维特比算法工具类

维特比算法工具类

Author: linyiqun

Others/DataMining_Viterbi/ViterbiTool.java

@@ -0,0 +1,240 @@
+packageDataMining_Viterbi;
+
+importjava.io.BufferedReader;
+importjava.io.File;
+importjava.io.FileReader;
+importjava.io.IOException;
+importjava.util.ArrayList;
+importjava.util.HashMap;
+importjava.util.Map;
+
+/**
+ * 维特比算法工具类
+ * 
+ * @author lyq
+ * 
+ */
+publicclassViterbiTool{
+// 状态转移概率矩阵文件地址
+privateStringstmFilePath;
+// 混淆矩阵文件地址
+privateStringconfusionFilePath;
+// 初始状态概率
+privatedouble[] initStatePro;
+// 观察到的状态序列
+publicString[] observeStates;
+// 状态转移矩阵值
+privatedouble[][] stMatrix;
+// 混淆矩阵值
+privatedouble[][] confusionMatrix;
+// 各个条件下的潜在特征概率值
+privatedouble[][] potentialValues;
+// 潜在特征
+privateArrayList<String> potentialAttrs;
+// 属性值列坐标映射图
+privateHashMap<String, Integer> name2Index;
+// 列坐标属性值映射图
+privateHashMap<Integer, String> index2name;
+
+publicViterbiTool(StringstmFilePath, StringconfusionFilePath,
+double[] initStatePro, String[] observeStates){
+this.stmFilePath = stmFilePath;
+this.confusionFilePath = confusionFilePath;
+this.initStatePro = initStatePro;
+this.observeStates = observeStates;
+
+initOperation();
+ }
+
+/**
+ * 初始化数据操作
+ */
+privatevoidinitOperation(){
+double[] temp;
+intindex;
+ArrayList<String[]> smtDatas;
+ArrayList<String[]> cfDatas;
+
+smtDatas = readDataFile(stmFilePath);
+cfDatas = readDataFile(confusionFilePath);
+
+index = 0;
+this.stMatrix = newdouble[smtDatas.size()][];
+for (String[] array : smtDatas){
+temp = newdouble[array.length];
+for (inti = 0; i < array.length; i++){
+try{
+temp[i] = Double.parseDouble(array[i]);
+ } catch (NumberFormatExceptione){
+temp[i] = -1;
+ }
+ }
+
+// 将转换后的值赋给数组中
+this.stMatrix[index] = temp;
+index++;
+ }
+
+index = 0;
+this.confusionMatrix = newdouble[cfDatas.size()][];
+for (String[] array : cfDatas){
+temp = newdouble[array.length];
+for (inti = 0; i < array.length; i++){
+try{
+temp[i] = Double.parseDouble(array[i]);
+ } catch (NumberFormatExceptione){
+temp[i] = -1;
+ }
+ }
+
+// 将转换后的值赋给数组中
+this.confusionMatrix[index] = temp;
+index++;
+ }
+
+this.potentialAttrs = newArrayList<>();
+// 添加潜在特征属性
+for (Strings : smtDatas.get(0)){
+this.potentialAttrs.add(s);
+ }
+// 去除首列无效列
+potentialAttrs.remove(0);
+
+this.name2Index = newHashMap<>();
+this.index2name = newHashMap<>();
+
+// 添加名称下标映射关系
+for (inti = 1; i < smtDatas.get(0).length; i++){
+this.name2Index.put(smtDatas.get(0)[i], i);
+// 添加下标到名称的映射
+this.index2name.put(i, smtDatas.get(0)[i]);
+ }
+
+for (inti = 1; i < cfDatas.get(0).length; i++){
+this.name2Index.put(cfDatas.get(0)[i], i);
+ }
+ }
+
+/**
+ * 从文件中读取数据
+ */
+privateArrayList<String[]> readDataFile(StringfilePath){
+Filefile = newFile(filePath);
+ArrayList<String[]> dataArray = newArrayList<String[]>();
+
+try{
+BufferedReaderin = newBufferedReader(newFileReader(file));
+Stringstr;
+String[] tempArray;
+while ((str = in.readLine()) != null){
+tempArray = str.split(" ");
+dataArray.add(tempArray);
+ }
+in.close();
+ } catch (IOExceptione){
+e.getStackTrace();
+ }
+
+returndataArray;
+ }
+
+/**
+ * 根据观察特征计算隐藏的特征概率矩阵
+ */
+privatevoidcalPotencialProMatrix(){
+StringcurObserveState;
+// 观察特征和潜在特征的下标
+intosIndex;
+intpsIndex;
+doubletemp;
+doublemaxPro;
+// 混淆矩阵概率值，就是相关影响的因素概率
+doubleconfusionPro;
+
+this.potentialValues = newdouble[observeStates.length][potentialAttrs
+ .size() + 1];
+for (inti = 0; i < this.observeStates.length; i++){
+curObserveState = this.observeStates[i];
+osIndex = this.name2Index.get(curObserveState);
+maxPro = -1;
+
+// 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
+if (i == 0){
+for (Stringattr : this.potentialAttrs){
+psIndex = this.name2Index.get(attr);
+confusionPro = this.confusionMatrix[psIndex][osIndex];
+
+temp = this.initStatePro[psIndex - 1] * confusionPro;
+this.potentialValues[BaseNames.DAY1][psIndex] = temp;
+ }
+ } else{
+// 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
+for (StringtoDayAttr : this.potentialAttrs){
+psIndex = this.name2Index.get(toDayAttr);
+confusionPro = this.confusionMatrix[psIndex][osIndex];
+
+intindex;
+maxPro = -1;
+// 通过昨天的概率计算今天此特征的最大概率
+for (StringyAttr : this.potentialAttrs){
+index = this.name2Index.get(yAttr);
+temp = this.potentialValues[i - 1][index]
+ * this.stMatrix[index][psIndex];
+
+// 计算得到今天此潜在特征的最大概率
+if (temp > maxPro){
+maxPro = temp;
+ }
+ }
+
+this.potentialValues[i][psIndex] = maxPro * confusionPro;
+ }
+ }
+ }
+ }
+
+/**
+ * 根据同时期最大概率值输出潜在特征值
+ */
+privatevoidoutputResultAttr(){
+doublemaxPro;
+intmaxIndex;
+ArrayList<String> psValues;
+
+psValues = newArrayList<>();
+for (inti = 0; i < this.potentialValues.length; i++){
+maxPro = -1;
+maxIndex = 0;
+
+for (intj = 0; j < potentialValues[i].length; j++){
+if (this.potentialValues[i][j] > maxPro){
+maxPro = potentialValues[i][j];
+maxIndex = j;
+ }
+ }
+
+// 取出最大概率下标对应的潜在特征
+psValues.add(this.index2name.get(maxIndex));
+ }
+
+System.out.println("观察特征为：");
+for (Strings : this.observeStates){
+System.out.print(s + ", ");
+ }
+System.out.println();
+
+System.out.println("潜在特征为：");
+for (Strings : psValues){
+System.out.print(s + ", ");
+ }
+System.out.println();
+ }
+
+/**
+ * 根据观察属性，得到潜在属性信息
+ */
+publicvoidcalHMMObserve(){
+calPotencialProMatrix();
+outputResultAttr();
+ }
+}