【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;
}
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