【deep learning学习笔记】注释yusugomori的DA代码 --- dA.cpp --模型准备

辅助函数和构造函数。

#include <iostream>
#include <math.h>
#include "dA.h"
using namespace std;

// To generate a value between min and max in a uniform distribution
double uniform(double min, double max)
{
  return rand() / (RAND_MAX + 1.0) * (max - min) + min;
}

// To get the result of n-binomial test by the p probability
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;
}

// To get the result of sigmoid function
double sigmoid(double x)
{
  return 1.0 / (1.0 + exp(-x));
}

dA::dA ( int size,        // N
         int n_v,        // n_visible
         int n_h,        // n_hidden
         double **w,    // W
         double *hb,    // hbias
         double* vb        // vbias
		 )
{
	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;
}