theplu::yat::utility::WeNNI Class Reference

Weighted Nearest Neighbour Imputation. More...

#include <yat/utility/WeNNI.h>

Inheritance diagram for theplu::yat::utility::WeNNI:

Public Member Functions
	WeNNI (const MatrixBase &matrix, const MatrixBase &weight, const unsigned int neighbours)
unsigned int	estimate (void)
	Function doing WeNNI imputation. More...
const Matrix &	imputed_data_raw (void) const
const utility::Matrix &	imputed_data (void) const
const std::vector< size_t > &	not_imputed (void) const

Protected Member Functions
std::vector< std::pair< size_t, double > >	calculate_distances (const size_t) const
std::vector< size_t >	nearest_neighbours (const size_t, const std::vector< std::pair< size_t, double > > &) const

Protected Attributes
const utility::MatrixBase &	data_
utility::Matrix	imputed_data_
unsigned int	neighbours_
std::vector< size_t >	not_imputed_
const utility::MatrixBase &	weight_

Detailed Description

Weighted Nearest Neighbour Imputation.

WeNNI is a continuous weights generalization of the (binary weights) kNNI algorithm presented by Troyanskaya et al. A reference to this paper is found in the NNI document referred to in the NNI class documentation. The NNI document also describes WeNNI in depth.

Note

Missing values should be represented with a zero weight. WeNNI will treat the corresponding data values as zero, i.e., this implies that NaNs and Infs with zero weight will not have any impact on calculations.a

See also

NNI and kNNI

Constructor & Destructor Documentation

WeNNI()

theplu::yat::utility::WeNNI::WeNNI	(	const MatrixBase &	matrix,
		const MatrixBase &	weight,
		const unsigned int	neighbours
	)

Constructor

Member Function Documentation

calculate_distances()

std::vector<std::pair<size_t,double> > theplu::yat::utility::NNI::calculate_distances ( const size_t  ) const

protectedinherited

where C is the number of columns

estimate()

unsigned int theplu::yat::utility::WeNNI::estimate ( void  )

virtual

Function doing WeNNI imputation.

Perform WeNNI on data in matrix with continuous uncertainty weights in weight using neighbours for the new impute value.

The return value can be used as an indication of how well the imputation worked. The return value should be zero if proper pre-processing of data is done. An example of bad data is a matrix with a column of zero weights, another is a corresponding situation with a row with all weights zero.

Returns

The number of rows that have at least one value not imputed.

Implements theplu::yat::utility::NNI.

imputed_data()

const utility::Matrix& theplu::yat::utility::NNI::imputed_data ( void  ) const

inherited

Returns

A const reference to the modified data.

imputed_data_raw()

const Matrix& theplu::yat::utility::WeNNI::imputed_data_raw

(

void

)

const

Returns

A const reference to imputed_data_raw.

nearest_neighbours()

std::vector<size_t> theplu::yat::utility::NNI::nearest_neighbours ( const size_t  , const std::vector< std::pair< size_t, double > > &    ) const

protectedinherited

Contributing nearest neighbours are added up to the user set number, and neighbours are disqualified if their element (column) weight is zero

not_imputed()

const std::vector<size_t>& theplu::yat::utility::NNI::not_imputed ( void  ) const

inherited

Returns

indices of rows in data matrix not imputed

Member Data Documentation

data_

const utility::MatrixBase& theplu::yat::utility::NNI::data_

protectedinherited

original data matrix

imputed_data_

utility::Matrix theplu::yat::utility::NNI::imputed_data_

protectedinherited

data after imputation

neighbours_

unsigned int theplu::yat::utility::NNI::neighbours_

protectedinherited

number of neighbor to use

not_imputed_

std::vector<size_t> theplu::yat::utility::NNI::not_imputed_

protectedinherited

which rows are not imputed due to lack of data

weight_

const utility::MatrixBase& theplu::yat::utility::NNI::weight_

protectedinherited

weight matrix

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

• yat/utility/WeNNI.h