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src/main/java/org/dromara/easyai/tools/MeanClustering.java

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+package org.dromara.easyai.tools;
+
+
+import org.dromara.easyai.entity.RGBNorm;
+
+import java.util.ArrayList;
+import java.util.List;
+import java.util.Random;
+
+//K均值聚类
+public class MeanClustering {
+    protected List<float[]> matrixList = new ArrayList<>();//聚类集合
+    private int length;//向量长度(模型需要返回)
+    protected int speciesQuantity;//种类数量(模型需要返回)
+    private final int maxTimes;//最大迭代次数
+    protected List<RGBNorm> matrices = new ArrayList<>();//均值K模型(模型需要返回)
+
+    public List<RGBNorm> getMatrices() {
+        return matrices;
+    }
+
+    public float[] getResultByNorm() {
+        MeanSort meanSort = new MeanSort();
+        float[] dm = new float[matrices.size() * length];
+        matrices.sort(meanSort);
+        for (int i = 0; i < matrices.size(); i++) {
+            RGBNorm rgbNorm = matrices.get(i);
+            float[] rgb = rgbNorm.getRgb();
+            for (int j = 0; j < rgb.length; j++) {
+                dm[i * rgb.length + j] = rgb[j];
+            }
+        }
+        return dm;
+    }
+
+    public MeanClustering(int speciesQuantity, int maxTimes) throws Exception {
+        this.speciesQuantity = speciesQuantity;//聚类的数量
+        this.maxTimes = maxTimes;
+    }
+
+    public void setFeature(float[] feature) throws Exception {
+        if (matrixList.isEmpty()) {
+            matrixList.add(feature);
+            length = feature.length;
+        } else {
+            if (length == feature.length) {
+                matrixList.add(feature);
+            } else {
+                throw new Exception("vector length is different");
+            }
+        }
+    }
+
+    private void averageMatrix() {
+        for (float[] rgb : matrixList) {//遍历当前集合
+            float min = -1;
+            int id = 0;
+            for (int i = 0; i < speciesQuantity; i++) {
+                RGBNorm rgbNorm = matrices.get(i);
+                float dist = rgbNorm.getEDist(rgb);
+                if (min == -1 || dist < min) {
+                    min = dist;
+                    id = i;
+                }
+            }
+            //进簇
+            RGBNorm rgbNorm = matrices.get(id);
+            rgbNorm.setColor(rgb);
+        }
+        //重新计算均值
+        for (RGBNorm rgbNorm : matrices) {
+            rgbNorm.norm();
+        }
+    }
+
+    private boolean isNext() {
+        boolean isNext = false;
+        for (RGBNorm rgbNorm : matrices) {
+            isNext = rgbNorm.compare();
+            if (isNext) {
+                break;
+            }
+        }
+        return isNext;
+    }
+
+    private void clear() {
+        for (RGBNorm rgbNorm : matrices) {
+            rgbNorm.clear();
+        }
+    }
+
+    public void start() throws Exception {//开始聚类
+        if (matrixList.size() > 1) {
+            Random random = new Random();
+            for (int i = 0; i < speciesQuantity; i++) {//初始化均值向量
+                int index = random.nextInt(matrixList.size());
+                float[] rgb = matrixList.get(index);
+                RGBNorm rgbNorm = new RGBNorm(rgb, length);
+                //要进行深度克隆
+                matrices.add(rgbNorm);
+            }
+            //进行两者的比较
+            boolean isNext;
+            for (int i = 0; i < maxTimes; i++) {
+                //System.out.println("聚类：" + i);
+                averageMatrix();
+                isNext = isNext();
+                if (isNext && i < maxTimes - 1) {
+                    clear();
+                } else {
+                    break;
+                }
+            }
+        } else {
+            throw new Exception("matrixList number less than 2");
+        }
+    }
+}