#include <iostream>
#include <string>
#include <vector>
#include <map>
#include <algorithm>
#include <cmath>
using namespace std;
#define MAXLEN 6//输入每行的数据个数
//多叉树的实现
//1 广义表
//2 父指针表示法，适于经常找父结点的应用
//3 子女链表示法，适于经常找子结点的应用
//4 左长子，右兄弟表示法,实现比较麻烦
//5 每个结点的所有孩子用vector保存
//教训:数据结构的设计很重要，本算法采用5比较合适，同时
//注意维护剩余样例和剩余属性信息，建树时横向遍历考循环属性的值，
//纵向遍历靠递归调用
vector <vector <string> > state;//实例集
vector <string> item(MAXLEN);//对应一行实例集
vector <string> attribute_row;//保存首行即属性行数据
string end("end");//输入结束
string yes("yes");
string no("no");
string blank("");
map<string,vector < string > > map_attribute_values;//存储属性对应的所有的值
int tree_size = 0;
struct Node{//决策树节点
string attribute;//属性值
string arrived_value;//到达的属性值
vector<Node *> childs;//所有的孩子
Node(){
attribute = blank;
arrived_value = blank;
}
};
Node * root;
//根据数据实例计算属性与值组成的map
void ComputeMapFrom2DVector(){
unsigned int i,j,k;
bool exited = false;
vector<string> values;
for(i = 1; i < MAXLEN-1; i++){//按照列遍历
for (j = 1; j < state.size(); j++){
for (k = 0; k < values.size(); k++){
if(!values[k].compare(state[j][i])) exited = true;
}
if(!exited){
values.push_back(state[j][i]);//注意Vector的插入都是从前面插入的，注意更新it，始终指向vector头
}
exited = false;
}
map_attribute_values[state[0][i]] = values;
values.erase(values.begin(), values.end());
}
}
//根据具体属性和值来计算熵
double ComputeEntropy(vector <vector <string> > remain_state, string attribute, string value,bool ifparent){
vector<int> count (2,0);
unsigned int i,j;
bool done_flag = false;//哨兵值
for(j = 1; j < MAXLEN; j++){
if(done_flag) break;
if(!attribute_row[j].compare(attribute)){
for(i = 1; i < remain_state.size(); i++){
if((!ifparent&&!remain_state[i][j].compare(value)) || ifparent){//ifparent记录是否算父节点
if(!remain_state[i][MAXLEN - 1].compare(yes)){
count[0]++;
}
else count[1]++;
}
}
done_flag = true;
}
}
if(count[0] == 0 || count[1] == 0 ) return 0;//全部是正实例或者负实例
//具体计算熵 根据[+count[0],-count[1]],log2为底通过换底公式换成自然数底数
double sum = count[0] + count[1];
double entropy = -count[0]/sum*log(count[0]/sum)/log(2.0) - count[1]/sum*log(count[1]/sum)/log(2.0);
return entropy;
}
//计算按照属性attribute划分当前剩余实例的信息增益
double ComputeGain(vector <vector <string> > remain_state, string attribute){
unsigned int j,k,m;
//首先求不做划分时的熵
double parent_entropy = ComputeEntropy(remain_state, attribute, blank, true);
double children_entropy = 0;
//然后求做划分后各个值的熵
vector<string> values = map_attribute_values[attribute];
vector<double> ratio;
vector<int> count_values;
int tempint;
for(m = 0; m < values.size(); m++){
tempint = 0;
for(k = 1; k < MAXLEN - 1; k++){
if(!attribute_row[k].compare(attribute)){
for(j = 1; j < remain_state.size(); j++){
if(!remain_state[j][k].compare(values[m])){
tempint++;
}
}
}
}
count_values.push_back(tempint);
}
for(j = 0; j < values.size(); j++){
ratio.push_back((double)count_values[j] / (double)(remain_state.size()-1));
}
double temp_entropy;
for(j = 0; j < values.size(); j++){
temp_entropy = ComputeEntropy(remain_state, attribute, values[j], false);
children_entropy += ratio[j] * temp_entropy;
}
return (parent_entropy - children_entropy);
}
int FindAttriNumByName(string attri){
for(int i = 0; i < MAXLEN; i++){
if(!state[0][i].compare(attri)) return i;
}
cerr<<"can't find the numth of attribute"<<endl;
return 0;
}
//找出样例中占多数的正/负性
string MostCommonLabel(vector <vector <string> > remain_state){
int p = 0, n = 0;
for(unsigned i = 0; i < remain_state.size(); i++){
if(!remain_state[i][MAXLEN-1].compare(yes)) p++;
else n++;
}
if(p >= n) return yes;
else return no;
}
//判断样例是否正负性都为label
bool AllTheSameLabel(vector <vector <string> > remain_state, string label){
int count = 0;
for(unsigned int i = 0; i < remain_state.size(); i++){
if(!remain_state[i][MAXLEN-1].compare(label)) count++;
}
if(count == remain_state.size()-1) return true;
else return false;
}
//计算信息增益，DFS构建决策树
//current_node为当前的节点
//remain_state为剩余待分类的样例
//remian_attribute为剩余还没有考虑的属性
//返回根结点指针
Node * BulidDecisionTreeDFS(Node * p, vector <vector <string> > remain_state, vector <string> remain_attribute){
//if(remain_state.size() > 0){
//printv(remain_state);
//}
if (p == NULL)
p = new Node();
//先看搜索到树叶的情况
if (AllTheSameLabel(remain_state, yes)){
p->attribute = yes;
return p;
}
if (AllTheSameLabel(remain_state, no)){
p->attribute = no;
return p;
}
if(remain_attribute.size() == 0){//所有的属性均已经考虑完了,还没有分尽
string label = MostCommonLabel(remain_state);
p->attribute = label;
return p;
}
double max_gain = 0, temp_gain;
vector <string>::iterator max_it = remain_attribute.begin();
vector <string>::iterator it1;
for(it1 = remain_attribute.begin(); it1 < remain_attribute.end(); it1++){
temp_gain = ComputeGain(remain_state, (*it1));
if(temp_gain > max_gain) {
max_gain = temp_gain;
max_it = it1;
}
}
//下面根据max_it指向的属性来划分当前样例，更新样例集和属性集
vector <string> new_attribute;
vector <vector <string> > new_state;
for(vector <string>::iterator it2 = remain_attribute.begin(); it2 < remain_attribute.end(); it2++){
if((*it2).compare(*max_it)) new_attribute.push_back(*it2);
}
//确定了最佳划分属性，注意保存
p->attribute = *max_it;
vector <string> values = map_attribute_values[*max_it];
int attribue_num = FindAttriNumByName(*max_it);
new_state.push_back(attribute_row);
for(vector <string>::iterator it3 = values.begin(); it3 < values.end(); it3++){
for(unsigned int i = 1; i < remain_state.size(); i++){
if(!remain_state[i][attribue_num].compare(*it3)){
new_state.push_back(remain_state[i]);
}
}
Node * new_node = new Node();
new_node->arrived_value = *it3;
if(new_state.size() == 0){//表示当前没有这个分支的样例，当前的new_node为叶子节点
new_node->attribute = MostCommonLabel(remain_state);
}
else
BulidDecisionTreeDFS(new_node, new_state, new_attribute);
//递归函数返回时即回溯时需要1 将新结点加入父节点孩子容器 2清除new_state容器
p->childs.push_back(new_node);
new_state.erase(new_state.begin()+1,new_state.end());//注意先清空new_state中的前一个取值的样例，准备遍历下一个取值样例
}
return p;
}
void Input(){
string s;
while(cin>>s,s.compare(end) != 0){//-1为输入结束
item[0] = s;
for(int i = 1;i < MAXLEN; i++){
cin>>item[i];
}
state.push_back(item);//注意首行信息也输入进去，即属性
}
for(int j = 0; j < MAXLEN; j++){
attribute_row.push_back(state[0][j]);
}
}
void PrintTree(Node *p, int depth){
for (int i = 0; i < depth; i++) cout << '\t';//按照树的深度先输出tab
if(!p->arrived_value.empty()){
cout<<p->arrived_value<<endl;
for (int i = 0; i < depth+1; i++) cout << '\t';//按照树的深度先输出tab
}
cout<<p->attribute<<endl;
for (vector<Node*>::iterator it = p->childs.begin(); it != p->childs.end(); it++){
PrintTree(*it, depth + 1);
}
}
void FreeTree(Node *p){
if (p == NULL)
return;
for (vector<Node*>::iterator it = p->childs.begin(); it != p->childs.end(); it++){
FreeTree(*it);
}
delete p;
tree_size++;
}
int main(){
Input();
vector <string> remain_attribute;
string outlook("Outlook");
string Temperature("Temperature");
string Humidity("Humidity");
string Wind("Wind");
remain_attribute.push_back(outlook);
remain_attribute.push_back(Temperature);
remain_attribute.pu
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