决策树算法C4.5 

数据仓库与数据挖掘

}

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; }

数据仓库与数据挖掘

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 {

数据仓库与数据挖掘

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++; }

数据仓库与数据挖掘

if (-1 == iAttributeValue[l]) {

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