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贝叶斯模型

class="模型" name="code">package bayes;

import java.util.HashMap;

import java.util.HashSet;

import java.util.Map;

import java.util.Set;

public class Model {

public Set categorySet = new HashSet();

public Set keyWordsSet = new HashSet();

public Map probabilityMap = new HashMap();

}

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? ? 贝叶斯主类

package bayes;

import java.io.BufferedReader;

import java.io.BufferedWriter;

import java.io.FileReader;

import java.io.FileWriter;

import java.util.Arrays;

import java.util.HashMap;

import java.util.HashSet;

import java.util.Map;

import java.util.Set;

public class Bayes {

/**

* P(B|a1,a2,a3)= cBa1 * cBa2 * cBa3 / (cB * cB) / ( cBa1 * cBa2 * cba3 / (cB * cB) + cAa1 * cAa2 * cAa3 / (cA * cA) )

*

* @param source

* @param model

* @return

*/

public static Map getValue(String source, Model model) {

if (model != null && model.keyWordsSet != null && model.categorySet != null && source != null) {

Set keyWordSet = new HashSet();

for (String key : model.keyWordsSet) {

if (source.contains(key)) {

keyWordSet.add(key);

}

}

if (keyWordSet.size() > 0) {

Map probabilityMap = new HashMap();

double sumProbalitity = 0;

for (String category : model.categorySet) {

double numerator = 1;

double denominator = 1;

int index = 0;

for (String keyword : keyWordSet) {

if (index > 0) {

denominator = denominator * getProbalityValue(model, category);

}

numerator = numerator * getProbalityValue(model, category + "-" + keyword);

index = index + 1;

}

double probalisty = Double.valueOf(numerator / denominator);

sumProbalitity = sumProbalitity + probalisty;

probabilityMap.put(category, probalisty);

}

Map rtnMap = new HashMap();

if (sumProbalitity > 0) {

for (String category : model.categorySet) {

rtnMap.put(category, Double.valueOf(probabilityMap.get(category) / sumProbalitity));

}

} else {

for (String category : model.categorySet) {

rtnMap.put(category, Double.valueOf(1.0 / model.categorySet.size()));

}

}

return rtnMap;

}

}

return null;

}

public static long getProbalityValue(Model model, String key) {

long rtn = 0;

if (model.probabilityMap.containsKey(key)) {

rtn = model.probabilityMap.get(key);

}

return rtn;

}

public static Model train(String[] categorys, String[][] data, String[] keyWords) {

if (categorys != null && data != null && data.length == categorys.length && categorys.length > 1 && keyWords != null && keyWords.length > 1) {

Model model = new Model();

model.categorySet.addAll(Arrays.asList(categorys));

model.keyWordsSet.addAll(Arrays.asList(keyWords));

for (int i = 0; i < categorys.length; i++) {

calculateProbability(categorys[i], data[i], model);

}

return model;

} else {

System.out.println("data error!");

}

return null;

}

private static void calculateProbability(String category, String[] categoryData, Model model) {

for (String source : categoryData) {

addCategoryKeywordCount(category, model);

for (String keywork : model.keyWordsSet) {

if (source.contains(keywork)) {

addCategoryKeywordCount(keywork, model);

addCategoryKeywordCount(category + "-" + keywork, model);

}

}

}

}

private static void addCategoryKeywordCount(String key, Model model) {

Long count = null;

count = model.probabilityMap.get(key);

if (count != null) {

count = count + 1;

} else {

count = 1L;

}

model.probabilityMap.put(key, count);

}

public static void saveModel(String fileName, Model model) {

try (BufferedWriter writer = new BufferedWriter(new FileWriter(fileName));) {

for (String category : model.categorySet) {

writer.write(category);

writer.write(",");

}

writer.write("\n");

for (String keyword : model.keyWordsSet) {

writer.write(keyword);

writer.write(",");

}

writer.write("\n");

for (String key : model.probabilityMap.keySet()) {

writer.write(key);

writer.write(":");

writer.write(model.probabilityMap.get(key).toString());

writer.write("\n");

}

writer.write("\n");

} catch (Exception e) {

System.out.println("save Model error");

}

}

public static Model loadModel(String fileName) {

Model model = new Model();

try (BufferedReader reader = new BufferedReader(new FileReader(fileName));) {

String categoryLine = reader.readLine();

model.categorySet.addAll(getStringSet(categoryLine, ","));

String keyWorksLine = reader.readLine();

model.keyWordsSet.addAll(getStringSet(keyWorksLine, ","));

String probalilityLine = reader.readLine();

while (probalilityLine != null) {

if (probalilityLine.trim().length() > 0) {

String[] itemStr = (probalilityLine + ":").split(":");

if (itemStr.length == 2) {

String key = itemStr[0];

Long probalility = Long.valueOf(itemStr[0]);

model.probabilityMap.put(key, probalility);

} else {

System.out.println("Error model line:" + probalilityLine);

}

}

probalilityLine = reader.readLine();

}

} catch (Exception e) {

System.out.println("load model error");

}

return model;

}

public static Set getStringSet(String sourceStr, String splitor) {

Set rtn = new HashSet();

if (sourceStr != null && splitor != null) {

String[] strs = sourceStr.split(splitor);

if (strs != null && strs.length > 0) {

for (String str : strs) {

if (str != null) {

rtn.add(str.trim());

}

}

}

}

return rtn;

}

}

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