adding machine learning class

src/machineLearning/machineLearning.cpp

+/*
+ * machineLearning.h
+ * Created by Michael Zbyszynski on 10 Jan 2016
+ * Copyright © 2017 Goldsmiths. All rights reserved.
+ */
+
+#include "machineLearning.h"
+
+RAPIDMIX_BEGIN_NAMESPACE
+
+void trainingData2rapidLib (const trainingData &newTrainingData, std::vector<trainingExample> &trainingSet) {
+    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);
+        }
+    }
+};
+
+template<>
+bool machineLearning<classification>::train(const trainingData &newTrainingData) {
+    std::vector<trainingExample> trainingSet;
+    trainingData2rapidLib(newTrainingData, trainingSet);
+    return classification::train(trainingSet);
+}
+
+template<>
+bool machineLearning<regression>::train(const trainingData &newTrainingData) {
+    std::vector<trainingExample> trainingSet;
+    trainingData2rapidLib(newTrainingData, trainingSet);
+    return regression::train(trainingSet);
+}
+
+
+RAPIDMIX_END_NAMESPACE

src/machineLearning/machineLearning.h

+/*
+ * machineLearning.h
+ * Created by Michael Zbyszynski on 10 Jan 2016
+ * Copyright © 2017 Goldsmiths. All rights reserved.
+ */
+
+#ifndef machineLearning_h
+#define machineLearning_h
+
+#include "rapidMix.h"
+#include "trainingData.h"
+
+////////// Include all of the machine learning algorithms here
+#include "classification.h"
+#include "regression.h"
+#include "rapidXmmTools.h"
+#include "gvf.h"
+
+
+
+RAPIDMIX_BEGIN_NAMESPACE
+
+//* Host class for machine learning algorithms */
+template <typename MachineLearningModule>
+class machineLearning : public MachineLearningModule {
+public:
+    
+    //* Constructors */
+    machineLearning() : MachineLearningModule() {};
+    
+    template<class T>
+    machineLearning(T type) : MachineLearningModule(type) {};
+    
+    //* this function becomes specialized in the implementation */
+    bool train(const trainingData &newTrainingData);
+    
+    // Could overload this, or specialize, or both
+    std::vector<double> run(const std::vector<double> &inputVector) {
+        return MachineLearningModule::process(inputVector);
+    }
+    
+    bool reset() {
+        return MachineLearningModule::reset();
+    }
+    
+private:
+    MachineLearningModule module;
+};
+
+////////// typedefs for calling different algorithms
+typedef machineLearning<classification> staticClassification;
+typedef machineLearning<regression> staticRegression;
+
+typedef xmmToolConfig xmmConfig;
+typedef machineLearning<xmmGmmTool> xmmStaticClassification;
+typedef machineLearning<xmmGmrTool> xmmStaticRegression;
+typedef machineLearning<xmmHmmTool> xmmTemporalClassification;
+typedef machineLearning<xmmHmrTool> xmmTemporalRegression;
+
+typedef machineLearning<GVF> gvf;
+
+RAPIDMIX_END_NAMESPACE
+
+#endif

src/rapidmix.h

+/*
+ * rapidmix.h
+ * Created by Michael Zbyszynski on 12 Jan 2017
+ * Copyright © 2017 Goldsmiths. All rights reserved.
+ */
+
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