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Commit d8b5c81

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author
linyiqun
committed
维特比算法工具类
维特比算法工具类
1 parent 2cef388 commit d8b5c81

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package DataMining_Viterbi;
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import java.io.BufferedReader;
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import java.io.File;
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import java.io.FileReader;
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import java.io.IOException;
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import java.util.ArrayList;
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import java.util.HashMap;
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import java.util.Map;
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/**
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* 维特比算法工具类
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*
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* @author lyq
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*
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*/
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public class ViterbiTool {
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// 状态转移概率矩阵文件地址
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private String stmFilePath;
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// 混淆矩阵文件地址
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private String confusionFilePath;
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// 初始状态概率
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private double[] initStatePro;
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// 观察到的状态序列
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public String[] observeStates;
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// 状态转移矩阵值
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private double[][] stMatrix;
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// 混淆矩阵值
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private double[][] confusionMatrix;
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// 各个条件下的潜在特征概率值
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private double[][] potentialValues;
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// 潜在特征
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private ArrayList<String> potentialAttrs;
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// 属性值列坐标映射图
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private HashMap<String, Integer> name2Index;
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// 列坐标属性值映射图
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private HashMap<Integer, String> index2name;
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public ViterbiTool(String stmFilePath, String confusionFilePath,
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double[] initStatePro, String[] observeStates) {
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this.stmFilePath = stmFilePath;
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this.confusionFilePath = confusionFilePath;
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this.initStatePro = initStatePro;
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this.observeStates = observeStates;
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initOperation();
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}
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/**
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* 初始化数据操作
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*/
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private void initOperation() {
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double[] temp;
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int index;
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ArrayList<String[]> smtDatas;
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ArrayList<String[]> cfDatas;
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smtDatas = readDataFile(stmFilePath);
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cfDatas = readDataFile(confusionFilePath);
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index = 0;
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this.stMatrix = new double[smtDatas.size()][];
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for (String[] array : smtDatas) {
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temp = new double[array.length];
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for (int i = 0; i < array.length; i++) {
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try {
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temp[i] = Double.parseDouble(array[i]);
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} catch (NumberFormatException e) {
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temp[i] = -1;
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}
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}
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// 将转换后的值赋给数组中
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this.stMatrix[index] = temp;
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index++;
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}
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index = 0;
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this.confusionMatrix = new double[cfDatas.size()][];
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for (String[] array : cfDatas) {
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temp = new double[array.length];
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for (int i = 0; i < array.length; i++) {
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try {
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temp[i] = Double.parseDouble(array[i]);
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} catch (NumberFormatException e) {
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temp[i] = -1;
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}
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}
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// 将转换后的值赋给数组中
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this.confusionMatrix[index] = temp;
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index++;
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}
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this.potentialAttrs = new ArrayList<>();
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// 添加潜在特征属性
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for (String s : smtDatas.get(0)) {
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this.potentialAttrs.add(s);
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}
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// 去除首列无效列
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potentialAttrs.remove(0);
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this.name2Index = new HashMap<>();
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this.index2name = new HashMap<>();
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// 添加名称下标映射关系
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for (int i = 1; i < smtDatas.get(0).length; i++) {
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this.name2Index.put(smtDatas.get(0)[i], i);
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// 添加下标到名称的映射
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this.index2name.put(i, smtDatas.get(0)[i]);
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}
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for (int i = 1; i < cfDatas.get(0).length; i++) {
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this.name2Index.put(cfDatas.get(0)[i], i);
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}
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}
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/**
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* 从文件中读取数据
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*/
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private ArrayList<String[]> readDataFile(String filePath) {
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File file = new File(filePath);
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ArrayList<String[]> dataArray = new ArrayList<String[]>();
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try {
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BufferedReader in = new BufferedReader(new FileReader(file));
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String str;
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String[] tempArray;
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while ((str = in.readLine()) != null) {
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tempArray = str.split(" ");
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dataArray.add(tempArray);
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}
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in.close();
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} catch (IOException e) {
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e.getStackTrace();
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}
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return dataArray;
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}
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/**
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* 根据观察特征计算隐藏的特征概率矩阵
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*/
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private void calPotencialProMatrix() {
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String curObserveState;
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// 观察特征和潜在特征的下标
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int osIndex;
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int psIndex;
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double temp;
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double maxPro;
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// 混淆矩阵概率值,就是相关影响的因素概率
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double confusionPro;
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this.potentialValues = new double[observeStates.length][potentialAttrs
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.size() + 1];
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for (int i = 0; i < this.observeStates.length; i++) {
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curObserveState = this.observeStates[i];
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osIndex = this.name2Index.get(curObserveState);
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maxPro = -1;
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// 因为是第一个观察特征,没有前面的影响,根据初始状态计算
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if (i == 0) {
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for (String attr : this.potentialAttrs) {
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psIndex = this.name2Index.get(attr);
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confusionPro = this.confusionMatrix[psIndex][osIndex];
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temp = this.initStatePro[psIndex - 1] * confusionPro;
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this.potentialValues[BaseNames.DAY1][psIndex] = temp;
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}
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} else {
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// 后面的潜在特征受前一个特征的影响,以及当前的混淆因素影响
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for (String toDayAttr : this.potentialAttrs) {
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psIndex = this.name2Index.get(toDayAttr);
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confusionPro = this.confusionMatrix[psIndex][osIndex];
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int index;
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maxPro = -1;
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// 通过昨天的概率计算今天此特征的最大概率
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for (String yAttr : this.potentialAttrs) {
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index = this.name2Index.get(yAttr);
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temp = this.potentialValues[i - 1][index]
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* this.stMatrix[index][psIndex];
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// 计算得到今天此潜在特征的最大概率
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if (temp > maxPro) {
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maxPro = temp;
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}
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}
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this.potentialValues[i][psIndex] = maxPro * confusionPro;
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}
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}
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}
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}
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/**
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* 根据同时期最大概率值输出潜在特征值
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*/
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private void outputResultAttr() {
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double maxPro;
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int maxIndex;
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ArrayList<String> psValues;
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psValues = new ArrayList<>();
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for (int i = 0; i < this.potentialValues.length; i++) {
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maxPro = -1;
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maxIndex = 0;
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for (int j = 0; j < potentialValues[i].length; j++) {
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if (this.potentialValues[i][j] > maxPro) {
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maxPro = potentialValues[i][j];
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maxIndex = j;
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}
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}
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// 取出最大概率下标对应的潜在特征
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psValues.add(this.index2name.get(maxIndex));
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}
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System.out.println("观察特征为:");
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for (String s : this.observeStates) {
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System.out.print(s + ", ");
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}
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System.out.println();
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System.out.println("潜在特征为:");
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for (String s : psValues) {
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System.out.print(s + ", ");
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}
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System.out.println();
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}
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/**
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* 根据观察属性,得到潜在属性信息
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*/
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public void calHMMObserve() {
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calPotencialProMatrix();
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outputResultAttr();
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}
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}

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