andrew@0: /*
andrew@0:  *  bayesianArray.cpp
andrew@0:  *  bayesianTest5
andrew@0:  *
andrew@0:  *  Created by Andrew Robertson on 08/05/2010.
andrew@0:  *  Copyright 2010 __MyCompanyName__. All rights reserved.
andrew@0:  *
andrew@0:  */
andrew@0: 
andrew@0: #include "bayesianArray.h"
andrew@0: #include "math.h"
andrew@0: #include "ofMain.h"
andrew@0: 
andrew@0: bayesianArray::bayesianArray(){
andrew@0: 	likelihoodNoise = 0.5;
andrew@0: 	likelihoodMean = ARRAY_SIZE/2;
andrew@0: 	likelihoodStdDev = ARRAY_SIZE / 12;
andrew@0: initialiseArray();
andrew@0: }
andrew@0: 
andrew@0: void bayesianArray::initialiseArray(){
andrew@0: 
andrew@0: 	//maximumIndex = 12;//change this
andrew@0: 	setGaussianPrior(ARRAY_SIZE/2, ARRAY_SIZE/1);	
andrew@0: 	setGaussianLikelihood(ARRAY_SIZE/2, ARRAY_SIZE/1);//likelihoodMean, likelihoodStdDev);
andrew@0: 	
andrew@0: 	calculatePosterior();
andrew@0: 	renormalisePosterior();
andrew@0: 	posteriorDecayRate = 0.06;
andrew@0: 	
andrew@0: 	eighthNoteProportion = 0.35;//must be less than 0.5 to discriminate - was 0.4
andrew@0: 	earlySixteenthNoteProportion = 0;
andrew@0: 	lateSixteenthNoteProportion = 0;
andrew@0: 	decayNoiseAmount = 0.1;
andrew@0: 	decayNoiseStdDev = ARRAY_SIZE/24;
andrew@0: 	standardDeviation = likelihoodStdDev;
andrew@0: 	setDecayNoiseGaussian(ARRAY_SIZE/2, decayNoiseStdDev);
andrew@0: 	
andrew@0: 	setGaussianLikelihood(likelihoodMean, likelihoodStdDev);
andrew@0: }
andrew@0: 
andrew@0: 
andrew@0: void bayesianArray::setGaussianPrior(float mean, float StdDev){
andrew@0: int i;
andrew@0: 	for (i=0;i<ARRAY_SIZE;i++){
andrew@0: 	prior[i] = (1/(StdDev*sqrt(2*PI)))*exp(-1*(i-mean)*(i-mean)/(2*StdDev*StdDev));
andrew@0: 	//posterior[i] = prior[i];
andrew@0: 	}
andrew@0: }
andrew@0: 
andrew@0: void bayesianArray::setGaussianPosterior(float mean, float StdDev){
andrew@0: int i;
andrew@0: 	for (i=0;i<ARRAY_SIZE;i++){
andrew@0: 	posterior[i] = (1/(StdDev*sqrt(2*PI)))*exp(-1*(i-mean)*(i-mean)/(2*StdDev*StdDev));
andrew@0: 	}
andrew@0: }
andrew@0: 
andrew@0: void bayesianArray::setGaussianLikelihoodForBeats(float mean, float StdDev){
andrew@0: 	//this has eighth and sixteenth positions included
andrew@0: 	
andrew@0: if (mean >= 0 && mean <= ARRAY_SIZE){
andrew@0: int i;	float eighthDifference;
andrew@0: int eighthPosition = ((int)mean + ARRAY_SIZE/2)%ARRAY_SIZE;
andrew@0: int earlySixteenthPosition = ((int)mean + (3*ARRAY_SIZE/4))%ARRAY_SIZE;;
andrew@0: int lateSixteenthPosition = ((int)mean + (ARRAY_SIZE/4))%ARRAY_SIZE;;
andrew@0: 
andrew@0: float mainDifference, sixteenthDifference;
andrew@0: float gaussianProportion = 1 - likelihoodNoise;
andrew@0: float mainProportion = (1 - eighthNoteProportion - earlySixteenthNoteProportion - lateSixteenthNoteProportion);
andrew@0: 
andrew@0: 	for (i=0;i < ARRAY_SIZE;i++){
andrew@0: 	
andrew@0: 	mainDifference = min( fabs(i-mean) , (double)(i + ARRAY_SIZE - mean));
andrew@0: 	likelihood[i] = gaussianProportion * mainProportion * (1/(StdDev*sqrt(2*PI)))*exp(-1*(mainDifference)*(mainDifference)/(2*StdDev*StdDev)) ;
andrew@0: 	
andrew@0: 	eighthDifference = min( abs(i - eighthPosition) , i + ARRAY_SIZE - eighthPosition);
andrew@0: 	eighthDifference = min(eighthDifference , (float)(ARRAY_SIZE + eighthPosition - i ));
andrew@0: 	//for e.g. +0.43, or -0.47 we require the gaussian around the half note too
andrew@0: 	likelihood[i] += gaussianProportion * eighthNoteProportion * (1/(StdDev*sqrt(2*PI)))*exp(-1*(eighthDifference)*(eighthDifference)/(2*StdDev*StdDev)) ;
andrew@0: 	
andrew@0: 	sixteenthDifference = min( abs(i - earlySixteenthPosition) , i + ARRAY_SIZE - earlySixteenthPosition);
andrew@0: 	sixteenthDifference = min(sixteenthDifference , (float)(ARRAY_SIZE + earlySixteenthPosition - i ));
andrew@0: 	//for e.g. +0.43, or -0.47 we require the gaussian around the half note too
andrew@0: 	likelihood[i] += gaussianProportion * earlySixteenthNoteProportion * (1/(StdDev*sqrt(2*PI)))*exp(-1*(sixteenthDifference)*(sixteenthDifference)/(2*StdDev*StdDev)) ;
andrew@0: 	
andrew@0: 	sixteenthDifference = min( abs(i - lateSixteenthPosition) , i + ARRAY_SIZE - lateSixteenthPosition);
andrew@0: 	sixteenthDifference = min(sixteenthDifference , (float)(ARRAY_SIZE + lateSixteenthPosition - i ));
andrew@0: 	//for e.g. +0.43, or -0.47 we require the gaussian around the half note too
andrew@0: 	likelihood[i] += gaussianProportion * lateSixteenthNoteProportion * (1/(StdDev*sqrt(2*PI)))*exp(-1*(sixteenthDifference)*(sixteenthDifference)/(2*StdDev*StdDev)) ;
andrew@0: 	
andrew@0: 
andrew@0: 
andrew@0: 	likelihood[i] += (likelihoodNoise / ARRAY_SIZE);
andrew@0: 	//likelihood[i] = (float) max(gaussianProportion * (1/(StdDev*sqrt(2*PI)))*exp(-1*(i-mean)*(i-mean)/(2*StdDev*StdDev)) , 
andrew@0: 	//(double) (likelihoodNoise / ARRAY_SIZE) );
andrew@0: 	}
andrew@0: //	renormaliseArray(&likelihood[0], ARRAY_SIZE);
andrew@0: }//end if mean within limits
andrew@0: }
andrew@0: 
andrew@0: 
andrew@0: void bayesianArray::setGaussianLikelihood(float mean, float StdDev){
andrew@0: if (mean >= 0 && mean <= ARRAY_SIZE){
andrew@0: int i;	float eighthDifference;
andrew@0: int eighthPosition = ((int)mean + ARRAY_SIZE/2)%ARRAY_SIZE;
andrew@0: float mainDifference;
andrew@0: float gaussianProportion = 1 - likelihoodNoise;
andrew@0: 
andrew@0: 	for (i=0;i < ARRAY_SIZE;i++){
andrew@0: 	
andrew@0: 	mainDifference = min( fabs(i-mean) , (double)(i + ARRAY_SIZE - mean));
andrew@0: 	//without * (1 - eighthNoteProportion)
andrew@0: 	likelihood[i] = gaussianProportion  * (1/(StdDev*sqrt(2*PI)))*exp(-1*(mainDifference)*(mainDifference)/(2*StdDev*StdDev)) ;
andrew@0: 		
andrew@0: 	likelihood[i] += (likelihoodNoise / ARRAY_SIZE);
andrew@0: 	//likelihood[i] = (float) max(gaussianProportion * (1/(StdDev*sqrt(2*PI)))*exp(-1*(i-mean)*(i-mean)/(2*StdDev*StdDev)) , 
andrew@0: 	//(double) (likelihoodNoise / ARRAY_SIZE) );
andrew@0: 	}
andrew@0: //	renormaliseArray(&likelihood[0], ARRAY_SIZE);
andrew@0: }//end if mean within limits
andrew@0: }
andrew@0: 
andrew@0: void bayesianArray::calculatePosterior(){
andrew@0: int i;
andrew@0: 	for (i=0;i < ARRAY_SIZE;i++){
andrew@0: 	posterior[i] = likelihood[i] * prior[i];
andrew@0: 	}
andrew@0: 	//renormalisePosterior();
andrew@0: }
andrew@0: 
andrew@0: 
andrew@0: float bayesianArray::getMaximum(float *ptr, int length){
andrew@0: int i;
andrew@0: float max = 0;
andrew@0: 	for (i=0;i < length;i++){	
andrew@0: 		if (*(ptr+i)>max)
andrew@0: 		max = *(ptr+i);
andrew@0: 	}
andrew@0: 	maximumValue = max;
andrew@0: 	return max;
andrew@0: }
andrew@0: 
andrew@0: float* bayesianArray::getMaximumEstimate(float *ptr, int length){
andrew@0: float returnArray[2];
andrew@0: int i;
andrew@0: float max = 0;
andrew@0: maximumIndex = 0;
andrew@0: 	for (i=0;i < length;i++){	
andrew@0: 		if (*(ptr+i)>max){
andrew@0: 		max = *(ptr+i);
andrew@0: 		maximumIndex = i;
andrew@0: 		}
andrew@0: 	}
andrew@0: 	returnArray[0] = max;
andrew@0: 	returnArray[1] = maximumIndex;
andrew@0: 	maximumValue = max;
andrew@0: 	return &returnArray[0];
andrew@0: }
andrew@0: 
andrew@0: 
andrew@0: 
andrew@0: double bayesianArray::getIntegratedEstimateIndex(){
andrew@0: 	int i;
andrew@0: 	float integratedQuantity = 0;
andrew@0: 	float integratedTotal = 0;
andrew@0: 	double integratedIndex = 0;
andrew@0: 	for (i=0;i < ARRAY_SIZE;i++){	
andrew@0: 		integratedQuantity += posterior[i];//the values of the probability distribution
andrew@0: 		integratedTotal += i*posterior[i];
andrew@0: 	}
andrew@0: 	if (integratedQuantity > 0){
andrew@0: 		integratedIndex = integratedTotal / integratedQuantity;
andrew@0: 	}
andrew@0: 	integratedEstimate = (float) integratedIndex;
andrew@0: 	return integratedIndex;
andrew@0: }
andrew@0: 
andrew@0: 
andrew@0: double bayesianArray::calculateStandardDeviation(){
andrew@0: 
andrew@0: 	double total = 0;
andrew@0: 	double pdfSum;
andrew@0: 	double variance = 0;
andrew@0: 	for (int i=0;i < ARRAY_SIZE;i++){	
andrew@0: //*posterior[i] * 		
andrew@0: 		total += posterior[i] * (i - integratedEstimate) * (i - integratedEstimate);//the values of the probability distribution
andrew@0: 		pdfSum += posterior[i];
andrew@0: 	}
andrew@0: 	
andrew@0: 	if (pdfSum > 0)
andrew@0: 	variance = total / pdfSum;
andrew@0: 	else 
andrew@0: 	variance = ARRAY_SIZE;
andrew@0: 
andrew@0: 	standardDeviation = sqrt(variance);
andrew@0: 	return standardDeviation;
andrew@0: }
andrew@0: 
andrew@0: 
andrew@0: 
andrew@0: void bayesianArray::renormaliseArray(float *ptr, int length){
andrew@0: int i;
andrew@0: float totalArea = 0;
andrew@0: 	for (i=0;i < length;i++){
andrew@0: 		totalArea += *(ptr+i);
andrew@0: 	}
andrew@0: 	
andrew@0: 	for (i=0;i < length;i++){
andrew@0: 		*(ptr+i) /= totalArea;
andrew@0: 	}
andrew@0: 	
andrew@0: }
andrew@0: 
andrew@0: void bayesianArray::resetPrior(){
andrew@0: 	int i;
andrew@0: 	for (i=0;i<ARRAY_SIZE;i++){
andrew@0: 	prior[i] = posterior[i];
andrew@0: 	}
andrew@0: }
andrew@0: 
andrew@0: void bayesianArray::renormalisePosterior(){
andrew@0: int i;
andrew@0: float totalArea = 0;
andrew@0: 	for (i=0;i < ARRAY_SIZE;i++){
andrew@0: 		totalArea += posterior[i];
andrew@0: 	}
andrew@0: 	
andrew@0: 	for (i=0;i < ARRAY_SIZE;i++){
andrew@0: 		posterior[i] /= totalArea;
andrew@0: 	}
andrew@0: 	
andrew@0: }
andrew@0: 
andrew@0: void bayesianArray::decayPosterior(){
andrew@0: 	float *pointer;
andrew@0: 	pointer = getMaximumEstimate(&posterior[0], ARRAY_SIZE);	
andrew@0: 	 float maximum;
andrew@0: 	 maximum = *pointer;
andrew@0: 	int i;
andrew@0: 	for (i=0;i<ARRAY_SIZE;i++){
andrew@0: 	posterior[i] += (maximum - posterior[i]) * posteriorDecayRate * 0.01;;//usded to be * maximum not minus value
andrew@0: 	}
andrew@0: 	maximumIndex = *(pointer+1);
andrew@0: }
andrew@0: 
andrew@0: void bayesianArray::setDecayNoiseGaussian(float mean, float StdDev){
andrew@0: int i;
andrew@0: 	for (i=0;i<ARRAY_SIZE;i++){
andrew@0: 	decayNoiseArray[i] = (1/(StdDev*sqrt(2*PI)))*exp(-1*(i-mean)*(i-mean)/(2*StdDev*StdDev));
andrew@0: 	}
andrew@0: }
andrew@0: 
andrew@0: void bayesianArray::decayPosteriorWithGaussianNoise(){
andrew@0: 
andrew@0: 	int i;
andrew@0: 	float currentMaximum = getMaximum(&posterior[0], ARRAY_SIZE);
andrew@0: 	for (i=0;i<ARRAY_SIZE;i++){
andrew@0: 	posterior[i] += decayNoiseArray[(i - (int)maximumIndex + ((3*ARRAY_SIZE)/2)) % ARRAY_SIZE] * currentMaximum * decayNoiseAmount;
andrew@0: 	//posteriorDecayRate * 0.01;;//usded to be * maximum not minus value
andrew@0: 	}
andrew@0: 
andrew@0: }
andrew@0: 
andrew@0: void bayesianArray::resetMaximumPosterior(){
andrew@0: int i;
andrew@0: float max = 0;
andrew@0: 	for (i=0;i < ARRAY_SIZE;i++){	
andrew@0: 		if (posterior[i]>max){
andrew@0: 		maximumIndex = i;
andrew@0: 		max = posterior[i];
andrew@0: 		}
andrew@0: 	}
andrew@0: }
andrew@0: 
andrew@0: void bayesianArray::translateDistribution(int translationIndex){
andrew@0: int tmpIndex;
andrew@0: 		//copy array
andrew@0: 		int i;
andrew@0: 		for (i=0;i < ARRAY_SIZE;i++){
andrew@0: 		tempPosteriorArray[i] =	posterior[i] ;
andrew@0: 		}
andrew@0: 		//translate values
andrew@0: 	for (i=0;i < ARRAY_SIZE;i++){
andrew@0: 	tmpIndex = (i + translationIndex + ARRAY_SIZE)%ARRAY_SIZE;
andrew@0: 	posterior[tmpIndex] = tempPosteriorArray[i]; 
andrew@0: 	}
andrew@0: 	//now delete tmp array
andrew@0: }
andrew@0: 
andrew@0: