【deep learning学习笔记】注释yusugomori的LR代码 --- LogisticRegression.cpp

模型实现代码，关键是train函数和predict函数，都很容易。

 

#include <iostream>
#include <string>
#include <math.h>
#include "LogisticRegression.h"
using namespace std;LogisticRegression::LogisticRegression(int size, 	// Nint in, 	// n_inint out		// n_out) 
{N = size;n_in = in;n_out = out;// initialize W, b// W[n_out][n_in], b[n_out]W = new double*[n_out];for(int i=0; i<n_out; i++) W[i] = new double[n_in];b = new double[n_out];for(int i=0; i<n_out; i++) {for(int j=0; j<n_in; j++) {W[i][j] = 0;}b[i] = 0;}
}LogisticRegression::~LogisticRegression() 
{for(int i=0; i<n_out; i++) delete[] W[i];delete[] W;delete[] b;
}void LogisticRegression::train (int *x, 	// the input from input nodes in training setint *y, 	// the output from output nodes in training setdouble lr	// the learning rate) 
{// the probability of P(y|x)double *p_y_given_x = new double[n_out];// the tmp variable which is not necessary being an arraydouble *dy = new double[n_out];// step 1: calculate the output of softmax given inputfor(int i=0; i<n_out; i++) {// initializep_y_given_x[i] = 0;for(int j=0; j<n_in; j++) {// the weight of networksp_y_given_x[i] += W[i][j] * x[j];}// the biasp_y_given_x[i] += b[i];}// the softmax valuesoftmax(p_y_given_x);// step 2: update the weight of networks// w_new = w_old + learningRate * differential (导数)//		 = w_old + learningRate * x (1{y_i=y} - p_yi_given_x) //		 = w_old + learningRate * x * (y - p_y_given_x)for(int i=0; i<n_out; i++) {dy[i] = y[i] - p_y_given_x[i];for(int j=0; j<n_in; j++) {W[i][j] += lr * dy[i] * x[j] / N;}b[i] += lr * dy[i] / N;}delete[] p_y_given_x;delete[] dy;
}void LogisticRegression::softmax (double *x) 
{double max = 0.0;double sum = 0.0;// step1: get the max in the X vectorfor(int i=0; i<n_out; i++) if(max < x[i]) max = x[i];// step 2: normalization and softmax// normalize -- 'x[i]-max', it's not necessary in traditional LR.// I wonder why it appears here? for(int i=0; i<n_out; i++) {x[i] = exp(x[i] - max);sum += x[i];} for(int i=0; i<n_out; i++) x[i] /= sum;
}void LogisticRegression::predict(int *x, 	// the input from input nodes in testing setdouble *y	// the calculated softmax probability) 
{// get the softmax output value given the current networksfor(int i=0; i<n_out; i++) {y[i] = 0;for(int j=0; j<n_in; j++) {y[i] += W[i][j] * x[j];}y[i] += b[i];}softmax(y);
}