RANSAC< TModelType > Class Template Reference

This class provides a generic implementation of the RANSAC outlier detection algorithm. Is implemented and tested after the SciPy reference: http://wiki.scipy.org/Cookbook/RANSAC. More...

#include <OpenMS/MATH/MISC/RANSAC.h>

Static Public Member Functions
static std::vector< std::pair< double, double > >	ransac (const std::vector< std::pair< double, double > > &pairs, const RANSACParam &p)
	alias for ransac() with full params More...
static std::vector< std::pair< double, double > >	ransac (const std::vector< std::pair< double, double > > &pairs, size_t n, size_t k, double t, size_t d, bool relative_d=false, int(*rng)(int)=NULL)
	This function provides a generic implementation of the RANSAC outlier detection algorithm. Is implemented and tested after the SciPy reference: http://wiki.scipy.org/Cookbook/RANSAC. More...

Detailed Description

template<typename TModelType = RansacModelLinear>
class OpenMS::Math::RANSAC< TModelType >

This class provides a generic implementation of the RANSAC outlier detection algorithm. Is implemented and tested after the SciPy reference: http://wiki.scipy.org/Cookbook/RANSAC.

Member Function Documentation

ransac() [1/2]

static std::vector<std::pair<double, double> > ransac ( const std::vector< std::pair< double, double > > &  pairs, const RANSACParam &  p  )

inlinestatic

alias for ransac() with full params

References RANSACParam::d, RANSACParam::k, RANSACParam::n, RANSACParam::relative_d, RANSACParam::rng, and RANSACParam::t.

ransac() [2/2]

static std::vector<std::pair<double, double> > ransac ( const std::vector< std::pair< double, double > > &  pairs, size_t  n, size_t  k, double  t, size_t  d, bool  relative_d = false, int(*)(int)  rng = NULL  )

inlinestatic

This function provides a generic implementation of the RANSAC outlier detection algorithm. Is implemented and tested after the SciPy reference: http://wiki.scipy.org/Cookbook/RANSAC.

If possible, restrict 'n' to the minimal number of points which the model requires to make a fit, i.e. n=2 for linear, n=3 for quadratic. Any higher number will result in increasing the chance of including an outlier, hence a lost iteration.

While iterating, this RANSAC implementation will consider any model which explains more data points than the currently best model as even better. If the data points are equal, RSS (residual sum of squared error) will be used.

Making 'd' a relative measure (1-99%) is useful if you cannot predict how many points RANSAC will actually receive at runtime, but you have a rough idea how many percent will be outliers. E.g. if you expect 20% outliers, then setting d=60, relative_d=true (i.e. 60% inliers with some margin for error) is a good bet for a larger input set. (Consider that 2-3 data points will be used for the initial model already – they cannot possibly become inliers).

Parameters

pairs	Input data (paired data of type <dim1, dim2>)
n	The minimum number of data points required to fit the model
k	The maximum number of iterations allowed in the algorithm
t	Threshold value for determining when a data point fits a model. Corresponds to the maximal squared deviation in units of the _second_ dimension (dim2).
d	The number of close data values (according to 't') required to assert that a model fits well to data
relative_d	Should 'd' be interpreted as percentages (0-100) of data input size
rng	Custom RNG function (useful for testing with fixed seeds)

Returns

A vector of pairs fitting the model well; data will be unsorted

References RANSACParam::k, RANSACParam::n, RANSACParam::relative_d, RANSACParam::rng, and RANSACParam::t.