551 lines
18 KiB
Cython
551 lines
18 KiB
Cython
import numpy as np
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from scipy import sparse
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from ..utils._cython_blas cimport _dot
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from ..utils._typedefs cimport float64_t, int32_t, intp_t
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cdef extern from *:
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ctypedef char* const_char_p "const char*"
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################################################################################
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# Includes
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cdef extern from "_svm_cython_blas_helpers.h":
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ctypedef double (*dot_func)(int, const double*, int, const double*, int)
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cdef struct BlasFunctions:
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dot_func dot
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cdef extern from "svm.h":
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cdef struct svm_csr_node
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cdef struct svm_csr_model
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cdef struct svm_parameter
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cdef struct svm_csr_problem
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char *svm_csr_check_parameter(svm_csr_problem *, svm_parameter *)
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svm_csr_model *svm_csr_train(svm_csr_problem *, svm_parameter *, int *, BlasFunctions *) nogil
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void svm_csr_free_and_destroy_model(svm_csr_model** model_ptr_ptr)
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cdef extern from "libsvm_sparse_helper.c":
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# this file contains methods for accessing libsvm 'hidden' fields
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svm_csr_problem * csr_set_problem (
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char *, intp_t *, char *, intp_t *, char *, char *, char *, int)
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svm_csr_model *csr_set_model(svm_parameter *param, int nr_class,
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char *SV_data, intp_t *SV_indices_dims,
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char *SV_indices, intp_t *SV_intptr_dims,
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char *SV_intptr,
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char *sv_coef, char *rho, char *nSV,
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char *probA, char *probB)
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svm_parameter *set_parameter (int , int , int , double, double ,
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double , double , double , double,
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double, int, int, int, char *, char *, int,
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int)
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void copy_sv_coef (char *, svm_csr_model *)
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void copy_n_iter (char *, svm_csr_model *)
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void copy_support (char *, svm_csr_model *)
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void copy_intercept (char *, svm_csr_model *, intp_t *)
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int copy_predict (char *, svm_csr_model *, intp_t *, char *, BlasFunctions *)
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int csr_copy_predict_values (intp_t *data_size, char *data, intp_t *index_size,
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char *index, intp_t *intptr_size, char *size,
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svm_csr_model *model, char *dec_values, int nr_class, BlasFunctions *)
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int csr_copy_predict (intp_t *data_size, char *data, intp_t *index_size,
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char *index, intp_t *intptr_size, char *size,
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svm_csr_model *model, char *dec_values, BlasFunctions *) nogil
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int csr_copy_predict_proba (intp_t *data_size, char *data, intp_t *index_size,
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char *index, intp_t *intptr_size, char *size,
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svm_csr_model *model, char *dec_values, BlasFunctions *) nogil
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int copy_predict_values(char *, svm_csr_model *, intp_t *, char *, int, BlasFunctions *)
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int csr_copy_SV (char *values, intp_t *n_indices,
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char *indices, intp_t *n_indptr, char *indptr,
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svm_csr_model *model, int n_features)
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intp_t get_nonzero_SV (svm_csr_model *)
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void copy_nSV (char *, svm_csr_model *)
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void copy_probA (char *, svm_csr_model *, intp_t *)
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void copy_probB (char *, svm_csr_model *, intp_t *)
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intp_t get_l (svm_csr_model *)
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intp_t get_nr (svm_csr_model *)
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int free_problem (svm_csr_problem *)
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int free_model (svm_csr_model *)
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int free_param (svm_parameter *)
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int free_model_SV(svm_csr_model *model)
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void set_verbosity(int)
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def libsvm_sparse_train (int n_features,
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const float64_t[::1] values,
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const int32_t[::1] indices,
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const int32_t[::1] indptr,
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const float64_t[::1] Y,
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int svm_type, int kernel_type, int degree, double gamma,
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double coef0, double eps, double C,
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const float64_t[::1] class_weight,
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const float64_t[::1] sample_weight,
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double nu, double cache_size, double p, int
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shrinking, int probability, int max_iter,
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int random_seed):
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"""
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Wrap svm_train from libsvm using a scipy.sparse.csr matrix
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Work in progress.
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Parameters
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----------
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n_features : number of features.
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XXX: can we retrieve this from any other parameter ?
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X : array-like, dtype=float, size=[N, D]
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Y : array, dtype=float, size=[N]
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target vector
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...
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Notes
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-------------------
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See sklearn.svm.predict for a complete list of parameters.
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"""
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cdef svm_parameter *param
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cdef svm_csr_problem *problem
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cdef svm_csr_model *model
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cdef const_char_p error_msg
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if len(sample_weight) == 0:
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sample_weight = np.ones(Y.shape[0], dtype=np.float64)
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else:
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assert sample_weight.shape[0] == indptr.shape[0] - 1, \
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"sample_weight and X have incompatible shapes: " + \
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"sample_weight has %s samples while X has %s" % \
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(sample_weight.shape[0], indptr.shape[0] - 1)
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# we should never end up here with a precomputed kernel matrix,
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# as this is always dense.
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assert(kernel_type != 4)
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# set libsvm problem
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problem = csr_set_problem(
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<char *> &values[0],
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<intp_t *> indices.shape,
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<char *> &indices[0],
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<intp_t *> indptr.shape,
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<char *> &indptr[0],
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<char *> &Y[0],
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<char *> &sample_weight[0],
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kernel_type,
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)
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cdef int32_t[::1] \
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class_weight_label = np.arange(class_weight.shape[0], dtype=np.int32)
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# set parameters
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param = set_parameter(
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svm_type,
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kernel_type,
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degree,
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gamma,
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coef0,
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nu,
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cache_size,
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C,
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eps,
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p,
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shrinking,
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probability,
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<int> class_weight.shape[0],
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<char *> &class_weight_label[0] if class_weight_label.size > 0 else NULL,
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<char *> &class_weight[0] if class_weight.size > 0 else NULL, max_iter,
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random_seed,
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)
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# check parameters
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if (param == NULL or problem == NULL):
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raise MemoryError("Seems we've run out of memory")
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error_msg = svm_csr_check_parameter(problem, param)
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if error_msg:
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free_problem(problem)
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free_param(param)
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raise ValueError(error_msg)
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cdef BlasFunctions blas_functions
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blas_functions.dot = _dot[double]
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# call svm_train, this does the real work
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cdef int fit_status = 0
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with nogil:
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model = svm_csr_train(problem, param, &fit_status, &blas_functions)
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cdef intp_t SV_len = get_l(model)
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cdef intp_t n_class = get_nr(model)
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cdef int[::1] n_iter
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n_iter = np.empty(max(1, n_class * (n_class - 1) // 2), dtype=np.intc)
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copy_n_iter(<char *> &n_iter[0], model)
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# copy model.sv_coef
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# we create a new array instead of resizing, otherwise
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# it would not erase previous information
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cdef float64_t[::1] sv_coef_data
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sv_coef_data = np.empty((n_class-1)*SV_len, dtype=np.float64)
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copy_sv_coef (<char *> &sv_coef_data[0] if sv_coef_data.size > 0 else NULL, model)
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cdef int32_t[::1] support
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support = np.empty(SV_len, dtype=np.int32)
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copy_support(<char *> &support[0] if support.size > 0 else NULL, model)
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# copy model.rho into the intercept
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# the intercept is just model.rho but with sign changed
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cdef float64_t[::1]intercept
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intercept = np.empty(n_class*(n_class-1)//2, dtype=np.float64)
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copy_intercept (<char *> &intercept[0], model, <intp_t *> intercept.shape)
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# copy model.SV
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# we erase any previous information in SV
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# TODO: custom kernel
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cdef intp_t nonzero_SV
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nonzero_SV = get_nonzero_SV (model)
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cdef float64_t[::1] SV_data
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cdef int32_t[::1] SV_indices, SV_indptr
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SV_data = np.empty(nonzero_SV, dtype=np.float64)
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SV_indices = np.empty(nonzero_SV, dtype=np.int32)
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SV_indptr = np.empty(<intp_t>SV_len + 1, dtype=np.int32)
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csr_copy_SV(
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<char *> &SV_data[0] if SV_data.size > 0 else NULL,
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<intp_t *> SV_indices.shape,
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<char *> &SV_indices[0] if SV_indices.size > 0 else NULL,
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<intp_t *> SV_indptr.shape,
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<char *> &SV_indptr[0] if SV_indptr.size > 0 else NULL,
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model,
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n_features,
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)
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support_vectors_ = sparse.csr_matrix(
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(SV_data, SV_indices, SV_indptr), (SV_len, n_features)
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)
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# copy model.nSV
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# TODO: do only in classification
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cdef int32_t[::1]n_class_SV
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n_class_SV = np.empty(n_class, dtype=np.int32)
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copy_nSV(<char *> &n_class_SV[0], model)
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# # copy probabilities
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cdef float64_t[::1] probA, probB
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if probability != 0:
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if svm_type < 2: # SVC and NuSVC
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probA = np.empty(n_class*(n_class-1)//2, dtype=np.float64)
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probB = np.empty(n_class*(n_class-1)//2, dtype=np.float64)
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copy_probB(<char *> &probB[0], model, <intp_t *> probB.shape)
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else:
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probA = np.empty(1, dtype=np.float64)
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probB = np.empty(0, dtype=np.float64)
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copy_probA(<char *> &probA[0], model, <intp_t *> probA.shape)
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else:
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probA = np.empty(0, dtype=np.float64)
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probB = np.empty(0, dtype=np.float64)
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svm_csr_free_and_destroy_model (&model)
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free_problem(problem)
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free_param(param)
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return (
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support.base,
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support_vectors_,
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sv_coef_data.base,
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intercept.base,
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n_class_SV.base,
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probA.base,
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probB.base,
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fit_status,
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n_iter.base,
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)
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def libsvm_sparse_predict (const float64_t[::1] T_data,
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const int32_t[::1] T_indices,
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const int32_t[::1] T_indptr,
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const float64_t[::1] SV_data,
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const int32_t[::1] SV_indices,
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const int32_t[::1] SV_indptr,
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const float64_t[::1] sv_coef,
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const float64_t[::1]
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intercept, int svm_type, int kernel_type, int
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degree, double gamma, double coef0, double
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eps, double C,
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const float64_t[:] class_weight,
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double nu, double p, int
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shrinking, int probability,
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const int32_t[::1] nSV,
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const float64_t[::1] probA,
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const float64_t[::1] probB):
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"""
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Predict values T given a model.
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For speed, all real work is done at the C level in function
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copy_predict (libsvm_helper.c).
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We have to reconstruct model and parameters to make sure we stay
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in sync with the python object.
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See sklearn.svm.predict for a complete list of parameters.
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Parameters
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----------
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X : array-like, dtype=float
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Y : array
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target vector
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Returns
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-------
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dec_values : array
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predicted values.
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"""
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cdef float64_t[::1] dec_values
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cdef svm_parameter *param
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cdef svm_csr_model *model
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cdef int32_t[::1] \
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class_weight_label = np.arange(class_weight.shape[0], dtype=np.int32)
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cdef int rv
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param = set_parameter(
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svm_type,
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kernel_type,
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degree,
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gamma,
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coef0,
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nu,
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100.0, # cache size has no effect on predict
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C,
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eps,
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p,
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shrinking,
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probability,
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<int> class_weight.shape[0],
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<char *> &class_weight_label[0] if class_weight_label.size > 0 else NULL,
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<char *> &class_weight[0] if class_weight.size > 0 else NULL,
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-1,
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-1, # random seed has no effect on predict either
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)
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model = csr_set_model(
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param, <int> nSV.shape[0],
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<char *> &SV_data[0] if SV_data.size > 0 else NULL,
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<intp_t *>SV_indices.shape,
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<char *> &SV_indices[0] if SV_indices.size > 0 else NULL,
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<intp_t *> SV_indptr.shape,
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<char *> &SV_indptr[0] if SV_indptr.size > 0 else NULL,
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<char *> &sv_coef[0] if sv_coef.size > 0 else NULL,
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<char *> &intercept[0],
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<char *> &nSV[0],
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<char *> &probA[0] if probA.size > 0 else NULL,
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<char *> &probB[0] if probB.size > 0 else NULL,
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)
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# TODO: use check_model
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dec_values = np.empty(T_indptr.shape[0]-1)
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cdef BlasFunctions blas_functions
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blas_functions.dot = _dot[double]
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with nogil:
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rv = csr_copy_predict(
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<intp_t *> T_data.shape,
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<char *> &T_data[0],
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<intp_t *> T_indices.shape,
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<char *> &T_indices[0],
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<intp_t *> T_indptr.shape,
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<char *> &T_indptr[0],
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model,
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<char *> &dec_values[0],
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&blas_functions,
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)
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if rv < 0:
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raise MemoryError("We've run out of memory")
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# free model and param
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free_model_SV(model)
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free_model(model)
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free_param(param)
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return dec_values.base
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def libsvm_sparse_predict_proba(
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const float64_t[::1] T_data,
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const int32_t[::1] T_indices,
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const int32_t[::1] T_indptr,
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const float64_t[::1] SV_data,
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const int32_t[::1] SV_indices,
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const int32_t[::1] SV_indptr,
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const float64_t[::1] sv_coef,
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const float64_t[::1]
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intercept, int svm_type, int kernel_type, int
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degree, double gamma, double coef0, double
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eps, double C,
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const float64_t[:] class_weight,
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double nu, double p, int shrinking, int probability,
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const int32_t[::1] nSV,
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const float64_t[::1] probA,
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const float64_t[::1] probB,
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):
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"""
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Predict values T given a model.
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"""
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cdef float64_t[:, ::1] dec_values
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cdef svm_parameter *param
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cdef svm_csr_model *model
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cdef int32_t[::1] \
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class_weight_label = np.arange(class_weight.shape[0], dtype=np.int32)
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param = set_parameter(
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svm_type,
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kernel_type,
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degree,
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gamma,
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coef0,
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nu,
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100.0, # cache size has no effect on predict
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C,
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eps,
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p,
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shrinking,
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probability,
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<int> class_weight.shape[0],
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<char *> &class_weight_label[0] if class_weight_label.size > 0 else NULL,
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<char *> &class_weight[0] if class_weight.size > 0 else NULL,
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-1,
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-1, # random seed has no effect on predict either
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)
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model = csr_set_model(
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param,
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<int> nSV.shape[0],
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<char *> &SV_data[0] if SV_data.size > 0 else NULL,
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<intp_t *> SV_indices.shape,
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<char *> &SV_indices[0] if SV_indices.size > 0 else NULL,
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<intp_t *> SV_indptr.shape,
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<char *> &SV_indptr[0] if SV_indptr.size > 0 else NULL,
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<char *> &sv_coef[0] if sv_coef.size > 0 else NULL,
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<char *> &intercept[0],
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<char *> &nSV[0],
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<char *> &probA[0] if probA.size > 0 else NULL,
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<char *> &probB[0] if probB.size > 0 else NULL,
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)
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# TODO: use check_model
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cdef intp_t n_class = get_nr(model)
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cdef int rv
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dec_values = np.empty((T_indptr.shape[0]-1, n_class), dtype=np.float64)
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cdef BlasFunctions blas_functions
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blas_functions.dot = _dot[double]
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with nogil:
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rv = csr_copy_predict_proba(
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<intp_t *> T_data.shape,
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<char *> &T_data[0],
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<intp_t *> T_indices.shape,
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<char *> &T_indices[0],
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<intp_t *> T_indptr.shape,
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<char *> &T_indptr[0],
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model,
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<char *> &dec_values[0, 0],
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&blas_functions,
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)
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if rv < 0:
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raise MemoryError("We've run out of memory")
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# free model and param
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free_model_SV(model)
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free_model(model)
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free_param(param)
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return dec_values.base
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def libsvm_sparse_decision_function(
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const float64_t[::1] T_data,
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const int32_t[::1] T_indices,
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const int32_t[::1] T_indptr,
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const float64_t[::1] SV_data,
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const int32_t[::1] SV_indices,
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const int32_t[::1] SV_indptr,
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const float64_t[::1] sv_coef,
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const float64_t[::1]
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intercept, int svm_type, int kernel_type, int
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degree, double gamma, double coef0, double
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eps, double C,
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const float64_t[:] class_weight,
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double nu, double p, int shrinking, int probability,
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const int32_t[::1] nSV,
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const float64_t[::1] probA,
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const float64_t[::1] probB,
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):
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"""
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Predict margin (libsvm name for this is predict_values)
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|
|
|
We have to reconstruct model and parameters to make sure we stay
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in sync with the python object.
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"""
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cdef float64_t[:, ::1] dec_values
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cdef svm_parameter *param
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cdef intp_t n_class
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|
|
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cdef svm_csr_model *model
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cdef int32_t[::1] \
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class_weight_label = np.arange(class_weight.shape[0], dtype=np.int32)
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param = set_parameter(
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svm_type,
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kernel_type,
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degree,
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|
gamma,
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|
coef0,
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nu,
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|
100.0, # cache size has no effect on predict
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|
C,
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|
eps,
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|
p,
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|
shrinking,
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|
probability,
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<int> class_weight.shape[0],
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<char *> &class_weight_label[0] if class_weight_label.size > 0 else NULL,
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<char *> &class_weight[0] if class_weight.size > 0 else NULL,
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-1,
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|
-1,
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|
)
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|
|
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model = csr_set_model(
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param,
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<int> nSV.shape[0],
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<char *> &SV_data[0] if SV_data.size > 0 else NULL,
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<intp_t *> SV_indices.shape,
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|
<char *> &SV_indices[0] if SV_indices.size > 0 else NULL,
|
|
<intp_t *> SV_indptr.shape,
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|
<char *> &SV_indptr[0] if SV_indptr.size > 0 else NULL,
|
|
<char *> &sv_coef[0] if sv_coef.size > 0 else NULL,
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|
<char *> &intercept[0],
|
|
<char *> &nSV[0],
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|
<char *> &probA[0] if probA.size > 0 else NULL,
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|
<char *> &probB[0] if probB.size > 0 else NULL,
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|
)
|
|
|
|
if svm_type > 1:
|
|
n_class = 1
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|
else:
|
|
n_class = get_nr(model)
|
|
n_class = n_class * (n_class - 1) // 2
|
|
|
|
dec_values = np.empty((T_indptr.shape[0] - 1, n_class), dtype=np.float64)
|
|
cdef BlasFunctions blas_functions
|
|
blas_functions.dot = _dot[double]
|
|
if csr_copy_predict_values(
|
|
<intp_t *> T_data.shape,
|
|
<char *> &T_data[0],
|
|
<intp_t *> T_indices.shape,
|
|
<char *> &T_indices[0],
|
|
<intp_t *> T_indptr.shape,
|
|
<char *> &T_indptr[0],
|
|
model,
|
|
<char *> &dec_values[0, 0],
|
|
n_class,
|
|
&blas_functions,
|
|
) < 0:
|
|
raise MemoryError("We've run out of memory")
|
|
# free model and param
|
|
free_model_SV(model)
|
|
free_model(model)
|
|
free_param(param)
|
|
|
|
return dec_values.base
|
|
|
|
|
|
def set_verbosity_wrap(int verbosity):
|
|
"""
|
|
Control verbosity of libsvm library
|
|
"""
|
|
set_verbosity(verbosity)
|