example_dmlfw_linear_regression.c

Example usage:

#include <stdio.h>
#include <stdlib.h>
#include <dmlfw_algorithms.h>
#include <dmlfw_error.h>
 
int main(void) {
    char err[512], dbg[512];
    dmlfw_gradient_descent_options *opts = dmlfw_gradient_descent_options_create_new();
    dmlfw_mat_double *x; // Assume initialized input feature matrix
    dmlfw_column_vec_double *y; // Assume initialized target vector
 
    dmlfw_gradient_descent_options_set_learning_rate(opts, 0.01);
    dmlfw_gradient_descent_options_set_number_of_iterations(opts, 1000);
 
    dmlfw_column_vec_double *model = dmlfw_linear_regression_fit_using_batch_gradient_descent(
        opts, x, y, 0.1, NULL);
    if (dmlfw_error()) {
        dmlfw_get_error_string(err, sizeof(err));
        dmlfw_get_debug_string(dbg, sizeof(dbg));
        printf("Training error: %s\nDebug info: %s\n", err, dbg);
        dmlfw_gradient_descent_options_destroy(opts);
        return EXIT_FAILURE;
    }
 
    dmlfw_column_vec_double *predictions = dmlfw_linear_regression_predict(x, model);
    if (dmlfw_error()) {
        dmlfw_get_error_string(err, sizeof(err));
        dmlfw_get_debug_string(dbg, sizeof(dbg));
        printf("Prediction error: %s\nDebug info: %s\n", err, dbg);
        dmlfw_column_vec_double_destroy(model);
        dmlfw_gradient_descent_options_destroy(opts);
        return EXIT_FAILURE;
    }
 
    // Use predictions...
 
    dmlfw_column_vec_double_destroy(predictions);
    dmlfw_column_vec_double_destroy(model);
    dmlfw_gradient_descent_options_destroy(opts);
 
    return EXIT_SUCCESS;
}