#include <iostream>
#include <math.h>
#include "utils.h"
#include "HiddenLayer.h"
#include "RBM.h"
#include "LogisticRegression.h"
#include "DBN.h"
using namespace std;
using namespace utils;


// DBN
DBN::DBN(int size, int n_i, int *hls, int n_o, int n_l) {
 int input_size;
 
 N = size;
 n_ins = n_i;
 hidden_layer_sizes = hls;
 n_outs = n_o;
 n_layers = n_l;

 sigmoid_layers = new HiddenLayer*[n_layers];
 rbm_layers = new RBM*[n_layers];

 // construct multi-layer
 for(int i=0; i<n_layers; i++) {
 if(i == 0) {
 input_size = n_ins;
 } else {
 input_size = hidden_layer_sizes[i-1];
 }

 // construct sigmoid_layer
 sigmoid_layers[i] = new HiddenLayer(N, input_size, hidden_layer_sizes[i], NULL, NULL);

 // construct rbm_layer
 rbm_layers[i] = new RBM(N, input_size, hidden_layer_sizes[i],\
 sigmoid_layers[i]->W, sigmoid_layers[i]->b, NULL);
 }

 // layer for output using LogisticRegression
 log_layer = new LogisticRegression(N, hidden_layer_sizes[n_layers-1], n_outs);
}

DBN::~DBN() {
 delete log_layer;

 for(int i=0; i<n_layers; i++) {
 delete sigmoid_layers[i];
 delete rbm_layers[i];
 }
 delete[] sigmoid_layers;
 delete[] rbm_layers;
}


void DBN::pretrain(int *input, double lr, int k, int epochs) {
 int *layer_input;
 int prev_layer_input_size;
 int *prev_layer_input;

 int *train_X = new int[n_ins];

 for(int i=0; i<n_layers; i++) { // layer-wise

 for(int epoch=0; epoch<epochs; epoch++) { // training epochs

 for(int n=0; n<N; n++) { // input x1...xN
 // initial input
 for(int m=0; m<n_ins; m++) train_X[m] = input[n * n_ins + m];

 // layer input
 for(int l=0; l<=i; l++) {

 if(l == 0) {
 layer_input = new int[n_ins];
 for(int j=0; j<n_ins; j++) layer_input[j] = train_X[j];
 } else {
 if(l == 1) prev_layer_input_size = n_ins;
 else prev_layer_input_size = hidden_layer_sizes[l-2];

 prev_layer_input = new int[prev_layer_input_size];
 for(int j=0; j<prev_layer_input_size; j++) prev_layer_input[j] = layer_input[j];
 delete[] layer_input;

 layer_input = new int[hidden_layer_sizes[l-1]];

 sigmoid_layers[l-1]->sample_h_given_v(prev_layer_input, layer_input);
 delete[] prev_layer_input;
 }
 }

 rbm_layers[i]->contrastive_divergence(layer_input, lr, k);
 }

 }
 }

 delete[] train_X;
 delete[] layer_input;
}

void DBN::finetune(int *input, int *label, double lr, int epochs) {
 int *layer_input;
 // int prev_layer_input_size;
 int *prev_layer_input;

 int *train_X = new int[n_ins];
 int *train_Y = new int[n_outs];

 for(int epoch=0; epoch<epochs; epoch++) {
 for(int n=0; n<N; n++) { // input x1...xN
 // initial input
 for(int m=0; m<n_ins; m++) train_X[m] = input[n * n_ins + m];
 for(int m=0; m<n_outs; m++) train_Y[m] = label[n * n_outs + m];

 // layer input
 for(int i=0; i<n_layers; i++) {
 if(i == 0) {
 prev_layer_input = new int[n_ins];
 for(int j=0; j<n_ins; j++) prev_layer_input[j] = train_X[j];
 } else {
 prev_layer_input = new int[hidden_layer_sizes[i-1]];
 for(int j=0; j<hidden_layer_sizes[i-1]; j++) prev_layer_input[j] = layer_input[j];
 delete[] layer_input;
 }


 layer_input = new int[hidden_layer_sizes[i]];
 sigmoid_layers[i]->sample_h_given_v(prev_layer_input, layer_input);
 delete[] prev_layer_input;
 }

 log_layer->train(layer_input, train_Y, lr);
 }
 // lr *= 0.95;
 }

 delete[] layer_input;
 delete[] train_X;
 delete[] train_Y;
}

void DBN::predict(int *x, double *y) {
 double *layer_input;
 // int prev_layer_input_size;
 double *prev_layer_input;

 double linear_output;

 prev_layer_input = new double[n_ins];
 for(int j=0; j<n_ins; j++) prev_layer_input[j] = x[j];

 // layer activation
 for(int i=0; i<n_layers; i++) {
 layer_input = new double[sigmoid_layers[i]->n_out];

 for(int k=0; k<sigmoid_layers[i]->n_out; k++) {
 linear_output = 0.0;

 for(int j=0; j<sigmoid_layers[i]->n_in; j++) {
 linear_output += sigmoid_layers[i]->W[k][j] * prev_layer_input[j];
 }
 linear_output += sigmoid_layers[i]->b[k];
 layer_input[k] = sigmoid(linear_output);
 }
 delete[] prev_layer_input;

 if(i < n_layers-1) {
 prev_layer_input = new double[sigmoid_layers[i]->n_out];
 for(int j=0; j<sigmoid_layers[i]->n_out; j++) prev_layer_input[j] = layer_input[j];
 delete[] layer_input;
 }
 }
 
 for(int i=0; i<log_layer->n_out; i++) {
 y[i] = 0;
 for(int j=0; j<log_layer->n_in; j++) {
 y[i] += log_layer->W[i][j] * layer_input[j];
 }
 y[i] += log_layer->b[i];
 }
 
 log_layer->softmax(y);


 delete[] layer_input;
}


// HiddenLayer
HiddenLayer::HiddenLayer(int size, int in, int out, double **w, double *bp) {
 N = size;
 n_in = in;
 n_out = out;

 if(w == NULL) {
 W = new double*[n_out];
 for(int i=0; i<n_out; i++) W[i] = new double[n_in];
 double a = 1.0 / n_in;

 for(int i=0; i<n_out; i++) {
 for(int j=0; j<n_in; j++) {
 W[i][j] = uniform(-a, a);
 }
 }
 } else {
 W = w;
 }

 if(bp == NULL) {
 b = new double[n_out];
 } else {
 b = bp;
 }
}

HiddenLayer::~HiddenLayer() {
 for(int i=0; i<n_out; i++) delete W[i];
 delete[] W;
 delete[] b;
}

double HiddenLayer::output(int *input, double *w, double b) {
 double linear_output = 0.0;
 for(int j=0; j<n_in; j++) {
 linear_output += w[j] * input[j];
 }
 linear_output += b;
 return sigmoid(linear_output);
}

void HiddenLayer::sample_h_given_v(int *input, int *sample) {
 for(int i=0; i<n_out; i++) {
 sample[i] = binomial(1, output(input, W[i], b[i]));
 }
}


// RBM 
RBM::RBM(int size, int n_v, int n_h, double **w, double *hb, double *vb) {
 N = size;
 n_visible = n_v;
 n_hidden = n_h;

 if(w == NULL) {
 W = new double*[n_hidden];
 for(int i=0; i<n_hidden; i++) W[i] = new double[n_visible];
 double a = 1.0 / n_visible;

 for(int i=0; i<n_hidden; i++) {
 for(int j=0; j<n_visible; j++) {
 W[i][j] = uniform(-a, a);
 }
 }
 } else {
 W = w;
 }

 if(hb == NULL) {
 hbias = new double[n_hidden];
 for(int i=0; i<n_hidden; i++) hbias[i] = 0;
 } else {
 hbias = hb;
 }

 if(vb == NULL) {
 vbias = new double[n_visible];
 for(int i=0; i<n_visible; i++) vbias[i] = 0;
 } else {
 vbias = vb;
 }
}

RBM::~RBM() {
 // for(int i=0; i<n_hidden; i++) delete[] W[i];
 // delete[] W;
 // delete[] hbias;
 delete[] vbias;
}


void RBM::contrastive_divergence(int *input, double lr, int k) {
 double *ph_mean = new double[n_hidden];
 int *ph_sample = new int[n_hidden];
 double *nv_means = new double[n_visible];
 int *nv_samples = new int[n_visible];
 double *nh_means = new double[n_hidden];
 int *nh_samples = new int[n_hidden];

 /* CD-k */
 sample_h_given_v(input, ph_mean, ph_sample);

 for(int step=0; step<k; step++) {
 if(step == 0) {
 gibbs_hvh(ph_sample, nv_means, nv_samples, nh_means, nh_samples);
 } else {
 gibbs_hvh(nh_samples, nv_means, nv_samples, nh_means, nh_samples);
 }
 }

 for(int i=0; i<n_hidden; i++) {
 for(int j=0; j<n_visible; j++) {
 // W[i][j] += lr * (ph_sample[i] * input[j] - nh_means[i] * nv_samples[j]) / N;
 W[i][j] += lr * (ph_mean[i] * input[j] - nh_means[i] * nv_samples[j]) / N;
 }
 hbias[i] += lr * (ph_sample[i] - nh_means[i]) / N;
 }

 for(int i=0; i<n_visible; i++) {
 vbias[i] += lr * (input[i] - nv_samples[i]) / N;
 }

 delete[] ph_mean;
 delete[] ph_sample;
 delete[] nv_means;
 delete[] nv_samples;
 delete[] nh_means;
 delete[] nh_samples;
}

void RBM::sample_h_given_v(int *v0_sample, double *mean, int *sample) {
 for(int i=0; i<n_hidden; i++) {
 mean[i] = propup(v0_sample, W[i], hbias[i]);
 sample[i] = binomial(1, mean[i]);
 }
}

void RBM::sample_v_given_h(int *h0_sample, double *mean, int *sample) {
 for(int i=0; i<n_visible; i++) {
 mean[i] = propdown(h0_sample, i, vbias[i]);
 sample[i] = binomial(1, mean[i]);
 }
}

double RBM::propup(int *v, double *w, double b) {
 double pre_sigmoid_activation = 0.0;
 for(int j=0; j<n_visible; j++) {
 pre_sigmoid_activation += w[j] * v[j];
 }
 pre_sigmoid_activation += b;
 return sigmoid(pre_sigmoid_activation);
}

double RBM::propdown(int *h, int i, double b) {
 double pre_sigmoid_activation = 0.0;
 for(int j=0; j<n_hidden; j++) {
 pre_sigmoid_activation += W[j][i] * h[j];
 }
 pre_sigmoid_activation += b;
 return sigmoid(pre_sigmoid_activation);
}

void RBM::gibbs_hvh(int *h0_sample, double *nv_means, int *nv_samples, \
 double *nh_means, int *nh_samples) {
 sample_v_given_h(h0_sample, nv_means, nv_samples);
 sample_h_given_v(nv_samples, nh_means, nh_samples);
}

void RBM::reconstruct(int *v, double *reconstructed_v) {
 double *h = new double[n_hidden];
 double pre_sigmoid_activation;

 for(int i=0; i<n_hidden; i++) {
 h[i] = propup(v, W[i], hbias[i]);
 }

 for(int i=0; i<n_visible; i++) {
 pre_sigmoid_activation = 0.0;
 for(int j=0; j<n_hidden; j++) {
 pre_sigmoid_activation += W[j][i] * h[j];
 }
 pre_sigmoid_activation += vbias[i];

 reconstructed_v[i] = sigmoid(pre_sigmoid_activation);
 }

 delete[] h;
}


// LogisticRegression
LogisticRegression::LogisticRegression(int size, int in, int out) {
 N = size;
 n_in = in;
 n_out = out;

 W = new double*[n_out];
 for(int i=0; i<n_out; i++) W[i] = new double[n_in];
 b = new double[n_out];

 for(int i=0; i<n_out; i++) {
 for(int j=0; j<n_in; j++) {
 W[i][j] = 0;
 }
 b[i] = 0;
 }
}

LogisticRegression::~LogisticRegression() {
 for(int i=0; i<n_out; i++) delete[] W[i];
 delete[] W;
 delete[] b;
}


void LogisticRegression::train(int *x, int *y, double lr) {
 double *p_y_given_x = new double[n_out];
 double *dy = new double[n_out];

 for(int i=0; i<n_out; i++) {
 p_y_given_x[i] = 0;
 for(int j=0; j<n_in; j++) {
 p_y_given_x[i] += W[i][j] * x[j];
 }
 p_y_given_x[i] += b[i];
 }
 softmax(p_y_given_x);

 for(int i=0; i<n_out; i++) {
 dy[i] = y[i] - p_y_given_x[i];

 for(int j=0; j<n_in; j++) {
 W[i][j] += lr * dy[i] * x[j] / N;
 }

 b[i] += lr * dy[i] / N;
 }
 
 delete[] p_y_given_x;
 delete[] dy;
}

void LogisticRegression::softmax(double *x) {
 double max = 0.0;
 double sum = 0.0;
 
 for(int i=0; i<n_out; i++) if(max < x[i]) max = x[i];
 for(int i=0; i<n_out; i++) {
 x[i] = exp(x[i] - max);
 sum += x[i];
 } 

 for(int i=0; i<n_out; i++) x[i] /= sum;
}

void LogisticRegression::predict(int *x, double *y) {
 for(int i=0; i<n_out; i++) {
 y[i] = 0;
 for(int j=0; j<n_in; j++) {
 y[i] += W[i][j] * x[j];
 }
 y[i] += b[i];
 }

 softmax(y);
}





void test_dbn() {
 srand(0);

 double pretrain_lr = 0.1;
 int pretraining_epochs = 1000;
 int k = 1;
 double finetune_lr = 0.1;
 int finetune_epochs = 500;

 int train_N = 6;
 int test_N = 3;
 int n_ins = 6;
 int n_outs = 2;
 int hidden_layer_sizes[] = {3, 3};
 int n_layers = sizeof(hidden_layer_sizes) / sizeof(hidden_layer_sizes[0]);

 // training data
 int train_X[6][6] = {
 {1, 1, 1, 0, 0, 0},
 {1, 0, 1, 0, 0, 0},
 {1, 1, 1, 0, 0, 0},
 {0, 0, 1, 1, 1, 0},
 {0, 0, 1, 1, 0, 0},
 {0, 0, 1, 1, 1, 0}
 };

 int train_Y[6][2] = {
 {1, 0},
 {1, 0},
 {1, 0},
 {0, 1},
 {0, 1},
 {0, 1}
 };


 
 // construct DBN
 DBN dbn(train_N, n_ins, hidden_layer_sizes, n_outs, n_layers);

 // pretrain
 dbn.pretrain(*train_X, pretrain_lr, k, pretraining_epochs);

 // finetune
 dbn.finetune(*train_X, *train_Y, finetune_lr, finetune_epochs);
 

 // test data
 int test_X[3][6] = {
 {1, 1, 0, 0, 0, 0},
 {0, 0, 0, 1, 1, 0},
 {1, 1, 1, 1, 1, 0}
 };

 double test_Y[3][2];


 // test
 for(int i=0; i<test_N; i++) {
 dbn.predict(test_X[i], test_Y[i]);
 for(int j=0; j<n_outs; j++) {
 cout << test_Y[i][j] << " ";
 }
 cout << endl;
 }

}





int main() {
 test_dbn();
 return 0;
}