c++实现的随机森林的代码，看不懂，请问谁能给点具体的解释，十分感谢

[jxq123_]

#ifndef _DECISION_TREE_H_
#define _DECISION_TREE_H_
#include <string>
#include <vector>
#include <set>
#include <ctime>
#include <algorithm>
#include <cmath>

using namespace std;

//the data structure for a tuple
struct TupleData
{
vector<int> A;
char label;
};

struct TNode
{
int attrNum;
int attr;
char label;
};

struct decision_tree
{
TNode node;
vector<decision_tree*> childs;
};

void init(char * trainname, char * testname);
int readData(vector<TupleData> &data, const char* fileName);
int stringtoint(string s);
void sub_init();
void calculate_ArrtNum();
void calculate_attributes();
void RandomSelectData(vector<TupleData> &data, vector<TupleData> &subdata);
double Entropy(double p, double s);
int creat_classifier(decision_tree *&p, const vector<TupleData> &samples, vector<int> &attributes);
int BestGainArrt(const vector<TupleData> &samples, vector<int> &attributes);
bool Allthesame(const vector<TupleData> &samples, char ch);
char Majorityclass(const vector<TupleData> &samples);
void RandomSelectAttr(vector<int> &data, vector<int> &subdata);
char testClassifier(decision_tree *p, TupleData d);
void testData();
void freeClassifier(decision_tree *p);
void freeArrtNum();
void showResult();
#endif //_DECISION_TREE_H_


#include <iostream>
#include <fstream>
#include <sstream>
#include "random_forest.h"

using namespace std;

vector<decision_tree*> alltrees;

vector<TupleData> trainAll,
train,
test;

vector<int> attributes;

int trainAllNum=0;
int testAllNum=0;
int MaxAttr;
int *ArrtNum;
unsigned int F;
int tree_num=100;
const int leafattrnum=-1;
int TP=0,
FN=0,
FP=0,
TN=0,
TestP=0,
TestN=0;

void init(char * trainname, char * testname)
{
trainAllNum=readData(trainAll, trainname);
testAllNum=readData(test, testname);
calculate_attributes();
double temp=(double)trainAllNum;
temp=log(temp)/log(2.0);
// F=round(temp)+1;
F = (unsigned int)floor(temp+0.5)+1;
if(F>MaxAttr) F=MaxAttr;
//cout<<"f="<<F<<endl;
}

void sub_init()
{
RandomSelectData(trainAll, train);
calculate_ArrtNum();
}


int readData(vector<TupleData> &data, const char* fileName)
{
ifstream fin;
fin.open(fileName);
string line;

int datanum=0;

while(getline(fin,line))
{
TupleData d;
istringstream stream(line);
string str;
while(stream>>str)
{
if(str.find('+')==0)
{
d.label='+';
}
else if(str.find('-')==0)
{
d.label='-';
}
else
{
int j=stringtoint(str);
d.A.push_back(j);
}
}

data.push_back(d);
datanum++;
}

fin.close();
return datanum;
}

void RandomSelectData(vector<TupleData> &data, vector<TupleData> &subdata)
{
int index;
subdata.clear();
int d=0;
while (d < trainAllNum)
{
index = rand() % trainAllNum;
subdata.push_back(data.at(index));
d++;
}
}

void calculate_attributes()
{
TupleData d=trainAll.at(0);
MaxAttr=d.A.size();
attributes.clear();

for (int i = 0; i < MaxAttr; i++)
{
attributes.push_back(i);
}

ArrtNum=new int[MaxAttr];
}


int stringtoint(string s)
{
int sum=0;
for(int i=0; s[i]!='\0';i++)
{
int j=int(s[i])-48;
sum=sum*10+j;
}
return sum;
}

void calculate_ArrtNum()
{
for(int i=0; i<MaxAttr;i++) ArrtNum[i]=0;
for (vector<TupleData>::const_iterator it = train.begin(); it != train.end(); it++)
{
int i=0;
for (vector<int>::const_iterator intt=(*it).A.begin(); intt!=(*it).A.end();intt++)
{
int valuemax=(*intt)+1; //(*it).A.at(i)???
if(valuemax>ArrtNum[i]) ArrtNum[i]=valuemax;
i++;
}
}
}


double Entropy(double p, double s)
{
double n = s - p;
double result = 0;
if (n != 0)
result += - double(n) / s * log(double(n) / s) / log(2.0);
if (p != 0)
result += double(-p) / s * log(double(p) / s) / log(2.0);
return result;
}

int creat_classifier(decision_tree *&p, const vector<TupleData> &samples, vector<int> &attributes)
{
if (p == NULL)
p = new decision_tree();
if (Allthesame(samples, '+'))
{
p->node.label = '+';
p->node.attrNum = leafattrnum;
p->childs.clear();
return 1;
}
if (Allthesame(samples, '-'))
{
p->node.label = '-';
p->node.attrNum = leafattrnum;
p->childs.clear();
return 1;
}
if (attributes.size() == 0)
{
p->node.label = Majorityclass(samples);
p->node.attrNum = leafattrnum;
p->childs.clear();
return 1;
}
p->node.attrNum = BestGainArrt(samples, attributes);

p->node.label = ' ';

vector<int> newAttributes;
for (vector<int>::iterator it = attributes.begin(); it != attributes.end(); it++)
if ((*it) != p->node.attrNum)
newAttributes.push_back((*it));

int maxvalue=ArrtNum[p->node.attrNum];
vector<TupleData>* subSamples = new vector<TupleData>[maxvalue];
for (int i = 0; i < maxvalue; i++)
subSamples[i].clear();

for (vector<TupleData>::const_iterator it = samples.begin(); it != samples.end(); it++)
{
subSamples[(*it).A.at(p->node.attrNum)].push_back((*it));
}

decision_tree *child;
for (int i = 0; i < maxvalue; i++)
{
child = new decision_tree;
child->node.attr = i;
if (subSamples[i].size() == 0)
child->node.label = Majorityclass(samples);
else
creat_classifier(child, subSamples[i], newAttributes);
p->childs.push_back(child);
}
delete[] subSamples;
return 0;
}

int BestGainArrt(const vector<TupleData> &samples, vector<int> &attributes)
{
int attr,
bestAttr = 0,
p = 0,
s = (int)samples.size();

for (vector<TupleData>::const_iterator it = samples.begin(); it != samples.end(); it++)
{
if ((*it).label == '+')
p++;
}

double infoD;
double bestResult = 0;
infoD=Entropy(p, s);

vector<int> m_attributes;
RandomSelectAttr(attributes, m_attributes);

for (vector<int>::iterator it = m_attributes.begin(); it != m_attributes.end(); it++)
{
attr = (*it);
double result = infoD;

int maxvalue=ArrtNum[attr];
int* subN = new int[maxvalue];
int* subP = new int[maxvalue];
int* sub = new int[maxvalue];
for (int i = 0; i < maxvalue; i++)
{
subN[i] = 0;
subP[i] = 0;
sub[i]=0;
}
for (vector<TupleData>::const_iterator jt = samples.begin(); jt != samples.end(); jt++)
{
if ((*jt).label == '+')
subP[(*jt).A.at(attr)] ++;
else
subN[(*jt).A.at(attr)] ++;
sub[(*jt).A.at(attr)]++;
}

double SplitInfo=0;
for(int i=0; i<maxvalue; i++)
{
double partsplitinfo;
partsplitinfo=-double(sub[i])/s*log(double(sub[i])/s)/log(2.0);
SplitInfo=SplitInfo+partsplitinfo;
}

double infoattr=0;
for (int i = 0; i < maxvalue; i++)
{
double partentropy;
partentropy=Entropy(subP[i], subP[i] + subN[i]);
infoattr=infoattr+((double)(subP[i] + subN[i])/(double)(s))*partentropy;
}
result=result-infoattr;
result=result/SplitInfo;

if (result > bestResult)
{
bestResult = result;
bestAttr = attr;
}
delete[] subN;
delete[] subP;
delete[] sub;
}

if (bestResult == 0)
{
bestAttr=attributes.at(0);
}
return bestAttr;
}

void RandomSelectAttr(vector<int> &data, vector<int> &subdata)
{
int index;
unsigned int dataNum=data.size();
subdata.clear();
if(dataNum<=F)
{
for (vector<int>::iterator it = data.begin(); it != data.end(); it++)
{
int attr = (*it);
subdata.push_back(attr);
}
}
else
{
set<int> AttrSet;
AttrSet.clear();
while (AttrSet.size() < F)
{
index = rand() % dataNum;
if (AttrSet.count(index) == 0)
{
AttrSet.insert(index);
subdata.push_back(data.at(index));
}
}
}
}

bool Allthesame(const vector<TupleData> &samples, char ch)
{
for (vector<TupleData>::const_iterator it = samples.begin(); it != samples.end(); it++)
if ((*it).label != ch)
return false;
return true;
}

char Majorityclass(const vector<TupleData> &samples)
{
int p = 0, n = 0;
for (vector<TupleData>::const_iterator it = samples.begin(); it != samples.end(); it++)
if ((*it).label == '+')
p++;
else
n++;
if (p >= n)
return '+';
else
return '-';
}

char testClassifier(decision_tree *p, TupleData d)
{
if (p->node.label != ' ')
return p->node.label;
int attrNum = p->node.attrNum;
if (d.A.at(attrNum) < 0)
return ' ';
return testClassifier(p->childs.at(d.A.at(attrNum)), d);
}

void testData()
{
for (vector<TupleData>::iterator it = test.begin(); it != test.end(); it++)
{
if((*it).label=='+') TestP++;
else TestN++;

int p=0, n=0;
for(int i=0;i<tree_num;i++)
{
if(testClassifier(alltrees.at(i), (*it))=='+') p++;
else n++;
}

if(p>n)
{
if((*it).label=='+') TP++;
else FP++;
}
else
{
if((*it).label=='+') FN++;
else TN++;
}
}
}


void freeClassifier(decision_tree *p)
{
if (p == NULL)
return;
for (vector<decision_tree*>::iterator it = p->childs.begin(); it != p->childs.end(); it++)
{
freeClassifier(*it);
}
delete p;
}

void freeArrtNum()
{
delete[] ArrtNum;
}

void showResult()
{
cout<<"Train size: "<< trainAllNum<<endl;
cout<<"Test size: "<<testAllNum<<endl;
cout << "True positive: " << TP << endl;
cout << "False negative: "<< FN<<endl;
cout << "False positive: "<<FP<<endl;
cout << "True negative: "<<TN<<endl;

// cout << TP << endl;
// cout << FN<<endl;
// cout <<FP<<endl;
// cout <<TN<<endl;
}

int main(int argc, char **argv)
{
char * trainfile=argv[1];
char * testfile=argv[2];

//cout<<"input the F and tree_num"<<endl;
//cin>>F>>tree_num;

srand((unsigned)time(NULL));

init(trainfile, testfile);

for(int i=0; i<tree_num; i++)
{
sub_init();
decision_tree * root=NULL;
creat_classifier(root, train, attributes);
alltrees.push_back(root);
}

testData();

for (vector<decision_tree *>::const_iterator it = alltrees.begin(); it != alltrees.end(); it++)
{
freeClassifier((*it));
}

freeArrtNum();

showResult();
return 0;
}

[爷就是这个范儿]

自己单步调试就会了

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[小静12395]

是不是下载了个随机森林的C++程序，里面还有决策树代码？我下了个 运行时有错误 我都不知道怎么改 你会么

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[ran337287]

http://blog.csdn.net/wsj998689aa/article/details/44680267看看这个

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[leowilliam]

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