#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:
    
    {
        return binary(prop_up(v));
    }
    {
        return binary(prop_down(h));
    }
public:
    rbm() {}
    rbm(int v_size, int h_size) :
        weights(
randu(h_size, v_size)/100 - 0.05),
    {
    }
    {
    }
    {
    }
    {
        vt = v;
        for (int i = 0; i < k; i++) {
            ht = vtoh(vt);
            vt = htov(ht);
        }
    }
    {
        ht = h;
        for (int i = 0; i < k; i++) {
            vt = htov(ht);
            ht = vtoh(vt);
        }
    }
    void train(
const array &in,
 
               double lr = 0.1,
               int num_epochs = 15,
               int batch_size = 100,
               int k = 1, bool verbose = false)
    {
        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 = vtoh(v_pos);
 
                gibbs_hvh(v_neg, h_neg, h_pos, k);
                
                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);
            }
        }
        if (verbose) printf("\n");
    }
};
int rbm_demo(bool console, int perc)
{
    printf("** ArrayFire RBM 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();
    rbm network(train_feats.
dims(1), 2000);
    network.train(train_feats,
                  0.1, 
                  15,  
                  100, 
                  1,   
                  true);
    
    for (int ii = 0; ii < 5; ii++) {
        network.gibbs_vhv(res, tmp, in);
        in  = 
moddims(in , dims[0], dims[1]);
        res = 
moddims(res, dims[0], dims[1]);
        printf("Reconstructed Error for image %2d: %.4f\n", ii,
               sum<float>(
abs(in - res)) / feature_size);
    }
    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 rbm_demo(console, perc);
        std::cerr << ae.
what() << std::endl;
    }
}