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dA.java
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Yusuke Sugomori committed Mar 21, 2013
1 parent eafd9bd commit 6d6b412
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6 changes: 3 additions & 3 deletions java/DBN/src/RBM.java
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Expand Up @@ -4,9 +4,9 @@ public class RBM {
public int N;
public int n_visible;
public int n_hidden;
double[][] W;
double[] hbias;
double[] vbias;
public double[][] W;
public double[] hbias;
public double[] vbias;
public Random rng;

public double uniform(double min, double max) {
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8 changes: 4 additions & 4 deletions java/RBM/src/RBM.java
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Original file line number Diff line number Diff line change
Expand Up @@ -4,9 +4,9 @@ public class RBM {
public int N;
public int n_visible;
public int n_hidden;
double[][] W;
double[] hbias;
double[] vbias;
public double[][] W;
public double[] hbias;
public double[] vbias;
public Random rng;

public double uniform(double min, double max) {
Expand Down Expand Up @@ -207,7 +207,7 @@ private static void test_rbm() {
for(int i=0; i<test_N; i++) {
rbm.reconstruct(test_X[i], reconstructed_X[i]);
for(int j=0; j<n_visible; j++) {
System.out.printf("%.5f", reconstructed_X[i][j]);
System.out.printf("%.5f ", reconstructed_X[i][j]);
}
System.out.println();
}
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207 changes: 207 additions & 0 deletions java/dA/src/dA.java
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@@ -0,0 +1,207 @@
import java.util.Random;

public class dA {
public int N;
public int n_visible;
public int n_hidden;
public double[][] W;
public double[] hbias;
public double[] vbias;
public Random rng;


public double uniform(double min, double max) {
return rng.nextDouble() * (max - min) + min;
}

public int binomial(int n, double p) {
if(p < 0 || p > 1) return 0;

int c = 0;
double r;

for(int i=0; i<n; i++) {
r = rng.nextDouble();
if (r < p) c++;
}

return c;
}

public static double sigmoid(double x) {
return 1.0 / (1.0 + Math.pow(Math.E, -x));
}

public dA(int N, int n_visible, int n_hidden,
double[][] W, double[] hbias, double[] vbias, Random rng) {
this.N = N;
this.n_visible = n_visible;
this.n_hidden = n_hidden;

if(rng == null) this.rng = new Random(1234);
else this.rng = rng;

if(W == null) {
this.W = new double[this.n_hidden][this.n_visible];
double a = 1.0 / this.n_visible;

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

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

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

public void get_corrupted_input(int[] x, int[] tilde_x, double p) {
for(int i=0; i<n_visible; i++) {
if(x[i] == 0) {
tilde_x[i] = 0;
} else {
tilde_x[i] = binomial(1, p);
}
}
}

// Encode
public void get_hidden_values(int[] x, double[] y) {
for(int i=0; i<n_hidden; i++) {
y[i] = 0;
for(int j=0; j<n_visible; j++) {
y[i] += W[i][j] * x[j];
}
y[i] += hbias[i];
y[i] = sigmoid(y[i]);
}
}

// Decode
public void get_reconstructed_input(double[] y, double[] z) {
for(int i=0; i<n_visible; i++) {
z[i] = 0;
for(int j=0; j<n_hidden; j++) {
z[i] += W[j][i] * y[j];
}
z[i] += vbias[i];
z[i] = sigmoid(z[i]);
}
}

public void train(int[] x, double lr, double corruption_level) {
int[] tilde_x = new int[n_visible];
double[] y = new double[n_hidden];
double[] z = new double[n_visible];

double[] L_vbias = new double[n_visible];
double[] L_hbias = new double[n_hidden];

double p = 1 - corruption_level;

get_corrupted_input(x, tilde_x, p);
get_hidden_values(tilde_x, y);
get_reconstructed_input(y, z);

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

// hbias
for(int i=0; i<n_hidden; i++) {
L_hbias[i] = 0;
for(int j=0; j<n_visible; j++) {
L_hbias[i] += W[i][j] * L_vbias[j];
}
L_hbias[i] *= y[i] * (1 - y[i]);
hbias[i] += lr * L_hbias[i] / N;
}

// W
for(int i=0; i<n_hidden; i++) {
for(int j=0; j<n_visible; j++) {
W[i][j] += lr * (L_hbias[i] * tilde_x[j] + L_vbias[j] * y[i]) / N;
}
}
}

public void reconstruct(int[] x, double[] z) {
double[] y = new double[n_hidden];

get_hidden_values(x, y);
get_reconstructed_input(y, z);
}


private static void test_dA() {
Random rng = new Random(123);

double learning_rate = 0.1;
double corruption_level = 0.3;
int training_epochs = 100;

int train_N = 10;
int test_N = 2;
int n_visible = 20;
int n_hidden = 5;

int[][] train_X = {
{1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0},
{1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0},
{1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0},
{1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0},
{0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0},
{0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1},
{0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1},
{0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1},
{0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1},
{0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0}
};

dA da = new dA(train_N, n_visible, n_hidden, null, null, null, rng);

// train
for(int epoch=0; epoch<training_epochs; epoch++) {
for(int i=0; i<train_N; i++) {
da.train(train_X[i], learning_rate, corruption_level);
}
}

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

double[][] reconstructed_X = new double[test_N][n_visible];

// test
for(int i=0; i<test_N; i++) {
da.reconstruct(test_X[i], reconstructed_X[i]);
for(int j=0; j<n_visible; j++) {
System.out.printf("%.5f ", reconstructed_X[i][j]);
}
System.out.println();
}
}

public static void main(String[] args) {
test_dA();
}
}

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