view hackday/BayesianArrayStructure.cpp @ 36:5a1b0c6fa1fb

Added class to read in the csv Annotation file, then write out the respective difference between the performed piece as followed here, and the annotation of RWC by Ewert and Muller

/*
 *  BayesianArrayStructure.cpp
 *  midiCannamReader
 *
 *  Created by Andrew on 17/07/2011.
 *  Copyright 2011 QMUL. All rights reserved.
 *
 */

//look at reset speed to one - what does this do? - get rid of?


#include "BayesianArrayStructure.h"

BayesianArrayStructure::BayesianArrayStructure(){
	printf("Bayesian structure: DeFault constructor called");
	usingIntegratedTempoEstimate = true;// use max index
	
	relativeSpeedLikelihoodStdDev = 5.0;
	
	prior.createVector(1);
	likelihood.createVector(1);
	posterior.createVector(1);

	
	speedPriorValue = 1.0;
	speedEstimate = speedPriorValue;
	
	lastEventTime = 0;//ofGetElapsedTimeMillis();

	tmpBestEstimate = 0;
	crossUpdateTimeThreshold = 60;
	priorWidth = 50;
	
}

BayesianArrayStructure::BayesianArrayStructure(int length){
	printf("BAYESIAN STURTUCRE CREATED LENGTH: %i\n", length);
	//this constructor isnt called  it seems
	prior.createVector(length);
	likelihood.createVector(length);
	posterior.createVector(length);
	
	lastEventTime = 0;
	

}



void BayesianArrayStructure::resetSize(int length){
	printf("BAYESIAN STRUCTURE size is : %i\n", length);
	
	prior.createVector(length);
	likelihood.createVector(length);
	posterior.createVector(length);
	
	acceleration.createVector(length);
	
}



void BayesianArrayStructure::resetSpeedToOne(){
	relativeSpeedPrior.zero();
	relativeSpeedPosterior.zero();
	relativeSpeedLikelihood.zero();
	
	
	relativeSpeedPosterior.addGaussianShape(100, 20, 0.8);
	relativeSpeedPosterior.renormalise();
	relativeSpeedPosterior.getMaximum();
	
	setSpeedPrior(speedPriorValue);
	speedEstimate = speedPriorValue;
	
	prior.zero();
	posterior.zero();

	posterior.addToIndex(0, 1);
	posterior.renormalise();
	

	//acceleration.addGaussianShape(2000, 20, 0.8);
	
}

void BayesianArrayStructure::setSpeedPrior(double f){	
	speedPriorValue = f;
	int index = relativeSpeedPosterior.getRealTermsAsIndex(speedPriorValue);
	relativeSpeedPosterior.zero();
	relativeSpeedPosterior.addGaussianShape(index, priorWidth, 0.8);
	relativeSpeedPosterior.renormalise();
	relativeSpeedPosterior.getMaximum();
	relativeSpeedPrior.copyFromDynamicVector(relativeSpeedPosterior);
	printf("BAYES STRUCTU ' SPEED PRIOR %f . index %i\n", speedPriorValue, index);
	
}

void BayesianArrayStructure::resetSpeedSize(int length){
	printf("BAYESIAN SPEED size is : %i\n", length);
	
	relativeSpeedPrior.createVector(length);
	relativeSpeedLikelihood.createVector(length);
	relativeSpeedPosterior.createVector(length);
	tmpPosteriorForStorage.createVector(length);
	
	

}

void BayesianArrayStructure::setRelativeSpeedScalar(double f){
	relativeSpeedPrior.scalar = f;
	relativeSpeedPosterior.scalar = f;
	relativeSpeedLikelihood.scalar = f;
}


void BayesianArrayStructure::setPositionDistributionScalar(double f){
	if (f > 0){
	prior.scalar = f;
	posterior.scalar = f;
	likelihood.scalar = f;
	}
}

void BayesianArrayStructure::simpleExample(){
	relativeSpeedPosterior.getMaximum();
	relativeSpeedPrior.copyFromDynamicVector(relativeSpeedPosterior);
}

void BayesianArrayStructure::copyPriorToPosterior(){

	for (int i = 0;i < prior.arraySize;i++){
		posterior.array[i] = prior.array[i];
	}
}

void BayesianArrayStructure::setStartPlaying(){
	
	lastEventTime = 0;
	bestEstimate = 0;
	lastBestEstimateUpdateTime = 0;
	if (*realTimeMode)
		lastBestEstimateUpdateTime = ofGetElapsedTimeMillis();
	//cannot just be zero - offline bug
	printf("start playing - best estimate %f\n", lastBestEstimateUpdateTime);
	
	resetArrays();
}

void BayesianArrayStructure::resetArrays(){
	//called when we start playing
	
	prior.zero();
	likelihood.zero();
	posterior.zero();
	
	updateCounter = 0;
	
	posterior.offset = -1000;
	setNewDistributionOffsets(0);
	
	int zeroIndex = posterior.getRealTermsAsIndex(0);
	
	posterior.addGaussianShape(zeroIndex, 500, 1);
	//	posterior.addToIndex(0, 1);
	likelihood.addConstant(1);
	
	updateCounter = 0;
	

	printf("bayes reset arrays - best estimate %f\n", lastBestEstimateUpdateTime);
	
	setSpeedPrior(speedPriorValue);
}


void BayesianArrayStructure::zeroArrays(){
	prior.zero();
	likelihood.zero();
	posterior.zero();
	
	relativeSpeedPrior.zero();
	relativeSpeedPosterior.zero();
	relativeSpeedLikelihood.zero();

}


void BayesianArrayStructure::updateTmpBestEstimate(const double& timeDifference){
	//input is the time since the start of playing
	
//	double timeDiff = ofGetElapsedTimeMillis() - lastEventTime;//lastBestEstimateUpdateTime;
	
	tmpBestEstimate = posterior.getIndexInRealTerms(posterior.MAPestimate) + timeDifference*relativeSpeedPosterior.getIndexInRealTerms(relativeSpeedPosterior.integratedEstimate);
	// 
	//printf("tmp best %f and best %f time diff %f posterior MAP %f at speed %f\n", tmpBestEstimate, bestEstimate, timeDifference, posterior.getIndexInRealTerms(posterior.MAPestimate), relativeSpeedPosterior.getIndexInRealTerms(relativeSpeedPosterior.integratedEstimate));
	//lastBestEstimateUpdateTime = ofGetElapsedTimeMillis();
}
	
void BayesianArrayStructure::updateBestEstimate(const double& timeDifference){
//	double timeDiff = ofGetElapsedTimeMillis() - lastEventTime;//
	
	double timeDiff = timeDifference;
	
	//Using timedifferencfe here will make it go wrong. Is time since beginning of playing
	
	if (*realTimeMode)
		timeDiff = ofGetElapsedTimeMillis() - lastBestEstimateUpdateTime;
	
	double speedEstimate;
	if (usingIntegratedTempoEstimate)
		speedEstimate = relativeSpeedPosterior.getIntegratedEstimate();
	else
		speedEstimate = relativeSpeedPosterior.MAPestimate;
	
	
	speedEstimate = relativeSpeedPosterior.getIndexInRealTerms(speedEstimate);
	//relativeSpeedPosterior.getIndexInRealTerms(relativeSpeedPosterior.MAPestimate)
	bestEstimate = posterior.getIndexInRealTerms(posterior.MAPestimate) + timeDiff*speedEstimate;
	
//	bestEstimate = tmpBestEstimate;
}

void BayesianArrayStructure::calculatePosterior(){
	//posterior.doProduct(prior, likelihood);
	
	int i;
	for (i = 0;i < posterior.length;i++){
		posterior.array[i] = likelihood.array[i] * prior.array[i];
	}
	
	
	posterior.renormalise();
	


	 
}




void BayesianArrayStructure::setNewDistributionOffsets(const double& newOffset){
	prior.offset = newOffset;
	likelihood.offset = newOffset;
	//posterior.offset = newOffset;
}


void BayesianArrayStructure::crossUpdateArrays(DynamicVector& position, DynamicVector& speed, double timeDifference){
	//set the cutoff for offset of position first! XXX
	
//	printf("time difference %f, ", timeDifference);
	
	double timeDifferenceInPositionVectorUnits = timeDifference / prior.scalar;
	

	prior.zero();//kill prior
	calculateNewPriorOffset(timeDifference);//set new prior offset here
	
	if (timeDifferenceInPositionVectorUnits > crossUpdateTimeThreshold)
		complexCrossUpdate(timeDifferenceInPositionVectorUnits);
	else
		translateByMaximumSpeed(timeDifferenceInPositionVectorUnits);	
			

	updateCounter++;
	prior.renormalise();

}

void BayesianArrayStructure::complexCrossUpdate(const double& timeDifferenceInPositionVectorUnits){
	int distanceMoved, newPriorIndex;
	
	double speedValue = relativeSpeedPosterior.offset;
	
	for (int i = 0;i < relativeSpeedPosterior.arraySize;i++){
		
	//	double speedValue = relativeSpeedPosterior.getIndexInRealTerms(i);//so for scalar 0.01, 50 -> speed value of 0.5
		
		//so we have moved 
		distanceMoved = round(timeDifferenceInPositionVectorUnits * speedValue);//round the value
	
		if (relativeSpeedPosterior.array[i] != 0){
			double speedContribution = relativeSpeedPosterior.array[i];
			//	printf("speed [%i] gives %f moved %i in %f units \n", i, speedValue, distanceMoved, timeDifferenceInPositionVectorUnits);
			
			newPriorIndex = posterior.offset - prior.offset + distanceMoved;
			
			for (int postIndex = 0;postIndex < posterior.arraySize;postIndex++){
				//old posterior contributing to new prior
			//	newPriorIndex = postIndex + posterior.offset - prior.offset + distanceMoved;
				
				if (newPriorIndex >= 0 && newPriorIndex < prior.arraySize){
					prior.addToIndex(newPriorIndex, posterior.array[postIndex]*speedContribution);
				}
				
				newPriorIndex++;//optimised code - the commented line above explains how this works
			}//end for
			
			
		}//if not zero
		speedValue += relativeSpeedPosterior.scalar;
		//optimised line
		//as we wanted:
		//	double speedValue = relativeSpeedPosterior.getIndexInRealTerms(i);//so for scalar 0.01, 50 -> speed value of 0.5
	}//end speed
}



void BayesianArrayStructure::translateByMaximumSpeed(const double& timeDifferenceInPositionVectorUnits){

	int distanceMoved, newPriorIndex;
	
	double speedValue = relativeSpeedPosterior.getIndexInRealTerms(relativeSpeedPosterior.integratedEstimate);
	//so for scalar 0.01, 50 -> speed value of 0.5
	double speedContribution = relativeSpeedPosterior.array[relativeSpeedPosterior.integratedEstimate];
		//so we have moved 
		distanceMoved = round(timeDifferenceInPositionVectorUnits * speedValue);//round the value
					//	printf("speed [%i] gives %f moved %i in %f units \n", i, speedValue, distanceMoved, timeDifferenceInPositionVectorUnits);
			
			for (int postIndex = 0;postIndex < posterior.arraySize;postIndex++){
				//old posterior contributing to new prior
				newPriorIndex = postIndex + posterior.offset - prior.offset + distanceMoved;
				if (newPriorIndex >= 0 && newPriorIndex < prior.arraySize){
					prior.addToIndex(newPriorIndex, posterior.array[postIndex]*speedContribution);
				}
				
			}
	
}

void BayesianArrayStructure::addGaussianNoiseToSpeedPosterior(const double& std_dev){
	tmpPosteriorForStorage.copyFromDynamicVector(relativeSpeedPosterior);
	
	for (int i = 0;i < relativeSpeedPosterior.length;i++){
		tmpPosteriorForStorage.addGaussianShape(i, std_dev, relativeSpeedPosterior.array[i]);
		}
												
	tmpPosteriorForStorage.renormalise();
	
	relativeSpeedPosterior.copyFromDynamicVector(tmpPosteriorForStorage);											
}


void BayesianArrayStructure::addTriangularNoiseToSpeedPosterior(const double& std_dev){
	tmpPosteriorForStorage.copyFromDynamicVector(relativeSpeedPosterior);
	
	for (int i = 0;i < relativeSpeedPosterior.length;i++){
		//adding a linear amount depending on distance
		tmpPosteriorForStorage.addTriangularShape(i, std_dev*2.0, relativeSpeedPosterior.array[i]);
	}
	
	tmpPosteriorForStorage.renormalise();
	
	relativeSpeedPosterior.copyFromDynamicVector(tmpPosteriorForStorage);											
}

void BayesianArrayStructure::calculateNewPriorOffset(const double& timeDifference){
	
	double maxSpeed = relativeSpeedPosterior.getIndexInRealTerms(relativeSpeedPosterior.integratedEstimate);
	//	printf("Maxspeed is %f\n", maxSpeed);
	
	double priorMax = posterior.getMaximum();
	double distanceTravelled = maxSpeed * (timeDifference / prior.scalar);
	double newMaxLocation = posterior.MAPestimate + distanceTravelled;
	//	printf("MAP: %i, tim df %f, distance %f, new location %f\n", posterior.MAPestimate, timeDifference, distanceTravelled, newMaxLocation);
	
}


void BayesianArrayStructure::decaySpeedDistribution(double timeDifference){
	
	// commented for the moment
	 double relativeAmount = max(1.0, timeDifference/1000.);
//	printf("decay %f around %i \n", timeDifference, relativeSpeedPosterior.MAPestimate);
	relativeAmount *= speedDecayAmount;
	relativeSpeedPosterior.renormalise();
	relativeSpeedPosterior.addGaussianShape(relativeSpeedPosterior.MAPestimate, speedDecayWidth, relativeAmount);
	
	relativeSpeedPosterior.renormalise();
	double newMax = relativeSpeedPosterior.getMaximum();
	
	//old code
//	relativeSpeedPosterior.addGaussianShape(relativeSpeedPosterior.MAPestimate, speedDecayWidth, 10);
	//relativeSpeedPosterior.addConstant(1);
	
	/*
	relativeSpeedPrior.copyFromDynamicVector(relativeSpeedPosterior);
	relativeSpeedLikelihood.zero();
	relativeSpeedLikelihood.addConstant(0.2);
	relativeSpeedLikelihood.addGaussianShape(relativeSpeedPosterior.maximumValue, speedDecayWidth, relativeAmount);
	relativeSpeedPosterior.doProduct(relativeSpeedPrior, relativeSpeedLikelihood);
	relativeSpeedPosterior.renormalise();
	 */
	

	
}

void BayesianArrayStructure::setLikelihoodToConstant(){
	//set new likelihood
	relativeSpeedLikelihood.zero();
	relativeSpeedLikelihood.addConstant(speedLikelihoodNoise);
}


void BayesianArrayStructure::updateTempoLikelihood(const double& speedRatio, const double& matchFactor){
	
	//speedratio is speed of played relative to the recording
	
	double index = relativeSpeedLikelihood.getRealTermsAsIndex(speedRatio);
	//	printf("index of likelihood would be %f for ratio %f\n", index, speedRatio);
	if (index >= 0 && index < relativeSpeedPrior.length){	
		relativeSpeedLikelihood.addGaussianShape(index , relativeSpeedLikelihoodStdDev, matchFactor);
	}
}
	


void BayesianArrayStructure::updateTempoDistribution(){

	//copy posterior to prior
	relativeSpeedPrior.copyFromDynamicVector(relativeSpeedPosterior);

	//update
	relativeSpeedPosterior.doProduct(relativeSpeedPrior, relativeSpeedLikelihood);

	//normalise
	relativeSpeedPosterior.renormalise();
		
	relativeSpeedPosterior.getMaximum();	
	
//	relativeSpeedPosterior.updateIntegratedEstimate();
	relativeSpeedPosterior.updateLimitedIntegratedEstimate();
	
	speedEstimate = relativeSpeedPosterior.getIndexInRealTerms(relativeSpeedPosterior.integratedEstimate);
}


void BayesianArrayStructure::calculateTempoUpdate(){
	//copy posterior to prior
	relativeSpeedPrior.copyFromDynamicVector(relativeSpeedPosterior);
	
	//update
	relativeSpeedPosterior.doProduct(relativeSpeedPrior, relativeSpeedLikelihood);
	
	//normalise
	relativeSpeedPosterior.renormalise();
	
	relativeSpeedPosterior.getMaximum();	
	
}


void BayesianArrayStructure::drawArrays(){
	
	//bayesArray.drawFloatArray(&bayesArray.prior[0], 0, 200);
	//bayesArray.drawFloatArray(&bayesArray.prior[0], 0, 200);
	
	int displaySize = prior.arraySize;
	ofSetColor(0,0,255);
	prior.drawVector(0, displaySize);
	ofSetColor(0,255,0);
	likelihood.drawVector(0, displaySize);
	ofSetColor(255,0,255);
	posterior.drawVector(0, displaySize);
	
	
	
}


void BayesianArrayStructure::drawTempoArrays(){
	ofSetColor(0,0,255);
//	relativeSpeedPrior.drawVector(0, relativeSpeedPrior.arraySize);
	
	ofSetColor(0,150,255);
	relativeSpeedLikelihood.drawVector(0, relativeSpeedLikelihood.arraySize);
	
//	relativeSpeedLikelihood.drawConstrainedVector(0, 199, 0, 1000);// relativeSpeedLikelihood.arraySize);	
	ofSetColor(255,0,0);
	relativeSpeedPosterior.drawVector(0, relativeSpeedPosterior.arraySize);
	
//	ofSetColor(0,0,255);
//	tmpPosteriorForStorage.drawVector(0, tmpPosteriorForStorage.arraySize);
	
	ofSetColor(255,255, 255);
	ofLine(screenWidth/2, 0, screenWidth/2, ofGetHeight());//middle of screen
	
	ofSetColor(25, 0, 250);
	double fractionOfScreen = ((double)relativeSpeedPosterior.integratedEstimate / relativeSpeedPosterior.length);
	ofLine(screenWidth * fractionOfScreen, 0, screenWidth * fractionOfScreen, ofGetHeight());
	
	ofSetColor(255, 0, 20);
	fractionOfScreen = ((double)relativeSpeedPosterior.MAPestimate / relativeSpeedPosterior.length);
	ofLine(screenWidth * fractionOfScreen, 0, screenWidth * fractionOfScreen, ofGetHeight());
	 
	
}


void BayesianArrayStructure::drawArraysRelativeToTimeframe(const double& startTimeMillis, const double& endTimeMillis){

	screenWidth = ofGetWidth();
	
	int startArrayIndex = 0;
	
	if (prior.getIndexInRealTerms(prior.arraySize-1) > startTimeMillis){
		//i.e. the array is on the page
	
	while (prior.getIndexInRealTerms(startArrayIndex) < startTimeMillis){
		startArrayIndex++;
	}
	int endArrayIndex = prior.arraySize-1;
	//could find constraints here
	if (prior.getIndexInRealTerms(prior.arraySize-1) > endTimeMillis)
		endArrayIndex = (floor)((endTimeMillis - prior.offset)/prior.scalar);
	
	//so we need to figure where start and end array are on screen
	int startScreenPosition, endScreenPosition;
	double screenWidthMillis = endTimeMillis - startTimeMillis;
		
	startScreenPosition = (prior.getIndexInRealTerms(startArrayIndex) - startTimeMillis)*screenWidth/screenWidthMillis;
	endScreenPosition = (double)(prior.getIndexInRealTerms(endArrayIndex) - startTimeMillis)*screenWidth/screenWidthMillis;
		
	ofSetColor(0,0,100);
	string relativeString = " offset "+ofToString(prior.offset, 1);//starttimes("+ofToString(startTimeMillis)+", "+ofToString(endTimeMillis);
	relativeString += ": index "+ofToString(startArrayIndex)+" , "+ofToString(endArrayIndex)+" [";
//	relativeString += ofToString(prior.getIndexInRealTerms(endArrayIndex), 3)+"] (sc-width:"+ofToString(screenWidthMillis, 1)+")  ";
	relativeString += " mapped to screen "+ofToString(startScreenPosition)+" , "+ofToString(endScreenPosition);
//	ofDrawBitmapString(relativeString, 100, 180);
	
		
		
	ofSetColor(100,100,100);//255, 255, 0);
	likelihood.drawConstrainedVector(startArrayIndex, endArrayIndex, startScreenPosition, endScreenPosition);

//	ofSetColor(0,0,200);
	ofSetColor(170,170,170);//00,200);
	prior.drawConstrainedVector(startArrayIndex, endArrayIndex, startScreenPosition, endScreenPosition);
		
		ofSetColor(0,0,150);	
//	ofSetColor(200, 0, 0);
	posterior.drawConstrainedVector(startArrayIndex, endArrayIndex, startScreenPosition, endScreenPosition);
		
		
//	ofSetColor(0, 200, 255);
//	acceleration.drawConstrainedVector(startArrayIndex, endArrayIndex, startScreenPosition, endScreenPosition);
		
		
	}

}


/*
 
 void BayesianArrayStructure::updateTempoDistribution(const double& speedRatio, const double& matchFactor){
 //speedratio is speed of played relative to the recording
 
 double index = relativeSpeedLikelihood.getRealTermsAsIndex(speedRatio);
 //	printf("\nindex of likelihood would be %f\n", index);
 if (index >= 0 && index < relativeSpeedPrior.length){
 //then we can do update
 
 //set new likelihood
 relativeSpeedLikelihood.zero();
 relativeSpeedLikelihood.addConstant(speedLikelihoodNoise);
 
 relativeSpeedLikelihood.addGaussianShape(index , 5, 0.5*matchFactor);
 
 
 //copy posterior to prior
 relativeSpeedPrior.copyFromDynamicVector(relativeSpeedPosterior);
 
 //update
 relativeSpeedPosterior.doProduct(relativeSpeedPrior, relativeSpeedLikelihood);
 
 //normalise
 relativeSpeedPosterior.renormalise();
 
 relativeSpeedPosterior.getMaximum();	
 }//end if within range
 
 
 }
 
 */