From 331a92f9b1c644d2f3d86cc823d497d71d21a502 Mon Sep 17 00:00:00 2001 From: inter Date: Thu, 4 Sep 2025 14:09:41 +0800 Subject: [PATCH] Add File --- .../regressionForest/RegressionForest.java | 269 ++++++++++++++++++ 1 file changed, 269 insertions(+) create mode 100644 src/main/java/org/dromara/easyai/regressionForest/RegressionForest.java diff --git a/src/main/java/org/dromara/easyai/regressionForest/RegressionForest.java b/src/main/java/org/dromara/easyai/regressionForest/RegressionForest.java new file mode 100644 index 0000000..6d58c4e --- /dev/null +++ b/src/main/java/org/dromara/easyai/regressionForest/RegressionForest.java @@ -0,0 +1,269 @@ +package org.dromara.easyai.regressionForest; + +import org.dromara.easyai.matrixTools.Matrix; +import org.dromara.easyai.matrixTools.MatrixOperation; +import org.dromara.easyai.tools.Frequency; + +import java.util.*; + +/** + * @param + * @DATA + * @Author LiDaPeng + * @Description 回归森林 + */ +public class RegressionForest extends Frequency { + private float[] 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 float[] results;//结果数组 + private float min;//结果最小值 + private float max;//结果最大值 + private Matrix pc;//需要映射的基 + private int cosSize = 20;//cos 分成几份 + private TreeMap forestMap = new TreeMap<>();//节点列表 + private final MatrixOperation matrixOperation = new MatrixOperation(); + + public int getCosSize() { + return cosSize; + } + + public void setCosSize(int cosSize) { + this.cosSize = cosSize; + } + + public RegressionForest(int size, int featureNub, float shrinkParameter, int minGrain) throws Exception {//初始化 + if (size > 0 && featureNub > 0) { + this.featureNub = featureNub; + w = new float[featureNub]; + results = new float[size]; + conditionMatrix = new Matrix(size, featureNub); + resultMatrix = new Matrix(size, 1); + createG(); + forest = new Forest(featureNub, shrinkParameter, pc, forestMap, 1, minGrain); + forestMap.put(1, forest); + forest.setW(w); + forest.setConditionMatrix(conditionMatrix); + forest.setResultMatrix(resultMatrix); + } else { + throw new Exception("size and featureNub too small"); + } + } + + public float getDist(Matrix featureMatrix, float result) throws Exception {//获取特征误差结果 + Forest forestFinish = getRegion(forest, featureMatrix); + //计算误差 + float[] w = forestFinish.getW(); + float sigma = 0; + for (int i = 0; i < w.length; i++) { + float nub; + if (i < w.length - 1) { + nub = w[i] * featureMatrix.getNumber(0, i); + } else { + nub = w[i]; + } + sigma = sigma + nub; + } + return (float)Math.abs(result - sigma); + } + + private Forest getRegion(Forest forest, Matrix matrix) throws Exception { + float median = forest.getMedian(); + float result = forest.getMappingFeature(matrix); + if (result > median && forest.getForestRight() != null) {//向右走 + forest = forest.getForestRight(); + } else if (result <= median && forest.getForestLeft() != null) {//向左走 + forest = forest.getForestLeft(); + } else { + return forest; + } + return getRegion(forest, matrix); + } + + private Forest getLimitRegion(Forest forest, boolean isMax) { + Forest forestSon; + if (isMax) { + forestSon = forest.getForestRight(); + } else { + forestSon = forest.getForestLeft(); + } + if (forestSon != null) { + return getLimitRegion(forestSon, isMax); + } else { + return forest; + } + } + + private void createG() throws Exception {//生成新基 + float[] cg = new float[featureNub - 1]; + Random random = new Random(); + float sigma = 0; + for (int i = 0; i < featureNub - 1; i++) { + float rm = random.nextFloat(); + cg[i] = rm; + sigma = sigma + (float)Math.pow(rm, 2); + } + float cosOne = 1.0f / cosSize; + float[] ag = new float[cosSize - 1];//装一个维度内所有角度的余弦值 + for (int i = 0; i < cosSize - 1; i++) { + float cos = cosOne * (i + 1); + ag[i] = (float)Math.sqrt(sigma / (1 / (float)Math.pow(cos, 2) - 1)); + } + int x = (cosSize - 1) * featureNub; + pc = new Matrix(x, featureNub); + for (int i = 0; i < featureNub; i++) {//遍历所有的固定基 + //以某个固定基摆动的所有新基集合的矩阵 + Matrix matrix = new Matrix(ag.length, featureNub); + for (int j = 0; j < ag.length; j++) { + for (int k = 0; k < featureNub; k++) { + if (k != i) { + if (k < i) { + matrix.setNub(j, k, cg[k]); + } else { + matrix.setNub(j, k, cg[k - 1]); + } + } else { + matrix.setNub(j, k, ag[j]); + } + } + } + //将一个固定基内摆动的新基都装到最大的集合内 + int index = (cosSize - 1) * i; + push(pc, matrix, index); + } + } + + //将两个矩阵从上到下进行合并 + private void push(Matrix mother, Matrix son, int index) throws Exception { + if (mother.getY() == son.getY()) { + int x = index + son.getX(); + int y = mother.getY(); + int start = 0; + for (int i = index; i < x; i++) { + for (int j = 0; j < y; j++) { + mother.setNub(i, j, son.getNumber(start, j)); + } + start++; + } + } else { + throw new Exception("matrix Y is not equals"); + } + } + + public void insertFeature(float[] feature, float result) throws Exception {//插入数据 + if (feature.length == featureNub - 1) { + 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, 1.0f); + resultMatrix.setNub(xIndex, 0, result); + } + } + xIndex++; + } else { + throw new Exception("feature length is not equals"); + } + } + + public void startStudy() throws Exception {//开始进行分段 + if (forest != null) { + //计算方差 + forest.setResultVariance(variance(results)); + float[] limit = getLimit(results); + min = limit[0]; + max = limit[1]; + start(forest); + //进行回归 + regression(); + //进行剪枝 + pruning(); + } 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); + } + } + + private void pruning() {//剪枝 + //先获取当前最大id + int max = forestMap.lastKey(); + int layersNub = (int) ((float)Math.log(max) / (float)Math.log(2));//当前的层数 + int lastMin = (int) (float)Math.pow(2, layersNub);//最后一层最小的id + if (layersNub > 1) {//先遍历最后一层 + for (Map.Entry entry : forestMap.entrySet()) { + if (entry.getKey() >= lastMin) { + Forest forest = entry.getValue(); + forest.pruning(); + } + } + } + //每一层从下到上进行剪枝 + for (int i = layersNub - 1; i > 0; i--) { + int min = (int) (float)Math.pow(2, i);//最后一层最小的id + int maxNub = (int) (float)Math.pow(2, i + 1); + for (Map.Entry entry : forestMap.entrySet()) { + int key = entry.getKey(); + if (key >= min && key < maxNub) {//在范围内，进行剪枝 + entry.getValue().pruning(); + } else if (key >= maxNub) { + break; + } + } + } + //遍历所有节点，将删除的节点移除 + List list = new ArrayList<>(); + for (Map.Entry entry : forestMap.entrySet()) { + int key = entry.getKey(); + Forest forest = entry.getValue(); + if (forest.isRemove()) { + list.add(key); + } + } + for (int key : list) { + forestMap.remove(key); + } + } + + private void regression() throws Exception {//开始进行回归 + if (forest != null) { + regressionTree(forest); + } else { + 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 {//对分段进行线性回归 + Matrix conditionMatrix = forest.getConditionMatrix(); + Matrix resultMatrix = forest.getResultMatrix(); + Matrix ws = matrixOperation.getLinearRegression(conditionMatrix, resultMatrix); + float[] w = forest.getW(); + for (int i = 0; i < ws.getX(); i++) { + w[i] = ws.getNumber(i, 0); + } + + } +} \ No newline at end of file