#include <stdio.h>
#include <vector>
#include <string>
#include <math.h>
#include "mnist_common.h"
float accuracy(
const array& predicted, 
const array& target)
 
{
    array val, plabels, tlabels;
 
    max(val, tlabels, target, 1);
 
    max(val, plabels, predicted, 1);
 
    return 100 * count<float>(plabels == tlabels) / tlabels.
elements();
 
}
float abserr(
const array& predicted, 
const array& target)
 
{
    return 100 * sum<float>(
abs(predicted - target)) / predicted.
elements();
 
}
{
    return a / b;
}
{
}
          const array &X, 
const array &Y, 
double lambda = 1.0)
 
{
    
    
    
    
    array H = predict(X, Weights);
 
    
    
    array Jreg = 0.5 * 
sum(lambdat * Weights * Weights);
 
    
    J = (Jerr + Jreg) / m;
    
    dJ = (
matmulTN(X, D) + lambdat * Weights) / m;
}
            double alpha = 0.1,
            double lambda = 1.0,
            double maxerr = 0.01,
            int maxiter = 1000,
            bool verbose = false)
{
    
    float err = 0;
    for (int i = 0; i < maxiter; i++) {
        
        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);
        }
        
        Weights = Weights - alpha * dJ;
    }
    printf("Training stopped after %d iterations\n", maxiter);
    return Weights;
}
void benchmark_softmax_regression(
const array &train_feats,
 
                                  const array &train_targets,
 
{
    array Weights = train(train_feats, train_targets, 0.1, 1.0, 0.01, 1000);
 
    const int iter = 100;
    for (int i = 0; i < iter; i++) {
        array test_outputs  = predict(test_feats , Weights);
 
    }
    printf(
"Prediction time: %4.4lf s\n", 
timer::stop() / iter);
}
int logit_demo(bool console, int perc)
{
    array train_images, train_targets;
 
    array test_images, test_targets;
 
    int num_train, num_test, num_classes;
    
    float frac = (float)(perc) / 100.0;
    setup_mnist<true>(&num_classes, &num_train, &num_test,
                      train_images, test_images,
                      train_targets, test_targets, frac);
    
    int feature_length = train_images.
elements() / num_train;
 
    array train_feats = 
moddims(train_images, feature_length, num_train).
T();
 
    array test_feats  = 
moddims(test_images , feature_length, num_test ).
T();
 
    train_targets = train_targets.
T();
    test_targets  = test_targets.
T();
    
    train_feats = 
join(1, 
constant(1, num_train, 1), train_feats);
    test_feats  = 
join(1, 
constant(1, num_test , 1), test_feats );
    
    array Weights = train(train_feats, train_targets,
 
                          0.1,  
                          1.0,  
                          0.01, 
                          1000, 
                          true);
    
    array train_outputs = predict(train_feats, Weights);
 
    array test_outputs  = predict(test_feats , Weights);
 
    printf("Accuracy on training data: %2.2f\n",
           accuracy(train_outputs, train_targets ));
    printf("Accuracy on testing data: %2.2f\n",
           accuracy(test_outputs , test_targets ));
    printf("Maximum error on testing data: %2.2f\n",
           abserr(test_outputs , test_targets ));
    benchmark_softmax_regression(train_feats, train_targets, test_feats);
    if (!console) {
        test_outputs = test_outputs.
T();
        
        display_results<true>(test_images, test_outputs, test_targets.
T(), 20);
    }
    return 0;
}
int main(int argc, char** argv)
{
    int device   = argc > 1 ? atoi(argv[1]) : 0;
    bool console = argc > 2 ? argv[2][0] == '-' : false;
    int perc     = argc > 3 ? atoi(argv[3]) : 60;
    try {
        return logit_demo(console, perc);
        std::cerr << ae.
what() << std::endl;
    }
}