[classification] Training structures for logistic regression and stochastic (minibatch) gradient descent update

src/logistic_regression_trainer.c

@@ -0,0 +1,109 @@
+#include "logistic_regression_trainer.h"
+
+void logistic_regression_trainer_destroy(logistic_regression_trainer_t *self) {
+    if (self == NULL) return;
+
+    if (self->feature_ids != NULL) {
+        trie_destroy(self->feature_ids);
+    }
+
+    if (self->label_ids != NULL) {
+        kh_destroy(str_uint32, self->label_ids);
+    }
+
+    if (self->weights != NULL) {
+        matrix_destroy(self->weights);
+    }
+
+    free(self);
+}
+
+logistic_regression_trainer_t *logistic_regression_trainer_init(trie_t *feature_ids, khash_t(str_uint32) *label_ids) {
+    if (feature_ids == NULL || label_ids == NULL) return NULL;
+
+    logistic_regression_trainer_t *trainer = malloc(sizeof(logistic_regression_trainer_t));
+    if (trainer == NULL) return NULL;
+
+    trainer->feature_ids = feature_ids;
+    // Add one feature for the bias unit
+    trainer->num_features = trie_num_keys(feature_ids) + 1;
+
+    trainer->label_ids = label_ids;
+    trainer->num_labels = kh_size(label_ids);
+
+    trainer->weights = matrix_new_zeros(trainer->num_features, trainer->num_labels);
+
+    trainer->lambda = DEFAULT_LAMBDA;
+    trainer->iters = 0;
+    trainer->epochs = 0;
+    trainer->gamma_0 = DEFAULT_GAMMA_0;
+    trainer->gamma = DEFAULT_GAMMA;
+
+    return trainer;
+
+exit_trainer_created:
+    logistic_regression_trainer_destroy(trainer);
+    return NULL;
+}
+
+
+static matrix_t *model_expectation(sparse_matrix_t *x, matrix_t *theta) {
+    matrix_t *p_y = matrix_new_zeros(x->m, theta->n);
+    if (p_y == NULL) return NULL;
+
+    if(logistic_regression_model_expectation(theta, x, p_y)) {
+        return p_y;
+    } else {
+        matrix_destroy(p_y);
+        return NULL;
+    }
+}
+
+double logistic_regression_trainer_batch_cost(logistic_regression_trainer_t *self, feature_count_array *features, cstring_array *labels) {
+    size_t m = self->weights->m;
+    size_t n = self->weights->n;
+
+    sparse_matrix_t *x = feature_matrix(self->feature_ids, features);
+    uint32_array *y = label_vector(self->label_ids, labels);
+    matrix_t *p_y = matrix_new_zeros(x->m, n);
+
+    double cost = logistic_regression_cost_function(self->weights, x, y, p_y, self->lambda);
+
+    matrix_destroy(p_y);
+    uint32_array_destroy(y);
+    sparse_matrix_destroy(x);
+    return cost;    
+}
+
+
+bool logistic_regression_trainer_train_batch(logistic_regression_trainer_t *self, feature_count_array *features, cstring_array *labels) {
+    size_t m = self->weights->m;
+    size_t n = self->weights->n;
+
+    matrix_t *gradient = matrix_new_zeros(m, n);
+
+    sparse_matrix_t *x = feature_matrix(self->feature_ids, features);
+    uint32_array *y = label_vector(self->label_ids, labels);
+
+    matrix_t *p_y = matrix_new_zeros(x->m, n);
+
+    bool ret = false;
+
+    if (!logistic_regression_gradient(self->weights, gradient, x, y, p_y, self->lambda)) {
+        log_error("Gradient failed\n");
+        goto exit_matrices_created;
+    }
+
+    size_t data_len = m * n;
+
+    ret = stochastic_gradient_descent(self->weights, gradient, self->gamma);
+
+    self->iters++;
+
+exit_matrices_created:
+    matrix_destroy(gradient);
+    matrix_destroy(p_y);
+    uint32_array_destroy(y);
+    sparse_matrix_destroy(x);
+    return ret;
+}