随机森林代码修改：按照target训练和预测，target包含刀号，进给和转速倍率

	修改：     RadomForestSAC/RamdomForestCalculate.cpp
	修改：     RadomForestSAC/RamdomForestCalculate.h
	修改：     RadomForestSAC/RandomForestClassifier.cpp
	修改：     RadomForestSAC/RandomForestClassifier.h
	修改：     RadomForestSAC/common.h
	修改：     main.cpp
	修改：     package/E3/SigerCalculation

SigerCalculation/RadomForestSAC/RamdomForestCalculate.h

@@ -17,6 +17,9 @@
 #include <QSqlError>
 #include <QTimer>
 
+#ifndef SAC_FEATURE_DIM
+#define SAC_FEATURE_DIM 10
+#endif
 
 // 如果 Dc_SacData 是结构体/类，需要注册元类型
 Q_DECLARE_METATYPE(Dc_SacData)
@@ -48,7 +51,10 @@ struct StreamInfo
     }
 };
 
-
+struct SqlFeaRecord{
+    std::vector<std::vector<float> > sample;
+    std::vector<int> label;
+};
 
 class RamdomForestCalculate : public QObject
 {
@@ -56,6 +62,7 @@ class RamdomForestCalculate : public QObject
 
 public:
     explicit RamdomForestCalculate(QObject *parent = nullptr);
+    std::vector<float> QstrToFeature(const QString& qstr);
 
     //1.查询功率样本(废弃函数)
     QString QueryPowerData(const StreamInfo& alarmInfo);
@@ -64,7 +71,8 @@ public:
     void SendStreamAlarminfo(const StreamInfo& alarmInfo);
 
     //3.查询函数
-    QList<SACRecord> queryRecordsByLabel(const QString& dbFileName, int targetLabel);
+    bool queryRecordsByLabel(const QString &dbFileName,
+            std::map<std::string, std::pair<std::vector<SACRecord>, std::vector<SACRecord>>>& data);
 
     //4.接受DC消息，保存振动数据
     int Save_SACDATA(const Dc_SacData& sacdata);
@@ -73,7 +81,7 @@ public:
     int judge_alarm();
 
     //6.解析查询到的振动值
-    void DealRecord(const QList<SACRecord> &record,std::vector<std::vector<float>>& result1,std::vector<int> &result2);
+    void DealRecord(const std::map<std::string, std::pair<std::vector<SACRecord>, std::vector<SACRecord>>>& data, std::map<std::string, SqlFeaRecord>& result);
 
     //7.重新训练
     void Retrain();
@@ -86,9 +94,7 @@ private:
     //QString m_name;
     //DBFunc *dbFunc;
     TcpServerManager *m_tcpServerManager;
-
-    RandomForestClassifier* Classifier;
-    bool IfHaveModel = false;
+    std::map<std::string, RandomForestClassifier> m_rf_map;
 };
 
 

SigerCalculation/RadomForestSAC/RandomForestClassifier.cpp

@@ -4,6 +4,16 @@
 #include "common.h"
 #include "spdlog/spdlog.h"
 
+RandomForestClassifier::RandomForestClassifier(std::string target, int initial_dim, int label_num)
+{
+    target = m_target;
+    feature_dim = initial_dim;
+    label_dim = label_num;
+}
+
+const std::string& RandomForestClassifier::getTarget() const {return m_target;}
+void RandomForestClassifier::set_target(const std::string& target) {m_target=target;}
+
 std::vector<sample_type> RandomForestClassifier::convert_to_dlib_format(const std::vector<std::vector<float>>& X)
 {
     std::vector<sample_type> result;
@@ -136,12 +146,12 @@ bool RandomForestClassifier::train(const std::vector<std::vector<float>>& X, con
     }
 
     if (X.size() != y.size()) {
-        DEBUG_PRINT("invalid param: size_X=%ld size_y=%ld\n", X.size(), y.size());
+        DEBUG_PRINT("invalid param: size_X=%lu size_y=%lu\n", X.size(), y.size());
         return false;
     }
 
     if (X[0].size() != static_cast<size_t>(feature_dim)){
-        DEBUG_PRINT("invalid param: X[0].size()=%ld feature_dim=%d\n", X[0].size(), feature_dim);
+        DEBUG_PRINT("invalid param: X[0].size()=%lu feature_dim=%d\n", X[0].size(), feature_dim);
         return false;
     }
 
@@ -190,9 +200,8 @@ bool RandomForestClassifier::train(const std::vector<std::vector<float>>& X, con
 
     try
     {
-        serialize("rf_model.dat") << forest;
-        serialize("scaler_mean.dat") << scaler_mean;
-        serialize("scaler_std.dat") << scaler_std;
+        std::string filename = (!m_target.empty()) ? (m_target + "_rf_model.dat") : "rf_model.dat";
+        serialize(filename) << scaler_mean << scaler_std << forest;
         DEBUG_PRINT("save random-forest model success\n");
     }
     catch(std::exception &e) {
@@ -294,25 +303,18 @@ double RandomForestClassifier::predict(const std::vector<float>& sample)
 {
     if (sample.size() != static_cast<size_t>(feature_dim))
     {
-        printf("invalid param: sample_size=%ld feature_dim=%d\n", sample.size(), feature_dim);
-
-        SPDLOG_LOGGER_DEBUG(spdlog::get("logger"),"invalid param: sample_size-------sample.size:{}--------feature_dim{}",sample.size(),feature_dim);
-
+        SPDLOG_LOGGER_DEBUG(spdlog::get("logger"), "[predict] invalid param: sample_size={} feature_dim={}", sample.size(), feature_dim);
         return -1;
     }
 
     if (forest.get_num_trees() == 0) {
         try {
-            deserialize("rf_model.dat") >> forest;
-            deserialize("scaler_mean.dat") >> scaler_mean;
-            deserialize("scaler_std.dat") >> scaler_std;
+            std::string filename = (!m_target.empty()) ? (m_target + "_rf_model.dat") : "rf_model.dat";
+            deserialize(filename) >> scaler_mean >> scaler_std >> forest;
             feature_dim = scaler_mean.size();
         }
         catch (const std::exception& e) {
-            DEBUG_PRINT("load model failure: %s\n", e.what());
-
-            SPDLOG_LOGGER_DEBUG(spdlog::get("logger"),"load model failure:--------------");
-
+            SPDLOG_LOGGER_DEBUG(spdlog::get("logger"), "load model failure: {}\n", e.what());
             return -1;
         }
     }
@@ -325,13 +327,3 @@ double RandomForestClassifier::predict(const std::vector<float>& sample)
     double prediction = forest(scaled);
     return prediction;
 }
-
-std::vector<double> RandomForestClassifier::predict_batch(const std::vector<std::vector<float>>& samples)
-{
-    std::vector<double> result;
-    for (const auto& s : samples) {
-        result.push_back(predict(s));
-    }
-    return result;
-}
-

SigerCalculation/RadomForestSAC/RandomForestClassifier.h

@@ -20,6 +20,7 @@ private:
     random_forest_regression_function<> forest;
     std::vector<double> scaler_mean;
     std::vector<double> scaler_std;
+    std::string m_target;
     int feature_dim;
     int label_dim;
 
@@ -39,13 +40,13 @@ private:
 
 public:
     RandomForestClassifier() : feature_dim(10), label_dim(4) {}
-    RandomForestClassifier(int initial_dim, int label_num) : feature_dim(initial_dim), label_dim(label_num) {}
-
+    RandomForestClassifier(std::string target, int initial_dim=10, int label_num=4);
     bool train(const std::vector<std::vector<float>>& X, const std::vector<int>& y,
                float test_size = 0.0f, int n_estimators = 100);
 
     double predict(const std::vector<float>& sample);
-    std::vector<double> predict_batch(const std::vector<std::vector<float>>& samples);
+    const std::string& getTarget() const;
+    void set_target(const std::string& target);
 };
 
 #endif // RANDOM_FOREST_CLASSIFIER_H

SigerCalculation/RadomForestSAC/common.h

@@ -9,7 +9,8 @@ enum {
 };
 
 //#define DEBUG_PRINT(fmt, ...) debug_printf(" [%s] " fmt, __func__, ##__VA_ARGS__)
-#define DEBUG_PRINT(fmt, ...) printf("[%s] " fmt, __func__, ##__VA_ARGS__)
+//#define DEBUG_PRINT(fmt, ...) printf("[%s] " fmt, __func__, ##__VA_ARGS__)
+#define DEBUG_PRINT
 void debug_printf(const char *fmt, ...);
 void measure_time(struct timeval *start);
 bool isFeatureValid(const std::vector<float>& fea);

SigerCalculation/main.cpp

@@ -47,7 +47,6 @@ int main(int argc, char *argv[])
 
     MonitorLogfile m_Logfile;
 
-
     RamdomForestCalculate RandomCaculate;
 
     //qRegisterMetaType<Dc_SacData>("Dc_SacData");