Function train

examples/machine_learning/perceptron.cpp:33–53

}

array train(const array &X, const array &Y, double alpha = 0.1,
            double maxerr = 0.05, int maxiter = 1000, bool verbose = false) {
    // Initialize parameters to 0
    array Weights = constant(0, X.dims(1), Y.dims(1));

    for (int i = 0; i < maxiter; i++) {
        array P   = predict(X, Weights);
        array err = Y - P;

        float mean_abs_err = mean<float>(abs(err));
        if (mean_abs_err < maxerr) break;

        if (verbose && (i + 1) % 25 == 0) {
            printf("Iter: %d, Err: %.4f\n", i + 1, mean_abs_err);
        }

        Weights = Weights + alpha * matmulTN(X, err);
    }

    return Weights;
}

void benchmark_perceptron(const array &train_feats, const array &train_targets,
                          const array test_feats) {