struct problem * csr_set_problem (char *values, npy_intp *n_indices,
char *indices, npy_intp *n_indptr, char *indptr, char *Y,
npy_intp n_features, double bias) {
struct problem *problem;
problem = malloc (sizeof (struct problem));
if (problem == NULL) return NULL;
problem->l = (int) n_indptr[0] -1;
if (bias > 0){
problem->n = (int) n_features + 1;
} else {
problem->n = (int) n_features;
}
problem->y = (double *) Y;
problem->x = csr_to_sparse((double *) values, n_indices, (int *) indices,
n_indptr, (int *) indptr, bias, n_features);
problem->bias = bias;
if (problem->x == NULL) {
free(problem);
return NULL;
}
return problem;
}
int csr_copy_predict_values(npy_intp n_features, npy_intp *data_size,
char *data, npy_intp *index_size, char
*index, npy_intp *indptr_shape, char
*intptr, struct model *model_, char
*dec_values, int nr_class)
{
struct feature_node **predict_nodes;
npy_intp i;
predict_nodes = csr_to_sparse((double *) data, index_size,
(int *) index, indptr_shape, (int *) intptr, model_->bias, n_features);
if (predict_nodes == NULL)
return -1;
for (i = 0; i < indptr_shape[0] - 1; ++i) {
predict_values(model_, predict_nodes[i],
((double *) dec_values) + i*nr_class);
free(predict_nodes[i]);
}
free(predict_nodes);
return 0;
}
int csr_copy_predict_proba(npy_intp n_features, npy_intp *data_size,
char *data, npy_intp *index_size,
char *index, npy_intp *indptr_shape,
char *indptr, struct model *model_,
char *dec_values)
{
struct feature_node **predict_nodes;
int i;
double *tx = (double *) dec_values;
predict_nodes = csr_to_sparse((double *) data, index_size,
(int *) index, indptr_shape,
(int *) indptr, model_->bias,
n_features);
if (predict_nodes == NULL)
return -1;
for(i=0; i<indptr_shape[0] - 1; ++i) {
predict_probability(model_, predict_nodes[i], tx);
tx += model_->nr_class;
free(predict_nodes[i]);
}
free(predict_nodes);
return 0;
}
