自然语言处理将WordTemple独立出来，并取消单例

src/main/java/org/wlld/naturalLanguage/Talk.java

@@ -13,9 +13,17 @@ import java.util.List;
  * @date 4:14 下午 2020/2/23
  */
 public class Talk {
-    private List<WorldBody> allWorld = WordTemple.get().getAllWorld();//所有词集合
+    private List<WorldBody> allWorld;//所有词集合
-    private RandomForest randomForest = WordTemple.get().getRandomForest();//获取随机森林模型
+    private RandomForest randomForest;//获取随机森林模型
-    private List<List<String>> wordTimes = WordTemple.get().getWordTimes();
+    private List<List<String>> wordTimes;
+    private WordTemple wordTemple;
+
+    public Talk(WordTemple wordTemple) {
+        this.wordTemple = wordTemple;
+        allWorld = wordTemple.getAllWorld();//所有词集合
+        randomForest = wordTemple.getRandomForest();//获取随机森林模型
+        wordTimes = wordTemple.getWordTimes();
+    }
 
     public List<Integer> talk(String sentence) throws Exception {
         List<Integer> typeList = new ArrayList<>();
@@ -69,7 +77,7 @@ public class Talk {
                     features.add(nub);
                 }
                 int type = 0;
-                if (ArithUtil.div(wrong, wordNumber) < WordTemple.get().getGarbageTh()) {
+                if (ArithUtil.div(wrong, wordNumber) < wordTemple.getGarbageTh()) {
                     LangBody langBody = new LangBody();
                     langBody.setA1(features.get(0));
                     langBody.setA2(features.get(1));
@@ -135,7 +143,7 @@ public class Talk {
             listWord = body.getWorldBodies();//这个body报了一次空指针
             word.setWordFrequency(body.getWordFrequency());
         }
-        Tokenizer tokenizer = new Tokenizer();
+        Tokenizer tokenizer = new Tokenizer(wordTemple);
         tokenizer.radiation(words);
 
     }

src/main/java/org/wlld/naturalLanguage/TemplateReader.java

@@ -20,7 +20,7 @@ public class TemplateReader {//模板读取类
      * @param charsetName 文字编码(一般使用UTF-8)
      * @throws Exception 找不到文字抛出异常
      */
-    public void read(String url, String charsetName) throws Exception {
+    public void read(String url, String charsetName, WordTemple wordTemple) throws Exception {
         this.charsetName = charsetName;
         File file = new File(url);
         InputStream is = new FileInputStream(file);
@@ -68,11 +68,12 @@ public class TemplateReader {//模板读取类
                 }
             }
         }
-        word();
+        word(wordTemple);
     }
 
-    public void word() throws Exception {
+    public void word(WordTemple wordTemple) throws Exception {
-        Tokenizer tokenizer = new Tokenizer();
+        //将模版注入分词器进行分词
+        Tokenizer tokenizer = new Tokenizer(wordTemple);
         tokenizer.start(model);
     }
 

src/main/java/org/wlld/naturalLanguage/Tokenizer.java

@@ -13,10 +13,18 @@ import java.util.*;
  * @date 7:42 上午 2020/2/23
  */
 public class Tokenizer extends Frequency {
-    private List<Sentence> sentences = WordTemple.get().getSentences();//所有断句
+    private List<Sentence> sentences;//所有断句
-    private List<WorldBody> allWorld = WordTemple.get().getAllWorld();//所有词集合
+    private List<WorldBody> allWorld;//所有词集合
-    private List<List<String>> wordTimes = WordTemple.get().getWordTimes();//所有词编号
+    private List<List<String>> wordTimes;//所有词编号
     private Word nowWord;//上一次出现的关键字
+    private WordTemple wordTemple;
+
+    public Tokenizer(WordTemple wordTemple) {
+        this.wordTemple = wordTemple;
+        sentences = wordTemple.getSentences();//所有断句
+        allWorld = wordTemple.getAllWorld();//所有词集合
+        wordTimes = wordTemple.getWordTimes();//所有词编号
+    }
 
     public void start(Map<Integer, List<String>> model) throws Exception {
         //model的主键是类别，值是该类别语句的集合
@@ -59,7 +67,7 @@ public class Tokenizer extends Frequency {
     }
 
     private void number() {//分词编号
-        System.out.println("开始编码:" + sentences.size());
+        System.out.println("开始编码:" + (sentences.size() + 1));
         for (Sentence sentence : sentences) {
             List<Integer> features = sentence.getFeatures();
             List<String> sentenceList = sentence.getKeyWords();
@@ -85,10 +93,12 @@ public class Tokenizer extends Frequency {
         }
         column.add("key");
         DataTable dataTable = new DataTable(column);
-        dataTable.setKey("key");
+        dataTable.setKey("key");//确认结果集主键
         //初始化随机森林
-        RandomForest randomForest = new RandomForest(11);
+        RandomForest randomForest = new RandomForest(wordTemple.getTreeNub());
-        WordTemple.get().setRandomForest(randomForest);//保存随机森林到模版
+        randomForest.setTrustTh(wordTemple.getTrustTh());
+        randomForest.setTrustPunishment(wordTemple.getTrustPunishment());
+        wordTemple.setRandomForest(randomForest);//保存随机森林到模版
         randomForest.init(dataTable);
         for (Sentence sentence : sentences) {
             LangBody langBody = new LangBody();

src/main/java/org/wlld/naturalLanguage/WordTemple.java

@@ -11,33 +11,30 @@ import java.util.List;
  * @date 4:15 下午 2020/2/23
  */
 public class WordTemple {
-    private static WordTemple Word_Temple = new WordTemple();
     private List<Sentence> sentences = new ArrayList<>();//所有断句
     private List<WorldBody> allWorld = new ArrayList<>();//所有词集合
     private List<List<String>> wordTimes = new ArrayList<>();//词编号
     private RandomForest randomForest;//保存的随机森林模型
+    //四大参数
     private double garbageTh = 0.5;//垃圾分类的阈值默认0.5
     private double trustPunishment = 0.1;//信任惩罚
+    private double trustTh = 0.1;//信任阈值,相当于一次信任惩罚的数值
+    private int treeNub = 11;//丛林里面树的数量
 
-    public WordModel getModel() {//获取模型
+    public int getTreeNub() {
-        WordModel wordModel = new WordModel();
+        return treeNub;
-        wordModel.setAllWorld(allWorld);
-        wordModel.setWordTimes(wordTimes);
-        wordModel.setGarbageTh(garbageTh);
-        wordModel.setTrustPunishment(trustPunishment);
-        wordModel.setTrustTh(randomForest.getTrustTh());
-        wordModel.setRfModel(randomForest.getModel());
-        return wordModel;
     }
 
-    public void insertModel(WordModel wordModel) throws Exception {//注入模型
+    public void setTreeNub(int treeNub) {
-        allWorld = wordModel.getAllWorld();
+        this.treeNub = treeNub;
-        wordTimes = wordModel.getWordTimes();
+    }
-        garbageTh = wordModel.getGarbageTh();
+
-        trustPunishment = wordModel.getTrustPunishment();
+    public double getTrustTh() {
-        randomForest = new RandomForest();
+        return trustTh;
-        randomForest.setTrustTh(wordModel.getTrustTh());
+    }
-        randomForest.insertModel(wordModel.getRfModel());
+
+    public void setTrustTh(double trustTh) {
+        this.trustTh = trustTh;
     }
 
     public double getTrustPunishment() {
@@ -64,9 +61,6 @@ public class WordTemple {
         this.randomForest = randomForest;
     }
 
-    private WordTemple() {
-    }
-
     public List<List<String>> getWordTimes() {
         return wordTimes;
     }
@@ -75,10 +69,6 @@ public class WordTemple {
         this.wordTimes = wordTimes;
     }
 
-    public static WordTemple get() {
-        return Word_Temple;
-    }
-
     public List<Sentence> getSentences() {
         return sentences;
     }

src/main/java/org/wlld/randomForest/RandomForest.java

@@ -13,6 +13,15 @@ public class RandomForest {
     private Random random = new Random();
     private Tree[] forest;
     private double trustTh = 0.1;//信任阈值
+    private double trustPunishment = 0.1;//信任惩罚
+
+    public double getTrustPunishment() {
+        return trustPunishment;
+    }
+
+    public void setTrustPunishment(double trustPunishment) {
+        this.trustPunishment = trustPunishment;
+    }
 
     public double getTrustTh() {
         return trustTh;
@@ -33,21 +42,6 @@ public class RandomForest {
         }
     }
 
-    public void insertModel(RfModel rfModel) throws Exception {//注入模型
-        if (rfModel != null) {
-            Map<Integer, Node> nodeMap = rfModel.getNodeMap();
-            forest = new Tree[nodeMap.size()];
-            for (Map.Entry<Integer, Node> entry : nodeMap.entrySet()) {
-                int key = entry.getKey();
-                Tree tree = new Tree();
-                forest[key] = tree;
-                tree.setRootNode(entry.getValue());
-            }
-        } else {
-            throw new Exception("model is null");
-        }
-    }
-
     public RfModel getModel() {//获取模型
         RfModel rfModel = new RfModel();
         Map<Integer, Node> nodeMap = new HashMap<>();
@@ -89,6 +83,7 @@ public class RandomForest {
         return type;
     }
 
+    //rf初始化
     public void init(DataTable dataTable) throws Exception {
         //一棵树属性的数量
         if (dataTable.getSize() > 4) {
@@ -96,7 +91,7 @@ public class RandomForest {
             //int kNub = dataTable.getSize() - 1;
             // System.out.println("knNub==" + kNub);
             for (int i = 0; i < forest.length; i++) {
-                Tree tree = new Tree(getRandomData(dataTable, kNub));
+                Tree tree = new Tree(getRandomData(dataTable, kNub), trustPunishment);
                 forest[i] = tree;
             }
         } else {
@@ -119,6 +114,7 @@ public class RandomForest {
         }
     }
 
+    //从总属性列表中随机挑选属性kNub个属性数量
     private DataTable getRandomData(DataTable dataTable, int kNub) throws Exception {
         Set<String> attr = dataTable.getKeyType();
         Set<String> myName = new HashSet<>();

src/main/java/org/wlld/randomForest/Tree.java

@@ -18,6 +18,7 @@ public class Tree {//决策树
     private List<Integer> endList;//最终结果分类
     private List<Node> lastNodes = new ArrayList<>();//最后一层节点集合
     private Random random = new Random();
+    private double trustPunishment;//信任惩罚
 
     public Node getRootNode() {
         return rootNode;
@@ -36,11 +37,13 @@ public class Tree {//决策树
         private double gainRatio;//信息增益率
     }
 
-    public Tree() {
+    public Tree(double trustPunishment) {
+        this.trustPunishment = trustPunishment;
     }
 
-    public Tree(DataTable dataTable) throws Exception {
+    public Tree(DataTable dataTable, double trustPunishment) throws Exception {
         if (dataTable != null && dataTable.getKey() != null) {
+            this.trustPunishment = trustPunishment;
             this.dataTable = dataTable;
         } else {
             throw new Exception("dataTable is empty");
@@ -221,7 +224,7 @@ public class Tree {//决策树
     private void punishment(TreeWithTrust treeWithTrust) {//信任惩罚
         //System.out.println("惩罚");
         double trust = treeWithTrust.getTrust();//获取当前信任值
-        trust = ArithUtil.mul(trust, WordTemple.get().getTrustPunishment());
+        trust = ArithUtil.mul(trust, trustPunishment);
         treeWithTrust.setTrust(trust);
     }
 

src/test/java/org/wlld/LangTest.java

@@ -70,17 +70,15 @@ public class LangTest {
     public static void test() throws Exception {
         //创建模板读取累
         TemplateReader templateReader = new TemplateReader();
+        WordTemple wordTemple = new WordTemple();//初始化语言模版
+        wordTemple.setTreeNub(9);
         //读取语言模版，第一个参数是模版地址，第二个参数是编码方式 (教程里的第三个参数已经省略)
         //同时也是学习过程
-        templateReader.read("D:\\b/a.txt", "UTF-8");
+        templateReader.read("/Users/lidapeng/Desktop/myDocument/model.txt", "UTF-8", wordTemple);
-        //学习结束获取模型参数
+        Talk talk = new Talk(wordTemple);
-       // WordModel wordModel = WordTemple.get().getModel();
-        //不用学习注入模型参数
-       // WordTemple.get().insertModel(wordModel);
-        Talk talk = new Talk();
         //输入语句进行识别，若有标点符号会形成LIST中的每个元素
         //返回的集合中每个值代表了输入语句，每个标点符号前语句的分类
-        List<Integer> list = talk.talk("有个快递尽快代我邮寄出去");
+        List<Integer> list = talk.talk("空调坏了");
         System.out.println(list);
     }
 }