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// naiveBayesClassify.cpp : 定义控制台应用程序的入口点。////#include "stdafx.h"#include<iostream>#include<iomanip>#include<string>#include<map>#include<vector>#include <tchar.h>//#include<math.h>using namespace std;//save the training datatypedef vector<vector<string>> strDVect;//save all possible condition probabilitystypedef map<string, map<string, map<string, double>>> thriMap;//service for thriMaptypedef map<string, map<string, double>> doubleMap;typedef map<string, double> strMap;//save the kind of aim attribute valuetypedef map<string, int> aimMap;//kind of every attribute including aim attribute valuetypedef map<string, int> attMap;//const for denominator and numeratordouble K = 1;//sampletypedef vector<string> sampleVect;//check the three dimensions map and return 4 statesint existRecord(thriMap prob, string aimValue, string attName, string attValue){thriMap::const_iterator iterThri = prob.find(aimValue);if (iterThri == prob.end())return 1;doubleMap::const_iterator iterDouble = iterThri->second.find(attName);if (iterDouble == iterThri->second.end())return 2;strMap::const_iterator iterSingle = iterDouble->second.find(attValue);if (iterSingle == iterDouble->second.end())return 3;return 4;}void show(thriMap prob){thriMap::iterator iterThri = prob.begin();for (; iterThri != prob.end(); iterThri++){doubleMap::iterator iterDouble = iterThri->second.begin();for (; iterDouble != iterThri->second.end(); iterDouble++){strMap::iterator iterSingle = iterDouble->second.begin();for (; iterSingle != iterDouble->second.end(); iterSingle++){cout << iterThri->first << " " << iterDouble->first << " " << iterSingle->first << " " << iterSingle->second << endl;}}}}//get all possible statistics//three dimensions hash prob, first key notes aim attribute value,second key notes refence attribute name,//third key notes reference attribute value, the value notes the refence attribute value numbervoid getConditionStat(strDVect datas, thriMap &prob, aimMap &aimNum){int i, j, k, m, n;m = datas.size();n = datas[0].size();doubleMap doubleMapIns;strMap strMapIns;thriMap::iterator iterThri;doubleMap::iterator iterDouble;for (i = 1; i < m; i++){//save all possible statisticsfor (j = 0; j < n - 1; j++){switch (existRecord(prob, datas[i][n - 1], datas[0][j], datas[i][j])){case 1://must clearstrMapIns.clear();doubleMapIns.clear();strMapIns.insert(make_pair(datas[i][j], 1));doubleMapIns.insert(make_pair(datas[0][j], strMapIns));prob.insert(make_pair(datas[i][n - 1], doubleMapIns));break;case 2:strMapIns.clear();strMapIns.insert(make_pair(datas[i][j], 1));prob.find(datas[i][n - 1])->second.insert(make_pair(datas[0][j], strMapIns));break;case 3:prob.find(datas[i][n - 1])->second.find(datas[0][j])->second.insert(make_pair(datas[i][j], 1));break;case 4:prob.find(datas[i][n - 1])->second.find(datas[0][j])->second.find(datas[i][j])->second++;break;}}//save the kind of aim attribute valueif (aimNum.find(datas[i][n - 1]) != aimNum.end()){aimNum.find(datas[i][n - 1])->second++;}else{aimNum.insert(make_pair(datas[i][n - 1], 1));}}}void getConditionProb(thriMap &prob, aimMap aimNum, attMap attKind){//save all possible condition probabilitysthriMap::iterator iterThri = prob.begin();for (; iterThri != prob.end(); iterThri++){doubleMap::iterator iterDouble = iterThri->second.begin();for (; iterDouble != iterThri->second.end(); iterDouble++){strMap::iterator iterSingle = iterDouble->second.begin();for (; iterSingle != iterDouble->second.end(); iterSingle++){//add const K,L to  denominator and numerator//the part of "attKind.find(iterDouble->first)->second*K" add the weight of kind of refence attribute。iterSingle->second = (iterSingle->second + K) / (attKind.find(iterDouble->first)->second*K + aimNum.find(iterThri->first)->second);}}}}void getClassification(strDVect datas, thriMap prob, aimMap aimNum, attMap attKind, sampleVect sampleIns, int records){//save all the probabilitydouble sum = 0;double max = 0;double pp;string classKind;//获得极大后验假设double h_map;aimMap::const_iterator iterAim = aimNum.begin();for (; iterAim != aimNum.end(); iterAim++){//get prior probability//the part of "((--attKind.end())->second*K" add the weight of the kind of aim attribute。pp = (double)(iterAim->second + K) / ((--attKind.end())->second*K + records);//获得极大使然假设double h_ml = 1;for (int i = 0; i < sampleIns.size(); i++){if (existRecord(prob, iterAim->first, datas[0][i], sampleIns.at(i)) != 4){cout << "there is no value of attrubute " << datas[0][i] << endl;h_ml *= K;}else{h_ml *= prob.find(iterAim->first)->second.find(datas[0][i])->second.find(sampleIns.at(i))->second;}}h_map = pp*h_ml;if (h_map > max){max = h_map;classKind = iterAim->first;}sum += h_map;}//normalizemax = max / sum;cout << "the the most class is " << classKind << ", and the probability is " << max << endl;}int _tmain(int argc, _TCHAR* argv[]){int i, j, m, n;//m:number of training data,n: number of attributesm = 15; n = 5;strDVect datas(m);for (i = 0; i < m; i++){datas[i].resize(n);}//first row save attributesdatas[0][0] = "age";datas[0][1] = "inco";datas[0][2] = "student";datas[0][3] = "credit_rating";datas[0][4] = "class:buys_computer";datas[1][0] = "<=30";datas[1][1] = "high";datas[1][2] = "no";datas[1][3] = "fair";datas[1][4] = "no";datas[2][0] = "<=30";datas[2][1] = "high";datas[2][2] = "no";datas[2][3] = "excellent";datas[2][4] = "no";datas[3][0] = "31..40";datas[3][1] = "high";datas[3][2] = "no";datas[3][3] = "fair";datas[3][4] = "yes";datas[4][0] = ">40";datas[4][1] = "medi";datas[4][2] = "no";datas[4][3] = "fair";datas[4][4] = "yes";datas[5][0] = ">40";datas[5][1] = "low";datas[5][2] = "yes";datas[5][3] = "fair";datas[5][4] = "yes";datas[6][0] = ">40";datas[6][1] = "low";datas[6][2] = "yes";datas[6][3] = "excellent";datas[6][4] = "no";datas[7][0] = "31..40";datas[7][1] = "low";datas[7][2] = "yes";datas[7][3] = "excellent";datas[7][4] = "yes";datas[8][0] = "<=30";datas[8][1] = "medi";datas[8][2] = "no";datas[8][3] = "fair";datas[8][4] = "no";datas[9][0] = "<=30";datas[9][1] = "low";datas[9][2] = "yes";datas[9][3] = "fair";datas[9][4] = "yes";datas[10][0] = ">40";datas[10][1] = "medi";datas[10][2] = "yes";datas[10][3] = "fair";datas[10][4] = "yes";datas[11][0] = "<=30";datas[11][1] = "medi";datas[11][2] = "yes";datas[11][3] = "excellent";datas[11][4] = "yes";datas[12][0] = "31..40";datas[12][1] = "medi";datas[12][2] = "no";datas[12][3] = "excellent";datas[12][4] = "yes";datas[13][0] = "31..40";datas[13][1] = "high";datas[13][2] = "yes";datas[13][3] = "fair";datas[13][4] = "yes";datas[14][0] = ">40";datas[14][1] = "medi";datas[14][2] = "no";datas[14][3] = "excellent";datas[14][4] = "no";for (i = 0; i < m; i++){for (j = 0; j < n; j++){cout.width(15);cout << setiosflags(ios::left) << datas[i][j];}cout << endl;}thriMap prob;aimMap aimNum;attMap attKind;attKind.insert(make_pair("age", 3));attKind.insert(make_pair("inco", 3));attKind.insert(make_pair("student", 2));attKind.insert(make_pair("credit_rating", 2));attKind.insert(make_pair("class:buys_computer", 2));getConditionStat(datas, prob, aimNum);//init KK = (double)1 / (m - 1);getConditionProb(prob, aimNum, attKind);show(prob);sampleVect sampleIns;sampleIns.push_back("31..40");sampleIns.push_back("high");sampleIns.push_back("no");sampleIns.push_back("fair");getClassification(datas, prob, aimNum, attKind, sampleIns, m - 1);system("pause");}