#include <stdio.h>
#include <vector>
#include <string>
#include <math.h>
#include "mnist_common.h"
using std::vector;
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();
 
}
{
    return out * (1 - out);
}
double error(
const array &out,
 
{
    array dif = (out - pred);
 
    return sqrt((
double)(sum<float>(dif * dif)));
 
}
class ann {
private:
    int num_layers;
    vector<array> weights;
    
    vector<array> forward_propagate(
const array& input);
    void back_propagate(const vector<array> signal,
                        const double &alpha);
public:
    
    ann(vector<int> layers, 
double range=0.05);
    
    
    double train(
const array &input, 
const array &target,
 
                 double alpha = 1.0,
                 int max_epochs = 300,
                 int batch_size = 100,
                 double maxerr = 1.0,
                 bool verbose = false);
};
{
    
}
vector<array> ann::forward_propagate(
const array& input)
{
    
    vector<array> signal(num_layers);
    signal[0] = input;
    for (int i = 0; i < num_layers - 1; i++) {
        array in = add_bias(signal[i]);
 
    }
    return signal;
}
void ann::back_propagate(const vector<array> signal,
                         const double &alpha)
{
    
    array out = signal[num_layers  - 1];
 
    array err = (out - target);
 
    for (int i = num_layers - 2; i >= 0; i--) {
        array in = add_bias(signal[i]);
 
        array delta = (deriv(out) * err).T();
 
        
        
        out = signal[i];
        
    }
}
ann::ann(vector<int> layers, 
double range) :
    num_layers(layers.size()),
    weights(layers.size() - 1)
{
    
    for (int i = 0; i < num_layers - 1; i++) {
        weights[i] = range * 
randu(layers[i] + 1, layers[i + 1]) - range/2;
    }
}
{
    vector<array> signal = forward_propagate(input);
    array out = signal[num_layers - 1];
 
    return out;
}
double ann::train(
const array &input, 
const array &target,
 
                  double alpha, int max_epochs, int batch_size,
                  double maxerr, bool verbose)
{
    const int num_samples = input.
dims(0);
 
    const int num_batches = num_samples / batch_size;
    double err = 0;
    
    for (int i = 0; i < max_epochs; i++) {
        for (int j = 0; j < num_batches - 1; j++) {
            int st = j * batch_size;
            int en = st + batch_size - 1;
            
            vector<array> signals = forward_propagate(x);
            array out = signals[num_layers - 1];
 
            
            back_propagate(signals, y, alpha);
        }
        
        int st = (num_batches - 1) * batch_size;
        int en = num_samples - 1;
        err = error(out, target(
seq(st, en), 
span));
        
        if (err < maxerr) {
            printf("Converged on Epoch: %4d\n", i + 1);
            return err;
        }
        if (verbose) {
            if ((i + 1) % 10 == 0) printf("Epoch: %4d, Error: %0.4f\n", i+1, err);
        }
    }
    return err;
}
int ann_demo(bool console, int perc)
{
    printf("** ArrayFire ANN Demo **\n\n");
    array train_images, test_images;
 
    array train_target, test_target;
 
    int num_classes, num_train, num_test;
    
    float frac = (float)(perc) / 100.0;
    setup_mnist<true>(&num_classes, &num_train, &num_test,
                      train_images, test_images, train_target, test_target, frac);
    int feature_size = train_images.
elements() / num_train;
 
    
    array train_feats = 
moddims(train_images, feature_size, num_train).
T();
 
    array test_feats  = 
moddims(test_images , feature_size, num_test ).
T();
 
    train_target = train_target.
T();
    test_target  = test_target.
T();
    
    vector<int> layers;
    layers.push_back(train_feats.
dims(1));
    layers.push_back(100);
    layers.push_back(50);
    layers.push_back(num_classes);
    
    ann network(layers);
    
    timer::start();
    network.train(train_feats, train_target,
                  2.0, 
                  250, 
                  100, 
                  0.5, 
                  true); 
    double train_time = timer::stop();
    
    array train_output = network.predict(train_feats);
 
    array test_output  = network.predict(test_feats );
 
    
    timer::start();
    for (int i = 0; i < 100; i++) {
        network.predict(test_feats);
    }
    double test_time = timer::stop() / 100;
    printf("\nTraining set:\n");
    printf("Accuracy on training data: %2.2f\n",
           accuracy(train_output, train_target));
    printf("\nTest set:\n");
    printf("Accuracy on testing  data: %2.2f\n",
           accuracy(test_output , test_target ));
    printf("\nTraining time: %4.4lf s\n", train_time);
    printf("Prediction time: %4.4lf s\n\n", test_time);
    if (!console) {
        
        test_output = test_output.
T();
        display_results<true>(test_images, test_output, test_target.
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 ann_demo(console, perc);
        std::cerr << ae.
what() << std::endl;
    }
}