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prt_cv_opt_param

PURPOSE ^

Function to pass optional (advanced) parameters into the classifier.

SYNOPSIS ^

function param = prt_cv_opt_param(PRT,ID,model_id)

DESCRIPTION ^

 Function to pass optional (advanced) parameters into the classifier. 

 This is primarily used for prediction methods that need to know something
 about the experimental design that is normally not accessible to ordinary
 (i.e. generic) prediction functions (e.g. task onsets or TR). Examples of
 this kind of method include multi-class classifier using kernel
 regression (MCKR) and the machine that implements nested cross-validation

 Inputs:
 -------
 PRT:      data structure
 ID:       id matrix for the current cross-validation fold
 model_id: which model are we working on?

 Outputs:
 --------
 param.id_fold:   the id matrix for this fold
 param.model_id:  id for the model being computed
 param.PRT:       PRT data structure

 Notes:
 --------
 The outputs (param.xxx) are provided for use by the classifier
 
__________________________________________________________________________
 Copyright (C) 2011 Machine Learning & Neuroimaging Laboratory

CROSS-REFERENCE INFORMATION ^

This function calls: This function is called by:

SOURCE CODE ^

0001 function param = prt_cv_opt_param(PRT,ID,model_id)
0002 % Function to pass optional (advanced) parameters into the classifier.
0003 %
0004 % This is primarily used for prediction methods that need to know something
0005 % about the experimental design that is normally not accessible to ordinary
0006 % (i.e. generic) prediction functions (e.g. task onsets or TR). Examples of
0007 % this kind of method include multi-class classifier using kernel
0008 % regression (MCKR) and the machine that implements nested cross-validation
0009 %
0010 % Inputs:
0011 % -------
0012 % PRT:      data structure
0013 % ID:       id matrix for the current cross-validation fold
0014 % model_id: which model are we working on?
0015 %
0016 % Outputs:
0017 % --------
0018 % param.id_fold:   the id matrix for this fold
0019 % param.model_id:  id for the model being computed
0020 % param.PRT:       PRT data structure
0021 %
0022 % Notes:
0023 % --------
0024 % The outputs (param.xxx) are provided for use by the classifier
0025 %
0026 %__________________________________________________________________________
0027 % Copyright (C) 2011 Machine Learning & Neuroimaging Laboratory
0028 
0029 % Written by A Marquand
0030 % $Id$
0031 
0032 param.id_fold  = ID;
0033 param.model_id = model_id;
0034 param.PRT      = PRT;
0035 
0036 end

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