Home > hit > clustering > hit_remove_duplicates.m

hit_remove_duplicates

PURPOSE ^

HIT_REMOVE_DUPLICATES Remove duplicates of xi-points before clustering.

SYNOPSIS ^

function [topreserve,isequalto]=hit_remove_duplicates(LDs,xi)

DESCRIPTION ^

HIT_REMOVE_DUPLICATES Remove duplicates of xi-points before clustering.

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 DESCRIPTION
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 [topreserve,isequalto]=hit_remove_duplicates(LDs,xi)
 
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 INPUT
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 LDs: structure containing information about local datasets (LDs)
 and local models

    LDs.X{i}:  matrix of regressors belonging to the i-th local dataset
    (each row is a point).
   LDs.y{i}: vector of outputs belonging to the i-th local dataset.
   LDs.pos{i}: vector of indexes of datapoints in the i-th local
   dataset, e.g. Xid(LDs.pos{i}(1),:) is the first regressor in the i-th
   LD. 
   LDs.weights{i} weight associated to the i-th datapoint used in
   weighted LS for computing mode PVs. 
   LDs.meanX{i} average of regressors in the i-th local dataset. 
   LDs.models{i} parameters of the i-th local model.
   LDs.models_var{i} INVERSE variance of the i-th local model.
   LDs.X_ivar{i} INVERSE of the variance of the the regressors in the
   i-th LD.Xid: matrix containing the datapoints in the regressor set.
       Each row is a datapoint.

 xi: structure containing information about the xi-points (they are either
 FVs or LPVs).

    xi.points{i}: xi-point based on the i-th LDs.
   xi.IR{i}: INVERSE of the covariance of the i-th xi-point.
   xi.weights{i}: scalar confidence measure of the i-th feature vector.

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 OUTPUT                                                                                                   
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 topreserve: indexes of xi-points that must be preserved.
 isequalto{i}: indexes of xi-points that are equal to the point indexed by
 topreserve(i). It can be an empty vector (if a point does not have
 duplicates)

CROSS-REFERENCE INFORMATION ^

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