annotate maths/MathUtilities.cpp @ 73:dcb555b90924

* Key detector: when returning key strengths, use the peak value of the
  three underlying chromagram correlations (from 36-bin chromagram)
  corresponding to each key, instead of the mean.
  Rationale: This is the same method as used when returning the key value,
  and it's nice to have the same results in both returned value and plot.
  The peak performed better than the sum with a simple test set of triads,
  so it seems reasonable to change the plot to match the key output rather
  than the other way around.

* FFT: kiss_fftr returns only the non-conjugate bins, synthesise the rest
  rather than leaving them (perhaps dangerously) undefined.  Fixes an
  uninitialised data error in chromagram that could cause garbage results
  from key detector.

* Constant Q: remove precalculated values again, I reckon they're not
  proving such a good tradeoff.

/* -*- c-basic-offset: 4 indent-tabs-mode: nil -*-  vi:set ts=8 sts=4 sw=4: */

/*
    QM DSP Library

    Centre for Digital Music, Queen Mary, University of London.
    This file copyright 2005-2006 Christian Landone.
    All rights reserved.
*/

#include "MathUtilities.h"

#include <iostream>
#include <cmath>


double MathUtilities::mod(double x, double y)
{
    double a = floor( x / y );

    double b = x - ( y * a );
    return b;
}

double MathUtilities::princarg(double ang)
{
    double ValOut;

    ValOut = mod( ang + M_PI, -2 * M_PI ) + M_PI;

    return ValOut;
}

void MathUtilities::getAlphaNorm(const double *data, unsigned int len, unsigned int alpha, double* ANorm)
{
    unsigned int i;
    double temp = 0.0;
    double a=0.0;
	
    for( i = 0; i < len; i++)
    {
	temp = data[ i ];
		
	a  += ::pow( fabs(temp), alpha );
    }
    a /= ( double )len;
    a = ::pow( a, ( 1.0 / (double) alpha ) );

    *ANorm = a;
}

double MathUtilities::getAlphaNorm( const std::vector <double> &data, unsigned int alpha )
{
    unsigned int i;
    unsigned int len = data.size();
    double temp = 0.0;
    double a=0.0;
	
    for( i = 0; i < len; i++)
    {
	temp = data[ i ];
		
	a  += ::pow( fabs(temp), alpha );
    }
    a /= ( double )len;
    a = ::pow( a, ( 1.0 / (double) alpha ) );

    return a;
}

double MathUtilities::round(double x)
{
    double val = (double)floor(x + 0.5);
  
    return val;
}

double MathUtilities::median(const double *src, unsigned int len)
{
    unsigned int i, j;
    double tmp = 0.0;
    double tempMedian;
    double medianVal;
 
    double* scratch = new double[ len ];//Vector < double > sortedX = Vector < double > ( size );

    for ( i = 0; i < len; i++ )
    {
	scratch[i] = src[i];
    }

    for ( i = 0; i < len - 1; i++ )
    {
	for ( j = 0; j < len - 1 - i; j++ )
	{
	    if ( scratch[j + 1] < scratch[j] )
	    {
		// compare the two neighbors
		tmp = scratch[j]; // swap a[j] and a[j+1]
		scratch[j] = scratch[j + 1];
		scratch[j + 1] = tmp;
	    }
	}
    }
    int middle;
    if ( len % 2 == 0 )
    {
	middle = len / 2;
	tempMedian = ( scratch[middle] + scratch[middle - 1] ) / 2;
    }
    else
    {
	middle = ( int )floor( len / 2.0 );
	tempMedian = scratch[middle];
    }

    medianVal = tempMedian;

    delete [] scratch;
    return medianVal;
}

double MathUtilities::sum(const double *src, unsigned int len)
{
    unsigned int i ;
    double retVal =0.0;

    for(  i = 0; i < len; i++)
    {
	retVal += src[ i ];
    }

    return retVal;
}

double MathUtilities::mean(const double *src, unsigned int len)
{
    double retVal =0.0;

    double s = sum( src, len );
	
    retVal =  s  / (double)len;

    return retVal;
}

double MathUtilities::mean(const std::vector<double> &src,
                           unsigned int start,
                           unsigned int count)
{
    double sum = 0.;
	
    for (int i = 0; i < count; ++i)
    {
        sum += src[start + i];
    }

    return sum / count;
}

void MathUtilities::getFrameMinMax(const double *data, unsigned int len, double *min, double *max)
{
    unsigned int i;
    double temp = 0.0;
    double a=0.0;

    if (len == 0) {
        *min = *max = 0;
        return;
    }
	
    *min = data[0];
    *max = data[0];

    for( i = 0; i < len; i++)
    {
	temp = data[ i ];

	if( temp < *min )
	{
	    *min =  temp ;
	}
	if( temp > *max )
	{
	    *max =  temp ;
	}
		
    }
}

int MathUtilities::getMax( double* pData, unsigned int Length, double* pMax )
{
	unsigned int index = 0;
	unsigned int i;
	double temp = 0.0;
	
	double max = pData[0];

	for( i = 0; i < Length; i++)
	{
		temp = pData[ i ];

		if( temp > max )
		{
			max =  temp ;
			index = i;
		}
		
   	}

	if (pMax) *pMax = max;


	return index;
}

int MathUtilities::getMax( const std::vector<double> & data, double* pMax )
{
	unsigned int index = 0;
	unsigned int i;
	double temp = 0.0;
	
	double max = data[0];

	for( i = 0; i < data.size(); i++)
	{
		temp = data[ i ];

		if( temp > max )
		{
			max =  temp ;
			index = i;
		}
		
   	}

	if (pMax) *pMax = max;


	return index;
}

void MathUtilities::circShift( double* pData, int length, int shift)
{
	shift = shift % length;
	double temp;
	int i,n;

	for( i = 0; i < shift; i++)
	{
		temp=*(pData + length - 1);

		for( n = length-2; n >= 0; n--)
		{
			*(pData+n+1)=*(pData+n);
		}

        *pData = temp;
    }
}

int MathUtilities::compareInt (const void * a, const void * b)
{
  return ( *(int*)a - *(int*)b );
}

void MathUtilities::normalise(double *data, int length, NormaliseType type)
{
    switch (type) {

    case NormaliseNone: return;

    case NormaliseUnitSum:
    {
        double sum = 0.0;
        for (int i = 0; i < length; ++i) {
            sum += data[i];
        }
        if (sum != 0.0) {
            for (int i = 0; i < length; ++i) {
                data[i] /= sum;
            }
        }
    }
    break;

    case NormaliseUnitMax:
    {
        double max = 0.0;
        for (int i = 0; i < length; ++i) {
            if (fabs(data[i]) > max) {
                max = fabs(data[i]);
            }
        }
        if (max != 0.0) {
            for (int i = 0; i < length; ++i) {
                data[i] /= max;
            }
        }
    }
    break;

    }
}

void MathUtilities::normalise(std::vector<double> &data, NormaliseType type)
{
    switch (type) {

    case NormaliseNone: return;

    case NormaliseUnitSum:
    {
        double sum = 0.0;
        for (int i = 0; i < data.size(); ++i) sum += data[i];
        if (sum != 0.0) {
            for (int i = 0; i < data.size(); ++i) data[i] /= sum;
        }
    }
    break;

    case NormaliseUnitMax:
    {
        double max = 0.0;
        for (int i = 0; i < data.size(); ++i) {
            if (fabs(data[i]) > max) max = fabs(data[i]);
        }
        if (max != 0.0) {
            for (int i = 0; i < data.size(); ++i) data[i] /= max;
        }
    }
    break;

    }
}

void MathUtilities::adaptiveThreshold(std::vector<double> &data)
{
    int sz = int(data.size());
    if (sz == 0) return;

    std::vector<double> smoothed(sz);
	
    int p_pre = 8;
    int p_post = 7;

    for (int i = 0; i < sz; ++i) {

        int first = std::max(0,      i - p_pre);
        int last  = std::min(sz - 1, i + p_post);

        smoothed[i] = mean(data, first, last - first + 1);
    }

    for (int i = 0; i < sz; i++) {
        data[i] -= smoothed[i];
        if (data[i] < 0.0) data[i] = 0.0;
    }
}

bool
MathUtilities::isPowerOfTwo(int x)
{
    if (x < 2) return false;
    if (x & (x-1)) return false;
    return true;
}

int
MathUtilities::nextPowerOfTwo(int x)
{
    if (isPowerOfTwo(x)) return x;
    int n = 1;
    while (x) { x >>= 1; n <<= 1; }
    return n;
}

int
MathUtilities::previousPowerOfTwo(int x)
{
    if (isPowerOfTwo(x)) return x;
    int n = 1;
    x >>= 1;
    while (x) { x >>= 1; n <<= 1; }
    return n;
}

int
MathUtilities::nearestPowerOfTwo(int x)
{
    if (isPowerOfTwo(x)) return x;
    int n0 = previousPowerOfTwo(x), n1 = nearestPowerOfTwo(x);
    if (x - n0 < n1 - x) return n0;
    else return n1;
}