diff --git a/src/main/java/org/wlld/regressionForest/Forest.java b/src/main/java/org/wlld/regressionForest/Forest.java
index da4ebb1..d905124 100644
--- a/src/main/java/org/wlld/regressionForest/Forest.java
+++ b/src/main/java/org/wlld/regressionForest/Forest.java
@@ -97,7 +97,7 @@ public class Forest extends Frequency {
                 pc1 = g;
             }
         }
-        //找到非原始基最离散的新基
+        //找到非原始基最离散的新基:
         if (max > maxOld) {//使用新基
             isOldG = false;
         } else {//使用原有基
@@ -150,17 +150,18 @@ public class Forest extends Frequency {
     }
 
     public void pruning() {//进行后剪枝
+        System.out.println("执行了剪枝====");
         if (forestLeft != null) {
             double leftDist = getDist(forestLeft.getW());
-            System.out.println("左剪枝阈值：" + leftDist);
             if (leftDist < shrinkParameter) {//剪枝
+                System.out.println("成功左剪枝阈值：" + leftDist + ",阈值:" + shrinkParameter);
                 forestLeft = null;
             }
         }
         if (forestRight != null) {
             double rightDist = getDist(forestRight.getW());
-            System.out.println("右剪枝阈值：" + rightDist);
             if (rightDist < shrinkParameter) {
+                System.out.println("成功右剪枝阈值：" + rightDist + ",阈值:" + shrinkParameter);
                 forestRight = null;
             }
         }
@@ -170,6 +171,7 @@ public class Forest extends Frequency {
     public void cut() throws Exception {
         int y = resultMatrix.getX();
         if (y > 200) {
+            System.out.println("-======================");
             double[] dm = findG();
             int z = y / 2;
             median = dm[z];
diff --git a/src/main/java/org/wlld/regressionForest/RegressionForest.java b/src/main/java/org/wlld/regressionForest/RegressionForest.java
index 26ada20..f334ce1 100644
--- a/src/main/java/org/wlld/regressionForest/RegressionForest.java
+++ b/src/main/java/org/wlld/regressionForest/RegressionForest.java
@@ -215,8 +215,8 @@ public class RegressionForest extends Frequency {
         if (forest != null) {
             forest.pruning();
             Forest forestRight = forest.getForestRight();
-            pruningTree(forestRight);
             Forest forestLeft = forest.getForestLeft();
+            pruningTree(forestRight);
             pruningTree(forestLeft);
         }
     }
diff --git a/src/test/java/coverTest/ForestTest.java b/src/test/java/coverTest/ForestTest.java
index f85a0ac..188ede0 100644
--- a/src/test/java/coverTest/ForestTest.java
+++ b/src/test/java/coverTest/ForestTest.java
@@ -19,10 +19,10 @@ public class ForestTest {
 
     public static void test() throws Exception {//对分段回归进行测试
         int size = 2000;
-        RegressionForest regressionForest = new RegressionForest(size, 3, 0.02);
+        RegressionForest regressionForest = new RegressionForest(size, 3, 0.01);
         regressionForest.setCosSize(40);
         List<double[]> a = fun(0.1, 0.2, 0.3, size, 2, 1);
-        List<double[]> b = fun(0.3, 0.2, 0.1, size, 2, 2);
+        List<double[]> b = fun(0.7, 0.3, 0.1, size, 2, 2);
         for (int i = 0; i < 1000; i++) {
             double[] featureA = a.get(i);
             double[] featureB = b.get(i);
@@ -32,21 +32,24 @@ public class ForestTest {
             regressionForest.insertFeature(testB, featureB[2]);
         }
         regressionForest.startStudy();
+        ///
+        List<double[]> a1 = fun(0.1, 0.2, 0.3, size, 2, 1);
+        List<double[]> b1 = fun(0.3, 0.2, 0.6, size, 2, 2);
         double sigma = 0;
         for (int i = 0; i < 1000; i++) {
-            double[] feature = a.get(i);
+            double[] feature = a1.get(i);
             double[] test = new double[]{feature[0], feature[1]};
             double dist = regressionForest.getDist(test, feature[2]);
-            sigma = sigma + Math.pow(dist, 2);
+            sigma = sigma + dist;
         }
         double avs = sigma / size;
         System.out.println("a误差：" + avs);
         sigma = 0;
         for (int i = 0; i < 1000; i++) {
-            double[] feature = b.get(i);
+            double[] feature = b1.get(i);
             double[] test = new double[]{feature[0], feature[1]};
             double dist = regressionForest.getDist(test, feature[2]);
-            sigma = sigma + Math.pow(dist, 2);
+            sigma = sigma + dist;
         }
         double avs2 = sigma / size;
         System.out.println("b误差：" + avs2);