - 2, indices=True) - for cv in [loo, lpo, kf, skf, lolo, lopo, b, ss, ps]: + loo = cval.LeaveOneOut(4) + lpo = cval.LeavePOut(4, 2) + kf = cval.KFold(4, 2) + skf = cval.StratifiedKFold(y, 2) + lolo = cval.LeaveOneLabelOut(labels) + lopo = cval.LeavePLabelOut(labels, 2) + ps = cval.PredefinedSplit([1, 1, 2, 2]) + ss = cval.ShuffleSplit(2) + for cv in [loo, lpo, kf, skf, lolo, lopo, ss, ps]: for train, test in cv: assert_not_equal(np.asarray(train).dtype.kind, 'b') assert_not_equal(np.asarray(train).dtype.kind, 'b') @@ -900,10 +853,9 @@ def test_cross_val_generator_with_default_indices(): skf = cval.StratifiedKFold(y, 2) lolo = cval.LeaveOneLabelOut(labels) lopo = cval.LeavePLabelOut(labels, 2) - b = cval.Bootstrap(2) # only in index mode ss = cval.ShuffleSplit(2) ps = cval.PredefinedSplit([1, 1, 2, 2]) - for cv in [loo, lpo, kf, skf, lolo, lopo, b, ss, ps]: + for cv in [loo, lpo, kf, skf, lolo, lopo, ss, ps]: for train, test in cv: assert_not_equal(np.asarray(train).dtype.kind, 'b') assert_not_equal(np.asarray(train).dtype.kind, 'b') @@ -911,57 +863,6 @@ def test_cross_val_generator_with_default_indices(): y[train], y[test] -@ignore_warnings -def test_cross_val_generator_mask_indices_same(): - # Test that the cross validation generators return the same results when - # indices=True and when indices=False - y = np.array([0, 0, 0, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2]) - labels = np.array([1, 1, 2, 3, 3, 3, 4]) - - loo_mask = cval.LeaveOneOut(5, indices=False) - loo_ind = cval.LeaveOneOut(5, indices=True) - lpo_mask = cval.LeavePOut(10, 2, indices=False) - lpo_ind = cval.LeavePOut(10, 2, indices=True) - kf_mask = cval.KFold(10, 5, indices=False, shuffle=True, random_state=1) - kf_ind = cval.KFold(10, 5, indices=True, shuffle=True, random_state=1) - skf_mask = cval.StratifiedKFold(y, 3, indices=False) - skf_ind = cval.StratifiedKFold(y, 3, indices=True) - lolo_mask = cval.LeaveOneLabelOut(labels, indices=False) - lolo_ind = cval.LeaveOneLabelOut(labels, indices=True) - lopo_mask = cval.LeavePLabelOut(labels, 2, indices=False) - lopo_ind = cval.LeavePLabelOut(labels, 2, indices=True) - ps_mask = cval.PredefinedSplit([1, 1, 2, 2], indices=False) - ps_ind = cval.PredefinedSplit([1, 1, 2, 2], indices=True) - - for cv_mask, cv_ind in [(loo_mask, loo_ind), (lpo_mask, lpo_ind), - (kf_mask, kf_ind), (skf_mask, skf_ind), - (lolo_mask, lolo_ind), (lopo_mask, lopo_ind), - (ps_mask, ps_ind)]: - for (train_mask, test_mask), (train_ind, test_ind) in \ - zip(cv_mask, cv_ind): - assert_array_equal(np.where(train_mask)[0], train_ind) - assert_array_equal(np.where(test_mask)[0], test_ind) - - -@ignore_warnings -def test_bootstrap_errors(): - assert_raises(ValueError, cval.Bootstrap, 10, train_size=100) - assert_raises(ValueError, cval.Bootstrap, 10, test_size=100) - assert_raises(ValueError, cval.Bootstrap, 10, train_size=1.1) - assert_raises(ValueError, cval.Bootstrap, 10, test_size=1.1) - assert_raises(ValueError, cval.Bootstrap, 10, train_size=0.6, - test_size=0.5) - - -@ignore_warnings -def test_bootstrap_test_sizes(): - assert_equal(cval.Bootstrap(10, test_size=0.2).test_size, 2) - assert_equal(cval.Bootstrap(10, test_size=1).test_size, 1) - assert_equal(cval.Bootstrap(10, train_size=1.).train_size, 10) - assert_equal(cval.Bootstrap(10, test_size=2).test_size, 2) - assert_equal(cval.Bootstrap(10, test_size=None).test_size, 5) - - def test_shufflesplit_errors(): assert_raises(ValueError, cval.ShuffleSplit, 10, test_size=2.0) assert_raises(ValueError, cval.ShuffleSplit, 10, test_size=1.0) @@ -982,26 +883,6 @@ def test_shufflesplit_reproducible(): assert_array_equal(list(a for a, b in ss), list(a for a, b in ss)) -@ignore_warnings -def test_cross_indices_exception(): - X = coo_matrix(np.array([[1, 2], [3, 4], [5, 6], [7, 8]])) - y = np.array([1, 1, 2, 2]) - labels = np.array([1, 2, 3, 4]) - loo = cval.LeaveOneOut(4, indices=False) - lpo = cval.LeavePOut(4, 2, indices=False) - kf = cval.KFold(4, 2, indices=False) - skf = cval.StratifiedKFold(y, 2, indices=False) - lolo = cval.LeaveOneLabelOut(labels, indices=False) - lopo = cval.LeavePLabelOut(labels, 2, indices=False) - - assert_raises(ValueError, cval.check_cv, loo, X, y) - assert_raises(ValueError, cval.check_cv, lpo, X, y) - assert_raises(ValueError, cval.check_cv, kf, X, y) - assert_raises(ValueError, cval.check_cv, skf, X, y) - assert_raises(ValueError, cval.check_cv, lolo, X, y) - assert_raises(ValueError, cval.check_cv, lopo, X, y) - - def test_safe_split_with_precomputed_kernel(): clf = SVC() clfp = SVC(kernel="precomputed") diff --git a/sklearn/tests/test_hmm.py b/sklearn/tests/test_hmm.py deleted file mode 100644 index 0a0d8de295520..0000000000000 --- a/sklearn/tests/test_hmm.py +++ /dev/null @@ -1,707 +0,0 @@ -from __future__ import print_function -import numpy as np - -from numpy.testing import assert_array_equal, assert_array_almost_equal -from unittest import TestCase - -from sklearn.datasets.samples_generator import make_spd_matrix -from sklearn import hmm -from sklearn import mixture -from sklearn.utils.extmath import logsumexp -from sklearn.utils import check_random_state - -from nose import SkipTest - -rng = np.random.RandomState(0) -np.seterr(all='warn') - - -class TestBaseHMM(TestCase): - - def setUp(self): - self.prng = np.random.RandomState(9) - - class StubHMM(hmm._BaseHMM): - - def _compute_log_likelihood(self, X): - return self.framelogprob - - def _generate_sample_from_state(self): - pass - - def _init(self): - pass - - def setup_example_hmm(self): - # Example from http://en.wikipedia.org/wiki/Forward-backward_algorithm - h = self.StubHMM(2) - h.transmat_ = [[0.7, 0.3], [0.3, 0.7]] - h.startprob_ = [0.5, 0.5] - framelogprob = np.log([[0.9, 0.2], - [0.9, 0.2], - [0.1, 0.8], - [0.9, 0.2], - [0.9, 0.2]]) - # Add dummy observations to stub. - h.framelogprob = framelogprob - return h, framelogprob - - def test_init(self): - h, framelogprob = self.setup_example_hmm() - for params in [('transmat_',), ('startprob_', 'transmat_')]: - d = dict((x[:-1], getattr(h, x)) for x in params) - h2 = self.StubHMM(h.n_components, **d) - self.assertEqual(h.n_components, h2.n_components) - for p in params: - assert_array_almost_equal(getattr(h, p), getattr(h2, p)) - - def test_set_startprob(self): - h, framelogprob = self.setup_example_hmm() - startprob = np.array([0.0, 1.0]) - h.startprob_ = startprob - assert np.allclose(startprob, h.startprob_) - - def test_set_transmat(self): - h, framelogprob = self.setup_example_hmm() - transmat = np.array([[0.8, 0.2], [0.0, 1.0]]) - h.transmat_ = transmat - assert np.allclose(transmat, h.transmat_) - - def test_do_forward_pass(self): - h, framelogprob = self.setup_example_hmm() - - logprob, fwdlattice = h._do_forward_pass(framelogprob) - - reflogprob = -3.3725 - self.assertAlmostEqual(logprob, reflogprob, places=4) - - reffwdlattice = np.array([[0.4500, 0.1000], - [0.3105, 0.0410], - [0.0230, 0.0975], - [0.0408, 0.0150], - [0.0298, 0.0046]]) - assert_array_almost_equal(np.exp(fwdlattice), reffwdlattice, 4) - - def test_do_backward_pass(self): - h, framelogprob = self.setup_example_hmm() - - bwdlattice = h._do_backward_pass(framelogprob) - - refbwdlattice = np.array([[0.0661, 0.0455], - [0.0906, 0.1503], - [0.4593, 0.2437], - [0.6900, 0.4100], - [1.0000, 1.0000]]) - assert_array_almost_equal(np.exp(bwdlattice), refbwdlattice, 4) - - def test_do_viterbi_pass(self): - h, framelogprob = self.setup_example_hmm() - - logprob, state_sequence = h._do_viterbi_pass(framelogprob) - - refstate_sequence = [0, 0, 1, 0, 0] - assert_array_equal(state_sequence, refstate_sequence) - - reflogprob = -4.4590 - self.assertAlmostEqual(logprob, reflogprob, places=4) - - def test_score_samples(self): - h, framelogprob = self.setup_example_hmm() - nobs = len(framelogprob) - - logprob, posteriors = h.score_samples([]) - - assert_array_almost_equal(posteriors.sum(axis=1), np.ones(nobs)) - - reflogprob = -3.3725 - self.assertAlmostEqual(logprob, reflogprob, places=4) - - refposteriors = np.array([[0.8673, 0.1327], - [0.8204, 0.1796], - [0.3075, 0.6925], - [0.8204, 0.1796], - [0.8673, 0.1327]]) - assert_array_almost_equal(posteriors, refposteriors, decimal=4) - - def test_hmm_score_samples_consistent_with_gmm(self): - n_components = 8 - nobs = 10 - h = self.StubHMM(n_components) - - # Add dummy observations to stub. - framelogprob = np.log(self.prng.rand(nobs, n_components)) - h.framelogprob = framelogprob - - # If startprob and transmat are uniform across all states (the - # default), the transitions are uninformative - the model - # reduces to a GMM with uniform mixing weights (in terms of - # posteriors, not likelihoods). - logprob, hmmposteriors = h.score_samples([]) - - assert_array_almost_equal(hmmposteriors.sum(axis=1), np.ones(nobs)) - - norm = logsumexp(framelogprob, axis=1)[:, np.newaxis] - gmmposteriors = np.exp(framelogprob - np.tile(norm, (1, n_components))) - assert_array_almost_equal(hmmposteriors, gmmposteriors) - - def test_hmm_decode_consistent_with_gmm(self): - n_components = 8 - nobs = 10 - h = self.StubHMM(n_components) - - # Add dummy observations to stub. - framelogprob = np.log(self.prng.rand(nobs, n_components)) - h.framelogprob = framelogprob - - # If startprob and transmat are uniform across all states (the - # default), the transitions are uninformative - the model - # reduces to a GMM with uniform mixing weights (in terms of - # posteriors, not likelihoods). - viterbi_ll, state_sequence = h.decode([]) - - norm = logsumexp(framelogprob, axis=1)[:, np.newaxis] - gmmposteriors = np.exp(framelogprob - np.tile(norm, (1, n_components))) - gmmstate_sequence = gmmposteriors.argmax(axis=1) - assert_array_equal(state_sequence, gmmstate_sequence) - - def test_base_hmm_attributes(self): - n_components = 20 - startprob = self.prng.rand(n_components) - startprob = startprob / startprob.sum() - transmat = self.prng.rand(n_components, n_components) - transmat /= np.tile(transmat.sum(axis=1) - [:, np.newaxis], (1, n_components)) - - h = self.StubHMM(n_components) - - self.assertEqual(h.n_components, n_components) - - h.startprob_ = startprob - assert_array_almost_equal(h.startprob_, startprob) - - self.assertRaises(ValueError, h.__setattr__, 'startprob_', - 2 * startprob) - self.assertRaises(ValueError, h.__setattr__, 'startprob_', []) - self.assertRaises(ValueError, h.__setattr__, 'startprob_', - np.zeros((n_components - 2, 2))) - - h.transmat_ = transmat - assert_array_almost_equal(h.transmat_, transmat) - self.assertRaises(ValueError, h.__setattr__, 'transmat_', - 2 * transmat) - self.assertRaises(ValueError, h.__setattr__, 'transmat_', []) - self.assertRaises(ValueError, h.__setattr__, 'transmat_', - np.zeros((n_components - 2, n_components))) - - -def train_hmm_and_keep_track_of_log_likelihood(hmm, obs, n_iter=1, **kwargs): - hmm.n_iter = 1 - hmm.fit(obs) - loglikelihoods = [] - for n in range(n_iter): - hmm.n_iter = 1 - hmm.init_params = '' - hmm.fit(obs) - loglikelihoods.append(sum(hmm.score(x) for x in obs)) - return loglikelihoods - - -class GaussianHMMBaseTester(object): - - def setUp(self): - self.prng = prng = np.random.RandomState(10) - self.n_components = n_components = 3 - self.n_features = n_features = 3 - self.startprob = prng.rand(n_components) - self.startprob = self.startprob / self.startprob.sum() - self.transmat = prng.rand(n_components, n_components) - self.transmat /= np.tile(self.transmat.sum(axis=1)[:, np.newaxis], - (1, n_components)) - self.means = prng.randint(-20, 20, (n_components, n_features)) - self.covars = { - 'spherical': (1.0 + 2 * np.dot(prng.rand(n_components, 1), - np.ones((1, n_features)))) ** 2, - 'tied': (make_spd_matrix(n_features, random_state=0) - + np.eye(n_features)), - 'diag': (1.0 + 2 * prng.rand(n_components, n_features)) ** 2, - 'full': np.array([make_spd_matrix(n_features, random_state=0) - + np.eye(n_features) - for x in range(n_components)]), - } - self.expanded_covars = { - 'spherical': [np.eye(n_features) * cov - for cov in self.covars['spherical']], - 'diag': [np.diag(cov) for cov in self.covars['diag']], - 'tied': [self.covars['tied']] * n_components, - 'full': self.covars['full'], - } - - def test_bad_covariance_type(self): - hmm.GaussianHMM(20, self.covariance_type) - self.assertRaises(ValueError, hmm.GaussianHMM, 20, - 'badcovariance_type') - - def test_score_samples_and_decode(self): - h = hmm.GaussianHMM(self.n_components, self.covariance_type) - h.means_ = self.means - h.covars_ = self.covars[self.covariance_type] - - # Make sure the means are far apart so posteriors.argmax() - # picks the actual component used to generate the observations. - h.means_ = 20 * h.means_ - - gaussidx = np.repeat(np.arange(self.n_components), 5) - nobs = len(gaussidx) - obs = self.prng.randn(nobs, self.n_features) + h.means_[gaussidx] - - ll, posteriors = h.score_samples(obs) - - self.assertEqual(posteriors.shape, (nobs, self.n_components)) - assert_array_almost_equal(posteriors.sum(axis=1), np.ones(nobs)) - - viterbi_ll, stateseq = h.decode(obs) - assert_array_equal(stateseq, gaussidx) - - def test_sample(self, n=1000): - h = hmm.GaussianHMM(self.n_components, self.covariance_type) - # Make sure the means are far apart so posteriors.argmax() - # picks the actual component used to generate the observations. - h.means_ = 20 * self.means - h.covars_ = np.maximum(self.covars[self.covariance_type], 0.1) - h.startprob_ = self.startprob - - samples = h.sample(n)[0] - self.assertEqual(samples.shape, (n, self.n_features)) - - def test_fit(self, params='stmc', n_iter=5, verbose=False, **kwargs): - h = hmm.GaussianHMM(self.n_components, self.covariance_type) - h.startprob_ = self.startprob - h.transmat_ = hmm.normalize( - self.transmat + np.diag(self.prng.rand(self.n_components)), 1) - h.means_ = 20 * self.means - h.covars_ = self.covars[self.covariance_type] - - # Create training data by sampling from the HMM. - train_obs = [h.sample(n=10)[0] for x in range(10)] - - # Mess up the parameters and see if we can re-learn them. - h.n_iter = 0 - h.fit(train_obs) - - trainll = train_hmm_and_keep_track_of_log_likelihood( - h, train_obs, n_iter=n_iter, params=params, **kwargs)[1:] - - # Check that the loglik is always increasing during training - if not np.all(np.diff(trainll) > 0) and verbose: - print('Test train: %s (%s)\n %s\n %s' - % (self.covariance_type, params, trainll, np.diff(trainll))) - delta_min = np.diff(trainll).min() - self.assertTrue( - delta_min > -0.8, - "The min nll increase is %f which is lower than the admissible" - " threshold of %f, for model %s. The likelihoods are %s." - % (delta_min, -0.8, self.covariance_type, trainll)) - - def test_fit_works_on_sequences_of_different_length(self): - obs = [self.prng.rand(3, self.n_features), - self.prng.rand(4, self.n_features), - self.prng.rand(5, self.n_features)] - - h = hmm.GaussianHMM(self.n_components, self.covariance_type) - # This shouldn't raise - # ValueError: setting an array element with a sequence. - h.fit(obs) - - def test_fit_with_length_one_signal(self): - obs = [self.prng.rand(10, self.n_features), - self.prng.rand(8, self.n_features), - self.prng.rand(1, self.n_features)] - h = hmm.GaussianHMM(self.n_components, self.covariance_type) - # This shouldn't raise - # ValueError: zero-size array to reduction operation maximum which has no identity - h.fit(obs) - - def test_fit_with_priors(self, params='stmc', n_iter=5, verbose=False): - startprob_prior = 10 * self.startprob + 2.0 - transmat_prior = 10 * self.transmat + 2.0 - means_prior = self.means - means_weight = 2.0 - covars_weight = 2.0 - if self.covariance_type in ('full', 'tied'): - covars_weight += self.n_features - covars_prior = self.covars[self.covariance_type] - - h = hmm.GaussianHMM(self.n_components, self.covariance_type) - h.startprob_ = self.startprob - h.startprob_prior = startprob_prior - h.transmat_ = hmm.normalize( - self.transmat + np.diag(self.prng.rand(self.n_components)), 1) - h.transmat_prior = transmat_prior - h.means_ = 20 * self.means - h.means_prior = means_prior - h.means_weight = means_weight - h.covars_ = self.covars[self.covariance_type] - h.covars_prior = covars_prior - h.covars_weight = covars_weight - - # Create training data by sampling from the HMM. - train_obs = [h.sample(n=10)[0] for x in range(10)] - - # Mess up the parameters and see if we can re-learn them. - h.n_iter = 0 - h.fit(train_obs[:1]) - - trainll = train_hmm_and_keep_track_of_log_likelihood( - h, train_obs, n_iter=n_iter, params=params)[1:] - - # Check that the loglik is always increasing during training - if not np.all(np.diff(trainll) > 0) and verbose: - print('Test MAP train: %s (%s)\n %s\n %s' - % (self.covariance_type, params, trainll, np.diff(trainll))) - # XXX: Why such a large tolerance? - self.assertTrue(np.all(np.diff(trainll) > -0.5)) - - def test_fit_non_ergodic_transmat(self): - startprob = np.array([1, 0, 0, 0, 0]) - transmat = np.array([[0.9, 0.1, 0, 0, 0], - [0, 0.9, 0.1, 0, 0], - [0, 0, 0.9, 0.1, 0], - [0, 0, 0, 0.9, 0.1], - [0, 0, 0, 0, 1.0]]) - - h = hmm.GaussianHMM(n_components=5, - covariance_type='full', startprob=startprob, - transmat=transmat, n_iter=100, init_params='st') - - h.means_ = np.zeros((5, 10)) - h.covars_ = np.tile(np.identity(10), (5, 1, 1)) - - obs = [h.sample(10)[0] for _ in range(10)] - - h.fit(obs=obs) - - -class TestGaussianHMMWithSphericalCovars(GaussianHMMBaseTester, TestCase): - covariance_type = 'spherical' - - def test_fit_startprob_and_transmat(self): - self.test_fit('st') - - -class TestGaussianHMMWithDiagonalCovars(GaussianHMMBaseTester, TestCase): - covariance_type = 'diag' - - -class TestGaussianHMMWithTiedCovars(GaussianHMMBaseTester, TestCase): - covariance_type = 'tied' - - -class TestGaussianHMMWithFullCovars(GaussianHMMBaseTester, TestCase): - covariance_type = 'full' - - -class MultinomialHMMTestCase(TestCase): - """Using examples from Wikipedia - - - http://en.wikipedia.org/wiki/Hidden_Markov_model - - http://en.wikipedia.org/wiki/Viterbi_algorithm - """ - - def setUp(self): - self.prng = np.random.RandomState(9) - self.n_components = 2 # ('Rainy', 'Sunny') - self.n_symbols = 3 # ('walk', 'shop', 'clean') - self.emissionprob = [[0.1, 0.4, 0.5], [0.6, 0.3, 0.1]] - self.startprob = [0.6, 0.4] - self.transmat = [[0.7, 0.3], [0.4, 0.6]] - - self.h = hmm.MultinomialHMM(self.n_components, - startprob=self.startprob, - transmat=self.transmat) - self.h.emissionprob_ = self.emissionprob - - def test_set_emissionprob(self): - h = hmm.MultinomialHMM(self.n_components) - emissionprob = np.array([[0.8, 0.2, 0.0], [0.7, 0.2, 1.0]]) - h.emissionprob = emissionprob - assert np.allclose(emissionprob, h.emissionprob) - - def test_wikipedia_viterbi_example(self): - # From http://en.wikipedia.org/wiki/Viterbi_algorithm: - # "This reveals that the observations ['walk', 'shop', 'clean'] - # were most likely generated by states ['Sunny', 'Rainy', - # 'Rainy'], with probability 0.01344." - observations = [0, 1, 2] - logprob, state_sequence = self.h.decode(observations) - self.assertAlmostEqual(np.exp(logprob), 0.01344) - assert_array_equal(state_sequence, [1, 0, 0]) - - def test_decode_map_algorithm(self): - observations = [0, 1, 2] - h = hmm.MultinomialHMM(self.n_components, startprob=self.startprob, - transmat=self.transmat, algorithm="map",) - h.emissionprob_ = self.emissionprob - logprob, state_sequence = h.decode(observations) - assert_array_equal(state_sequence, [1, 0, 0]) - - def test_predict(self): - observations = [0, 1, 2] - state_sequence = self.h.predict(observations) - posteriors = self.h.predict_proba(observations) - assert_array_equal(state_sequence, [1, 0, 0]) - assert_array_almost_equal(posteriors, [ - [0.23170303, 0.76829697], - [0.62406281, 0.37593719], - [0.86397706, 0.13602294], - ]) - - def test_attributes(self): - h = hmm.MultinomialHMM(self.n_components) - - self.assertEqual(h.n_components, self.n_components) - - h.startprob_ = self.startprob - assert_array_almost_equal(h.startprob_, self.startprob) - self.assertRaises(ValueError, h.__setattr__, 'startprob_', - 2 * self.startprob) - self.assertRaises(ValueError, h.__setattr__, 'startprob_', []) - self.assertRaises(ValueError, h.__setattr__, 'startprob_', - np.zeros((self.n_components - 2, self.n_symbols))) - - h.transmat_ = self.transmat - assert_array_almost_equal(h.transmat_, self.transmat) - self.assertRaises(ValueError, h.__setattr__, 'transmat_', - 2 * self.transmat) - self.assertRaises(ValueError, h.__setattr__, 'transmat_', []) - self.assertRaises(ValueError, h.__setattr__, 'transmat_', - np.zeros((self.n_components - 2, self.n_components))) - - h.emissionprob_ = self.emissionprob - assert_array_almost_equal(h.emissionprob_, self.emissionprob) - self.assertRaises(ValueError, h.__setattr__, 'emissionprob_', []) - self.assertRaises(ValueError, h.__setattr__, 'emissionprob_', - np.zeros((self.n_components - 2, self.n_symbols))) - self.assertEqual(h.n_symbols, self.n_symbols) - - def test_score_samples(self): - idx = np.repeat(np.arange(self.n_components), 10) - nobs = len(idx) - obs = [int(x) for x in np.floor(self.prng.rand(nobs) * self.n_symbols)] - - ll, posteriors = self.h.score_samples(obs) - - self.assertEqual(posteriors.shape, (nobs, self.n_components)) - assert_array_almost_equal(posteriors.sum(axis=1), np.ones(nobs)) - - def test_sample(self, n=1000): - samples = self.h.sample(n)[0] - self.assertEqual(len(samples), n) - self.assertEqual(len(np.unique(samples)), self.n_symbols) - - def test_fit(self, params='ste', n_iter=5, verbose=False, **kwargs): - h = self.h - - # Create training data by sampling from the HMM. - train_obs = [h.sample(n=10)[0] for x in range(10)] - - # Mess up the parameters and see if we can re-learn them. - h.startprob_ = hmm.normalize(self.prng.rand(self.n_components)) - h.transmat_ = hmm.normalize(self.prng.rand(self.n_components, - self.n_components), axis=1) - h.emissionprob_ = hmm.normalize( - self.prng.rand(self.n_components, self.n_symbols), axis=1) - - trainll = train_hmm_and_keep_track_of_log_likelihood( - h, train_obs, n_iter=n_iter, params=params, **kwargs)[1:] - - # Check that the loglik is always increasing during training - if not np.all(np.diff(trainll) > 0) and verbose: - print('Test train: (%s)\n %s\n %s' % (params, trainll, - np.diff(trainll))) - self.assertTrue(np.all(np.diff(trainll) > -1.e-3)) - - def test_fit_emissionprob(self): - self.test_fit('e') - - def test_fit_with_init(self, params='ste', n_iter=5, verbose=False, - **kwargs): - h = self.h - learner = hmm.MultinomialHMM(self.n_components) - - # Create training data by sampling from the HMM. - train_obs = [h.sample(n=10)[0] for x in range(10)] - - # use init_function to initialize paramerters - learner._init(train_obs, params) - - trainll = train_hmm_and_keep_track_of_log_likelihood( - learner, train_obs, n_iter=n_iter, params=params, **kwargs)[1:] - - # Check that the loglik is always increasing during training - if not np.all(np.diff(trainll) > 0) and verbose: - print() - print('Test train: (%s)\n %s\n %s' % (params, trainll, - np.diff(trainll))) - self.assertTrue(np.all(np.diff(trainll) > -1.e-3)) - - -def create_random_gmm(n_mix, n_features, covariance_type, prng=0): - prng = check_random_state(prng) - g = mixture.GMM(n_mix, covariance_type=covariance_type) - g.means_ = prng.randint(-20, 20, (n_mix, n_features)) - mincv = 0.1 - g.covars_ = { - 'spherical': (mincv + mincv * np.dot(prng.rand(n_mix, 1), - np.ones((1, n_features)))) ** 2, - 'tied': (make_spd_matrix(n_features, random_state=prng) - + mincv * np.eye(n_features)), - 'diag': (mincv + mincv * prng.rand(n_mix, n_features)) ** 2, - 'full': np.array( - [make_spd_matrix(n_features, random_state=prng) - + mincv * np.eye(n_features) for x in range(n_mix)]) - }[covariance_type] - g.weights_ = hmm.normalize(prng.rand(n_mix)) - return g - - -class GMMHMMBaseTester(object): - - def setUp(self): - self.prng = np.random.RandomState(9) - self.n_components = 3 - self.n_mix = 2 - self.n_features = 2 - self.covariance_type = 'diag' - self.startprob = self.prng.rand(self.n_components) - self.startprob = self.startprob / self.startprob.sum() - self.transmat = self.prng.rand(self.n_components, self.n_components) - self.transmat /= np.tile(self.transmat.sum(axis=1)[:, np.newaxis], - (1, self.n_components)) - - self.gmms_ = [] - for state in range(self.n_components): - self.gmms_.append(create_random_gmm( - self.n_mix, self.n_features, self.covariance_type, - prng=self.prng)) - - def test_attributes(self): - h = hmm.GMMHMM(self.n_components, covariance_type=self.covariance_type) - - self.assertEqual(h.n_components, self.n_components) - - h.startprob_ = self.startprob - assert_array_almost_equal(h.startprob_, self.startprob) - self.assertRaises(ValueError, h.__setattr__, 'startprob_', - 2 * self.startprob) - self.assertRaises(ValueError, h.__setattr__, 'startprob_', []) - self.assertRaises(ValueError, h.__setattr__, 'startprob_', - np.zeros((self.n_components - 2, self.n_features))) - - h.transmat_ = self.transmat - assert_array_almost_equal(h.transmat_, self.transmat) - self.assertRaises(ValueError, h.__setattr__, 'transmat_', - 2 * self.transmat) - self.assertRaises(ValueError, h.__setattr__, 'transmat_', []) - self.assertRaises(ValueError, h.__setattr__, 'transmat_', - np.zeros((self.n_components - 2, self.n_components))) - - def test_score_samples_and_decode(self): - h = hmm.GMMHMM(self.n_components, gmms=self.gmms_) - # Make sure the means are far apart so posteriors.argmax() - # picks the actual component used to generate the observations. - for g in h.gmms_: - g.means_ *= 20 - - refstateseq = np.repeat(np.arange(self.n_components), 5) - nobs = len(refstateseq) - obs = [h.gmms_[x].sample(1).flatten() for x in refstateseq] - - ll, posteriors = h.score_samples(obs) - - self.assertEqual(posteriors.shape, (nobs, self.n_components)) - assert_array_almost_equal(posteriors.sum(axis=1), np.ones(nobs)) - - viterbi_ll, stateseq = h.decode(obs) - assert_array_equal(stateseq, refstateseq) - - def test_sample(self, n=1000): - h = hmm.GMMHMM(self.n_components, self.covariance_type, - startprob=self.startprob, transmat=self.transmat, - gmms=self.gmms_) - samples = h.sample(n)[0] - self.assertEqual(samples.shape, (n, self.n_features)) - - def test_fit(self, params='stmwc', n_iter=5, verbose=False, **kwargs): - h = hmm.GMMHMM(self.n_components, covars_prior=1.0) - h.startprob_ = self.startprob - h.transmat_ = hmm.normalize( - self.transmat + np.diag(self.prng.rand(self.n_components)), 1) - h.gmms_ = self.gmms_ - - # Create training data by sampling from the HMM. - train_obs = [h.sample(n=10, random_state=self.prng)[0] - for x in range(10)] - - # Mess up the parameters and see if we can re-learn them. - h.n_iter = 0 - h.fit(train_obs) - h.transmat_ = hmm.normalize(self.prng.rand(self.n_components, - self.n_components), axis=1) - h.startprob_ = hmm.normalize(self.prng.rand(self.n_components)) - - trainll = train_hmm_and_keep_track_of_log_likelihood( - h, train_obs, n_iter=n_iter, params=params)[1:] - - if not np.all(np.diff(trainll) > 0) and verbose: - print('Test train: (%s)\n %s\n %s' % (params, trainll, - np.diff(trainll))) - - # XXX: this test appears to check that training log likelihood should - # never be decreasing (up to a tolerance of 0.5, why?) but this is not - # the case when the seed changes. - raise SkipTest("Unstable test: trainll is not always increasing " - "depending on seed") - - self.assertTrue(np.all(np.diff(trainll) > -0.5)) - - def test_fit_works_on_sequences_of_different_length(self): - obs = [self.prng.rand(3, self.n_features), - self.prng.rand(4, self.n_features), - self.prng.rand(5, self.n_features)] - - h = hmm.GMMHMM(self.n_components, covariance_type=self.covariance_type) - # This shouldn't raise - # ValueError: setting an array element with a sequence. - h.fit(obs) - - -class TestGMMHMMWithDiagCovars(GMMHMMBaseTester, TestCase): - covariance_type = 'diag' - - def test_fit_startprob_and_transmat(self): - self.test_fit('st') - - def test_fit_means(self): - self.test_fit('m') - - -class TestGMMHMMWithTiedCovars(GMMHMMBaseTester, TestCase): - covariance_type = 'tied' - - -class TestGMMHMMWithFullCovars(GMMHMMBaseTester, TestCase): - covariance_type = 'full' - - -def test_normalize_1D(): - A = rng.rand(2) + 1.0 - for axis in range(1): - Anorm = hmm.normalize(A, axis) - assert np.all(np.allclose(Anorm.sum(axis), 1.0)) - - -def test_normalize_3D(): - A = rng.rand(2, 2, 2) + 1.0 - for axis in range(3): - Anorm = hmm.normalize(A, axis) - assert np.all(np.allclose(Anorm.sum(axis), 1.0)) diff --git a/sklearn/tests/test_learning_curve.py b/sklearn/tests/test_learning_curve.py index c062abfab7189..afe8ce189d6d9 100644 --- a/sklearn/tests/test_learning_curve.py +++ b/sklearn/tests/test_learning_curve.py @@ -228,7 +228,7 @@ def test_learning_curve_with_boolean_indices(): n_redundant=0, n_classes=2, n_clusters_per_class=1, random_state=0) estimator = MockImprovingEstimator(20) - cv = KFold(n=30, n_folds=3, indices=False) + cv = KFold(n=30, n_folds=3) train_sizes, train_scores, test_scores = learning_curve( estimator, X, y, cv=cv, train_sizes=np.linspace(0.1, 1.0, 10)) assert_array_equal(train_sizes, np.linspace(2, 20, 10)) diff --git a/sklearn/utils/__init__.py b/sklearn/utils/__init__.py index 1728195e7eab4..7b3be8ce7109c 100644 --- a/sklearn/utils/__init__.py +++ b/sklearn/utils/__init__.py @@ -186,7 +186,7 @@ def resample(*arrays, **options): ------- resampled_arrays : sequence of arrays or scipy.sparse matrices with same \ shape[0] - Sequence of resampled views of the collections. The original arrays are + Sequence of resampled views of the collections. The original arrays are not impacted. Examples @@ -224,7 +224,6 @@ def resample(*arrays, **options): See also -------- - :class:`sklearn.cross_validation.Bootstrap` :func:`sklearn.utils.shuffle` """ random_state = check_random_state(options.pop('random_state', None)) diff --git a/sklearn/utils/extmath.py b/sklearn/utils/extmath.py index bf9b935859c93..48fd151103bd4 100644 --- a/sklearn/utils/extmath.py +++ b/sklearn/utils/extmath.py @@ -18,7 +18,7 @@ from scipy import linalg from scipy.sparse import issparse -from . import check_random_state, deprecated +from . import check_random_state from .fixes import np_version from ._logistic_sigmoid import _log_logistic_sigmoid from ..externals.six.moves import xrange @@ -437,7 +437,7 @@ def pinvh(a, cond=None, rcond=None, lower=True): zero. If None or -1, suitable machine precision is used. - + lower : boolean Whether the pertinent array data is taken from the lower or upper triangle of a. (Default: lower) @@ -539,7 +539,7 @@ def svd_flip(u, v, u_based_decision=True): Parameters ---------- u, v : ndarray - u and v are the output of `linalg.svd` or + u and v are the output of `linalg.svd` or `sklearn.utils.extmath.randomized_svd`, with matching inner dimensions so one can compute `np.dot(u * s, v)`. @@ -569,14 +569,6 @@ def svd_flip(u, v, u_based_decision=True): return u, v -@deprecated('to be removed in 0.17; use scipy.special.expit or log_logistic') -def logistic_sigmoid(X, log=False, out=None): - """Logistic function, ``1 / (1 + e ** (-x))``, or its log.""" - from .fixes import expit - fn = log_logistic if log else expit - return fn(X, out) - - def log_logistic(X, out=None): """Compute the log of the logistic function, ``log(1 / (1 + e ** -x))``. @@ -696,7 +688,7 @@ def _batch_mean_variance_update(X, old_mean, old_variance, old_sample_count): def _deterministic_vector_sign_flip(u): """Modify the sign of vectors for reproducibility - + Flips the sign of elements of all the vectors (rows of u) such that the absolute maximum element of each vector is positive. diff --git a/sklearn/utils/tests/test_extmath.py b/sklearn/utils/tests/test_extmath.py index 643a4420a6bee..22db7d74f5f94 100644 --- a/sklearn/utils/tests/test_extmath.py +++ b/sklearn/utils/tests/test_extmath.py @@ -3,7 +3,6 @@ # Denis Engemann # # License: BSD 3 clause -import warnings import numpy as np from scipy import sparse @@ -25,7 +24,7 @@ from sklearn.utils.extmath import row_norms from sklearn.utils.extmath import weighted_mode from sklearn.utils.extmath import cartesian -from sklearn.utils.extmath import log_logistic, logistic_sigmoid +from sklearn.utils.extmath import log_logistic from sklearn.utils.extmath import fast_dot, _fast_dot from sklearn.utils.extmath import svd_flip from sklearn.utils.extmath import _batch_mean_variance_update @@ -318,8 +317,6 @@ def test_logistic_sigmoid(): naive_log_logistic = lambda x: np.log(naive_logistic(x)) x = np.linspace(-2, 2, 50) - with warnings.catch_warnings(record=True): - assert_array_almost_equal(logistic_sigmoid(x), naive_logistic(x)) assert_array_almost_equal(log_logistic(x), naive_log_logistic(x)) extreme_x = np.array([-100., 100.]) @@ -444,9 +441,9 @@ def test_incremental_variance_ddof(): incremental_count = batch.shape[0] sample_count = batch.shape[0] else: - result = _batch_mean_variance_update(batch, incremental_means, - incremental_variances, - sample_count) + result = _batch_mean_variance_update( + batch, incremental_means, incremental_variances, + sample_count) (incremental_means, incremental_variances, incremental_count) = result sample_count += batch.shape[0]