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@yusugomori yusugomori/RBM.java
Last active Dec 15, 2015

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Restricted Boltzmann Machine
import java.util.Random;
public class RBM {
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 RBM(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 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;
}
}
public void 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]);
}
}
public void 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]);
}
}
public double 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);
}
public double 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);
}
public void 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);
}
public void 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);
}
}
}
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