view maths/Histogram.h @ 298:255e431ae3d4

* 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: */

// Histogram.h: interface for the THistogram class.
//
//////////////////////////////////////////////////////////////////////


#ifndef HISTOGRAM_H
#define HISTOGRAM_H


#include <valarray>

/*! \brief A histogram class

  This class computes the histogram of a vector.

\par Template parameters
	
	- T type of input data (can be any: float, double, int, UINT, etc...)
	- TOut type of output data: float or double. (default is double)

\par Moments:

	The moments (average, standard deviation, skewness, etc.) are computed using 
the algorithm of the Numerical recipies (see Numerical recipies in C, Chapter 14.1, pg 613).

\par Example:

	This example shows the typical use of the class:
\code
	// a vector containing the data
	vector<float> data;
	// Creating histogram using float data and with 101 containers,
	THistogram<float> histo(101);
	// computing the histogram
	histo.compute(data);
\endcode

Once this is done, you can get a vector with the histogram or the normalized histogram (such that it's area is 1):
\code
	// getting normalized histogram
	vector<float> v=histo.getNormalizedHistogram();
\endcode

\par Reference
	
	Equally spaced acsissa function integration (used in #GetArea): Numerical Recipies in C, Chapter 4.1, pg 130.

\author Jonathan de Halleux, [e-mail], 2002
*/

template<class T, class TOut = double>
class THistogram  
{
public:
    //! \name Constructors
    //@{
    /*! Default constructor
      \param counters the number of histogram containers (default value is 10)
    */
    THistogram(unsigned int counters = 10);
    virtual ~THistogram()				{ clear();};
    //@}

    //! \name Histogram computation, update
    //@{
    /*! Computes histogram of vector v
      \param v vector to compute histogram
      \param computeMinMax set to true if min/max of v have to be used to get the histogram limits	
	
      This function computes the histogram of v and stores it internally.
      \sa Update, GeTHistogram
    */
    void compute( const std::vector<T>& v, bool computeMinMax = true);
    //! Update histogram with the vector v
    void update( const std::vector<T>& v);
    //! Update histogram with t
    void update( const T& t);
    //@}

    //! \name Resetting functions
    //@{
    //! Resize the histogram. Warning this function clear the histogram.
    void resize( unsigned int counters );
    //! Clears the histogram
    void clear()						{ m_counters.clear();};
    //@}

    //! \name Setters
    //@{
    /*! This function sets the minimum of the histogram spectrum. 
      The spectrum is not recomputed, use it with care
    */
    void setMinSpectrum( const T& min )					{	m_min = min; computeStep();};
    /*! This function sets the minimum of the histogram spectrum. 
      The spectrum is not recomputed, use it with care
    */
    void setMaxSpectrum( const T& max )					{	m_max = max; computeStep();};
    //@}
    //! \name Getters
    //@{
    //! return minimum of histogram spectrum
    const T& getMinSpectrum() const							{	return m_min;};
    //! return maximum of histogram spectrum
    const T& getMaxSpectrum() const							{	return m_max;};
    //! return step size of histogram containers
    TOut getStep() const									{	return m_step;};
    //! return number of points in histogram
    unsigned int getSum() const;
    /*! \brief returns area under the histogram 

    The Simpson rule is used to integrate the histogram.
    */
    TOut getArea() const;

    /*! \brief Computes the moments of the histogram

    \param data dataset
    \param ave mean
    \f[ \bar x = \frac{1}{N} \sum_{j=1}^N x_j\f]
    \param adev mean absolute deviation
    \f[ adev(X) = \frac{1}{N} \sum_{j=1}^N | x_j - \bar x |\f]
    \param var average deviation:
    \f[ \mbox{Var}(X) = \frac{1}{N-1} \sum_{j=1}^N (x_j - \bar x)^2\f]
    \param sdev standard deviation:
    \f[ \sigma(X) = \sqrt{var(\bar x) }\f]
    \param skew skewness
    \f[ \mbox{Skew}(X) = \frac{1}{N}\sum_{j=1}^N \left[ \frac{x_j - \bar x}{\sigma}\right]^3\f]
    \param kurt kurtosis
    \f[ \mbox{Kurt}(X) = \left\{ \frac{1}{N}\sum_{j=1}^N \left[ \frac{x_j - \bar x}{\sigma}\right]^4 \right\} - 3\f]

    */
    static void getMoments(const std::vector<T>& data, TOut& ave, TOut& adev, TOut& sdev, TOut& var, TOut& skew, TOut& kurt);

    //! return number of containers
    unsigned int getSize() const							{	return m_counters.size();};
    //! returns i-th counter
    unsigned int operator [] (unsigned int i) const {
//	ASSERT( i < m_counters.size() );
        return m_counters[i];
    };
    //! return the computed histogram
    const std::vector<unsigned int>& geTHistogram() const	{	return m_counters;};
    //! return the computed histogram, in TOuts
    std::vector<TOut> geTHistogramD() const;
    /*! return the normalized computed histogram 

    \return the histogram such that the area is equal to 1
    */
    std::vector<TOut> getNormalizedHistogram() const;
    //! returns left containers position
    std::vector<TOut> getLeftContainers() const;
    //! returns center containers position
    std::vector<TOut> getCenterContainers() const;
    //@}
protected:
    //! Computes the step
    void computeStep()								{	m_step = (TOut)(((TOut)(m_max-m_min)) / (m_counters.size()-1));};
    //! Data accumulators
    std::vector<unsigned int> m_counters;
    //! minimum of dataset
    T m_min;
    //! maximum of dataset
    T m_max;
    //! width of container
    TOut m_step;
};

template<class T, class TOut>
THistogram<T,TOut>::THistogram(unsigned int counters)
    : m_counters(counters,0), m_min(0), m_max(0), m_step(0)
{

}

template<class T, class TOut>
void THistogram<T,TOut>::resize( unsigned int counters )
{
    clear();

    m_counters.resize(counters,0);

    computeStep();
}

template<class T, class TOut>
void THistogram<T,TOut>::compute( const std::vector<T>& v, bool computeMinMax)
{
    using namespace std;
    unsigned int i;
    int index;

    if (m_counters.empty())
	return;

    if (computeMinMax)
    {
	m_max = m_min = v[0];
	for (i=1;i<v.size();i++)
	{
	    m_max = std::max( m_max, v[i]);
	    m_min = std::min( m_min, v[i]);
	}
    }

    computeStep();

    for (i = 0;i < v.size() ; i++)
    {
	index=(int) floor( ((TOut)(v[i]-m_min))/m_step ) ;

	if (index >= m_counters.size() || index < 0)
	    return;

	m_counters[index]++;
    }
}

template<class T, class TOut>
void THistogram<T,TOut>::update( const std::vector<T>& v)
{
    if (m_counters.empty())
	return;

    computeStep();

    TOut size = m_counters.size();

    int index;
    for (unsigned int i = 0;i < size ; i++)
    {
	index = (int)floor(((TOut)(v[i]-m_min))/m_step);

	if (index >= m_counters.size() || index < 0)
	    return;

	m_counters[index]++;
    }
}

template<class T, class TOut>
void THistogram<T,TOut>::update( const T& t)
{	
    int index=(int) floor( ((TOut)(t-m_min))/m_step ) ;

    if (index >= m_counters.size() || index < 0)
	return;

    m_counters[index]++;
};

template<class T, class TOut>
std::vector<TOut> THistogram<T,TOut>::geTHistogramD() const
{
    std::vector<TOut> v(m_counters.size());
    for (unsigned int i = 0;i<m_counters.size(); i++)
	v[i]=(TOut)m_counters[i];

    return v;
}

template <class T, class TOut>
std::vector<TOut> THistogram<T,TOut>::getLeftContainers() const
{
    std::vector<TOut> x( m_counters.size());

    for (unsigned int i = 0;i<m_counters.size(); i++)
	x[i]= m_min + i*m_step;

    return x;
}

template <class T, class TOut>
std::vector<TOut> THistogram<T,TOut>::getCenterContainers() const
{
    std::vector<TOut> x( m_counters.size());

    for (unsigned int i = 0;i<m_counters.size(); i++)
	x[i]= m_min + (i+0.5)*m_step;

    return x;
}

template <class T, class TOut>
unsigned int THistogram<T,TOut>::getSum() const
{
    unsigned int sum = 0;
    for (unsigned int i = 0;i<m_counters.size(); i++)
	sum+=m_counters[i];

    return sum;
}

template <class T, class TOut>
TOut THistogram<T,TOut>::getArea() const
{
    const size_t n=m_counters.size();
    TOut area=0;

    if (n>6)
    {
	area=3.0/8.0*(m_counters[0]+m_counters[n-1])
	    +7.0/6.0*(m_counters[1]+m_counters[n-2])
	    +23.0/24.0*(m_counters[2]+m_counters[n-3]);
	for (unsigned int i=3;i<n-3;i++)
	{
	    area+=m_counters[i];
	}
    }
    else if (n>4)
    {
	area=5.0/12.0*(m_counters[0]+m_counters[n-1])
	    +13.0/12.0*(m_counters[1]+m_counters[n-2]);
	for (unsigned int i=2;i<n-2;i++)
	{
	    area+=m_counters[i];
	}
    }
    else if (n>1)
    {
	area=1/2.0*(m_counters[0]+m_counters[n-1]);
	for (unsigned int i=1;i<n-1;i++)
	{
	    area+=m_counters[i];
	}
    }
    else 
	area=0;

    return area*m_step;
}

template <class T, class TOut>
std::vector<TOut> THistogram<T,TOut>::getNormalizedHistogram() const
{
    std::vector<TOut> normCounters( m_counters.size());
    TOut area = (TOut)getArea();

    for (unsigned int i = 0;i<m_counters.size(); i++)
    {
	normCounters[i]= (TOut)m_counters[i]/area;
    }

    return normCounters;
};

template <class T, class TOut>
void THistogram<T,TOut>::getMoments(const std::vector<T>& data, TOut& ave, TOut& adev, TOut& sdev, TOut& var, TOut& skew, TOut& kurt)
{
    int j;
    double ep=0.0,s,p;
    const size_t n = data.size();

    if (n <= 1)
	// nrerror("n must be at least 2 in moment");
	return;

    s=0.0; // First pass to get the mean.
    for (j=0;j<n;j++)
	s += data[j];
	
    ave=s/(n);
    adev=var=skew=kurt=0.0; 
    /* Second pass to get the first (absolute), second,
       third, and fourth moments of the
       deviation from the mean. */

    for (j=0;j<n;j++) 
    {
	adev += fabs(s=data[j]-(ave));
	ep += s;
	var += (p=s*s);
	skew += (p *= s);
	kurt += (p *= s);
    }


    adev /= n;
    var=(var-ep*ep/n)/(n-1); // Corrected two-pass formula.
    sdev=sqrt(var); // Put the pieces together according to the conventional definitions. 
    if (var) 
    {
	skew /= (n*(var)*(sdev));
	kurt=(kurt)/(n*(var)*(var))-3.0;
    } 
    else
	//nrerror("No skew/kurtosis when variance = 0 (in moment)");
	return;
}

#endif