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linedance-app/venv/lib/python3.12/site-packages/sklearn/svm/_liblinear.pyx

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+"""
+Wrapper for liblinear
+
+Author: fabian.pedregosa@inria.fr
+"""
+
+import  numpy as np
+
+from sklearn.utils._cython_blas cimport _dot, _axpy, _scal, _nrm2
+from sklearn.utils._typedefs cimport float32_t, float64_t, int32_t
+
+include "_liblinear.pxi"
+
+
+def train_wrap(
+    object X,
+    const float64_t[::1] Y,
+    bint is_sparse,
+    int solver_type,
+    double eps,
+    double bias,
+    double C,
+    const float64_t[:] class_weight,
+    int max_iter,
+    unsigned random_seed,
+    double epsilon,
+    const float64_t[::1] sample_weight
+):
+    cdef parameter *param
+    cdef problem *problem
+    cdef model *model
+    cdef char_const_ptr error_msg
+    cdef int len_w
+    cdef bint X_has_type_float64 = X.dtype == np.float64
+    cdef char * X_data_bytes_ptr
+    cdef const float64_t[::1] X_data_64
+    cdef const float32_t[::1] X_data_32
+    cdef const int32_t[::1] X_indices
+    cdef const int32_t[::1] X_indptr
+
+    if is_sparse:
+        X_indices = X.indices
+        X_indptr = X.indptr
+        if X_has_type_float64:
+            X_data_64 = X.data
+            X_data_bytes_ptr = <char *> &X_data_64[0]
+        else:
+            X_data_32 = X.data
+            X_data_bytes_ptr = <char *> &X_data_32[0]
+
+        problem = csr_set_problem(
+            X_data_bytes_ptr,
+            X_has_type_float64,
+            <char *> &X_indices[0],
+            <char *> &X_indptr[0],
+            (<int32_t>X.shape[0]),
+            (<int32_t>X.shape[1]),
+            (<int32_t>X.nnz),
+            bias,
+            <char *> &sample_weight[0],
+            <char *> &Y[0]
+        )
+    else:
+        X_as_1d_array = X.reshape(-1)
+        if X_has_type_float64:
+            X_data_64 = X_as_1d_array
+            X_data_bytes_ptr = <char *> &X_data_64[0]
+        else:
+            X_data_32 = X_as_1d_array
+            X_data_bytes_ptr = <char *> &X_data_32[0]
+
+        problem = set_problem(
+            X_data_bytes_ptr,
+            X_has_type_float64,
+            (<int32_t>X.shape[0]),
+            (<int32_t>X.shape[1]),
+            (<int32_t>np.count_nonzero(X)),
+            bias,
+            <char *> &sample_weight[0],
+            <char *> &Y[0]
+        )
+
+    cdef int32_t[::1] class_weight_label = np.arange(class_weight.shape[0], dtype=np.intc)
+    param = set_parameter(
+        solver_type,
+        eps,
+        C,
+        class_weight.shape[0],
+        <char *> &class_weight_label[0] if class_weight_label.size > 0 else NULL,
+        <char *> &class_weight[0] if class_weight.size > 0 else NULL,
+        max_iter,
+        random_seed,
+        epsilon
+    )
+
+    error_msg = check_parameter(problem, param)
+    if error_msg:
+        free_problem(problem)
+        free_parameter(param)
+        raise ValueError(error_msg)
+
+    cdef BlasFunctions blas_functions
+    blas_functions.dot = _dot[double]
+    blas_functions.axpy = _axpy[double]
+    blas_functions.scal = _scal[double]
+    blas_functions.nrm2 = _nrm2[double]
+
+    # early return
+    with nogil:
+        model = train(problem, param, &blas_functions)
+
+    # FREE
+    free_problem(problem)
+    free_parameter(param)
+    # destroy_param(param)  don't call this or it will destroy class_weight_label and class_weight
+
+    # coef matrix holder created as fortran since that's what's used in liblinear
+    cdef float64_t[::1, :] w
+    cdef int nr_class = get_nr_class(model)
+
+    cdef int labels_ = nr_class
+    if nr_class == 2:
+        labels_ = 1
+    cdef int32_t[::1] n_iter = np.zeros(labels_, dtype=np.intc)
+    get_n_iter(model, <int *> &n_iter[0])
+
+    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[0, 0], model, nr_feature)
+    else:
+        len_w = (nr_class) * nr_feature
+        w = np.empty((nr_class, nr_feature), order='F')
+        copy_w(&w[0, 0], model, len_w)
+
+    free_and_destroy_model(&model)
+
+    return w.base, n_iter.base
+
+
+def set_verbosity_wrap(int verbosity):
+    """
+    Control verbosity of libsvm library
+    """
+    set_verbosity(verbosity)