Home > prediction > ccl_learnp_pred_lwlinear.m

ccl_learnp_pred_lwlinear

PURPOSE ^

Yp = ccl_learnp_pred_lwlinear(X,model)

SYNOPSIS ^

function Yp = ccl_learnp_pred_lwlinear(X,model)

DESCRIPTION ^

 Yp = ccl_learnp_pred_lwlinear(X,model)

 Locally weighted model prediction

 Input:

   X                               State inputs
   model                           Model parameters

 Ouput:

   Yp                              Prediction

CROSS-REFERENCE INFORMATION ^

This function calls: This function is called by:

SOURCE CODE ^

0001 function Yp = ccl_learnp_pred_lwlinear(X,model)
0002 % Yp = ccl_learnp_pred_lwlinear(X,model)
0003 %
0004 % Locally weighted model prediction
0005 %
0006 % Input:
0007 %
0008 %   X                               State inputs
0009 %   model                           Model parameters
0010 %
0011 % Ouput:
0012 %
0013 %   Yp                              Prediction
0014 
0015 
0016 
0017 
0018 % CCL: A MATLAB library for Constraint Consistent Learning
0019 % Copyright (C) 2007  Matthew Howard
0020 % Contact: matthew.j.howard@kcl.ac.uk
0021 %
0022 % This library is free software; you can redistribute it and/or
0023 % modify it under the terms of the GNU Lesser General Public
0024 % License as published by the Free Software Foundation; either
0025 % version 2.1 of the License, or (at your option) any later version.
0026 %
0027 % This library is distributed in the hope that it will be useful,
0028 % but WITHOUT ANY WARRANTY; without even the implied warranty of
0029 % MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
0030 % Lesser General Public License for more details.
0031 %
0032 % You should have received a copy of the GNU Library General Public
0033 % License along with this library; if not, write to the Free
0034 % Software Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA.
0035 
0036 N  = size(X,2);       % get no. data points
0037 
0038 % find feature vectors
0039 Phi = model.phi(X);
0040 dimPhi = size(Phi,1);
0041 
0042 % find weights
0043 W = model.W(X);
0044 Nc = size(W,1);   % get no. data points, dimensionality
0045 
0046 % predict training data
0047 for nc=1:Nc
0048 Yp(:,:,nc)=((repmat(W(nc,:),dimPhi,1).*Phi)'*model.w(:,:,nc))';
0049 %Yp(:,:,nc) = sum(repmat(model.w(:,:,nc),1,N).*Phi).*W(nc,:);
0050 end
0051 Yp = sum(Yp,3)./repmat(sum(W,1),size(Yp,1),1);
0052

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