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src/main/java/org/dromara/easyai/recommend/RecommendCodeManager.java

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+package org.dromara.easyai.recommend;
+
+import org.dromara.easyai.matrixTools.Matrix;
+import org.dromara.easyai.config.RZ;
+import org.dromara.easyai.function.Tanh;
+import org.dromara.easyai.i.OutBack;
+import org.dromara.easyai.rnnNerveCenter.NerveManager;
+import org.dromara.easyai.rnnNerveEntity.SensoryNerve;
+import org.dromara.easyai.tools.IdCreator;
+
+import java.util.HashMap;
+import java.util.List;
+import java.util.Map;
+import java.util.concurrent.locks.ReentrantReadWriteLock;
+
+public class RecommendCodeManager {//推荐id管理
+    private final NerveManager nerveManager;
+    private final float studyTh;
+    private final int dim;
+    private final ReentrantReadWriteLock lock = new ReentrantReadWriteLock();
+
+    public RecommendCodeManager(boolean initPower, RecommendConfig recommendConfig) throws Exception {
+        studyTh = recommendConfig.getStudyTh();
+        dim = recommendConfig.getDimension();
+        nerveManager = new NerveManager(31, dim, 31, 1, new Tanh(),
+                0.01f, RZ.L1, 0.0001f);
+        nerveManager.setSoftMax(false);
+        nerveManager.initRnn(initPower, false);
+    }
+
+    public float[] getFeatures(int feature) {
+        float[] features = new float[31];
+        for (int i = 0; i < 31; i++) {
+            int t = 1 << i;
+            if ((feature & t) != 0) {//存在
+                features[i] = 1;
+            }
+        }
+        return features;
+    }
+
+    public Map<Integer, Float> getMap(float[] feature) {
+        Map<Integer, Float> map = new HashMap<>();
+        for (int i = 0; i < feature.length; i++) {
+            int t = i + 1;
+            map.put(t, feature[i]);
+        }
+        return map;
+    }
+
+    private Matrix getSubMatrix(float sub) throws Exception {
+        Matrix subMatrix = new Matrix(2, dim);
+        for (int i = 0; i < dim; i++) {
+            subMatrix.setNub(1, i, sub);
+        }
+        return subMatrix;
+    }
+
+    public void study(List<CodeBody> codeBodyList) throws Exception {
+        for (int i = 0; i < codeBodyList.size(); i++) {
+            CodeBody myCodeBody = codeBodyList.get(i);
+            float myPower = myCodeBody.getPower();
+            if (myPower >= studyTh) {
+                float[] myId = getFeatures(myCodeBody.getId());
+                Map<Integer, Float> myMapId = getMap(myId);
+                for (int j = 0; j < codeBodyList.size(); j++) {
+                    if (i != j) {
+                        CodeBody codeBody = codeBodyList.get(j);
+                        Matrix sub = getSubMatrix((myPower - codeBody.getPower()) / dim);
+                        float[] id = getFeatures(codeBody.getId());
+                        Map<Integer, Float> mapId = getMap(id);
+                        lock.writeLock().lock();
+                        studyNerve(1, myId, sub, mapId, true, null);
+                        studyNerve(1, id, sub, myMapId, true, null);
+                        lock.writeLock().unlock();
+                    }
+                }
+            }
+        }
+    }
+
+    public float[] getMappingId(int id) throws Exception {//获取映射id
+        lock.readLock().lock();
+        CodeBack codeBack = new CodeBack();
+        codeBack.setMyFeature(new float[31]);
+        Matrix subMatrix = new Matrix(2, dim);
+        long nextId = IdCreator.get().nextId();
+        float[] feature = getFeatures(id);
+        studyNerve(nextId, feature, subMatrix, null, false, codeBack);
+        lock.readLock().unlock();
+        return codeBack.getMyFeature();
+    }
+
+    private void studyNerve(long eventId, float[] features, Matrix rnnMatrix, Map<Integer, Float> E, boolean isStudy, OutBack convBack) throws Exception {
+        List<SensoryNerve> sensoryNerves = nerveManager.getSensoryNerves();
+        if (sensoryNerves.size() == features.length) {
+            for (int i = 0; i < sensoryNerves.size(); i++) {
+                sensoryNerves.get(i).postMessage(eventId, features[i], isStudy, E, convBack, false, rnnMatrix);
+            }
+        } else {
+            throw new Exception("size not equals,feature size:" + features.length + ",sensorySize:" + sensoryNerves.size());
+        }
+    }
+}