添加语言分类器

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

@@ -0,0 +1,90 @@
+package org.wlld.naturalLanguage;
+
+/**
+ * @author lidapeng
+ * @description
+ * @date 6:43 下午 2020/2/23
+ */
+public class LangBody {
+    private int a1;
+    private int a2;
+    private int a3;
+    private int a4;
+    private int a5;
+    private int a6;
+    private int a7;
+    private int a8;
+    private int key;
+
+    public int getA1() {
+        return a1;
+    }
+
+    public void setA1(int a1) {
+        this.a1 = a1;
+    }
+
+    public int getA2() {
+        return a2;
+    }
+
+    public void setA2(int a2) {
+        this.a2 = a2;
+    }
+
+    public int getA3() {
+        return a3;
+    }
+
+    public void setA3(int a3) {
+        this.a3 = a3;
+    }
+
+    public int getA4() {
+        return a4;
+    }
+
+    public void setA4(int a4) {
+        this.a4 = a4;
+    }
+
+    public int getA5() {
+        return a5;
+    }
+
+    public void setA5(int a5) {
+        this.a5 = a5;
+    }
+
+    public int getA6() {
+        return a6;
+    }
+
+    public void setA6(int a6) {
+        this.a6 = a6;
+    }
+
+    public int getA7() {
+        return a7;
+    }
+
+    public void setA7(int a7) {
+        this.a7 = a7;
+    }
+
+    public int getA8() {
+        return a8;
+    }
+
+    public void setA8(int a8) {
+        this.a8 = a8;
+    }
+
+    public int getKey() {
+        return key;
+    }
+
+    public void setKey(int key) {
+        this.key = key;
+    }
+}

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

@@ -1,6 +1,5 @@
 package org.wlld.naturalLanguage;
 
-import org.omg.Messaging.SYNC_WITH_TRANSPORT;
 
 import java.util.ArrayList;
 import java.util.List;
@@ -14,8 +13,13 @@ public class Sentence {
     private Word firstWord;
     private List<Word> waitWords = new ArrayList<>();//词
     private List<String> keyWords;//分词结果下标按照时间序列排序
+    private List<Integer> features = new ArrayList<>();//时序特征
     private int key;
 
+    public List<Integer> getFeatures() {
+        return features;
+    }
+
     public List<String> getKeyWords() {
         return keyWords;
     }

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

@@ -1,6 +1,8 @@
 package org.wlld.naturalLanguage;
 
 
+import org.wlld.randomForest.RandomForest;
+
 import java.util.ArrayList;
 import java.util.List;
 
@@ -11,8 +13,10 @@ import java.util.List;
  */
 public class Talk {
     private List<WorldBody> allWorld = WordTemple.get().getAllWorld();//所有词集合
+    private RandomForest randomForest = WordTemple.get().getRandomForest();//获取随机森林模型
+    private List<List<String>> wordTimes = WordTemple.get().getWordTimes();
 
-    public void talk(String sentence) {
+    public void talk(String sentence) throws Exception {
         String rgm = null;
         if (sentence.indexOf(",") > -1) {
             rgm = ",";
@@ -33,9 +37,48 @@ public class Talk {
             sentences.add(sentenceWords);
         }
         restructure(sentences);
-        for (Sentence sentence1 : sentences) {
-            System.out.println(sentence1.getKeyWords());
+        //进行识别
+        if (randomForest != null) {
+            for (Sentence sentence1 : sentences) {
+                List<Integer> features = sentence1.getFeatures();
+                List<String> keyWords = sentence1.getKeyWords();
+                for (int i = 0; i < 8; i++) {
+                    int nub = 0;
+                    List<String> words = wordTimes.get(i);
+                    String word = keyWords.get(i);
+                    if (word != null) {
+                        nub = getNub(words, word);
+                    }
+                    features.add(nub);
+                }
+                LangBody langBody = new LangBody();
+                langBody.setA1(features.get(0));
+                langBody.setA2(features.get(1));
+                langBody.setA3(features.get(2));
+                langBody.setA4(features.get(3));
+                langBody.setA5(features.get(4));
+                langBody.setA6(features.get(5));
+                langBody.setA7(features.get(6));
+                langBody.setA8(features.get(7));
+                int type = randomForest.forest(langBody);
+                System.out.println("type==" + type);
+            }
+        } else {
+            System.out.println("随机森林没有训练");
+        }
+    }
+
+    private int getNub(List<String> words, String testWord) {
+        int nub = 0;
+        int size = words.size();
+        for (int i = 0; i < size; i++) {
+            String word = words.get(i);
+            if (testWord.hashCode() == word.hashCode() && testWord.equals(word)) {
+                nub = i + 1;
+                break;
+            }
         }
+        return nub;
     }
 
     private void catchSentence(String sentence, Sentence sentenceWords) {//把句子拆开

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

@@ -65,7 +65,7 @@ public class TemplateReader {//模板读取类
         word();
     }
 
-    public void word() {
+    public void word() throws Exception {
         Tokenizer tokenizer = new Tokenizer();
         tokenizer.start(model);
     }

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

@@ -1,11 +1,11 @@
 package org.wlld.naturalLanguage;
 
+import org.wlld.randomForest.DataTable;
+import org.wlld.randomForest.RandomForest;
 import org.wlld.tools.ArithUtil;
 import org.wlld.tools.Frequency;
 
-import java.util.ArrayList;
-import java.util.List;
-import java.util.Map;
+import java.util.*;
 
 /**
  * @author lidapeng
@@ -15,9 +15,10 @@ import java.util.Map;
 public class Tokenizer extends Frequency {
     private List<Sentence> sentences = WordTemple.get().getSentences();//所有断句
     private List<WorldBody> allWorld = WordTemple.get().getAllWorld();//所有词集合
+    private List<List<String>> wordTimes = WordTemple.get().getWordTimes();//所有词编号
     private Word nowWord;//上一次出现的关键字
 
-    public void start(Map<Integer, List<String>> model) {
+    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
         for (Map.Entry<Integer, List<String>> mod : model.entrySet()) {
             if (mod.getKey() != 0) {
@@ -35,13 +36,81 @@ public class Tokenizer extends Frequency {
         }
         restructure();//对集合中的词进行词频统计
         //这里分词已经结束,对词进行编号
-        //test();
+        number();
+        //进入随机森林进行学习
+        study();
     }
 
-    private void test() {//分词测试类
+    private void number() {//分词编号
         for (Sentence sentence : sentences) {
-            System.out.println(sentence.getKeyWords());
+            List<Integer> features = sentence.getFeatures();
+            List<String> sentenceList = sentence.getKeyWords();
+            int size = sentenceList.size();//时间序列的深度
+            for (int i = 0; i < size; i++) {
+                if (!wordTimes.contains(i)) {
+                    wordTimes.add(new ArrayList<>());
+                }
+                List<String> list = wordTimes.get(i);
+                int nub = list.size();
+                features.add(nub);
+                list.add(sentenceList.get(i));
+            }
+        }
+    }
+
+    private void study() throws Exception {
+        Set<String> column = new HashSet<>();
+        for (int i = 0; i < 8; i++) {
+            int t = i + 1;
+            column.add("a" + t);
+        }
+        column.add("key");
+        DataTable dataTable = new DataTable(column);
+        dataTable.setKey("key");
+        //初始化随机森林
+        RandomForest randomForest = new RandomForest(5);
+        WordTemple.get().setRandomForest(randomForest);//保存随机森林到模版
+        randomForest.init(dataTable);
+        for (Sentence sentence : sentences) {
+            LangBody langBody = new LangBody();
+            List<Integer> features = sentence.getFeatures();
+            langBody.setKey(sentence.getKey());
+            for (int i = 0; i < 8; i++) {
+                int nub = 0;
+                if (features.contains(i)) {
+                    nub = features.get(i);
+                }
+                int t = i + 1;
+                switch (t) {
+                    case 1:
+                        langBody.setA1(nub);
+                        break;
+                    case 2:
+                        langBody.setA2(nub);
+                        break;
+                    case 3:
+                        langBody.setA3(nub);
+                        break;
+                    case 4:
+                        langBody.setA4(nub);
+                        break;
+                    case 5:
+                        langBody.setA5(nub);
+                        break;
+                    case 6:
+                        langBody.setA6(nub);
+                        break;
+                    case 7:
+                        langBody.setA7(nub);
+                        break;
+                    case 8:
+                        langBody.setA8(nub);
+                        break;
+                }
+            }
+            randomForest.insert(langBody);
         }
+        randomForest.study();
     }
 
     private void restructure() {//对句子里面的Word进行词频统计

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

@@ -1,5 +1,7 @@
 package org.wlld.naturalLanguage;
 
+import org.wlld.randomForest.RandomForest;
+
 import java.util.ArrayList;
 import java.util.List;
 
@@ -12,10 +14,28 @@ 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
+
+    public RandomForest getRandomForest() {
+        return randomForest;
+    }
+
+    public void setRandomForest(RandomForest randomForest) {
+        this.randomForest = randomForest;
+    }
 
     private WordTemple() {
     }
 
+    public List<List<String>> getWordTimes() {
+        return wordTimes;
+    }
+
+    public void setWordTimes(List<List<String>> wordTimes) {
+        this.wordTimes = wordTimes;
+    }
+
     public static WordTemple get() {
         return Word_Temple;
     }