Function naive_bayes_demo

examples/machine_learning/naive_bayes.cpp:104–141

}

void naive_bayes_demo(bool console, int perc) {
    array train_images, train_labels;
    array test_images, test_labels;
    int num_train, num_test, num_classes;

    // Load mnist data
    float frac = (float)(perc) / 100.0;
    setup_mnist<false>(&num_classes, &num_train, &num_test, train_images,
                       test_images, train_labels, test_labels, frac);

    int feature_length = train_images.elements() / num_train;
    array train_feats  = moddims(train_images, feature_length, num_train);
    array test_feats   = moddims(test_images, feature_length, num_test);

    // Get training parameters
    array mu, sig2;
    float *priors = new float[num_classes];
    naive_bayes_train(priors, mu, sig2, train_feats, train_labels, num_classes);

    // Predict the classes
    array res_labels =
        naive_bayes_predict(priors, mu, sig2, test_feats, num_classes);
    delete[] priors;

    // Results
    printf("Trainng samples: %4d, Testing samples: %4d\n", num_train, num_test);
    printf("Accuracy on testing  data: %2.2f\n",
           accuracy(res_labels, test_labels));

    benchmark_nb(train_feats, test_feats, train_labels, num_classes);

    if (!console) {
        test_images = test_images.T();
        test_labels = test_labels.T();

        display_results<false>(test_images, res_labels, test_labels, 20);
    }
}

int main(int argc, char **argv) {
    int device   = argc > 1 ? atoi(argv[1]) : 0;