BX = ccl_basis_rbf ( X, C, s2 ) Calculate normalised Gaussian radial basis function of input X
0001 function BX = ccl_basis_rbf ( X, C, s2 ) 0002 % BX = ccl_basis_rbf ( X, C, s2 ) 0003 % 0004 % Calculate normalised Gaussian radial basis function of input X 0005 0006 % Input: 0007 % 0008 % X Input data 0009 % C Centre 0010 % s2 Variance 0011 % 0012 % Output: 0013 % 0014 % BX Normalised gaussian radial basis function of X 0015 0016 0017 0018 0019 % CCL: A MATLAB library for Constraint Consistent Learning 0020 % Copyright (C) 2007 Matthew Howard 0021 % Contact: matthew.j.howard@kcl.ac.uk 0022 % 0023 % This library is free software; you can redistribute it and/or 0024 % modify it under the terms of the GNU Lesser General Public 0025 % License as published by the Free Software Foundation; either 0026 % version 2.1 of the License, or (at your option) any later version. 0027 % 0028 % This library is distributed in the hope that it will be useful, 0029 % but WITHOUT ANY WARRANTY; without even the implied warranty of 0030 % MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU 0031 % Lesser General Public License for more details. 0032 % 0033 % You should have received a copy of the GNU Library General Public 0034 % License along with this library; if not, write to the Free 0035 % Software Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA. 0036 0037 D = ccl_math_distances(C,X) ; % distance between C and X 0038 BX = exp(-0.5/s2*D) ; % radial basis function of X 0039 BX = BX.*repmat(sum(BX).^(-1),size(C,2),1); % normalise 0040 end