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"""
Wrapper for liblinear
Author: fabian.pedregosa@inria.fr
"""
import numpy as np
cimport numpy as np
cimport liblinear
np.import_array()
def train_wrap(X, np.ndarray[np.float64_t, ndim=1, mode='c'] Y,
bint is_sparse, int solver_type, double eps, double bias,
double C, np.ndarray[np.float64_t, ndim=1] class_weight,
int max_iter, unsigned random_seed, double epsilon):
cdef parameter *param
cdef problem *problem
cdef model *model
cdef char_const_ptr error_msg
cdef int len_w
if is_sparse:
problem = csr_set_problem(
(<np.ndarray[np.float64_t, ndim=1, mode='c']>X.data).data,
(<np.ndarray[np.int32_t, ndim=1, mode='c']>X.indices).shape,
(<np.ndarray[np.int32_t, ndim=1, mode='c']>X.indices).data,
(<np.ndarray[np.int32_t, ndim=1, mode='c']>X.indptr).shape,
(<np.ndarray[np.int32_t, ndim=1, mode='c']>X.indptr).data,
Y.data, (<np.int32_t>X.shape[1]), bias)
else:
problem = set_problem(
(<np.ndarray[np.float64_t, ndim=2, mode='c']>X).data,
Y.data,
(<np.ndarray[np.float64_t, ndim=2, mode='c']>X).shape,
bias)
cdef np.ndarray[np.int32_t, ndim=1, mode='c'] \
class_weight_label = np.arange(class_weight.shape[0], dtype=np.intc)
param = set_parameter(solver_type, eps, C, class_weight.shape[0],
class_weight_label.data, class_weight.data,
max_iter, random_seed, epsilon)
error_msg = check_parameter(problem, param)
if error_msg:
free_problem(problem)
free_parameter(param)
raise ValueError(error_msg)
# early return
with nogil:
model = train(problem, param)
# coef matrix holder created as fortran since that's what's used in liblinear
cdef np.ndarray[np.float64_t, ndim=2, mode='fortran'] w
cdef int nr_class = get_nr_class(model)
cdef int labels_ = nr_class
if nr_class == 2:
labels_ = 1
cdef np.ndarray[np.int32_t, ndim=1, mode='c'] n_iter = np.zeros(labels_, dtype=np.intc)
get_n_iter(model, <int *>n_iter.data)
cdef int nr_feature = get_nr_feature(model)
if bias > 0: nr_feature = nr_feature + 1
if nr_class == 2 and solver_type != 4: # solver is not Crammer-Singer
w = np.empty((1, nr_feature),order='F')
copy_w(w.data, model, nr_feature)
else:
len_w = (nr_class) * nr_feature
w = np.empty((nr_class, nr_feature),order='F')
copy_w(w.data, model, len_w)
### FREE
free_and_destroy_model(&model)
free_problem(problem)
free_parameter(param)
# destroy_param(param) don't call this or it will destroy class_weight_label and class_weight
return w, n_iter
def set_verbosity_wrap(int verbosity):
"""
Control verbosity of libsvm library
"""
set_verbosity(verbosity)
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