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src/main/java/org/dromara/easyai/regressionForest/LinearRegression.java

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+package org.dromara.easyai.regressionForest;
+
+
+import org.dromara.easyai.matrixTools.Matrix;
+import org.dromara.easyai.matrixTools.MatrixOperation;
+
+/**
+ * @param
+ * @DATA
+ * @Author LiDaPeng
+ * @Description rgb回归 Y = r *wr + g * wg + b* wb
+ */
+public class LinearRegression {
+    private float w1;
+    private float w2;
+    private float b;
+    private Matrix XY;//坐标矩阵
+    private Matrix NormSequence;//内积序列矩阵
+    private int xIndex = 0;//记录插入数量
+    private boolean isRegression = false;//是否进行了回归
+    private float avg;//结果平均值
+    private final MatrixOperation matrixOperation = new MatrixOperation();
+
+    public LinearRegression(int size) {//初始化rgb矩阵
+        XY = new Matrix(size, 3);
+        NormSequence = new Matrix(size, 1);
+        xIndex = 0;
+        avg = 0;
+    }
+
+    public LinearRegression() {
+
+    }
+
+    public void insertXY(float[] xy, float sequence) throws Exception {//rgb插入矩阵
+        if (xy.length == 2) {
+            XY.setNub(xIndex, 0, xy[0]);
+            XY.setNub(xIndex, 1, xy[1]);
+            XY.setNub(xIndex, 2, 1.0f);
+            NormSequence.setNub(xIndex, 0, sequence);
+            xIndex++;
+        } else {
+            throw new Exception("rgb length is not equals three");
+        }
+    }
+
+    public float getValue(float x, float y) {//获取值
+        if (isRegression) {
+            return w1 * x + w2 * y + b;
+        }
+        return avg;
+    }
+
+    public float getCos(Matrix vector) throws Exception {//获取该直线与指定向量之间的余弦
+        Matrix matrix = new Matrix(1, 3);
+        matrix.setNub(0, 0, w1);
+        matrix.setNub(0, 1, w2);
+        matrix.setNub(0, 2, b);
+        return matrixOperation.getNormCos(matrix, vector);
+    }
+
+    public void regression() throws Exception {//开始进行回归
+        if (xIndex > 0) {
+            Matrix ws = matrixOperation.getLinearRegression(XY, NormSequence);
+            if (ws.getX() == 1 && ws.getY() == 1) {//矩阵奇异
+                isRegression = false;
+                for (int i = 0; i < xIndex; i++) {
+                    avg = avg + NormSequence.getNumber(xIndex, 0);
+                }
+                avg = avg / xIndex;
+            } else {
+                w1 = ws.getNumber(0, 0);
+                w2 = ws.getNumber(1, 0);
+                b = ws.getNumber(2, 0);
+                isRegression = true;
+            }
+            // System.out.println("wr==" + wr + ",wg==" + wg + ",b==" + b);
+        } else {
+            throw new Exception("regression matrix size is zero");
+        }
+    }
+
+    public float getW1() {
+        return w1;
+    }
+
+    public void setW1(float w1) {
+        this.w1 = w1;
+    }
+
+    public float getW2() {
+        return w2;
+    }
+
+    public void setW2(float w2) {
+        this.w2 = w2;
+    }
+
+    public float getB() {
+        return b;
+    }
+
+    public void setB(float b) {
+        this.b = b;
+    }
+}