增加分段回归

src/main/java/org/wlld/regressionForest/Forest.java

@@ -17,7 +17,7 @@ public class Forest extends Frequency {
     private Matrix resultMatrix;//结果矩阵
     private Forest forestLeft;//左森林
     private Forest forestRight;//右森林
-    private int size;
+    private int featureSize;
     private double min;//下限
     private double max;//上限
     private double resultVariance;//结果矩阵方差
@@ -25,9 +25,10 @@ public class Forest extends Frequency {
     private double shrinkParameter;//方差收缩参数
     private double[] w;
 
-    public Forest(int size, double shrinkParameter) {
-        this.size = size;
+    public Forest(int featureSize, double shrinkParameter) {
+        this.featureSize = featureSize;
         this.shrinkParameter = shrinkParameter;
+        w = new double[featureSize];
     }
 
     public double getResultVariance() {
@@ -48,10 +49,10 @@ public class Forest extends Frequency {
             Arrays.sort(dm);//排序
             int z = y / 2;
             median = dm[z];
-            forestLeft = new Forest(size, shrinkParameter);
-            forestRight = new Forest(size, shrinkParameter);
-            Matrix conditionMatrixLeft = new Matrix(z, size);//条件矩阵左
-            Matrix conditionMatrixRight = new Matrix(y - z, size);//条件矩阵右
+            forestLeft = new Forest(featureSize, shrinkParameter);
+            forestRight = new Forest(featureSize, shrinkParameter);
+            Matrix conditionMatrixLeft = new Matrix(z, featureSize);//条件矩阵左
+            Matrix conditionMatrixRight = new Matrix(y - z, featureSize);//条件矩阵右
             Matrix resultMatrixLeft = new Matrix(z, 1);//结果矩阵左
             Matrix resultMatrixRight = new Matrix(y - z, 1);//结果矩阵右
             forestLeft.setConditionMatrix(conditionMatrixLeft);
@@ -65,14 +66,14 @@ public class Forest extends Frequency {
             for (int i = 0; i < y; i++) {
                 double nub = resultMatrix.getNumber(i, 0);//结果矩阵
                 if (nub > median) {//进入右森林并计算右森林结果矩阵方差
-                    for (int j = 0; j < size; j++) {//进入右森林的条件矩阵
+                    for (int j = 0; j < featureSize; j++) {//进入右森林的条件矩阵
                         conditionMatrixRight.setNub(rightIndex, j, conditionMatrix.getNumber(i, j));
                     }
                     resultRight[rightIndex] = nub;
                     resultMatrixRight.setNub(rightIndex, 0, nub);
                     rightIndex++;
                 } else {//进入左森林并计算左森林结果矩阵方差
-                    for (int j = 0; j < size; j++) {//进入右森林的条件矩阵
+                    for (int j = 0; j < featureSize; j++) {//进入右森林的条件矩阵
                         conditionMatrixLeft.setNub(leftIndex, j, conditionMatrix.getNumber(i, j));
                     }
                     resultLeft[leftIndex] = nub;
@@ -137,4 +138,12 @@ public class Forest extends Frequency {
     public void setW(double[] w) {
         this.w = w;
     }
+
+    public Forest getForestLeft() {
+        return forestLeft;
+    }
+
+    public Forest getForestRight() {
+        return forestRight;
+    }
 }

src/main/java/org/wlld/regressionForest/RegressionForest.java

@@ -2,6 +2,7 @@ package org.wlld.regressionForest;
 
 import org.wlld.MatrixTools.Matrix;
 import org.wlld.MatrixTools.MatrixOperation;
+import org.wlld.tools.Frequency;
 
 /**
  * @param
@@ -9,30 +10,44 @@ import org.wlld.MatrixTools.MatrixOperation;
  * @Author LiDaPeng
  * @Description 回归森林
  */
-public class RegressionForest {
+public class RegressionForest extends Frequency {
     private double[] w;
     private Matrix conditionMatrix;//条件矩阵
     private Matrix resultMatrix;//结果矩阵
+    private Forest forest;
     private int featureNub;//特征数量
     private int xIndex = 0;//记录插入位置
+    private double[] results;//结果数组
+    private double min;//结果最小值
+    private double max;//结果最大值
 
     public RegressionForest(int size, int featureNub) throws Exception {//初始化
         if (size > 0 && featureNub > 0) {
             this.featureNub = featureNub;
             w = new double[size];
+            results = new double[size];
             conditionMatrix = new Matrix(size, featureNub);
             resultMatrix = new Matrix(size, 1);
+            forest = new Forest(featureNub, 0.9);
+            forest.setW(w);
+            forest.setConditionMatrix(conditionMatrix);
+            forest.setResultMatrix(resultMatrix);
         } else {
             throw new Exception("size and featureNub too small");
         }
     }
 
+    public void getDist(double[] feature, double result) {//获取特征误差结果
+
+    }
+
     public void insertFeature(double[] feature, double result) throws Exception {//插入数据
         if (feature.length == featureNub) {
             for (int i = 0; i < featureNub; i++) {
                 if (i < featureNub - 1) {
                     conditionMatrix.setNub(xIndex, i, feature[i]);
                 } else {
+                    results[xIndex] = result;
                     conditionMatrix.setNub(xIndex, i, 1.0);
                     resultMatrix.setNub(xIndex, 0, result);
                 }
@@ -43,14 +58,54 @@ public class RegressionForest {
         }
     }
 
+    public void start() throws Exception {//开始进行分段
+        if (forest != null) {
+            double[] limit = getLimit(results);
+            min = limit[0];
+            max = limit[1];
+            start(forest);
+        } else {
+            throw new Exception("rootForest is null");
+        }
+    }
+
+    private void start(Forest forest) throws Exception {
+        forest.cut();
+        Forest forestLeft = forest.getForestLeft();
+        Forest forestRight = forest.getForestRight();
+        if (forestLeft != null && forestRight != null) {
+            start(forestLeft);
+            start(forestRight);
+        }
+
+    }
+
     public void regression() throws Exception {//开始进行回归
-        if (xIndex > 0) {
-            Matrix ws = MatrixOperation.getLinearRegression(conditionMatrix, resultMatrix);
-            for (int i = 0; i < ws.getX(); i++) {
-                w[i] = ws.getNumber(i, 0);
-            }
+        if (forest != null) {
+            regressionTree(forest);
         } else {
-            throw new Exception("regression matrix size is zero");
+            throw new Exception("rootForest is null");
+        }
+    }
+
+    private void regressionTree(Forest forest) throws Exception {
+        regression(forest);
+        Forest forestLeft = forest.getForestLeft();
+        Forest forestRight = forest.getForestRight();
+        if (forestLeft != null && forestRight != null) {
+            regressionTree(forestLeft);
+            regressionTree(forestRight);
+        }
+
+    }
+
+    private void regression(Forest forest) throws Exception {
+        Matrix conditionMatrix = forest.getConditionMatrix();
+        Matrix resultMatrix = forest.getResultMatrix();
+        double[] w = forest.getW();
+        Matrix ws = MatrixOperation.getLinearRegression(conditionMatrix, resultMatrix);
+        for (int i = 0; i < ws.getX(); i++) {
+            w[i] = ws.getNumber(i, 0);
         }
     }
 }