examples/ofx/prob_drums+ml+myo/bin/data/thung

+<group>
+	<0>100</0>
+	<1>0</1>
+	<2>44</2>
+	<3>0</3>
+	<4>100</4>
+	<5>0</5>
+	<6>45</6>
+	<7>0</7>
+	<8>100</8>
+	<9>0</9>
+	<10>45</10>
+	<11>0</11>
+</group>

examples/ofx/prob_drums+ml+myo/src/main.cpp

+#include "ofMain.h"
+#include "ofApp.h"
+
+//========================================================================
+int main( ){
+	ofSetupOpenGL(1024,768,OF_WINDOW);			// <-------- setup the GL context
+
+	// this kicks off the running of my app
+	// can be OF_WINDOW or OF_FULLSCREEN
+	// pass in width and height too:
+	ofRunApp(new ofApp());
+
+}

examples/ofx/prob_drums+ml+myo/src/ofApp.cpp

+#include <array>
+
+#include "ofApp.h"
+
+//--------------------------------------------------------------
+void ofApp::setup(){
+    
+    myo.setup();
+    
+    //gui
+    probsClear.addListener(this, &ofApp::probsClearPressed);
+    resetModel.addListener(this, &ofApp::resetModelPressed);
+    
+    guiTong.setup("tong");
+    guiTong.add(tong0.setup("1", 50, 0, 100));
+    guiTong.add(tong1.setup("2", 0, 0, 100));
+    guiTong.add(tong2.setup("3", 90, 0, 100));
+    guiTong.add(tong3.setup("4", 0, 0, 100));
+    guiTong.add(tong4.setup("5", 90, 0, 100));
+    guiTong.add(tong5.setup("6", 0, 0, 100));
+    guiTong.add(tong6.setup("7", 0, 0, 100));
+    guiTong.add(tong7.setup("8", 60, 0, 100));
+    guiTong.add(tong8.setup("9", 0, 0, 100));
+    guiTong.add(tong9.setup("10", 90, 0, 100));
+    guiTong.add(tong10.setup("11", 0, 0, 100));
+    guiTong.add(tong11.setup("12", 0, 0, 100));
+    ofColor green(0, 255, 0);
+    guiTong.setHeaderBackgroundColor(green);
+    
+    guiThung.setup("thung", "thung", 250, 10);
+    guiThung.add(thung0.setup("1", 100, 0, 100));
+    guiThung.add(thung1.setup("2", 0, 0, 100));
+    guiThung.add(thung2.setup("3", 10, 0, 100));
+    guiThung.add(thung3.setup("4", 0, 0, 100));
+    guiThung.add(thung4.setup("5", 60, 0, 100));
+    guiThung.add(thung5.setup("6", 0, 0, 100));
+    guiThung.add(thung6.setup("7", 10, 0, 100));
+    guiThung.add(thung7.setup("8", 0, 0, 100));
+    guiThung.add(thung8.setup("9", 60, 0, 100));
+    guiThung.add(thung9.setup("10", 0, 0, 100));
+    guiThung.add(thung10.setup("11", 30, 0, 100));
+    guiThung.add(thung11.setup("12", 0, 0, 100));
+    ofColor yellow(255, 255, 0);
+    guiThung.setHeaderBackgroundColor(yellow);
+    
+    guiGeneral.setup("general", "general", 500, 10);
+    guiGeneral.add(gain.setup("gain", 1., 0., 1.));
+    guiGeneral.add(modelControl.setup("run model", false));
+    guiGeneral.add(resetModel.setup("reset model"));
+    guiGeneral.add(inputDevice.setup("myo", false));
+    guiGeneral.add(probsClear.setup("clear"));
+    
+    //This will make life easier later
+    allSliders.push_back(tong0);
+    allSliders.push_back(tong1);
+    allSliders.push_back(tong2);
+    allSliders.push_back(tong3);
+    allSliders.push_back(tong4);
+    allSliders.push_back(tong5);
+    allSliders.push_back(tong6);
+    allSliders.push_back(tong7);
+    allSliders.push_back(tong8);
+    allSliders.push_back(tong9);
+    allSliders.push_back(tong10);
+    allSliders.push_back(tong11);
+    allSliders.push_back(thung0);
+    allSliders.push_back(thung1);
+    allSliders.push_back(thung2);
+    allSliders.push_back(thung3);
+    allSliders.push_back(thung4);
+    allSliders.push_back(thung5);
+    allSliders.push_back(thung6);
+    allSliders.push_back(thung7);
+    allSliders.push_back(thung8);
+    allSliders.push_back(thung9);
+    allSliders.push_back(thung10);
+    allSliders.push_back(thung11);
+    
+    //RapidLib
+    trained = false;
+    
+    //loading samples
+    saron_sbpl1.load(ofToDataPath("saron-sbpl1.wav"));
+    saron_sbpl2.load(ofToDataPath("saron-sbpl2.wav"));
+    saron_sbpl3.load(ofToDataPath("saron-sbpl3.wav"));
+    saron_sbpl4.load(ofToDataPath("saron-sbpl4.wav"));
+    saron_sbpl5.load(ofToDataPath("saron-sbpl5.wav"));
+    
+    ciblon_tong.load(ofToDataPath("drums-ciblon-medium-tong.wav"));
+    ciblon_thung.load(ofToDataPath("drums-ciblon-medium-thung.wav"));
+    
+    //maxi Clock
+    myClock.setTicksPerBeat(4);//This sets the number of ticks per beat
+    myClock.setTempo(120);// This sets the tempo in Beats Per Minute
+    
+    //audio setup
+    sampleRate 	= 44100;
+    bufferSize	= 512;
+    
+    ofxMaxiSettings::setup(sampleRate, 2, initialBufferSize);
+    ofSoundStreamSetup(2,2,this, sampleRate, bufferSize, 4);
+}
+
+//--------------------------------------------------------------
+void ofApp::update(){
+
+    if (inputDevice) { //Only when Myo is toggled on
+        
+        //Simple gain control
+        float emg = myo.getDevices()[0]->getEmgSamples()[4];
+        streamBuf.pushToWindow(double(emg));
+        gain = streamBuf.rms() * 0.01;
+        
+        //Machine learning with quarternions
+        double myoX = myo.getDevices()[0]->getQuaternion().x();
+        double myoY = myo.getDevices()[0]->getQuaternion().y();
+        double myoZ = myo.getDevices()[0]->getQuaternion().z();
+        double myoW = myo.getDevices()[0]->getQuaternion().w();
+        
+        if (inputDevice) {
+            if (recordingState > 0) {
+                trainingExample tempExample;
+                tempExample.input = { myoX, myoY, myoZ, myoW };
+                for (int i = 0; i < allSliders.size() ; ++i) {
+                    tempExample.output.push_back(double(allSliders[i]));
+                }
+                trainingSet.push_back(tempExample);
+            } else if (trained && modelControl == 1) {
+                std::vector<double> inputVec;
+                inputVec.push_back(myoX);
+                inputVec.push_back(myoY);
+                inputVec.push_back(myoZ);
+                inputVec.push_back(myoW);
+                std::vector<double> output = myNN.run(inputVec);
+                
+                for (int i = 0; i < output.size(); ++i) {
+                    allSliders[i] = int(output[i]);
+                }
+            }
+        }
+    }
+}
+
+//--------------------------------------------------------------
+void ofApp::exit(){
+    myo.stop();
+}
+
+
+//--------------------------------------------------------------
+void ofApp::draw(){
+    //ofClear(0);
+    ofDrawBitmapString(currentBeat, 100, 300);
+    ofDrawBitmapString("Hold space to record", 200, 300);
+    
+    guiTong.draw();
+    guiThung.draw();
+    guiGeneral.draw();
+    
+    if (inputDevice) {
+        ofSetColor(255);
+        for ( int i=0; i<myo.getDevices().size(); i++ ) {
+            stringstream s;
+            s << "id: " << myo.getDevices()[i]->getId() << endl;
+            s << "which: " << myo.getDevices()[i]->getWhichArm() << endl;
+            s << "pose: " << myo.getDevices()[i]->getPose() << endl;
+            s << "accel:          ";
+            s << myo.getDevices()[i]->getAccel().x << ",";
+            s << myo.getDevices()[i]->getAccel().y << ",";
+            s << myo.getDevices()[i]->getAccel().z << endl;
+            s << "gyro:           ";
+            s << myo.getDevices()[i]->getGyro().x << ",";
+            s << myo.getDevices()[i]->getGyro().y << ",";
+            s << myo.getDevices()[i]->getGyro().z << endl;
+            s << "quaternion:     ";
+            s << myo.getDevices()[i]->getQuaternion().x() << ",";
+            s << myo.getDevices()[i]->getQuaternion().y() << ",";
+            s << myo.getDevices()[i]->getQuaternion().z() << ",";
+            s << myo.getDevices()[i]->getQuaternion().w() << endl;
+            s << "roll/pitch/yaw: ";
+            s << myo.getDevices()[i]->getRoll() << ",";
+            s << myo.getDevices()[i]->getPitch() << ",";
+            s << myo.getDevices()[i]->getYaw() << endl;
+            s << "raw data:       ";
+            for ( int j=0; j<8; j++ ) {
+                s << myo.getDevices()[i]->getEmgSamples()[j];
+                s << ",";
+            }
+            s << endl;
+            ofSetColor(0);
+            ofDrawBitmapString(s.str(), 10, 400 + i*100);
+        }
+    }
+    
+}
+
+//--------------------------------------------------------------
+void ofApp::probsClearPressed() {
+    std::cout << "clearing probs" << std::endl;
+    for (int i = 0; i < allSliders.size(); ++i) {
+        allSliders[i] = 0;
+    }
+}
+
+//--------------------------------------------------------------
+void ofApp::resetModelPressed() {
+    std::cout << "resetting models" << std::endl;
+    myNN.reset();
+    trainingSet.clear();
+    modelControl = false;
+}
+
+//--------------------------------------------------------------
+bool eventTest(int prob) {
+    int testRand = rand() % 100;
+    if (testRand < prob) {
+        return true;
+    }
+    return false;
+}
+
+//--------------------------------------------------------------
+void ofApp::audioOut(float * output, int bufferSize, int nChannels) {
+    //probs = { tong0, tong1, tong2, tong3, tong4, tong5, tong6, tong7, tong8, tong9, tong10, tong11 };
+    //probs2 = { thung0, thung1, thung2, thung3, thung4, thung5, thung6, thung7, thung8, thung9, thung10, thung11 };
+    bool beatsTong[12];
+    bool beats2[12];
+    for (int i = 0; i < 12; ++i) {
+        beatsTong[i] = eventTest(allSliders[i]);
+        beats2[i] = eventTest(allSliders[i + 12]);
+    }
+    int lastCount = 0;
+    int testMe = 0;
+    for (int i = 0; i < bufferSize; i++){
+        
+        myClock.ticker();
+        if (myClock.tick) {
+            if (beatsTong[currentBeat]) {
+                ciblon_tong.trigger();
+            }
+            if (beats2[currentBeat]) {
+                ciblon_thung.trigger();
+            }
+            currentBeat = (currentBeat + 1) % 12;
+        }
+        
+        outputs[0] = ciblon_tong.playOnce() * gain;
+        outputs[1] = ciblon_thung.playOnce() * gain;
+        output[i*nChannels    ] = outputs[0];
+        output[i*nChannels + 1] = outputs[1];
+    }
+    
+}
+
+//--------------------------------------------------------------
+void ofApp::keyPressed(int key){
+    //std::cout << "key: " << key << std::endl;
+    switch(key) {
+        case 32:
+            recordingState = 1;
+            break;
+        case 13:
+            modelControl = (modelControl) ? false : true;
+            break;
+    }
+}
+
+//--------------------------------------------------------------
+void ofApp::keyReleased(int key){
+    recordingState = 0;
+    if (trainingSet.size() > 0) {
+        trained = myNN.train(trainingSet);
+        std::cout << "trained: " << trained << std::endl;
+    }
+}
+
+//--------------------------------------------------------------
+void ofApp::mouseMoved(int x, int y ){
+    if (inputDevice == false) { //don't do this with Myo is on
+        if (recordingState > 0) {
+            trainingExample tempExample;
+            tempExample.input = { double(x), double(y) };
+            for (int i = 0; i < allSliders.size() ; ++i) {
+                tempExample.output.push_back(double(allSliders[i]));
+            }
+            trainingSet.push_back(tempExample);
+        } else if (trained && modelControl == 1) {
+            std::vector<double> inputVec;
+            inputVec.push_back(double(x));
+            inputVec.push_back(double(y));
+            std::vector<double> output = myNN.run(inputVec);
+            
+            for (int i = 0; i < output.size(); ++i) {
+                allSliders[i] = int(output[i]);
+            }
+        }
+    }
+}
+
+//--------------------------------------------------------------
+void ofApp::mouseDragged(int x, int y, int button){
+    
+}
+
+//--------------------------------------------------------------
+void ofApp::mousePressed(int x, int y, int button){
+    
+}
+
+//--------------------------------------------------------------
+void ofApp::mouseReleased(int x, int y, int button){
+    
+}
+
+//--------------------------------------------------------------
+void ofApp::mouseEntered(int x, int y){
+    
+}
+
+//--------------------------------------------------------------
+void ofApp::mouseExited(int x, int y){
+    
+}
+
+//--------------------------------------------------------------
+void ofApp::windowResized(int w, int h){
+    
+}
+
+//--------------------------------------------------------------
+void ofApp::gotMessage(ofMessage msg){
+    
+}
+
+//--------------------------------------------------------------
+void ofApp::dragEvent(ofDragInfo dragInfo){
+    
+}

examples/ofx/prob_drums+ml+myo/src/ofApp.h

+#pragma once
+
+#include <vector>
+
+#include "ofMain.h"
+#include "ofxGui.h"
+#include "ofxMyo.h"
+#include "ofxMaxim.h"
+#include "regression.h"
+#include "rapidStream.h"
+
+class ofApp : public ofBaseApp{
+    
+public:
+    void setup();
+    void update();
+    void draw();
+    void exit();
+    
+    void keyPressed(int key);
+    void keyReleased(int key);
+    void mouseMoved(int x, int y );
+    void mouseDragged(int x, int y, int button);
+    void mousePressed(int x, int y, int button);
+    void mouseReleased(int x, int y, int button);
+    void mouseEntered(int x, int y);
+    void mouseExited(int x, int y);
+    void windowResized(int w, int h);
+    void dragEvent(ofDragInfo dragInfo);
+    void gotMessage(ofMessage msg);
+    
+    void probsClearPressed();
+    void resetModelPressed();
+    
+    ofxIntSlider thung0, thung1, thung2, thung3;
+    ofxIntSlider thung4, thung5, thung6, thung7;
+    ofxIntSlider thung8, thung9, thung10, thung11;
+    std::vector<ofxIntSlider> thungs;
+    ofxPanel guiThung;
+
+    ofxIntSlider tong0, tong1, tong2, tong3;
+    ofxIntSlider tong4, tong5, tong6, tong7;
+    ofxIntSlider tong8, tong9, tong10, tong11;
+    ofxPanel guiTong;
+    
+    ofxFloatSlider gain;
+    ofxToggle inputDevice;
+    ofxToggle modelControl;
+    ofxButton resetModel;
+    ofxButton probsClear;
+    ofxPanel guiGeneral;
+    
+    std::vector<ofxIntSlider> allSliders;
+    
+    //Maxi
+    void audioOut(float * output, int bufferSize, int nChannels);
+    //void audioIn(float * input, int bufferSize, int nChannels);
+    
+    int	bufferSize;
+    int	initialBufferSize;
+    int sampleRate;
+    
+private:
+    
+    ofxMyo::Myo myo;
+    
+    //---------Maxi---------------//
+    double outputs[2];
+    
+    maxiSample saron_sbpl1;
+    maxiSample saron_sbpl2;
+    maxiSample saron_sbpl3;
+    maxiSample saron_sbpl4;
+    maxiSample saron_sbpl5;
+    
+    maxiSample ciblon_tong;
+    maxiSample ciblon_thung;
+    
+    maxiClock myClock;
+   
+    int currentBeat;
+    
+    //---------RapidLib---------------//
+    regression myNN;
+    std::vector<trainingExample> trainingSet;
+    int recordingState;
+    bool trained;
+    
+    rapidStream streamBuf = rapidStream(25);
+    
+};

rmix_build_test.sh

+#!/bin/bash
+
+#Make sure we've got the latest version
+git pull
+git submodule update --init --recursive
+
+rm -rf build/
+mkdir build
+cd build
+cmake ..
+make
+./rapidmixTest
+cp ./helloRapidMix ../examples/HelloRapidMix/helloRapidMix
\ No newline at end of file

src/machineLearning/machineLearning.cpp

-/*
- * machineLearning.cpp
- * Created by Michael Zbyszynski on 10 Jan 2016
+/**
+ * @file machineLearning.cpp
+ * @author Michael Zbyszynski
+ * @date 10 Jan 2016
+ * @copyright
  * 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));
+namespace rapidmix {
+    
+    //////////////////////////////////////////////////////////////////////// Generic train
+    template <class MachineLearningModule>
+    bool machineLearning<MachineLearningModule>::train(const trainingData &newTrainingData) {
+        return MachineLearningModule::train(newTrainingData);
+    }
+    
+    /////////////////////////////////////////////////////////////////////// RapidLib specializations
+    
+    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 {
+                    tempExample.output.push_back(double(h));
+                }
+                trainingSet.push_back(tempExample);
             }
-            trainingSet.push_back(tempExample);
         }
+    };
+    
+    /////////////////////////////////////////////////////////////////////// RapidLib classification
+    template<>
+    bool machineLearning<classification>::train(const trainingData &newTrainingData) {
+        std::vector<trainingExample> trainingSet;
+        labels.clear();
+        for (int i = 0; i < newTrainingData.trainingSet.size(); ++i) {
+            labels.push_back(newTrainingData.trainingSet[i].label);
+        }
+        trainingData2rapidLib(newTrainingData, trainingSet);
+        return classification::train(trainingSet);
     }
-};
-
-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);
-}
-
-template<>
-bool machineLearning<seriesClassification>::train(const trainingData &newTrainingData) {
-    std::vector<std::vector<trainingExample> > seriesSet;
-    for (int i = 1; i < newTrainingData.trainingSet.size(); ++i) { //each phrase
-        std::vector<trainingExample> tempSeries;
-        for (int j = 0; j < newTrainingData.trainingSet[i].elements.size(); ++j) { //each element
-            trainingExample tempExample;
-            tempExample.input = newTrainingData.trainingSet[i].elements[j].input;
-            tempSeries.push_back(tempExample);
+    
+    template<>
+    std::string machineLearning<classification>::run(const std::vector<double> &inputVector, const std::string &label) {
+        int classIndex = classification::run(inputVector)[0];
+        return labels[classIndex];
+    };
+    
+    /////////////////////////////////////////////////////////////////////// RapidLib regression
+    template<>
+    bool machineLearning<regression>::train(const trainingData &newTrainingData) {
+        std::vector<trainingExample> trainingSet;
+        trainingData2rapidLib(newTrainingData, trainingSet);
+        return regression::train(trainingSet);
+    }
+    
+    /////////////////////////////////////////////////////////////////////// RapidLib seriesClassification
+    template<>
+    bool machineLearning<seriesClassification>::train(const trainingData &newTrainingData) {
+        std::vector<trainingSeries> seriesSet;
+        for (int i = 0; i < newTrainingData.trainingSet.size(); ++i) { //each phrase
+            trainingSeries tempSeries;
+            tempSeries.label = newTrainingData.trainingSet[i].label;
+            for (int j = 0; j < newTrainingData.trainingSet[i].elements.size(); ++j) { //each element
+                tempSeries.input.push_back(newTrainingData.trainingSet[i].elements[j].input);
+            }
+            seriesSet.push_back(tempSeries);
         }
-        seriesSet.push_back(tempSeries);
+        return seriesClassification::train(seriesSet);
+    }
+    
+    template<>
+    std::string machineLearning<seriesClassification>::run(const std::vector<std::vector<double> > &inputSeries) {
+        return seriesClassification::run(inputSeries);
+    }
+    
+    
+    /////////////////////////////////////////////////////////////////////// GVF
+    template<>
+    bool machineLearning<rapidGVF>::train(const trainingData &newTrainingData) {
+        return rapidGVF::train(newTrainingData);
     }
-    return seriesClassification::trainTrainingSet(seriesSet);
-}
-
-template<>
-int machineLearning<seriesClassification>::run(const std::vector<std::vector<double> > &inputSeries) {
-    return seriesClassification::run(inputSeries);
 }
-
-
-RAPIDMIX_END_NAMESPACE

src/machineLearning/machineLearning.h

-/*
- * machineLearning.h
- * Created by Michael Zbyszynski on 10 Jan 2016
+/**
+ * @file machineLearning.h
+ * @author Michael Zbyszynski on 10 Jan 2016
+ * @copyright
  * Copyright © 2017 Goldsmiths. All rights reserved.
+ *
+ * @ingroup machinelearning
  */
 
 #ifndef machineLearning_h
 #define machineLearning_h
 
-#include "rapidMix.h"
-#include "trainingData.h"
+#include <vector>
+#include "../rapidmix.h"
 
 ////////// Include all of the machine learning algorithms here
 #include "classification.h"
 #include "regression.h"
 #include "seriesClassification.h"
-#include "rapidXmmTools.h"
-#include "rapidGVF.h"
+#include "./rapidXMM/rapidXMM.h"
+#include "./rapidGVF/rapidGVF.h"
 
-// forward declaration
-namespace rapidmix { class trainingData; }
-
-RAPIDMIX_BEGIN_NAMESPACE
-
-//* Host class for machine learning algorithms */
-template <typename MachineLearningModule>
-class machineLearning : public MachineLearningModule {
-public:
+namespace rapidmix {
     
-    //* Constructors */
-    machineLearning() : MachineLearningModule() {};
+    // forward declaration
+    class trainingData;
     
-    template<class T>
-    machineLearning(T type) : MachineLearningModule(type) {};
+    /** @brief A generic ouptut struct to fit all kinds of models */
+    typedef struct runResults_t {
+        std::vector<double> likelihoods;
+        std::vector<double> regression;
+        std::vector<double> progressions;
+        std::string likeliest;
+    } runResults;
     
-    //* this function becomes specialized in the implementation */
-    bool train(const trainingData &newTrainingData);
+    /**
+     * @brief 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) {};
+        
+        /**
+         * @brief This function becomes specialized in the implementation
+         */
+        bool train(const trainingData &newTrainingData);
+        
+        //* this function is not being specialized
+        std::vector<double> run(const std::vector<double> &inputVector) {
+            return MachineLearningModule::run(inputVector);
+        }
+        
+        // This is a hack while I think about how to do this. -MZ //
+        std::string run(const std::vector<double> &inputVector, const std::string &label);
+        
+        //* This is the one I'm using for DTW */
+        std::string run(const std::vector<std::vector<double> > &inputSeries);
+        
+        bool reset() {
+            return MachineLearningModule::reset();
+        }
+        
+    private:
+        MachineLearningModule module;
+        
+        //this holds string labels
+        std::vector<std::string> labels; //FIXME: This probably should be pushed down into rapidLib?
+        std::string getLabel(int value);
+        
+    };
     
-    //* this function is not being specialized
-    std::vector<double> run(const std::vector<double> &inputVector) {
-        return MachineLearningModule::run(inputVector);
-    }
+    ////////// typedefs for calling different algorithms
     
-    //* This is the one I'm using for DTW */
-    int run(const std::vector<std::vector<double> > &inputSeries);
+    ///// RapidLib
     
-    bool reset() {
-        return MachineLearningModule::reset();
-    }
+    /** @brief static classification using KNN from RapidLib */
+    typedef machineLearning<classification> staticClassification;
     
-private:
-    MachineLearningModule module;
-};
-
-////////// typedefs for calling different algorithms
-
-typedef machineLearning<classification> staticClassification;
-typedef machineLearning<regression> staticRegression;
-typedef machineLearning<seriesClassification> dtwTemporalClassification;
-
-typedef xmmToolConfig xmmConfig;
-typedef machineLearning<xmmGmmTool> xmmStaticClassification;
-typedef machineLearning<xmmGmrTool> xmmStaticRegression;
-typedef machineLearning<xmmHmmTool> xmmTemporalClassification;
-typedef machineLearning<xmmHmrTool> xmmTemporalRegression;
-
-typedef machineLearning<rapidGVF> gvfTemporalVariation;
-
-
-RAPIDMIX_END_NAMESPACE
+    /** @brief static regression using Neural Networks from RapidLib */
+    typedef machineLearning<regression> staticRegression;
+    
+    /** @brief temporal classification using Dynamic Time Warping from RapidLib */
+    typedef machineLearning<seriesClassification> dtwTemporalClassification;
+    
+    ///// XMM
+    
+    /** @brief configuration for XMM based algorithms */
+    typedef xmmToolConfig xmmConfig;
+    
+    /** @brief static classification using Gaussian Mixture Models from XMM */
+    typedef machineLearning<rapidXmmGmm> xmmStaticClassification;
+    
+    /** @brief static regression using Gaussian Mixture Models from XMM */
+    typedef machineLearning<rapidXmmGmr> xmmStaticRegression;
+    
+    /** @brief temporal classification using Hierarchical Hidden Markov Models from XMM */
+    typedef machineLearning<rapidXmmHmm> xmmTemporalClassification;
+    
+    /** @brief temporal regression using Hierarchical Hidden Markov Models from XMM */
+    typedef machineLearning<rapidXmmHmr> xmmTemporalRegression;
+    
+    ///// GVF
+    
+    /** @brief temporal variation estimation using GVF library */
+    typedef machineLearning<rapidGVF> gvfTemporalVariation;
+    
+    
+}
 
 #endif

src/machineLearning/rapidGVF/rapidGVF.cpp

@@ -6,30 +6,17 @@
 //
 
 #include "rapidGVF.h"
-#include "trainingData.h"
+#include "../trainingData.h"
 
-rapidGVF::rapidGVF()
-{
-    // Initialised with default configuration
-    this->gvf = new GVF();
-    this->currentGesture = GVFGesture();
-}
+rapidGVF::rapidGVF() {}
 
-rapidGVF::~rapidGVF()
-{
-    delete this->gvf;
-    this->currentGesture = NULL;
-}
-
-template<>
-bool rapidmix::machineLearning<rapidGVF>::train(const trainingData &newTrainingData) {
-    
-    return rapidGVF::train(newTrainingData);
-}
+rapidGVF::~rapidGVF() {}
 
-bool rapidGVF::train(const rapidmix::trainingData &newTrainingData) {
+bool rapidGVF::train(const rapidmix::trainingData &newTrainingData)
+{
     
-    if (newTrainingData.trainingSet.size() < 1) {
+    if (newTrainingData.trainingSet.size() < 1)
+    {
         // no recorded phrase
         return false;
     }
@@ -39,71 +26,77 @@ bool rapidGVF::train(const rapidmix::trainingData &newTrainingData) {
         return false;
     }
     
-    if(gvf->getState() != GVF::STATE_LEARNING)
+    if(gvf.getState() != GVF::STATE_LEARNING)
     {
-        gvf->setState(GVF::STATE_LEARNING);
+        gvf.setState(GVF::STATE_LEARNING);
     }
     
     //Go through every phrase
-
-    for (int h = 1; h < newTrainingData.trainingSet.size(); ++h) {
-        this->gvf->startGesture();
-        for (int i = 0; i < newTrainingData.trainingSet[h].elements.size(); ++i) {
-            
+    for (int h = 0; h < newTrainingData.trainingSet.size(); ++h)
+    {
+        gvf.startGesture();
+        for (int i = 0; i < newTrainingData.trainingSet[h].elements.size(); ++i)
+        {
             std::vector<double> vd = newTrainingData.trainingSet[h].elements[i].input;
-            
+
             // Using template <class InputIterator> vector to change for vec<double> to vec<float>
             std::vector<float> vf(vd.begin(), vd.end());
             this->currentGesture.addObservation(vf);
         }
-        this->gvf->addGestureTemplate(this->currentGesture);
+        gvf.addGestureTemplate(this->currentGesture);
     }
     return true;
 }
 
-template<>
-std::vector<double> rapidmix::machineLearning<rapidGVF>::run(const std::vector<double> &inputVector) {
-    
-    return rapidGVF::process(inputVector);
-}
-
-std::vector<double> rapidGVF::process(const std::vector<double> &inputVector){
-    
+std::vector<double> rapidGVF::run(const std::vector<double> &inputVector)
+{
     
-    if (inputVector.size() == 0) {
+    if (inputVector.size() == 0)
+    {
         return std::vector<double>();
     }
     
-    gvf->restart();
+    gvf.restart();
     
-    if(gvf->getState() != GVF::STATE_FOLLOWING)
+    if (gvf.getState() != GVF::STATE_FOLLOWING)
     {
-        gvf->setState(GVF::STATE_FOLLOWING);
+        gvf.setState(GVF::STATE_FOLLOWING);
     }
     
     // Using template <class InputIterator> vector to change for vec<double> to vec<float>
     std::vector<float> vf(inputVector.begin(),inputVector.end());
     
     this->currentGesture.addObservation(vf);
-    this->outcomes = this->gvf->update(this->currentGesture.getLastObservation());
+    outcomes = gvf.update(this->currentGesture.getLastObservation());
     
     std::vector<double> output;
-    output.push_back(this->outcomes.likeliestGesture);
-    output.insert(output.end(), this->outcomes.likelihoods.begin(), this->outcomes.likelihoods.end());
-    output.insert(output.end(), this->outcomes.alignments.begin(), this->outcomes.alignments.end());
-//    output.insert(output.end(), this->outcomes.dynamics.begin(), this->outcomes.dynamics.end());
-//    output.insert(output.end(), this->outcomes.scalings.begin(), this->outcomes.scalings.end());
-//    output.insert(output.end(), this->outcomes.rotations.begin(), this->outcomes.rotations.end());
-//    
+    output.push_back(outcomes.likeliestGesture);
+    output.insert(output.end(), outcomes.likelihoods.begin(), outcomes.likelihoods.end());
+    output.insert(output.end(), outcomes.alignments.begin(), outcomes.alignments.end());
     return output;
 }
 
+const std::vector<float> rapidGVF::getLikelihoods()
+{
+    return outcomes.likelihoods;
+};
 
+const std::vector<float> rapidGVF::getAlignments()
+{
+    return outcomes.alignments;
+};
 
+const std::vector<std::vector<float> > * rapidGVF::getDynamics()
+{
+    return &outcomes.dynamics;
+};
 
+const std::vector<std::vector<float> > * rapidGVF::getScalings()
+{
+    return &outcomes.scalings;
+};
 
-
-
-
-
-
+const std::vector<std::vector<float> > * rapidGVF::getRotations()
+{
+    return &outcomes.rotations;
+};

src/machineLearning/rapidGVF/rapidGVF.h

-//
-//  rapidGVF.h
-//
-//  Created by Francisco on 04/05/2017.
-//  Copyright © 2017 Goldsmiths. All rights reserved.
-//
+/**
+ * @file rapidGVF.h
+ * Created by Francisco on 04/05/2017.
+ * Copyright © 2017 Goldsmiths. All rights reserved.
+ *
+ * @ingroup machinelearning
+ */
 
 #ifndef rapidGVF_h
 #define rapidGVF_h
@@ -14,19 +15,27 @@
 
 namespace rapidmix { class trainingData; }
 
+/**
+ * @brief This class is an adapter for the GVF library from Baptiste Caramiaux
+ */
 class rapidGVF {
-    
-public: 
-    rapidGVF();
 
+public:
+    rapidGVF();
     ~rapidGVF();
-    
+
     bool train(const rapidmix::trainingData &newTrainingData);
+    std::vector<double> run(const std::vector<double> &inputVector);
+    //TODO: needs a "reset" message
+
+    const std::vector<float> getLikelihoods();
+    const std::vector<float> getAlignments();
+    const std::vector<std::vector<float> > * getDynamics();
+    const std::vector<std::vector<float> > * getScalings();
+    const std::vector<std::vector<float> > * getRotations();
     
-    std::vector<double> process(const std::vector<double> &inputVector);
-    
-protected:
-    GVF * gvf;
+private:
+    GVF gvf;
     GVFGesture currentGesture;
     GVFOutcomes outcomes;
 };

src/machineLearning/rapidXMM/rapidXMM.cpp

 #include "rapidXMM.h"
-#include "trainingData.h"
-#include "machineLearning.h"
+#include "../trainingData.h"
+#include "../machineLearning.h"
 
 static bool trainingData2xmmTrainingSet(const rapidmix::trainingData& data, xmm::TrainingSet& set) {
-  if (data.trainingSet.size() <= 1) {
-    // no recorded phrase (only default one)
+  if (data.trainingSet.size() < 1) {
     return false;
   }
 
-  if (data.trainingSet.size() > 1 && data.trainingSet[1].elements.size() == 0) {
+  if (data.trainingSet.size() > 0 && data.trainingSet[0].elements.size() == 0) {
     // empty recorded phrase
     return false;
   }
 
-  rapidmix::trainingData::element el = data.trainingSet[1].elements[0];
+  rapidmix::trainingData::element el = data.trainingSet[0].elements[0];
   int dimIn = static_cast<int>(el.input.size());
   int dimOut = static_cast<int>(el.output.size());
 
@@ -43,9 +42,7 @@ static bool trainingData2xmmTrainingSet(const rapidmix::trainingData& data, xmm:
   set.clear();
 
   //for (auto &phrase : data.trainingSet) {
-  // changed to look starting from index 1
-  // because phrase at index 0 is for unordered elements
-  for (int i = 1; i < data.trainingSet.size(); ++i) {
+  for (int i = 0; i < data.trainingSet.size(); ++i) {
     const rapidmix::trainingData::phrase &phrase = data.trainingSet[i];
     xp.clear();
     xp.label.set(phrase.label);
@@ -84,25 +81,27 @@ bool xmmTool<SingleClassModel, Model>::train(const rapidmix::trainingData& newTr
 template <class SingleClassModel, class Model>
 Json::Value xmmTool<SingleClassModel, Model>::toJSON(/*std::string modelType*/) {
   Json::Value root;
-  Json::Value metadata;
-  Json::Value modelSet;
 
-  metadata["creator"] = "Rapid API C++";
-  metadata["version"] = "v0.1.1"; //TODO: This should be a macro someplace
-  metadata["family"] = "xmm";
-  root["metadata"] = metadata;
+  root["docType"] = "rapid-mix:ml-model";
+  root["docVersion"] = RAPIDMIX_JSON_DOC_VERSION;
 
-  modelSet.append(model.toJson());
-  root["modelSet"] = modelSet;
+  Json::Value target;
+
+  target["name"] = "xmm";
+  target["version"] = "v1.0.0";
+
+  root["target"] = target;
+  root["payload"] = model.toJson();
 
   return root;
 }
 
 template <class SingleClassModel, class Model>
 bool xmmTool<SingleClassModel, Model>::fromJSON(Json::Value &jm) {
-  if (jm["metadata"]["family"].asString().compare("xmm") == 0 &&
-      jm["modelSet"].size() > 0) {
-    model.fromJson(jm["modelSet"][0]);
+  if (jm["docType"].asString().compare("rapid-mix:ml-model") == 0 &&
+      jm["target"]["name"].asString().compare("xmm") == 0 &&
+      jm["payload"].size() > 0) {
+    model.fromJson(jm["payload"]);
     model.reset();
     return true;
   }
@@ -146,14 +145,14 @@ bool xmmTool<SingleClassModel, Model>::readJSON(const std::string &filepath) {
 
 //============================== xmmGmmTool ==================================//
 
-std::vector<double> xmmGmmTool::run(const std::vector<double>& inputVector) {
+std::vector<double> rapidXmmGmm::run(const std::vector<double>& inputVector) {
   xmmTool::preProcess(inputVector);
   return model.results.smoothed_normalized_likelihoods;
 }
 
 //============================== xmmGmrTool ==================================//
 
-std::vector<double> xmmGmrTool::run(const std::vector<double>& inputVector) {
+std::vector<double> rapidXmmGmr::run(const std::vector<double>& inputVector) {
   xmmTool::preProcess(inputVector);
   std::vector<float> *res = &model.results.output_values;
   std::vector<double> dRes(res->begin(), res->end());
@@ -162,7 +161,7 @@ std::vector<double> xmmGmrTool::run(const std::vector<double>& inputVector) {
 
 //============================== xmmHmmTool ==================================//
 
-std::vector<double> xmmHmmTool::run(const std::vector<double>& inputVector) {
+std::vector<double> rapidXmmHmm::run(const std::vector<double>& inputVector) {
   xmmTool::preProcess(inputVector);
   std::vector<double> res;
 
@@ -178,7 +177,7 @@ std::vector<double> xmmHmmTool::run(const std::vector<double>& inputVector) {
 
 //============================== xmmHmrTool ==================================//
 
-std::vector<double> xmmHmrTool::run(const std::vector<double>& inputVector) {
+std::vector<double> rapidXmmHmr::run(const std::vector<double>& inputVector) {
   xmmTool::preProcess(inputVector);
   std::vector<float> *res = &model.results.output_values;
   std::vector<double> dRes(res->begin(), res->end());
@@ -186,16 +185,27 @@ std::vector<double> xmmHmrTool::run(const std::vector<double>& inputVector) {
 }
 
 ///////////////////////////////////////////////////////////////////////////
-///// generic train method and forward declaration of specialized templates 
+///// generic train method and forward declaration of specialized templates
 ///////////////////////////////////////////////////////////////////////////
 
+template class xmmTool<xmm::GMM, xmm::GMM>;
+template class xmmTool<xmm::HMM, xmm::HierarchicalHMM>;
+
+template class xmmStaticTool<xmm::GMM, xmm::GMM>;
+template class xmmTemporalTool<xmm::HMM, xmm::HierarchicalHMM>;
+
+//I wonder why this can't be defined in machineLearning.cpp? -MZ
+
+// It is needed by the template instantiations below.
+// You get an undefined symbols error otherwise.
+
 template <class MachineLearningModule>
 bool rapidmix::machineLearning<MachineLearningModule>::train(const trainingData &newTrainingData) {
     return MachineLearningModule::train(newTrainingData);
 }
 
-template class rapidmix::machineLearning<xmmGmmTool>;
-template class rapidmix::machineLearning<xmmGmrTool>;
-template class rapidmix::machineLearning<xmmHmmTool>;
-template class rapidmix::machineLearning<xmmHmrTool>;
+template class rapidmix::machineLearning<rapidXmmGmm>;
+template class rapidmix::machineLearning<rapidXmmGmr>;
+template class rapidmix::machineLearning<rapidXmmHmm>;
+template class rapidmix::machineLearning<rapidXmmHmr>;
 

src/machineLearning/rapidXMM/rapidXMM.h

 /**
- * @file rapidXmmTools.h
+ * @file rapidXMM.h
  * @author joseph larralde
- * 
+ *
  * @copyright
  * Copyright (C) 2016 - 2017 by IRCAM - Centre Pompidou, Paris, France.
  * All rights reserved.
@@ -140,7 +140,7 @@ protected:
     mCfg.gaussians.set(cfg.gaussians);
     mCfg.relative_regularization.set(cfg.relativeRegularization);
     mCfg.absolute_regularization.set(cfg.absoluteRegularization);
-    
+
     xmm::MultiClassRegressionEstimator mcre;
     switch (cfg.regressionEstimator) {
       case xmmLikeliestRegression:
@@ -224,14 +224,11 @@ public:
 /**
  * @brief Static classification using Gaussian Mixture Models
  */
-class xmmGmmTool : public xmmStaticTool<xmm::GMM, xmm::GMM> {
-// class GMM : public xmmStaticTool<xmm::GMM, xmm::GMM> {
+class rapidXmmGmm : public xmmStaticTool<xmm::GMM, xmm::GMM> {
 public:
-  xmmGmmTool(xmmToolConfig cfg = xmmToolConfig()) :
-  // GMM(xmmToolConfig cfg = xmmToolConfig()) :
+  rapidXmmGmm(xmmToolConfig cfg = xmmToolConfig()) :
   xmmStaticTool<xmm::GMM, xmm::GMM>(cfg, false) {}
-  ~xmmGmmTool() {}
-  // ~GMM() {}
+  ~rapidXmmGmm() {}
 
   std::vector<double> run(const std::vector<double>& inputVector);
 };
@@ -239,14 +236,11 @@ public:
 /**
  * @brief Static regression using Gaussian Mixture Models
  */
-class xmmGmrTool : public xmmStaticTool<xmm::GMM, xmm::GMM> {
-// class GMR : public xmmStaticTool<xmm::GMM, xmm::GMM> {
+class rapidXmmGmr : public xmmStaticTool<xmm::GMM, xmm::GMM> {
 public:
-  xmmGmrTool(xmmToolConfig cfg = xmmToolConfig()) :
-  // GMR(xmmToolConfig cfg = xmmToolConfig()) :
+  rapidXmmGmr(xmmToolConfig cfg = xmmToolConfig()) :
   xmmStaticTool<xmm::GMM, xmm::GMM>(cfg, true) {}
-  ~xmmGmrTool() {}
-  // ~GMR() {}
+  ~rapidXmmGmr() {}
 
   std::vector<double> run(const std::vector<double>& inputVector);
 };
@@ -254,14 +248,11 @@ public:
 /**
  * @brief Temporal classification using Hierarchical Hidden Markov Models
  */
-class xmmHmmTool : public xmmTemporalTool<xmm::HMM, xmm::HierarchicalHMM> {
-// class HMM : public xmmTemporalTool<xmm::HMM, xmm::HierarchicalHMM> {
+class rapidXmmHmm : public xmmTemporalTool<xmm::HMM, xmm::HierarchicalHMM> {
 public:
-  xmmHmmTool(xmmToolConfig cfg = xmmToolConfig()) :
-  // HMM(xmmToolConfig cfg = xmmToolConfig()) :
+  rapidXmmHmm(xmmToolConfig cfg = xmmToolConfig()) :
   xmmTemporalTool<xmm::HMM, xmm::HierarchicalHMM>(cfg, false) {}
-  ~xmmHmmTool() {}
-  // ~HMM() {}
+  ~rapidXmmHmm() {}
 
   std::vector<double> run(const std::vector<double>& inputVector);
 };
@@ -269,14 +260,11 @@ public:
 /**
  * @brief Temporal regression using Hierarchical Hidden Markov Models
  */
-class xmmHmrTool : public xmmTemporalTool<xmm::HMM, xmm::HierarchicalHMM> {
-// class HMR : public xmmTemporalTool<xmm::HMM, xmm::HierarchicalHMM> {
+class rapidXmmHmr : public xmmTemporalTool<xmm::HMM, xmm::HierarchicalHMM> {
 public:
-  xmmHmrTool(xmmToolConfig cfg = xmmToolConfig()) :
-  // HMR(xmmToolConfig cfg = xmmToolConfig()) :
+  rapidXmmHmr(xmmToolConfig cfg = xmmToolConfig()) :
   xmmTemporalTool<xmm::HMM, xmm::HierarchicalHMM>(cfg, true) {}
-  ~xmmHmrTool() {}
-  // ~HMR() {}
+  ~rapidXmmHmr() {}
 
   std::vector<double> run(const std::vector<double>& inputVector);
 };

src/machineLearning/rapidXmmTools/rapidXmmTools.cpp

-#include "rapidXmmTools.h"
-#include "trainingData.h"
-#include "machineLearning.h"
-
-static bool trainingData2xmmTrainingSet(const rapidmix::trainingData& data, xmm::TrainingSet& set) {
-  if (data.trainingSet.size() <= 1) {
-    // no recorded phrase (only default one)
-    return false;
-  }
-
-  if (data.trainingSet.size() > 1 && data.trainingSet[1].elements.size() == 0) {
-    // empty recorded phrase
-    return false;
-  }
-
-  rapidmix::trainingData::element el = data.trainingSet[1].elements[0];
-  int dimIn = static_cast<int>(el.input.size());
-  int dimOut = static_cast<int>(el.output.size());
-
-  // translate and return true if data and set are compatible
-  // don't translate and return false otherwise
-
-  if (dimOut > 0 != set.bimodal()) {
-    return false;
-  }
-
-  xmm::Phrase xp;
-
-  if (set.bimodal()) {
-    set.dimension.set(dimIn + dimOut);
-    set.dimension_input.set(dimIn);
-    xp = xmm::Phrase(xmm::MemoryMode::OwnMemory, xmm::Multimodality::Bimodal);
-    xp.dimension.set(dimIn + dimOut);
-    xp.dimension_input.set(dimIn);
-  } else {
-    set.dimension.set(dimIn);
-    set.dimension_input.set(0);
-    xp = xmm::Phrase(xmm::MemoryMode::OwnMemory, xmm::Multimodality::Unimodal);
-    xp.dimension.set(dimIn);
-    xp.dimension_input.set(0);
-  }
-
-  set.clear();
-
-  //for (auto &phrase : data.trainingSet) {
-  // changed to look starting from index 1
-  // because phrase at index 0 is for unordered elements
-  for (int i = 1; i < data.trainingSet.size(); ++i) {
-    const rapidmix::trainingData::phrase &phrase = data.trainingSet[i];
-    xp.clear();
-    xp.label.set(phrase.label);
-
-    for (auto &element : phrase.elements) {
-      std::vector<float> obsIn(element.input.begin(), element.input.end());
-      std::vector<float> obsOut(element.output.begin(), element.output.end());
-      std::vector<float> obs;
-      obs.insert(obs.end(), obsIn.begin(), obsIn.end());
-      obs.insert(obs.end(), obsOut.begin(), obsOut.end());
-      xp.record(obs);
-    }
-
-    set.addPhrase(static_cast<int>(set.size()), xp);
-  }
-
-  return true;
-}
-
-//=============================== xmmTool ====================================//
-
-template <class SingleClassModel, class Model>
-bool xmmTool<SingleClassModel, Model>::train(const rapidmix::trainingData& newTrainingData) {
-  if (trainingData2xmmTrainingSet(newTrainingData, set)) {
-    model.train(&set);
-    model.reset();
-    return true;
-  }
-
-  return false;
-}
-
-////////// private JSON data manipulation methods :
-
-//TODO: add a type field (gmm/gmr/hmm/hmr) in metadata when family is xmm
-template <class SingleClassModel, class Model>
-Json::Value xmmTool<SingleClassModel, Model>::toJSON(/*std::string modelType*/) {
-  Json::Value root;
-  Json::Value metadata;
-  Json::Value modelSet;
-
-  metadata["creator"] = "Rapid API C++";
-  metadata["version"] = "v0.1.1"; //TODO: This should be a macro someplace
-  metadata["family"] = "xmm";
-  root["metadata"] = metadata;
-
-  modelSet.append(model.toJson());
-  root["modelSet"] = modelSet;
-
-  return root;
-}
-
-template <class SingleClassModel, class Model>
-bool xmmTool<SingleClassModel, Model>::fromJSON(Json::Value &jm) {
-  if (jm["metadata"]["family"].asString().compare("xmm") == 0 &&
-      jm["modelSet"].size() > 0) {
-    model.fromJson(jm["modelSet"][0]);
-    model.reset();
-    return true;
-  }
-
-  return false;
-}
-
-////////// public JSON file manipulation interface :
-
-template <class SingleClassModel, class Model>
-std::string xmmTool<SingleClassModel, Model>::getJSON() {
-  Json::Value result = toJSON();
-  return result.toStyledString();
-}
-
-template <class SingleClassModel, class Model>
-void xmmTool<SingleClassModel, Model>::writeJSON(const std::string &filepath) {
-  Json::Value root = toJSON();
-  std::ofstream jsonOut;
-  jsonOut.open (filepath);
-  Json::StyledStreamWriter writer;
-  writer.write(jsonOut, root);
-  jsonOut.close();
-}
-
-template <class SingleClassModel, class Model>
-bool xmmTool<SingleClassModel, Model>::putJSON(const std::string &jsonMessage) {
-  Json::Value parsedFromString;
-  Json::Reader reader;
-  bool parsingSuccessful = reader.parse(jsonMessage, parsedFromString);
-  return (parsingSuccessful && fromJSON(parsedFromString));
-}
-
-template <class SingleClassModel, class Model>
-bool xmmTool<SingleClassModel, Model>::readJSON(const std::string &filepath) {
-  Json::Value root;
-  std::ifstream file(filepath);
-  file >> root;
-  return fromJSON(root);
-}
-
-//============================== xmmGmmTool ==================================//
-
-std::vector<double> xmmGmmTool::run(const std::vector<double>& inputVector) {
-  xmmTool::preProcess(inputVector);
-  return model.results.smoothed_normalized_likelihoods;
-}
-
-//============================== xmmGmrTool ==================================//
-
-std::vector<double> xmmGmrTool::run(const std::vector<double>& inputVector) {
-  xmmTool::preProcess(inputVector);
-  std::vector<float> *res = &model.results.output_values;
-  std::vector<double> dRes(res->begin(), res->end());
-  return dRes;
-}
-
-//============================== xmmHmmTool ==================================//
-
-std::vector<double> xmmHmmTool::run(const std::vector<double>& inputVector) {
-  xmmTool::preProcess(inputVector);
-  std::vector<double> res;
-
-  int i(0);
-  for (auto &m : model.models) {
-    res.push_back(model.results.smoothed_normalized_likelihoods[i]);
-    res.push_back(m.second.results.progress);
-    i++;
-  }
-
-  return res;
-}
-
-//============================== xmmHmrTool ==================================//
-
-std::vector<double> xmmHmrTool::run(const std::vector<double>& inputVector) {
-  xmmTool::preProcess(inputVector);
-  std::vector<float> *res = &model.results.output_values;
-  std::vector<double> dRes(res->begin(), res->end());
-  return dRes;
-}
-
-///////////////////////////////////////////////////////////////////////////
-///// generic train method and forward declaration of specialized templates 
-///////////////////////////////////////////////////////////////////////////
-
-template <class MachineLearningModule>
-bool rapidmix::machineLearning<MachineLearningModule>::train(const trainingData &newTrainingData) {
-    return MachineLearningModule::train(newTrainingData);
-}
-
-template class rapidmix::machineLearning<xmmGmmTool>;
-template class rapidmix::machineLearning<xmmGmrTool>;
-template class rapidmix::machineLearning<xmmHmmTool>;
-template class rapidmix::machineLearning<xmmHmrTool>;
-

src/machineLearning/rapidXmmTools/rapidXmmTools.h

-#ifndef _RAPID_XMM_TOOLS_H_
-#define _RAPID_XMM_TOOLS_H_
-
-// this works !
-#ifndef EXTERNAL_JSONCPP_PATH
-// #define EXTERNAL_JSONCPP_PATH "../../../../json/json.h" // relative to xmmJson.h
-#define EXTERNAL_JSONCPP_PATH "json.h"
-#endif /* EXTERNAL_JSONCPP_PATH */
-
-#include "xmm.h"
-
-// forward declaration
-namespace rapidmix { class trainingData; }
-
-// original defined in xmmModelConfiguration.hpp
-enum xmmRegressionEstimator {
-  xmmLikeliestRegression,
-  xmmMixtureRegression
-};
-
-// original defined in xmmGaussianDistribution.hpp
-enum xmmCovarianceMode {
-  xmmFullCovariance,
-  xmmDiagonalCovariance
-};
-
-// original defined in xmmHmmParameters.hpp
-enum xmmHmmTransitionMode {
-  xmmHmmLeftRightTransition,
-  xmmHmmErgodicTransition
-};
-
-// original defined in xmmHmmParameters.hpp
-enum xmmHmmRegressionEstimator {
-  xmmHmmFullRegression,
-  xmmHmmWindowedRegression,
-  xmmHmmLikeliestRegression
-};
-
-// this is the optional argument of machineLearning<xmmWhateverTool>'s constructors
-struct xmmToolConfig {
-  xmmToolConfig() :
-  gaussians(1),
-  relativeRegularization(0.01),
-  absoluteRegularization(0.01),
-  regressionEstimator(xmmMixtureRegression),
-  covarianceMode(xmmFullCovariance),
-  states(5),
-  hierarchical(true),
-  hmmTransitionMode(xmmHmmErgodicTransition),
-  hmmRegressionEstimator(xmmHmmFullRegression),
-  likelihoodWindow(5) {}
-
-  // general parameters :
-  uint32_t gaussians;
-  float relativeRegularization;
-  float absoluteRegularization;
-  xmmRegressionEstimator regressionEstimator;
-  xmmCovarianceMode covarianceMode;
-
-  // hmm specific :
-  uint32_t states;
-  bool hierarchical;
-  xmmHmmTransitionMode hmmTransitionMode;
-  xmmHmmRegressionEstimator hmmRegressionEstimator;
-
-  // run-time parameter :
-  uint32_t likelihoodWindow;
-};
-
-//========================== template base class =============================//
-
-template <class SingleClassModel, class Model>
-class xmmTool {
-protected:
-  xmmTool(bool bimodal) {
-    model = Model(bimodal);
-    model.configuration.multithreading = xmm::MultithreadingMode::Sequential;
-    model.configuration.changed = true;
-
-    set = xmm::TrainingSet(xmm::MemoryMode::OwnMemory,
-      bimodal
-      ? xmm::Multimodality::Bimodal
-      : xmm::Multimodality::Unimodal
-    );
-  }
-
-  virtual void preProcess(const std::vector<double> &inputVector) {
-    std::vector<float> fv(inputVector.begin(), inputVector.end());
-    model.filter(fv);
-  }
-
-public:
-  virtual ~xmmTool() {}
-
-  virtual bool train(const rapidmix::trainingData &newTrainingData);
-
-  virtual bool reset() {
-    model.reset();
-    return true;
-  }
-
-  /** Get a JSON representation of the model in the form of a styled string */
-  virtual std::string getJSON();
-  /** Write a JSON model description to specified file path */
-  virtual void writeJSON(const std::string &filepath);
-  /** configure empty model with string. See getJSON() */
-  virtual bool putJSON(const std::string &jsonMessage);
-  /** read a JSON file at file path and build a modelSet from it */
-  virtual bool readJSON(const std::string &filepath);
-
-protected:
-  Model model;
-  xmm::TrainingSet set;
-
-  Json::Value toJSON();
-  bool fromJSON(Json::Value &jm);
-};
-
-//======================= base class for GMM models ==========================//
-
-template <class SingleClassModel, class Model>
-class xmmStaticTool : public xmmTool<SingleClassModel, Model> {
-protected:
-  xmmStaticTool(xmmToolConfig cfg, bool bimodal) :
-  xmmTool<SingleClassModel, Model>(bimodal) {
-    xmm::Configuration<SingleClassModel>& mCfg = this->model.configuration;
-
-    mCfg.gaussians.set(cfg.gaussians);
-    mCfg.relative_regularization.set(cfg.relativeRegularization);
-    mCfg.absolute_regularization.set(cfg.absoluteRegularization);
-    
-    xmm::MultiClassRegressionEstimator mcre;
-    switch (cfg.regressionEstimator) {
-      case xmmLikeliestRegression:
-        mcre = xmm::MultiClassRegressionEstimator::Likeliest;
-      case xmmMixtureRegression:
-      default:
-        mcre = xmm::MultiClassRegressionEstimator::Mixture;
-        break;
-    }
-    mCfg.multiClass_regression_estimator = mcre;
-
-    xmm::GaussianDistribution::CovarianceMode gdcm;
-    switch (cfg.covarianceMode) {
-      case xmmFullCovariance:
-        gdcm = xmm::GaussianDistribution::CovarianceMode::Full;
-        break;
-      case xmmDiagonalCovariance:
-      default:
-        gdcm = xmm::GaussianDistribution::CovarianceMode::Diagonal;
-        break;
-    }
-    mCfg.covariance_mode.set(gdcm);
-
-    mCfg.changed = true;
-
-    this->model.shared_parameters->likelihood_window.set(cfg.likelihoodWindow);
-  }
-
-public:
-  virtual ~xmmStaticTool() {}
-};
-
-//======================= base class for HMM models ==========================//
-
-template <class SingleClassModel, class Model>
-class xmmTemporalTool : public xmmStaticTool<SingleClassModel, Model> {
-protected:
-  xmmTemporalTool(xmmToolConfig cfg, bool bimodal) :
-  xmmStaticTool<SingleClassModel, Model>(cfg, bimodal) {
-    xmm::Configuration<SingleClassModel>& mCfg = this->model.configuration;
-
-    mCfg.states.set(cfg.states);
-    mCfg.hierarchical.set(cfg.hierarchical);
-
-    xmm::HMM::TransitionMode htm;
-    switch (cfg.hmmTransitionMode) {
-      case xmmHmmLeftRightTransition:
-        htm = xmm::HMM::TransitionMode::LeftRight;
-        break;
-      case xmmHmmErgodicTransition:
-      default:
-        htm = xmm::HMM::TransitionMode::Ergodic;
-        break;
-    }
-    mCfg.transition_mode.set(htm);
-
-    xmm::HMM::RegressionEstimator hre;
-    switch (cfg.hmmRegressionEstimator) {
-      case xmmHmmFullRegression:
-        hre = xmm::HMM::RegressionEstimator::Full;
-        break;
-      case xmmHmmWindowedRegression:
-        hre = xmm::HMM::RegressionEstimator::Windowed;
-        break;
-      case xmmHmmLikeliestRegression:
-      default:
-        hre = xmm::HMM::RegressionEstimator::Likeliest;
-        break;
-    }
-    mCfg.regression_estimator.set(hre);
-
-    mCfg.changed = true;
-  }
-
-public:
-  virtual ~xmmTemporalTool() {}
-};
-
-//================== actual classes used in machineLearning.h ================//
-
-class xmmGmmTool : public xmmStaticTool<xmm::GMM, xmm::GMM> {
-public:
-  xmmGmmTool(xmmToolConfig cfg = xmmToolConfig()) :
-  xmmStaticTool<xmm::GMM, xmm::GMM>(cfg, false) {}
-  ~xmmGmmTool() {}
-
-  std::vector<double> run(const std::vector<double>& inputVector);
-};
-
-class xmmGmrTool : public xmmStaticTool<xmm::GMM, xmm::GMM> {
-public:
-  xmmGmrTool(xmmToolConfig cfg = xmmToolConfig()) :
-  xmmStaticTool<xmm::GMM, xmm::GMM>(cfg, true) {}
-  ~xmmGmrTool() {}
-
-  std::vector<double> run(const std::vector<double>& inputVector);
-};
-
-class xmmHmmTool : public xmmTemporalTool<xmm::HMM, xmm::HierarchicalHMM> {
-public:
-  xmmHmmTool(xmmToolConfig cfg = xmmToolConfig()) :
-  xmmTemporalTool<xmm::HMM, xmm::HierarchicalHMM>(cfg, false) {}
-  ~xmmHmmTool() {}
-
-  std::vector<double> run(const std::vector<double>& inputVector);
-};
-
-class xmmHmrTool : public xmmTemporalTool<xmm::HMM, xmm::HierarchicalHMM> {
-public:
-  xmmHmrTool(xmmToolConfig cfg = xmmToolConfig()) :
-  xmmTemporalTool<xmm::HMM, xmm::HierarchicalHMM>(cfg, true) {}
-  ~xmmHmrTool() {}
-
-  std::vector<double> run(const std::vector<double>& inputVector);
-};
-
-#endif /* _RAPID_XMM_TOOLS_H_ */

src/machineLearning/trainingData.cpp

-/*
- * trainingData.cpp
- * Created by Michael Zbyszynski on 2 Feb 2017
- * Copyright © 2017 Goldsmiths. All rights reserved.
+/**
+ * @file trainingData.cpp
+ * @author Michael Zbyszynski
+ * @date 2 Feb 2017
+ * @copyright Copyright © 2017 Goldsmiths. All rights reserved.
  */
 
-#include <fstream>
 #include "trainingData.h"
 
-RAPIDMIX_BEGIN_NAMESPACE
-
-trainingData::trainingData () {
-    phrase defaultPhrase = {0, "default"};
-    trainingSet.push_back(defaultPhrase);
-    currentId = 1;
-    targetPhrase = 0;
-};
-
-uint32_t trainingData::assignCurrentId() {
-    uint32_t returnVal = currentId;
-    ++currentId;
-    return returnVal;
-}
-
-
-uint32_t trainingData::startRecording() {
-    phrase tempPhrase = { assignCurrentId(), std::to_string(tempPhrase.uniqueId) }; //TODO: Is this label helpful? -MZ
-    trainingSet.push_back(tempPhrase);
-    targetPhrase = int(trainingSet.size() - 1);
-    return tempPhrase.uniqueId;
-};
-
-uint32_t trainingData::startRecording(std::string label) {
-    phrase tempPhrase = { assignCurrentId(), label };
-    labels.insert(std::make_pair(label, labels.size()));
-    trainingSet.push_back(tempPhrase);
-    targetPhrase = int(trainingSet.size() - 1);
-    return tempPhrase.uniqueId;
-    
-};
-
-uint32_t trainingData::addElement(std::vector<double>input, std::vector<double> output) {
-    element newElement;
-    newElement.uniqueId = assignCurrentId();
-    newElement.input = input;
-    newElement.output = output;
-    newElement.timeStamp = NULL;
-    trainingSet[targetPhrase].elements.push_back(newElement);
-    return newElement.uniqueId;
-}
-
-uint32_t trainingData::addElement(std::vector<double>input) {
-    element newElement;
-    newElement.uniqueId = assignCurrentId();
-    newElement.input = input;
-    newElement.timeStamp = NULL;
-    trainingSet[targetPhrase].elements.push_back(newElement);
-    return newElement.uniqueId;
-}
-
-
-void trainingData::stopRecording() {
-    targetPhrase = 0; //direct elements to default phrase
-}
-
-std::string trainingData::getLabel(int value) {
-    std::string theLabel = "not found";
-    for (const auto& element : labels) {
-        if (element.second == value) {
-            theLabel = element.first;
-        }
+namespace rapidmix {
+    
+    trainingData::trainingData () {
+        currentId = 0;
+        targetPhrase = 0;
+    };
+    
+    uint32_t trainingData::assignCurrentId() {
+        uint32_t returnVal = currentId;
+        ++currentId;
+        return returnVal;
     }
-    return theLabel;
-}
-
-std::vector<std::string> trainingData::getColumnNames() {
-    return trainingSet[targetPhrase].columnNames;
-}
-
-void trainingData::setColumnNames(std::vector<std::string> column_names) {
-    trainingSet[targetPhrase].columnNames = column_names;
-}
-
-Json::Value trainingData::parse2json() {
-    Json::Value root;
-    Json::Value metadata;
-    Json::Value trainingSetJSON;
-    
-    metadata["creator"] = "RAPID-MIX API C++";
-    metadata["version"] = RAPIDMIX_VERSION;
-    
-    //Go through all the phrases
-    for (int i = 0; i < trainingSet.size(); ++i) {
-        Json::Value thisPhrase;
-        thisPhrase.append(trainingSet[i].uniqueId);
-        thisPhrase.append(trainingSet[i].label);
-      
-        Json::Value column_names;
-        for (int j = 0; j < trainingSet[i].columnNames.size(); ++j) {
-            column_names.append(trainingSet[i].columnNames[j]);
-        }
-        thisPhrase.append(column_names);
+    
+    std::vector<trainingData::phrase>::iterator trainingData::createNewPhrase(std::string label) {
+        phrase tempPhrase = { assignCurrentId(), label };
+        trainingSet.push_back(tempPhrase);
+        std::vector<trainingData::phrase>::iterator it = --trainingSet.end();
+        return it;
+    };
+    
+    uint32_t trainingData::startRecording() {
+        phrase tempPhrase = { assignCurrentId(), std::to_string(tempPhrase.uniqueId) }; //TODO: Is this label helpful? -MZ
+        trainingSet.push_back(tempPhrase);
+        targetPhrase = int(trainingSet.size() - 1);
+        return tempPhrase.uniqueId;
+    };
+    
+    uint32_t trainingData::startRecording(const std::string &label) {
+        phrase tempPhrase = { assignCurrentId(), label };
+        trainingSet.push_back(tempPhrase);
+        targetPhrase = int(trainingSet.size() - 1);
+        return tempPhrase.uniqueId;
         
-        Json::Value elements;
-        for (int j = 0; j < trainingSet[i].elements.size(); ++j) {
-            Json::Value singleElement;
+    };
+    
+    uint32_t trainingData::addElement(const std::vector<double> &input, const std::vector<double> &output) {
+        element newElement;
+        newElement.uniqueId = assignCurrentId();
+        newElement.input = input;
+        newElement.output = output;
+        newElement.timeStamp = 0.0;
+        trainingSet[targetPhrase].elements.push_back(newElement);
+        return newElement.uniqueId;
+    }
+    
+    uint32_t trainingData::addElement(const std::vector<double> &input) {
+        element newElement;
+        newElement.uniqueId = assignCurrentId();
+        newElement.input = input;
+        newElement.timeStamp = 0.0;
+        trainingSet[targetPhrase].elements.push_back(newElement);
+        return newElement.uniqueId;
+    }
+    
+    
+    void trainingData::stopRecording() {
+        //TODO: This doesn't do much. -MZ
+    }
+    
+    uint32_t trainingData::recordSingleElement(const std::string &label, const std::vector<double> &input) {
+        startRecording(label);
+        int returnId = addElement(input);
+        stopRecording();
+        return returnId;
+    };
+    
+    uint32_t trainingData::recordSingleElement(const std::vector<double> &input, const std::vector<double> &output) {
+        startRecording();
+        int returnId = addElement(input, output);
+        stopRecording();
+        return returnId;
+    };
+    
+    uint32_t trainingData::recordSingleElement(const std::string &label, const std::vector<double> &input, const std::vector<double> &output) {
+        startRecording(label);
+        int returnId = addElement(input, output);
+        stopRecording();
+        return returnId;
+    };
+    
+    std::vector<std::string> trainingData::getColumnNames() {
+        return trainingSet[targetPhrase].columnNames;
+    }
+    
+    void trainingData::setColumnNames(const std::vector<std::string> &column_names) {
+        trainingSet[targetPhrase].columnNames = column_names;
+    }
+    
+    Json::Value trainingData::parse2json() {
+        Json::Value root;
+        Json::Value metadata;
+        Json::Value trainingSetJSON;
+        
+        metadata["creator"] = "RAPID-MIX API C++";
+        metadata["version"] = RAPIDMIX_VERSION;
+        
+        //Go through all the phrases
+        for (int i = 0; i < trainingSet.size(); ++i) {
+            Json::Value thisPhrase;
+            thisPhrase.append(trainingSet[i].uniqueId);
+            thisPhrase.append(trainingSet[i].label);
             
-            Json::Value elementInput;
-            for (int k = 0; k < trainingSet[i].elements[j].input.size(); ++k) {
-                elementInput.append(trainingSet[i].elements[j].input[k]);
+            Json::Value column_names;
+            for (int j = 0; j < trainingSet[i].columnNames.size(); ++j) {
+                column_names.append(trainingSet[i].columnNames[j]);
             }
-            singleElement.append(elementInput);
+            thisPhrase.append(column_names);
             
-            Json::Value elementOutput;
-            for (int k = 0; k < trainingSet[i].elements[j].output.size(); ++k) {
-                elementOutput.append(trainingSet[i].elements[j].output[k]);
+            Json::Value elements;
+            for (int j = 0; j < trainingSet[i].elements.size(); ++j) {
+                Json::Value singleElement;
+                
+                Json::Value elementInput;
+                for (int k = 0; k < trainingSet[i].elements[j].input.size(); ++k) {
+                    elementInput.append(trainingSet[i].elements[j].input[k]);
+                }
+                singleElement.append(elementInput);
+                
+                Json::Value elementOutput;
+                for (int k = 0; k < trainingSet[i].elements[j].output.size(); ++k) {
+                    elementOutput.append(trainingSet[i].elements[j].output[k]);
+                }
+                singleElement.append(elementOutput);
+                
+                singleElement.append(trainingSet[i].elements[j].timeStamp);
+                elements.append(singleElement);
             }
-            singleElement.append(elementOutput);
+            thisPhrase.append(elements);
             
-            singleElement.append(trainingSet[i].elements[j].timeStamp);
-            elements.append(singleElement);
+            trainingSetJSON.append(thisPhrase);
         }
-        thisPhrase.append(elements);
         
-        trainingSetJSON.append(thisPhrase);
+        root["metadata"] = metadata;
+        root["trainingSet"] = trainingSetJSON;
+        return root;
     }
     
-    root["metadata"] = metadata;
-    root["trainingSet"] = trainingSetJSON;
-    return root;
-}
-
-std::string trainingData::getJSON() {
-    Json::Value root = parse2json();
-    return root.toStyledString();
-}
-
-void trainingData::writeJSON(const std::string &filepath) {
-    Json::Value root = parse2json();
-    std::ofstream jsonOut;
-    jsonOut.open (filepath);
-    Json::StyledStreamWriter writer;
-    writer.write(jsonOut, root);
-    jsonOut.close();
-    
-}
-
-void trainingData::json2trainingSet(const Json::Value &root) {
-    trainingSet = {};
-    for (const Json::Value& jsonPhrase : root["trainingSet"]) {
+    std::string trainingData::getJSON() {
+        Json::Value root = parse2json();
+        return root.toStyledString();
+    }
     
-        phrase tempPhrase = { jsonPhrase[0].asUInt(), jsonPhrase[1].asString()    };
-        
-        for (int i = 0; i < jsonPhrase[2].size(); ++i) {
-            tempPhrase.columnNames.push_back(jsonPhrase[2][i].asString());
-        }
+    void trainingData::writeJSON(const std::string &filepath) {
+        Json::Value root = parse2json();
+        std::ofstream jsonOut;
+        jsonOut.open (filepath);
+        Json::StyledStreamWriter writer;
+        writer.write(jsonOut, root);
+        jsonOut.close();
         
-        for (int i = 0; i < jsonPhrase[3].size(); ++i) {
-            element tempElement;
-            for (int j = 0; j < jsonPhrase[3][i][0].size(); ++j) {
-                tempElement.input.push_back(jsonPhrase[3][i][0][j].asDouble());
-            }
-            for (int j = 0; j < jsonPhrase[3][i][1].size(); ++j) {
-                tempElement.output.push_back(jsonPhrase[3][i][1][j].asDouble());
+    }
+    
+    void trainingData::json2trainingSet(const Json::Value &root) {
+        trainingSet = {};
+        for (const Json::Value& jsonPhrase : root["trainingSet"]) {
+            
+            phrase tempPhrase = { jsonPhrase[0].asUInt(), jsonPhrase[1].asString()    };
+            
+            for (int i = 0; i < jsonPhrase[2].size(); ++i) {
+                tempPhrase.columnNames.push_back(jsonPhrase[2][i].asString());
             }
-            tempElement.timeStamp = jsonPhrase[3][i][2].asDouble();
             
-            tempPhrase.elements.push_back(tempElement);
+            for (int i = 0; i < jsonPhrase[3].size(); ++i) {
+                element tempElement;
+                for (int j = 0; j < jsonPhrase[3][i][0].size(); ++j) {
+                    tempElement.input.push_back(jsonPhrase[3][i][0][j].asDouble());
+                }
+                for (int j = 0; j < jsonPhrase[3][i][1].size(); ++j) {
+                    tempElement.output.push_back(jsonPhrase[3][i][1][j].asDouble());
+                }
+                tempElement.timeStamp = jsonPhrase[3][i][2].asDouble();
+                
+                tempPhrase.elements.push_back(tempElement);
+            }
+            trainingSet.push_back(tempPhrase);
         }
-        trainingSet.push_back(tempPhrase);
     }
-}
-
-bool trainingData::putJSON(const std::string &jsonMessage) {
-    Json::Value parsedFromString;
-    Json::Reader reader;
-    bool parsingSuccessful = reader.parse(jsonMessage, parsedFromString);
-    if (parsingSuccessful)
-    {
-        json2trainingSet(parsedFromString);
+    
+    bool trainingData::putJSON(const std::string &jsonMessage) {
+        Json::Value parsedFromString;
+        Json::Reader reader;
+        bool parsingSuccessful = reader.parse(jsonMessage, parsedFromString);
+        if (parsingSuccessful)
+        {
+            json2trainingSet(parsedFromString);
+        }
+        return parsingSuccessful;
     }
-    return parsingSuccessful;
-}
-
-bool trainingData::readJSON(const std::string &filepath) {
-    Json::Value root;
-    std::ifstream file(filepath);
-    file >> root;
-    json2trainingSet(root);
-    return true; //TODO: check something first
+    
+    bool trainingData::readJSON(const std::string &filepath) {
+        Json::Value root;
+        std::ifstream file(filepath);
+        file >> root;
+        json2trainingSet(root);
+        return true; //TODO: check something first
+    }
+    
 }
-
-
-RAPIDMIX_END_NAMESPACE

src/machineLearning/trainingData.h

-/*
- * trainingData.h
- * Created by Michael Zbyszynski on 2 Feb 2017
+/**
+ * @file trainingData.h
+ * @author Michael Zbyszynski
+ * @date 2 Feb 2017
+ * @copyright
  * Copyright © 2017 Goldsmiths. All rights reserved.
+ *
+ * @ingroup machinelearning
  */
 
 #ifndef trainingData_h
@@ -11,78 +15,100 @@
 #include <string>
 #include <unordered_map>
 
-#include "rapidMix.h"
+#include "../rapidmix.h"
 #include "json.h"
 
-RAPIDMIX_BEGIN_NAMESPACE
-
-/** This is used by both NN and KNN models for training */
-class trainingData {
-  
-public:
-    
-    trainingData();
-    
-    struct element{
-        uint32_t uniqueId; //MZ: Does this scope of this id need to extend beyond this instantiation?
-        std::vector<double> input;
-        std::vector<double> output;
-        double timeStamp;
-    };
-    
-    struct phrase {
-        uint32_t uniqueId;
-        std::string label; //TODO: Need to work this with templates
-        std::vector<std::string> columnNames;
-        std::vector<element> elements;
+namespace rapidmix {
+    
+    /** This is used by both NN and KNN models for training */
+    class trainingData {
+        
+    public:
+        
+        trainingData();
+        
+        struct element{
+            uint32_t uniqueId; //MZ: Does this scope of this id need to extend beyond this instantiation?
+            std::vector<double> input;
+            std::vector<double> output;
+            double timeStamp;
+        };
+        
+        struct phrase {
+            uint32_t uniqueId;
+            std::string label; //TODO: Need to work this with templates
+            std::vector<std::string> columnNames; //equal to the number of inputs
+            std::vector<element> elements;
+            
+            
+            //TODO: This is just a design idea right now.
+            void addElement (const std::vector<double> &input, const std::vector<double> &output)
+            {
+                element tempElement;
+//                tempElement.uniqueId = assignCurrentId();  //TODO: how to do this? Do we need this?
+                tempElement.input = input;
+                tempElement.output = output;
+                //tempElement.timeStamp = NULL;
+                this->elements.push_back(tempElement);
+            }
+        };
+        
+        std::vector<phrase> trainingSet;
+        
+        //TODO: Deleting phrases (last or by label)
+        //Design ideas to make phrase building stateless:
+        std::vector<phrase>::iterator createNewPhrase(); //??? Do we need this?
+        std::vector<phrase>::iterator createNewPhrase(std::string label);
+        
+        /** Create a new phrase that can be recorded into. Returns phrase id */
+        uint32_t startRecording(); //FIXME: this should go away. -MZ
+        
+        /** Create new phrase, with a label, that can be recorded into. Returns phrase id */
+        uint32_t startRecording(const std::string &label);
+        
+        /** Add an element with input and output to the phrase that is recording,
+         or to the default phrase if recording is stopped. Returns phrase id. */
+        uint32_t addElement(const std::vector<double> &input, const std::vector<double> &output);
+        
+        /** Add an element with just input to the phrase that is recording,
+         or to the default phrase if recording is stopped. Returns phrase id. */
+        uint32_t addElement(const std::vector<double> &input);
+        
+        void stopRecording();
+        
+        /** Create a phrase with a single element that has a label and input. Returns phrase id. */
+        uint32_t recordSingleElement(const std::string &label, const std::vector<double> &input);
+        
+        /** Create a phrase with a single element that has input, and output. Returns phrase id. */
+        uint32_t recordSingleElement(const std::vector<double> &input, const std::vector<double> &output);
+        
+        /** Create a phrase with a single element that has a label, input, and output. Returns phrase id. */
+        uint32_t recordSingleElement(const std::string &label, const std::vector<double> &input, const std::vector<double> &output);
+        
+        std::vector<std::string> getColumnNames();
+        void setColumnNames(const std::vector<std::string> &columnNames);
+        
+        /** Get a JSON representation of the data set in the form of a styled string */
+        std::string getJSON();
+        /** Write a JSON version of the training set to specified file path */
+        void writeJSON(const std::string &filepath);
+        /** populate a data set with string. See getJSON() */
+        bool putJSON(const std::string &jsonMessage);
+        /** read a JSON file at file path and build a training set from it */
+        bool readJSON(const std::string &filepath);
+        
+    private:
+        int targetPhrase;
+        uint32_t currentId;
+        
+        //* Returns and increments current id */
+        uint32_t assignCurrentId();
+        
+        Json::Value parse2json();
+        void json2trainingSet(const Json::Value &newTrainingData);
+        
     };
     
-    std::vector<phrase> trainingSet;
-    
-    //* Create a new phrase that can be recorded into. Returns phrase id */
-    uint32_t startRecording();
-    
-    //* Create new phrase, with a label, that can be recorded into. Returns phrase id */
-    uint32_t startRecording(std::string label);
-    
-    //* Add an element with input and output to the phrase that is recording, or to the default phrase if recording is stopped. Returns phrase id. */
-    uint32_t addElement(std::vector<double>input, std::vector<double> output);
-    
-    //* Add an element with just input to the phrase that is recording, or to the default phrase if recording is stopped. Returns phrase id. */
-    uint32_t addElement(std::vector<double>input);
-    
-    void stopRecording();
-    
-    std::vector<std::string> getColumnNames();
-    void setColumnNames(std::vector<std::string> columnNames);
-  
-    
-    /** Get a JSON representation of the data set in the form of a styled string */
-    std::string getJSON();
-    /** Write a JSON version of the training set to specified file path */
-    void writeJSON(const std::string &filepath);
-    /** populate a data set with string. See getJSON() */
-    bool putJSON(const std::string &jsonMessage);
-    /** read a JSON file at file path and build a training set from it */
-    bool readJSON(const std::string &filepath);
-
-    
-    //this holds string labels
-    std::unordered_map<std::string, int> labels;
-    std::string getLabel(int value);
-    
-private:
-    int targetPhrase;
-    uint32_t currentId;
-    
-    //* Returns and increments current id */
-    uint32_t assignCurrentId();
-    
-    Json::Value parse2json();
-    void json2trainingSet(const Json::Value &newTrainingData);
-    
-};
-
-RAPIDMIX_END_NAMESPACE
+}
 
 #endif

src/rapidmix.h

-/*
- * rapidmix.h
- * Created by Michael Zbyszynski on 12 Jan 2017
+/**
+ * @file    rapidmix.h
+ * @author  Michael Zbyszynski
+ * @date    12 Jan 2017
+ *
+ * @copyright
  * Copyright © 2017 Goldsmiths. All rights reserved.
  */
 
+/**
+ * @mainpage RAPID-MIX API
+ *
+ * @section Introduction
+ *
+ * Hello, API ! <br>
+ * All the classes, functions and structs documented here belong to the
+ * <code>rapidmix</code> namespace.
+ *
+ * @section Examples
+ * @subsection Simple classification
+ * TODO
+ */
+
 /////////////////////////////////////////////////////////////////////////////
 //  ______            _     _       ___  ____         ___  ______ _____    //
 //  | ___ \          (_)   | |      |  \/  (_)       / _ \ | ___ \_   _|   //
@@ -19,16 +36,14 @@
 #define rapidmix_h
 
 #define RAPIDMIX_VERSION_MAJOR 2
-#define RAPIDMIX_VERSION_MINOR 1
+#define RAPIDMIX_VERSION_MINOR 3
 #define RAPIDMIX_VERSION_PATCH 0
-#define RAPIDMIX_VERSION "2.0.1"
-#define RAPIDMIX_REVISION "13-JULY-2017"
-
-#define RAPIDMIX_BEGIN_NAMESPACE namespace rapidmix {
-#define RAPIDMIX_END_NAMESPACE }
+#define RAPIDMIX_VERSION "2.3.1"
+#define RAPIDMIX_JSON_DOC_VERSION "1.0.0"
+#define RAPIDMIX_REVISION "2-MAY-2018"
 
-#include "machineLearning.h"
-#include "trainingData.h"
-#include "signalProcessing.h"
+#include "machineLearning/machineLearning.h"
+#include "machineLearning/trainingData.h"
+#include "signalProcessing/signalProcessing.h"
 
 #endif

src/signalProcessing/rapidPiPoTools/rapidPiPoHost.cpp

+#include "rapidmix.h"
 #include "rapidPiPoHost.h"
 #include <iostream>
 #include <fstream>
 
 //=========================== H O S T === U T I L S ==========================//
 
-/*
-static const unsigned int maxWordLen = 256;
-
-static bool getPiPoInstanceAndAttrName(const char *attrName,
-                                       char *instanceName,
-                                       char *pipoAttrName)
-{
-  const char *dot = std::strrchr(attrName, '.');
-
-  if (dot != NULL)
-  {
-    long pipoAttrNameLen = dot - attrName;
-    std::strcpy(pipoAttrName, dot + 1);
-
-    if (pipoAttrNameLen > maxWordLen)
-    {
-      pipoAttrNameLen = maxWordLen;
-    }
-
-    std::strncpy(instanceName, attrName, pipoAttrNameLen);
-    instanceName[pipoAttrNameLen] = '\0';
-
-    return true;
-  }
-
-  return false;
-}
-//*/
-
-
 static void fromPiPoStreamAttributes(PiPoStreamAttributes &src,
-                                     pipoStreamAttributes &dst)
+                                     RapidPiPoStreamAttributes &dst)
 {
   unsigned int numCols = src.dims[0];
   unsigned int numLabels = src.numLabels;
-  
+
   if (numLabels > PIPO_MAX_LABELS) {
     numLabels = PIPO_MAX_LABELS;
   }
-  
+
   if (numLabels > numCols) {
     numLabels = numCols;
   }
@@ -76,7 +47,7 @@ static void fromPiPoStreamAttributes(PiPoStreamAttributes &src,
 }
 
 
-static void toPiPoStreamAttributes(pipoStreamAttributes &src,
+static void toPiPoStreamAttributes(RapidPiPoStreamAttributes &src,
                                    PiPoStreamAttributes &dst)
 {
   const char *labs[src.labels.size()];
@@ -101,258 +72,31 @@ static void toPiPoStreamAttributes(pipoStreamAttributes &src,
 
 //========================= H O S T === M E T H O D S ========================//
 
-PiPoHost::PiPoHost() :
-inputStreamAttrs(PIPO_MAX_LABELS),
-outputStreamAttrs(PIPO_MAX_LABELS)
-{
-  PiPoCollection::init();
-  this->out = new PiPoOut(this);
-  this->graph = nullptr;
-}
-
-PiPoHost::~PiPoHost()
-{
-  if (this->graph != nullptr)
-  {
-    delete this->graph;
-  }
-
-  delete this->out;
-}
-
-bool
-PiPoHost::setGraph(std::string name)
-{
-  if (this->graph != nullptr)
-  {
-    delete this->graph;
-  }
-
-  this->graph = PiPoCollection::create(name);
-
-  if (this->graph != NULL)
-  {
-    this->graphName = name;
-    this->graph->setReceiver((PiPo *)this->out);
-    return true;
-  }
-
-  this->graph = nullptr;
-  this->graphName = "undefined";
-  return false;
-}
-
-void
-PiPoHost::clearGraph()
-{
-  if (this->graph != nullptr)
-  {
-    delete this->graph;
-    this->graph = nullptr;
-  }
-}
-
-
-void
-PiPoHost::onNewFrameOut(double time, std::vector<PiPoValue> &frame)
-{
-  std::cout << time << std::endl;
-  std::cout << "please override this method" << std::endl;
-}
-
-std::vector<PiPoValue>
-PiPoHost::getLastFrameOut()
-{
-  return this->out->getLastFrame();
-}
-
-
-
 int
-PiPoHost::setInputStreamAttributes(pipoStreamAttributes &sa, bool propagate)
+RapidPiPoHost::setInputStreamAttributes(RapidPiPoStreamAttributes &rpsa)
 {
-  toPiPoStreamAttributes(sa, inputStreamAttrs);
-
-  if (propagate)
-  {
-    return this->propagateInputStreamAttributes();
-  }
-    
-  return 0;
+  PiPoStreamAttributes psa;
+  toPiPoStreamAttributes(rpsa, psa);
+  return PiPoHost::setInputStreamAttributes(psa);
 }
 
-pipoStreamAttributes
-PiPoHost::getOutputStreamAttributes()
-{
-  pipoStreamAttributes sa;
-  fromPiPoStreamAttributes(this->outputStreamAttrs, sa);
-  return sa;
-}
-
-int
-PiPoHost::frames(double time, double weight, PiPoValue *values, unsigned int size,
-                 unsigned int num)
-{
-  return this->graph->frames(time, weight, values, size, num);
-}
-
-
-
-// bool
-// setAttr(const std::string &attrName, bool value)
-// {
-
-// }
-
-// bool
-// setAttr(const std::string &attrName, int value)
-// {
-
-// }
-
-bool
-PiPoHost::setAttr(const std::string &attrName, double value)
-{
-  PiPo::Attr *attr = this->graph->getAttr(attrName.c_str());
-  
-  if (attr != NULL)
-  {
-    int iAttr = attr->getIndex();
-    return this->graph->setAttr(iAttr, value);
-  }
-
-  return false;
-}
-
-bool
-PiPoHost::setAttr(const std::string &attrName, const std::vector<double> &values)
-{
-  PiPo::Attr *attr = this->graph->getAttr(attrName.c_str());
-
-  if (attr != NULL)
-  {
-    int iAttr = attr->getIndex();
-    double vals[values.size()];
-    unsigned int i = 0;
-
-    for (auto &value : values)
-    {
-      vals[i] = value;
-      i++;
-    }
-
-    return this->graph->setAttr(iAttr, &vals[0], static_cast<unsigned int>(values.size()));
-  }
-
-  return false;
-}
-
-bool
-PiPoHost::setAttr(const std::string &attrName, const std::string &value) // for enums
-{
-  PiPo::Attr *attr = this->graph->getAttr(attrName.c_str());
-  
-  if (attr != NULL)
-  {
-    // int iAttr = attr->getIndex();
-    PiPo::Type type = attr->getType();
-  
-    if (type == PiPo::Type::Enum)
-    {
-      std::vector<const char *> *list = attr->getEnumList();
-      
-      for (int i = 0; i < list->size(); i++)
-      {
-        if (strcmp(list->at(i), value.c_str()) == 0)
-        {
-          attr->set(0, i);
-          return true;
-        }
-      }
-    }
-  }
-
-  return false;
-}
-
-std::vector<std::string>
-PiPoHost::getAttrNames()
-{
-  std::vector<std::string> res;
-
-  for (unsigned int i = 0; i < this->graph->getNumAttrs(); ++i)
-  {
-    res.push_back(this->graph->getAttr(i)->getName());
-  }
-
-  return res;
-}
-
-double
-PiPoHost::getDoubleAttr(const std::string &attrName)
-{
-    PiPo::Attr *attr = this->graph->getAttr(attrName.c_str());
-    
-    if (attr != NULL) {
-        // int iAttr = attr->getIndex();
-        PiPo::Type type = attr->getType();
-        
-        if (type == PiPo::Type::Double) {
-            return attr->getDbl(0);
-        }
-    }
-    
-    return 0;
-}
-
-std::vector<double>
-PiPoHost::getDoubleArrayAttr(const std::string &attrName)
-{
-    std::vector<double> res;
-    PiPo::Attr *attr = this->graph->getAttr(attrName.c_str());
-    
-    if (attr != NULL) {
-        // int iAttr = attr->getIndex();
-        PiPo::Type type = attr->getType();
-        
-        if (type == PiPo::Type::Double) {
-            for (int i = 0; i < attr->getSize(); ++i) {
-                res.push_back(attr->getDbl(i));
-            }
-        }
-    }
-    
-    return res;
-}
-
-std::string
-PiPoHost::getEnumAttr(const std::string &attrName)
+void
+RapidPiPoHost::onNewFrame(double time, double weight, PiPoValue *values, unsigned int size)
 {
-    PiPo::Attr *attr = this->graph->getAttr(attrName.c_str());
-    
-    if (attr != NULL) {
-        // int iAttr = attr->getIndex();
-        PiPo::Type type = attr->getType();
-        
-        if (type == PiPo::Type::Enum) {
-            return attr->getStr(0);
-        }
-    }
-
-    return "";
+  std::cout << "I received " << size << " new frame(s)" << std::endl;
 }
 
-//============================= JSON FORMATTING ==============================//
+//----------------------------- JSON FORMATTING ------------------------------//
 
 std::string
-PiPoHost::getJSON()
+RapidPiPoHost::getJSON()
 {
   Json::Value result = toJSON();
   return result.toStyledString();
 }
 
 void
-PiPoHost::writeJSON(const std::string &filepath)
+RapidPiPoHost::writeJSON(const std::string &filepath)
 {
   Json::Value root = toJSON();
   std::ofstream jsonOut;
@@ -363,7 +107,7 @@ PiPoHost::writeJSON(const std::string &filepath)
 }
 
 bool
-PiPoHost::putJSON(const std::string &jsonMessage)
+RapidPiPoHost::putJSON(const std::string &jsonMessage)
 {
   Json::Value parsedFromString;
   Json::Reader reader;
@@ -372,7 +116,7 @@ PiPoHost::putJSON(const std::string &jsonMessage)
 }
 
 bool
-PiPoHost::readJSON(const std::string &filepath)
+RapidPiPoHost::readJSON(const std::string &filepath)
 {
   Json::Value root;
   std::ifstream file(filepath);
@@ -380,23 +124,26 @@ PiPoHost::readJSON(const std::string &filepath)
   return fromJSON(root);
 }
 
-//======================== PROTECTED HOST METHODS ============================//
+//-------------------------- PRIVATE HOST METHODS ----------------------------//
 
 Json::Value
-PiPoHost::toJSON() 
+RapidPiPoHost::toJSON()
 {
   Json::Value root;
-  Json::Value metadata;
-  Json::Value pipodata;
 
-  metadata["creator"] = "RAPID-MIX API";// C++";
-  metadata["version"] = "v0.1.1"; //TODO: This should be a macro someplace
-  metadata["family"] = "pipo";
-    
-  root["metadata"] = metadata;
+  //*
+  root["docType"] = "rapid-mix:signal-processing";
+  root["docVersion"] = RAPIDMIX_JSON_DOC_VERSION;
+
+  Json::Value target;
+  target["name"] = "pipo";
+  target["version"] = PIPO_SDK_VERSION;
 
+  root["target"] = target;
+
+  Json::Value pipodata;
   pipodata["description"] = this->graphName;
-    
+
   Json::Value inputStream;
   inputStream["hasTimeTags"] = inputStreamAttrs.hasTimeTags;
   inputStream["rate"] = inputStreamAttrs.rate;
@@ -407,7 +154,7 @@ PiPoHost::toJSON()
   inputStream["hasVarSize"] = inputStreamAttrs.hasVarSize;
   inputStream["domain"] = inputStreamAttrs.domain;
   inputStream["maxFrames"] = inputStreamAttrs.maxFrames;
-  
+
   Json::Value outputStream;
   outputStream["hasTimeTags"] = outputStreamAttrs.hasTimeTags;
   outputStream["rate"] = outputStreamAttrs.rate;
@@ -422,9 +169,9 @@ PiPoHost::toJSON()
   Json::Value streams;
   streams["input"] =  inputStream;
   streams["output"] =  outputStream;
-  
+
   pipodata["streamAttributes"] = streams;
-    
+
   Json::Value params;
   int n = this->graph->getNumAttrs();
   params.resize(static_cast<Json::ArrayIndex>(n));
@@ -434,331 +181,111 @@ PiPoHost::toJSON()
     Json::Value param;
     PiPo::Attr *a = this->graph->getAttr(i);
     param["name"] = a->getName();
-    param["value"] = a->getDbl(0);
+    switch (a->getType())
+    {
+      case PiPo::Bool:
+        param["type"] = "Bool";
+        param["value"] = a->getInt(0);
+        break;
+      case PiPo::Enum:
+        param["type"] = "Enum";
+        param["value"] = std::string(a->getStr(0));
+        break;
+      case PiPo::String:
+        param["type"] = "String";
+        param["value"] = std::string(a->getStr(0));
+        break;
+      case PiPo::Int:
+        param["type"] = "Int";
+        param["value"] = Json::Value(Json::arrayValue);
+        for (int i = 0; i < a->getSize(); ++i)
+        {
+          param["value"].append(a->getInt(i));
+        }
+        break;
+      case PiPo::Double:
+        param["type"] = "Double";
+        param["value"] = Json::Value(Json::arrayValue);
+        for (int i = 0; i < a->getSize(); ++i)
+        {
+          param["value"].append(a->getDbl(i));
+        }
+        break;
+      default:
+        std::cout << a->getType() << std::endl;
+        break;
+    }
     params[i] = param;
   }
-    
+
   pipodata["parameters"] = params;
 
-  root["pipodata"] = pipodata;
+  root["payload"] = pipodata;
+  //*/
 
   return root;
 }
 
 bool
-PiPoHost::fromJSON(Json::Value &jv)
+RapidPiPoHost::fromJSON(Json::Value &jv)
 {
-  if (jv["metadata"]["family"].asString().compare("pipo") == 0 &&
-    jv["pipodata"].size() > 0) {
+  //*
+  if (jv["docType"].asString().compare("rapid-mix:signal-processing") == 0 &&
+      //jv["docVersion"].asString().compare(RAPIDMIX_JSON_DOC_VERSION) == 0 &&
+      jv["target"]["name"].asString().compare("pipo") == 0 &&
+      jv["payload"].size() > 0) {
 
-    this->setGraph(jv["pipodata"]["description"].asString());
-
-    Json::Value inputStream = jv["pipodata"]["streamAttributes"]["input"];
-    // setInputStreamAttributes(
-    //   inputStream["hasTimeTags"].getDbl()//,
-    //   //...
-    // );
-    return true;
-  }
-  
-  return false;
-}
+    this->setGraph(jv["payload"]["description"].asString());
 
-//========================= PRIVATE HOST METHODS =============================//
+    Json::Value params = jv["payload"]["parameters"];
+    unsigned int nAttrs = params.size();
 
-int
-PiPoHost::propagateInputStreamAttributes()
-{
-  if (this->graph != nullptr)
-  {
-    return this->graph->streamAttributes(this->inputStreamAttrs.hasTimeTags,
-                                         this->inputStreamAttrs.rate,
-                                         this->inputStreamAttrs.offset,
-                                         this->inputStreamAttrs.dims[0],
-                                         this->inputStreamAttrs.dims[1],
-                                         this->inputStreamAttrs.labels,
-                                         this->inputStreamAttrs.hasVarSize,
-                                         this->inputStreamAttrs.domain,
-                                         this->inputStreamAttrs.maxFrames);
-  }
-    
-  return 0;
-}
+    for (unsigned int i = 0; i < nAttrs; ++i)
+    {
+      std::string type = params[i]["type"].asString();
+      const char *name = params[i]["name"].asString().c_str();
 
-void
-PiPoHost::setOutputAttributes(bool hasTimeTags, double rate, double offset,
-                              unsigned int width, unsigned int height,
-                              const char **labels, bool hasVarSize,
-                              double domain, unsigned int maxFrames) {
-  if (labels != NULL) {
-    int numLabels = width;
-    
-    if (numLabels > PIPO_MAX_LABELS) {
-      numLabels = PIPO_MAX_LABELS;
-    }
-    
-    for (unsigned int i = 0; i < numLabels; i++) {
-      try {
-        this->outputStreamAttrs.labels[i] = labels[i];        
-      } catch(std::exception e) {
-        this->outputStreamAttrs.labels[i] = "unnamed";
+      if (type.compare("Bool") == 0)
+      {
+        this->setAttr(name, params[i]["value"].asInt());
       }
-    }
-    
-    this->outputStreamAttrs.numLabels = numLabels;
-  } else {
-    this->outputStreamAttrs.numLabels = 0;
-  }
-  
-  this->outputStreamAttrs.hasTimeTags = hasTimeTags;
-  this->outputStreamAttrs.rate = rate;
-  this->outputStreamAttrs.offset = offset;
-  this->outputStreamAttrs.dims[0] = width;
-  this->outputStreamAttrs.dims[1] = height;
-  this->outputStreamAttrs.hasVarSize = hasVarSize;
-  this->outputStreamAttrs.domain = domain;
-  this->outputStreamAttrs.maxFrames = maxFrames; 
-}
-
-
-
-// void
-// PiPoHost::streamAttributesChanged(PiPo *pipo, PiPo::Attr *attr) {
-//   this->propagateInputAttributes();
-// }
-
-// void
-// PiPoHost::signalError(PiPo *pipo, std::string errorMsg) {
-//   // todo
-// }
-
-// void
-// PiPoHost::signalWarning(PiPo *pipo, std::string warningMsg) {
-//   // todo
-// }
-
-//--------------------- INPUT STREAM ATTRIBUTES SETTERS ----------------------//
-
-/*
-void
-PiPoHost::setInputHasTimeTags(bool hasTimeTags, bool propagate) {
-  this->inputStreamAttrs.hasTimeTags = hasTimeTags;
-
-  if (propagate) {
-    this->propagateInputAttributes();
-  }
-}
-
-#define MIN_PIPO_SAMPLERATE (1.0 / 31536000000.0) // once a year
-#define MAX_PIPO_SAMPLERATE (96000000000.0)
-
-void
-PiPoHost::setInputFrameRate(double rate, bool propagate) {
-  if (rate <= MIN_PIPO_SAMPLERATE) {
-    this->inputStreamAttrs.rate = MIN_PIPO_SAMPLERATE;
-  } else if (rate >= MAX_PIPO_SAMPLERATE) {
-    this->inputStreamAttrs.rate = MAX_PIPO_SAMPLERATE;
-  } else {
-    this->inputStreamAttrs.rate = rate;
-  }
-
-  if (propagate) {
-    this->propagateInputAttributes();
-  }
-}
-
-void
-PiPoHost::setInputFrameOffset(double offset, bool propagate) {
-  this->inputStreamAttrs.offset = offset;
-
-  if (propagate) {
-    this->propagateInputAttributes();
-  }
-}
-
-void
-PiPoHost::setInputDims(int width, int height, bool propagate) {
-  this->inputStreamAttrs.dims[0] = width;
-  this->inputStreamAttrs.dims[1] = height;
-  
-  if (propagate) {
-    this->propagateInputAttributes();
-  }
-}
-
-void
-PiPoHost::setInputLabels(const std::vector<std::string> &labels, bool propagate) {
-  const char *labs[labels.size()];
-
-  for (unsigned int i = 0; i < labels.size(); ++i) {
-    labs[i] = labels[i].c_str();
-  }
-
-  this->inputStreamAttrs.labels = &labs[0];
-
-  if (propagate) {
-    this->propagateInputAttributes();
-  }
-}
-
-void
-PiPoHost::setInputHasVarSize(bool hasVarSize, bool propagate) {
-  this->inputStreamAttrs.hasVarSize = hasVarSize;
-
-  if (propagate) {
-    this->propagateInputAttributes();
-  }
-}
-
-void
-PiPoHost::setInputDomain(double domain, bool propagate) {
-  this->inputStreamAttrs.domain = domain;
-
-  if (propagate) {
-    this->propagateInputAttributes();
-  }
-}
-
-void
-PiPoHost::setInputMaxFrames(int maxFrames, bool propagate) {
-  this->inputStreamAttrs.maxFrames = maxFrames;
-
-  if (propagate) {
-    this->propagateInputAttributes();
-  }
-}
-//*/
-
-//--------------------- INPUT STREAM ATTRIBUTES GETTERS ----------------------//
-
-/*
-bool
-PiPoHost::getInputHasTimeTags() {
-  return this->inputStreamAttrs.hasTimeTags;
-}
-
-double
-PiPoHost::getInputFrameRate() {
-  return this->inputStreamAttrs.rate;
-}
-
-double
-PiPoHost::getInputFrameOffset() {
-  return this->inputStreamAttrs.offset;
-}
-
-void
-PiPoHost::getInputDims(int &width, int &height) {
-  width = this->inputStreamAttrs.dims[0];
-  height = this->inputStreamAttrs.dims[1];
-}
-
-void
-PiPoHost::getInputLabels(std::vector<std::string> &labels) {
-  //for (unsigned int i = 0; i < )
-}
-
-bool
-PiPoHost::getInputHasVarSize() {
-  return this->inputStreamAttrs.hasVarSize;
-}
-
-double
-PiPoHost::getInputDomain() {
-  return this->inputStreamAttrs.domain;
-}
-
-int
-PiPoHost::getInputMaxFrames() {
-  return this->inputStreamAttrs.maxFrames;
-}
-//*/
-
-//--------------------- OUTPUT STREAM ATTRIBUTES GETTERS ---------------------//
-
-// void
-// PiPoHost::setOutputAttributes(bool hasTimeTags, double rate, double offset,
-//                               unsigned int width, unsigned int height,
-//                               const char **labels, bool hasVarSize,
-//                               double domain, unsigned int maxFrames) {
-//   if (labels != NULL) {
-//     int numLabels = width;
-    
-//     if (numLabels > PIPO_MAX_LABELS) {
-//       numLabels = PIPO_MAX_LABELS;
-//     }
-    
-//     for (unsigned int i = 0; i < numLabels; i++) {
-//       try {
-//         this->outputStreamAttrs.labels[i] = labels[i];        
-//       } catch(std::exception e) {
-//         this->outputStreamAttrs.labels[i] = "unnamed";
-//       }
-//     }
-    
-//     this->outputStreamAttrs.numLabels = numLabels;
-//   } else {
-//     this->outputStreamAttrs.numLabels = 0;
-//   }
-  
-//   this->outputStreamAttrs.hasTimeTags = hasTimeTags;
-//   this->outputStreamAttrs.rate = rate;
-//   this->outputStreamAttrs.offset = offset;
-//   this->outputStreamAttrs.dims[0] = width;
-//   this->outputStreamAttrs.dims[1] = height;
-//   this->outputStreamAttrs.hasVarSize = hasVarSize;
-//   this->outputStreamAttrs.domain = domain;
-//   this->outputStreamAttrs.maxFrames = maxFrames; 
-// }
-
-/*
-bool
-PiPoHost::getOutputHasTimeTags() {
-  return this->outputStreamAttrs.hasTimeTags;
-}
-
-double
-PiPoHost::getOutputFrameRate() {
-  return this->outputStreamAttrs.rate;
-}
-
-double
-PiPoHost::getOutputFrameOffset() {
-  return this->outputStreamAttrs.offset;
-}
+      else if (type.compare("Enum") == 0 || type.compare("String") == 0)
+      {
+        this->setAttr(name, params[i]["value"].asString());
+      }
+      else if (type.compare("Int") == 0)
+      {
+        std::vector<int> values;
 
-void
-PiPoHost::getOutputDims(int &width, int &height) {
-  width = this->outputStreamAttrs.dims[0];
-  height = this->outputStreamAttrs.dims[1];
-}
+        for (int j = 0; j < params[i]["value"].size(); ++j)
+        {
+          values.push_back(params[i]["value"][j].asInt());
+        }
 
-void
-PiPoHost::getOutputLabels(std::vector<std::string> &labels) {
-  labels.clear();
+        this->setAttr(name, values);
+      }
+      else if (type.compare("Double") == 0)
+      {
+        std::vector<double> values;
 
-  for (unsigned int i = 0; this->outputStreamAttrs.numLabels; ++i) {
+        for (int j = 0; j < params[i]["value"].size(); ++j)
+        {
+          values.push_back(params[i]["value"][j].asDouble());
+        }
 
-    if (this->outputStreamAttrs.labels[i] != NULL) {
-      labels.push_back(std::string(this->outputStreamAttrs.labels[i]));
-    } else {
-      labels.push_back("unnamed");
+        this->setAttr(name, values);
+      }
     }
-  }
-}
 
-bool
-PiPoHost::getOutputHasVarSize() {
-  return this->outputStreamAttrs.hasVarSize;
-}
 
-double
-PiPoHost::getOutputDomain() {
-  return this->outputStreamAttrs.domain;
-}
+    Json::Value inputStream = jv["pipodata"]["streamAttributes"]["input"];
+    // setInputStreamAttributes(
+    //   inputStream["hasTimeTags"].getDbl()//,
+    //   //...
+    // );
+    return true;
+  }
+  //*/
 
-int
-PiPoHost::getOutputMaxFrames() {
-  return this->outputStreamAttrs.maxFrames;
+  return false;
 }
-//*/