增加垃圾词语分类

.idea/compiler.xml

@@ -6,8 +6,8 @@
         <sourceOutputDir name="target/generated-sources/annotations" />
         <sourceTestOutputDir name="target/generated-test-sources/test-annotations" />
         <outputRelativeToContentRoot value="true" />
-        <module name="myBrain" />
         <module name="ImageMarket" />
+        <module name="myBrain" />
       </profile>
     </annotationProcessing>
   </component>

pom.xml

@@ -6,7 +6,7 @@
 
     <groupId>com.github</groupId>
     <artifactId>ImageMarket</artifactId>
-    <version>1.0.0</version>
+    <version>1.0.2</version>
 
     <name>myBrain</name>
     <!-- FIXME change it to the project's website -->

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

@@ -2,6 +2,7 @@ package org.wlld.naturalLanguage;
 
 
 import org.wlld.randomForest.RandomForest;
+import org.wlld.tools.ArithUtil;
 
 import java.util.ArrayList;
 import java.util.List;
@@ -16,7 +17,8 @@ public class Talk {
     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) throws Exception {
+    public List<Integer> talk(String sentence) throws Exception {
+        List<Integer> typeList = new ArrayList<>();
         String rgm = null;
         if (sentence.indexOf(",") > -1) {
             rgm = ",";
@@ -41,29 +43,38 @@ public class Talk {
         if (randomForest != null) {
             for (Sentence sentence1 : sentences) {
                 List<Integer> features = sentence1.getFeatures();
-                List<String> keyWords = sentence1.getKeyWords();
+                List<String> keyWords = sentence1.getKeyWords();//拆分的关键词
+                int wrong = 0;
+                int wordNumber = keyWords.size();
                 for (int i = 0; i < 8; i++) {
                     int nub = 0;
                     if (keyWords.size() > i) {
                         List<String> words = wordTimes.get(i);
                         nub = getNub(words, keyWords.get(i));
+                        if (nub == 0) {//出现了不认识的词
+                            wrong++;
+                        }
                     }
                     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);
+                int type = 0;
+                if (ArithUtil.div(wrong, wordNumber) < WordTemple.get().getGarbageTh()) {
+                    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));
+                    type = randomForest.forest(langBody);
+                }
+                typeList.add(type);
             }
+            return typeList;
         } else {
-            System.out.println("随机森林没有训练");
+            throw new Exception("forest is not study");
         }
     }
 

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

@@ -69,7 +69,7 @@ public class Tokenizer extends Frequency {
         DataTable dataTable = new DataTable(column);
         dataTable.setKey("key");
         //初始化随机森林
-        RandomForest randomForest = new RandomForest(5);
+        RandomForest randomForest = new RandomForest(7);
         WordTemple.get().setRandomForest(randomForest);//保存随机森林到模版
         randomForest.init(dataTable);
         for (Sentence sentence : sentences) {

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

@@ -2,19 +2,9 @@ package org.wlld.naturalLanguage;
 
 public class WordConst {
     public static double Word_Noise = 0.7;//收缩程度
-    public static final int SHOP = 1;//购买类型
-    public static final int FOOD = 3;//食物类型
-    public static final int DRINK = 4;//饮品类型
-    public static final int OTHER = 5;//家庭日用(油盐酱醋卫生纸之类的)
-    public static final int SMOKE = 10;//烟草
-    public static final int ADD = 6;//订单增0.5037412492
-    public static final int DEL = 7;//订单删
-    public static final int UPDATE = 8;//订单改
-    public static final int SELECT = 9;//订单查
-    public static final int TALK = 2;//聊天类型
-    public static final int ALL = 11;//全文本
-    public static final int CHANGE = 12;//分类文本
-    public static final int DROP = 13;//消文本
-    public static final int CURD = 14;//对订单增删改查类型
-    public static final int ANS = 0;//聊天回复
+    public static final int Water = 2;//送水
+    public static final int Nanny = 3;//保姆
+    public static final int Unlock = 4;//开锁
+    public static final int Express = 5;//快递
+    public static final int Telephone = 6;//充话费
 }

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

@@ -16,6 +16,15 @@ public class WordTemple {
     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
+    private double garbageTh = 0.5;//垃圾分类的阈值默认0.7
+
+    public double getGarbageTh() {
+        return garbageTh;
+    }
+
+    public void setGarbageTh(double garbageTh) {
+        this.garbageTh = garbageTh;
+    }
 
     public RandomForest getRandomForest() {
         return randomForest;

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

@@ -48,6 +48,7 @@ public class RandomForest {
         for (int i = 0; i < forest.length; i++) {
             Tree tree = forest[i];
             int type = tree.judge(object);
+            //System.out.println(type);
             if (map.containsKey(type)) {
                 map.put(type, map.get(type) + 1);
             } else {
@@ -81,7 +82,7 @@ public class RandomForest {
 
     public void study() throws Exception {//学习
         for (int i = 0; i < forest.length; i++) {
-            System.out.println("开始学习==" + i + ",treeNub==" + forest.length);
+            //System.out.println("开始学习==" + i + ",treeNub==" + forest.length);
             Tree tree = forest[i];
             tree.study();
         }

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

@@ -4,6 +4,8 @@ import org.wlld.naturalLanguage.IOConst;
 import org.wlld.naturalLanguage.Talk;
 import org.wlld.naturalLanguage.TemplateReader;
 
+import java.util.List;
+
 /**
  * @author lidapeng
  * @description
@@ -16,11 +18,9 @@ public class LangTest {
 
     public static void test() throws Exception {
         TemplateReader templateReader = new TemplateReader();
-        templateReader.read("/Users/lidapeng/Desktop/myDocment/a2.txt", "UTF-8", IOConst.NOT_WIN);
+        templateReader.read("/Users/lidapeng/Desktop/myDocment/a1.txt", "UTF-8", IOConst.NOT_WIN);
         Talk talk = new Talk();
-        talk.talk("我要吃面包");
-        talk.talk("我渴了");
-        talk.talk("我要去看望你");
-        talk.talk("我买两盒烟");
+        List<Integer> list = talk.talk("我草尼玛");
+        System.out.println(list);
     }
 }