#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 rbm {
private:
public:
    rbm(int v_size, int h_size) :
        weights(
randu(h_size, v_size)/100.f),
    {
    }
    {
    }
    void train(
const array &in, 
double lr, 
int num_epochs, 
int batch_size, 
bool verbose)
 
    {
        const int num_samples = in.
dims(0);
 
        const int num_batches = num_samples / batch_size;
        for (int i = 0; i <  num_epochs; i++) {
            double err = 0;
            for (int j = 0; j < num_batches - 1; j++) {
                int st = j * batch_size;
                int en = 
std::min(num_samples - 1, st + batch_size - 1);
 
                int num = en - st + 1;
                array h_pos = sigmoid_binary(
tile(h_bias, num) +
 
                array v_neg = sigmoid_binary(
tile(v_bias, num) +
 
                array h_neg = sigmoid_binary(
tile(h_bias, num) +
 
                array delta_w = lr * (c_pos - c_neg) / num;
 
                array delta_vb = lr * 
sum(v_pos - v_neg) / num;
 
                array delta_hb = lr * 
sum(h_pos - h_neg) / num;
 
                weights += delta_w;
                v_bias += delta_vb;
                h_bias += delta_hb;
                if (verbose) {
                    err += error(v_pos, v_neg);
                }
            }
            if (verbose) {
                printf("Epoch %d: Reconstruction error: %0.4f\n", i + 1, err / num_batches);
            }
        }
    }
    {
    }
};
class dbn {
private:
    const int in_size;
    const int out_size;
    const int num_hidden;
    const int num_total;
    std::vector<array> weights;
    std::vector<int> hidden;
    {
        
    }
    vector<array> forward_propagate(
const array& input)
    {
        
        vector<array> signal(num_total);
        signal[0] = input;
        for (int i = 0; i < num_total - 1; i++) {
            array in = add_bias(signal[i]);
 
        }
        return signal;
    }
    void back_propagate(const vector<array> signal,
                        const double &alpha)
    {
        
        array out = signal[num_total  - 1];
 
        array err = (out - target);
 
        for (int i = num_total - 2; i >= 0; i--) {
            array in = add_bias(signal[i]);
 
            array delta = (deriv(out) * err).T();
 
            
            
            out = signal[i];
            
        }
    }
public:
    dbn(const int in_sz, const int out_sz,
        const std::vector<int> hidden_layers) :
        in_size(in_sz),
        out_size(out_sz),
        num_hidden(hidden_layers.size()),
        num_total(hidden_layers.size() + 2),
        weights(hidden_layers.size() + 1),
        hidden(hidden_layers)
    {
    }
    void train(
const array &input, 
const array &target,
 
               double lr_rbm = 1.0,
               double lr_nn  = 1.0,
               const int epochs_rbm = 15,
               const int epochs_nn = 300,
               const int batch_size = 100,
               double maxerr = 1.0, bool verbose=false)
    {
        
        for (int i = 0; i < num_hidden; i++) {
            if (verbose) {
                printf("Training Hidden Layer %d\n", i);
            }
            int visible = (i == 0) ? in_size : hidden[i - 1];
            rbm r(visible, hidden[i]);
            r.train(X, lr_rbm, epochs_rbm, batch_size, verbose);
            X = r.prop_up(X);
            weights[i] = r.get_weights();
            if (verbose) {
                printf("\n");
            }
        }
        weights[num_hidden] = 0.05 * 
randu(hidden[num_hidden - 1] + 1, out_size) - 0.0025;
        const int num_samples = input.
dims(0);
 
        const int num_batches = num_samples / batch_size;
        
        for (int i = 0; i < epochs_nn; i++) {
            for (int j = 0; j < num_batches; j++) {
                int st = j * batch_size;
                int en = 
std::min(num_samples - 1, st + batch_size - 1);
 
                
                vector<array> signals = forward_propagate(x);
                array out = signals[num_total - 1];
 
                
                back_propagate(signals, y, lr_nn);
            }
            
            int st = (num_batches - 1) * batch_size;
            int en = num_samples - 1;
            double err = error(out, target(
seq(st, en), 
span));
 
            
            if (err < maxerr) {
                printf("Converged on Epoch: %4d\n", i + 1);
                return;
            }
            if (verbose) {
                if ((i + 1) % 10 == 0) printf("Epoch: %4d, Error: %0.4f\n", i+1, err);
            }
        }
    }
    {
        vector<array> signal = forward_propagate(input);
        array out = signal[num_total - 1];
 
        return out;
    }
};
int dbn_demo(bool console, int perc)
{
    printf("** ArrayFire DBN 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(100);
    layers.push_back(50);
    
    dbn network(train_feats.
dims(1), num_classes, layers);
    
    network.train(train_feats, train_target,
                  0.2,  
                  4.0,  
                  15,   
                  250,  
                  100,  
                  0.5,  
                  true);
    
    array train_output = network.predict(train_feats);
 
    array test_output  = network.predict(test_feats );
 
    
    for (int i = 0; i < 100; i++) {
        network.predict(test_feats);
    }
    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 dbn_demo(console, perc);
        std::cerr << ae.
what() << std::endl;
    }
}