cpp dA

Yusuke Sugomori · Yusuke Sugomori · commit 272a0fc9fca0 · 2013-02-12T15:30:42.000+09:00
diff --git a/cpp/dA.cpp b/cpp/dA.cpp
@@ -0,0 +1,244 @@
+#include <iostream>
+#include <math.h>
+using namespace std;
+
+
+double uniform(double min, double max) {
+  return rand() / (RAND_MAX + 1.0) * (max - min) + min;
+}
+
+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 = rand() / (RAND_MAX + 1.0);
+    if (r < p) c++;
+  }
+
+  return c;
+}
+
+double sigmoid(double x) {
+  return 1.0 / (1.0 + exp(-x));
+}
+
+
+
+class dA {
+
+public:
+  int N;
+  int n_visible;
+  int n_hidden;
+  double **W;
+  double *hbias;
+  double *vbias;
+  dA(int, int, int , double**, double*, double*);
+  ~dA();
+  void get_corrupted_input(int*, int*, double);
+  void get_hidden_values(int*, double*);
+  void get_reconstructed_input(double*, double*);
+  void train(int*, double, double);
+  void reconstruct(int*, double*);
+};
+
+
+dA::dA(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;
+  }
+}
+
+dA::~dA() {
+  for(int i=0; i<n_hidden; i++) delete[] W[i];
+  delete[] W;
+  delete[] hbias;
+  delete[] vbias;
+}
+
+void dA::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
+void dA::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
+void dA::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]);
+  }
+}
+
+void dA::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;
+    }
+  }
+
+  delete[] L_hbias;
+  delete[] L_vbias;
+  delete[] z;
+  delete[] y;
+  delete[] tilde_x;
+}
+
+void dA::reconstruct(int *x, double *z) {
+  double *y = new double[n_hidden];
+
+  // for(int i=0; i<n_visible; i++) z[i] = 0;
+  for(int i=0; i<n_hidden; i++) y[i] = 0;
+
+  get_hidden_values(x, y);
+  get_reconstructed_input(y, z);
+
+  delete[] y;
+}
+
+
+
+void test_dA() {
+  srand(0);
+  
+  double learning_rate = 0.1;
+  double corruption_level = 0.3;
+  int training_epochs = 50;
+
+  int train_N = 10;
+  int test_N = 2;
+  int n_visible = 20;
+  int n_hidden = 5;
+
+  // training data
+  int train_X[10][20] = {
+    {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}
+  };
+
+  // construct dA
+  dA da(train_N, n_visible, n_hidden, NULL, NULL, NULL);
+
+  // 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[2][20] = {
+    {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[2][20] = {0};
+
+
+  // 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++) {
+      printf("%.5f ", reconstructed_X[i][j]);
+    }
+    cout << endl;
+  }
+
+  cout << endl;
+}
+
+
+
+int main() {
+  test_dA();
+  return 0;
+}
