inline double_array *averaged_perceptron_predict_scores(averaged_perceptron_t *self, cstring_array *features) {
if (self->scores == NULL || self->scores->n == 0) self->scores = double_array_new_zeros((size_t)self->num_classes);
double_array_set(self->scores->a, self->scores->n, 0.0);
double *scores = self->scores->a;
uint32_t i = 0;
char *feature;
uint32_t feature_id;
uint32_t *indptr = self->weights->indptr->a;
uint32_t *indices = self->weights->indices->a;
double *data = self->weights->data->a;
cstring_array_foreach(features, i, feature, {
if (!averaged_perceptron_get_feature_id(self, feature, &feature_id)) {
continue;
}
for (int col = indptr[feature_id]; col < indptr[feature_id + 1]; col++) {
uint32_t class_id = indices[col];
scores[class_id] += data[col];
}
})
return self->scores;
void crf_context_beta_score(crf_context_t *self) {
double *cur = NULL;
double *row = self->row->a;
double *row_trans = self->row_trans->a;
const double *next = NULL;
const double *state = NULL;
const double *state_trans = NULL;
const double *trans = NULL;
const size_t T = self->num_items;
const size_t L = self->num_labels;
double *scale = self->scale_factor->a;
/* Compute the beta scores at (T-1, *). */
cur = beta_score(self, T - 1);
double scale_t = scale[T - 1];
double_array_set(cur, scale_t, L);
/* Compute the beta scores at (t, *). */
for (ssize_t t = T - 2; t >= 0; t--) {
cur = beta_score(self, t);
next = beta_score(self, t + 1);
state = exp_state_score(self, t + 1);
double_array_raw_copy(row, next, L);
double_array_mul_array(row, state, L);
/* Compute the beta score at (t, i). */
for (int i = 0; i < L; i++) {
trans = exp_trans_score(self, i);
double_array_raw_copy(row_trans, row, L);
double_array_mul_array(row_trans, trans, L);
state_trans = exp_state_trans_score(self, t + 1, i);
cur[i] = double_array_dot(state_trans, row_trans, L);
}
scale_t = scale[t];
double_array_mul(cur, scale_t, L);
}
}