changes to rapidGVF

src/machineLearning/machineLearning.h

@@ -14,7 +14,7 @@
 #include "classification.h"
 #include "regression.h"
 #include "rapidXmmTools.h"
-//#include "gvf.h"
+#include "rapidGVF.h"
 
 // forward declaration
 namespace rapidmix { class trainingData; }
@@ -58,11 +58,9 @@ typedef machineLearning<xmmGmmTool> xmmStaticClassification;
 typedef machineLearning<xmmGmrTool> xmmStaticRegression;
 typedef machineLearning<xmmHmmTool> xmmTemporalClassification;
 typedef machineLearning<xmmHmrTool> xmmTemporalRegression;
-//*/
 
-/*
-typedef machineLearning<GVF> gvf;
-//*/
+typedef machineLearning<rapidGVF> gvfTemporalVariation;
+
 
 RAPIDMIX_END_NAMESPACE
 

src/machineLearning/rapidGVF/rapidGVF.cpp

@@ -5,92 +5,97 @@
 //  Copyright © 2017 Goldsmiths. All rights reserved.
 //
 
-#include "gvf.h"
+#include "rapidGVF.h"
+#include "trainingData.h"
 
 
 
-namespace rapidmix {
+rapidGVF::rapidGVF()
+{
     
     
+}
+
+rapidGVF::~rapidGVF()
+{
+
     
-    bool trainingData2GVF (const trainingData &newTrainingData, std::vector<std::vector<float>> &trainingSet) {
-        
-        if (newTrainingData.trainingSet.size() <= 1) {
-            // no recorded phrase (only default one)
-            return false;
-        }
-        
-        if (newTrainingData.trainingSet.size() > 1 && newTrainingData.trainingSet[1].elements.size() == 0) {
-            // empty recorded phrase
-            return false;
-        }
-        
-        for (int h = 0; h < newTrainingData.trainingSet.size(); ++h) { //Go through every phrase
-            
-            for (int i = 0; i < newTrainingData.trainingSet[h].elements.size(); ++i) { //...and every element
-                
-                //            trainingExample tempExample;
-                //            tempExample.input = newTrainingData.trainingSet[h].elements[i].input;
-                //
-                //            if (newTrainingData.trainingSet[h].elements[i].output.size() > 0) {
-                //                tempExample.output = newTrainingData.trainingSet[h].elements[i].output;
-                //            } else {
-                //                std::unordered_map<std::string, int>::const_iterator mappedLabel = newTrainingData.labels.find(newTrainingData.trainingSet[h].label);
-                //                tempExample.output.push_back(double(mappedLabel->second));
-                //            }
-                //            trainingSet.push_back(tempExample);
-            }
-        }
-        
-        return true;
-    };
+}
+
+
+bool rapidGVF::trainingData2GVF(const rapidmix::trainingData &newTrainingData, std::vector<std::vector<float>> &trainingSet) {
     
-    template<>
-    bool machineLearning<GVF>::train(const trainingData &newTrainingData) {
-        
-        //
-        //     std::vector<trainingExample> trainingSet;
-        //
-        //     trainingData2GVF(newTrainingData, trainingSet);
-        //
-        //     g
-        //
-        //     return GVF::train(trainingSet);
-        //
-        //
-        //
-        //     vector<vector<float>> trainingData;
-        //
-        //     for (vector<vector<vector<float>>>::iterator trainingGesture = trainingData.begin(); trainingGesture != trainingData.end(); trainingGesture++)
-        //     {
-        //     currentGesture.clear();
-        //     for (vector<vector<float>>::iterator frame = (*trainingGesture).begin(); frame != (*trainingGesture).end(); frame++)
-        //     {
-        //      Fill the template
-        //     currentGesture.addObservation(*frame);
-        //     }
-        //      Add the template at the end of the gesture
-        //     gvf->addGestureTemplate(currentGesture);
-        //     }
-        //     
-        
-        return true;
+    if (newTrainingData.trainingSet.size() <= 1) {
+        // no recorded phrase (only default one)
+        return false;
     }
     
-    template<>
-    std::vector<double> machineLearning<GVF>::process(const std::vector<double> &inputVector) {
+    if (newTrainingData.trainingSet.size() > 1 && newTrainingData.trainingSet[1].elements.size() == 0) {
+        // empty recorded phrase
+        return false;
+    }
+    
+    for (int h = 0; h < newTrainingData.trainingSet.size(); ++h) { //Go through every phrase
         
-        return std::vector<double>();
+        for (int i = 0; i < newTrainingData.trainingSet[h].elements.size(); ++i) { //...and every element
+            
+            //            trainingExample tempExample;
+            //            tempExample.input = newTrainingData.trainingSet[h].elements[i].input;
+            //
+            //            if (newTrainingData.trainingSet[h].elements[i].output.size() > 0) {
+            //                tempExample.output = newTrainingData.trainingSet[h].elements[i].output;
+            //            } else {
+            //                std::unordered_map<std::string, int>::const_iterator mappedLabel = newTrainingData.labels.find(newTrainingData.trainingSet[h].label);
+            //                tempExample.output.push_back(double(mappedLabel->second));
+            //            }
+            //            trainingSet.push_back(tempExample);
+        }
     }
     
-//    template<>
-//    bool machineLearning<GVF>::reset() {
-//        
-//        return true;
-//    }
-//    
+    return true;
+};
+
+
+bool rapidGVF::train(const rapidmix::trainingData &newTrainingData) {
+    
+    //
+    //     std::vector<trainingExample> trainingSet;
+    //
+    //     trainingData2GVF(newTrainingData, trainingSet);
+    //
+    //     g
+    //
+    //     return GVF::train(trainingSet);
+    //
+    //
+    //
+    //     vector<vector<float>> trainingData;
+    //
+    //     for (vector<vector<vector<float>>>::iterator trainingGesture = trainingData.begin(); trainingGesture != trainingData.end(); trainingGesture++)
+    //     {
+    //     currentGesture.clear();
+    //     for (vector<vector<float>>::iterator frame = (*trainingGesture).begin(); frame != (*trainingGesture).end(); frame++)
+    //     {
+    //      Fill the template
+    //     currentGesture.addObservation(*frame);
+    //     }
+    //      Add the template at the end of the gesture
+    //     gvf->addGestureTemplate(currentGesture);
+    //     }
+    //
+    
+    return true;
+}
+
+
+
+std::vector<double> rapidGVF::process(const std::vector<double> &inputVector){
     
-    template class rapidmix::machineLearning<GVF>;
+    
+    return vector<double>();
 }
 
 
+
+
+

src/machineLearning/rapidGVF/rapidGVF.h

@@ -5,31 +5,29 @@
 //  Copyright © 2017 Goldsmiths. All rights reserved.
 //
 
-#ifndef gvf_h
-#define gvf_h
+#ifndef rapidGVF_h
+#define rapidGVF_h
 
+#include <vector>
 #include <string>
 
-
 #include "GVF.h"
-#include "trainingData.h"
-#include "machineLearning.h"
 
-namespace rapidmix {
-    
-//    typedef machineLearning<GVF> gvf;
-    
-    bool trainingData2GVF (const trainingData &newTrainingData, vector<vector<float>> &trainingSet);
+
+namespace rapidmix { class trainingData; }
+
+class rapidGVF {
     
-    template<>
-    bool machineLearning<GVF>::train(const trainingData &newTrainingData);
+    rapidGVF();
+
+    ~rapidGVF();
     
-    template<>
-    std::vector<double> machineLearning<GVF>::process(const std::vector<double> &inputVector);
+    bool train(const rapidmix::trainingData &newTrainingData);
     
-    template<>
-    bool machineLearning<GVF>::reset();
+    bool trainingData2GVF(const rapidmix::trainingData &newTrainingData, std::vector<std::vector<float>> &trainingSet);
     
-}
+    std::vector<double> process(const std::vector<double> &inputVector);
+};
+
 
 #endif