diff --git a/src/main/java/org/wlld/randomForest/Tree.java b/src/main/java/org/wlld/randomForest/Tree.java
index c49d27f..a55e704 100644
--- a/src/main/java/org/wlld/randomForest/Tree.java
+++ b/src/main/java/org/wlld/randomForest/Tree.java
@@ -1,6 +1,5 @@
 package org.wlld.randomForest;
 
-import org.wlld.tools.ArithUtil;
 
 import java.lang.reflect.Method;
 import java.util.*;
@@ -50,7 +49,7 @@ public class Tree {//决策树
     }
 
     private double log2(double p) {
-        return ArithUtil.div(Math.log(p), Math.log(2));
+        return Math.log(p) / Math.log(2);
     }
 
     private double getEnt(List<Integer> list) {
@@ -67,14 +66,14 @@ public class Tree {//决策树
         double ent = 0;
         //求信息熵
         for (Map.Entry<Integer, Integer> entry1 : myType.entrySet()) {
-            double g = ArithUtil.div(entry1.getValue(), list.size());//每个类别的概率
-            ent = ArithUtil.add(ent, ArithUtil.mul(g, log2(g)));
+            double g = (double) entry1.getValue() / (double) list.size();//每个类别的概率
+            ent = ent + g * log2(g);
         }
         return -ent;
     }
 
     private double getGain(double ent, double dNub, double gain) {
-        return ArithUtil.add(gain, ArithUtil.mul(ent, dNub));
+        return gain + ent * dNub;
     }
 
     private List<Node> createNode(Node node) {
@@ -122,19 +121,19 @@ public class Tree {//决策树
                     sonNode.typeId = entry.getKey();//该属性值
                     int myNub = list.size();//该属性值下数据的数量
                     double ent = getEnt(list);//该属性值 的信息熵
-                    double dNub = ArithUtil.div(myNub, fatherNub);//该属性值在 父级样本中出现的概率
-                    IV = ArithUtil.add(ArithUtil.mul(dNub, log2(dNub)), IV);
+                    double dNub = (double) myNub / (double) fatherNub;//该属性值在 父级样本中出现的概率
+                    IV = dNub * log2(dNub) + IV;
                     gain = getGain(ent, dNub, gain);
                 }
                 Gain gain1 = new Gain();
                 gainMap.put(name, gain1);
-                gain1.gain = ArithUtil.sub(fatherEnt, gain);//信息增益
+                gain1.gain = fatherEnt - gain;//信息增益
                 if (IV != 0) {
-                    gain1.gainRatio = ArithUtil.div(gain1.gain, -IV);//增益率
+                    gain1.gainRatio = gain1.gain / -IV;//增益率
                 } else {
                     gain1.gainRatio = 1000000;
                 }
-                sigmaG = ArithUtil.add(gain1.gain, sigmaG);
+                sigmaG = gain1.gain + sigmaG;
                 i++;
             }
             double avgGain = sigmaG / i;
@@ -143,9 +142,9 @@ public class Tree {//决策树
             System.out.println("平均信息增益==============================" + avgGain);
             for (Map.Entry<String, Gain> entry : gainMap.entrySet()) {
                 Gain gain = entry.getValue();
-                System.out.println("主键:" + entry.getKey() + ",平均信息增益:" + avgGain + ",可用属性数量:" + gainMap.size()
-                        + "该属性信息增益:" + gain.gain + ",该属性增益率：" + gain.gainRatio + ",当前最高增益率:" + gainRatio);
-                if (gainMap.size() == 1 || (gain.gain >= avgGain && (gain.gainRatio >= gainRatio || gainRatio == -2))) {
+//                System.out.println("主键:" + entry.getKey() + ",平均信息增益:" + avgGain + ",可用属性数量:" + gainMap.size()
+//                        + "该属性信息增益:" + gain.gain + ",该属性增益率：" + gain.gainRatio + ",当前最高增益率:" + gainRatio);
+                if (gainMap.size() == 1 || ((gain.gain >= avgGain || Math.abs(gain.gain - avgGain) < 0.000001) && (gain.gainRatio >= gainRatio || gainRatio == -2))) {
                     gainRatio = gain.gainRatio;
                     key = entry.getKey();
                 }
@@ -222,7 +221,7 @@ 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, trustPunishment);
+        trust = trust * trustPunishment;
         treeWithTrust.setTrust(trust);
     }
 
@@ -312,7 +311,7 @@ public class Tree {//决策树
         int fatherType = getType(father.fatherList);
         int nub = getRightPoint(father.fatherList, fatherType);
         //父级该样本正确率
-        double rightFather = ArithUtil.div(nub, father.fatherList.size());
+        double rightFather = (double) nub / (double) father.fatherList.size();
         int rightNub = 0;
         int rightAllNub = 0;
         for (int i = 0; i < sonNodes.size(); i++) {
@@ -322,7 +321,7 @@ public class Tree {//决策树
             rightNub = rightNub + right;
             rightAllNub = rightAllNub + list.size();
         }
-        double rightPoint = ArithUtil.div(rightNub, rightAllNub);//子节点正确率
+        double rightPoint = (double) rightNub / (double) rightAllNub;//子节点正确率
         if (rightPoint <= rightFather) {
             isRemove = true;
         }