--- /dev/null
+## Copyright (C) 2005 Michel D. Schmid <michaelschmid@users.sourceforge.net>
+##
+##
+## This program is free software; you can redistribute it and/or modify it
+## under the terms of the GNU General Public License as published by
+## the Free Software Foundation; either version 2, or (at your option)
+## any later version.
+##
+## This program is distributed in the hope that it will be useful, but
+## WITHOUT ANY WARRANTY; without even the implied warranty of
+## MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
+## General Public License for more details.
+##
+## You should have received a copy of the GNU General Public License
+## along with this program; see the file COPYING. If not, see
+## <http://www.gnu.org/licenses/>.
+
+## -*- texinfo -*-
+## @deftypefn {Function File} {}{@var{net}} = __newnetwork(@var{numInputs},@var{numLayers},@var{numOutputs},@var{networkType})
+## @code{__newnetwork} create a custom 'zero'-network
+##
+##
+## @example
+## net = __newnetwork(numInputs,numLayers,numOutputs,networkType)
+##
+## numInputs : number of input vectors, actually only 1 allowed
+## numLayers : number of layers
+## numOutputs: number of output vectors, actually only 1 allowed
+## networkType: e.g. feed-forward-network "newff"
+## @end example
+##
+## @example
+## net = __newnetwork(1,2,1,"newff")
+## 1 input layer, two hidden layers, one output layer
+## and the network type
+## @end example
+##
+## @noindent
+## @end deftypefn
+
+## Author: Michel D. Schmid
+
+function net = __newnetwork(numInputs,numLayers,numOutputs,networkType)
+
+ ## check range of input arguments
+ error(nargchk(4,4,nargin))
+
+ ## check input args
+ if ( !isposint(numInputs) )
+ error("network: at least 1 input must be defined! ")
+ # this can't happen actually, only one is allowed and this
+ # one is hard coded
+ elseif ( !isposint(numLayers) )
+ error("network: at least 1 hidden- and one output layer must be defined! ")
+ endif
+ ## second check for numLayers... must be at least "2" for the
+ ## newff, this means at least 1 hidden and 1 output layer
+ if (strcmp(networkType,"newff") && (numLayers<2))
+ error("network: not enough layers are defined! ")
+ endif
+
+ ## define network type
+ net.networkType = networkType;
+
+ ## ZERO NETWORK
+ net.numInputs = 0;
+ net.numLayers = 0;
+ net.numInputDelays = 0;
+ net.numLayerDelays = 0;
+ # the next five parameters aren't used till now, they are used
+ # only for matlab nnet type compatibility ==> saveMLPStruct
+ net.biasConnect = []; # not used parameter till now
+ net.inputConnect = []; # not used parameter till now
+ net.layerConnect = []; # not used parameter till now
+ net.outputConnect = []; # not used parameter till now
+ net.targetConnect = []; # not used parameter till now
+ net.numOutputs = 0;
+ net.numTargets = 0;
+ net.inputs = cell(0,1);
+ net.layers = cell(0,1);
+ net.biases = cell(0,1);
+ net.inputWeights = cell(0,0);
+ net.layerWeights = cell(0,0);
+ net.outputs = cell(1,0);
+ net.targets = cell(1,0);
+ net.performFcn = "";
+ net.performParam = [];
+ net.trainFcn = "";
+ net.trainParam = [];
+ net.IW = {};
+ net.LW = {};
+ net.b = cell(0,1);
+ net.userdata.note = "Put your custom network information here.";
+
+
+ ## ARCHITECTURE
+
+ ## define everything with "inputs"
+ net.numInputs = numInputs;
+ ## actually, it's only possible to have "one" input vector
+ net.inputs{1,1}.range = [0 0];
+ net.inputs{1,1}.size = 0;
+ net.inputs{1,1}.userdata = "Put your custom informations here!";
+
+ ## define everything with "layers"
+ net.numLayers = numLayers;
+ net = newLayers(net,numLayers);
+
+ ## define unused variables, must be defined for saveMLPStruct
+ net.biasConnect = [0; 0];
+ net.inputConnect = [0; 0];
+ net.layerConnect = [0 0; 0 0];
+ net.outputConnect = [0 0];
+ net.targetConnect = [0 0];
+ net.numInputDelays = 0;
+ net.numLayerDelays = 0;
+
+ ## define everything with "outputs"
+ net.numOutputs = numOutputs;
+ net.outputs = cell(1,numLayers);
+ for i=1:numLayers
+ if (i==numLayers)
+ net.outputs{i}.size = 1; # nothing else allowed till now
+ net.outputs{i}.userdata = "Put your custom informations here!";
+ else
+ net.outputs{i} = [];
+ endif
+ endfor
+
+ ## define everything with "biases"
+ net = newBiases(net,numLayers);
+
+
+
+#=====================================================
+#
+# Additional ARCHITECTURE Functions
+#
+#=====================================================
+ function net = newLayers(net,numLayers)
+
+ ## check range of input arguments
+ error(nargchk(2,2,nargin))
+
+ ## check type of arguments
+ if ( !isscalar(numLayers) || !isposint(numLayers) )
+ error("second argument must be a positive integer scalar value!")
+ endif
+ if ( !__checknetstruct(net) )
+ error("first argument must be a network structure!")
+ endif
+
+ for iRuns=1:numLayers
+ net.layers{iRuns,1}.dimension = 0;
+ net.layers{iRuns,1}.netInputFcn = "";
+ net.layers{iRuns,1}.size = 0;
+### TODO: test with newff net.layers{iRuns,1}.transferFcn = "tansig";
+ net.layers{iRuns,1}.transferFcn = "";
+ net.layers{iRuns,1}.userdata = "Put your custom informations here!";
+ endfor
+
+ endfunction
+
+#-----------------------------------------------------
+
+ function net = newBiases(net,numLayers)
+
+ ## check range of input arguments
+ error(nargchk(2,2,nargin))
+
+ ## check type of arguments
+ if ( !isscalar(numLayers) || !isposint(numLayers) )
+ error("second argument must be a positive integer scalar value!")
+ endif
+ if ( !isstruct(net) )
+ error("first argument must be a network structure!")
+ endif
+
+ for iRuns=1:numLayers
+ net.biases{iRuns,1}.learn = 1;
+ net.biases{iRuns,1}.learnFcn = "";
+ net.biases{iRuns,1}.learnParam = "undefined...";
+ net.biases{iRuns,1}.size = 0;
+ net.biases{iRuns,1}.userdata = "Put your custom informations here!";
+ endfor
+
+ endfunction
+
+# ================================================================
+#
+# END Additional ARCHITECTURE Functions
+#
+# ================================================================
+
+endfunction
\ No newline at end of file
git://git.creatis.insa-lyon.fr / CreaPhase.git / blobdiff