yat  0.18pre
Public Member Functions | Protected Member Functions | Protected Attributes | List of all members
theplu::yat::utility::kNNI Class Reference

kNNimpute More...

#include <yat/utility/kNNI.h>

Inheritance diagram for theplu::yat::utility::kNNI:

Public Member Functions

 kNNI (const utility::Matrix &matrix, const utility::Matrix &weight, const unsigned int neighbours)
unsigned int estimate (void)
 Function doing kNNI imputation. More...
const utility::Matriximputed_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::Matrixdata_
utility::Matrix imputed_data_
unsigned int neighbours_
std::vector< size_t > not_imputed_
const utility::Matrixweight_

Detailed Description


kNNI is the binary weight implementation of NNI. This follows the work done by Troyanskaya et al. cited in the NNI document referred to in the NNI class documentation.

This is a special case of the WeNNI, but is maintained since it is faster than the more general WeNNI.

See also

Constructor & Destructor Documentation

theplu::yat::utility::kNNI::kNNI ( const utility::Matrix matrix,
const utility::Matrix weight,
const unsigned int  neighbours 


Member Function Documentation

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

$ d_{ij}^2=\frac {\sum_{k=1}^C w_{ik} w_{jk} (x_{ik}-x_{jk})^2 }{\sum_{k=l}^C w_{ik} w_{jk} } $ where C is the number of columns

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

Function doing kNNI imputation.

Perform kNNI on data in matrix with binary 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.

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

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

const utility::Matrix& theplu::yat::utility::NNI::imputed_data ( void  ) const
A const reference to the modified data.
std::vector<size_t> theplu::yat::utility::NNI::nearest_neighbours ( const size_t  ,
const std::vector< std::pair< size_t, double > > &   
) const

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

const std::vector<size_t>& theplu::yat::utility::NNI::not_imputed ( void  ) const
indices of rows in data matrix not imputed

Member Data Documentation

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

original data matrix

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

data after imputation

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

number of neighbor to use

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

which rows are not imputed due to lack of data

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

weight matrix

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

Generated on Sun Sep 27 2020 02:26:14 for yat by  doxygen 1.8.11