DGtal::OrderedLinearRegression Class Reference

Description of class 'OrderedLinearRegression'. More...

#include <DGtal/math/OrderedLinearRegression.h>

Public Member Functions
	~OrderedLinearRegression ()
	OrderedLinearRegression (double eps_zero=1e-8)
void	clear ()
template<class XIterator , class YIterator >
void	addSamples (XIterator begin_x, XIterator end_x, YIterator begin_y)
void	addSample (const double x, const double y)
void	forwardSLR (SimpleLinearRegression &linearModel, const unsigned int n=4, const double alpha=0.01) const
void	backwardSLR (SimpleLinearRegression &linearModel, const unsigned int n=4, const double alpha=0.01) const
void	selfDisplay (std::ostream &that_stream) const
bool	isValid () const

Private Member Functions
	OrderedLinearRegression (const OrderedLinearRegression &other)
OrderedLinearRegression &	operator= (const OrderedLinearRegression &other)

Private Attributes
double	myEpsilonZero
	Epsilon zero value. More...
unsigned int	myN
	Number of samples. More...
std::vector< double >	myY
	Ordinate values of sample points. More...
std::vector< double >	myX
	Abscissa values of sample points. More...

Detailed Description

Description of class 'OrderedLinearRegression'.

Aim: Utility based on SimpleLinearRegression to compute regression on ordered data. Hence, we can obtain linear fitting with interval trust from the left to the right (resp. from the right to the left) of the data.

Note

backport from ImaGene.

Definition at line 65 of file OrderedLinearRegression.h.

Constructor & Destructor Documentation

~OrderedLinearRegression()

DGtal::OrderedLinearRegression::~OrderedLinearRegression ( )

inline

Destructor.

Definition at line 73 of file OrderedLinearRegression.h.

    {}

OrderedLinearRegression() [1/2]

DGtal::OrderedLinearRegression::OrderedLinearRegression ( double  eps_zero = 1e-8 )

inline

Constructor. The object is empty (and invalid for regression).

Parameters

[in]

eps_zero

the value below which the absolute value of the determinant is considered null.

Definition at line 83 of file OrderedLinearRegression.h.

                                                     :
      myEpsilonZero(eps_zero),
      myN(0)
    {}

OrderedLinearRegression() [2/2]

DGtal::OrderedLinearRegression::OrderedLinearRegression ( const OrderedLinearRegression &  other )

private

Copy constructor.

Parameters

other

the object to clone. Forbidden by default.

Member Function Documentation

addSample()

void DGtal::OrderedLinearRegression::addSample ( const double  x, const double  y  )

inline

Adds the sample (x,y). Does not compute immediately the regression but copies the data. See 'forwardSLR' or 'backwardSLR' for computing the regression with the current samples.

Parameters

[in]	x	the x data.
[in]	y	the y data.

See also

forwardSLR backwardSLR

Definition at line 131 of file OrderedLinearRegression.h.

    {
      myX.push_back( x );
      myY.push_back( y );
      ++myN;
    }

References myN, myX, and myY.

Referenced by addSamples().

addSamples()

template<class XIterator , class YIterator >

void DGtal::OrderedLinearRegression::addSamples ( XIterator  begin_x, XIterator  end_x, YIterator  begin_y  )

inline

Adds the samples (x,y). Does not compute immediately the regression but copies the data. See 'forwardSLR' or 'backwardSLR' for computing the regression with the current samples.

Parameters

[in]	begin_x	an iterator on the first x-data
[in]	end_x	an iterator after the last x-data
[in]	begin_y	an iterator on the first y-data

See also

forwardSLR backwardSLR

Definition at line 111 of file OrderedLinearRegression.h.

      {
        for ( ; begin_x != end_x; ++begin_x, ++begin_y )
          {
            addSample( *begin_x, *begin_y );
          }
      }

References addSample().

Referenced by testSimpleRegressionOrdered().

backwardSLR()

void DGtal::OrderedLinearRegression::backwardSLR ( SimpleLinearRegression &  linearModel, const unsigned int  n = 4, const double  alpha = 0.01  ) const

inline

Returns the slope of the last straight part of the data. The straightness is evaluated through a statistic test based on a simple linear regression (SLR) model. It requires two parameters: n is the minimum number of samples to fit a linear model, 1-[ alpha] is the proportion of accepted linear model of the test (99%, alpha=0.01, means that 99% of all linear model with a Gaussian noise are accepted).

Parameters

[in]	n	the minimum number of samples greater than 3 (default value is 4).
[in]	alpha	is the proportion of rejected linear model (the ones with big variance, default value is 0.01).
[out]	linearModel	the SLR instance of the last straight part of the data.

Definition at line 200 of file OrderedLinearRegression.h.

    {
      linearModel.setEpsilonZero(myEpsilonZero);
      linearModel.clear();
      std::vector<double>::const_reverse_iterator itx = myX.rbegin(); 
      std::vector<double>::const_reverse_iterator itxe = myX.rend(); 
      std::vector<double>::const_reverse_iterator ity = myY.rbegin(); 
      linearModel.addSamples( itx, itx + n, ity );
      linearModel.computeRegression();
      itx += n;
      ity += n;
      unsigned int l = myX.size() - n + 1;
      for ( ; itx != itxe; ++itx, ++ity, --l )
        {
          std::pair<double,double> ic;
          ic = linearModel.trustIntervalForY( *itx, alpha );
          if ( ( *ity < ic.first ) || ( *ity > ic.second ) )
            break;
          linearModel.addSample( *itx, *ity );
          linearModel.computeRegression();
        }
    }

References DGtal::SimpleLinearRegression::addSample(), DGtal::SimpleLinearRegression::addSamples(), DGtal::SimpleLinearRegression::clear(), DGtal::SimpleLinearRegression::computeRegression(), myEpsilonZero, myX, myY, DGtal::SimpleLinearRegression::setEpsilonZero(), and DGtal::SimpleLinearRegression::trustIntervalForY().

Referenced by testSimpleRegressionOrdered().

clear()

void DGtal::OrderedLinearRegression::clear ( )

inline

Clears the data.

Definition at line 91 of file OrderedLinearRegression.h.

    {
      myX.clear();
      myY.clear();
      myN = 0;
    }

References myN, myX, and myY.

forwardSLR()

void DGtal::OrderedLinearRegression::forwardSLR ( SimpleLinearRegression &  linearModel, const unsigned int  n = 4, const double  alpha = 0.01  ) const

inline

Returns the slope of the first straight part of the data. The straightness is evaluated through a statistic test based on a simple linear regression (SLR) model. It requires two parameters: n is the minimum number of samples to fit a linear model, 1-[ alpha] is the proportion of accepted linear model of the test (99%, alpha=0.01, means that 99% of all linear model with a Gaussian noise are accepted).

Parameters

[in]	n	the minimum number of samples greater than 3 (default value is 4).
[in]	alpha	is the proportion of rejected linear model (the ones with big variance, default value is 0.01).
[out]	linearModel	the SLR instance of the first straight part of the data.

Definition at line 157 of file OrderedLinearRegression.h.

    {
      linearModel.setEpsilonZero(myEpsilonZero);
      linearModel.clear();
      std::vector<double>::const_iterator itx = myX.begin(); 
      std::vector<double>::const_iterator itxe = myX.end(); 
      std::vector<double>::const_iterator ity = myY.begin(); 
      linearModel.addSamples( itx, itx + n, ity );
      linearModel.computeRegression();
      itx += n;
      ity += n;
      unsigned int l = myX.size() - n + 1;
      for ( ; itx != itxe; ++itx, ++ity, --l )
        {
          std::pair<double,double> ic;
          ic = linearModel.trustIntervalForY( *itx, alpha );
          if ( ( *ity < ic.first ) || ( *ity > ic.second ) )
            break;
          linearModel.addSample( *itx, *ity );
          linearModel.computeRegression();
        }
    }

References DGtal::SimpleLinearRegression::addSample(), DGtal::SimpleLinearRegression::addSamples(), DGtal::SimpleLinearRegression::clear(), DGtal::SimpleLinearRegression::computeRegression(), myEpsilonZero, myX, myY, DGtal::SimpleLinearRegression::setEpsilonZero(), and DGtal::SimpleLinearRegression::trustIntervalForY().

Referenced by testSimpleRegressionOrdered().

isValid()

bool DGtal::OrderedLinearRegression::isValid ( ) const

inline

Checks the validity/consistency of the object.

Returns

'true' if the object is valid, 'false' otherwise.

Definition at line 241 of file OrderedLinearRegression.h.

    {
      return true;
    }

operator=()

OrderedLinearRegression & DGtal::OrderedLinearRegression::operator= ( const OrderedLinearRegression &  other )

private

Assignment.

Parameters

other

the object to copy.

Returns

a reference on 'this'. Forbidden by default.

selfDisplay()

void DGtal::OrderedLinearRegression::selfDisplay ( std::ostream &  that_stream ) const

inline

Writes/Displays the object on an output stream.

Parameters

that_stream

the output stream where the object is written.

Definition at line 232 of file OrderedLinearRegression.h.

    {
      that_stream << "[OrderedLinearRegression]  Number of samples="<< myN;
    }

References myN.

Field Documentation

myEpsilonZero

double DGtal::OrderedLinearRegression::myEpsilonZero

private

Epsilon zero value.

Definition at line 252 of file OrderedLinearRegression.h.

Referenced by backwardSLR(), and forwardSLR().

myN

unsigned int DGtal::OrderedLinearRegression::myN

private

Number of samples.

Definition at line 255 of file OrderedLinearRegression.h.

Referenced by addSample(), clear(), and selfDisplay().

myX

std::vector<double> DGtal::OrderedLinearRegression::myX

private

Abscissa values of sample points.

Definition at line 261 of file OrderedLinearRegression.h.

Referenced by addSample(), backwardSLR(), clear(), and forwardSLR().

myY

std::vector<double> DGtal::OrderedLinearRegression::myY

private

Ordinate values of sample points.

Definition at line 258 of file OrderedLinearRegression.h.

Referenced by addSample(), backwardSLR(), clear(), and forwardSLR().

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The documentation for this class was generated from the following file:

• OrderedLinearRegression.h