跪求C4.5算法，C语言的……

具体算法步骤如下； 1创建节点N 2如果训练集为空，在返回节点N标记为Failure 3如果训练集中的所有记录都属于同一个类别，则以该类别标记节点N 4如果候选属性为空，则返回N作为叶节点，标记为训练集中最普通的类； 5for each 候选属性 attribute_list 6if 候选属性是联系的then 7对该属性进行离散化 8选择候选属性attribute_list中具有最高信息增益的属性D 9标记节点N为属性D 10for each 属性D的一致值d 11由节点N长出一个条件为D=d的分支 12设s是训练集中D=d的训练样本的集合 13if s为空 14加上一个树叶，标记为训练集中最普通的类 15else加上一个有C4.5（R - {D},C，s）返回的点

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C++代码你可以参考下

C4.5算法源代码（C++）

// C4.5_test.cpp : Defines the entry point for the console application.

//

#include "stdafx.h"

#include stdio.h

#include math.h

#include "malloc.h"

#include stdlib.h

const int MAX = 10;

int** iInput;

int i = 0;//列数

int j = 0;//行数

void build_tree(FILE *fp, int* iSamples, int* iAttribute,int ilevel);//输出规则

int choose_attribute(int* iSamples, int* iAttribute);//通过计算信息增益率选出test_attribute

double info(double dTrue,double dFalse);//计算期望信息

double entropy(double dTrue, double dFalse, double dAll);//求熵

double splitinfo(int* list,double dAll);

int check_samples(int *iSamples);//检查samples是否都在同一个类里

int check_ordinary(int *iSamples);//检查最普通的类

int check_attribute_null(int *iAttribute);//检查attribute是否为空

void get_attributes(int *iSamples,int *iAttributeValue,int iAttribute);

int _tmain(int argc, _TCHAR* argv[])

{

FILE *fp;

FILE *fp1;

char iGet;

int a = 0;

int b = 0;//a,b是循环变量

int* iSamples;

int* iAttribute;

fp = fopen("c:\\input.txt","r");

if (NULL == fp)

{

printf("error\n");

return 0;

}

iGet = getc(fp);

while (('\n' != iGet)(EOF != iGet))

{

if (',' == iGet)

{

i++;

}

iGet = getc(fp);

}

i++;

iAttribute = (int *)malloc(sizeof(int)*i);

for (int k = 0; ki; k++)

{

iAttribute[k] = (int)malloc(sizeof(int));

iAttribute[k] = 1;

}

while (EOF != iGet)

{

if ('\n' == iGet)

{

j++;

}

iGet = getc(fp);

}

j++;

iInput = (int **)malloc(sizeof(int*)*j);

iSamples = (int *)malloc(sizeof(int)*j);

for (a = 0;a j;a++)

{

iInput[a] = (int *)malloc(sizeof(int)*i);

iSamples[a] = (int)malloc(sizeof(int));

iSamples[a] = a;

}

a = 0;

fclose(fp);

fp=fopen("c:\\input.txt","r");

iGet = getc(fp);

while(EOF != iGet)

{

if ((',' != iGet)('\n' != iGet))

{

iInput[a][b] = iGet - 48;

b++;

}

if (b == i)

{

a++;

b = 0;

}

iGet = getc(fp);

}

fp1 = fopen("d:\\output.txt","w");

build_tree(fp1,iSamples,iAttribute,0);

fclose(fp);

return 0;

}

void build_tree(FILE * fp, int* iSamples, int* iAttribute,int level)//

{

int iTest_Attribute = 0;

int iAttributeValue[MAX];

int k = 0;

int l = 0;

int m = 0;

int *iSamples1;

for (k = 0; kMAX; k++)

{

iAttributeValue[k] = -1;

}

if (0 == check_samples(iSamples))

{

fprintf(fp,"result: %d\n",iInput[iSamples[0]][i-1]);

return;

}

if (1 == check_attribute_null(iAttribute))

{

fprintf(fp,"result: %d\n",check_ordinary(iSamples));

return;

}

iTest_Attribute = choose_attribute(iSamples,iAttribute);

iAttribute[iTest_Attribute] = -1;

get_attributes(iSamples,iAttributeValue,iTest_Attribute);

k = 0;

while ((-1 != iAttributeValue[k])(k MAX))

{

l = 0;

m = 0;

while ((-1 != iSamples[l])(l j))

{

if (iInput[iSamples[l]][iTest_Attribute] == iAttributeValue[k])

{

m++;

}

l++;

}

iSamples1 = (int *)malloc(sizeof(int)*(m+1));

l = 0;

m = 0;

while ((-1 != iSamples[l])(l j))

{

if (iInput[iSamples[l]][iTest_Attribute] == iAttributeValue[k])

{

iSamples1[m] = iSamples[l];

m++;

}

l++;

}

iSamples1[m] = -1;

if (-1 == iSamples1[0])

{

fprintf(fp,"result: %d\n",check_ordinary(iSamples));

return;

}

fprintf(fp,"level%d: %d = %d\n",level,iTest_Attribute,iAttributeValue[k]);

build_tree(fp,iSamples1,iAttribute,level+1);

k++;

}

}

int choose_attribute(int* iSamples, int* iAttribute)

{

int iTestAttribute = -1;

int k = 0;

int l = 0;

int m = 0;

int n = 0;

int iTrue = 0;

int iFalse = 0;

int iTrue1 = 0;

int iFalse1 = 0;

int iDepart[MAX];

int iRecord[MAX];

double dEntropy = 0.0;

double dGainratio = 0.0;

double test = 0.0;

for (k = 0;kMAX;k++)

{

iDepart[k] = -1;

iRecord[k] = 0;

}

k = 0;

while ((l!=2)(k(i - 1)))

{

if (iAttribute[k] == -1)

{

l++;

}

k++;

}

if (l == 1)

{

for (k = 0;k(k-1);k++)

{

if (iAttribute[k] == -1)

{

return iAttribute[k];

}

}

}

for (k = 0;k (i-1);k++)

{

l = 0;

iTrue = 0;

iFalse = 0;

if (iAttribute[k] != -1)

{

while ((-1 != iSamples[l])(l j))

{

if (0 == iInput[iSamples[l]][i-1])

{

iFalse++;

}

if (1 == iInput[iSamples[l]][i-1])

{

iTrue++;

}

l++;

}

for (n = 0;nl;n++)//计算该属性有多少不同的值并记录

{

m = 0;

while((iDepart[m]!=-1)(m!=MAX))

{

if (iInput[iSamples[n]][iAttribute[k]] == iDepart[m])

{

break;

}

m++;

}

if (-1 == iDepart[m])

{

iDepart[m] = iInput[iSamples[n]][iAttribute[k]];

}

}

while ((iDepart[m] != -1)(m!=MAX))

{

for (n = 0;nl;n++)

{

if (iInput[iSamples[n]][iAttribute[k]] == iDepart[m])

{

if (1 == iInput[iSamples[n]][i-1])

{

iTrue1++;

}

if (0 == iInput[iSamples[n]][i-1])

{

iFalse1++;

}

iRecord[m]++;

}

}

dEntropy += entropy((double)iTrue1,(double)iFalse1,(double)l);

iTrue1 = 0;

iFalse1 = 0;

m++;

}

double dSplitinfo = splitinfo(iRecord,(double)l);

if (-1 == iTestAttribute)

{

iTestAttribute = k;

dGainratio = (info((double)iTrue,(double)iFalse)-dEntropy)/dSplitinfo;

}

else

{

test = (info((double)iTrue,(double)iFalse)-dEntropy)/dSplitinfo;

if (dGainratio test)

{

iTestAttribute = k;

dGainratio = test;

}

}

}

}

return iTestAttribute;

}

double info(double dTrue,double dFalse)

{

double dInfo = 0.0;

dInfo = ((dTrue/(dTrue+dFalse))*(log(dTrue/(dTrue+dFalse))/log(2.0))+(dFalse/(dTrue+dFalse))*(log(dFalse/(dTrue+dFalse))/log(2.0)))*(-1);

return dInfo;

}

double entropy(double dTrue, double dFalse, double dAll)

{

double dEntropy = 0.0;

dEntropy = (dTrue + dFalse)*info(dTrue,dFalse)/dAll;

return dEntropy;

}

double splitinfo(int* list,double dAll)

{

int k = 0;

double dSplitinfo = 0.0;

while (0!=list[k])

{

dSplitinfo -= ((double)list[k]/(double)dAll)*(log((double)list[k]/(double)dAll));

k++;

}

return dSplitinfo;

}

int check_samples(int *iSamples)

{

int k = 0;

int b = 0;

while ((-1 != iSamples[k])(k j-1))

{

if (iInput[k][i-1] != iInput[k+1][i-1])

{

b = 1;

break;

}

k++;

}

return b;

}

int check_ordinary(int *iSamples)

{

int k = 0;

int iTrue = 0;

int iFalse = 0;

while ((-1 != iSamples[k])(k i))

{

if (0 == iInput[iSamples[k]][i-1])

{

iFalse++;

}

else

{

iTrue++;

}

k++;

}

if (iTrue = iFalse)

{

return 1;

}

else

{

return 0;

}

}

int check_attribute_null(int *iAttribute)

{

int k = 0;

while (k (i-1))

{

if (-1 != iAttribute[k])

{

return 0;

}

k++;

}

return 1;

}

void get_attributes(int *iSamples,int *iAttributeValue,int iAttribute)

{

int k = 0;

int l = 0;

while ((-1 != iSamples[k])(k j))

{

l = 0;

while (-1 != iAttributeValue[l])

{

if (iInput[iSamples[k]][iAttribute] == iAttributeValue[l])

{

break;

}

l++;

}

if (-1 == iAttributeValue[l])

{

iAttributeValue[l] = iInput[iSamples[k]][iAttribute];

}

k++;

}

}

如何用C++语言计算一幅图像信息的熵

1、熵是描述区域的随机程度的，P=ΣC*logC，C是灰度概率值，当图像均匀时，各灰度值的概率基本相等，熵可以达到最大

2、例程：

#includeiostream.h

#includemath.h

int i,j;

double rowsum(double table[][4],int nrow)//定义第i行的边际概率函数

{

for(i=0;inrow;i++)

{

for( j=1;j4;j++)

table[i][0]+=table[i][j]; 

}

return 0;

}

double liesum(double table[4][4],int nlie)//定义第j列的边际概率函数

{ for( j=0;jnlie;j++)

{

for( i=1;i4;i++)

table[0][j]+=table[i][j];

}

return 0;

}

void main()

{ 

double p[4][4]={{1.0/8.0,1.0/16.0,1.0/32.0,1.0/32.0},{1.0/16.0,1.0/8.0,1.0/32.0,1.0/32.0},

{1.0/16.0,1.0/16.0,1.0/16.0,1.0/16.0},{1.0/4.0,0.0,0.0,0.0}};

for ( i=0;i4;i++)//输出概率矩阵

{

for ( j=0;j4;j++)

coutp[i][j]" ";

coutendl;      

}coutendl; 

rowsum(p,4);//调用函数输出第i行的边际概率 

for (i =0;i4;i++)

{cout"第"i"行的边际概率p""["i"]""是"p[i][0]endl;}coutendl;

liesum(p,4);//调用函数输出第j列的边际概率

for ( j =0;j4;j++)

{cout"第"j"列的条件概率p""["j"]""是"p[0][j]endl;}coutendl;

// double p[4][4];

double H1=0.0;

for( i=0;i4;i++)

{H1+=p[i][0]*(log((1.0/p[i][0])/log(2.0)));}

double H2=0.0;

for( j=0;j4;j++)

{H2+=p[0][j]*(log((1.0/p[0][j])/log(2.0)));}

double H3=0.0; 

for(i=0;i3;i++)

for(j=0;j4;j++) 

{H3+=p[i][j]*(log(1.0/p[i][j])/log(2.0));}

H3+=p[4][1]*(log(1.0/p[4][1])/log(2.0));

cout"X的熵：H(X)="H1endl;

cout"Y的熵：H(Y)="H2endl;

cout"(X,Y)的熵：H(X,Y)="H3endl;

coutendl;

cout"条件熵：H(X|Y)="H3-H2endl;

cout"条件熵：H(Y|X)="H3-H1endl;

cout"互信息：I(X;Y)="H1+H2-H3endl;

int size=4;//定义联合概率p为维数组

double *p;

p=new double[size]; 

for ( i=0;i4;i++)//联合概率计算

{

for ( j=0;j4;j++)

{

/*int nSize; 

scanf( "%d", nSize ); 

int *p = ( int* )malloc( sizeof( int ) * nSize ); 

for( int i = 0; i  nSize; i++ ) 

p[ i ] = 0;

double table[4][4];

p[i]=pp[0][i]*table[i][j];

cout"联合概率""p""["i"]""["j"]""是"p[i]endl;

}

}

for ( i=0;i4;i++)//联合熵的计算

{

for ( j=0;j4;j++)

{   

// H+=p[i][j]*log(1.0/p[i][j]);

H+=p[i]*(log((1.0/p[i])/log(2.0)));

}

}

cout"联合H(x,y)熵为"Hendl;

delete []p; */

}

用c语言求信源熵怎么编程

#include stdio.h

#include string.h

#includemath.h

int main()//是少了main函数，程序里面一定要有main函数的

{

double p[100];//每个信源的概率

int n;//信源个数

int i;

double sum=0;

scanf("%d",n);

for(i=0;in;i++)

{

scanf("%lf",p[i]);

sum+=-p[i]*(log(p[i])/log(2.0));

}

printf("%lf\n",sum);

return 0;

}

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