Function train

examples/machine_learning/logistic_regression.cpp:66–96

}

array train(const array &X, const array &Y, double alpha = 0.1,
            double lambda = 1.0, double maxerr = 0.01, int maxiter = 1000,
            bool verbose = false) {
    // Initialize parameters to 0
    array Weights = constant(0, X.dims(1), Y.dims(1));

    array J, dJ;
    float err = 0;

    for (int i = 0; i < maxiter; i++) {
        // Get the cost and gradient
        cost(J, dJ, Weights, X, Y, lambda);

        err = max<float>(abs(J));
        if (err < maxerr) {
            printf("Iteration %4d Err: %.4f\n", i + 1, err);
            printf("Training converged\n");
            return Weights;
        }

        if (verbose && ((i + 1) % 10 == 0)) {
            printf("Iteration %4d Err: %.4f\n", i + 1, err);
        }

        // Update the parameters via gradient descent
        Weights = Weights - alpha * dJ;
    }

    printf("Training stopped after %d iterations\n", maxiter);
    return Weights;
}

void benchmark_logistic_regression(const array &train_feats,
                                   const array &train_targets,