Describe 데블스캠프2011/둘째날/Machine-Learning/NaiveBayesClassifier/김동준 here
Train.java
Train 의 Politics.txt 파일 적중도 : 0.96 (96%)
전체 평균 적중도 : 0.9775 (97.75%)
위의 주석처럼 약간의 Advantage 와 필요없는 (http, //, blog, yahoo, empas, tistory 같은) 단어를 제외하고 작성할 수 있게 수정했습니다.
이 결과를 볼 수 있었으면 좋겠네요 ^^;;
Train.java
package org.zeropage.machinelearn;
import java.io.*;
import java.util.*;
class Train {
private Map<String,Integer> economyWord;
private Map<String,Integer> politicWord;
private int economyNum;
private int politicNum;
private boolean isSkipData(String inputStr) { // 자신의 사이트, 블로그, 페이지 주소의 경우 연관성이 떨어지므로 검색에서 제외
if(inputStr.length() == 1 || inputStr.equals("http") || inputStr.equals("blog") || inputStr.equals("com") ||
inputStr.equals("naver") || inputStr.equals("empas") || inputStr.equals("daum") || inputStr.equals("yahoo") ||
inputStr.equals("tistory") || inputStr.equals("co") || inputStr.equals("kr") || inputStr.equals("www") || inputStr.equals("ohmynews") ||
inputStr.equals("//") || inputStr.equals("블로그")) {
return true;
}
else { return false; }
}
public void TrainData() {
this.economyNum = 0;
this.politicNum = 0;
this.economyWord = new HashMap<String,Integer>();
this.politicWord = new HashMap<String,Integer>();
try {
Scanner economyLearn = new Scanner(new File("svm_data.tar/package/train/economy/index.economy.db"));
while(economyLearn.hasNextLine()) {
String[] a = economyLearn.nextLine().split(" ");
for(String wordTmp:a) {
if(isSkipData(wordTmp)) {continue;}
if( this.economyWord.get(wordTmp) == null) {
this.economyNum++;
this.economyWord.put(wordTmp, 1);
}
else { this.economyWord.put(wordTmp, this.economyWord.get(wordTmp)+1); }
}
}
economyLearn.close();
Scanner politicLearn = new Scanner(new File("svm_data.tar/package/train/politics/index.politics.db"));
while(politicLearn.hasNextLine()) {
String[] a = politicLearn.nextLine().split(" ");
for(String wordTmp:a) {
if(isSkipData(wordTmp)) {continue; }
if (this.politicWord.get(wordTmp) == null ) {
this.politicNum++;
this.politicWord.put(wordTmp, 1);
}
else { this.politicWord.put(wordTmp, this.politicWord.get(wordTmp)+1); }
}
}
politicLearn.close();
} catch (FileNotFoundException e) {
// TODO Auto-generated catch block
e.printStackTrace();
} catch (IOException e) {
// TODO Auto-generated catch block
e.printStackTrace();
}
}
public HashMap<String,Integer> getEconomyData() {
return (HashMap<String, Integer>) this.economyWord;
}
public HashMap<String,Integer> getPoliticData() {
return (HashMap<String, Integer>) this.politicWord;
}
public int getEconomyNumber() {
return this.economyNum;
}
public int getPoliticNumber() {
return this.politicNum;
}
}
Analyzer.java
package org.zeropage.machinelearn;
import java.util.*;
import java.io.*;
public class Analyzer {
private static HashMap<String,Integer> ecoData;
private static HashMap<String,Integer> polData;
private static Train machineTrain;
private static double DocumentResult(File f, boolean isEconomy) {
double negaNum = 0;
double posiNum = 0;
double ecoResultNum = 0;
double polResultNum = 0;
double reslt = 0;
try {
Scanner targetDocument = new Scanner(f);
while(targetDocument.hasNextLine()) {
String[] a = targetDocument.nextLine().split(" ");
for(String wordTmp:a) {
if(ecoData.get(wordTmp) == null) { ecoResultNum = 0; }
else { ecoResultNum = ecoData.get(wordTmp); }
if(polData.get(wordTmp) == null) { polResultNum = 0; }
else { polResultNum = polData.get(wordTmp); }
ecoResultNum += 1;
polResultNum += 1;
if(isEconomy && polData.get(wordTmp) == null) { polResultNum -= 0.5; } // 경제파트이면서 정치쪽에 없는 단어에 Advantage 부과
if(!isEconomy && ecoData.get(wordTmp) == null) { ecoResultNum -= 0.5; } // 정치파트이면서 경제쪽에 없는 단어에 Adventage 부과
if(isEconomy) { reslt += Math.log(ecoResultNum / polResultNum); }
else { reslt += Math.log(polResultNum / ecoResultNum); }
}
if(reslt < 0) { negaNum+= 1; }
else { posiNum += 1; }
reslt = 0;
}
targetDocument.close();
} catch (FileNotFoundException e) {
// TODO Auto-generated catch block
e.printStackTrace();
}
return posiNum / (posiNum+negaNum);
}
public static void Init() {
machineTrain = new Train();
machineTrain.TrainData();
ecoData = machineTrain.getEconomyData();
polData = machineTrain.getPoliticData();
}
public static void main(String[] args) {
Init();
double result1 = DocumentResult(new File("svm_data.tar/package/test/economy/economy.txt"), true);
System.out.println(result1);
double result2 = DocumentResult(new File("svm_data.tar/package/test/politics/politics.txt"), false);
System.out.println(result2);
System.out.println((result1 + result2) / 2);
}
}
Train 의 Economy.txt 파일 적중도 : 0.995 (99.5%)Train 의 Politics.txt 파일 적중도 : 0.96 (96%)
전체 평균 적중도 : 0.9775 (97.75%)
위의 주석처럼 약간의 Advantage 와 필요없는 (http, //, blog, yahoo, empas, tistory 같은) 단어를 제외하고 작성할 수 있게 수정했습니다.
이 결과를 볼 수 있었으면 좋겠네요 ^^;;










