Mercurial > forge

--- a/main/nnet/COPYING	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,337 +0,0 @@
-                    GNU GENERAL PUBLIC LICENSE
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--- a/main/nnet/DESCRIPTION	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,12 +0,0 @@
-Name: nnet
-Version: 0.1.13
-Date: 2010-12-02
-Author: various authors
-Maintainer: Octave-Forge community <octave-dev@lists.sourceforge.net>
-Title: Neural Networks
-Description: A feed forward multi-layer neural network.
-Depends: octave (>= 3.0.0)
-Autoload: no
-License: GPLv3+
-Url: http://octave.sf.net
-Url: http://octnnettb.sourceforge.net
--- a/main/nnet/INDEX	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,31 +0,0 @@
-nnet >> Neural Networks
-
-Creation
- newff
-
-Data preprocessing & postprocessing
- mapstd
- prestd
- poststd
- trastd
-
-Simulation
- sim
-
-Training
- train
-
-Transfer functions
- logsig
- purelin
- radbas
- tansig
-
-Utility
- dividerand
- ind2vec
- isposint
- min_max
- saveMLPStruct
- subset
- vec2ind
\ No newline at end of file
--- a/main/nnet/Makefile	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,12 +0,0 @@
-sinclude ../../Makeconf
-
-PKG_FILES = COPYING DESCRIPTION INDEX $(wildcard inst/*) \
-	 doc/pdf/neuralNetworkPackageForOctaveUsersGuide.pdf \
-	$(wildcard doc/latex/user/*.tex) \
-	$(wildcard doc/latex/user/examples/*.tex) \
-	$(wildcard doc/latex/user/examples/1/*.tex) \
-	$(wildcard doc/latex/user/examples/1/*.txt) \
-	$(wildcard doc/latex/user/examples/1/*.m) \
-	$(wildcard doc/latex/user/octave/neuroPackage/*.tex) \
-	$(wildcard doc/latex/user/octave/neuroPackage/graphics/*.eps) \
-	$(wildcard doc/latex/user/octave/neuroPackage/graphics/*.pdf)
--- a/main/nnet/NEWS	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,36 +0,0 @@
-Summary of important user-visible changes for Neural Networks package 0.2.0:
-----------------------------------------------------------------------------
-
- ** The following functions are new:
-
-      minmax
-
- ** The following functions have been deprecated (see their help text
-    for the recommended alternatives):
-
-      min_max
-
- ** The new function `minmax' meant to replace `min_max' can now handle single
-    row matrices and cell arrays in a Matlab compatible way.
-
- ** The following functions have been made private:
-
-      __analyzerows.m       __printBiasConnect.m     __printNumLayerDelays.m
-      __calcjacobian.m      __printBiases.m          __printNumLayers.m
-      __calcperf.m          __printB.m               __printNumOutputs.m
-      __checknetstruct.m    __printInitFcn.m         __printNumTargets.m
-      __copycoltopos1.m     __printInitParam.m       __printOutputConnect.m
-      __dlogsig.m           __printInputConnect.m    __printOutputs.m
-      __dpurelin.m          __printInputs.m          __printPerformFcn.m
-      __dradbas.m           __printInputWeights.m    __printPerformParam.m
-      __dtansig.m           __printIW.m              __printTargetConnect.m
-      __getx.m              __printLayerConnect.m    __printTargets.m
-      __init.m              __printLayers.m          __printTrainFcn.m
-      __mae.m               __printLayerWeights.m    __printTrainParam.m
-      __mse.m               __printLW.m              __randomisecols.m
-      __newnetwork.m        __printMLPHeader.m       __rerangecolumns.m
-      __optimizedatasets.m  __printNetworkType.m     __setx.m
-      __printAdaptFcn.m     __printNumInputDelays.m  __trainlm.m
-      __printAdaptParam.m   __printNumInputs.m
-
- ** Package is no longer automatically loaded.
--- a/main/nnet/doc/docReadme	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,29 +0,0 @@
-The directory nnet/doc contains two subdirectories.
-1. latex: This includes five (5) subdirectories. All of them
-          are used to create the newest version of the documentation.
-
-		  asymptote: Includes asy-scripts to create graphics, used in
-          subdirectory users/octave/neuroToolbox/graphics. For pdf output
-          the graphic files must be in pdf format. For dvi output, the
-          graphics must be in eps format. That's why I create each time
-          both types. Used asymptote is version: 1.42 win version
-
-          common: Contains some .tex files which are used in both documentations.
-          developer as users guide.
-
-          developers: Contains .tex files with a lot of informations about
-          how I did the most parts of this package. Not all of them are
-          completly written ... :-(
-
-          perl:  Contains two files. The first one is a "modul" to put somewhere
-          in a lib directory in the perl installation. This file ends with .pm.
-          The second file (createTestDocu.pl) is a short perl script to generate
-		  some .tex files for the developer guide which contains all the test
-		  cases programmed in the function files. In the file createTestDocu.pl
-		  must be changed some variables to the local system likewise it must be
-		  copied to the "developers" directory!
-
-          users: The user's guide. The official documentation to the neural network
-          package!
-
-2. pdf:  Contains documentation generated of the latex directory.
--- a/main/nnet/doc/latex/asymptote/mlp4-2-3.asy	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,122 +0,0 @@
-import graph;
-size(100,0);
-
-// define sizes for neurons
-pair center1 = (10,10);
-pair center2 = (10,30);
-pair center3 = (10,50);
-pair center4 = (10,70);
-pair center1hidden = (50,30);
-pair center2hidden = (50,50);
-pair center1out = (90,20);
-pair center2out = (90,40);
-pair center3out = (90,60);
-real radius  = (5);
-
-
-// define circle for neurons
-path in1 = circle(center1,radius);
-path in2 = circle(center2,radius);
-path in3 = circle(center3,radius);
-path in4 = circle(center4,radius);
-path hn1 = circle(center1hidden,radius);
-path hn2 = circle(center2hidden,radius);
-path on1 = circle(center1out,radius);
-path on2 = circle(center2out,radius);
-path on3 = circle(center3out,radius);
-
-// draw neurons
-draw(in1);
-draw(in2);
-draw(in3);
-draw(in4);
-draw(hn1);
-draw(hn2);
-draw(on1);
-draw(on2);
-draw(on3);
-
-// now draw arrows from input
-// to hidden neurons
-// (starts from bottom to top)
-
-// first input
-pair z11=(16,10);
-pair z12=(45,27);
-draw(z11--z12,Arrow);
-// first input
-pair z21=(16,30);
-pair z22=(44,29);
-draw(z21--z22,Arrow);
-// first input
-pair z31=(16,50);
-pair z32=(44,31);
-draw(z31--z32,Arrow);
- // first input
-pair z41=(16,70);
-pair z42=(45,33);
-draw(z41--z42,Arrow);
-
-// second input
-pair z11=(16,10);
-pair z12=(45,47);
-draw(z11--z12,Arrow);
-// second input
-pair z21=(16,30);
-pair z22=(44,49);
-draw(z21--z22,Arrow);
-// second input
-pair z31=(16,50);
-pair z32=(44,51);
-draw(z31--z32,Arrow);
-// second input
-pair z41=(16,70);
-pair z42=(45,53);
-draw(z41--z42,Arrow);
-
-// now draw arrows from hidden
-// to output neurons
-// (starts from bottom to top)
-
-// first hidden
-pair z11=(56,30);
-pair z12=(84,19);
-draw(z11--z12,Arrow);
-// first hidden
-pair z21=(56,30);
-pair z22=(84,39);
-draw(z21--z22,Arrow);
-// first hidden
-pair z21=(56,30);
-pair z22=(84,59);
-draw(z21--z22,Arrow);
-// second hidden
-pair z31=(56,50);
-pair z32=(84,21);
-draw(z31--z32,Arrow);
- // second hidden
-pair z41=(56,50);
-pair z42=(84,41);
-draw(z41--z42,Arrow);
- // second hidden
-pair z41=(56,50);
-pair z42=(84,61);
-draw(z41--z42,Arrow);
-
-// now define text for input neurons
-// p1 for the top neuron
-label(scale(0.5)*minipage("\centering \textbf{p1}",0.5cm),(0,70));
-label(scale(0.5)*minipage("\centering \textbf{p2}",0.5cm),(0,50));
-label(scale(0.5)*minipage("\centering \textbf{p3}",0.5cm),(0,30));
-label(scale(0.5)*minipage("\centering \textbf{p4}",0.5cm),(0,10));
-
-// now define text for hidden neurons
-// n1 for the top neuron
-label(scale(0.5)*minipage("\centering \textbf{n1}",0.5cm),(50,60));
-label(scale(0.5)*minipage("\centering \textbf{n2}",0.5cm),(50,40));
-
-// now define text for output neurons
-// a1 for the top neuron
-label(scale(0.5)*minipage("\centering \textbf{a1}",0.5cm),(100,60));
-label(scale(0.5)*minipage("\centering \textbf{a2}",0.5cm),(100,40));
-label(scale(0.5)*minipage("\centering \textbf{a3}",0.5cm),(100,20));
--- a/main/nnet/doc/latex/asymptote/mlp4-2-3.eps	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,680 +0,0 @@
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Binary file main/nnet/doc/latex/asymptote/mlp4-2-3.pdf has changed
--- a/main/nnet/doc/latex/asymptote/transferFunctions/logsig.asy	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,18 +0,0 @@
-import graph;
-size(100,0);
-
-real f(real x) {return 1/(1+exp(-x));}
-pair F(real x) {return (x,f(x));}
-
-
-xaxis("$n$",EndArrow);
-yaxis("$a$",-1.75,1.75,EndArrow);
-
-draw(graph(f,-2.5,2.5,operator ..));
-draw((-2.5,-1)--(2.5,-1),currentpen+dashed);
-draw((-2.5,1)--(2.5,1),currentpen+dashed);
-
-label("$a = logsig(n) $",(0,-2.00));
-label("$0$",(0.2,-0.3));
-label("$-1$",(0.6,-1.35));
-label("$+1$",(0.75,1.35));
\ No newline at end of file
--- a/main/nnet/doc/latex/asymptote/transferFunctions/logsiglogo.asy	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,17 +0,0 @@
-// logsig symbol for nnet
-
-// define size of outer square = 1cm
-unitsize(1cm);
-draw(unitsquare);
-
-// in the middle one short line from left to right
-draw((0.1,0.3)--(0.9,0.3));
-
-// now draw logsig
-import graph;
-
-real f(real x) {return tanh(x);}
-draw(shift(0.5,0.5)*((scale(0.2)*graph(f,-2.0,2.0,operator ..))));
-//shift(2,1);
-
-//scale(real 0.5);
\ No newline at end of file
--- a/main/nnet/doc/latex/asymptote/transferFunctions/purelin.asy	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,19 +0,0 @@
-// purelin
-import graph;
-size(100,0);
-
-real f(real x) {return 1*x;}
-pair F(real x) {return (x,f(x));}
-
-
-xaxis("$n$",EndArrow);
-yaxis("$a$",-1.75,1.75,EndArrow);
-
-draw(graph(f,-2.5,2.5,operator ..));
-draw((-2.5,-1)--(2.5,-1),currentpen+dashed);
-draw((-2.5,1)--(2.5,1),currentpen+dashed);
-
-label("$a = purelin(n) $",(0,-2.00));
-label("$0$",(0.2,-0.3));
-label("$-1$",(0.6,-1.35));
-label("$+1$",(0.75,1.35));
\ No newline at end of file
--- a/main/nnet/doc/latex/asymptote/transferFunctions/purelinlogo.asy	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,17 +0,0 @@
-// purelin symbol for nnet
-
-// define size of outer square = 1cm
-unitsize(1cm);
-draw(unitsquare);
-
-// in the middle one short line from left to right
-draw((0.1,0.5)--(0.9,0.5));
-
-// now draw purelin
-import graph;
-
-real f(real x) {return 1*x;}
-draw(shift(0.5,0.5)*((scale(0.2)*graph(f,-2.0,2.0,operator ..))));
-//shift(2,1);
-
-//scale(real 0.5);
\ No newline at end of file
--- a/main/nnet/doc/latex/asymptote/transferFunctions/tansig.asy	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,18 +0,0 @@
-import graph;
-size(100,0);
-
-real f(real x) {return tanh(x);}
-pair F(real x) {return (x,f(x));}
-
-
-xaxis("$n$",EndArrow);
-yaxis("$a$",-1.75,1.75,EndArrow);
-
-draw(graph(f,-2.5,2.5,operator ..));
-draw((-2.5,-1)--(2.5,-1),currentpen+dashed);
-draw((-2.5,1)--(2.5,1),currentpen+dashed);
-
-label("$a = tansig(n) $",(0,-2.00));
-label("$0$",(0.2,-0.3));
-label("$-1$",(0.6,-1.35));
-label("$+1$",(0.75,1.35));
\ No newline at end of file
--- a/main/nnet/doc/latex/asymptote/transferFunctions/tansiglogo.asy	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,17 +0,0 @@
-// tansig symbol for nnet
-
-// define size of outer square = 1cm
-unitsize(1cm);
-draw(unitsquare);
-
-// in the middle one short line from left to right
-draw((0.1,0.5)--(0.9,0.5));
-
-// now draw tansig
-import graph;
-
-real f(real x) {return tanh(x);}
-draw(shift(0.5,0.5)*((scale(0.2)*graph(f,-2.0,2.0,operator ..))));
-//shift(2,1);
-
-//scale(real 0.5);
\ No newline at end of file
--- a/main/nnet/doc/latex/common/bibliography.tex	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,39 +0,0 @@
-% Preamble
-
-%\documentclass[a4paper]{report}
-
-%\usepackage[ngerman]{babel}
-%\usepackage[T1]{fontenc}
-%\usepackage[ansinew]{inputenc}
-
-
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-% start text here!!
-
-%\begin{document}
-
-\begin{thebibliography}{XXXXXXX}
-
-\bibitem [1]{1} John W. Eaton
-
-GNU Octave Manual, Edition 3, PDF-Version, February 1997
-
-\bibitem [2]{2} The MathWorks, Inc.
-
-MATLAB Online-Help
-
-\bibitem [3]{3} Steven W. Smith
-
-The Scientist and Engineer's Guide to Digital Signal Processing
-ISBN 0-9660176-3-3, California Technical Publishing, 1997
-
-\bibitem [4]{4} Martin T. Hagan, Howard B. Demuth, Mark Beale
-
-Neural Network Design, ISBN 0971732108, PWS Publishing Company, USA, Boston, 1996
-
-
-
-
-
-\end{thebibliography}
-%\end{document}
\ No newline at end of file
--- a/main/nnet/doc/latex/common/version.tex	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,1 +0,0 @@
-Version: 0.1.9.1
\ No newline at end of file
--- a/main/nnet/doc/latex/developers/algorithm/LevenbergMarquardt.tex	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,39 +0,0 @@
-\section{Levenberg Marquardt}
-This algorithm will be programed with \cite{4}.
-
-\subsection{Sensitivity Matrix}
-How does this looks like?\\
-\begin{enumerate}
-	\item for a 1-1-1 MLP
-	\item for a 2-1-1 MLP
-	\item for a 1-2-1 MLP
-\end{enumerate}
-
-\subsubsection{1-1-1 MLP}
-In this case, the MLP holds one input neuron, one hidden neuron and one output neuron. The number of weights needed for this MLP is 4 (2 weights, 2 biases).\\
-
-It needs two sensitivity matrices because the two layers. Each sensitivity matrix will hold 1 element.
-This is taken from \cite{4}, example P12.5 page 12-44. Attention, in this example are two data sets used, this is the reason for the 4 elements...!
-
-\subsubsection{2-1-1 MLP}
-In this case, the MLP holds two input neurons, one hidden neuron and one output neuron. The number of weights needed for this MLP is 5 (3 weights, 2 biases).\\
-
-It needs also two sensitivity matrices because the two layers. Actually, the input is not only a scalar, it is a vector with 2 elements. Even though, again after \cite{4}. I think the sensitivity matrices will hold only 1 element. So the number of elements will bi proportional to the number of hidden neurons and the number of output neurons.
-
-\subsubsection{1-2-1 MLP}
-In this case, the MLP holds one input neuron, two hidden neurons and one output neuron. The number of weights needed for this MLP is 7 (4 weights, 3 biases).\\
-
-It needs also two sensitivity matrices because the two layers. Actually, the input is again only a scalar.
-Now calculating $n_1^1$ will result in a row vector with 2 elements. $n_1^2$ will hold only one element and so we have 3 elements in the sensitivity matrix.\\
-
-We can say, the number of hidden neurons is responsible for the dimension of the sensitivity matrices.
-For example, a 4-3-1 MLP with 100 data sets will generate following sensitivity matrix for the first layer:
-$\tilde{\textbf{S}}^1 = [\tilde{\textbf{S}}^1_1 | \tilde{\textbf{S}}^1_2 | ... | \tilde{\textbf{S}}^1_{100}]$\\
-\noindent $\tilde{\textbf{S}}^1_1$ will hold 3 elements  $\tilde{\textbf{S}}^1_1 = [\tilde{\textbf{S}}^1_{1,1} ~ \tilde{\textbf{S}}^1_{2,1} ~ \tilde{\textbf{S}}^1_{3,1}]^T$;
-$\tilde{\textbf{S}}^1_2 = [\tilde{\textbf{S}}^1_{1,2} ~ \tilde{\textbf{S}}^1_{2,2} ~ \tilde{\textbf{S}}^1_{3,2}]^T$ and so on. So matrix will have a size of 3x100 for $\tilde{\textbf{S}}^1_{1}$
-and a size of 1x100 for $\tilde{\textbf{S}}^1_{2}$.\\
-
-By the way, the jacobian matrix will be a 100x20 matrix ..
-
-
-
--- a/main/nnet/doc/latex/developers/algorithm/algorithmen.tex	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,5 +0,0 @@
-\chapter{Algorithm}
-Here are some general thoughts about calculating parts are used in algorithm.
-
-\input{algorithm/LevenbergMarquardt}
-
--- a/main/nnet/doc/latex/developers/analyzing/analyzingNewff.tex	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,231 +0,0 @@
-\section{analyzing newff}
-First, \textit{newff} will be analyzed for a X-X-X mlp. This means, maximum 3 layers, including the input layer. Or in words, one input- one hidden- and one output-layer. The number of neurons are choosable.
-
-Following command will be used, to create a new feed-forward neural network:\\
-\noindent MLPnet = newff(mMinMaxElements,[nHiddenNeurons nOutputNeurons],...\newline
-\{'tansig','purelin'\},'trainlm','learngdm','mse');\\
-
-newff is the matlab command, mMinMaxElements is a $Rx2$-Matrix with minimum and maximum values of the inputs. $R$ is equal to the number of input neurons. [nHiddenNeurons nOutputNeurons] are the scalar values, to define the number of neurons in the hidden and output layer. One value, for each layer. \{'tansig','purelin'\} are the transfer functions, for each layer. This means, 'tansig' for the hidden layer and 'purelin' for the output layer. 'trainlm' is the training algorithm, in this case, Levenberg-Marquardt. 'learngdm' is the learn algorithm and 'mse' is the performance function, \textbf{m}ean-\textbf{s}quare-\textbf{e}rror.\\
-MLPnet will be a structure with following content:
-
-\begin{verbatim}
-Neural Network object:
-
-    architecture:
-
-         numInputs: 1
-         numLayers: 2
-       biasConnect: [1; 1]
-      inputConnect: [1; 0]
-      layerConnect: [0 0; 1 0]
-     outputConnect: [0 1]
-     targetConnect: [0 1]
-
-        numOutputs: 1  (read-only)
-        numTargets: 1  (read-only)
-    numInputDelays: 0  (read-only)
-    numLayerDelays: 0  (read-only)
-
-    subobject structures:
-
-            inputs: {1x1 cell} of inputs
-            layers: {2x1 cell} of layers
-           outputs: {1x2 cell} containing 1 output
-           targets: {1x2 cell} containing 1 target
-            biases: {2x1 cell} containing 2 biases
-      inputWeights: {2x1 cell} containing 1 input weight
-      layerWeights: {2x2 cell} containing 1 layer weight
-
-    functions:
-
-          adaptFcn: 'trains'
-           initFcn: 'initlay'
-        performFcn: 'mse'
-          trainFcn: 'trainlm'
-
-    parameters:
-
-        adaptParam: .passes
-         initParam: (none)
-      performParam: (none)
-        trainParam: .epochs, .goal, .max_fail, .mem_reduc,
-                    .min_grad, .mu, .mu_dec, .mu_inc,
-                    .mu_max, .show, .time
-
-    weight and bias values:
-
-                IW: {2x1 cell} containing 1 input weight matrix
-                LW: {2x2 cell} containing 1 layer weight matrix
-                 b: {2x1 cell} containing 2 bias vectors
-
-    other:
-
-          userdata: (user stuff)
-\end{verbatim}
-\textit{numInputs: 1}: one input layer\\
-\noindent \textit{numLayers: 2}: one hidden and one output layer\\
-\noindent \textit{biasConnect: [1; 1]}: unknown till now!!\\
-\noindent \textit{inputConnect: [1; 0]}: unknown till now!!\\
-\noindent \textit{layerConnect: [0 0; 1 0]}: unknown till now!!\\
-\noindent \textit{outputConnect: [0 1]}: unknown till now!!\\
-\noindent \textit{targetConnect: [0 1]}: unknown till now!!\\
-\noindent \textit{numOutputs: 1  (read-only)}: unknown till now!!\\
-\noindent \textit{numTargets: 1  (read-only)}: unknown till now!!\\
-\noindent \textit{numInputDelays: 0  (read-only)}: unknown till now!!\\
-\noindent \textit{numLayerDelays: 0  (read-only)}: unknown till now!!\\
-\noindent \textit{inputs: {1x1 cell} of inputs}: input layer definition\\
-Because we have defined only one input layer, you can see the detailed definition with
-following command in the matlab prompt:\\
-\begin{verbatim}
-	MLPnet.inputs{1}
-
-ans =
-
-       range: [26x2 double]
-        size: 26
-    userdata: [1x1 struct]
-\end{verbatim}
-range are the min. and max. values of the inputs. size is the number of input neurons and userdata are user specified inputs...!\\
-\noindent \textit{layers: {2x1 cell} of layers}: hidden and output layer definition\\
-The dimension of $2x1 cell$ is because we have one hidden and one output layer. So too see the details of the hidden layer definitions, we have to enter:
-\begin{verbatim}
-K>> MLPnet.layers{1}
-
-ans =
-
-     dimensions: 2
-    distanceFcn: ''
-      distances: []
-        initFcn: 'initnw'
-    netInputFcn: 'netsum'
-      positions: [0 1]
-           size: 2
-    topologyFcn: 'hextop'
-    transferFcn: 'tansig'
-       userdata: [1x1 struct]
-\end{verbatim}
-and for the output layer:
-\begin{verbatim}
-K>> MLPnet.layers{2}
-
-ans =
-
-     dimensions: 1
-    distanceFcn: ''
-      distances: []
-        initFcn: 'initnw'
-    netInputFcn: 'netsum'
-      positions: 0
-           size: 1
-    topologyFcn: 'hextop'
-    transferFcn: 'purelin'
-       userdata: [1x1 struct]
-\end{verbatim}
-
-\noindent \textit{outputs: {1x2 cell} containing 1 output}: output layer definitions\\
-\begin{verbatim}
-K>> MLPnet.outputs
-
-ans =
-
-     []    [1x1 struct]
-\end{verbatim}
-How knows, why this is a $1x2 cell$? The next command will also show the detailed definition! Of course, realy simple.
-\begin{verbatim}
-K>> MLPnet.outputs{2}
-
-ans =
-
-        size: 1
-    userdata: [1x1 struct]
-\end{verbatim}
-
-\noindent \textit{targets: {1x2 cell} containing 1 target}: unknow till now\\
-
-\noindent \textit{biases: {2x1 cell} containing 2 biases}: detailed definitions, for the biases\\
-\begin{verbatim}
-K>> MLPnet.biases
-
-ans =
-
-    [1x1 struct]
-    [1x1 struct]
-
-K>> MLPnet.biases{1}
-
-ans =
-
-       initFcn: ''
-         learn: 1
-      learnFcn: 'learngdm'
-    learnParam: [1x1 struct]
-          size: 2
-      userdata: [1x1 struct]
-
-K>> MLPnet.biases{2}
-
-ans =
-
-       initFcn: ''
-         learn: 1
-      learnFcn: 'learngdm'
-    learnParam: [1x1 struct]
-          size: 1
-      userdata: [1x1 struct]
-\end{verbatim}
-      inputWeights: {2x1 cell} containing 1 input weight
-      layerWeights: {2x2 cell} containing 1 layer weight
-
-
-\paragraph{weight and bias values:}
-
-\subparagraph{IW:}
-\begin{verbatim}
-K>> MLPnet.IW
-
-ans =
-
-    [2x26 double]
-               []
-\end{verbatim}
-
-\subparagraph{LW:}
-\begin{verbatim}
-K>> MLPnet.LW
-
-ans =
-
-              []     []
-    [1x2 double]     []
-\end{verbatim}
-
-\subparagraph{b:}
-\begin{verbatim}
-K>> MLPnet.b
-
-ans =
-
-    [2x1 double]
-    [   -0.3908]
-\end{verbatim}
-
-
-\paragraph{net.trainParam:}
-Output for the Levenberg-Marquardt train algorithm.
-\begin{verbatim}
-K>> MLPnet.trainParam
-
-ans =
-
-       epochs: 100
-         goal: 0
-     max_fail: 5
-    mem_reduc: 1
-     min_grad: 1.0000e-010
-           mu: 0.0010
-       mu_dec: 0.1000
-       mu_inc: 10
-       mu_max: 1.0000e+010
-         show: 25
-         time: Inf
-\end{verbatim}
\ No newline at end of file
--- a/main/nnet/doc/latex/developers/analyzing/matlab.tex	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,4 +0,0 @@
-\chapter{analyzing matlab functions}
-
-\input{analyzing/analyzingNewff}
-\input{analyzing/analyzingNewp}
--- a/main/nnet/doc/latex/developers/codeConvention.tex	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,12 +0,0 @@
-\section{Code convention}
-
-The main function of this toolbox will be programed with help of the book in \cite{4}.
-So the variables will have the same names like in the book with one exception: Variables, only with one letter, will have two letters, e.g.
-\begin{itemize}
-	\item $X \rightarrow Xx$
-	\item $Q \rightarrow Qq$
-	\item $a \rightarrow aa$
-\end{itemize}
-and so on ...\\
-
-This is only to make it possible to search for variable names.
\ No newline at end of file
--- a/main/nnet/doc/latex/developers/codingGuideline/codingGuideline.tex	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,51 +0,0 @@
-\chapter{Coding Guideline}
-Some genereal descriptions why a variable has a chosen name. This is valid for the complete
-toolbox... or so :-)\\
-Here is only the description of variable names, which aren't visible to the user. Visible names are
-described in the User's Guide!\\
-The variable identifiers are taken from \cite{4}. One difference is purposeful added. If a variable has only one letter, a second small letter will be added to make it searchable. Who has ever tried to search a variable called "t"?
-
-\section{Variable identifier}
-
-\begin{tabbing}
-\hspace*{1em} \= \textcolor{blue}{Identifier} \hspace*{3em}\= \textcolor{blue}{Description:} \\
-  \textbf{Aa} \> \> hold the network values after transfer function.\\
-  blf \>  \> \textbf{b}atch \textbf{l}earning \textbf{f}unction \\
-  btf  \>  \> \textbf{b}atch \textbf{t}rainings \textbf{f}unction \\
-  \textbf{Jj} \>  \> Jacobi matrix \\
-  \textbf{Nn}  \> \> hold the network values before transfer function.\\
-  net	\> \> structure which holds the neural network informations \\
-  pf \>  \> \textbf{p}erformance \textbf{f}unction \\
-  \textbf{Pp}				\>						\>input matrix; nInputs x nDataSets  \\
-  \textbf{Pr}	\>		\> input range, this is a Nx2 matrix, that's why the capitalized P \\
-  trf \>  \> \textbf{tr}ansfer \textbf{f}unction \\
-  \textbf{Tt} \>  \> target matrix, nTargets x nDataSets\\
-  \textbf{ss}	\> \> row vector with numbers of neurons in the layers, for each layer, one entry \\
-  \textbf{vE}	\> \> row vector with errors... size depends on network structure. \\
-  VV \>  \> Validation structure \\
-  \textbf{xx} \>  \> Weight vector in column format\\
-
-\end{tabbing}
-
-\subsection{Nn}
-\textbf{Nn} is a cell array and has one entry for each layer. In reality, this will have 2 or 3 layers.\\
-In \textbf{Nn\{1,1\}} are the values for the first hidden layer. The size of this matrix depends
-on the number of neurons used for this layer.\\
-In \textbf{Nn\{2,1\}} are the values for the second hidden layer or the output layer. The size of this matrix depends
-on the number of neurons used for this layer and so on ...\\
-\textbf{Nn\{x,1\}} where \textbf{x} can be $\infty$.\\
-
-\subsection{Aa}
-\textbf{Aa} is a cell array and has one entry for each layer.\\
-In \textbf{Aa\{1,1\}} are the values for the first hidden layer. The size of this matrix depends
-on the number of neurons used for this layer.\\
-In \textbf{Aa\{2,1\}} are the values for the second hidden layer or the output layer. The size of this matrix depends
-on the number of neurons used for this layer.\\
-See \textbf{Nn} for a more detailed description.\\
-
-\subsection{vE}
-\textbf{vE} is also a cell array which holds in the last (second) element the error vector. It's not completly clear, why in the last (second) element.\\
-The number of rows depends on the number of output neurons. For one output neuron, \textbf{vE} holds only one row, for 2 output neurons, this holds of course 2 rows, and so on.
-
-\subsection{Jj}
-This is the short term for the Jacobi matrix.
\ No newline at end of file
--- a/main/nnet/doc/latex/developers/examples/example1.tex	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,40 +0,0 @@
-\section{Example 1}
-
-This MLP is designed with 2-2-1. This is not a complete example but it will help to understand the
-dimensions of all matrices and vectores are used inside the Levenberg-Marquardt algorithm.
-
-\subsection{Data matrices}
-The input matrix will be defined like in equation \eqref{equ:mInput} and the output matrix like in
-equation \eqref{equ:mOutput}.
-
-\begin{equation}
-  mInput = \left[ \begin{array}{c c c c}
-												1 & 2 & 3 & 1   \\
-												1	& 1 & 1 & 2 	\\
-												1 & 2 & 1 & 2		\\
-												\end{array}
-					\right]
-		\label{equ:mInput}
-\end{equation}
-
-\begin{equation}
-  mOutput = \left[ \begin{array}{c c c c}
-												1 & 1.5 & 2 & 3   \\
-									 \end{array}
-					\right]
-		\label{equ:mOutput}
-\end{equation}
-
-
-
-
-\subsection{Weight matrices}
-The first layer matrix will hold 2x3 weights. The second layer matrix will hold 1x2 weights.
-The first bias holds 3x1 weights and the second holds only a scalar element.
-
-\subsection{Sensitivity Matrices}
-This part is right now not so clear in my mind. What is the dimension of these two matrices?
-The first layer sensitivity matrix should be about 2x71. Number of hidden neurons in the rows and number of train data sets in the columns.\\
-
-In the actual version, the dimension is about 71x71 .. so it seems to have a mistake inside the algorithm :-(
-
--- a/main/nnet/doc/latex/developers/examples/example2.tex	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,15 +0,0 @@
-\section{Example 2}
-
-This MLP is designed with 26-2-1. Why starting easy if it's possible to start difficult?
-The input matrix which will be given to the training algorithm has the dimension of 26x71. The jacobian matrix should reach a size of 71x57.
-
-\subsection{Weight matrices}
-The first layer matrix will hold 2x26 weights. The second layer matrix will hold 1x2 weights.
-The first bias holds 26x1 weights and the second holds only a scalar element.
-
-\subsection{Sensitivity Matrices}
-This part is right now not so clear in my mind. What is the dimension of these two matrices?
-The first layer sensitivity matrix should be about 2x71. Number of hidden neurons in the rows and number of train data sets in the columns.\\
-
-In the actual version, the dimension is about 71x71 .. so it seems to have a mistake inside the algorithm :-(
-
--- a/main/nnet/doc/latex/developers/examples/examples.tex	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,4 +0,0 @@
-\chapter{Examples}
-
-
-\input{examples/example1}
\ No newline at end of file
--- a/main/nnet/doc/latex/developers/funcindex/funcindex.tex	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,5 +0,0 @@
-\chapter{Function Index}
-
-\section{Who calles who}
-\input{funcindex/funcindexCalled}
-
--- a/main/nnet/doc/latex/developers/funcindex/funcindexCalled.tex	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,321 +0,0 @@
-\begin{verbatim}
-Function-File: isposint
-=============================
-\end{verbatim}
-\begin{verbatim}
-Function-File: logsig
-=============================
-\end{verbatim}
-\begin{verbatim}
-Function-File: min_max
-=============================
-\end{verbatim}
-\begin{verbatim}
-Function-File: newff
-=============================
-__init
-__newnetwork
-tansig
-train
-\end{verbatim}
-\begin{verbatim}
-Function-File: poststd
-=============================
-\end{verbatim}
-\begin{verbatim}
-Function-File: prestd
-=============================
-\end{verbatim}
-\begin{verbatim}
-Function-File: purelin
-=============================
-\end{verbatim}
-\begin{verbatim}
-Function-File: saveMLPStruct
-=============================
-__checknetstruct
-__printAdaptFcn
-__printAdaptParam
-__printB
-__printBiasConnect
-__printBiases
-__printIW
-__printInitFcn
-__printInitParam
-__printInputConnect
-__printInputWeights
-__printInputs
-__printLW
-__printLayerConnect
-__printLayerWeights
-__printLayers
-__printMLPHeader
-__printNetworkType
-__printNumInputDelays
-__printNumInputs
-__printNumLayerDelays
-__printNumLayers
-__printNumOutputs
-__printNumTargets
-__printOutputConnect
-__printOutputs
-__printPerformFcn
-__printPerformParam
-__printTargetConnect
-__printTargets
-__printTrainFcn
-__printTrainParam
-\end{verbatim}
-\begin{verbatim}
-Function-File: sim
-=============================
-__checknetstruct
-logsig
-purelin
-tansig
-\end{verbatim}
-\begin{verbatim}
-Function-File: subset
-=============================
-__optimizedatasets
-\end{verbatim}
-\begin{verbatim}
-Function-File: tansig
-=============================
-\end{verbatim}
-\begin{verbatim}
-Function-File: train
-=============================
-__checknetstruct
-__trainlm
-\end{verbatim}
-\begin{verbatim}
-Function-File: trastd
-=============================
-\end{verbatim}
-\begin{verbatim}
-Function-File: __analyzerows
-=============================
-\end{verbatim}
-\begin{verbatim}
-Function-File: __calcjacobian
-=============================
-__dlogsig
-__dpurelin
-__dtansig
-logsig
-purelin
-tansig
-\end{verbatim}
-\begin{verbatim}
-Function-File: __calcperf
-=============================
-__mse
-logsig
-purelin
-tansig
-\end{verbatim}
-\begin{verbatim}
-Function-File: __checknetstruct
-=============================
-\end{verbatim}
-\begin{verbatim}
-Function-File: __copycoltopos1
-=============================
-\end{verbatim}
-\begin{verbatim}
-Function-File: __dlogsig
-=============================
-\end{verbatim}
-\begin{verbatim}
-Function-File: __dpurelin
-=============================
-\end{verbatim}
-\begin{verbatim}
-Function-File: __dtansig
-=============================
-\end{verbatim}
-\begin{verbatim}
-Function-File: __getx
-=============================
-__checknetstruct
-\end{verbatim}
-\begin{verbatim}
-Function-File: __init
-=============================
-__checknetstruct
-newff
-\end{verbatim}
-\begin{verbatim}
-Function-File: __mae
-=============================
-\end{verbatim}
-\begin{verbatim}
-Function-File: __mse
-=============================
-\end{verbatim}
-\begin{verbatim}
-Function-File: __newnetwork
-=============================
-__checknetstruct
-isposint
-newff
-train
-\end{verbatim}
-\begin{verbatim}
-Function-File: __optimizedatasets
-=============================
-__analyzerows
-__randomisecols
-__rerangecolumns
-\end{verbatim}
-\begin{verbatim}
-Function-File: __printAdaptFcn
-=============================
-\end{verbatim}
-\begin{verbatim}
-Function-File: __printAdaptParam
-=============================
-\end{verbatim}
-\begin{verbatim}
-Function-File: __printB
-=============================
-\end{verbatim}
-\begin{verbatim}
-Function-File: __printBiasConnect
-=============================
-\end{verbatim}
-\begin{verbatim}
-Function-File: __printBiases
-=============================
-\end{verbatim}
-\begin{verbatim}
-Function-File: __printInitFcn
-=============================
-\end{verbatim}
-\begin{verbatim}
-Function-File: __printInitParam
-=============================
-\end{verbatim}
-\begin{verbatim}
-Function-File: __printInputConnect
-=============================
-\end{verbatim}
-\begin{verbatim}
-Function-File: __printInputs
-=============================
-\end{verbatim}
-\begin{verbatim}
-Function-File: __printInputWeights
-=============================
-\end{verbatim}
-\begin{verbatim}
-Function-File: __printIW
-=============================
-\end{verbatim}
-\begin{verbatim}
-Function-File: __printLayerConnect
-=============================
-\end{verbatim}
-\begin{verbatim}
-Function-File: __printLayers
-=============================
-\end{verbatim}
-\begin{verbatim}
-Function-File: __printLayerWeights
-=============================
-\end{verbatim}
-\begin{verbatim}
-Function-File: __printLW
-=============================
-\end{verbatim}
-\begin{verbatim}
-Function-File: __printMLPHeader
-=============================
-\end{verbatim}
-\begin{verbatim}
-Function-File: __printNetworkType
-=============================
-newff
-\end{verbatim}
-\begin{verbatim}
-Function-File: __printNumInputDelays
-=============================
-\end{verbatim}
-\begin{verbatim}
-Function-File: __printNumInputs
-=============================
-\end{verbatim}
-\begin{verbatim}
-Function-File: __printNumLayerDelays
-=============================
-\end{verbatim}
-\begin{verbatim}
-Function-File: __printNumLayers
-=============================
-\end{verbatim}
-\begin{verbatim}
-Function-File: __printNumOutputs
-=============================
-\end{verbatim}
-\begin{verbatim}
-Function-File: __printNumTargets
-=============================
-\end{verbatim}
-\begin{verbatim}
-Function-File: __printOutputConnect
-=============================
-\end{verbatim}
-\begin{verbatim}
-Function-File: __printOutputs
-=============================
-\end{verbatim}
-\begin{verbatim}
-Function-File: __printPerformFcn
-=============================
-\end{verbatim}
-\begin{verbatim}
-Function-File: __printPerformParam
-=============================
-\end{verbatim}
-\begin{verbatim}
-Function-File: __printTargetConnect
-=============================
-\end{verbatim}
-\begin{verbatim}
-Function-File: __printTargets
-=============================
-\end{verbatim}
-\begin{verbatim}
-Function-File: __printTrainFcn
-=============================
-train
-\end{verbatim}
-\begin{verbatim}
-Function-File: __printTrainParam
-=============================
-train
-\end{verbatim}
-\begin{verbatim}
-Function-File: __randomisecols
-=============================
-\end{verbatim}
-\begin{verbatim}
-Function-File: __rerangecolumns
-=============================
-__copycoltopos1
-\end{verbatim}
-\begin{verbatim}
-Function-File: __setx
-=============================
-__checknetstruct
-\end{verbatim}
-\begin{verbatim}
-Function-File: __trainlm
-=============================
-__calcjacobian
-__calcperf
-__getx
-__setx
-isposint
-\end{verbatim}
--- a/main/nnet/doc/latex/developers/introduction.tex	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,28 +0,0 @@
-\chapter{Introduction}
-This documentation isn't a well defined and structured docu for the neural network toolbox.
-It's more a \textit{collection of my ideas and minds}.
-
-
-\section{Installed system}
-I'm developing and testing the actual version of the neural network toolbox on following
-program versions:
-
-\begin{itemize}
-  \item Octave 2.9.5
-  \item octave-forge-2006.01.28
-  \item OctPlot svn version
-\end{itemize}
-
-.. and on another system
-
-\begin{itemize}
-  \item Octave 2.9.12
-  \item octave-forge packages ...
-  \item OctPlot svn version
-\end{itemize}
-
-
-\input{numbering}
-\input{codeConvention}
-
-
Binary file main/nnet/doc/latex/developers/neuralNetworkPackageForOctaveDevelopersGuide.dvi has changed
Binary file main/nnet/doc/latex/developers/neuralNetworkPackageForOctaveDevelopersGuide.pdf has changed
--- a/main/nnet/doc/latex/developers/neuralNetworkPackageForOctaveDevelopersGuide.tcp	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,12 +0,0 @@
-[FormatInfo]
-Type=TeXnicCenterProjectInformation
-Version=4
-
-[ProjectInfo]
-MainFile=neuralNetworkPackageForOctaveDevelopersGuide.tex
-UseBibTeX=0
-UseMakeIndex=1
-ActiveProfile=LaTeX => PDF
-ProjectLanguage=de
-ProjectDialect=DE
-
--- a/main/nnet/doc/latex/developers/neuralNetworkPackageForOctaveDevelopersGuide.tex	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,83 +0,0 @@
-% Preambel
-\documentclass[a4paper,openany]{report}
-
-
-\usepackage{a4wide}
-\usepackage[ansinew]{inputenc}
-\usepackage[T1]{fontenc}
-\RequirePackage{ifpdf}
-
-\usepackage{hyperref}
-	\hypersetup{%
-  colorlinks=true,   % activates colored references
-  pdfpagemode=None,  % PDF-Viewer starts without content et.al.
-  pdfstartview=FitH, % PDF-Viewer uses a defined page width
-  %linkbordercolor=111,
-  % citebordercolor=111,
-  citecolor=blue,
-  linkcolor=blue}
-
-\ifpdf
-  \usepackage[pdftex]{graphicx}
-	  \DeclareGraphicsExtensions{.pdf}
-\else
-  \usepackage[dvips]{graphicx}
-	  \DeclareGraphicsExtensions{.eps}
-\fi
-
-\usepackage{fancyhdr}
-\usepackage{booktabs}
-%\usepackage[dvips]{rotating}
-\usepackage{multirow}
-\usepackage{multicol}
-
-\usepackage{color}
-\usepackage{amsmath}
-\usepackage{alltt}
-%\usepackage{array}
-%\usepackage{colortbl}
-
-\clubpenalty = 10000
-\widowpenalty = 10000 \displaywidowpenalty = 10000
-
-\definecolor{hellgrau}{gray}{0.95}
-\definecolor{dunkelgrau}{gray}{0.55}
-
-
-
-\renewcommand{\headrulewidth}{0pt} % no head rule
-\renewcommand{\footrulewidth}{0pt} % no foot rule
-
-
-
-
-%\nointend
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-% start text here!!
-
-
-
-\begin{document}
-\pagenumbering{roman}
-\input{title}
-
-\tableofcontents
-\pagenumbering{arabic}
-
-\include{introduction}
-\include{octave/octave}
-\include{codingGuideline/codingGuideline}
-\include{algorithm/algorithmen}
-\include{funcindex/funcindex}
-\include{tests/test}
-\include{analyzing/matlab}
-
-\appendix
-\include{examples/examples}
-\include{../common/bibliography}
-
-
-
-\end{document}
-
-
--- a/main/nnet/doc/latex/developers/numbering.tex	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,9 +0,0 @@
-\section{Version numbers of the neural network toolbox}
-
-The first number describes the major release. Version number V1.0 will be the first toolbox release which should have the same functions like the Matlab R14 SP3 neural network Toolbox.\\
-
-The second number defines the finished functions. So to start, only the MLPs will realised and so this will be the number V0.1.0.\\
-
-The third number defines the status of the actual development and function. V0.0.1 means no release with MLP, actually, everything under construction... ;-D.\\
-
-Right now it's version V0.1.3 which means MLP works and currently the transfer function logsig is added.
\ No newline at end of file
--- a/main/nnet/doc/latex/developers/octave/directorys.tex	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,59 +0,0 @@
-\section{Functions location}
-
-\subsection{Directory \textit{nnet/nnet}}
-
-\noindent Available functions:
-\begin{enumerate}
-	\item min\_max
-	\item network
-  \item newff
-\end{enumerate}
-
-
-\subsection{Directory \textit{nnet/nnutils}}
-This directory holds a further subdirectory: \textit{saveMLPStructure}.\\
-
-\noindent Available functions:
-\begin{enumerate}
-  \item saveMLPStruct
-
-\end{enumerate}
-
-Function \textit{saveMLPStruct} doesn't exist in MATLAB(TM). This is a helper function to print a neural network architecture.
-
-\subsubsection{saveMLPStructure}
-Inside this directory are a lot of different functions which will help to print a
-text file to display the neural network architecture. The look will be the same like
-you would open the type \textit{network} in MATLAB(TM).
-
-\begin{itemize}
-  \item printAdaptFcn
-  \item printAdaptParam
-  \item printB
-  \item printBiasConnect
-  \item printBiases
-  \item printInitFcn
-  \item printInputConnect
-  \item printInputs
-  \item printInputWeights
-  \item printIW
-  \item printLayerConnect
-  \item printLayers
-  \item printLayerWeights
-  \item printLW
-  \item printMLPHeader
-  \item printNumInputDelays
-  \item printNumInputs
-  \item printNumLayerDelays
-  \item printNumLayers
-  \item printNumOutputs
-  \item printNumTargets
-  \item printOutputConnect
-  \item printOutputs
-  \item printPerformFcn
-  \item printPerformParam
-  \item printTargetConnect
-  \item printTargets
-  \item printTrainFcn
-  \item printTrainParam
-\end{itemize}
\ No newline at end of file
--- a/main/nnet/doc/latex/developers/octave/functions/isposintOct.tex	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,28 +0,0 @@
-\section{isposint}
-Checks if value is a positive integer.\\
-
-\subsubsection{Syntax:}
-
-$f = isposint(val)$\\
-$f = 0$ if val is negative integer or float and is 0 if val is a positive float.\\
-
-\subsubsection{Example 1:}
-val = 5.3;\\
-isposint(val)\\
-ans = 0\\
-
-\subsubsection{Example 2:}
-val = -5;\\
-isposint(val)\\
-ans = 0\\
-
-\subsubsection{Example 3:}
-val = 0;\\
-isposint(val)\\
-ans = 1\\
-
-\subsubsection{Example 4:}
-val = 5;\\
-isposint(val)\\
-ans = 1\\
-
--- a/main/nnet/doc/latex/developers/octave/functions/min_max.tex	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,14 +0,0 @@
-\subsection{min\_max}
-Checks for minimal and maximal values of an input matrix for \textbf{newff}.\\
-
-\subsubsection{Syntax:}
-
-$pr = min\_max(mInputs)$\\
-
-\subsubsection{Description:}
-\textit{mInputs} must be a matrix with input training data sets. This means in the case, for a 9-2-1 MLP
-(this means 9 input-, 2 hidden- and 1 output-neuron) with 100 input training data sets, the matrix must be
-an 9x100 matrix. \textit{pr} will then be a 9x2 matrix with minimal values in the first column and maximal values in the second column. If a row holds 2 zeros, a warning will appear (no information in this row!).
-
-\subsubsection{Important:}
-The equival function in MATLAB(TM) is called \textit{minmax}. This is not possible because the functions \textit{min} and \textit{max} in Octave are programed in minmax.cc!
\ No newline at end of file
--- a/main/nnet/doc/latex/developers/octave/functions/newff.tex	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,14 +0,0 @@
-\subsection{min\_max}
-Checks for minimal and maximal values of an input matrix for \textbf{newff}.\\
-
-\subsubsection{Syntax:}
-
-$pr = min\_max(mInputs)$\\
-
-\subsubsection{Description:}
-\textit{mInputs} must be a matrix with input training data sets. This means in the case, for a 9-2-1 MLP
-(this means 9 input-, 2 hidden- and 1 output-neuron) with 100 input training data sets, the matrix must be
-an 9x100 matrix. \textit{pr} will then be a 9x2 matrix with minimal values in the first column and maximal values in the second column. If a row holds 2 zeros, a warning will appear (no information in this row!).
-
-\subsubsection{Important:}
-The equival function in MATLAB(TM) is called \textit{minmax}. This is not possible because the functions \textit{min} and \textit{max} in Octave are programed in minmax.cc!
\ No newline at end of file
--- a/main/nnet/doc/latex/developers/octave/functions/poststd.tex	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,14 +0,0 @@
-\subsection{min\_max}
-Checks for minimal and maximal values of an input matrix for \textbf{newff}.\\
-
-\subsubsection{Syntax:}
-
-$pr = min\_max(mInputs)$\\
-
-\subsubsection{Description:}
-\textit{mInputs} must be a matrix with input training data sets. This means in the case, for a 9-2-1 MLP
-(this means 9 input-, 2 hidden- and 1 output-neuron) with 100 input training data sets, the matrix must be
-an 9x100 matrix. \textit{pr} will then be a 9x2 matrix with minimal values in the first column and maximal values in the second column. If a row holds 2 zeros, a warning will appear (no information in this row!).
-
-\subsubsection{Important:}
-The equival function in MATLAB(TM) is called \textit{minmax}. This is not possible because the functions \textit{min} and \textit{max} in Octave are programed in minmax.cc!
\ No newline at end of file
--- a/main/nnet/doc/latex/developers/octave/functions/prestd.tex	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,14 +0,0 @@
-\subsection{min\_max}
-Checks for minimal and maximal values of an input matrix for \textbf{newff}.\\
-
-\subsubsection{Syntax:}
-
-$pr = min\_max(mInputs)$\\
-
-\subsubsection{Description:}
-\textit{mInputs} must be a matrix with input training data sets. This means in the case, for a 9-2-1 MLP
-(this means 9 input-, 2 hidden- and 1 output-neuron) with 100 input training data sets, the matrix must be
-an 9x100 matrix. \textit{pr} will then be a 9x2 matrix with minimal values in the first column and maximal values in the second column. If a row holds 2 zeros, a warning will appear (no information in this row!).
-
-\subsubsection{Important:}
-The equival function in MATLAB(TM) is called \textit{minmax}. This is not possible because the functions \textit{min} and \textit{max} in Octave are programed in minmax.cc!
\ No newline at end of file
--- a/main/nnet/doc/latex/developers/octave/functions/sim.tex	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,14 +0,0 @@
-\subsection{min\_max}
-Checks for minimal and maximal values of an input matrix for \textbf{newff}.\\
-
-\subsubsection{Syntax:}
-
-$pr = min\_max(mInputs)$\\
-
-\subsubsection{Description:}
-\textit{mInputs} must be a matrix with input training data sets. This means in the case, for a 9-2-1 MLP
-(this means 9 input-, 2 hidden- and 1 output-neuron) with 100 input training data sets, the matrix must be
-an 9x100 matrix. \textit{pr} will then be a 9x2 matrix with minimal values in the first column and maximal values in the second column. If a row holds 2 zeros, a warning will appear (no information in this row!).
-
-\subsubsection{Important:}
-The equival function in MATLAB(TM) is called \textit{minmax}. This is not possible because the functions \textit{min} and \textit{max} in Octave are programed in minmax.cc!
\ No newline at end of file
--- a/main/nnet/doc/latex/developers/octave/functions/train.tex	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,14 +0,0 @@
-\subsection{min\_max}
-Checks for minimal and maximal values of an input matrix for \textbf{newff}.\\
-
-\subsubsection{Syntax:}
-
-$pr = min\_max(mInputs)$\\
-
-\subsubsection{Description:}
-\textit{mInputs} must be a matrix with input training data sets. This means in the case, for a 9-2-1 MLP
-(this means 9 input-, 2 hidden- and 1 output-neuron) with 100 input training data sets, the matrix must be
-an 9x100 matrix. \textit{pr} will then be a 9x2 matrix with minimal values in the first column and maximal values in the second column. If a row holds 2 zeros, a warning will appear (no information in this row!).
-
-\subsubsection{Important:}
-The equival function in MATLAB(TM) is called \textit{minmax}. This is not possible because the functions \textit{min} and \textit{max} in Octave are programed in minmax.cc!
\ No newline at end of file
--- a/main/nnet/doc/latex/developers/octave/functions/trastd.tex	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,14 +0,0 @@
-\subsection{min\_max}
-Checks for minimal and maximal values of an input matrix for \textbf{newff}.\\
-
-\subsubsection{Syntax:}
-
-$pr = min\_max(mInputs)$\\
-
-\subsubsection{Description:}
-\textit{mInputs} must be a matrix with input training data sets. This means in the case, for a 9-2-1 MLP
-(this means 9 input-, 2 hidden- and 1 output-neuron) with 100 input training data sets, the matrix must be
-an 9x100 matrix. \textit{pr} will then be a 9x2 matrix with minimal values in the first column and maximal values in the second column. If a row holds 2 zeros, a warning will appear (no information in this row!).
-
-\subsubsection{Important:}
-The equival function in MATLAB(TM) is called \textit{minmax}. This is not possible because the functions \textit{min} and \textit{max} in Octave are programed in minmax.cc!
\ No newline at end of file
--- a/main/nnet/doc/latex/developers/octave/octave.tex	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,4 +0,0 @@
-\chapter{Octave's available functions}
-
-\section{Available functions}
-\input{octave/functions/min_max}
\ No newline at end of file
--- a/main/nnet/doc/latex/developers/tests/__analyzerows.tex	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,23 +0,0 @@
-\begin{verbatim}
-%!shared b, retmat
-%! disp("testing __analyzerows")
-%! b = [1 0 0 1; 1 0 0 0; 1 2 0 1];
-%! retmat = __analyzerows(b);
-%!assert(retmat(1,1)==1);#%!assert(retmat(1,1)==1);
-%!assert(retmat(2,1)==1);
-%!assert(retmat(3,1)==0);
-%! b = [1 0 0 2; 1 0 0 0; 1 1 1 1];
-%! retmat = __analyzerows(b);
-%!assert(retmat(1,2)==0);
-%!assert(retmat(2,2)==0);
-%!assert(retmat(3,2)==1);
-%! b = [1 0 0 2; 1 0 0 0; 1 1 1 1];
-%! retmat = __analyzerows(b);
-%!assert(retmat(1,3)==2);
-%!assert(retmat(2,3)==0);
-%!assert(retmat(3,3)==0);
-%! retmat = __analyzerows(b);
-%!assert(retmat(1,4)==1);
-%!assert(retmat(2,4)==0);
-%!assert(retmat(3,4)==0);
-\end{verbatim}
--- a/main/nnet/doc/latex/developers/tests/__copycoltopos1.tex	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,11 +0,0 @@
-\begin{verbatim}
-%!shared a, retmat
-%! disp("testing __copycoltopos1")
-%! a = [0 1 2 3 4; 5 6 7 8 9];
-%! retmat = __copycoltopos1(a,3);
-%!assert(retmat(1,1)==2);
-%!assert(retmat(2,1)==7);
-%! retmat = __copycoltopos1(a,5);
-%!assert(retmat(1,1)==4);
-%!assert(retmat(2,1)==9);
-\end{verbatim}
--- a/main/nnet/doc/latex/developers/tests/__optimizedatasets.tex	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,23 +0,0 @@
-\begin{verbatim}
-%!shared retmatrix, matrix
-%! disp("testing __optimizedatasets")
-%! matrix = [1 2 3 2 1 2 3 0 5 4 3 2 2 2 2 2 2; \
-%!			 0 1 1 0 0 0 0 0 0 0 0 0 0 0 1 1 0; \
-%!			-1 3 2 4 9 1 1 1 1 1 9 1 1 1 9 9 0; \
-%!			 2 3 2 3 2 2 2 2 3 3 3 3 1 1 1 1 1];
-%! ## The last row is equal to the neural network targets
-%! retmatrix = __optimizedatasets(matrix,9,1);
-%! ## the above statement can't be tested with assert!
-%! ## it contains random values! So pass a "success" message
-%!assert(1==1);
-%! matrix = [1 2 3 2 1 2 3 0 5 4 3 2 2 2 2 2 2; \
-%!			 0 1 1 0 0 0 0 0 0 0 0 0 0 0 1 1 0; \
-%!			-1 3 2 4 9 1 1 1 1 1 9 1 1 1 9 9 0; \
-%!			 2 3 2 3 2 2 2 2 3 3 3 3 1 1 1 1 1];
-%! ## The last row is equal to the neural network targets
-%! retmatrix = __optimizedatasets(matrix,9,1,0);
-%!assert(retmatrix(1,1)==5);
-%!assert(retmatrix(2,1)==0);
-%!assert(retmatrix(3,1)==1);
-%!assert(retmatrix(4,1)==3);
-\end{verbatim}
--- a/main/nnet/doc/latex/developers/tests/__randomisecols.tex	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,4 +0,0 @@
-\begin{verbatim}
-%!# no test possible, contains randperm which is using
-%!# some randome functions
-\end{verbatim}
--- a/main/nnet/doc/latex/developers/tests/__rerangecolumns.tex	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,32 +0,0 @@
-\begin{verbatim}
-%!shared matrix,analyzeMatrix,nTrainSets, returnmatrix
-%! disp("testing __rerangecolumns")
-%! matrix = [0 1 0 0 0 0 1 0 1 1;  \
-%!			 4 4 4 4 4 4 4 4 4 4;  \
-%!        -1.1 -1.1 2 3 4 3.2 1 8 9 10; \
-%!           0 1.1 3 4 5 2 10 10 2 3; \
-%!          -1 1 1 1 1 2 3 4 1 5];
-%! analyzeMatrix = [1 0 0 0; 0 1 0 0; 0 0 2 1; 0 0 1 2; 0 0 1 1];
-%! nTrainSets = 8;
-%! returnmatrix = __rerangecolumns(matrix,analyzeMatrix,nTrainSets);
-%!assert(returnmatrix(1,1)==1);
-%!assert(returnmatrix(2,1)==4);
-%!assert(returnmatrix(3,1)==1);
-%!assert(returnmatrix(4,1)==10);
-%!assert(returnmatrix(5,1)==3);
-%! matrix = [0 1 0 0 0 0 1 0 1 1; 			\
-%!			 4 4 4 4 4 4 4 4 4 4; 			\
-%!          -1.1 -1.1 2 3 4 3.2 1 8 9 10; 	\
-%!           0 1.1 3 4 5 2 10 10 2 3; 		\
-%!          -1 1 1 1 1 2 3 4 1 5;     		\
-%!			 0 1 2 1 2 1 2 3 4 5;];  # the last row is euqal to the nnet targets
-%! analyzeMatrix = [1 0 0 0; 0 1 0 0; 0 0 2 1; 0 0 1 2; 0 0 1 1];
-%! nTrainSets = 8;
-%! returnmatrix = __rerangecolumns(matrix,analyzeMatrix,nTrainSets);
-%!assert(returnmatrix(1,1)==1);
-%!assert(returnmatrix(2,1)==4);
-%!assert(returnmatrix(3,1)==1);
-%!assert(returnmatrix(4,1)==10);
-%!assert(returnmatrix(5,1)==3);
-%!assert(returnmatrix(6,1)==2);
-\end{verbatim}
--- a/main/nnet/doc/latex/developers/tests/isposint.tex	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,11 +0,0 @@
-\begin{verbatim}
-%!shared
-%! disp("testing isposint")
-%!assert(isposint(1)) # this should pass
-%!assert(isposint(0.5),0) # should return zero
-%!assert(isposint(-1),0) # should return zero
-%!assert(isposint(-1.5),0) # should return zero
-%!assert(isposint(0),0) # should return zero
-%!fail("isposint([0 0])","Input argument must not be a vector, only scalars are allowed!")
-%!fail("isposint('testString')","Input argument must not be a vector, only scalars are allowed!")
-\end{verbatim}
--- a/main/nnet/doc/latex/developers/tests/min_max.tex	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,9 +0,0 @@
-\begin{verbatim}
-%!shared
-%! disp("testing min_max")
-%!test fail("min_max(1)","Argument must be a matrix.")
-%!test fail("min_max('testString')","Argument must be a matrix.")
-%!test fail("min_max(cellA{1}=1)","Argument must be a matrix.")
-%!test fail("min_max([1+1i, 2+2i])","Argument must be a matrix.")
-%!test fail("min_max([1+1i, 2+2i; 3+1i, 4+2i])","Argument has illegal type.")
-\end{verbatim}
--- a/main/nnet/doc/latex/developers/tests/newff.tex	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,32 +0,0 @@
-\begin{verbatim}
-%!shared
-%! disp("testing newff")
-%!test
-%! Pr = [1;2];
-%! fail("newff(Pr,[1 1],{'tansig','purelin'},'trainlm','unused','mse')","Input ranges must be a two column matrix!")
-%!test
-%! Pr = [1 2 ; 4  6];
-%! assert(__checknetstruct(newff(Pr,[1 1],{'tansig','purelin'},'trainlm','unused','mse')))
-%!test
-%! Pr = [1 2 3; 4 5 6];
-%! fail("newff(Pr,[1 1],{'tansig','purelin'},'trainlm','unused','mse')","Input ranges must be a two column matrix!")
-%!test
-%! Pr = [5 3; 4 5];
-%! fail("newff(Pr,[1 1],{'tansig','purelin'},'trainlm','unused','mse')",\
-%!  "Input ranges has values in the second column larger as in the same row of the first column.")
-%!test
-%! Pr = [1 2 ; 4 6];
-%! fail("newff(Pr,[1 1; 2 3],{'tansig','purelin'},'trainlm','unused','mse')",\
-%!  "Layer sizes is not a row vector.")
-%!test
-%! Pr = [1 2 ; 4 6];
-%! assert(__checknetstruct(newff(Pr,[ 2 3],{'tansig','purelin'},'trainlm','unused','mse')))
-%!test
-%! Pr = [1 2 ; 4 6];
-%! fail("newff(Pr,[1],{'tansig','purelin'},'trainlm','unused','mse')",\
-%!  "There must be at least one hidden layer and one output layer!")
-%!test
-%! Pr = [1 2 ; 4 6];
-%! fail("newff(Pr,[-1 1],{'tansig','purelin'},'trainlm','unused','mse')",\
-%!  "Layer sizes is not a row vector of positive integers.")
-\end{verbatim}
--- a/main/nnet/doc/latex/developers/tests/prestd.tex	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,7 +0,0 @@
-\begin{verbatim}
-%!shared Pp, Tt, pn
-%!  Pp = [1 2 3 4; -1 3 2 -1];
-%!  Tt = [3 4 5 6];
-%!  [pn,meanp,stdp] = prestd(Pp);
-%!assert(pn,[-1.16190 -0.38730 0.38730 1.16190; -0.84887 1.09141 0.60634 -0.84887],0.00001);
-\end{verbatim}
--- a/main/nnet/doc/latex/developers/tests/purelin.tex	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,7 +0,0 @@
-\begin{verbatim}
-%!assert(purelin(2),2);
-%!assert(purelin(-2),-2);
-%!assert(purelin(0),0);
-%!error  # this test must throw an error!
-%! assert(purelin(2),1);
-\end{verbatim}
--- a/main/nnet/doc/latex/developers/tests/subset.tex	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,67 +0,0 @@
-\begin{verbatim}
-%!shared matrix, nTargets, mTrain, mTest, mVali
-%! disp("testing subset")
-%! matrix = [1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 18 20; \
-%!			 0 2 4 1 3 5 3 4 1 -1 -2 -9 -1 10 12 20 11 11 11 11; \
-%!			-2 2 2 2 2 0 0 0 0  0 10 12 13 12 13 44 33 32 98 11; \
-%!			 0 0 0 0 1 1 1 1 0  0  1  1  1  0  0  1  1  1  0  0; \
-%!           4 4 4 4 4 4 4 4 4  4  4  4  4  4  4  4  4  4  4  4; \
-%!           1 2 3 4 5 6 7 8 9 10 11 12 13 33 44 55 66 77 88 99];
-%! nTargets = 1; # the last row is equivalent to the target values.
-%! [mTrain, mTest, mVali] = subset(matrix,nTargets);  ############################
-%!assert(size(mTrain,2)==10);# 50% of 20
-%!assert(size(mTest,2)==6);# 1/3 of 20 = 6 (floor)
-%!assert(size(mVali,2)==4);# 1/6 of 20 = 4 (floor)
-%! # It's not possible to test the column order with this call!
-%! # randomizing is used! But all max and min values should be
-%! # in the training set
-%!assert(max(mTrain(1,:))==max(matrix(1,:)));
-%!assert(min(mTrain(1,:))==min(matrix(1,:)));
-%!assert(max(mTrain(2,:))==max(matrix(2,:)));
-%!assert(min(mTrain(2,:))==min(matrix(2,:)));
-%!assert(max(mTrain(3,:))==max(matrix(3,:)));
-%!assert(min(mTrain(3,:))==min(matrix(3,:)));
-%!assert(max(mTrain(4,:))==max(matrix(4,:)));
-%!assert(min(mTrain(4,:))==min(matrix(4,:)));
-%!
-%!
-%! [mTrain, mTest, mVali] = subset(matrix,nTargets,0);  ############################
-%!assert(size(mTrain,2)==10);# 50% of 20
-%!assert(size(mTest,2)==6);# 1/3 of 20 = 6 (floor)
-%!assert(size(mVali,2)==4);# 1/6 of 20 = 4 (floor)
-%!assert(mTrain==matrix(:,1:10));
-%!assert(mTest==matrix(:,11:16));
-%!assert(mVali==matrix(:,17:20));
-%!
-%!
-%! [mTrain, mTest, mVali] = subset(matrix,nTargets,2);  ############################
-%!assert(size(mTrain,2)==10);# 50% of 20
-%!assert(size(mTest,2)==6);# 1/3 of 20 = 6 (floor)
-%!assert(size(mVali,2)==4);# 1/6 of 20 = 4 (floor)
-%!assert(max(mTrain(1,:))==max(matrix(1,:)));
-%!assert(min(mTrain(1,:))==min(matrix(1,:)));
-%!assert(max(mTrain(2,:))==max(matrix(2,:)));
-%!assert(min(mTrain(2,:))==min(matrix(2,:)));
-%!assert(max(mTrain(3,:))==max(matrix(3,:)));
-%!assert(min(mTrain(3,:))==min(matrix(3,:)));
-%!assert(max(mTrain(4,:))==max(matrix(4,:)));
-%!assert(min(mTrain(4,:))==min(matrix(4,:)));
-%!
-%!
-%! ## next test ... optimize twice
-%! matrix = [1 2 3 4 5 6 7 20 8 10 11 12 13 14 15 16 17 18 18 9; \
-%!			 0 2 4 1 3 5 3 4 1 -1 -2 -9 -1 10 12 20 11 11 11 11; \
-%!			-2 2 2 2 2 0 0 0 0  0 10 12 13 12 13 44 33 32 98 11; \
-%!			 0 0 0 0 1 1 1 1 0  0  1  1  1  0  0  1  1  1  0  0; \
-%!           4 4 4 4 4 4 4 4 4  4  4  4  4  4  4  4  4  4  4  4; \
-%!           1 2 3 4 5 6 7 8 9 10 11 12 13 33 44 55 66 77 88 99];
-%! [mTrain, mTest, mVali] = subset(matrix,nTargets,2);  ############################
-%!assert(max(mTrain(1,:))==max(matrix(1,:)));
-%!assert(min(mTrain(1,:))==min(matrix(1,:)));
-%!assert(max(mTrain(2,:))==max(matrix(2,:)));
-%!assert(min(mTrain(2,:))==min(matrix(2,:)));
-%!assert(max(mTrain(3,:))==max(matrix(3,:)));
-%!assert(min(mTrain(3,:))==min(matrix(3,:)));
-%!assert(max(mTrain(4,:))==max(matrix(4,:)));
-%!assert(min(mTrain(4,:))==min(matrix(4,:)));
-\end{verbatim}
--- a/main/nnet/doc/latex/developers/tests/test.tex	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,24 +0,0 @@
-\chapter{Test}
-
-\section{isposint}
-\input{tests/isposint}
-\section{min\_max}
-\input{tests/min_max}
-\section{newff}
-\input{tests/newff}
-\section{prestd}
-\input{tests/prestd}
-\section{purelin}
-\input{tests/purelin}
-\section{subset}
-\input{tests/subset}
-\section{\_\_analyzerows}
-\input{tests/__analyzerows}
-\section{\_\_copycoltopos1}
-\input{tests/__copycoltopos1}
-\section{\_\_optimizedatasets}
-\input{tests/__optimizedatasets}
-\section{\_\_randomisecols}
-\input{tests/__randomisecols}
-\section{\_\_rerangecolumns}
-\input{tests/__rerangecolumns}
--- a/main/nnet/doc/latex/developers/title.tex	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,7 +0,0 @@
-
-\title{A neural network toolbox for Octave\\
-			Developer's Guide\\
-			  \input{../common/version}}
-
-\author{M. Schmid}
-\maketitle
--- a/main/nnet/doc/latex/developers/varietes.tex	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,96 +0,0 @@
-\section{Varietes}
-\label{chap:intro:sec:varietes}
-\subsection{Object-oriented programming}
-Some of the functions for Octave will have not the same name, like the matlab ones has.
-This difference is because the object-oriented programming technology. The object-oriented functions will have a name postfix in Octave.\\
-As example: \textit{isposint\_netw\_priv} means, \textit{isposint} is a private function from the matlab function \textit{network}.\\
-This is a kind of "`simulation"' for the object-oriented programming technology.\\
-
-\subsubsection{Data-Typ \textit{network} and m-file \textit{subsasgn}}
-Matlab has a further data type \textit{network}. A basic neural network will be initialized in a
-structure and than changed to the \textit{network} type with the \textit{class} command. The class command from Octave doesn't support the creation of this network type!
-In the Matlab m-file \textit{network.m} on row 256, the network type will be created! From this moment, structure subscription assignment will call the file subsasgn from the \textit{@network} directory. Back in the \textit{newff} m-file on row 134, the @network subsasgn will be called the first time.
-
-\paragraph{newff row 134; net.biasConnect} net=subsasgn(net,subscripts,v) will hold the following values:
-\begin{itemize}
-	\item net: the whole net structure
-	\item subscripts: 1x1 structure;\\
-		subscripts.type = '.'\\
-		subscripts.subs = 'biasConnect'
-	\item v: 2x1 double [1; 1]
-\end{itemize}
-
-\paragraph{newff row 135; net.inputConnect} net=subsasgn(net,subscripts,v) will hold the following values:
-\begin{itemize}
-	\item net: the whole net structure
-	\item subscripts: 1x2 structure;\\
-		subscripts(1).type = '.'\\
-		subscripts(1).subs = 'inputConnect'\\
-		subscripts(2).type = '()'\\
-		subscripts(2).subs = [1] [1]
-	\item v: 1x1 double 1
-\end{itemize}
-\textcolor{red}{bis hier sollte es erledigt sein...!}
-
-\paragraph{newff row 137; net.layerConnect} net=subsasgn(net,subscripts,v) will hold the following values:
-\begin{itemize}
-	\item net: the whole net structure
-	\item subscripts: 1x1 structure;\\
-		subscripts(1).type = '.'\\
-		subscripts(1).subs = 'layerConnect'
-	\item v: 2x2 logical [0 0; 1 0]
-\end{itemize}
-
-\paragraph{newff row 138; net.outputConnect} net=subsasgn(net,subscripts,v) will hold the following values:
-\begin{itemize}
-	\item net: the whole net structure
-	\item subscripts: 1x2 structure;\\
-		subscripts(1).type = '.'\\
-		subscripts(1).subs = 'outputConnect'\\
-		subscripts(2).type = '()'\\
-		subscripts(2).subs = '[2]'
-	\item v: 1x1 double = 1
-\end{itemize}
-
-\paragraph{newff row 139; net.targetConnect} net=subsasgn(net,subscripts,v) will hold the following values:
-\begin{itemize}
-	\item net: the whole net structure
-	\item subscripts: 1x2 structure;\\
-		subscripts(1).type = '.'\\
-		subscripts(1).subs = 'targetConnect'\\
-		subscripts(2).type = '()'\\
-		subscripts(2).subs = '[2]'
-	\item v: 1x1 double = 1
-\end{itemize}
-
-
-\subsection{Data types}
-
-\begin{table}
-	\centering
-	\begin{tabular}{c c c}
-		\toprule
-									& Matlab										& Octave\\
-		\midrule
-  								& double 											& double \\
-									& int8  											& \\
-									& int16 											& \\
-									& int32 											& \\
-									& int64 											& \\
-			Numeric			& single											& \\
-									& uint8 											& \\
-									& uint16											& \\
-									& uint32 											& \\
-									& uint64 											& \\
-									& complex 										& complex\\
-		\midrule
-			characters	& x														&  \\
-		\midrule
-			string			&															& \\
-		\midrule
-			cell				&	x														& x \\
-		\midrule
-			structure		& x 												  & x  \\
-		\bottomrule
-	\end{tabular}
-\end{table}
\ No newline at end of file
--- a/main/nnet/doc/latex/perl/analyzeOctaveSource.pm	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,66 +0,0 @@
-package analyzeOctaveSource;
-
-use File::Find;
-
-sub new {
- my $Objekt = shift;
- my $Referenz = {};
- bless($Referenz,$Objekt);
- return($Referenz);
-}
-
-sub readDirTree{
-	my $Objekt = shift;
-	my $Dir = shift;
-	my $fileExt = shift;
-	my $File;
-	# read directory
-	my @dirArray = ();
-
-	find sub { push @dirArray, $File::Find::name }, $Dir;
-
-	# remove all files except with file ending $fileExt
-	@DirArray = grep /.+\.$fileExt/ , @DirArray;
-	my $nFiles = @dirArray;
-	if ($nFiles==0){
-		print "No Octave files found.\n";
-		print "Octave files must end with *.m\n";
-		die " ==========================\n";
-	}
-	return @dirArray;
-}
-
-sub searchFilesContainTestLines{
-	my $Objekt = shift;
-	my @dirArray = shift;
-
-	#
-	my $fileName = "";
-	my @fileContent = ();
-	my @fileArray = ();
-	my @temp = ();
-	my $nTemp = "";
-
-	foreach (@dirArray){
-	  # open file and search for lines beginning with
-	  # !%
-	  if (-d $_){
-        # if directory, do nothing
-      }else{
-	    open(FILE,$_) or die "File $_ not found!\n";
-	    @fileContent = <FILE>;
-	    close FILE;
-        @temp = grep /^!%/, @fileContent;
-        $nTemp = @temp;
-        if ($nTemp>0){
-          @fileArray = $_
-        }
-	  }
-	}
-
-	return @fileArray;
-}
-
-
-1;
-
--- a/main/nnet/doc/latex/perl/createFunctionIndexDocu.pl	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,172 +0,0 @@
-#!/usr/bin/perl -w
-
-use strict;
-use diagnostics;# Force verbose warning diagnostics.
-use warnings;
-use English;
-
-#
-# Modules from the Standard Perl Library.
-
-
-#
-# Own modules
-use analyzeOctaveSource;
-
-
-#--- DEFINE VARIABLES -------------------------------
-my $Dir = "D:/daten/octave/neuroPackage/0.1.9/nnet/inst";
-my $fileExt = "m";
-my $funcIndDir = "D:/daten/octave/neuroPackage/0.1.9/nnet/doc/latex/developers/funcindex";
-my $relFuncDir = "funcindex/";
-my $funcFileExt = "tex";
-my $mainLatexFuncIndexFile = "funcindexCalled.tex";
-my $chapter = "Function Index";
-#--- END DEFINE VARIABLES ---------------------------
-
-
-my @filesArray = ();
-my @filesName = ();
-my $fileName = "";
-my $savePath = "";
-my $nTestLines = 0;
-my $m = 0;
-
-# analyze directory structure
-my $obj = analyzeOctaveSource->new();
-my @DirArray = $obj->readDirTree($Dir,$fileExt);
-
-# in @DirArray should be only names, ending with *.m
-# but if there is a directory, we will remove it at
-# the next line of code
-my @FuncReferences = grep /.+\.$fileExt/ , @DirArray; #/
-my @FuncNames = @FuncReferences;
-# now I have to remove the path and file extension
-foreach (@FuncNames)
-{
- 	s/\.m//g;   # removes file ending
- 	s/$Dir\///g; # removes any parts of the file & directory path
-}
-my @input = ();
-my @calledFunction = ();
-my $deleteFile = 1;
-my $anzahl_elemente = 0;
-# now analyze functions to see which other functions are called
-foreach my $FuncRef (@FuncReferences)
-{
-
-  open(FILE,$FuncRef); # opens e.g. 'subset.m'
-  @input = <FILE>; # read the complete content of the file to @input
-  # now remove all comment and test lines ..
-  @input = grep s/^\s+//, @input; # removes white-space characters at the
-  								  # beginning of a line
-  @input = grep /^[^#|%]/ , @input; # removes lines starting with # or %
-
-#   foreach (@input)
-#   {
-#     print "$_";
-#     sleep(1);
-#   }
-      my $actFuncName = "";
-  foreach my $FuncName (@FuncNames)
-  {
-
-    if ($FuncRef !~/$FuncName/)   # returns true if pattern is not found
-    {
-      # now search for each $FuncName
-      # inside of the @input array
-      # if one is found, put them to a list
-      # \todo ERRORs are still available
-      # if the $FuncName occures in another context!!
-      # such lines should be deleted!
-	  if (grep /$FuncName/,@input)
-	  {
-          push (@calledFunction, "$FuncName");
-      }
-    }else{
-      $actFuncName = $FuncName;
-    }
-  }
-  # now remove double entries of @calledFunction
-  undef my %saw;
-  @saw{@calledFunction} = ();
-  @calledFunction = sort keys %saw;  # remove sort if undesired
-  if (-e "$funcIndDir" . "/" . "$mainLatexFuncIndexFile")
-  {
-    # if the file exist, delete it
-    if ($deleteFile){
-      unlink("$funcIndDir" . "/" . "$mainLatexFuncIndexFile");
-      $deleteFile = 0;
-      open(DAT,">$funcIndDir" . "/" . "$mainLatexFuncIndexFile");
-      print DAT "\\begin{longtable}{ll}\n";
-      print DAT "\\textbf{main function}	&	\\textbf{called function} \\\\ \n";
-      print DAT "\\hline\n";
-	  $_ = $actFuncName;
-	  s/_/\\_/g;     # put a backslash for each underscore
-      print DAT "$_	";
-      $anzahl_elemente = @calledFunction;
-      if ($anzahl_elemente > 0)
-      {
-      	foreach (@calledFunction){
-          	  s/_/\\_/g;  # put a backslash for each underscore
-	    	print DAT " &	$_\\\\ \n";
-  	  	}
-	  }else{
-        print DAT " & \\\\ \n";
-      }
-
-  	  close(DAT);
-
-    }else{
-      # file doesn't have to be deleted
-      open(DAT,">>$funcIndDir" . "/" . "$mainLatexFuncIndexFile");
-      print DAT "\\hline\n";
-      	  $_ = $actFuncName;
-	  s/_/\\_/g;     # put a backslash for each underscore
-      print DAT "$_	";
-      $anzahl_elemente = @calledFunction;
-      if ($anzahl_elemente > 0)
-      {
-      	foreach (@calledFunction){
-          s/_/\\_/g;  # put a backslash for each underscore
-	    	print DAT " &	$_\\\\ \n";
-  	  	}
-	  }else{
-        print DAT " & \\\\ \n";
-      }
-  	    close(DAT);
-    }
-
-  }else{
-    # File doesn't exist yet
-    open(DAT,">$funcIndDir" . "/" . "$mainLatexFuncIndexFile");
-    print DAT "\\begin{longtable}{l l}\n";
-    print DAT "\\textbf{main function}	&	\\textbf{called function} \\\\ \n";
-    print DAT "\\hline\n";
-          	  $_ = $actFuncName;
-	  s/_/\\_/g;    # put a backslash for each underscore
-      print DAT "$_	";
-      $anzahl_elemente = @calledFunction;
-      if ($anzahl_elemente > 0)
-      {
-      	foreach (@calledFunction){
-            s/_/\\_/g;  # put a backslash for each underscore
-	    	print DAT " &	$_\\\\ \n";
-  	  	}
-	  }else{
-        print DAT " & \\\\ \n";
-      }
-  	  close(DAT);
-  }
-#  print DAT "Function-File: $actFuncName\n";
-#  print DAT "=============================\n";
-#   foreach (@calledFunction){
-# 	print DAT "$_\n";
-#   }
-
-  @calledFunction = ();
-
-}
-  open(DAT,">>$funcIndDir" . "/" . "$mainLatexFuncIndexFile");
-  print DAT "\\end{longtable}\n";
-  close(DAT);
--- a/main/nnet/doc/latex/perl/createTestDocu.pl	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,145 +0,0 @@
-#!/usr/bin/perl -w
-
-use strict;
-use diagnostics;# Force verbose warning diagnostics.
-use warnings;
-use English;
-
-#
-# Modules from the Standard Perl Library.
-
-
-#
-# Own modules
-use analyzeOctaveSource;
-
-
-#--- DEFINE VARIABLES -------------------------------
-my $Dir = "D:\\daten\\octave\\neuroPackage\\0.1.9\\nnet\\inst";
-my $fileExt = "m";
-my $testDir = "D:\\daten\\octave\\neuroPackage\\0.1.9\\nnet/doc/latex/developers/tests";
-my $relTestDir = "tests/";
-my $testFileExt = "tex";
-my $mainLatexTestFile = "test.tex";
-my $chapter = "Test";
-#--- END DEFINE VARIABLES ---------------------------
-
-
-my @filesArray = ();
-my @filesName = ();
-my $fileName = "";
-my $savePath = "";
-my $nTestLines = 0;
-my $m = 0;
-
-# analyze directory structure
-my $obj = analyzeOctaveSource->new();
-my @DirArray = $obj->readDirTree($Dir,$fileExt);
-
-# analyze file structure
-my $nFiles = @DirArray;
-if ($nFiles>=1){ # if $nFiles==0 readDirTree will die
-  # if we are in this branch, we should check the files
-  # to found those which content tests (lines beginning with
-  # !% )
-  foreach (@DirArray){
-	  # open file and search for lines beginning with
-	  # !%
-	  if (-d $_){
-        # if directory, do nothing
-      }else{
-      	print "$_\n";
-      	sleep(0.1);
-
-	    open(FILE,$_) or die "File $_ not found!\n";
-	    my @fileContent = <FILE>;
-	    chomp(@fileContent);
-	    $nTestLines = @fileContent;
-        my @temp = grep /^%!/, @fileContent;
-        my $nTemp = @temp;
-        if ($nTemp>0){ # this means,
-                       # test lines are available
-          # now create latex files without header
-          # take the same name like the *.m file
-          # and save in specified directory $testDir
-          # with file extens in $testFileExt
-          # use verbatim environment
-          @filesName = split(/\//, $_);
-          $fileName = $filesName[$#filesName];
-          # now remove file extension .m
-          @filesName = split(/\./,$fileName);
-          $savePath = ("$testDir" . "\\\\" . "$filesName[0]." . "$testFileExt");
-          open(OUTFILE, ">$savePath");
-          my $i = 0;
-          print OUTFILE "\\begin{verbatim}\n";
-          while ($i < $nTestLines){
-            if ($fileContent[$i]=~/^%!/){
-              print OUTFILE "$fileContent[$i]\n";
-            }
-            $i++;
-		  } # END while ($i <= $#fileContent)
-		  print OUTFILE "\\end{verbatim}\n";
-		  close OUTFILE;
-
-		  ## now set entries in the main test latex file ..
-          my $mainTestFile = ("$testDir" . "\\\\" . "$mainLatexTestFile");
-          if ($m==0){
-            open(TESTFILE,">$mainTestFile") or die "Could not found file $mainTestFile!\n";
-            print TESTFILE "\\chapter{$chapter}\n\n";
-
-			# test if back slash needed
-            # a back slash is needed if the sign "underscore"
-            # is used in the file name. This happens at each
-            # "sub function". There are two underscores!
-            my $tempString = "";
-            my $oldString = $filesName[0];
-			$_ = $filesName[0];
-			s/_/\\_/g; # s/ : search & replace pattern (everything between / /)
-					   # here: search underscore
-					   # if found, replace with \_ (two back slashes are needed
-					   # to get \_ as sign)
-					   # /g means: each occurence of pattern, otherwise, only one _
-					   # will be replaced
-
-			print "test file name: $_\n";
-            print TESTFILE "\\section{$_}\n";
-            $tempString = $relTestDir . $oldString;
-            print TESTFILE "\\input{$tempString}\n";
-          }else{
-            open(TESTFILE,">>$mainTestFile") or die "Could not found file $mainTestFile!\n";
-            # test if back slash needed
-            my $tempString = "";
-            my $oldString = $filesName[0];
-            # test if back slash needed
-            # a back slash is needed if the sign "underscore"
-            # is used in the file name. This happens at each
-            # "sub function". There are two underscores!
-            my $tempString = "";
-            my $oldString = $filesName[0];
-			$_ = $filesName[0];
-			s/_/\\_/g; # s/ : search & replace pattern (everything between / /)
-					   # here: search underscore
-					   # if found, replace with \_ (two back slashes are needed
-					   # to get \_ as sign)
-					   # /g means: each occurence of pattern, otherwise, only one _
-					   # will be replaced
-
-            print TESTFILE "\\section{$_}\n";
-            $tempString = $relTestDir . $oldString;
-            print TESTFILE "\\input{$tempString}\n";
-          }
-          $m++;
-          close TESTFILE;
-
-
-        }# END if($nTemp>0)
-        close FILE;
-
-	  }# END if(-d $_)
-
-	}# END foreach (@DirArray)
-
-}else{ # if $nFiles==0
-  print "No file found with valid file extension: .$fileExt.\n";
-  die;
-}
\ No newline at end of file
--- a/main/nnet/doc/latex/users/bibliography.tex	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,35 +0,0 @@
-% Preamble
-
-%\documentclass[a4paper]{report}
-
-%\usepackage[ngerman]{babel}
-%\usepackage[T1]{fontenc}
-%\usepackage[ansinew]{inputenc}
-
-
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-% start text here!!
-
-%\begin{document}
-
-\begin{thebibliography}{XXXXXXX}
-
-\bibitem [1]{1} John W. Eaton
-
-GNU Octave Manual, Edition 3, PDF-Version, February 1997
-
-\bibitem [2]{2} The MathWorks, Inc.
-
-MATLAB Help, MATLAB Version 7.1 (R14SP3), Neural Network Toolbox Version 4.0.6 (R14SP3)
-
-\bibitem [3]{3} Christopher M. Bishop
-
-Neural Networks for Pattern Recognition, OXFORD University Press, Great Clarendon Streed, Oxford OX2 6DP,
-ISBN 0-19-853864-2, 2002
-
-\bibitem [4]{4} Martin T. Hagen, Howard B. Demuth, Mark H. Beale
-
-NEURAL NETWORK DESIGN, PWS Publishing Company, 20 Park Plaza, Boston, MA 02116-4324, ISBN 053494332-2, 1996
-
-\end{thebibliography}
-%\end{document}
\ No newline at end of file
--- a/main/nnet/doc/latex/users/examples/1/1.tex	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,34 +0,0 @@
-\section{Example 1}
-You can find this example in the \textit{tests/MLP} directory of each release or from the subversion repository. I will do (more or less) a line by line walkthrough, so after this should be everything clear. I assume that you have some experience with multilayer perceptrons.
-
-\subsection{Introduction}
-Our problem can be solved with a monotonically increasing or decreasing surface. An input vector \textbf{p} (with 9 values) should be mapped onto one output value. Because we know that it can be solved with a monotonically increasing or decreasing surface, we can choose a 9-1-1 multi-layer perceptron (short: MLP).
-This means an MLP with 9 input neurons, only 1 hidden neuron and with 1 output neuron.
-
-\subsection{Code m-file}
-\input{examples/1/MLP9_1_1}
-
-\subsection{Walkthrough}
-Till line number 0023 there is realy nothing interesting.\\
-On line 0023 \& 0024 data will be loaded. This data matrix contains 13 columns. Column 4, 8 and 12 won't be used (this is because the datas are of a real world problem). Column 13 contains the target values.
-So on the lines 0049 till 0051 this will be splittet into the corresponding peaces. A short repetition about the datas: Each line is a data set with 9 input values and one target value. On line 0038 and 0039 the datas are transposed. So we have now in each column one data set.\\
-
-Now let's split the data matrix again in 3 pieces. The biggest part is for training the network. The second part for testing the trained network to be sure it's still possible to generalize with the net. And the third part, and the smallest one, for validate during training. This splitting happens on the lines 0041 till 0061.\\
-
-Line 0063 is the first special command from this toolbox. This command will be used to pre-standardize the input datas. Do it ever! Non linear transfer functions will squash the whole input range to an small second range e.g. the transfer function \textit{tansig} will squash the datas between -1 and +1.\\
-
-On line 0069 the next toolbox command will be used. This command \textit{min\_max} creates a $Rx2$ matrix of the complete input matrix. Don't ask me for what MATLAB(TM) this is using. I couldn't figure out it. One part is the number of input neurons, but for this, the range would not be needed. Who cares ;-)\\
-
-Now it's time to create a structure which holds the informations about the neural network. The command \textbf{newff} can do it for us. See the complete line and actually, please use it only on this way, each other try will fail! This means, you can change the number of input neurons, the number of hidden neurons and the number of output neurons of course. But don't change the train algorithm or the performance function.\\
-
-\textbf{saveMLPStruct} on line 0083 is a command which doesn't exist in MATLAB(TM). This will save the structure with the same informations you can see in MATLAB(TM) if you try to open the net-type.\\
-
-The validation part on line 0086 \& 0087 is important. The naming convention is for MATLAB(TM) compatibility. For validate, you have to define a structure with the name \textbf{VV}. Inside this structure you have to define actually \textbf{VV.P} \& \textbf{VV.T} for validate inputs and validate targets. Bye the way, you have to pre-standardize them like the training input matrix. Use for this the command \textbf{trastd} like on line 0090.\\
-
-\textbf{train} is the next toolbox command and of course one of the most important. Please also use this command like on line 0092. Nothing else will work.\\
-
-The second last step is to standardize again datas. This time the test datas. See line 0096 for this and the last step. Simulate the network. This can be done with the command \textbf{sim}. This will be a critical part if someone else will write a toolbox with this command name!\\
-
-I hope this short walkthrough will help for first steps. In next time, I will try to improve this documentation and of course, the toolbox commands. But time is realy rare.
-
-
--- a/main/nnet/doc/latex/users/examples/1/MLP9_1_1.m_template	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,96 +0,0 @@
-## Copyright (C) 2006 Michel D. Schmid  <michael.schmid@plexso.com>
-##
-## 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 3 of the License, 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; if not, see <http://www.gnu.org/licenses/>.
-
-## Author: Michel D. Schmid
-
-
-## load data
-mData = load("mData.txt","mData");
-mData = mData.mData;
-[nRows, nColumns] = size(mData);
-	# this file contains 13 columns.
-	# The first 12 columns are the inputs
-	# the last column is the output,
-	# remove column 4, 8 and 12!
-	# 89 rows.
-
-
-mOutput = mData(:,end);
-mInput = mData(:,1:end-1);
-mInput(:,[4 8 12]) = []; # delete column 4, 8 and 12
-
-## now prepare data
-mInput = mInput';
-mOutput = mOutput';
-
-# now split the data matrix in 3 pieces, train data, test data and validate data
-# the proportion should be about 1/2 train, 1/3 test and 1/6 validate data
-# in this neural network we have 12 weights, for each weight at least 3 train sets..
-# (that's a rule of thumb like 1/2, 1/3 and 1/6)
-# 1/2 of 89 = 44.5; let's take 44 for training
-nTrainSets = floor(nRows/2);
-# now the rest of the sets are again 100%
-# ==> 2/3 for test sets and 1/3 for validate sets
-nTestSets = (nRows-nTrainSets)/3*2;
-nValiSets = nRows-nTrainSets-nTestSets;
-
-mValiInput = mInput(:,1:nValiSets);
-mValliOutput = mOutput(:,1:nValiSets);
-mInput(:,1:nValiSets) = [];
-mOutput(:,1:nValiSets) = [];
-mTestInput = mInput(:,1:nTestSets);
-mTestOutput = mOutput(:,1:nTestSets);
-mInput(:,1:nTestSets) = [];
-mOutput(:,1:nTestSets) = [];
-mTrainInput = mInput(:,1:nTrainSets);
-mTrainOutput = mOutput(:,1:nTrainSets);
-
-[mTrainInputN,cMeanInput,cStdInput] = prestd(mTrainInput);# standardize inputs
-
-## comments: there is no reason to standardize the outputs because we have only
-# one output ...
-
-# define the max and min inputs for each row
-mMinMaxElements = min_max(mTrainInputN); # input matrix with (R x 2)...
-
-## define network
-nHiddenNeurons = 1;
-nOutputNeurons = 1;
-
-MLPnet = newff(mMinMaxElements,[nHiddenNeurons nOutputNeurons],\
-		{"tansig","purelin"},"trainlm","","mse");
-## for test purpose, define weights by hand
-MLPnet.IW{1,1}(:) = 1.5;
-MLPnet.LW{2,1}(:) = 0.5;
-MLPnet.b{1,1}(:) = 1.5;
-MLPnet.b{2,1}(:) = 0.5;
-
-saveMLPStruct(MLPnet,"MLP3test.txt");
-
-## define validation data new, for matlab compatibility
-VV.P = mValiInput;
-VV.T = mValliOutput;
-
-## standardize also the validate data
-VV.P = trastd(VV.P,cMeanInput,cStdInput);
-
-[net] = train(MLPnet,mTrainInputN,mTrainOutput,[],[],VV);
-
-# make preparations for net test and test MLPnet
-#  standardise input & output test data
-[mTestInputN] = trastd(mTestInput,cMeanInput,cStdInput);
-
-[simOut] = sim(net,mTestInputN);
-simOut
--- a/main/nnet/doc/latex/users/examples/1/MLP9_1_1.tex	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,103 +0,0 @@
-\noindent
-\ttfamily
-\hlstd{}\hlline{00001\ }\hlslc{\#\# Copyright (C) 2006 Michel D. Schmid}\hlstd{\ \ }\hlslc{$<$michael.schmid@plexso.com$>$}\\
-\hlline{00002\ }\hlstd{}\hlslc{\#\#}\\
-\hlline{00003\ }\hlstd{}\hlslc{\#\#}\\
-\hlline{00004\ }\hlstd{}\hlslc{\#\# This program is free software; you can redistribute it and/or modify it}\\
-\hlline{00005\ }\hlstd{}\hlslc{\#\# under the terms of the GNU General Public License as published by}\\
-\hlline{00006\ }\hlstd{}\hlslc{\#\# the Free Software Foundation; either version 2, or (at your option)}\\
-\hlline{00007\ }\hlstd{}\hlslc{\#\# any later version.}\\
-\hlline{00008\ }\hlstd{}\hlslc{\#\#}\\
-\hlline{00009\ }\hlstd{}\hlslc{\#\# This program is distributed in the hope that it will be useful, but}\\
-\hlline{00010\ }\hlstd{}\hlslc{\#\# WITHOUT ANY WARRANTY; without even the implied warranty of}\\
-\hlline{00011\ }\hlstd{}\hlslc{\#\# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.}\hlstd{\ \ }\hlslc{See the GNU}\\
-\hlline{00012\ }\hlstd{}\hlslc{\#\# General Public License for more details.}\\
-\hlline{00013\ }\hlstd{}\hlslc{\#\#}\\
-\hlline{00014\ }\hlstd{}\hlslc{\#\# You should have received a copy of the GNU General Public License}\\
-\hlline{00015\ }\hlstd{}\hlslc{\#\# along with this program; see the file COPYING.}\hlstd{\ \ }\hlslc{If not, write to the Free}\\
-\hlline{00016\ }\hlstd{}\hlslc{\#\# Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA}\\
-\hlline{00017\ }\hlstd{}\hlslc{\#\# 02110{-}1301, USA.}\\
-\hlline{00018\ }\hlstd{}\\
-\hlline{00019\ }\hlslc{\#\# Author: Michel D. Schmid}\\
-\hlline{00020\ }\hlstd{}\\
-\hlline{00021\ }\\
-\hlline{00022\ }\hlslc{\#\# load data}\\
-\hlline{00023\ }\hlstd{mData }\hlsym{= }\hlstd{}\hlkwc{load}\hlstd{}\hlsym{(}\hlstd{}\hlstr{"mData.txt"}\hlstd{}\hlsym{,}\hlstd{}\hlstr{"mData"}\hlstd{}\hlsym{);}\\
-\hlline{00024\ }\hlstd{mData }\hlsym{= }\hlstd{mData.mData}\hlsym{;}\\
-\hlline{00025\ }\hlstd{}\hlsym{{[}}\hlstd{nRows}\hlsym{, }\hlstd{nColumns}\hlsym{{]} = }\hlstd{}\hlkwc{size}\hlstd{}\hlsym{(}\hlstd{mData}\hlsym{);}\\
-\hlline{00026\ }\hlstd{}\hlstd{\ \ \ \ }\hlstd{}\hlslc{\# this file contains 13 columns.}\\
-\hlline{00027\ }\hlstd{}\hlstd{\ \ \ \ }\hlstd{}\hlslc{\# The first 12 columns are the inputs}\\
-\hlline{00028\ }\hlstd{}\hlstd{\ \ \ \ }\hlstd{}\hlslc{\# the last column is the output,}\\
-\hlline{00029\ }\hlstd{}\hlstd{\ \ \ \ }\hlstd{}\hlslc{\# remove column 4, 8 and 12!}\\
-\hlline{00030\ }\hlstd{}\hlstd{\ \ \ \ }\hlstd{}\hlslc{\# 89 rows.}\\
-\hlline{00031\ }\hlstd{\\
-\hlline{00032\ }\\
-\hlline{00033\ }mOutput }\hlsym{= }\hlstd{}\hlstd{mData}\hlstd{}\hlsym{(:,}\hlstd{}\hlkwa{end}\hlstd{}\hlsym{);}\\
-\hlline{00034\ }\hlstd{mInput }\hlsym{= }\hlstd{}\hlstd{mData}\hlstd{}\hlsym{(:,}\hlstd{}\hlnum{1}\hlstd{}\hlsym{:}\hlstd{}\hlkwa{end}\hlstd{}\hlsym{{-}}\hlstd{}\hlnum{1}\hlstd{}\hlsym{);}\\
-\hlline{00035\ }\hlstd{}\hlstd{mInput}\hlstd{}\hlsym{(:,{[}}\hlstd{}\hlnum{4 8 12}\hlstd{}\hlsym{{]}) = {[}{]}; }\hlstd{}\hlslc{\# delete column 4, 8 and 12}\\
-\hlline{00036\ }\hlstd{}\\
-\hlline{00037\ }\hlslc{\#\# now prepare data}\\
-\hlline{00038\ }\hlstd{mInput }\hlsym{= }\hlstd{mInput}\hlstd{';}\\
-\hlline{00039\ }\hlstd{mOutput = mOutput'}\hlstd{}\hlsym{;}\\
-\hlline{00040\ }\hlstd{}\\
-\hlline{00041\ }\hlslc{\# now split the data matrix in 3 pieces, train data, test data and validate data}\\
-\hlline{00042\ }\hlstd{}\hlslc{\# the proportion should be about 1/2 train, 1/3 test and 1/6 validate data}\\
-\hlline{00043\ }\hlstd{}\hlslc{\# in this neural network we have 12 weights, for each weight at least 3 train sets..}\\
-\hlline{00044\ }\hlstd{}\hlslc{\# (that's a rule of thumb like 1/2, 1/3 and 1/6)}\\
-\hlline{00045\ }\hlstd{}\hlslc{\# 1/2 of 89 = 44.5; let's take 44 for training}\\
-\hlline{00046\ }\hlstd{nTrainSets }\hlsym{= }\hlstd{}\hlkwc{floor}\hlstd{}\hlsym{(}\hlstd{nRows}\hlsym{/}\hlstd{}\hlnum{2}\hlstd{}\hlsym{);}\\
-\hlline{00047\ }\hlstd{}\hlslc{\# now the rest of the sets are again 100\%}\\
-\hlline{00048\ }\hlstd{}\hlslc{\# ==$>$ 2/3 for test sets and 1/3 for validate sets}\\
-\hlline{00049\ }\hlstd{nTestSets }\hlsym{= (}\hlstd{nRows}\hlsym{{-}}\hlstd{nTrainSets}\hlsym{)/}\hlstd{}\hlnum{3}\hlstd{}\hlsym{{*}}\hlstd{}\hlnum{2}\hlstd{}\hlsym{;}\\
-\hlline{00050\ }\hlstd{nValiSets }\hlsym{= }\hlstd{nRows}\hlsym{{-}}\hlstd{nTrainSets}\hlsym{{-}}\hlstd{nTestSets}\hlsym{;}\\
-\hlline{00051\ }\hlstd{\\
-\hlline{00052\ }mValiInput }\hlsym{= }\hlstd{}\hlstd{mInput}\hlstd{}\hlsym{(:,}\hlstd{}\hlnum{1}\hlstd{}\hlsym{:}\hlstd{nValiSets}\hlsym{);}\\
-\hlline{00053\ }\hlstd{mValliOutput }\hlsym{= }\hlstd{}\hlstd{mOutput}\hlstd{}\hlsym{(:,}\hlstd{}\hlnum{1}\hlstd{}\hlsym{:}\hlstd{nValiSets}\hlsym{);}\\
-\hlline{00054\ }\hlstd{}\hlstd{mInput}\hlstd{}\hlsym{(:,}\hlstd{}\hlnum{1}\hlstd{}\hlsym{:}\hlstd{nValiSets}\hlsym{) = {[}{]};}\\
-\hlline{00055\ }\hlstd{}\hlstd{mOutput}\hlstd{}\hlsym{(:,}\hlstd{}\hlnum{1}\hlstd{}\hlsym{:}\hlstd{nValiSets}\hlsym{) = {[}{]};}\\
-\hlline{00056\ }\hlstd{mTestInput }\hlsym{= }\hlstd{}\hlstd{mInput}\hlstd{}\hlsym{(:,}\hlstd{}\hlnum{1}\hlstd{}\hlsym{:}\hlstd{nTestSets}\hlsym{);}\\
-\hlline{00057\ }\hlstd{mTestOutput }\hlsym{= }\hlstd{}\hlstd{mOutput}\hlstd{}\hlsym{(:,}\hlstd{}\hlnum{1}\hlstd{}\hlsym{:}\hlstd{nTestSets}\hlsym{);}\\
-\hlline{00058\ }\hlstd{}\hlstd{mInput}\hlstd{}\hlsym{(:,}\hlstd{}\hlnum{1}\hlstd{}\hlsym{:}\hlstd{nTestSets}\hlsym{) = {[}{]};}\\
-\hlline{00059\ }\hlstd{}\hlstd{mOutput}\hlstd{}\hlsym{(:,}\hlstd{}\hlnum{1}\hlstd{}\hlsym{:}\hlstd{nTestSets}\hlsym{) = {[}{]};}\\
-\hlline{00060\ }\hlstd{mTrainInput }\hlsym{= }\hlstd{}\hlstd{mInput}\hlstd{}\hlsym{(:,}\hlstd{}\hlnum{1}\hlstd{}\hlsym{:}\hlstd{nTrainSets}\hlsym{);}\\
-\hlline{00061\ }\hlstd{mTrainOutput }\hlsym{= }\hlstd{}\hlstd{mOutput}\hlstd{}\hlsym{(:,}\hlstd{}\hlnum{1}\hlstd{}\hlsym{:}\hlstd{nTrainSets}\hlsym{);}\\
-\hlline{00062\ }\hlstd{}\\
-\hlline{00063\ }\hlsym{{[}}\hlstd{mTrainInputN}\hlsym{,}\hlstd{cMeanInput}\hlsym{,}\hlstd{cStdInput}\hlsym{{]} = }\hlstd{}\hlkwc{prestd}\hlstd{}\hlsym{(}\hlstd{mTrainInput}\hlsym{);}\hlstd{}\hlslc{\# standardize inputs}\\
-\hlline{00064\ }\hlstd{}\\
-\hlline{00065\ }\hlslc{\#\# comments: there is no reason to standardize the outputs because we have only}\\
-\hlline{00066\ }\hlstd{}\hlslc{\# one output ...}\\
-\hlline{00067\ }\hlstd{}\\
-\hlline{00068\ }\hlslc{\# define the max and min inputs for each row}\\
-\hlline{00069\ }\hlstd{mMinMaxElements }\hlsym{= }\hlstd{}\hlkwc{min\textunderscore max}\hlstd{}\hlsym{(}\hlstd{mTrainInputN}\hlsym{); }\hlstd{}\hlslc{\# input matrix with (R x 2)...}\\
-\hlline{00070\ }\hlstd{}\\
-\hlline{00071\ }\hlslc{\#\# define network}\\
-\hlline{00072\ }\hlstd{nHiddenNeurons }\hlsym{= }\hlstd{}\hlnum{1}\hlstd{}\hlsym{;}\\
-\hlline{00073\ }\hlstd{nOutputNeurons }\hlsym{= }\hlstd{}\hlnum{1}\hlstd{}\hlsym{;}\\
-\hlline{00074\ }\hlstd{\\
-\hlline{00075\ }MLPnet }\hlsym{= }\hlstd{}\hlkwc{newff}\hlstd{}\hlsym{(}\hlstd{mMinMaxElements}\hlsym{,{[}}\hlstd{nHiddenNeurons nOutputNeurons}\hlsym{{]},}\hlstd{$\backslash$\\
-\hlline{00076\ }}\hlstd{\ \ \ \ \ \ \ \ }\hlstd{}\hlsym{\{}\hlstd{}\hlstr{"tansig"}\hlstd{}\hlsym{,}\hlstd{}\hlstr{"purelin"}\hlstd{}\hlsym{\},}\hlstd{}\hlstr{"trainlm"}\hlstd{}\hlsym{,}\hlstd{}\hlstr{""}\hlstd{}\hlsym{,}\hlstd{}\hlstr{"mse"}\hlstd{}\hlsym{);}\\
-\hlline{00077\ }\hlstd{}\hlslc{\#\# for test purpose, define weights by hand}\\
-\hlline{00078\ }\hlstd{MLPnet.IW}\hlsym{\{}\hlstd{}\hlnum{1}\hlstd{}\hlsym{,}\hlstd{}\hlnum{1}\hlstd{}\hlsym{\}(:) = }\hlstd{}\hlnum{1.5}\hlstd{}\hlsym{;}\\
-\hlline{00079\ }\hlstd{MLPnet.LW}\hlsym{\{}\hlstd{}\hlnum{2}\hlstd{}\hlsym{,}\hlstd{}\hlnum{1}\hlstd{}\hlsym{\}(:) = }\hlstd{}\hlnum{0.5}\hlstd{}\hlsym{;}\\
-\hlline{00080\ }\hlstd{MLPnet.b}\hlsym{\{}\hlstd{}\hlnum{1}\hlstd{}\hlsym{,}\hlstd{}\hlnum{1}\hlstd{}\hlsym{\}(:) = }\hlstd{}\hlnum{1.5}\hlstd{}\hlsym{;}\\
-\hlline{00081\ }\hlstd{MLPnet.b}\hlsym{\{}\hlstd{}\hlnum{2}\hlstd{}\hlsym{,}\hlstd{}\hlnum{1}\hlstd{}\hlsym{\}(:) = }\hlstd{}\hlnum{0.5}\hlstd{}\hlsym{;}\\
-\hlline{00082\ }\hlstd{}\\
-\hlline{00083\ }\hlkwc{saveMLPStruct}\hlstd{}\hlsym{(}\hlstd{MLPnet}\hlsym{,}\hlstd{}\hlstr{"MLP3test.txt"}\hlstd{}\hlsym{);}\\
-\hlline{00084\ }\hlstd{}\\
-\hlline{00085\ }\hlslc{\#\# define validation data new, for matlab compatibility}\\
-\hlline{00086\ }\hlstd{VV.P }\hlsym{= }\hlstd{mValiInput}\hlsym{;}\\
-\hlline{00087\ }\hlstd{VV.T }\hlsym{= }\hlstd{mValliOutput}\hlsym{;}\\
-\hlline{00088\ }\hlstd{}\\
-\hlline{00089\ }\hlslc{\#\# standardize also the validate data}\\
-\hlline{00090\ }\hlstd{VV.P }\hlsym{= }\hlstd{}\hlkwc{trastd}\hlstd{}\hlsym{(}\hlstd{VV.P}\hlsym{,}\hlstd{cMeanInput}\hlsym{,}\hlstd{cStdInput}\hlsym{);}\\
-\hlline{00091\ }\hlstd{}\\
-\hlline{00092\ }\hlsym{{[}}\hlstd{net}\hlsym{{]} = }\hlstd{}\hlkwc{train}\hlstd{}\hlsym{(}\hlstd{MLPnet}\hlsym{,}\hlstd{mTrainInputN}\hlsym{,}\hlstd{mTrainOutput}\hlsym{,{[}{]},{[}{]},}\hlstd{VV}\hlsym{);}\\
-\hlline{00093\ }\hlstd{}\\
-\hlline{00094\ }\hlslc{\# make preparations for net test and test MLPnet}\\
-\hlline{00095\ }\hlstd{}\hlslc{\#}\hlstd{\ \ }\hlslc{standardise input \& output test data}\\
-\hlline{00096\ }\hlstd{}\hlsym{{[}}\hlstd{mTestInputN}\hlsym{{]} = }\hlstd{}\hlkwc{trastd}\hlstd{}\hlsym{(}\hlstd{mTestInput}\hlsym{,}\hlstd{cMeanInput}\hlsym{,}\hlstd{cStdInput}\hlsym{);}\\
-\hlline{00097\ }\hlstd{}\\
-\hlline{00098\ }\hlsym{{[}}\hlstd{simOut}\hlsym{{]} = }\hlstd{}\hlkwc{sim}\hlstd{}\hlsym{(}\hlstd{net}\hlsym{,}\hlstd{mTestInputN}\hlsym{);}\\
-\hlline{00099\ }\hlstd{simOut}\\
-\mbox{}
-\normalfont
\ No newline at end of file
--- a/main/nnet/doc/latex/users/examples/1/mData.txt	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,94 +0,0 @@
-# Created by Octave 2.9.5, Wed May 24 10:33:36 2006     <sim@TL3124>
-# name: mData
-# type: matrix
-# rows: 89
-# columns: 13
- 306 286 2 0 12 61 2 0 3 28 4 0 2
- 368 188 1 0 6 49 0 0 3 37 0 0 1
- 511 73 0 0 40 21 0 0 16 22 0 0 1
- 418 43 0 0 34 30 1 0 9 27 5 0 1
- 299 173 1 0 8 63 1 0 1 37 3 0 1
- 312 253 0 0 2 63 2 0 0 35 3 0 1
- 492 98 0 0 7 23 0 0 13 36 0 0 2
- 506 64 0 0 32 23 0 0 13 29 0 0 3
- 476 41 0 0 32 5 0 0 19 26 0 0 3
- 483 66 0 0 17 16 0 0 10 28 2 0 3
- 429 44 0 0 37 19 0 0 23 19 0 0 1
- 521 137 0 0 16 17 0 0 7 24 0 0 2
- 340 163 1 0 16 72 3 0 3 31 1 0 1
- 323 177 0 0 8 68 4 0 0 37 2 0 1
- 344 240 2 0 4 43 1 0 4 39 6 0 2
- 459 22 0 0 46 14 0 0 27 17 0 0 1
- 487 36 0 0 34 10 0 0 19 27 0 0 3
- 331 169 0 0 19 66 1 0 0 34 1 0 1
- 541 269 1 0 12 5 0 0 6 20 0 0 3
- 475 23 0 0 37 5 0 0 26 14 0 0 3
- 475 186 1 0 15 28 0 0 5 35 0 0 2
- 496 319 0 0 2 4 0 0 2 14 0 0 3
- 525 41 0 0 38 9 0 0 37 18 0 0 3
- 484 37 0 0 50 13 0 0 29 18 0 0 2
- 511 55 0 0 32 15 0 0 23 21 0 0 2
- 515 44 0 0 29 16 0 0 19 31 0 0 2
- 478 101 0 0 15 34 0 0 11 40 0 0 2
- 429 433 3 0 8 11 0 0 5 15 0 0 3
- 471 8 0 0 58 2 0 0 25 13 0 0 1
- 303 269 3 0 10 66 2 0 1 32 7 0 2
- 445 74 0 0 19 30 0 0 4 43 0 0 1
- 488 83 0 0 34 18 0 0 15 34 0 0 3
- 264 298 4 0 7 68 10 0 1 30 7 0 2
- 489 44 0 0 21 12 0 0 29 21 0 0 1
- 475 34 0 0 38 13 0 0 28 18 0 0 3
- 492 14 0 0 44 4 0 0 40 7 0 0 1
- 454 173 1 0 14 21 0 0 2 42 0 0 2
- 306 285 5 0 5 68 3 0 1 33 1 0 2
- 508 64 0 0 31 14 0 0 17 33 0 0 2
- 477 61 0 0 36 13 0 0 22 15 0 0 2
- 349 224 1 0 3 66 3 0 2 41 1 0 1
- 447 189 0 0 11 34 0 0 5 45 0 0 2
- 496 34 0 0 49 6 0 0 31 11 0 0 3
- 484 222 0 0 16 12 0 0 9 24 0 0 2
- 412 50 0 0 25 47 0 0 11 29 0 0 1
- 316 184 0 0 13 65 3 0 5 34 3 0 1
- 345 163 1 0 17 57 2 0 2 32 1 0 1
- 285 273 2 0 7 50 13 0 1 28 10 0 1
- 317 179 0 0 11 64 0 0 4 35 0 0 1
- 500 68 0 0 23 15 0 0 18 30 0 0 1
- 495 34 0 0 39 4 0 0 21 21 0 0 3
- 387 294 1 0 10 37 0 0 4 30 5 0 2
- 258 236 2 0 2 72 7 0 0 26 8 0 1
- 423 25 0 0 26 16 0 0 16 29 0 0 3
- 501 32 0 0 40 11 0 0 24 20 0 0 3
- 459 37 0 0 46 4 0 0 32 16 0 0 3
- 511 48 0 0 35 7 0 0 27 15 0 0 2
- 295 271 3 0 6 71 5 0 3 29 4 0 1
- 502 34 0 0 25 9 0 0 23 11 0 0 3
- 458 36 0 0 12 7 0 0 24 19 0 0 2
- 470 273 1 0 9 17 0 0 2 32 0 0 3
- 477 30 0 0 24 14 0 0 18 26 0 0 3
- 406 77 1 0 15 24 2 0 6 37 1 0 1
- 291 251 2 0 8 53 2 0 0 43 2 0 1
- 407 51 0 0 28 47 0 0 5 35 0 0 1
- 390 47 0 0 17 45 1 0 3 33 2 0 1
- 347 123 1 0 7 52 7 0 4 38 3 0 1
- 300 175 0 0 8 71 2 0 0 35 0 0 1
- 473 59 0 0 44 15 0 0 22 23 0 0 1
- 487 35 0 0 34 12 0 0 29 24 0 0 3
- 532 57 0 0 19 6 0 0 28 15 0 0 2
- 286 207 1 0 5 74 2 0 1 38 4 0 1
- 405 199 0 0 13 37 0 0 6 45 0 0 2
- 337 177 0 0 11 47 1 0 4 39 3 0 1
- 527 20 0 0 32 6 0 0 23 16 0 0 1
- 326 218 1 0 4 71 2 0 1 33 1 0 1
- 486 14 0 0 41 4 0 0 40 8 0 0 1
- 420 43 0 0 26 30 9 0 9 24 4 0 1
- 286 293 3 0 7 83 9 0 1 22 5 0 1
- 395 59 0 0 30 33 0 0 4 36 0 0 1
- 506 24 0 0 34 12 0 0 26 17 0 0 3
- 396 217 0 0 16 43 2 0 6 30 0 0 2
- 457 15 0 0 44 1 0 0 30 13 0 0 1
- 472 139 0 0 16 25 0 0 11 38 0 0 2
- 493 21 0 0 33 16 0 0 22 19 0 0 3
- 311 236 0 0 6 66 4 0 2 24 9 0 1
- 490 23 0 0 34 6 0 0 30 14 0 0 1
- 485 29 0 0 33 7 0 0 14 20 0 0 3
- 481 43 0 0 38 11 0 0 21 24 0 0 2
--- a/main/nnet/doc/latex/users/examples/2/2.tex	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,33 +0,0 @@
-\section{Example 2}
-You can find this example in this directory but renamed to \textit{MLP9\_1\_1.m\_template}.
-I will explain only differencies to the example1, so please read it first, if you haven't.
-
-\subsection{Introduction}
-Our problem can be solved with a monotonically increasing or decreasing surface. An input vector \textbf{p}
-(with 9 values) should be mapped onto one output value.
-Because we know that it can be solved with a monotonically increasing or decreasing surface,
-we can choose a 9-1-1 multi-layer perceptron (short: MLP).
-This means an MLP with 9 input neurons, only 1 hidden neuron and with 1 output neuron.
-
-\subsection{Code m-file}
-\input{examples/2/MLP9_1_1}
-
-\subsection{Walkthrough}
-The difference to the example1 starts below the line number 0035.\\
-The difference concerns only the pre-processing section where the data set is splittet into
-the three subsets. This time, the command \textbf{subset} is used, which makes the complete
-example about 19 lines shorter!
-On line 0023 \& 0024 data will be loaded. This data matrix contains 13 columns.
-Column 4, 8 and 12 won't be used (this is because the datas are of a real world problem).
-Column 13 contains the target values.\\
-
-Now on line 35 we have to merge the input and output targets again. Subset will take the complete
-matrix as argument! On line 42 happens the complete magic :-). Subset will return three
-subsets containing each time the input and output arguments. So this part must be splitet once more!
-But this is very easy and happens at some specific positions below.\\
-
-That's it, \textbf{subset} will help you to write short scripts!
-
-
-
-
--- a/main/nnet/doc/latex/users/examples/2/MLP9_1_1.m_template	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,78 +0,0 @@
-## Copyright (C) 2008 Michel D. Schmid  <michael.schmid@plexso.com>
-##
-## 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 3 of the License, 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; if not, see <http://www.gnu.org/licenses/>.
-
-## Author: Michel D. Schmid
-
-
-## load data
-mData = load("mData.txt","mData");
-mData = mData.mData;
-[nRows, nColumns] = size(mData);
-	# this file contains 13 columns.
-	# The first 12 columns are the inputs
-	# the last column is the output,
-	# remove column 4, 8 and 12!
-	# 89 rows.
-
-mOutput = mData(:,end);
-mInput = mData(:,1:end-1);
-mInput(:,[4 8 12]) = []; # delete column 4, 8 and 12
-
-mData = [mInput mOutput];
-
-# now split the data matrix in 3 pieces, train data, test data and validate data
-# the proportion should be about 1/2 train, 1/3 test and 1/6 validate data
-# in this neural network we have 12 weights, for each weight at least 3 train sets..
-# (that's a rule of thumb like 1/2, 1/3 and 1/6)
-# 1/2 of 89 = 44.5; let's take 44 for training
-[mTrain,mTest,mVali] = subset(mData',1);
-
-[mTrainInputN,cMeanInput,cStdInput] = prestd(mTrain(1:end-1,:));# standardize inputs
-
-## comments: there is no reason to standardize the outputs because we have only
-# one output ...
-
-# define the max and min inputs for each row
-mMinMaxElements = min_max(mTrainInputN); # input matrix with (R x 2)...
-
-## define network
-nHiddenNeurons = 1;
-nOutputNeurons = 1;
-
-MLPnet = newff(mMinMaxElements,[nHiddenNeurons nOutputNeurons],\
-		{"tansig","purelin"},"trainlm","","mse");
-## for test purpose, define weights by hand
-MLPnet.IW{1,1}(:) = 1.5;
-MLPnet.LW{2,1}(:) = 0.5;
-MLPnet.b{1,1}(:) = 1.5;
-MLPnet.b{2,1}(:) = 0.5;
-
-saveMLPStruct(MLPnet,"MLP3test.txt");
-
-## define validation data new, for matlab compatibility
-VV.P = mVali(1:end-1,:);
-VV.T = mVali(end,:);
-
-## standardize also the validate data
-VV.P = trastd(VV.P,cMeanInput,cStdInput);
-
-[net] = train(MLPnet,mTrainInputN,mTrain(end,:),[],[],VV);
-
-# make preparations for net test and test MLPnet
-#  standardise input & output test data
-[mTestInputN] = trastd(mTest(1:end-1,:),cMeanInput,cStdInput);
-
-[simOut] = sim(net,mTestInputN);
-simOut
--- a/main/nnet/doc/latex/users/examples/2/MLP9_1_1.tex	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,89 +0,0 @@
-\noindent
-\ttfamily
-\hlstd{}\hlline{00001\ }\hlslc{\#\# Copyright (C) 2008 Michel D. Schmid}\hlstd{\ \ }\hlslc{$<$michael.schmid@plexso.com$>$}\\
-\hlline{00002\ }\hlstd{}\hlslc{\#\#}\\
-\hlline{00003\ }\hlstd{}\hlslc{\#\#}\\
-\hlline{00004\ }\hlstd{}\hlslc{\#\#}\\
-\hlline{00005\ }\hlstd{}\hlslc{\#\# This program is free software;you can redistribute it and/or modify it}\\
-\hlline{00006\ }\hlstd{}\hlslc{\#\# under the terms of the GNU General Public License as published by}\\
-\hlline{00007\ }\hlstd{}\hlslc{\#\# the Free Software Foundation; either version 2, or (at your option)}\\
-\hlline{00008\ }\hlstd{}\hlslc{\#\# any later version.}\\
-\hlline{00009\ }\hlstd{}\hlslc{\#\#}\\
-\hlline{00010\ }\hlstd{}\hlslc{\#\# This program is distributed in the hope that it will be useful, but}\\
-\hlline{00011\ }\hlstd{}\hlslc{\#\# WITHOUT ANY WARRANTY; without even the implied warranty of}\\
-\hlline{00012\ }\hlstd{}\hlslc{\#\# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.}\hlstd{\ \ }\hlslc{See the GNU}\\
-\hlline{00013\ }\hlstd{}\hlslc{\#\# General Public License for more details.}\\
-\hlline{00014\ }\hlstd{}\hlslc{\#\#\\}
-\hlline{00015\ }\hlstd{}\hlslc{\#\# You should have received a copy of the GNU General Public License}\\
-\hlline{00016\ }\hlstd{}\hlslc{\#\# along with this program; see the file COPYING.}\hlstd{\ \ }\hlslc{If not, see}\\
-\hlline{00017\ }\hlstd{}\hlslc{\#\# http://www.gnu.org/licenses.}\\
-\hlline{00018\ }\hlstd{}\\
-\hlline{00019\ }\hlstd{}\hlslc{\#\# Author: Michel D. Schmid}\\
-\hlline{00020\ }\hlstd{}\\
-\hlline{00021\ }\hlstd{}\\
-\hlline{00022\ }\hlstd{}\hlslc{\#\# load data}\\
-\hlline{00023\ }\hlstd{mData }\hlsym{= }\hlstd{}\hlkwc{load}\hlstd{}\hlsym{(}\hlstd{}\hlstr{"mData.txt"}\hlstd{}\hlsym{,}\hlstd{}\hlstr{"mData"}\hlstd{}\hlsym{);}\\
-\hlline{00024\ }\hlstd{mData }\hlsym{= }\hlstd{mData.mData}\hlsym{;}\\
-\hlline{00025\ }\hlstd{}\hlsym{{[}}\hlstd{nRows}\hlsym{, }\hlstd{nColumns}\hlsym{{]} = }\hlstd{}\hlkwa{size}\hlstd{}\hlsym{(}\hlstd{mData}\hlsym{);}\\
-\hlline{00026\ }\hlstd{}\hlstd{\ \ \ \ }\hlslc{\# this file contains 13 columns.}\\
-\hlline{00027\ }\hlstd{\ \ \ \ }\hlslc{\# The first 12 columns are the inputs}\\
-\hlline{00028\ }\hlstd{\ \ \ \ }\hlslc{\# the last column is the output,}\\
-\hlline{00029\ }\hlstd{}\hlstd{\ \ \ \ }\hlslc{\# remove column 4, 8 and 12!}\\
-\hlline{00030\ }\hlstd{}\hlstd{\ \ \ \ }\hlslc{\# 89 rows.}\\
-\hlline{00031\ }\\
-\hlline{00032\ }\hlstd{mOutput }\hlsym{= }\hlstd{mData}\hlsym{(:,}\hlstd{}\hlkwa{end}\hlstd{}\hlsym{);}\\
-\hlline{00033\ }\hlstd{mInput }\hlsym{= }\hlstd{mData}\hlsym{(:,}\hlstd{}\hlnum{1}\hlstd{}\hlsym{:}\hlstd{}\hlkwa{end}\hlstd{}\hlsym{{-}}\hlstd{}\hlnum{1}\hlstd{}\hlsym{);}\\
-\hlline{00034\ }\hlstd{mInput}\hlsym{(:,{[}}\hlstd{}\hlnum{4 8 12}\hlstd{}\hlsym{{]}) = {[}{]}; }\hlslc{\# delete column 4, 8 and 12}\\
-\hlline{00035\ }\hlstd{\\
-\hlline{00036\ }mData }\hlsym{= {[}}\hlstd{mInput mOutput}\hlsym{{]};}\\
-\hlline{00037\ }\hlstd{}\\
-\hlline{00038\ }\hlstd{}\hlslc{\# now split the data matrix in 3 pieces, train data, test data and validate}\\
-\hlline{00039\ }\hlstd{}\hlslc{data}\\
-\hlline{00040\ }\hlstd{}\hlslc{\# the proportion should be about 1/2 train, 1/3 test and 1/6 validate data}\\
-\hlline{00041\ }\hlstd{}\hlslc{\# in this neural network we have 12 weights, for each weight at least 3}\\
-\hlline{00042\ }\hlstd{}\hlslc{train sets..}\\
-\hlline{00043\ }\hlstd{}\hlslc{\# that's a rule of thumb like 1/2, 1/3 and 1/6}\\
-\hlline{00044\ }\hlslc{\# 1/2 of 89 = 44.5; let's take 44 for training}\\
-\hlline{00045\ }\hlsym{{[}}\hlstd{mTrain}\hlsym{,}\hlstd{mTest}\hlsym{,}\hlstd{mVali}\hlsym{{]} = }\hlkwc{subset}\hlsym{(}\hlstd{mData',}\hlnum{1}\hlstd{);}\\
-\hlline{00046\ }\hlstd{}\\
-\hlline{00047\ }\hlstd{[mTrainInputN,cMeanInput,cStdInput] =\hlkwc{ prestd}(mTrain(\hlnum{1}:\hlkwa{end}-\hlnum{1},:));}\\
-\hlline{00048\ }\hlstd{\ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ }\hlslc{\#standardize inputs}\\
-\hlline{00049\ }\hlstd{}\\
-\hlline{00050\ }\hlslc{\#\# comments: there is no reason to standardize the outputs because we have}\\
-\hlline{00051\ }\hlslc{only}\\
-\hlline{00052\ }\hlslc{\# one output ...}\\
-\hlline{00053\ }\hlstd{}\\
-\hlline{00054\ }\hlstd{}\hlslc{\# define the max and min inputs for each row}\\
-\hlline{00055\ }\hlstd{mMinMaxElements = \hlkwc{min\textunderscore max}(mTrainInputN);} \hlslc{\# input matrix with (R x 2)...}\\
-\hlline{00056\ }\hlstd{}\\
-\hlline{00057\ }\hlstd{}\hlslc{\#\# define network}\\
-\hlline{00058\ }\hlstd{nHiddenNeurons = 1;}\\
-\hlline{00059\ }\hlstd{nOutputNeurons = 1;}\\
-\hlline{00060\ }\hlstd{}\\
-\hlline{00061\ }\hlstd{MLPnet = \hlkwc{newff}(mMinMaxElements,[nHiddenNeurons nOutputNeurons],$\backslash$}\\
-\hlline{00062\ }\hlstd{}\hlstd{\ \ \ \ \ \ \ \ }\hlstd{\{\hlstr{"tansig"},\hlstr{"purelin"}\},\hlstr{"trainlm"},\hlstr{""},\hlstr{"mse"});}\\
-\hlline{00063\ }\hlslc{\#\# for test purpose, define weights by hand}\\
-\hlline{00064\ }\hlstd{MLPnet.IW\{1,1\}(:) = 1.5;}\\
-\hlline{00065\ }\hlstd{MLPnet.LW\{2,1\}(:) = 0.5;}\\
-\hlline{00066\ }\hlstd{MLPnet.b\{1,1\}(:) = 1.5;}\\
-\hlline{00067\ }\hlstd{MLPnet.b\{2,1\}(:) = 0.5;}\\
-\hlline{00068\ }\hlstd{}\\
-\hlline{00069\ }\hlkwc{saveMLPStruct}\hlstd{(MLPnet,"MLP3test.txt");}\\
-\hlline{00070\ }\hlstd{}\\
-\hlline{00071\ }\hlslc{\#\# define validation data new, for matlab compatibility}\\
-\hlline{00072\ }\hlstd{VV.P = mVali(1:end{-}1,:);}\\
-\hlline{00073\ }\hlstd{VV.T = mVali(end,:);}\\
-\hlline{00074\ }\hlstd{}\\
-\hlline{00075\ }\hlslc{\#\# standardize also the validate data}\\
-\hlline{00076\ }\hlstd{VV.P = trastd(VV.P,cMeanInput,cStdInput);}\\
-\hlline{00077\ }\hlstd{}\\
-\hlline{00078\ }\hlstd{[net] = \hlkwc{train}(MLPnet,mTrainInputN,mTrain(end,:),[],[],VV);}\\
-\hlline{00079\ }\hlstd{}\\
-\hlline{00080\ }\hlslc{\# make preparations for net test and test MLPnet}\\
-\hlline{00081\ }\hlslc{\#\hlstd{\ \ }standardise input \& output test data}\\
-\hlline{00082\ }\hlstd{[mTestInputN] = \hlkwc{trastd}(mTest(1:end-1,:),cMeanInput,cStdInput);}\\
-\hlline{00083\ }\hlstd{}\\
-\hlline{00084\ }\hlstd{[simOut] = \hlkwc{sim}(net,mTestInputN);}\\
-\hlline{00085\ }\hlstd{simOut}\hlstd{}\\
-\mbox{}
-\normalfont
--- a/main/nnet/doc/latex/users/examples/2/highlight.sty	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,20 +0,0 @@
-% Style definition file generated by highlight 2.6.0, http://www.andre-simon.de/
-
-% Highlighting theme definition:
-
-\newcommand{\hlstd}[1]{\textcolor[rgb]{0,0,0}{#1}}
-\newcommand{\hlnum}[1]{\textcolor[rgb]{0.5,0,0.5}{\bf{#1}}}
-\newcommand{\hlesc}[1]{\textcolor[rgb]{1,0,1}{\bf{#1}}}
-\newcommand{\hlstr}[1]{\textcolor[rgb]{0.65,0.52,0}{#1}}
-\newcommand{\hldstr}[1]{\textcolor[rgb]{0,0,1}{#1}}
-\newcommand{\hlslc}[1]{\textcolor[rgb]{0.95,0.47,0}{#1}}
-\newcommand{\hlcom}[1]{\textcolor[rgb]{1,0.5,0}{#1}}
-\newcommand{\hldir}[1]{\textcolor[rgb]{0,0.5,0.75}{\bf{#1}}}
-\newcommand{\hlsym}[1]{\textcolor[rgb]{1,0,0.5}{\bf{#1}}}
-\newcommand{\hlline}[1]{\textcolor[rgb]{0.19,0.19,0.19}{#1}}
-\newcommand{\hlkwa}[1]{\textcolor[rgb]{0.73,0.47,0.47}{\bf{#1}}}
-\newcommand{\hlkwb}[1]{\textcolor[rgb]{0.5,0.5,0.75}{\bf{#1}}}
-\newcommand{\hlkwc}[1]{\textcolor[rgb]{0,0.5,0.75}{#1}}
-\newcommand{\hlkwd}[1]{\textcolor[rgb]{0,0.27,0.4}{#1}}
-\definecolor{bgcolor}{rgb}{0.93,0.93,0.93}
-
--- a/main/nnet/doc/latex/users/examples/2/mData.txt	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,94 +0,0 @@
-# Created by Octave 2.9.5, Wed May 24 10:33:36 2006     <sim@TL3124>
-# name: mData
-# type: matrix
-# rows: 89
-# columns: 13
- 306 286 2 0 12 61 2 0 3 28 4 0 2
- 368 188 1 0 6 49 0 0 3 37 0 0 1
- 511 73 0 0 40 21 0 0 16 22 0 0 1
- 418 43 0 0 34 30 1 0 9 27 5 0 1
- 299 173 1 0 8 63 1 0 1 37 3 0 1
- 312 253 0 0 2 63 2 0 0 35 3 0 1
- 492 98 0 0 7 23 0 0 13 36 0 0 2
- 506 64 0 0 32 23 0 0 13 29 0 0 3
- 476 41 0 0 32 5 0 0 19 26 0 0 3
- 483 66 0 0 17 16 0 0 10 28 2 0 3
- 429 44 0 0 37 19 0 0 23 19 0 0 1
- 521 137 0 0 16 17 0 0 7 24 0 0 2
- 340 163 1 0 16 72 3 0 3 31 1 0 1
- 323 177 0 0 8 68 4 0 0 37 2 0 1
- 344 240 2 0 4 43 1 0 4 39 6 0 2
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--- a/main/nnet/doc/latex/users/examples/examples.tex	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,7 +0,0 @@
-\chapter{Examples}
-
-
-
-
-\input{examples/1/1}
-\input{examples/2/2}
\ No newline at end of file
--- a/main/nnet/doc/latex/users/introduction.tex	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,16 +0,0 @@
-\chapter{Introduction}
-
-\section{Compatibility to Matlab's \texttrademark Neural Network Toolbox}
-The compatibility is one of the strongest targets during developing this toolbox.
-If I have to develope an incompatibility e.g. in naming the functions, it will be descriped
-in this documentation. Even though it should be clear that I can't make a one to one copy.  First,
-the m-files are copyrighted and second, octave doesn't yet support the object oriented-programming techonology.\\
-
-If you find a bug, any not described incompatibility or have some suggestions, please write me at
-michael.schmid@plexso.com. This will help improving this toolbox.
-
-
-\input{numbering}
-
-\input{knownIncompatibilities}
-
--- a/main/nnet/doc/latex/users/knownIncompatibilities.tex	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,11 +0,0 @@
-\section{Known incompatibilities}
-\label{chap:intro:sec:knownIncompatibilities}
-
-
-\subsection{Function names}
-
-\subsubsection{minmax}
-\textit{minmax} is in this toolbox called \textit{min\_max}. This is because Octave already has
-a function whichs name is \textit{minmax}. This is a c file and the functions \textit{min} and \textit{max} are therein realized.
-
-
--- a/main/nnet/doc/latex/users/neuralNetworkPackageForOctaveUsersGuide.tcp	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,12 +0,0 @@
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--- a/main/nnet/doc/latex/users/neuralNetworkPackageForOctaveUsersGuide.tex	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,102 +0,0 @@
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-\include{examples/examples}
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-
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--- a/main/nnet/doc/latex/users/neuralNetworkToolboxForOctaveUsersGuide.tcp	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
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--- a/main/nnet/doc/latex/users/neuralNetworkToolboxForOctaveUsersGuide.tex	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,102 +0,0 @@
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-
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-
-\tableofcontents
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-
-\include{introduction}
-\include{octave/neuroToolbox/neuroToolbox}
-\include{examples/examples}
-
-\include{bibliography}
-
-
-
-\end{document}
-
-
--- a/main/nnet/doc/latex/users/numbering.tex	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,7 +0,0 @@
-\section{Version numbers}
-
-The first number describes the major release. Version number V1.0 will be the first toolbox release which should have the same functions like the Matlab R14 SP3 neural network Toolbox.\\
-
-The second number defines the finished functions. So to start, only the MLPs will realised and so this will be the number V0.1.0.\\
-
-The third number defines the status of the actual development and function. V0.1.0 means a first release with MLP. Actually it works only with Levenberg-Marquardt algorithm and Mean-Square-Error as performance function.
\ No newline at end of file
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Binary file main/nnet/doc/latex/users/octave/neuroPackage/graphics/tansiglogo.pdf has changed
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+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,15 +0,0 @@
-\subsection{logsig}
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-\label{fig:logsigTransferFunction}
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-
-\begin{figure}[htb]
-\centering
-  \includegraphics{octave/neuroPackage/graphics/logsiglogo}
-\caption{Log-Sigmoid transfer function logo}
-\label{fig:logsigTransferFunctionLogo}
-\end{figure}
\ No newline at end of file
--- a/main/nnet/doc/latex/users/octave/neuroPackage/min_max.tex	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,27 +0,0 @@
-\subsection{min\_max}
-\textit{min\_max} get the minimal and maximal values of an training input matrix. So the dimension of this matrix must be an RxN matrix where R is the number of input neurons and N depends on the number of training sets.\\
-
-\noindent \textbf{\textcolor{brown}{Syntax:}}\\
-
-\noindent mMinMaxElements = min\_max(RxN);\\
-
-\noindent \textbf{\textcolor{brown}{Description:}}\\
-
-\noindent RxN: R x N matrix of min and max values for R input elements with N columns\\
-
-\noindent \textbf{\textcolor{brown}{Example:}}\\
-
-\begin{equation}
-	\left[
-		\begin{array}{cc}
-     	1 &  11 \\
-    	0  & 21
-   \end{array}
-	 \right]            = min\_max\left[
-	 															   \begin{array}{ccccc}
-	 															   3 & 1 & 3 & 5 & 11 \\
-	 															   12& 0 & 21& 8 & 6  \\
-	 															   \end{array}
-	 															   	 \right]
-\end{equation}
-
--- a/main/nnet/doc/latex/users/octave/neuroPackage/neuroPackage.tex	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,22 +0,0 @@
-\chapter{Neural Network Package for Octave}
-This chapter describes all functions available in the neural network package of Octave.
-
-Eventhough it will be as compatible as possible to the one of MATLAB(TM).
-
-\section{Available Functions}
-\input{octave/neuroPackage/min_max}
-\input{octave/neuroPackage/newff}
-\input{octave/neuroPackage/prestd}
-\input{octave/neuroPackage/poststd}
-\input{octave/neuroPackage/saveMLPStruct}
-\input{octave/neuroPackage/sim}
-\input{octave/neuroPackage/subset}
-\input{octave/neuroPackage/train}
-\input{octave/neuroPackage/trastd}
-
-
-\section{Transfer functions}
-\input{octave/neuroPackage/logsig}
-\input{octave/neuroPackage/purelin}
-\input{octave/neuroPackage/tansig}
-
--- a/main/nnet/doc/latex/users/octave/neuroPackage/newff.tex	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,27 +0,0 @@
-\subsection{newff}
-\textit{newff} is the short form for \textit{\textbf{new f}eed \textbf{f}orward network}. This command creates a feed-forward backpropagation network structure.\\
-
-\noindent \textbf{\textcolor{brown}{Syntax:}}\\
-
-\noindent net = newff(Rx2,[S1 S2 ... SN],\{TF1 TF2 ... TFN\},BTF,BLF,PF)\\
-
-\noindent \textbf{\textcolor{brown}{Description:}}\\
-
-\noindent Rx2: R x 2 matrix of min and max values for R input elements\\
-\noindent Si: Size of ith layer, for N layers\\
-\noindent TFi: Transfer function of ith layer, default = "tansig"\\
-\noindent BTF: Backpropagation network training function, default = "trainlm" \\
-\noindent BLF: Backpropagation weight/bias learning function, NOT USED, is only for MATLAB(TM) compatibility\\
-\noindent PF: Performance function, default = "mse"\\
-
-\noindent \textbf{\textcolor{brown}{Examples:}}\\
-
-\noindent net = newff(Rx2,[2 1])\\
-\noindent net = newff(Rx2,[2 1],\{"tansig","purelin"\});\\
-\noindent net = newff(Rx2,[2 1],\{"tansig","purelin"\},"trainlm");\\
-\noindent net = newff(Rx2,[2 1],\{"tansig","purelin"\},"trainlm","notUsed","mse");\\
-
-\noindent \textbf{\textcolor{brown}{Comments:}}\\
-In this version, you can have as much output neurons as you want. The same with the number of hidden layers. This means you can have one input layer, unrestricted number of hidden layers and one output layer. That's it.\\
-
-
--- a/main/nnet/doc/latex/users/octave/neuroPackage/poststd.tex	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,1 +0,0 @@
-\subsection{poststd}
\ No newline at end of file
--- a/main/nnet/doc/latex/users/octave/neuroPackage/prestd.tex	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,1 +0,0 @@
-\subsection{prestd}
\ No newline at end of file
--- a/main/nnet/doc/latex/users/octave/neuroPackage/purelin.tex	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,16 +0,0 @@
-\subsection{purelin}
-
-\begin{figure}[htb]
-\centering
-  \includegraphics{octave/neuroPackage/graphics/purelin}
-\caption{Linear transfer function}
-\label{fig:purelinTransferFunction}
-\end{figure}
-
-\begin{figure}[htb]
-\centering
-  \includegraphics{octave/neuroPackage/graphics/purelinlogo}
-\caption{Linear transfer function logo}
-\label{fig:purelinTransferFunctionLogo}
-\end{figure}
-
--- a/main/nnet/doc/latex/users/octave/neuroPackage/saveMLPStruct.tex	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,10 +0,0 @@
-\subsection{saveMLPStruct}
-This is an additional function which doesn't exist in the neural network toolbox of MathWorks (TM). To see the network structure, you can use this command and save the complete structure to a file. Open this file and you have the same view like you would open the \textit{network type} of MATLAB(TM).\\
-
-\noindent \textbf{\textcolor{brown}{Syntax:}}\\
-
-\noindent saveMLPStruct(net,"initNetwork.txt");\\
-
-
-
-
--- a/main/nnet/doc/latex/users/octave/neuroPackage/sim.tex	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,18 +0,0 @@
-\subsection{sim}
-
-\noindent \textbf{\textcolor{brown}{Syntax:}}\\
-
-\noindent simout = sim(net,P);\\
-
-\noindent \textbf{\textcolor{brown}{Description:}}\\
-
-\noindent  \textbf{Left-Hand-Side:}\\
-\noindent simout: Output values of the simulated network.\\
-
-\noindent  \textbf{Right-Hand-Side:}\\
-\noindent net: Network, created and trained with newff(...) and train(...)\\
-\noindent P: Input data\\
-
-
-
-
--- a/main/nnet/doc/latex/users/octave/neuroPackage/subset.tex	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,47 +0,0 @@
-\subsection{subset}
-
-\textit{subset} can be used to optimize the data sets for train, test and validation of a neural
-network.\\
-
-\noindent \textbf{\textcolor{brown}{Syntax:}}\\
-
-\noindent [mTrain, mTest, mVali] = subset(mData,nTargets,iOpti,fTest,fVali);\\
-
-\noindent \textbf{\textcolor{brown}{Description:}}\\
-
-\noindent  \textbf{Left-Hand-Side:}\\
-\noindent mTrain: (R+T) x M matrix with R input rows, T output rows and M columns
-				where M <= N.\\
-\noindent mTest:  (R+T) x S matrix with R input rows, T output rows and S columns
-				where S <= N.\\
-\noindent mVali:  (R+T) x U matrix with R input rows, T output rows and U columns
-				where U <= N. And U can only exist, if S also exist.\\
-
-\noindent  \textbf{Right-Hand-Side:}\\
-\noindent mData: (R+T) x N matrix with R input rows, T output rows and N columns\\
-\noindent nTargets: Number of T output rows\\
-\noindent iOpti: Integer value to define level of optimization.\\
-\noindent fTest: Fraction to define the percentage of data sets which should be used for testing. \\
-\noindent fVali: Fraction to define the percentage of data sets which should be used for testing.\\
-
-\noindent iOpti can have following values:\\
-0	: no optimization\\
-1	: will randomise the column order and rerange the columns containing min and max values to be in the train set\\
-2	:	will NOT randomise the column order, but rerange the columns containing min and max values to be in the train set\\
-
-\noindent fTest or fValie have following meaning:\\
-Each of this arguments can be a fraction or zero. The value 1 is not allowed! The sum of both values
-must also be smaller than 1!\\
-Example: fTest = 1/3\\
-
-\noindent \textbf{Default values}\\
-\noindent iOpti		= 1\\
-\noindent fTest		= 1/3\\
-\noindent fVali		= 1/6\\
-
-\noindent \textbf{\textcolor{brown}{Examples:}}\\
-
-\noindent mTrain = subset(mData,2,1,0,0)\\
-\noindent [mTrain,mTest] = subset(mData,2,1,1/3,0);\\
-\noindent [mTrain,mTest,mVali] = subset(mData,1);\\
-\noindent [mTrain,mTest,mVali] = subset(mData,1,1,1/3,1/6);\\
\ No newline at end of file
--- a/main/nnet/doc/latex/users/octave/neuroPackage/tansig.tex	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,29 +0,0 @@
-\subsection{tansig}
-
-\noindent
-I solved all of my real life problems with this transfer function if a non-linear function was used. In [4] page 2-6 the tansig is defined as in equation \eqref{equ:tansigTransferFunction}. A look on the MathWorks homepage with the keyword tansig will show that tansig is programed as in equation \eqref{equ:tansigTransferFunctionNnet}.
-
-\begin{equation}
-	a = \frac{e^n - e^{-n}}{e^n + e^{-n}}
-	\label{equ:tansigTransferFunction}
-\end{equation}
-
-\begin{equation}
-	a = \frac{2}{(1 + e^{-2*n})-1}
-	\label{equ:tansigTransferFunctionNnet}
-\end{equation}
-
-
-\begin{figure}[htb]
-\centering
-  \includegraphics{octave/neuroPackage/graphics/tansig}
-\caption{Tansig transfer function}
-\label{fig:tansigTransferFunction}
-\end{figure}
-
-\begin{figure}[htb]
-\centering
-  \includegraphics{octave/neuroPackage/graphics/tansiglogo}
-\caption{Tansig transfer function logo}
-\label{fig:tansigTransferFunctionLogo}
-\end{figure}
--- a/main/nnet/doc/latex/users/octave/neuroPackage/train.tex	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,34 +0,0 @@
-\subsection{train}
-
-\noindent \textbf{\textcolor{brown}{Syntax:}}\\
-
-\noindent net = train(MLPnet,P,T,[],[],VV);\\
-
-\noindent \textbf{\textcolor{brown}{Description:}}\\
-
-\noindent  \textbf{Left-Hand-Side:}\\
-\noindent net: Trained multi-layer network.\\
-
-\noindent  \textbf{Right-Hand-Side:}\\
-\noindent MLPnet: Multi-layer network, created with newff(...)\\
-\noindent P: Input data for training\\
-\noindent T: Target data for training\\
-\noindent []: Not used right now, only for compatibility with Matlab\\
-\noindent []: Not used right now, only for compatibility with Matlab\\
-\noindent VV: Validation data. Contains input and target values\\
-
-\noindent \textbf{\textcolor{brown}{Examples:}}\\
-
-\noindent net = train(MLPnet,P,T)\\
-\noindent net = train(MLPnet,P,T,[],[],VV)\\
-
-\noindent P,T must have the same number of rows as in newff(...).\\
-\noindent VV.P, VV.T must have the same number of rows as in newff(...)\\
-
-\noindent \textbf{\textcolor{brown}{Comments:}}\\
-\noindent Please be sure to put the validation values in a structure named
-VV.P and VV.T.\\
-VV can be changed, but not .P and .T!
-
-
-
--- a/main/nnet/doc/latex/users/octave/neuroPackage/trastd.tex	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,1 +0,0 @@
-\subsection{trastd}
\ No newline at end of file
--- a/main/nnet/doc/latex/users/octave/octave.tex	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,6 +0,0 @@
-\chapter{Octave}
-This chapter describes all functions available in the neural network toolbox in Octave.
-
-
-
-\input{octave/functions/isposint_netw_privOct}
\ No newline at end of file
--- a/main/nnet/doc/latex/users/title2.tex	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,8 +0,0 @@
-% start text here!!
-
-\title{A neural network package for Octave\\
-		User's Guide \\
-				\input{../common/version}}
-
-\author{Michel D. Schmid}
-\maketitle
Binary file main/nnet/doc/pdf/neuralNetworkPackageForOctaveDevelopersGuide.pdf has changed
Binary file main/nnet/doc/pdf/neuralNetworkPackageForOctaveUsersGuide.pdf has changed
--- a/main/nnet/inst/dhardlim.m	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,28 +0,0 @@
-## Copyright (C) 2007 Michel D. Schmid <michael.schmid@plexso.com>
-##
-## 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 3 of the License, 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; if not, see <http://www.gnu.org/licenses/>.
-
-## -*- texinfo -*-
-## @deftypefn {Function File} {}[@var{a} = dhardlim (@var{n})
-##
-## @end deftypefn
-
-## Author: Michel D. Schmid
-
-
-function a = dhardlim(n)
-
-  a = 0;
-
-endfunction
--- a/main/nnet/inst/dividerand.m	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,72 +0,0 @@
-## Copyright (C) 2009 Luiz Angelo Daros de Luca <luizluca@gmail.com>
-##
-## 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 3 of the License, 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; if not, see <http://www.gnu.org/licenses/>.
-
-## -*- texinfo -*-
-## @deftypefn {Function File} [@var{trainVectors},@var{validationVectors},@var{testVectors},@var{indexOfTrain},@var{indexOfValidation},@var{indexOfTest}] = dividerand (@var{allCases},@var{trainRatio},@var{valRatio},@var{testRatio})
-## Divide the vectors in training, validation and test group according to
-## the informed ratios
-##
-##
-## @example
-##
-## [trainVectors,validationVectors,testVectors,indexOfTrain,indexOfValidatio
-## n,indexOfTest] = dividerand(allCases,trainRatio,valRatio,testRatio)
-##
-## The ratios are normalized. This way:
-##
-## dividerand(xx,1,2,3) == dividerand(xx,10,20,30)
-##
-## @end example
-##
-## @end deftypefn
-
-
-function [trainVectors,validationVectors,testVectors,indexOfTrain,indexOfValidation,indexOfTest] = dividerand(allCases,trainRatio,valRatio,testRatio)
-  #
-  # Divide the vectors in training, validation and test group according to
-  # the informed ratios
-  #
-  # [trainVectors,validationVectors,testVectors,indexOfTrain,indexOfValidatio
-  # n,indexOfTest] = dividerand(allCases,trainRatio,valRatio,testRatio)
-  #
-  # The ratios are normalized. This way:
-  #
-  # dividerand(xx,1,2,3) == dividerand(xx,10,20,30)
-  #
-
-  ## Normalize ratios
-  total = trainRatio + valRatio + testRatio;
-  #trainRatio = trainRatio / total; not used
-  validationRatio = valRatio / total;
-  testRatio = testRatio / total;
-
-  ## Calculate the number of cases for each type
-  numerOfCases = size(allCases,2);
-  numberOfValidation = floor(validationRatio*numerOfCases);
-  numberOfTest = floor(testRatio*numerOfCases);
-  numberOfTrain = numerOfCases - numberOfValidation - numberOfTest;
-
-  ## Find their indexes
-  indexOfAll=randperm(numerOfCases);
-  indexOfValidation=sort(indexOfAll(1:numberOfValidation));
-  indexOfTest=sort(indexOfAll((1:numberOfTest)+numberOfValidation));
-  indexOfTrain=sort(indexOfAll((1:numberOfTrain)+numberOfTest+numberOfValidation));
-
-  ## Return vectors
-  trainVectors = allCases(:,indexOfTrain);
-  testVectors = allCases(:,indexOfTest);
-  validationVectors = allCases(:,indexOfValidation);
-endfunction
-
--- a/main/nnet/inst/dposlin.m	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,32 +0,0 @@
-## Copyright (C) 2007 Michel D. Schmid  <michael.schmid@plexso.com>
-##
-## 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 3 of the License, 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; if not, see <http://www.gnu.org/licenses/>.
-
-## -*- texinfo -*-
-## @deftypefn {Function File} {}@var{a}= poslin (@var{n})
-## @code{poslin} is a positive linear transfer function used
-## by neural networks
-## @end deftypefn
-
-## Author: Michel D. Schmid
-
-function a = dposlin(n)
-
-   if (n<0)
-     a = 0;
-   else
-     a = 1;
-   endif
-
-endfunction
--- a/main/nnet/inst/dsatlin.m	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,38 +0,0 @@
-## Copyright (C) 2007 Michel D. Schmid <michael.schmid@plexso.com>
-##
-## 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 3 of the License, 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; if not, see <http://www.gnu.org/licenses/>.
-
-## -*- texinfo -*-
-## @deftypefn {Function File} {}[@var{a} = dsatlin (@var{n})
-##
-## @end deftypefn
-
-## @seealso{dpurelin,dtansig,dlogsig}
-
-## Author: Michel D. Schmid
-
-
-function a = dsatlin(n)
-
-
-  # the derivative of satlin is easy:
-  # where satlin is constant, the derivative is 0
-  # else, because without variable n, the derivative is 1
-  if (n>=0 && n<=1)
-    a = 1;
-  else
-    a = 0;
-  endif
-
-endfunction
--- a/main/nnet/inst/dsatlins.m	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,38 +0,0 @@
-## Copyright (C) 2007 Michel D. Schmid <michael.schmid@plexso.com>
-##
-## 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 3 of the License, 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; if not, see <http://www.gnu.org/licenses/>.
-
-## -*- texinfo -*-
-## @deftypefn {Function File} {}[@var{a} = satlins (@var{n})
-## A neural feed-forward network will be trained with @code{trainlm}
-##
-## @end deftypefn
-
-## @seealso{purelin,tansig,logsig,satlin,hardlim,hardlims}
-
-## Author: Michel D. Schmid
-
-
-function a = dsatlins(n)
-
-  # comment see dsatlin
-  # a = 1 if (n>=-1 && n<=1),
-  # 0 otherwise
-  if (n>=-1 && n<=1)
-    a = 1;
-  else
-    a = 0;
-  endif
-
-endfunction
--- a/main/nnet/inst/hardlim.m	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,31 +0,0 @@
-## Copyright (C) 2007 Michel D. Schmid <michael.schmid@plexso.com>
-##
-## 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 3 of the License, 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; if not, see <http://www.gnu.org/licenses/>.
-
-## -*- texinfo -*-
-## @deftypefn {Function File} {}[@var{a} = hardlim (@var{n})
-##
-## @end deftypefn
-
-## @seealso{purelin,tansig}
-
-## Author: Michel D. Schmid <michael.schmid@plexso.com>
-
-
-function a = hardlim(n)
-
-  # a=1 if n>=0, a=0 otherwise
-  a = (n>=0);
-
-endfunction
--- a/main/nnet/inst/hardlims.m	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,35 +0,0 @@
-## Copyright (C) 2007 Michel D. Schmid <michael.schmid@plexso.com>
-##
-## 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 3 of the License, 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; if not, see <http://www.gnu.org/licenses/>.
-
-## -*- texinfo -*-
-## @deftypefn {Function File} {}[@var{a} = hardlims (@var{n})
-##
-## @end deftypefn
-
-## @seealso{purelin,tansig,hardlim}
-
-## Author: Michel D. Schmid <michael.schmid@plexso.com>
-
-
-function a = hardlims(n)
-
-  # a=1 if n>0, a=-1 otherwise
-  if n>=0
-    a=1;
-  else
-    a=-1;
-  endif
-
-endfunction
--- a/main/nnet/inst/ind2vec.m	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,41 +0,0 @@
-## Copyright (C) 2009 Luiz Angelo Daros de Luca <luizluca@gmail.com>
-##
-## 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 3 of the License, 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; if not, see <http://www.gnu.org/licenses/>.
-
-## -*- texinfo -*-
-## @deftypefn {Function File} {@var{vec}} = ind2vec (@var{ind})
-## @code{vec2ind} convert indices to vector
-##
-##
-## @example
-## EXAMPLE 1
-## vec = [1 2 3; 4 5 6; 7 8 9];
-##
-## ind = vec2ind(vec)
-## The prompt output will be:
-## ans =
-##    1 2 3 1 2 3 1 2 3
-## @end example
-##
-## @end deftypefn
-
-## @seealso{vec2ind}
-
-function [vector]=ind2vec(ind)
-  # Converts indices to vectors
-  #
-  #
-  vectors = length(ind);
-  vector = sparse(ind,1:vectors,ones(1,vectors));
-endfunction
--- a/main/nnet/inst/isposint.m	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,59 +0,0 @@
-## Copyright (C) 2005 Michel D. Schmid  <michael.schmid@plexso.com>
-##
-## 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 3 of the License, 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; if not, see <http://www.gnu.org/licenses/>.
-
-## -*- texinfo -*-
-## @deftypefn {Function File} {} @var{f} = isposint(@var{n})
-## @code{isposint} returns true for positive integer values.
-##
-## @example
-##   isposint(1)   # this returns TRUE
-##   isposint(0.5) # this returns FALSE
-##   isposint(0)   # this also return FALSE
-##   isposint(-1)  # this also returns FALSE
-## @end example
-##
-##
-## @end deftypefn
-
-## Author: Michel D. Schmid
-
-function f = isposint(n)
-
-  ## check range of input arguments
-  error(nargchk(1,1,nargin))
-
-  ## check input arg
-  if (length(n)>1)
-    error("Input argument must not be a vector, only scalars are allowed!")
-  endif
-
-  f = 1;
-  if ( (!isreal(n)) || (n<=0) || (round(n) != n) )
-    f = 0;
-  endif
-
-
-endfunction
-
-%!shared
-%! disp("testing isposint")
-%!assert(isposint(1)) # this should pass
-%!assert(isposint(0.5),0) # should return zero
-%!assert(isposint(-1),0) # should return zero
-%!assert(isposint(-1.5),0) # should return zero
-%!assert(isposint(0),0) # should return zero
-%!fail("isposint([0 0])","Input argument must not be a vector, only scalars are allowed!")
-%!fail("isposint('testString')","Input argument must not be a vector, only scalars are allowed!")
-
--- a/main/nnet/inst/logsig.m	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,39 +0,0 @@
-## Copyright (C) 2007 Michel D. Schmid <michael.schmid@plexso.com>
-##
-## 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 3 of the License, 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; if not, see <http://www.gnu.org/licenses/>.
-
-## -*- texinfo -*-
-## @deftypefn {Function File} {}@var{a} = logsig (@var{n})
-## @code{logsig} is a non-linear transfer function used to train
-## neural networks.
-## This function can be used in newff(...) to create a new feed forward
-## multi-layer neural network.
-##
-## @end deftypefn
-
-## @seealso{purelin,tansig}
-
-## Author: Michel D. Schmid
-
-
-function a = logsig(n)
-
-
-  a = 1 ./ (1 + exp(-n));
-  ## attention with critical values ==> infinite values
-  ## must be set to 1! Still the same problem as in "tansig"
-  i = find(!finite(a));
-  a(i) = sign(n(i));
-
-endfunction
--- a/main/nnet/inst/mapstd.m	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,327 +0,0 @@
-## Copyright (C) 2009 Luiz Angelo Daros de Luca <luizluca@gmail.com>
-##
-## 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 3 of the License, 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; if not, see <http://www.gnu.org/licenses/>.
-
-## -*- texinfo -*-
-## @deftypefn {Function File} [@var{YY},@var{PS}] = mapstd (@var{XX},@var{ymean},@var{ystd})
-## Map values to mean 0 and standard derivation to 1.
-##
-## @example
-## [YY,PS] = mapstd(XX,ymean,ystd)
-##
-##    Apply the conversion and returns YY as (YY-ymean)/ystd.
-##
-## [YY,PS] = mapstd(XX,FP)
-##
-##    Apply the conversion but using an struct to inform target mean/stddev.
-##    This is the same of [YY,PS]=mapstd(XX,FP.ymean, FP.ystd).
-##
-## YY = mapstd('apply',XX,PS)
-##
-##    Reapply the conversion based on a previous operation data.
-##    PS stores the mean and stddev of the first XX used.
-##
-## XX = mapstd('reverse',YY,PS)
-##
-##    Reverse a conversion of a previous applied operation.
-##
-## dx_dy = mapstd('dx',XX,YY,PS)
-##
-##    Returns the derivative of Y with respect to X.
-##
-## dx_dy = mapstd('dx',XX,[],PS)
-##
-##    Returns the derivative (less efficient).
-##
-## name = mapstd('name');
-##
-##    Returns the name of this convesion process.
-##
-## FP = mapstd('pdefaults');
-##
-##    Returns the default process parameters.
-##
-## names = mapstd('pnames');
-##
-##    Returns the description of the process parameters.
-##
-## mapstd('pcheck',FP);
-##
-##    Raises an error if FP has some inconsistent.
-## @end example
-##
-## @end deftypefn
-
-function [out1,out2]=mapstd(in1,in2,in3,in4)
-  #
-  # Map values to mean 0 and standard derivation to 1.
-  #
-  # [YY,PS] = mapstd(XX,ymean,ystd)
-  #
-  #    Apply the conversion and returns YY as (YY-ymean)/ystd.
-  #
-  # [YY,PS] = mapstd(XX,FP)
-  #
-  #    Apply the conversion but using an struct to inform target mean/stddev.
-  #    This is the same of [YY,PS]=mapstd(XX,FP.ymean, FP.ystd).
-  #
-  # YY = mapstd('apply',XX,PS)
-  #
-  #    Reapply the conversion based on a previous operation data.
-  #    PS stores the mean and stddev of the first XX used.
-  #
-  # XX = mapstd('reverse',YY,PS)
-  #
-  #    Reverse a conversion of a previous applied operation.
-  #
-  # dx_dy = mapstd('dx',XX,YY,PS)
-  #
-  #    Returns the derivative of Y with respect to X.
-  #
-  # dx_dy = mapstd('dx',XX,[],PS)
-  #
-  #    Returns the derivative (less efficient).
-  #
-  # name = mapstd('name');
-  #
-  #    Returns the name of this convesion process.
-  #
-  # FP = mapstd('pdefaults');
-  #
-  #    Returns the default process parameters.
-  #
-  # names = mapstd('pnames');
-  #
-  #    Returns the description of the process parameters.
-  #
-  # mapstd('pcheck',FP);
-  #
-  #    Raises an error if FP has some inconsistent.
-  #
-
-  if nargin==0
-    error("Not enough arguments.")
-  endif
-
-  # Defaults
-  ps.name="mapstd";
-  ps.ymean=0;
-  ps.ystd=1;
-
-  if ischar(in1)
-    switch in1
-        case "name"
-            if nargout>1
-                error("Too many output arguments");
-            endif
-            if nargin>1
-                error("Too many input arguments");
-            endif
-            out1="Map Mean and Standard Deviation";
-            return;
-        case "pdefaults"
-            if nargout>1
-                error("Too many output arguments");
-            endif
-            if nargin>1
-                error("Too many input arguments");
-            endif
-            out1=ps;
-        case "pcheck"
-            if nargout>1
-                error("Too many output arguments");
-            endif
-            if nargin<2
-                error("Not enough input arguments");
-            endif
-            if nargin>2
-                error("Too many input arguments");
-            endif
-
-            fp=in2;
-            if ~isstruct(fp)
-                error("FP must be a struct")
-            elseif ~isfield(fp,"ymean")
-                error("FP must include ymean field")
-            elseif ~isfield(fp,"ystd")
-                error("FP must include ystd field")
-            elseif isdouble(fp.ymean)
-                error("FP.ymean must be a real scalar value")
-            elseif isdouble(fp.ystd)
-                error("FP.ystd must be a real scalar value")
-            else
-                out1='';
-            endif
-            return;
-        # MATLAB uses pnames but documents as pdesc (that does not work)
-        case "pnames"
-            if nargout>1
-                error("Too many output arguments");
-            endif
-            if nargin>1
-                error("Too many input arguments");
-            endif
-            # MATLAB seems to be buggy in the second element
-            #out1={'Mean value for each row of Y.','Maximum value for each
-            #row of Y.'};
-            out1={"Mean value for each row of Y.","Standart deviation value for each row of Y."};
-        case "apply"
-            if nargin<3
-                error("Not enough input arguments");
-            endif
-            if nargin>3
-                error("Too many input arguments");
-            endif
-            if nargout>1
-                error("Too many output arguments");
-            endif
-            xx=in2;
-            ps=in3;
-            yy=apply(xx,ps);
-            out1=yy;
-            out2=ps;
-            return;
-        case "reverse"
-            if nargin<3
-                error("Not enough input arguments");
-            endif
-            if nargin>3
-                error("Too many input arguments");
-            endif
-            if nargout>1
-                error("Too many output arguments");
-            endif
-            yy=in2;
-            ps=in3;
-            xx=reverse(yy,ps);
-            out1=xx;
-            out2=ps;
-            return;
-        case "dx"
-            if nargin<3
-                error("Not enough input arguments");
-            endif
-            if nargin>3
-                error("Too many input arguments");
-            endif
-            if nargout>1
-                error("Too many output arguments");
-            endif
-            xx=in2;
-            yy=in3;
-            ps=in4;
-            xx_yy=derivate(xx,yy,ps);
-            out1=xx_yy;
-            return;
-    endswitch
-  else
-    xx=in1;
-    ps.xrows=size(xx,1);
-    ps.yrows=size(xx,1);
-    ps.xmean=mean(xx,2);
-    ps.xstd=std(xx,0,2);
-
-    if nargin==1
-        # All correct
-    elseif nargin==2
-        if isstruct(in2)
-            ps.ymean=in2.ymean;
-            ps.ystd=in2.ystd;
-        else
-            ps.ymean=in2;
-        endif
-    elseif nargin == 3
-        ps.ymean=in2;
-        ps.ystd=in3;
-    else
-        error("Too many input arguments");
-    endif
-
-    out1=apply(xx,ps);
-    out2=ps;
-  endif
-
-  # Verify args
-  function checkargs(values,ps)
-    # check xx is matrix
-    if ~isnumeric(values)
-        error("Just numeric values are accepted")
-    endif
-    # check ps is struct
-    if ~isstruct(ps)
-        error("PS should be a struct")
-    endif
-    # check ymean,ystd
-    if ~isa(ps.ymean,"double")
-        error("PS.ymean should be a double")
-    endif
-    if ~isa(ps.ystd,"double")
-        error("PS.ystd should be a double")
-    endif
-    if ~all(size(ps.ymean)==[1 1])
-        error("PS.ymean should be a scalar")
-    endif
-    if ~all(size(ps.ystd)==[1 1])
-        error("PS.ystd should be a scalar")
-    endif
-    # check xmean,ystd
-    if ~isnumeric(ps.xmean)
-        error("PS.xmean should be a numeric")
-    endif
-    if ~isnumeric(ps.xstd)
-        error("PS.xstd should be a numeric")
-    endif
-    if ~all(size(ps.xmean)==size(ps.xstd))
-        error("Size of PS.xmean and PS.xstd must match")
-    endif
-  endfunction
-
-  # Apply the mapping operation
-  function [yy]=apply(xx,ps)
-    checkargs(xx,ps)
-
-    if ~all(size(xx,1)==size(ps.xmean,1))
-        error("Size of XX rows should match PS.xmean and PS.xstd")
-    endif
-    # Avoid multiply/division by zero
-    ps.xstd(ps.xstd == 0) = 1;
-    yy=(xx - (ps.xmean*ones(1,size(xx,2)))) ./ (ps.xstd*ones(1,size(xx,2)));
-    yy=(yy + ps.ymean) .* ps.ystd;
-  endfunction
-
-  # Reverse the mapping operation
-  function [xx]=reverse(yy,ps)
-    checkargs(yy,ps)
-    if ~all(size(yy,1)==size(ps.xmean,1))
-        error("Size of YY rows should match PS.xmean and PS.xstd")
-    endif
-    # Avoid multiply/division by zero
-    ps.xstd(ps.xstd == 0) = 1;
-    yy=(yy ./ ps.ystd) - ps.ymean;
-    xx=(yy .* (ps.xstd*ones(1,size(yy,2)))) + (ps.xmean*ones(1,size(yy,2)));
-  endfunction
-
-  # I don't know why this exists but matlab implements it
-  function [dy_dx]=derivate(xx,yy,ps)
-    checkargs(yy,ps)
-    checkargs(xx,ps)
-
-    cols = size(xx,2);
-    diagonal = diag(ps.ystd ./ ps.xstd);
-    dy_dx = diagonal(:,:,ones(1,cols));
-  endfunction
-
-#end
-
-endfunction
--- a/main/nnet/inst/min_max.m	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,70 +0,0 @@
-## Copyright (C) 2005 Michel D. Schmid  <michael.schmid@plexso.com>
-##
-## 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 3 of the License, 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; if not, see <http://www.gnu.org/licenses/>.
-
-## -*- texinfo -*-
-## @deftypefn {Function File} {} @var{Pr} = min_max (@var{Pp})
-## @code{min_max} returns variable Pr with range of matrix rows
-##
-## @emph{warning}: @code{min_max} has been deprecated in favor of
-## @code{minmax}. This function will be removed from future versions of the
-## 'nnet' package".
-##
-## @example
-## PR - R x 2 matrix of min and max values for R input elements
-## @end example
-##
-## @example
-## Pp = [1 2 3; -1 -0.5 -3]
-## pr = min_max(Pp);
-## pr = [1 3; -0.5 -3];
-## @end example
-## @end deftypefn
-
-## Author: Michel D. Schmid
-
-function Pr = min_max(Pp)
-  persistent warned = false;
-  if (! warned)
-    warned = true;
-    warning ("Octave:deprecated-function",
-             "`min_max' has been deprecated in favor of `minmax'. This function will be removed from future versions of the `nnet' package");
-  endif
-
-  ## check number of input args
-  error(nargchk(1,1,nargin))
-
-  Pr = []; # returns an empty matrix
-  #if ismatrix(Pp)
-  if (!(size(Pp,1)==1) && !(size(Pp,2)==1)) # ismatrix(1) will return 1!!!
-    if isreal(Pp) # be sure, this is no complex matrix
-      Pr = [min(Pp,[],2) max(Pp,[],2)];
-    else
-      error("Argument has illegal type.")
-    endif
-  else
-    error("Argument must be a matrix.")
-  endif
-
-endfunction
-
-%!shared
-%! disp("testing min_max")
-%!test fail("min_max(1)","Argument must be a matrix.")
-%!test fail("min_max('testString')","Argument must be a matrix.")
-%!test fail("min_max(cellA{1}=1)","Argument must be a matrix.")
-%!test fail("min_max([1+1i, 2+2i])","Argument must be a matrix.")
-%!test fail("min_max([1+1i, 2+2i; 3+1i, 4+2i])","Argument has illegal type.")
-
-
--- a/main/nnet/inst/minmax.m	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,116 +0,0 @@
-## Copyright (C) 2012 Carnë Draug <carandraug+dev@gmail.com>
-##
-## 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 3 of the License, 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; if not, see <http://www.gnu.org/licenses/>.
-
-## -*- texinfo -*-
-## @deftypefn {Function File} {@var{Pr} =} minmax (@var{Pp})
-## Calculate maximum and mininum of rows.
-##
-## For each row of the matrix @var{Pp}, outputs its minimum and maximum on the
-## first and second column of @var{Pr} respectively.  @var{Pr} will have the
-## same number of rows as @var{Pp} and 2 columns.
-##
-## @group
-## @example
-## Pp = [5 7 9 2 5 0 6
-##       5 3 6 2 7 9 3
-##       8 3 2 3 5 6 8]
-## minmax (Pp)
-##   @result{} 0 9
-##      2 9
-##      0 8
-## @end example
-## @end group
-##
-## @var{Pp} can also be a cell array of matrices in wich case they all must have
-## the same number of columns, and all matrices on each row of cells must have
-## the same number of rows.  In this case, matrices of each row of @var{Pp} are
-## concatenated horizontally for calculating the minimum ad maximum values.
-## @var{Pr} will be a single column cell array with same number of rows as
-## @var{Pp}. For example:
-##
-## @group
-## @example
-## Pp = @{[0 1; 1 2; 4 6] [2 3; 8 0; 3 1] [9 1; 5 2; 4 8];
-##        [1 2; 9 7] [5 2; 3 1] [7 6; 0 3]@}
-## minmax (Pp)
-##   @result{} @{
-##        [1,1] =
-##           0   9
-##           0   8
-##           1   8
-##        [2,1] =
-##           1   7
-##           0   9
-##      @}
-## @end example
-## @end group
-##
-## If drawn on a table, it would look like:
-##
-## @verbatim
-##   2x3 cell array      2x1 cell array
-##
-##  0 1   2 3   9 1   >      0 9
-##  1 2   8 0   5 2   >      0 8
-##  4 6   3 1   4 8   >      1 8
-##
-##  1 2   5 2   7 6   >      1 7
-##  9 7   3 1   0 3   >      0 9
-## @end verbatim
-##
-## Note how on this example: the number of columns (3) in the cell array is
-## irrelevant but the output has the same number of rows (2); all matrices have
-## the same number of columns (2).
-##
-## @seealso {cell2mat, max, min}
-## @end deftypefn
-
-function Pr = minmax (Pp)
-
-  if (nargin != 1)
-    print_usage;
-  elseif (minmax_check (Pp))
-    Pr = single_minmax (Pp);
-  elseif (iscell (Pp) && ndims (Pp) == 2 && all (cellfun (@minmax_check, Pp(:))))
-    Pr_rows = cellfun (@rows, Pp(:,1));
-    if (!all (cellfun (@columns, Pp(:)) == columns (Pp{1})))
-      error ("minmax: all matrices must have the same number of columns.");
-    elseif (!all (bsxfun (@eq, cellfun (@rows, Pp), Pr_rows)(:)))
-      error ("minmax: all matrices in a row of cells must have same number of rows.");
-    endif
-    Pr = mat2cell (single_minmax (cell2mat (Pp)), Pr_rows, 2);
-  else
-    error ("minmax: input must be one, or a 2D cell array of, 2D non-complex matrix.");
-  endif
-
-endfunction
-
-function retval = minmax_check (val)
-  retval = isnumeric (val) && !iscomplex (val) && ndims (val) == 2;
-endfunction
-
-function Pr = single_minmax (Pp)
-  Pr = [min(Pp, [], 2) max(Pp, [], 2)];
-endfunction
-
-%!assert (minmax ([2 5 4; -2 6 5]), [2 5; -2 6]);                         # basic usage
-%!assert (minmax ([2 5 4]), [2 5]);                                       # single row, basic usage
-%!assert (minmax ({[0 1; -1 -2; 34 56] [2 3; 8 0; 21 23]; [1 -2; 9 7] [12 5; 13 11]}), ...
-%!        {[0 3; -2 8; 21 56]; [-2 12; 7 13]});                           # basic usage with cell arrays
-%!assert (minmax (1), [1 1]);                                             # matlab compatibility
-%!fail ("minmax ([i 2; 3 4])");                                           # do not accept complex values
-%!fail ("minmax (rand (2, 2, 2))");                                       # only 2D matrix
-%!fail ("minmax ({[0 1; 1 2] [2 3 2; 8 0 2]; [1 2] [9 7 3]})");           # number of columns must be the same everywhere
-%!fail ("minmax ({[0 1; 1 2] [2 3; 8 0; 5 5]; [1 2; 9 7] [1 5; 1 1]})");  # each row of cells must have matrices with same number of rows
--- a/main/nnet/inst/newff.m	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,277 +0,0 @@
-## Copyright (C) 2005 Michel D. Schmid  <michael.schmid@plexso.com>
-##
-## 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 3 of the License, 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; if not, see <http://www.gnu.org/licenses/>.
-
-## -*- texinfo -*-
-## @deftypefn {Function File} {@var{net}} = newff (@var{Pr},@var{ss},@var{trf},@var{btf},@var{blf},@var{pf})
-## @code{newff} create a feed-forward backpropagation network
-##
-## @example
-## Pr - R x 2 matrix of min and max values for R input elements
-## Ss - 1 x Ni row vector with size of ith layer, for N layers
-## trf - 1 x Ni list with transfer function of ith layer,
-##       default = "tansig"
-## btf - Batch network training function,
-##       default = "trainlm"
-## blf - Batch weight/bias learning function,
-##       default = "learngdm"
-## pf  - Performance function,
-##       default = "mse".
-## @end example
-##
-## @example
-## EXAMPLE 1
-## Pr = [0.1 0.8; 0.1 0.75; 0.01 0.8];
-##      it's a 3 x 2 matrix, this means 3 input neurons
-##
-## net = newff(Pr, [4 1], @{"tansig","purelin"@}, "trainlm", "learngdm", "mse");
-## @end example
-##
-## @end deftypefn
-
-## @seealso{sim, init, train}
-
-## Author: Michel D. Schmid
-
-function net = newff(Pr,ss,transFunc,trainFunc,notUsed,performFunc)
-
-  ## initial descriptipn
-  ##  newff(Pr,ss,transfunc,trainFunc,notUsed,performFunc)
-  ##  * Pr is a nx2 matrix with min and max values of standardized inputs
-  ##    Pr means: p-range
-  ##  * ss is a row vector, the first element describes the number
-  ##    of hidden neurons, the second element describes the number
-  ##    of output neurons
-  ##  * transFunc is a cell array of transfer function, standard is "tansig"
-  ##  * trainFunc is the training algorithm
-  ##  * notUsed exist only because we have only one train algorithm which doesn't
-  ##    need a weight learning function
-  ##  * performFunc is written for the performance function, standard is "mse"
-
-  ## check range of input arguments
-  error(nargchk(2,6,nargin))
-
-  ## get number of layers (without input layer)
-  nLayers = length(ss);
-
-  ## set defaults
-  if (nargin <3)
-    # the number of transfer functions depends on the number of
-    # hidden layers, so we have to create a loop here 30.09.09 (dd.mm.yy)
-	for i=1:nLayers
-	  if (i==nLayers)
-	    transFunc{i,1} = "purelin";
-	  else
-        transFunc{i,1}= "tansig";
-      endif
-    endfor
-  endif
-  if (nargin <4)
-    trainFunc = "trainlm";
-  endif
-  if (nargin <5)
-    notUsed = "noSense";
-  endif
-  if (nargin==5)
-    ## it doesn't matter what nargin 5 is ...!
-    ## it won't be used ... it's only for matlab compatibility
-    notUsed = "noSense"
-  endif
-  if (nargin <6)
-    performFunc = "mse";
-  endif
-
-  ## check input args
-  checkInputArgs(Pr,ss);
-
-  ## Standard architecture of neural network
-  net = __newnetwork(1,nLayers,1,"newff");
-  ## description:
-  ##	first argument: number of inputs, nothing else allowed till now
-  ## it's not the same like the number of neurons in this input
-  ## second argument: number of layers, including output layer
-  ## third argument: number of outputs, nothing else allowed till now
-  ## it's not the same like the number of neurons in this output
-
-  ## set inputs with limit of only ONE input
-  net.inputs{1}.range = Pr;
-  [nRows, nColumns] = size(Pr);
-  net.inputs{1}.size = nRows;
-
-  ## set size of IW
-  net.IW{1,1} = zeros(1,nRows);
-  ## set more needed empty cells
-  for iLayers = 2:nLayers
-    net.IW{iLayers,1} = [];
-    #  net.IW{2:nLayers,1} = [];    # old code
-  endfor
-  ## set number of bias, one per layer
-  for iBiases = 1:nLayers
-    net.b{iBiases,1} = 0;
-  endfor
-
-  ## set rest of layers
-
-  ## set size of LayerWeights LW
-  ## the numbers of rows and columns depends on the
-  ## number of hidden neurons and output neurons...
-  ## 2 hidden neurons match 2 columns ...
-  ## 2 output neurons match 2 rows ...
-  for i=2:nLayers
-    net.LW{i,i-1} = zeros(ss(i),ss(i-1));
-  endfor
-  for iLayers = 1:nLayers
-    net.layers{iLayers}.size = ss(iLayers);
-    net.layers{iLayers}.transferFcn = transFunc{iLayers};
-  endfor
-
-  ## define everything with "targets"
-  net.numTargets = ss(end);
-  net.targets = cell(1,nLayers);
-  for i=1:nLayers
-    if (i==nLayers)
-      net.targets{i}.size = ss(end);
-      ## next row of code is only for MATLAB(TM) compatibility
-      ## I never used this the last 4 years ...
-      net.targets{i}.userdata = "Put your custom informations here!";
-    else
-      net.targets{i} = [];
-    endif
-  endfor
-
-  ## Performance
-  net.performFcn = performFunc;
-
-  ## Adaption
-  for i=1:nLayers
-#    net.biases{i}.learnFcn = blf;
-#    net.layerWeights{i,:}.learnFcn = blf;
-    net.biases{i}.size = ss(i);
-  endfor
-
-  ## Training
-  net.trainFcn = trainFunc; # actually, only trainlm will exist
-  net = setTrainParam(net);
-  ## Initialization
-  net = __init(net);
-
-# ======================================================
-#
-# additional check functions...
-#
-# ======================================================
-  function checkInputArgs(Pr,ss)
-
-    ## check if Pr has correct format
-    if !isreal(Pr) || (size(Pr,2)!=2)
-      error("Input ranges must be a two column matrix!")
-    endif
-    if any(Pr(:,1) > Pr(:,2)) # check if numbers in the second column are larger as in the first one
-      error("Input ranges has values in the second column larger as in the same row of the first column.")
-    endif
-
-    ## check if ss has correct format, must be 1xR row vector
-    if (size(ss,1)!=1)
-      error("Layer sizes is not a row vector.")
-    endif
-    if (size(ss,2)<2)
-      error("There must be at least one hidden layer and one output layer!")
-    endif
-    for k=1:length(ss)
-      sk = ss(k);
-      if !isreal(sk) || any(sk<1) || any(round(sk)!=sk)
-        error("Layer sizes is not a row vector of positive integers.")
-      endif
-    endfor
-
-  endfunction
-# ======================================================
-#
-# additional set functions...
-#
-# ======================================================
-  function net = setTrainParam(net)
-
-    trainFunc = net.trainFcn;
-    switch(trainFunc)
-
-    case "trainlm"
-      net.trainParam.epochs = 100;
-      net.trainParam.goal = 0;
-      net.trainParam.max_fail = 5;
-      net.trainParam.mem_reduc = 1;
-      net.trainParam.min_grad = 1.0000e-010;
-      net.trainParam.mu = 0.0010;
-      net.trainParam.mu_dec = 0.1;
-      net.trainParam.mu_inc = 10;
-      net.trainParam.mu_max = 1.0000e+010;
-      net.trainParam.show = 50;
-      net.trainParam.time = Inf;
-    otherwise
-      error("newff:setTrainParam: this train algorithm isn't available till now!")
-    endswitch
-
-  endfunction
-# ========================================================
-
-
-endfunction
-
-%!shared
-%! disp("testing newff")
-
-# if input range Pr has only one column
-%!test
-%! Pr = [1;2];
-%! fail("newff(Pr,[1 1],{'tansig','purelin'},'trainlm','unused','mse')","Input ranges must be a two column matrix!")
-
-# if input range Pr has two columns
-%!test
-%! Pr = [1 2 ; 4  6];
-%! assert(__checknetstruct(newff(Pr,[1 1],{'tansig','purelin'},'trainlm','unused','mse')))
-  ## __checknetstruct returns TRUE is input arg is a network structure ...
-
-# if input range Pr has three columns
-%!test
-%! Pr = [1 2 3; 4 5 6];
-%! fail("newff(Pr,[1 1],{'tansig','purelin'},'trainlm','unused','mse')","Input ranges must be a two column matrix!")
-
-# if input range has in the second col greater values as in the first col ...
-%!test
-%! Pr = [5 3; 4 5];
-%! fail("newff(Pr,[1 1],{'tansig','purelin'},'trainlm','unused','mse')",\
-%!  "Input ranges has values in the second column larger as in the same row of the first column.")
-
-# check if ss has correct format
-%!test
-%! Pr = [1 2 ; 4 6];
-%! fail("newff(Pr,[1 1; 2 3],{'tansig','purelin'},'trainlm','unused','mse')",\
-%!  "Layer sizes is not a row vector.")
-
-# check if ss has correct format
-%!test
-%! Pr = [1 2 ; 4 6];
-%! assert(__checknetstruct(newff(Pr,[ 2 3],{'tansig','purelin'},'trainlm','unused','mse')))
-
-# check if ss has correct format
-%!test
-%! Pr = [1 2 ; 4 6];
-%! fail("newff(Pr,[1],{'tansig','purelin'},'trainlm','unused','mse')",\
-%!  "There must be at least one hidden layer and one output layer!")
-
-# check if ss has correct format
-%!test
-%! Pr = [1 2 ; 4 6];
-%! fail("newff(Pr,[-1 1],{'tansig','purelin'},'trainlm','unused','mse')",\
-%!  "Layer sizes is not a row vector of positive integers.")
--- a/main/nnet/inst/newp.m	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,140 +0,0 @@
-## Copyright (C) 2007 Michel D. Schmid  <michael.schmid@plexso.com>
-##
-## 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 3 of the License, 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; if not, see <http://www.gnu.org/licenses/>.
-
-## -*- texinfo -*-
-## @deftypefn {Function File} {@var{net}} = newp (@var{Pr},@var{ss},@var{transFunc},@var{learnFunc})
-## @code{newp} create a perceptron
-##
-## @example
-## PLEASE DON'T USE THIS FUNCTIONS, IT'S STILL NOT FINISHED!
-## =========================================================
-## @end example
-## @example
-## Pr - R x 2 matrix of min and max values for R input elements
-## ss - a scalar value with the number of neurons
-## transFunc - a string with the transfer function
-##       default = "hardlim"
-## learnFunc - a string with the learning function
-##       default = "learnp"
-## @end example
-##
-##
-## @end deftypefn
-
-## Author: Michel D. Schmid
-
-function net = newp(Pr,ss,transFunc,learnFunc)
-
-  ## initial descriptipn
-  ##  newp(Pr,ss,transFunc,learnFunc)
-  ##  * Pr is a nx2 matrix with min and max values of standardized inputs
-  ##    Pr means: p-range
-  ##  * ss is a scalar value which describes the number of neurons
-  ##    of output neurons
-  ##  * transFunc is the transfer function, standard is "hardlim"
-  ##  * learnFunc is the learning function, standard is "learnp"
-
-  ## check range of input arguments
-  error(nargchk(1,4,nargin))
-
-  ## set defaults
-  if (nargin <2)
-    ss = 1; # means one neuron
-  endif
-  if (nargin <3)
-    transFunc = "hardlim";
-  endif
-  if (nargin <4)
-    learnFunc = "learnp";
-  endif
-
-  ## check input args
-  checkInputArgs(Pr,ss);
-
-#   ## get number of layers (without input layer)
-#   nLayers = length(ss);
-
-  ## Standard architecture of neural network
-  net = __newnetwork(1,1,1,"newp");
-  ## description:
-  ##	first argument: number of inputs, nothing else allowed till now
-  ## it's not the same like the number of neurons in this input
-  ## second argument: number of layers, including output layer
-  ## third argument: number of outputs, nothing else allowed till now
-  ## it's not the same like the number of neurons in this output
-  ## fourth argument: network type
-
-
-  ## set inputs with limit of only ONE input
-  net.inputs{1}.range = Pr;
-  [nRows, nColumns] = size(Pr);
-  net.inputs{1}.size = nRows;
-
-  ## set size of IW
-  net.IW{1,1} = zeros(1,nRows);
-  ## set number of bias, one per layer
-  net.b{iBiases,1} = 0;
-
-  ## define everything with "layers"
-  net.numLayers = ss(end);
-  net.layers = cell(1,1);
-  net.layers{1}.size = ss(end);
-  net.layers{1}.transFcn = transFunc;
-  ## next row of code is only for MATLAB(TM) compatibility
-  ## I never used this the last 4 years ...
-  net.targets{i}.userdata = "Put your custom informations here!";
-
-  ## performance function
-  net.performFnc = "mae";
-
-  ## learning
-  net.biases{1}.learnFcn = learnFunc;
-  net.inputWeights{1,1}.learnFcn = learnFunc;
-
-  ## adaption
-  net.adaptFcn = "trains";
-
-  ## Training
-  net.trainFcn = "trainc";
-
-  ## Initialization
-  net = __init(net);
-
-# ======================================================
-#
-# additional check functions...
-#
-# ======================================================
-  function checkInputArgs(Pr,ss)
-
-    ## check if Pr has correct format
-    if !isreal(Pr) | (size(Pr,2)!=2)
-      error("Input ranges must be a two column matrix!")
-    endif
-    if any(Pr(:,1) > Pr(:,2)) # check if numbers in the second column are larger as in the first one
-      error("Input ranges has values in the second column larger as in the same row of the first column.")
-    endif
-
-    ## check if ss has correct format, must be a scalar value
-    if ( (size(ss,1)!=1) || (size(ss,2)!=1))
-      error("Layer sizes is not a scalar value.")
-    endif
-
-  endfunction
-
-# ========================================================
-
-
-endfunction
--- a/main/nnet/inst/poslin.m	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,32 +0,0 @@
-## Copyright (C) 2007 Michel D. Schmid  <michael.schmid@plexso.com>
-##
-## 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 3 of the License, 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; if not, see <http://www.gnu.org/licenses/>.
-
-## -*- texinfo -*-
-## @deftypefn {Function File} {}@var{a}= poslin (@var{n})
-## @code{poslin} is a positive linear transfer function used
-## by neural networks
-## @end deftypefn
-
-## Author: Michel D. Schmid
-
-function a = poslin(n)
-
-   if (n<0)
-     a = 0;
-   else
-     a = n;
-   endif
-
-endfunction
--- a/main/nnet/inst/poststd.m	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,78 +0,0 @@
-## Copyright (C) 2006 Michel D. Schmid  <michael.schmid@plexso.com>
-##
-## 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 3 of the License, 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; if not, see <http://www.gnu.org/licenses/>.
-
-## -*- texinfo -*-
-## @deftypefn {Function File} {}[@var{Pp},@var{Tt}] = poststd(@var{Pn},@var{meanp},,@var{stdP},@var{Tn},@var{meanT},@var{stdT})
-## @code{poststd} postprocesses the data which has been preprocessed by @code{prestd}.
-## @end deftypefn
-
-## @seealso{prestd,trastd}
-
-## Author: Michel D. Schmid
-
-function [Pp,Tt] = poststd(Pn,meanp,stdp,Tn,meant,stdt)
-
-  ## check range of input arguments
-  error(nargchk(3,6,nargin))
-  if (nargin==4)
-    error("4 input arguments are not allowed!");
-  endif
-  if (nargin==5)
-    error("5 input arguments are not allowed!");
-  endif
-
-  ## do first inputs
-  ## set all standard deviations which are zero to 1
-  [nRowsII, nColumnsII] = size(Pn);
-  rowZeros = zeros(nRowsII,1);
-  findZeros = find(stdp==0);
-  rowZeros(findZeros)=1;
-  nequal = !rowZeros;
-  if (sum(rowZeros) != 0)
-    warning("Some standard deviations are zero. Those inputs won't be transformed.");
-    meanpZero = meanp.*nequal;
-    stdpZero = stdp.*nequal + 1*rowZeros;
-  else
-    meanpZero = meanp;
-    stdpZero = stdp;
-  endif
-
-  ## calculate the postprocessed inputs
-  nColumnsIIone = ones(1,nColumnsII);
-  Pp = (stdpZero*nColumnsIIone).*Pn + meanpZero*nColumnsIIone;
-
-  ## do also targets
-  if ( nargin==6 )
-    # now set all standard deviations which are zero to 1
-    [nRowsIII, nColumnsIII] = size(stdt);
-    rowZeros = zeros(nRowsIII,1);
-    findZeros = find(stdt==0);
-    rowZeros(findZeros)=1;
-    nequal = !rowZeros;
-    if (sum(rowZeros) != 0)
-      warning("Some standard deviations are zero. Those targets won't be transformed.");
-      meantZero = meant.*nequal;
-      stdtZero = stdt.*nequal + 1*rowZeros;
-    else
-      meantZero = meant;
-      stdtZero = stdt;
-    endif
-
-    ## calculate the postprocessed targets
-    nColumnsIIIone = ones(1,nColumnsIII);
-    Tt = (stdtZero*nColumnsIIIone).*Tn + meantZero*nColumnsIIIone;
-  endif
-
-endfunction
--- a/main/nnet/inst/prestd.m	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,98 +0,0 @@
-## Copyright (C) 2005 Michel D. Schmid  <michael.schmid@plexso.com>
-##
-## 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 3 of the License, 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; if not, see <http://www.gnu.org/licenses/>.
-
-## -*- texinfo -*-
-## @deftypefn {Function File} {}[@var{pn},@var{meanp},@var{stdp},@var{tn},@var{meant},@var{stdt}] =prestd(@var{p},@var{t})
-## @code{prestd} preprocesses the data so that the mean is 0 and the standard deviation is 1.
-## @end deftypefn
-
-## @seealso{trastd}
-
-## Author: Michel D. Schmid
-
-function [pn,meanp,stdp,tn,meant,stdt] = prestd(Pp,Tt)
-
-  ## inital description
-  ## prestd(p,t)
-  ##  * p are the general descriptions for the inputs of
-  ##    neural networks
-  ##  * t is written for "targets" and these are the outputs
-  ##    of a neural network
-
-  ## some more detailed description:
-  ## for more informations about this
-  ## formula programmed in this file, see:
-  ## 1. http://en.wikipedia.org/wiki/Standard_score
-  ## 2. http://www.statsoft.com/textbook/stathome.html
-  ##    choose "statistical glossary", choose "standardization"
-
-  ## check range of input arguments
-  error(nargchk(1,2,nargin))
-
-  ## do first inputs
-  meanp = mean(Pp')';
-  stdp = std(Pp')';
-  [nRows,nColumns]=size(Pp);
-  rowOnes = ones(1,nColumns);
-
-  ## now set all standard deviations which are zero to 1
-  [nRowsII, nColumnsII] = size(stdp);
-  rowZeros = zeros(nRowsII,1); # returning a row containing only zeros
-  findZeros = find(stdp==0); # returning a vector containing index where zeros are
-  rowZeros(findZeros)=1; #
-  nequal = !rowZeros;
-  if (sum(rowZeros) != 0)
-    warning("Some standard deviations are zero. Those inputs won't be transformed.");
-    meanpZero = meanp.*nequal;
-    stdpZero = stdp.*nequal + 1*rowZeros;
-  else
-    meanpZero = meanp;
-    stdpZero = stdp;
-  endif
-
-  ## calculate the standardized inputs
-  pn = (Pp-meanpZero*rowOnes)./(stdpZero*rowOnes);
-
-  ## do also targets
-  if ( nargin==2 )
-    meant = mean(Tt')';
-    stdt = std(Tt')';
-
-    ## now set all standard deviations which are zero to 1
-    [nRowsIII, nColumnsIII] = size(stdt);
-    rowZeros = zeros(nRowsIII,1);
-    findZeros = find(stdt==0);
-    rowZeros(findZeros)=1;
-    nequal = !rowZeros;
-    if (sum(rowZeros) != 0)
-      warning("Some standard deviations are zero. Those targets won't be transformed.");
-      meantZero = meant.*nequal;
-      stdtZero = stdt.*nequal + 1*rowZeros;
-    else
-      meantZero = meant;
-      stdtZero = stdt;
-    endif
-
-    ## calculate the standardized targets
-    tn = (Tt-meantZero*rowOnes)./(stdtZero*rowOnes);
-  endif
-endfunction
-
-
-%!shared Pp, Tt, pn
-%!  Pp = [1 2 3 4; -1 3 2 -1];
-%!  Tt = [3 4 5 6];
-%!  [pn,meanp,stdp] = prestd(Pp);
-%!assert(pn,[-1.16190 -0.38730 0.38730 1.16190; -0.84887 1.09141 0.60634 -0.84887],0.00001);
--- a/main/nnet/inst/private/__analyzerows.m	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,115 +0,0 @@
-## Copyright (C) 2008 Michel D. Schmid  <michael.schmid@plexso.com>
-##
-## 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 3 of the License, 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; if not, see <http://www.gnu.org/licenses/>.
-
-## -*- texinfo -*-
-## @deftypefn {Function File} {} @var{retmatrix} = __analyzerows(@var{matrix})
-## @code{__analyzerows} takes a matrix as input argument and checks what kind of
-## data are contained in the rows.
-##   a.) binary values? Means the row contains only 0 and 1
-##   b.) unique values?
-##   c.) Min values are several times contained in the row
-##   d.) Max values are several times contained in the row
-## @end deftypefn
-
-## Author: mds
-
-function retmatrix = __analyzerows(matrix)
-
-  ## check number of inputs
-  error(nargchk(1,1,nargin));
-
-  nRows = size(matrix,1);   # get number or rows
-  retmatrix = zeros(nRows,4);
-  doneVec = zeros(nRows,1);
-
-  ## now let's check which rows are binary
-  i = 1;
-  while (i <= nRows)
-    vec = matrix(i,:);
-    n1 = find(vec==1);
-    n0 = find(vec==0);
-    if (length(n1)==0 || length(n0)==0)
-      #do nothing
-    else
-      if (length(vec)==(length(n1)+length(n0)))
-        # in this case, the vector contains only ones and zeros
-        retmatrix(i,1) = 1;
-        doneVec(i) = 1;
-      endif
-    endif
-    i += 1;
-  endwhile
-
-  ## now let's check which rows are unique
-  i = 1;
-  while (i <= nRows)
-    if (doneVec(i)==0)
-      vec = matrix(i,:);
-      n1 = find(vec==vec(1));
-      if (length(vec)==(length(n1)))
-        # in this case, the vector contains only unique data
-        retmatrix(i,2) = 1;
-        doneVec(i) = 1;
-      endif
-    endif
-  i += 1;
-  endwhile
-
-
-  ## now let's check how often we can find the min value
-  i = 1;
-  while (i <= nRows)
-	if (doneVec(i)==0)
-      vec = matrix(i,:);
-      n1 = min(vec);
-	  retmatrix(i,3) = length(find(n1==vec));
-	endif
-  i += 1;
-  endwhile
-
-  ## now let's check how often we can find the max value
-  i = 1;
-  while (i <= nRows)
-	if (doneVec(i)==0)
-      vec = matrix(i,:);
-      n1 = max(vec);
-	  retmatrix(i,4) = length(find(n1==vec));
-	endif
-  i += 1;
-  endwhile
-
-endfunction
-
-%!shared b, retmat
-%! disp("testing __analyzerows")
-%! b = [1 0 0 1; 1 0 0 0; 1 2 0 1];
-%! retmat = __analyzerows(b);
-%!assert(retmat(1,1)==1);#%!assert(retmat(1,1)==1);
-%!assert(retmat(2,1)==1);
-%!assert(retmat(3,1)==0);
-%! b = [1 0 0 2; 1 0 0 0; 1 1 1 1];
-%! retmat = __analyzerows(b);
-%!assert(retmat(1,2)==0);
-%!assert(retmat(2,2)==0);
-%!assert(retmat(3,2)==1);
-%! b = [1 0 0 2; 1 0 0 0; 1 1 1 1];
-%! retmat = __analyzerows(b);
-%!assert(retmat(1,3)==2);
-%!assert(retmat(2,3)==0);
-%!assert(retmat(3,3)==0);
-%! retmat = __analyzerows(b);
-%!assert(retmat(1,4)==1);
-%!assert(retmat(2,4)==0);
-%!assert(retmat(3,4)==0);
--- a/main/nnet/inst/private/__calcjacobian.m	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,281 +0,0 @@
-## Copyright (C) 2006 Michel D. Schmid   <michael.schmid@plexso.com>
-##
-## 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 3 of the License, 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; if not, see <http://www.gnu.org/licenses/>.
-
-## -*- texinfo -*-
-## @deftypefn {Function File} {}@var{Jj} = __calcjacobian (@var{net},@var{Im},@var{Nn},@var{Aa},@var{vE})
-## This function calculates the jacobian matrix. It's used inside the
-## Levenberg-Marquardt algorithm of the neural network toolbox.
-## PLEASE DO NOT USE IT ELSEWEHRE, it proparly will not work!
-## @end deftypefn
-
-## Author: Michel D. Schmid
-
-
-function [Jj] = __calcjacobian(net,Im,Nn,Aa,vE)
-
-  ## comment:
-  ## - return value Jj is jacobi matrix
-  ##   for this calculation, see "Neural Network Design; Hagan, Demuth & Beale page 12-45"
-
-
-  ## check range of input arguments
-  error(nargchk(5,5,nargin))
-
-  ## get signals from inside the network
-  bias  = net.b;
-
-  ## calculate some help matrices
-  mInputWeight = net.IW{1} * Im;
-  nLayers = net.numLayers;
-  for i=2:nLayers
-    mLayerWeight{i,1} = net.LW{i,i-1} * Aa{i-1,1};
-  endfor
-
-  ## calculate number of columns and rows in jacobi matrix
-  ## firstly, number of columns
-  a = ones(nLayers+1,1); # +1 is for the input
-  a(1) = net.inputs{1}.size;
-  for iLayers = 1:nLayers
-    a(iLayers+1) = net.layers{iLayers}.size;
-  endfor
-  nColumnsJacobiMatrix = 0;
-  for iLayers = 1:nLayers
-    nColumnsJacobiMatrix = (a(iLayers)+1)*a(iLayers+1) + nColumnsJacobiMatrix;
-  endfor
-  ## secondly, number of rows
-  ve = vE{nLayers,1};
-  nRowsJacobiMatrix = length(ve(:));
-
-
-  ## FIRST STEP -----------------------------------------------------
-  ## calculate the neuron outputs without the transfer function
-  ## - n1_1 = W^1*a_1^0+b^1: the ^x factor defined the xth train data set
-  ##   the _x factor defines the layer
-  ## **********  this datas should be hold in Nn
-  ## **********  should be calculated in "__calcperf"
-  ## **********  so Nn{1} means hidden layer
-  ## **********  so Nn{2} means second hidden layer or output layer
-  ## **********  and so on ...
-  ## END FIRST STEP -------------------------------------------------
-
-  ## now we can rerange the signals ... this will be done only for
-  ## matrix calculation ...
-  [nRowsError nColumnsError] = size(ve);
-  errorSize = size(ve(:),1); # this will calculate, if only one row
-  # of errors exist... in other words... two rows will be reranged to
-  # one row with the same number of elements.
-  rerangeIndex = floor([0:(errorSize-1)]/nRowsError)+1;
-  nLayers = net.numLayers;
-
-  for i = 1:nLayers
-    Nn{i,1} = Nn{i,1}(:,rerangeIndex);
-    Aa{i,1} = Aa{i,1}(:,rerangeIndex);
-    [nRows nColumns] = size(Nn{i,1});
-    bTemp = bias{i,1};
-    bias{i,1} = repmat(bTemp,1,nColumns);
-    bias{i,1} = bias{i,1}(:,rerangeIndex);
-  endfor
-  mInputWeight = mInputWeight(:,rerangeIndex);
-  for i=2:nLayers
-    mLayerWeight{i,1} = mLayerWeight{i,1}(:,rerangeIndex);
-  endfor
-  Im = Im(:,rerangeIndex);
-
-  ## define how the errors are connected
-  ## ATTENTION! this happens in row order...
-  numTargets = net.numTargets;
-  mIdentity = -eye(numTargets);
-  cols = size(mIdentity,2);
-  mIdentity = mIdentity(:,rem(0:(cols*nColumnsError-1),cols)+1);
-  errorConnect = cell(net.numLayers,1);
-  startPos = 0;
-  for i=net.numLayers
-    targSize = net.layers{i}.size;
-    errorConnect{i} = mIdentity(startPos+[1:targSize],:);
-    startPos = startPos + targSize;
-  endfor
-
-  ## SECOND STEP ----------------------------------------------
-  ## define and calculate the derivative matrix dF
-  ## - this is "done" by the two first derivative functions
-  ##   of the transfer functions
-  ##   e.g. __dpureline, __dtansig, __dlogsig and so on ...
-
-  ## calculate the sensitivity matrix tildeS
-  ## start at the end layer, this means of course the output layer,
-  ## the transfer function is selectable
-
-  ## for calculating the last layer
-  ## this should happen like following:
-  ## tildeSx = -dFx(n_x^x)
-  ## use mIdentity to calculate the number of targets correctly
-  ## for all other layers, use instead:
-  ## tildeSx(-1) = dF1(n_x^(x-1))(W^x)' * tildeSx;
-
-  for iLayers = nLayers:-1:1 # this will count from the last
-                             # layer to the first layer ...
-    n = Nn{iLayers}; # nLayers holds the value of the last layer...
-    ## which transfer function should be used?
-    if (iLayers==nLayers)
-      switch(net.layers{iLayers}.transferFcn)
-        case "radbas"
-          tildeSxTemp = __dradbas(n);
-        case "purelin"
-          tildeSxTemp = __dpurelin(n);
-        case "tansig"
-          n = tansig(n);
-          tildeSxTemp = __dtansig(n);
-        case "logsig"
-          n = logsig(n);
-          tildeSxTemp = __dlogsig(n);
-        otherwise
-          error(["transfer function argument: " net.layers{iLayers}.transferFcn  " is not valid!"])
-      endswitch
-      tildeSx{iLayers,1} = tildeSxTemp .* mIdentity;
-      n = bias{nLayers,1};
-      switch(net.layers{iLayers}.transferFcn)
-        case "radbas"
-          tildeSbxTemp = __dradbas(n);
-        case "purelin"
-          tildeSbxTemp = __dpurelin(n);
-        case "tansig"
-          n = tansig(n);
-          tildeSbxTemp = __dtansig(n);
-        case "logsig"
-          n = logsig(n);
-          tildeSbxTemp = __dlogsig(n);
-        otherwise
-          error(["transfer function argument: " net.layers{iLayers}.transferFcn  " is not valid!"])
-      endswitch
-      tildeSbx{iLayers,1} = tildeSbxTemp .* mIdentity;
-    endif
-
-    if (iLayers<nLayers)
-      dFx = ones(size(n));
-      switch(net.layers{iLayers}.transferFcn) ######## new lines ...
-        case "radbas"
-          nx = radbas(n);
-          dFx = __dradbas(nx);
-        case "purelin"
-	  nx = purelin(n);
-	  dFx = __dpurelin(nx);
-        case "tansig"         ######## new lines ...
-	  nx = tansig(n);
-	  dFx = __dtansig(nx);
-	case "logsig"    ######## new lines ...
-          nx = logsig(n);  ######## new lines ...
-	  dFx = __dlogsig(nx); ######## new lines ...
-	otherwise     ######## new lines ...
-	  error(["transfer function argument: " net.layers{iLayers}.transferFcn  " is not valid!"])######## new lines ...
-       endswitch ############# new lines ....
-	  LWtranspose = net.LW{iLayers+1,iLayers};
-      if iLayers<(nLayers-1)
-        mIdentity = -ones(net.layers{iLayers}.size,size(mIdentity,2));
-      endif
-
-      mTest = tildeSx{iLayers+1,1};
-      LWtranspose = LWtranspose' * mTest;
-      tildeSx{iLayers,1} = dFx .* LWtranspose;
-      tildeSxTemp = dFx .* LWtranspose;
-      tildeSbx{iLayers,1} = ones(size(nx)).*tildeSxTemp;
-    endif
-
-  endfor #  if iLayers = nLayers:-1:1
-  ## END SECOND STEP -------------------------------------------------
-
-  ## THIRD STEP ------------------------------------------------------
-  ## some problems occur if we have more than only one target... so how
-  ## does the jacobi matrix looks like?
-
-  ## each target will cause an extra row in the jacobi matrix, for
-  ## each training set..  this means, 2 targets --> double of rows in the
-  ## jacobi matrix ... 3 targets --> three times the number of rows like
-  ## with one target and so on.
-
-  ## now calculate jacobi matrix
-  ## to do this, define first the transposed of it
-  ## this makes it easier to calculate on the "batch" way, means all inputs
-  ## at the same time...
-  ## and it makes it easier to use the matrix calculation way..
-
-  JjTrans = zeros(nRowsJacobiMatrix,nColumnsJacobiMatrix)'; # transposed jacobi matrix
-
-  ## Weight Gradients
-  for i=1:net.numLayers
-    if i==1
-      newInputs = Im;
-      newTemps =  tildeSx{i,1};
-      gIW{i,1} = copyRows(newTemps,net.inputs{i}.size) .* copyRowsInt(newInputs,net.layers{i}.size);
-    endif
-    if i>1
-      Ad = cell2mat(Aa(i-1,1)');
-      newInputs = Ad;
-      newTemps = tildeSx{i,1};
-      gLW{i,1} = copyRows(newTemps,net.layers{i-1}.size) .* copyRowsInt(newInputs,net.layers{i}.size);
-    endif
-  endfor
-
-  for i=1:net.numLayers
-    [nRows, nColumns] = size(Im);
-    if (i==1)
-      nWeightElements = a(i)*a(i+1); # n inputs * n hidden neurons
-      JjTrans(1:nWeightElements,:) =  gIW{i}(1:nWeightElements,:);
-      nWeightBias = a(i+1);
-      start = nWeightElements;
-      JjTrans(start+1:start+nWeightBias,:) = tildeSbx{i,1};
-      start = start+nWeightBias;
-    endif
-    if (i>1)
-      nLayerElements = a(i)*a(i+1); # n hidden neurons * n output neurons
-      JjTrans(start+1:start+nLayerElements,:)=gLW{i}(1:nLayerElements,:);
-      start = start +  nLayerElements;
-      nLayerBias = a(i+1);
-      JjTrans(start+1:start+nLayerBias,:) = tildeSbx{i,1};
-      start = start + nLayerBias;
-    endif
-  endfor
-  Jj = JjTrans';
-  ## END THIRD STEP -------------------------------------------------
-
-
-#=======================================================
-#
-# additional functions
-#
-#=======================================================
-
-  function k = copyRows(k,m)
-    # make copies of the ROWS of Aa matrix
-
-    mRows = size(k,1);
-    k = k(rem(0:(mRows*m-1),mRows)+1,:);
-  endfunction
-
-# -------------------------------------------------------
-
-  function k = copyRowsInt(k,m)
-    # make copies of the ROWS of matrix with elements INTERLEAVED
-
-    mRows = size(k,1);
-    k = k(floor([0:(mRows*m-1)]/m)+1,:);
-  endfunction
-
-# =====================================================================
-#
-# END additional functions
-#
-# =====================================================================
-
-endfunction
--- a/main/nnet/inst/private/__calcperf.m	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,108 +0,0 @@
-## Copyright (C) 2006 Michel D. Schmid    <email: michael.schmid@plexso.com>
-##
-## 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 3 of the License, 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; if not, see <http://www.gnu.org/licenses/>.
-
-## -*- texinfo -*-
-## @deftypefn {Function File} {}[@var{perf}, @var{Ee}, @var{Aa}, @var{Nn}] = __calcperf (@var{net},@var{xx},@var{Im},@var{Tt})
-## @code{__calcperf} calculates the performance of a multi-layer neural network.
-## PLEASE DON'T USE IT ELSEWHERE, it proparly won't work.
-## @end deftypefn
-
-## Author: Michel D. Schmid
-
-
-function [perf,Ee,Aa,Nn] = __calcperf(net,xx,Im,Tt)
-
-  ## comment:
-  ## perf, net performance.. from input to output through the hidden layers
-  ## Aa, output values of the hidden and last layer (output layer)
-  ## is used for NEWFF network types
-
-  ## calculate bias terms
-  ## must have the same number of columns like the input matrix Im
-  [nRows, nColumns] = size(Im);
-  Btemp = cell(net.numLayers,1); # Btemp: bias matrix
-  ones1xQ = ones(1,nColumns);
-  for i= 1:net.numLayers
-    Btemp{i} = net.b{i}(:,ones1xQ);
-  endfor
-
-  ## shortcuts
-  IWtemp = cell(net.numLayers,net.numInputs,1);# IW: input weights ...
-  LWtemp = cell(net.numLayers,net.numLayers,1);# LW: layer weights ...
-  Aa = cell(net.numLayers,1);# Outputs hidden and output layer
-  Nn = cell(net.numLayers,1);# outputs before the transfer function
-  IW = net.IW; # input weights
-  LW = net.LW; # layer weights
-
-  ## calculate the whole network till outputs are reached...
-  for iLayers = 1:net.numLayers
-
-    ## calculate first input weights to weighted inputs..
-    ## this can be done with matrix calculation...
-    ## called "dotprod"
-    ## to do this, there must be a special matrix ...
-    ## e.g.  IW = [1 2 3 4 5; 6 7 8 9 10] * [ 1 2 3; 4 5 6; 7 8 9; 10 11 12; 1 2 3];
-    if (iLayers==1)
-      IWtemp{iLayers,1} = IW{iLayers,1} * Im;
-      onlyTempVar = [IWtemp(iLayers,1) Btemp(iLayers)];
-    else
-      IWtemp{iLayers,1} = [];
-    endif
-
-    ## now calculate layer weights to weighted layer outputs
-    if (iLayers>1)
-      Ad = Aa{iLayers-1,1};
-      LWtemp{iLayers,1} = LW{iLayers,iLayers-1} * Ad;
-      onlyTempVar = [LWtemp(iLayers,1) Btemp(iLayers)];
-    else
-      LWtemp{iLayers,1} = [];
-    endif
-
-    Nn{iLayers,1} = onlyTempVar{1};
-    for k=2:length(onlyTempVar)
-      Nn{iLayers,1} = Nn{iLayers,1} + onlyTempVar{k};
-    endfor
-
-    ## now calculate with the transfer functions the layer output
-    switch net.layers{iLayers}.transferFcn
-    case "purelin"
-      Aa{iLayers,1} = purelin(Nn{iLayers,1});
-    case "tansig"
-      Aa{iLayers,1} = tansig(Nn{iLayers,1});
-    case "logsig"
-      Aa{iLayers,1} = logsig(Nn{iLayers,1});
-    otherwise
-      error(["Transfer function: " net.layers{iLayers}.transferFcn " doesn't exist!"])
-    endswitch
-
-  endfor  # iLayers = 1:net.numLayers
-
-  ## now calc network error
-  Ee = cell(net.numLayers,1);
-
-  for i=net.numLayers
-    Ee{i,1} = Tt{i,1} - Aa{i,1};# Tt: target
-    # Ee will be the error vector cell array
-  endfor
-
-  ## now calc network performance
-  switch(net.performFcn)
-  case "mse"
-    perf = __mse(Ee);
-  otherwise
-    error("for performance functions, only mse is currently valid!")
-  endswitch
-
-endfunction
--- a/main/nnet/inst/private/__checknetstruct.m	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,47 +0,0 @@
-## Copyright (C) 2006 Michel D. Schmid  <michael.schmid@plexso.com>
-##
-## 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 3 of the License, 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; if not, see <http://www.gnu.org/licenses/>.
-
-## -*- texinfo -*-
-## @deftypefn {Function File} {}[@var{isTrue}] = __checknetstruct (@var{net})
-## This function will check if a valid structure seems to be a neural network
-## structure
-##
-## @noindent
-##
-## left side arguments:
-## @noindent
-##
-## right side arguments:
-## @noindent
-##
-##
-## @noindent
-## are equivalent.
-## @end deftypefn
-
-## @seealso{newff,prestd,trastd}
-
-## Author: Michel D. Schmid
-
-
-function isTrue = __checknetstruct(net)
-
-  isTrue = 0;
-  ## first check, if it's a structure
-  if (isstruct(net) && isfield(net,"networkType"))
-    isTrue = 1;
-  endif
-
-endfunction
--- a/main/nnet/inst/private/__copycoltopos1.m	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,43 +0,0 @@
-## Copyright (C) 2008 Michel D. Schmid  <michael.schmid@plexso.com>
-##
-## 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 3 of the License, 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; if not, see <http://www.gnu.org/licenses/>.
-
-## -*- texinfo -*-
-## @deftypefn {Function File} {} @var{retmatrix} = __copycoltopos1(@var{matrix},@var{colIndex})
-## @code{__copycoltopos1} copies the column of position colIndex to the first position.
-## Moves the rest of the matrix one position to the right.
-## @end deftypefn
-
-## Author: mds
-
-function retmatrix = __copycoltopos1(matrix,colIndex)
-
-  ## check number of inputs
-  error(nargchk(2,2,nargin));
-
-  temp = matrix(:,colIndex);
-  matrix(:,colIndex) = []; # delete col
-  retmatrix = [temp matrix ];
-
-endfunction
-
-%!shared a, retmat
-%! disp("testing __copycoltopos1")
-%! a = [0 1 2 3 4; 5 6 7 8 9];
-%! retmat = __copycoltopos1(a,3);
-%!assert(retmat(1,1)==2);
-%!assert(retmat(2,1)==7);
-%! retmat = __copycoltopos1(a,5);
-%!assert(retmat(1,1)==4);
-%!assert(retmat(2,1)==9);
--- a/main/nnet/inst/private/__dlogsig.m	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,30 +0,0 @@
-## Copyright (C) 2007 Michel D. Schmid <michael.schmid@plexso.com>
-##
-## 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 3 of the License, 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; if not, see <http://www.gnu.org/licenses/>.
-
-## -*- texinfo -*-
-## @deftypefn {Function File} {}[@var{a} = __dlogsig (@var{n})
-##
-## @end deftypefn
-
-## @seealso{__dpurelin,__dtansig}
-
-## Author: Michel D. Schmid
-
-
-function a = __dlogsig(n)
-
-  a = n.*(1-n);
-
-endfunction
--- a/main/nnet/inst/private/__dpurelin.m	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,33 +0,0 @@
-## Copyright (C) 2005 Michel D. Schmid  <michael.schmid@plexso.com>
-##
-## 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 3 of the License, 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; if not, see <http://www.gnu.org/licenses/>.
-
-## -*- texinfo -*-
-## @deftypefn {Function File} {} @var{a} = __dpurelin (@var{n})
-## @code{dpurelin}, first derivative of purelin
-## @example
-##
-## purelin is a linear transfer function used by neural networks
-## @end example
-##
-## @end deftypefn
-
-## Author: Michel D. Schmid
-
-function a = __dpurelin(n)
-
-   [nRows, nColumns] = size(n);
-   a = ones(nRows,nColumns);
-
-endfunction
--- a/main/nnet/inst/private/__dradbas.m	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,42 +0,0 @@
-## Copyright (C) 2010 Michel D. Schmid <michael.schmid@plexso.com>
-##
-## 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 3 of the License, 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; if not, see <http://www.gnu.org/licenses/>.
-
-## -*- texinfo -*-
-## @deftypefn {Function File} {} __dradbas (@var{n})
-## First derivative of the radial basis transfer function.
-##
-## @code{__dradbas(n) = exp(-n^2)*-2*x}
-##
-## @end deftypefn
-
-## Author: Michel D. Schmid
-
-
-function retval = __dradbas (n)
-
-  if (nargin != 1)
-    print_usage ();
-  else
-    retval = exp (-n^2)*(-2)*x;
-    # the derivative of exp(-n^2) must be calculated
-    # with help of the chain-rule!
-    # d/dx of e^x = e^x
-    # d/dx of -x^2 = -2x
-    # now calculate the product of both
-  endif
-endfunction
-
-
-#%!assert (radbas (3), exp (-3^2));
--- a/main/nnet/inst/private/__dtansig.m	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,36 +0,0 @@
-## Copyright (C) 2006 Michel D. Schmid  <michael.schmid@plexso.com>
-##
-## 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 3 of the License, 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; if not, see <http://www.gnu.org/licenses/>.
-
-## -*- texinfo -*-
-## @deftypefn {Function File} {}[@var{n} = __dtansig (@var{n})
-## first derivative of @code{tansig}
-##
-## @example
-##
-## tansig is a symmetric non linear transfer function
-## used by neural networks.
-## Input n must be calculated with "n = tansig(n)".
-## @end example
-##
-## @end deftypefn
-
-## Author: Michel D. Schmid
-
-
-function a = __dtansig(n)
-
-  a = 1-(n.*n);
-
-endfunction
--- a/main/nnet/inst/private/__getx.m	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,52 +0,0 @@
-## Copyright (C) 2005 Michel D. Schmid     <michael.schmid@plexso.com>
-##
-## 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 3 of the License, 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; if not, see <http://www.gnu.org/licenses/>.
-
-## -*- texinfo -*-
-## @deftypefn {Function File} {} @var{x} = __getx (@var{net})
-## @code{__getx} will rerange the weights in one columns vector.
-##
-##
-## @noindent
-## @end deftypefn
-
-
-## Author: Michel D. Schmid
-
-function x = __getx(net)
-
-  ## check number of inputs
-  error(nargchk(1,1,nargin));
-
-  ## check input args
-  ## check "net", must be a net structure
-  if !__checknetstruct(net)
-    error("Structure doesn't seem to be a neural network")
-  endif
-
-  ## inputs
-  x = net.IW{1,1}(:);
-  x = [x; net.b{1}(:)];
-
-  nNumLayers = net.numLayers;
-  for iLayers = 2:nNumLayers # 1 would be the input layer
-
-    ## layers
-    x = [x; net.LW{iLayers,iLayers-1}(:)];
-    x = [x; net.b{iLayers}(:)];
-
-  endfor
-
-
-endfunction
--- a/main/nnet/inst/private/__init.m	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,82 +0,0 @@
-## Copyright (C) 2005 Michel D. Schmid  <michael.schmid@plexso.com>
-##
-## 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 3 of the License, 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; if not, see <http://www.gnu.org/licenses/>.
-
-## -*- texinfo -*-
-## @deftypefn {Function File} {} @var{net} = __init (@var{net})
-## @code{__init} initializes a neural network. This will be done
-## with the function @code{rand} from octave.
-##
-## @example
-## net = __init(net);
-## @end example
-##
-## This function takes the octave function "rand" to init the
-## neural network weights.
-##
-## @noindent
-## @end deftypefn
-
-
-## Author: Michel D. Schmid
-
-function net=__init(net)
-
-  ## check number of inputs
-  error(nargchk(1,1,nargin));
-
-  ## check input
-  if ( !__checknetstruct(net) )
-    error("__init: wrong argument type, must be a structure!");
-  endif
-
-
-  if (strcmp(net.networkType,"newff"))
-
-    ## init with random numbers between +-1
-    ## input weight layer
-    mRand = rand(net.layers{1}.size,net.inputs{1}.size);
-    net.IW{1} = mRand*2-1;
-
-    ## hidden layers
-    nLayers = net.numLayers;
-    for i=2:nLayers
-      mRand = rand(net.layers{i}.size,net.layers{i-1}.size);
-      net.LW{i,i-1} = mRand*2-1;
-    endfor
-    for i=1:nLayers
-      mRand = rand(net.biases{i}.size,1);
-      net.b{i} = mRand*2-1;
-    endfor
-  elseif (strcmp(net.networkType,"newp"))
-
-    ## init with zeros
-    inputRows = size(net.inputs{1,1}.range,1);
-    net.IW{1} = zeros(inputRows,1);
-    net.b{1} = zeros(1,1);
-  endif
-
-  ## warn user of constant inputs
-  for i=1:net.numInputs
-    prange = net.inputs{i}.range;
-    if (any(prange(:,1) == prange(:,2)))
-      fprintf("\n")
-      fprintf("** Warning in INIT\n")
-      fprintf("** Network net.inputs{%g}.range has a row with equal min and max values.\n",i)
-      fprintf("** Constant inputs do not provide useful information.\n")
-      fprintf("\n")
-    end
-  end
-
-endfunction
--- a/main/nnet/inst/private/__mae.m	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,51 +0,0 @@
-## Copyright (C) 2007 Michel D. Schmid  <michael.schmid@plexso.com>
-##
-## 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 3 of the License, 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; if not, see <http://www.gnu.org/licenses/>.
-
-## -*- texinfo -*-
-## @deftypefn {Function File} {}@var{perf} = __mae (@var{E})
-## @code{__mse} returns the Mean-Square-Error of a vector E
-##
-## @example
-##
-## This function is used to calculate the perceptron performance
-## @end example
-##
-## @end deftypefn
-
-## @seealso{__mse}
-
-## Author: Michel D. Schmid
-
-function perf = __mae(E)
-
-  ## check number of inputs
-  error(nargchk(1,1,nargin));
-
-  if iscell(E)
-    perf = 0;
-    elements = 0;
-    for i=1:size(E,1)
-      for j=1:size(E,2)
-        perf = perf + sum(sum(E{i,j}.^2));
-        elements = elements + prod(size(E{i,j}));
-      endfor
-    endfor
-    perf = perf / elements;
-  else
-    error("Error vector should be a cell array!")
-  endif
-
-
-endfunction
--- a/main/nnet/inst/private/__mse.m	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,51 +0,0 @@
-## Copyright (C) 2005 Michel D. Schmid  <michael.schmid@plexso.com>
-##
-## 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 3 of the License, 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; if not, see <http://www.gnu.org/licenses/>.
-
-## -*- texinfo -*-
-## @deftypefn {Function File} {}@var{perf} = __mse (@var{E})
-## @code{__mse} returns the Mean-Square-Error of a vector E
-##
-## @example
-##
-## This function is used to calculate the network performance
-## @end example
-##
-## @end deftypefn
-
-## @seealso{__mae}
-
-## Author: Michel D. Schmid
-
-function perf = __mse(E)
-
-  ## check number of inputs
-  error(nargchk(1,1,nargin));
-
-  if iscell(E)
-    perf = 0;
-    elements = 0;
-    for i=1:size(E,1)
-      for j=1:size(E,2)
-        perf = perf + sum(sum(E{i,j}.^2));
-        elements = elements + prod(size(E{i,j}));
-      endfor
-    endfor
-    perf = perf / elements;
-  else
-    error("Error vector should be a cell array!")
-  endif
-
-
-endfunction
--- a/main/nnet/inst/private/__newnetwork.m	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,193 +0,0 @@
-## Copyright (C) 2005 Michel D. Schmid  <michael.schmid@plexso.com>
-##
-## 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 3 of the License, 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; 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
--- a/main/nnet/inst/private/__optimizedatasets.m	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,87 +0,0 @@
-## Copyright (C) 2008 Michel D. Schmid  <michael.schmid@plexso.com>
-##
-## 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 3 of the License, 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; if not, see <http://www.gnu.org/licenses/>.
-
-## -*- texinfo -*-
-## @deftypefn {Function File} {} @var{retmatrix} = __optimizedatasets (@var{matrix},@var{nTrainSets},@var{nTargets},@var{bRand})
-## @code{__optimizedatasets} reranges the data sets depending on the input arguments.
-## @code{matrix} is the data set matrix containing inputs and outputs (targets) in row order.
-## This means for example: the first three rows are inputs and the fourth row is an output row.
-## The second argument is used in the optimizing algorithm. All cols with min and max values must
-## be in the range of the train data sets. The third argument defines how much rows are equal to the
-## neural network targets. These rows must be at the end of the data set!
-## The fourth arguemnt is optional and defines if the data sets have to be randomised before
-## optimizing.
-## Default value for bRand is 1, means randomise the columns.
-## @end deftypefn
-
-## Author: mds
-
-function retmatrix = __optimizedatasets(matrix,nTrainSets,nTargets,bRand)
-
-  ## check number of inputs
-  error(nargchk(3,4,nargin));
-
-  # set default values
-  bRandomise = 1;
-
-  if (nargin==4)
-    bRandomise = bRand;
-  endif
-
-  # if needed, randomise the cols
-  if (bRandomise)
-    matrix = __randomisecols(matrix);
-  endif
-
-  # analyze matrix, which row contains what kind of data?
-  # a.) binary values? Means the row contains only 0 and 1
-  # b.) unique values?
-  # c.) Min values are several times contained in the row
-  # d.) Max values are several times contained in the row
-  matrix1 = matrix(1:end-nTargets,:);
-  analyzeMatrix = __analyzerows(matrix1);
-
-  # now sort "matrix" with help of analyzeMatrix
-  # following conditions must be kept:
-  # a.) rows containing unique values aren't sorted!
-  # b.) sort first rows which contains min AND max values only once
-  # c.) sort secondly rows which contains min OR max values only once
-  # d.) at last, sort binary data if still needed!
-  retmatrix = __rerangecolumns(matrix,analyzeMatrix,nTrainSets);
-
-
-endfunction
-
-%!shared retmatrix, matrix
-%! disp("testing __optimizedatasets")
-%! matrix = [1 2 3 2 1 2 3 0 5 4 3 2 2 2 2 2 2; \
-%!			 0 1 1 0 0 0 0 0 0 0 0 0 0 0 1 1 0; \
-%!			-1 3 2 4 9 1 1 1 1 1 9 1 1 1 9 9 0; \
-%!			 2 3 2 3 2 2 2 2 3 3 3 3 1 1 1 1 1];
-%! ## The last row is equal to the neural network targets
-%! retmatrix = __optimizedatasets(matrix,9,1);
-%! ## the above statement can't be tested with assert!
-%! ## it contains random values! So pass a "success" message
-%!assert(1==1);
-%! matrix = [1 2 3 2 1 2 3 0 5 4 3 2 2 2 2 2 2; \
-%!			 0 1 1 0 0 0 0 0 0 0 0 0 0 0 1 1 0; \
-%!			-1 3 2 4 9 1 1 1 1 1 9 1 1 1 9 9 0; \
-%!			 2 3 2 3 2 2 2 2 3 3 3 3 1 1 1 1 1];
-%! ## The last row is equal to the neural network targets
-%! retmatrix = __optimizedatasets(matrix,9,1,0);
-%!assert(retmatrix(1,1)==5);
-%!assert(retmatrix(2,1)==0);
-%!assert(retmatrix(3,1)==1);
-%!assert(retmatrix(4,1)==3);
--- a/main/nnet/inst/private/__printAdaptFcn.m	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,34 +0,0 @@
-## Copyright (C) 2006 Michel D. Schmid <michael.schmid@plexso.com>
-##
-## 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 3 of the License, 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; if not, see <http://www.gnu.org/licenses/>.
-
-## -*- texinfo -*-
-## @deftypefn {Function File} {} __printAdaptFcn (@var{fid})
-## @code{printMLPHeader} saves the header of a  neural network structure
-## to a *.txt file with identification @code{fid}.
-## @end deftypefn
-
-## Author: Michel D. Schmid
-
-function __printAdaptFcn(fid,net)
-
-  if isfield(net,"adaptFcn")
-    if isempty(net.adaptFcn)
-      fprintf(fid,"            adaptFcn:  '%s'\n","empty");
-    else
-      fprintf(fid,"            adaptFcn:  '%s'\n",net.adaptFcn);
-    endif
-  endif
-
-endfunction
--- a/main/nnet/inst/private/__printAdaptParam.m	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,34 +0,0 @@
-## Copyright (C) 2006 Michel D. Schmid <michael.schmid@plexso.com>
-##
-## 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 3 of the License, 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; if not, see <http://www.gnu.org/licenses/>.
-
-## -*- texinfo -*-
-## @deftypefn {Function File} {} __printAdaptParam (@var{fid})
-## @code{printMLPHeader} saves the header of a  neural network structure
-## to a *.txt file with identification @code{fid}.
-## @end deftypefn
-
-## Author: Michel D. Schmid
-
-function __printAdaptParam(fid,net)
-
-  if isfield(net,"adaptParam")
-    if isempty(net.adaptParam)
-      fprintf(fid,"          adaptParam:  '%s'\n","not yet used item");
-    else
-      fprintf(fid,"          adaptParam:  '%s'\n",net.adaptParam);
-    endif
-  endif
-
-endfunction
--- a/main/nnet/inst/private/__printB.m	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,46 +0,0 @@
-## Copyright (C) 2006 Michel D. Schmid <michael.schmid@plexso.com>
-##
-## 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 3 of the License, 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; if not, see <http://www.gnu.org/licenses/>.
-
-## -*- texinfo -*-
-## @deftypefn {Function File} {} __printB (@var{fid})
-## @code{printMLPHeader} saves the header of a  neural network structure
-## to a *.txt file with identification @code{fid}.
-## @end deftypefn
-
-## Author: Michel D. Schmid
-
-function __printB(fid,net)
-
-  if isfield(net,"b")
-    nBiases = 0;
-    # check if it's cell array
-    if iscell(net.b)
-      [nRows, nColumns] = size(net.b);
-      for i=1:nRows
-        for k=1:nColumns
-          if !isempty(net.b{i,k})
-            nBiases = nBiases+1;
-          endif
-        endfor
-      endfor
-      # insert enough spaces to put ":" to position 20
-      # insert 2 spaces for distance between ":" and "%"
-      fprintf(fid,"                   b: {%dx%d cell} containing %d bias vectors\n",nRows,nColumns,nBiases);
-    else
-      fprintf(fid,"unsure if this is possible\n")
-    endif
-  endif
-
-endfunction
--- a/main/nnet/inst/private/__printBiasConnect.m	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,58 +0,0 @@
-## Copyright (C) 2006 Michel D. Schmid <michael.schmid@plexso.com>
-##
-## 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 3 of the License, 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; if not, see <http://www.gnu.org/licenses/>.
-
-## -*- texinfo -*-
-## @deftypefn {Function File} {} __printBiasConnect (@var{fid})
-## @code{printMLPHeader} saves the header of a  neural network structure
-## to a *.txt file with identification @code{fid}.
-## @end deftypefn
-
-## Author: Michel D. Schmid
-
-
-function __printBiasConnect(fid,net)
-
-  if isfield(net,"biasConnect")
-    # net.biasConnect can be a matrix..!
-    # check if it's a matrix
-    if isscalar(net.biasConnect)
-      error("unsure if this is possible..")
-    elseif isnumeric(net.biasConnect)
-      if ismatrix(net.biasConnect)
-        if issquare(net.biasConnect)
-             # nothing prgrammed till now
-        elseif isvector(net.biasConnect)
-          # insert enough spaces to put ":" to position 20
-          # insert 2 spaces for distance between ":" and "%"
-          # print bracket for open
-          fprintf(fid,"         biasConnect:  [");
-          [nRows nColumns] = size(net.biasConnect);
-          for k = 1:1:nRows
-            for i = 1:1:nColumns
-              fprintf(fid,"%d",net.biasConnect(i*k));
-            endfor
-            if k!=nRows
-              #print ; for newline in matrix
-              fprintf(fid,";");
-            endif
-          endfor
-          # print last bracket
-          fprintf(fid,"] not yet used item\n");
-        endif  # if issquare..
-      endif #if ismatrix
-    endif # isscalar(net.biasConnect)
-  endif  # if isfield(...)
-
-endfunction
--- a/main/nnet/inst/private/__printBiases.m	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,38 +0,0 @@
-## Copyright (C) 2006 Michel D. Schmid <michael.schmid@plexso.com>
-##
-## 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 3 of the License, 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; if not, see <http://www.gnu.org/licenses/>.
-
-## -*- texinfo -*-
-## @deftypefn {Function File} {} __printBiases (@var{fid})
-## @code{printMLPHeader} saves the header of a  neural network structure
-## to a *.txt file with identification @code{fid}.
-## @end deftypefn
-
-## Author: Michel D. Schmid
-
-function __printBiases(fid,net)
-
-  if isfield(net,"biases")
-    # check if it's cell array
-    if iscell(net.biases)
-      [nRows, nColumns] = size(net.biases);
-      # insert enough spaces to put ":" to position 20
-      # insert 2 spaces for distance between ":" and "%"
-      fprintf(fid,"              biases: {%dx%d cell} containing %d biases\n",nRows,nColumns,length(net.biases));
-    else
-      fprintf(fid,"unsure if this is possible\n");
-    endif
-  endif
-
-endfunction
--- a/main/nnet/inst/private/__printIW.m	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,46 +0,0 @@
-## Copyright (C) 2006 Michel D. Schmid <michael.schmid@plexso.com>
-##
-## 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 3 of the License, 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; if not, see <http://www.gnu.org/licenses/>.
-
-## -*- texinfo -*-
-## @deftypefn {Function File} {} __printIW (@var{fid})
-## @code{printMLPHeader} saves the header of a  neural network structure
-## to a *.txt file with identification @code{fid}.
-## @end deftypefn
-
-## Author: Michel D. Schmid
-
-function __printIW(fid,net)
-
-  if isfield(net,"IW")
-    nInputs = 0;
-    # check if it's cell array
-    if iscell(net.IW)
-      [nRows, nColumns] = size(net.IW);
-      for i=1:nRows
-        for k=1:nColumns
-          if !isempty(net.IW{i,k})
-            nInputs = nInputs+1;
-          endif
-        endfor
-      endfor
-      # insert enough spaces to put ":" to position 20
-      # insert 2 spaces for distance between ":" and "%"
-      fprintf(fid,"                  IW: {%dx%d cell} containing %d input weight matrix\n",nRows,nColumns,nInputs);
-    else
-      fprintf(fid,"unsure if this is possible\n");
-    endif
-  endif
-
-endfunction
--- a/main/nnet/inst/private/__printInitFcn.m	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,35 +0,0 @@
-## Copyright (C) 2006 Michel D. Schmid <michael.schmid@plexso.com>
-##
-## 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 3 of the License, 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; if not, see <http://www.gnu.org/licenses/>.
-
-## -*- texinfo -*-
-## @deftypefn {Function File} {} __printInitFcn (@var{fid})
-## @code{printMLPHeader} saves the header of a  neural network structure
-## to a *.txt file with identification @code{fid}.
-## @end deftypefn
-
-## Author: Michel D. Schmid
-
-
-function __printInitFcn(fid,net)
-
-  if isfield(net,"initFcn")
-    if isempty(net.initFcn)
-      fprintf(fid,"             initFcn:  '%s'\n","empty");
-    else
-      fprintf(fid,"             initFcn:  '%s'\n",net.initFcn);
-    endif
-  endif
-
-endfunction
--- a/main/nnet/inst/private/__printInitParam.m	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,34 +0,0 @@
-## Copyright (C) 2006 Michel D. Schmid <michael.schmid@plexso.com>
-##
-## 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 3 of the License, 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; if not, see <http://www.gnu.org/licenses/>.
-
-## -*- texinfo -*-
-## @deftypefn {Function File} {} __printInitParam (@var{fid})
-## @code{printMLPHeader} saves the header of a  neural network structure
-## to a *.txt file with identification @code{fid}.
-## @end deftypefn
-
-## Author: Michel D. Schmid
-
-function __printInitParam(fid,net)
-
-  if isfield(net,"initParam")
-    if isempty(net.initParam)
-      fprintf(fid,"           initParam:  '%s'\n","not yet used item");
-    else
-      fprintf(fid,"           initParam:  '%s'\n",net.initParam);
-    endif
-  endif
-
-endfunction
--- a/main/nnet/inst/private/__printInputConnect.m	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,57 +0,0 @@
-## Copyright (C) 2006 Michel D. Schmid <michael.schmid@plexso.com>
-##
-## 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 3 of the License, 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; if not, see <http://www.gnu.org/licenses/>.
-
-## -*- texinfo -*-
-## @deftypefn {Function File} {} __printInputConnect (@var{fid})
-## @code{printMLPHeader} saves the header of a  neural network structure
-## to a *.txt file with identification @code{fid}.
-## @end deftypefn
-
-## Author: Michel D. Schmid
-
-function __printInputConnect(fid,net)
-
-  if isfield(net,"inputConnect")
-    # net.inputConnect can be a matrix..!
-    # check if it's a matrix
-    if isscalar(net.inputConnect)
-      error("unsure if this is possible..")
-    elseif isnumeric(net.inputConnect)
-      if ismatrix(net.inputConnect)
-        if issquare(net.inputConnect)
-          # nothing prgrammed till now
-        elseif isvector(net.inputConnect)
-          # insert enough spaces to put ":" to position 20
-          # insert 2 spaces for distance between ":" and "%"
-          # print bracket for open
-          fprintf(fid,"        inputConnect:  [");
-          [nRows nColumns] = size(net.inputConnect);
-          for k = 1:1:nRows
-            for i = 1:1:nColumns
-              fprintf(fid,"%d",net.inputConnect(i*k));
-            endfor
-            if k!=nRows
-              #print ; for newline in matrix
-              fprintf(fid,";");
-            endif
-          endfor
-          # print last bracket
-          fprintf(fid,"] not yet used item\n");
-        endif  # if issquare..
-      endif #if ismatrix
-    endif
-  endif
-
-endfunction
--- a/main/nnet/inst/private/__printInputWeights.m	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,40 +0,0 @@
-## Copyright (C) 2006 Michel D. Schmid <michael.schmid@plexso.com>
-##
-## 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 3 of the License, 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; if not, see <http://www.gnu.org/licenses/>.
-
-## -*- texinfo -*-
-## @deftypefn {Function File} {} __printInputsWeights (@var{fid})
-## @code{printMLPHeader} saves the header of a  neural network structure
-## to a *.txt file with identification @code{fid}.
-## @end deftypefn
-
-## Author: Michel D. Schmid
-
-function __printInputWeights(fid,net)
-
-  if isfield(net,"inputweights")
-    # check if it's cell array
-    if iscell(net.inputweights)
-      [nRows, nColumns] = size(net.inputweights);
-      # insert enough spaces to put ":" to position 20
-      # insert 2 spaces for distance between ":" and "%"
-      fprintf(fid,"        inputweights: {%dx%d cell} containing xx input weight\n",nRows,nColumns);
-    else
-      fprintf(fid,"unsure if this is possible\n");
-    endif
-  else
-    fprintf(fid,"field inputweights not found & not yet used item\n");
-  endif
-
-endfunction
--- a/main/nnet/inst/private/__printInputs.m	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,49 +0,0 @@
-## Copyright (C) 2006 Michel D. Schmid <michael.schmid@plexso.com>
-##
-## 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 3 of the License, 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; if not, see <http://www.gnu.org/licenses/>.
-
-## -*- texinfo -*-
-## @deftypefn {Function File} {} __printInputs (@var{fid})
-## @code{printMLPHeader} saves the header of a  neural network structure
-## to a *.txt file with identification @code{fid}.
-## @end deftypefn
-
-## Author: Michel D. Schmid
-
-function __printInputs(fid,net)
-
-  if isfield(net,"inputs")
-    # check if it's cell array
-    if iscell(net.inputs)
-      [nRows, nColumns] = size(net.inputs);
-      # insert enough spaces to put ":" to position 20
-      # insert 2 spaces for distance between ":" and "%"
-      fprintf(fid,"              inputs: {%dx%d cell} of inputs\n",nRows,nColumns);
-    else
-      fprintf(fid,"unsure if this is possible\n");
-    endif
-
-  endif
-
-endfunction
-
-
-
-
-
-
-
-
-
-
--- a/main/nnet/inst/private/__printLW.m	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,46 +0,0 @@
-## Copyright (C) 2006 Michel D. Schmid  <michael.schmid@plexso.com>
-##
-## 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 3 of the License, 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; if not, see <http://www.gnu.org/licenses/>.
-
-## -*- texinfo -*-
-## @deftypefn {Function File} {} __printLW (@var{fid})
-## @code{printMLPHeader} saves the header of a  neural network structure
-## to a *.txt file with identification @code{fid}.
-## @end deftypefn
-
-## Author: Michel D. Schmid
-
-function __printLW(fid,net)
-
-  if isfield(net,"LW")
-    nLayers = 0;
-    # check if it's cell array
-    if iscell(net.LW)
-      [nRows, nColumns] = size(net.LW);
-      for i=1:nRows
-        for k=1:nColumns
-          if !isempty(net.LW{i,k})
-            nLayers = nLayers+1;
-          endif
-        endfor
-      endfor
-      # insert enough spaces to put ":" to position 20
-      # insert 2 spaces for distance between ":" and "%"
-      fprintf(fid,"                  LW: {%dx%d cell} containing %d layer weight matrix\n",nRows,nColumns,nLayers);
-    else
-      fprintf(fid,"unsure if this is possible\n");
-    endif
-  endif
-
-endfunction
--- a/main/nnet/inst/private/__printLayerConnect.m	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,77 +0,0 @@
-## Copyright (C) 2006 Michel D. Schmid  <michael.schmid@plexso.com>
-##
-## 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 3 of the License, 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; if not, see <http://www.gnu.org/licenses/>.
-
-## -*- texinfo -*-
-## @deftypefn {Function File} {} __printLayerConnect (@var{fid})
-## @code{printMLPHeader} saves the header of a  neural network structure
-## to a *.txt file with identification @code{fid}.
-## @end deftypefn
-
-## Author: Michel D. Schmid
-
-function __printLayerConnect(fid,net)
-
-  if isfield(net,"layerConnect")
-    # net.layerConnect can be a matrix..!
-    # check if it's a matrix
-    if isscalar(net.layerConnect)
-      error("unsure if this is possible..")
-    elseif isnumeric(net.layerConnect)
-      if ismatrix(net.layerConnect)
-        if issquare(net.layerConnect)
-          # insert enough spaces to put ":" to position 20
-          # insert 2 spaces for distance between ":" and "%"
-          fprintf(fid,"        layerConnect:  [");
-          [nRows nColumns] = size(net.layerConnect);
-          for k = 1:1:nRows
-            for i = 1:1:nColumns
-              if i<nColumns
-                fprintf(fid,"%d ",net.layerConnect(i*k));
-              else
-                fprintf(fid,"%d",net.layerConnect(i*k));
-              endif
-            endfor
-            if k!=nRows
-              #print ; for newline in matrix
-              fprintf(fid,";");
-            endif
-          endfor
-          # print last bracket
-          fprintf(fid,"] not yet used item\n");
-        elseif isvector(net.layerConnect)
-        # insert enough spaces to put ":" to position 20
-        # insert 2 spaces for distance between ":" and "%"
-        # print bracket for open
-          fprintf(fid,"        layerConnect:  [");
-          [nRows nColumns] = size(net.layerConnect);
-             for k = 1:1:nRows
-               for i = 1:1:nColumns
-                 fprintf(fid,"%d",net.layerConnect(i*k));
-               endfor
-               if k!=nRows
-                 #print ; for newline in matrix
-                 fprintf(fid,";");
-               endif
-             endfor
-             # print last bracket
-             fprintf(fid,"] not yet used item\n");
-           endif  # if issquare..
-         endif #if ismatrix
-      endif
-    else
-      fprintf(fid," ERROR...");
-    endif
-
-endfunction
--- a/main/nnet/inst/private/__printLayerWeights.m	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,40 +0,0 @@
-## Copyright (C) 2006 Michel D. Schmid  <michael.schmid@plexso.com>
-##
-## 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 3 of the License, 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; if not, see <http://www.gnu.org/licenses/>.
-
-## -*- texinfo -*-
-## @deftypefn {Function File} {} __printLayerWeights (@var{fid})
-## @code{printMLPHeader} saves the header of a  neural network structure
-## to a *.txt file with identification @code{fid}.
-## @end deftypefn
-
-## Author: Michel D. Schmid
-
-function __printLayerWeights(fid,net)
-
-  if isfield(net,"layerweights")
-    # check if it's cell array
-    if iscell(net.layerweights)
-      [nRows, nColumns] = size(net.layerweights);
-      # insert enough spaces to put ":" to position 20
-      # insert 2 spaces for distance between ":" and "%"
-      fprintf(fid,"        layerweights: {%dx%d cell} containing xx layer weight\n",nRows,nColumns);
-    else
-      fprintf(fid,"layerweights:unsure if this is possible\n");
-    endif
-  else
-     fprintf(fid,"field layerweights not found & not yet used item\n");
-  endif
-
-endfunction
--- a/main/nnet/inst/private/__printLayers.m	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,38 +0,0 @@
-## Copyright (C) 2006 Michel D. Schmid  <michael.schmid@plexso.com>
-##
-## 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 3 of the License, 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; if not, see <http://www.gnu.org/licenses/>.
-
-## -*- texinfo -*-
-## @deftypefn {Function File} {} __printLayers (@var{fid})
-## @code{printMLPHeader} saves the header of a  neural network structure
-## to a *.txt file with identification @code{fid}.
-## @end deftypefn
-
-## Author: Michel D. Schmid
-
-function __printLayers(fid,net)
-
-  if isfield(net,"layers")
-   # check if it's cell array
-    if iscell(net.layers)
-      [nRows, nColumns] = size(net.layers);
-      # insert enough spaces to put ":" to position 20
-      # insert 2 spaces for distance between ":" and "%"
-      fprintf(fid,"              layers: {%dx%d cell} of layers\n",nRows,nColumns);
-    else
-      fprintf(fid,"unsure if this is possible\n");
-    endif
-  endif
-
-endfunction
--- a/main/nnet/inst/private/__printMLPHeader.m	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,41 +0,0 @@
-## Copyright (C) 2006 Michel D. Schmid   <michael.schmid@plexso.com>
-##
-## 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 3 of the License, 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; if not, see <http://www.gnu.org/licenses/>.
-
-## -*- texinfo -*-
-## @deftypefn {Function File} {} __printMLPHeader (@var{fid})
-## @code{__printMLPHeader} saves the header of a  neural network structure
-## to a *.txt file with identification @code{fid}.
-## @end deftypefn
-
-## Author: Michel D. Schmid
-
-function __printMLPHeader(fid)
-
-     # one empty row
-     fprintf(fid,"\n");
-     # write "net="
-     fprintf(fid,"net=\n");
-     # next empty row
-     fprintf(fid,"\n");
-     # write "Neural Network object:", insert two spaces..
-     fprintf(fid,"  Neural Network object:\n");
-     # next empty row
-     fprintf(fid,"\n");
-     # write "architecture:", insert two spaces..
-     fprintf(fid,"  architecture:\n");
-     # one more time an empty row
-     fprintf(fid,"\n");
-
-endfunction
--- a/main/nnet/inst/private/__printNetworkType.m	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,34 +0,0 @@
-## Copyright (C) 2006 Michel D. Schmid   <michael.schmid@plexso.com>
-##
-## 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 3 of the License, 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; if not, see <http://www.gnu.org/licenses/>.
-
-## -*- texinfo -*-
-## @deftypefn {Function File} {} __printMLPHeader (@var{fid})
-## @code{__printMLPHeader} saves the header of a  neural network structure
-## to a *.txt file with identification @code{fid}.
-## @end deftypefn
-
-## Author: Michel D. Schmid
-
-function __printNetworkType(fid,net)
-
-  if isfield(net,"networkType")
-    if strcmp(net.networkType,"newff")
-      fprintf(fid,"          Network type:  '%s'\n","Feed forward multi-layer network");
-    else
-      fprintf(fid,"          Network type:  '%s'\n","error: undefined network type");
-    endif
-  endif
-
-endfunction
--- a/main/nnet/inst/private/__printNumInputDelays.m	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,40 +0,0 @@
-## Copyright (C) 2006 Michel D. Schmid  <michael.schmid@plexso.com>
-##
-## 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 3 of the License, 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; if not, see <http://www.gnu.org/licenses/>.
-
-## -*- texinfo -*-
-## @deftypefn {Function File} {} __printNumInputDelays (@var{fid})
-## @code{printMLPHeader} saves the header of a  neural network structure
-## to a *.txt file with identification @code{fid}.
-## @end deftypefn
-
-## Author: Michel D. Schmid
-
-
-function __printNumInputDelays(fid,net)
-
-     ## now check the structure fields..
-     cellNetFields = fieldnames(net);
-     # search for numInputDelays
-     if isfield(net,"numInputDelays")
-        # test on scalar
-        if isscalar(net.numInputDelays)
-           fprintf(fid,"      numInputDelays:  %d  (read-only)\n",net.numInputDelays);
-        # net.numInputDelays must be an integer... till now, 11-01-2006
-        else
-            error("numInputDelays must be a scalar value!");
-        endif
-     endif
-
-endfunction
--- a/main/nnet/inst/private/__printNumInputs.m	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,42 +0,0 @@
-## Copyright (C) 2006 Michel D. Schmid  <michael.schmid@plexso.com>
-##
-## 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 3 of the License, 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; if not, see <http://www.gnu.org/licenses/>.
-
-## -*- texinfo -*-
-## @deftypefn {Function File} {} __printNumInputs (@var{fid})
-## @code{printMLPHeader} saves the header of a  neural network structure
-## to a *.txt file with identification @code{fid}.
-## @end deftypefn
-
-## Author: Michel D. Schmid
-
-
-function __printNumInputs(fid,net)
-
-     ## now check the structure fields..
-     cellNetFields = fieldnames(net);
-     # search for numInputs
-     if isfield(net,"numInputs")
-        # test on scalar
-        if isscalar(net.numInputs)
-           # insert enough spaces to put ":" to position 20
-           # insert 2 spaces for distance between ":" and "%"
-           fprintf(fid,"           numInputs:  %d\n",net.numInputs);
-        # net.numInputs must be an integer... till now, 11-01-2006
-        else
-            error("numInputs must be a scalar value!");
-        endif
-     endif
-
-endfunction
--- a/main/nnet/inst/private/__printNumLayerDelays.m	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,41 +0,0 @@
-## Copyright (C) 2006 Michel D. Schmid  <michael.schmid@plexso.com>
-##
-## 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 3 of the License, 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; if not, see <http://www.gnu.org/licenses/>.
-
-## -*- texinfo -*-
-## @deftypefn {Function File} {} __printNumLayerDelays (@var{fid})
-## @code{printMLPHeader} saves the header of a  neural network structure
-## to a *.txt file with identification @code{fid}.
-## @end deftypefn
-
-## Author: Michel D. Schmid
-
-function __printNumLayerDelays(fid,net)
-
-     ## now check the structure fields..
-     cellNetFields = fieldnames(net);
-     # search for numLayerDelays
-     if isfield(net,"numLayerDelays")
-        # test on scalar
-        if isscalar(net.numLayerDelays)
-          # insert enough spaces to put ":" to position 20
-          # insert 2 spaces for distance between ":" and "%"
-          fprintf(fid,"      numLayerDelays:  %d  (read-only)\n",net.numLayerDelays);
-        # net.numLayerDelays must be an integer... till now, 11-01-2006
-        else
-            error("numLayerDelays must be a scalar value!");
-        endif
-     endif
-
-endfunction
--- a/main/nnet/inst/private/__printNumLayers.m	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,37 +0,0 @@
-## Copyright (C) 2006 Michel D. Schmid  <michael.schmid@plexso.com>
-##
-## 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 3 of the License, 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; if not, see <http://www.gnu.org/licenses/>.
-
-## -*- texinfo -*-
-## @deftypefn {Function File} {} __printNumLayers (@var{fid})
-## @code{printMLPHeader} saves the header of a  neural network structure
-## to a *.txt file with identification @code{fid}.
-## @end deftypefn
-
-## Author: Michel D. Schmid
-
-function __printNumLayers(fid,net)
-
-     if isfield(net,"numLayers")
-        if isscalar(net.numLayers)
-					 # insert enough spaces to put ":" to position 20
-           # insert 2 spaces for distance between ":" and "%"
-           fprintf(fid,"           numLayers:  %d\n",net.numLayers);
-        # net.numLayers must be an integer... till now, 11-01-2006
-        else
-            error("numLayers must be a scalar value!");
-        endif
-     endif
-
-endfunction
--- a/main/nnet/inst/private/__printNumOutputs.m	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,41 +0,0 @@
-## Copyright (C) 2006 Michel D. Schmid  <michael.schmid@plexso.com>
-##
-## 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 3 of the License, 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; if not, see <http://www.gnu.org/licenses/>.
-
-## -*- texinfo -*-
-## @deftypefn {Function File} {} __printNumOutputs (@var{fid})
-## @code{printMLPHeader} saves the header of a  neural network structure
-## to a *.txt file with identification @code{fid}.
-## @end deftypefn
-
-## Author: Michel D. Schmid
-
-function __printNumOutputs(fid,net)
-
-     ## now check the structure fields..
-     cellNetFields = fieldnames(net);
-     # search for numOutputs
-     if isfield(net,"numOutputs")
-        # test on scalar
-        if isscalar(net.numOutputs)
-          # insert enough spaces to put ":" to position 20
-          # insert 2 spaces for distance between ":" and "%"
-          fprintf(fid,"          numOutputs:  %d  (read-only)\n",net.numOutputs);
-        # net.numOutputs must be an integer... till now, 11-01-2006
-        else
-            error("numOutputs must be a scalar value!");
-        endif
-     endif
-
-endfunction
--- a/main/nnet/inst/private/__printNumTargets.m	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,41 +0,0 @@
-## Copyright (C) 2006 Michel D. Schmid  <michael.schmid@plexso.com>
-##
-## 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 3 of the License, 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; if not, see <http://www.gnu.org/licenses/>.
-
-## -*- texinfo -*-
-## @deftypefn {Function File} {} __printNumTargets (@var{fid})
-## @code{printMLPHeader} saves the header of a  neural network structure
-## to a *.txt file with identification @code{fid}.
-## @end deftypefn
-
-## Author: Michel D. Schmid
-
-function __printNumTargets(fid,net)
-
-     ## now check the structure fields..
-     cellNetFields = fieldnames(net);
-     # search for numTargets
-     if isfield(net,"numTargets")
-        # test on scalar
-        if isscalar(net.numTargets)
-          # insert enough spaces to put ":" to position 20
-          # insert 2 spaces for distance between ":" and "%"
-          fprintf(fid,"          numTargets:  %d  (read-only)\n",net.numTargets);
-        # net.numTargets must be an integer... till now, 11-01-2006
-        else
-            error("numTargets must be a scalar value!");
-        endif
-     endif
-
-endfunction
--- a/main/nnet/inst/private/__printOutputConnect.m	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,79 +0,0 @@
-## Copyright (C) 2006 Michel D. Schmid  <michael.schmid@plexso.com>
-##
-## 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 3 of the License, 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; if not, see <http://www.gnu.org/licenses/>.
-
-## -*- texinfo -*-
-## @deftypefn {Function File} {} __printOutputConnect (@var{fid})
-## @code{printMLPHeader} saves the header of a  neural network structure
-## to a *.txt file with identification @code{fid}.
-## @end deftypefn
-
-## Author: Michel D. Schmid
-
-function __printOutputConnect(fid,net)
-
-  if isfield(net,"outputConnect")
-    # net.outputConnect can be a matrix..!
-    # check if it's a matrix
-    if isscalar(net.outputConnect)
-      error("unsure if this is possible..")
-    elseif isnumeric(net.outputConnect)
-      if ismatrix(net.outputConnect)
-        if issquare(net.outputConnect)
-          fprintf(fid,"       outputConnect:  [");
-          [nRows nColumns] = size(net.outputConnect);
-          for k = 1:1:nRows
-            for i = 1:1:nColumns
-              if i<nColumns
-                fprintf(fid,"%d ",net.outputConnect(i*k));
-              else
-                fprintf(fid,"%d",net.outputConnect(i*k));
-              endif
-            endfor
-            if k!=nRows
-              #print ; for newline in matrix
-              fprintf(fid,";");
-            endif
-          endfor
-          # print last bracket
-          fprintf(fid,"]\n");
-        elseif isvector(net.outputConnect)
-          # insert enough spaces to put ":" to position 20
-          # insert 2 spaces for distance between ":" and "%"
-          # print bracket for open
-          fprintf(fid,"       outputConnect:  [");
-          [nRows nColumns] = size(net.outputConnect);
-             for k = 1:1:nRows
-               for i = 1:1:nColumns
-                 if (i<nColumns)
-                   fprintf(fid,"%d ",net.outputConnect(i*k));
-                 else
-                   fprintf(fid,"%d",net.outputConnect(i*k));
-                 endif
-               endfor
-               if k!=nRows
-                 #print ; for newline in matrix
-                 fprintf(fid,";");
-               endif
-             endfor
-             # print last bracket
-             fprintf(fid,"] not yet used item\n");
-           endif  # if issquare..
-         endif #if ismatrix
-      endif
-    else
-      fprintf(fid," ERROR...");
-    endif
-
-endfunction
--- a/main/nnet/inst/private/__printOutputs.m	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,43 +0,0 @@
-## Copyright (C) 2006 Michel D. Schmid  <michael.schmid@plexso.com>
-##
-## 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 3 of the License, 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; if not, see <http://www.gnu.org/licenses/>.
-
-## -*- texinfo -*-
-## @deftypefn {Function File} {} __printOutputs (@var{fid})
-## @code{printMLPHeader} saves the header of a  neural network structure
-## to a *.txt file with identification @code{fid}.
-## @end deftypefn
-
-## Author: Michel D. Schmid
-
-
-function __printOutputs(fid,net)
-
-  if isfield(net,"outputs")
-    # check if it's cell array
-    if iscell(net.outputs)
-      [nRows, nColumns] = size(net.outputs);
-      # insert enough spaces to put ":" to position 20
-      # insert 2 spaces for distance between ":" and "%"
-      if (net.numOutputs>1)
-        fprintf(fid,"             outputs: {%dx%d cell} containing %d output\n",nRows,nColumns,net.numOutputs);
-      else
-        fprintf(fid,"             outputs: {%dx%d cell} containing %d output\n",nRows,nColumns,net.numOutputs);
-      endif
-    else
-      fprintf(fid,"unsure if this is possible\n");
-    endif
-  endif
-
-endfunction
--- a/main/nnet/inst/private/__printPerformFcn.m	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,34 +0,0 @@
-## Copyright (C) 2006 Michel D. Schmid  <michael.schmid@plexso.com>
-##
-## 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 3 of the License, 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; if not, see <http://www.gnu.org/licenses/>.
-
-## -*- texinfo -*-
-## @deftypefn {Function File} {} __printPerformFcn (@var{fid})
-## @code{printMLPHeader} saves the header of a  neural network structure
-## to a *.txt file with identification @code{fid}.
-## @end deftypefn
-
-## Author: Michel D. Schmid
-
-function __printPerformFcn(fid,net)
-
-  if isfield(net,"performFcn")
-    if isempty(net.performFcn)
-      fprintf(fid,"          performFcn:  '%s'\n","empty");
-    else
-      fprintf(fid,"          performFcn:  '%s'\n",net.performFcn);
-    endif
-  endif
-
-endfunction
--- a/main/nnet/inst/private/__printPerformParam.m	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,34 +0,0 @@
-## Copyright (C) 2006 Michel D. Schmid  <michael.schmid@plexso.com>
-##
-## 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 3 of the License, 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; if not, see <http://www.gnu.org/licenses/>.
-
-## -*- texinfo -*-
-## @deftypefn {Function File} {} printInputs (@var{fid})
-## @code{printMLPHeader} saves the header of a  neural network structure
-## to a *.txt file with identification @code{fid}.
-## @end deftypefn
-
-## Author: Michel D. Schmid
-
-function __printPerformParam(fid,net)
-
-  if isfield(net,"performParam")
-    if isempty(net.performParam)
-      fprintf(fid,"        performParam:  '%s'\n","not yet used item");
-    else
-      fprintf(fid,"        performParam:  '%s'\n",net.performParam);
-    endif
-  endif
-
-endfunction
--- a/main/nnet/inst/private/__printTargetConnect.m	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,82 +0,0 @@
-## Copyright (C) 2006 Michel D. Schmid  <michael.schmid@plexso.com>
-##
-## 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 3 of the License, 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; if not, see <http://www.gnu.org/licenses/>.
-
-## -*- texinfo -*-
-## @deftypefn {Function File} {} __printTargetConnect (@var{fid})
-## @code{printMLPHeader} saves the header of a  neural network structure
-## to a *.txt file with identification @code{fid}.
-## @end deftypefn
-
-## Author: Michel D. Schmid
-
-function __printTargetConnect(fid,net)
-
-  if isfield(net,"targetConnect")
-    # net.targetConnect can be a matrix..!
-    # check if it's a matrix
-    if isscalar(net.targetConnect)
-      error("unsure if this is possible..")
-    elseif isnumeric(net.targetConnect)
-      if ismatrix(net.targetConnect)
-        if issquare(net.targetConnect)
-          # insert enough spaces to put ":" to position 20
-          # insert 2 spaces for distance between ":" and "%"
-          fprintf(fid,"       targetConnect:  [");
-          [nRows nColumns] = size(net.targetConnect);
-          for k = 1:1:nRows
-            for i = 1:1:nColumns
-              if i<nColumns
-                fprintf(fid,"%d ",net.targetConnect(i*k));
-              else
-                fprintf(fid,"%d",net.targetConnect(i*k));
-              endif
-            endfor
-            if k!=nRows
-              #print ; for newline in matrix
-              fprintf(fid,";");
-            endif
-          endfor
-          # print last bracket
-          fprintf(fid,"]\n");
-        elseif isvector(net.targetConnect)
-          # insert enough spaces to put ":" to position 20
-          # insert 2 spaces for distance between ":" and "%"
-          # print bracket for open
-          fprintf(fid,"       targetConnect:  [");
-          [nRows nColumns] = size(net.targetConnect);
-             for k = 1:1:nRows
-               for i = 1:1:nColumns
-                 if (i<nColumns)
-                   fprintf(fid,"%d ",net.targetConnect(i*k));
-                 else
-                   fprintf(fid,"%d",net.targetConnect(i*k));
-                 endif
-               endfor
-               if k!=nRows
-                 #print ; for newline in matrix
-                 fprintf(fid,";");
-               endif
-             endfor
-             # print last bracket
-             fprintf(fid,"] not yet used item\n");
-           endif  # if issquare..
-         endif #if ismatrix
-      endif
-    else
-      fprintf(fid," ERROR...");
-    endif
-
-
-endfunction
--- a/main/nnet/inst/private/__printTargets.m	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,38 +0,0 @@
-## Copyright (C) 2006 Michel D. Schmid  <michael.schmid@plexso.com>
-##
-## 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 3 of the License, 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; if not, see <http://www.gnu.org/licenses/>.
-
-## -*- texinfo -*-
-## @deftypefn {Function File} {} __printTargets (@var{fid})
-## @code{printMLPHeader} saves the header of a  neural network structure
-## to a *.txt file with identification @code{fid}.
-## @end deftypefn
-
-## Author: Michel D. Schmid
-
-function __printTargets(fid,net)
-
-  if isfield(net,"targets")
-    # check if it's cell array
-    if iscell(net.targets)
-      [nRows, nColumns] = size(net.targets);
-      # insert enough spaces to put ":" to position 20
-      # insert 2 spaces for distance between ":" and "%"
-      fprintf(fid,"             targets: {%dx%d cell} containing %d targets\n",nRows,nColumns,net.numTargets);
-    else
-      fprintf(fid,"unsure if this is possible\n");
-    endif
-  endif
-
-endfunction
--- a/main/nnet/inst/private/__printTrainFcn.m	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,35 +0,0 @@
-## Copyright (C) 2006 Michel D. Schmid  <michael.schmid@plexso.com>
-##
-## 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 3 of the License, 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; if not, see <http://www.gnu.org/licenses/>.
-
-## -*- texinfo -*-
-## @deftypefn {Function File} {} __printTrainFcn (@var{fid})
-## @code{printMLPHeader} saves the header of a  neural network structure
-## to a *.txt file with identification @code{fid}.
-## @end deftypefn
-
-## Author: Michel D. Schmid
-
-
-function __printTrainFcn(fid,net)
-
-  if isfield(net,"trainFcn")
-    if isempty(net.trainFcn)
-      fprintf(fid,"            trainFcn:  '%s'\n","empty");
-    else
-      fprintf(fid,"            trainFcn:  '%s'\n",net.trainFcn);
-    endif
-  endif
-
-endfunction
--- a/main/nnet/inst/private/__printTrainParam.m	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,59 +0,0 @@
-## Copyright (C) 2006 Michel D. Schmid  <michael.schmid@plexso.com>
-##
-## 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 3 of the License, 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; if not, see <http://www.gnu.org/licenses/>.
-
-## -*- texinfo -*-
-## @deftypefn {Function File} {} __printTrainParam (@var{fid})
-## @code{printMLPHeader} saves the header of a  neural network structure
-## to a *.txt file with identification @code{fid}.
-## @end deftypefn
-
-## Author: Michel D. Schmid
-
-function __printTrainParam(fid,net)
-
-  if isfield(net,"trainParam")
-    str2 = "";
-    str3 = "";
-    if isempty(net.trainParam)
-      fprintf(fid,"          trainParam:  '%s'\n","not yet used item");
-    else
-      cellFieldNames = fieldnames(net.trainParam);
-      [nRows, nColumns] = size(cellFieldNames);
-      if (nRows<4)
-      else
-        for iRuns = 1:nRows
-          if (iRuns==1)
-            str1 =  ["." char(cellFieldNames(iRuns,1)) ", "];
-          endif
-          if (iRuns<=4 & iRuns>1)
-            str1 = [str1 "." char(cellFieldNames(iRuns,1)) ", "];
-          endif
-          if (iRuns>4 & iRuns<=8)
-            str2 = [str2 "." char(cellFieldNames(iRuns,1)) ", "];
-          endif
-          if (iRuns>8)
-            str3 = [str3 "." char(cellFieldNames(iRuns,1)) ", "];
-          endif
-        endfor
-        fprintf(fid,"          trainParam:  %s\n",str1);
-        fprintf(fid,"                       %s\n",str2);
-        fprintf(fid,"                       %s\n",str3);
-      endif
-    endif
-  else
-    fprintf(fid,"field trainparam not found\n");
-  endif
-
-endfunction
--- a/main/nnet/inst/private/__randomisecols.m	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,42 +0,0 @@
-## Copyright (C) 2008 Michel D. Schmid  <michael.schmid@plexso.com>
-##
-## 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 3 of the License, 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; if not, see <http://www.gnu.org/licenses/>.
-
-## -*- texinfo -*-
-## @deftypefn {Function File} {} @var{retmatrix} = __randomisecols (@var{matrix})
-## @code{__randomisecols} takes a matrix as input argument and changes the order
-## of the columns. The rows aren't affected.
-## @end deftypefn
-
-## Author: mds
-
-function [retmatrix] = __randomisecols(matrix)
-
-  ## check number of inputs
-  error(nargchk(1,1,nargin));
-
-  # get number of cols
-  nCols = size(matrix,2);
-
-  # now create random column order
-  colOrder = randperm(nCols);
-
-  # now sort the matrix new
-  retmatrix = matrix(:,[colOrder]);
-
-
-endfunction
-
-%!# no test possible, contains randperm which is using
-%!# some randome functions
--- a/main/nnet/inst/private/__rerangecolumns.m	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,166 +0,0 @@
-## Copyright (C) 2008 Michel D. Schmid  <michael.schmid@plexso.com>
-##
-## 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 3 of the License, 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; if not, see <http://www.gnu.org/licenses/>.
-
-## -*- texinfo -*-
-## @deftypefn {Function File} {} @var{retmatrix} = __rerangecolumns (@var{matrix},@var{analyzeMatrix},@var{nTrainSets})
-## @code{__rerangecolumns} reranges the data sets depending on the input arguments.
-## @code{matrix} is the data set matrix containing inputs and outputs (targets) in row order.
-## This means for example: the first three rows are inputs and the fourth row is an output row.
-## The second argument is used in the optimizing algorithm. This matrix contains informations about
-## the description of the rows data of matrix.
-## The third argument is used to be sure, rerange all the columns to the correct position.
-## @end deftypefn
-
-## Author: mds
-
-function retmatrix = __rerangecolumns(matrix,analyzeMatrix,nTrainSets)
-
-  ## check number of inputs
-  error(nargchk(3,3,nargin));
-
-  # set default values
-
-  # now sort "matrix" with help of analyzeMatrix
-  # following conditions must be kept:
-  # a.) rows containing unique values aren't sorted!
-  # b.) sort first rows which contains min AND max values only once
-  # c.) sort secondly rows which contains min OR max values only once
-  # d.) at last, sort binary data if still needed!
-
-  nRows = size(analyzeMatrix,1);   # get number of rows
-
-  ## create i-vector
-  i = 1;
-  iVec = [];
-  while (i <= nRows)
-    if ( (analyzeMatrix(i,3)==1) && (analyzeMatrix(i,4)==1) )
-      iVec = [iVec i];
-    endif
-    i += 1;
-  endwhile
-  i = 1;
-  while (i <= nRows)
-	if ( (analyzeMatrix(i,3)>1) || (analyzeMatrix(i,4)>1) )
-	  iVec = [iVec i];
-	endif
-	i += 1;
-  endwhile
-  i = 1;
-  while (i <= nRows)
-    if (analyzeMatrix(i,1)==1)
-      iVec = [iVec i];
-    endif
-  i += 1;
-  endwhile
-
-
-  ## now do main loop
-  j = 1;
-  i = iVec(j);
-  nRows = length(iVec);
-  while (j < nRows)
-    if (analyzeMatrix(i,2)==1)
-      # easiest case, nothing to do
-    else
-
-      # now let's see if min AND max values are only once in the row
-      if ( (analyzeMatrix(i,3)==1) && (analyzeMatrix(i,4)==1) )
-		# search at which index the min value is
-		minVal = min(matrix(i,:));
-        [rowInd, colInd] = find(matrix(i,:)==minVal);# colInd is searched
-        if (colInd >= nTrainSets ) # move column
-		  matrix = __copycoltopos1(matrix,colInd);
-        endif
-        # search at which index the max value is
-        maxVal = max(matrix(i,:));
-        [rowInd, colInd] = find(matrix(i,:)==maxVal);# colInd is searched
-        if (colInd >= nTrainSets ) # move column
-		  matrix = __copycoltopos1(matrix,colInd);
-        endif
-
-      else
-
-		# now here, we have to copy the rows, if min OR max values are more than once in a row
-        if ( (analyzeMatrix(i,3)>=1) || (analyzeMatrix(i,4)>=1) )
-
-		  # search at which index the min value is
-		  minVal = min(matrix(i,:));
-          [rowInd, colInd] = find(matrix(i,:)==minVal);# colInd is searched
-          if (colInd(1) >= nTrainSets ) # move column
-		    matrix = __copycoltopos1(matrix,colInd(1));
-          endif
-
-          # search at which index the max value is
-          maxVal = max(matrix(i,:));
-          [rowInd, colInd] = find(matrix(i,:) == maxVal);# colInd is searched
-          if (colInd(1) >= nTrainSets ) # move column
-		    matrix = __copycoltopos1(matrix,colInd(1));
-          endif
-
-		else
-		  # now sort binary data, if needed
-
-          # search at which index the 0-value is
-		  [rowInd, colInd] = find(matrix(i,:)==0);# colInd is searched
-          if (colInd(1) >= nTrainSets ) # move column
-		    matrix = __copycoltopos1(matrix,colInd(1));
-          endif
-          # search at which index the 1-value is
-          [rowInd, colInd] = find(matrix(i,:)==1);# colInd is searched
-          if (colInd(1) >= nTrainSets ) # move column
-		    matrix = __copycoltopos1(matrix,colInd(1));
-          endif
-
-        endif# END OF if ( (analyzeMatrix(i,3)>=1) || (analyzeMatrix(i,4)>=1) )
-
-      endif # END OF if ( (analyzeMatrix(i,3)==1) AND (analyzeMatrix(i,4)==1) )
-
-    endif # END OF if (analyzeMatrix(i,2)==1)
-    j += 1;
-    i = iVec(j);
-  endwhile
-  retmatrix = matrix;
-endfunction
-
-%!shared matrix,analyzeMatrix,nTrainSets, returnmatrix
-%! disp("testing __rerangecolumns")
-%! matrix = [0 1 0 0 0 0 1 0 1 1;  \
-%!			 4 4 4 4 4 4 4 4 4 4;  \
-%!        -1.1 -1.1 2 3 4 3.2 1 8 9 10; \
-%!           0 1.1 3 4 5 2 10 10 2 3; \
-%!          -1 1 1 1 1 2 3 4 1 5];
-%! analyzeMatrix = [1 0 0 0; 0 1 0 0; 0 0 2 1; 0 0 1 2; 0 0 1 1];
-%! nTrainSets = 8;
-%! returnmatrix = __rerangecolumns(matrix,analyzeMatrix,nTrainSets);
-%!assert(returnmatrix(1,1)==1);
-%!assert(returnmatrix(2,1)==4);
-%!assert(returnmatrix(3,1)==1);
-%!assert(returnmatrix(4,1)==10);
-%!assert(returnmatrix(5,1)==3);
-%! matrix = [0 1 0 0 0 0 1 0 1 1; 			\
-%!			 4 4 4 4 4 4 4 4 4 4; 			\
-%!          -1.1 -1.1 2 3 4 3.2 1 8 9 10; 	\
-%!           0 1.1 3 4 5 2 10 10 2 3; 		\
-%!          -1 1 1 1 1 2 3 4 1 5;     		\
-%!			 0 1 2 1 2 1 2 3 4 5;];  # the last row is euqal to the nnet targets
-%! analyzeMatrix = [1 0 0 0; 0 1 0 0; 0 0 2 1; 0 0 1 2; 0 0 1 1];
-%! nTrainSets = 8;
-%! returnmatrix = __rerangecolumns(matrix,analyzeMatrix,nTrainSets);
-%!assert(returnmatrix(1,1)==1);
-%!assert(returnmatrix(2,1)==4);
-%!assert(returnmatrix(3,1)==1);
-%!assert(returnmatrix(4,1)==10);
-%!assert(returnmatrix(5,1)==3);
-%!assert(returnmatrix(6,1)==2);
--- a/main/nnet/inst/private/__setx.m	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,59 +0,0 @@
-## Copyright (C) 2005 Michel D. Schmid  <michael.schmid@plexso.com>
-##
-## 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 3 of the License, 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; if not, see <http://www.gnu.org/licenses/>.
-
-## -*- texinfo -*-
-## @deftypefn {Function File} {} @var{net} = __setx (@var{net},@var{X2})
-## @code{__setx} sets the new weights to the neural network structure
-## @end deftypefn
-
-## @seealso{getx}
-
-## Author: Michel D. Schmid
-
-function net = __setx(net,xx)
-
-  ## check number of inputs
-  error(nargchk(2,2,nargin));
-
-  ## check input args
-  ## check "net", must be a net structure
-  if !__checknetstruct(net)
-    error("Structure doesn't seem to be a neural network")
-  endif
-
-  ## inputs
-  [nRows, nColumns] = size(net.IW{1,1});
-  nElementsIW = nRows*nColumns;
-  net.IW{1,1}(:) = xx(1:nElementsIW);
-
-  [nRows, nColumns] = size(net.b{1,1});
-  nElementsB1 = nRows*nColumns;
-  net.b{1,1}(:) = xx(1+nElementsIW:nElementsIW+nElementsB1);
-  start = nElementsIW + nElementsB1;
-
-  ## layers
-  nLayers = net.numLayers;
-  for i = 2:nLayers
-    [nRows, nColumns] = size(net.LW{i,i-1});
-    nElementsLW = nRows*nColumns;
-    net.LW{i,i-1}(:) = xx(1+start:start+nElementsLW);
-
-    [nRows, nColumns] = size(net.b{i,1});
-    nElementsBx = nRows*nColumns;
-    net.b{i,1}(:) = xx(1+start+nElementsLW:start+nElementsLW+nElementsBx);
-    start = start + nElementsLW + nElementsBx;
-  endfor
-
-endfunction
--- a/main/nnet/inst/private/__trainlm.m	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,359 +0,0 @@
-## Copyright (C) 2006 Michel D. Schmid <michael.schmid@plexso.com>
-##
-## 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 3 of the License, 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; if not, see <http://www.gnu.org/licenses/>.
-
-## -*- texinfo -*-
-## @deftypefn {Function File} {}[@var{netOut}] = __trainlm (@var{net},@var{mInputN},@var{mOutput},@var{[]},@var{[]},@var{VV})
-## A neural feed-forward network will be trained with @code{__trainlm}
-##
-## @example
-## [netOut,tr,out,E] = __trainlm(net,mInputN,mOutput,[],[],VV);
-## @end example
-## @noindent
-##
-## left side arguments:
-## @example
-## netOut: the trained network of the net structure @code{MLPnet}
-## tr :
-## out:
-## E  : Error
-## @end example
-## @noindent
-##
-## right side arguments:
-## @example
-## net    : the untrained network, created with @code{newff}
-## mInputN: normalized input matrix
-## mOutput: output matrix
-## []     : unused parameter
-## []     : unused parameter
-## VV     : validize structure
-## out:
-## E  : Error
-## @end example
-## @noindent
-##
-##
-## @noindent
-## are equivalent.
-## @end deftypefn
-
-## @seealso{newff,prestd,trastd}
-
-## Author: Michel D. Schmid
-
-## Comments: see in "A neural network toolbox for Octave User's Guide" [4]
-##  for variable naming... there have inputs or targets only one letter,
-## e.g. for inputs is P written. To write a program, this is stupid, you can't
-## search for 1 letter variable... that's why it is written here like Pp, or Tt
-## instead only P or T.
-
-function [net] = __trainlm(net,Im,Pp,Tt,VV)
-
-  ## check range of input arguments
-  error(nargchk(5,5,nargin))
-
-  ## Initialize
-  ##------------
-
-  ## get parameters for training
-  epochs   = net.trainParam.epochs;
-  goal     = net.trainParam.goal;
-  maxFail  = net.trainParam.max_fail;
-  minGrad  = net.trainParam.min_grad;
-  mu       = net.trainParam.mu;
-  muInc    = net.trainParam.mu_inc;
-  muDec    = net.trainParam.mu_dec;
-  muMax    = net.trainParam.mu_max;
-  show     = net.trainParam.show;
-  time     = net.trainParam.time;
-
-  ## parameter checking
-  checkParameter(epochs,goal,maxFail,minGrad,mu,...
-	               muInc,muDec,muMax,show,time);
-
-  ## Constants
-  shortStr = "TRAINLM";    # TODO: shortStr is longer as TRAINLM !!!!!!!!!!!
-  doValidation = !isempty(VV);
-  stop = "";
-
-
-  #startTime = clock(); # TODO: maybe this row can be placed
-                       # some rows later
-
-  ## the weights are used in column vector format
-  xx = __getx(net); # x is the variable with respect to, but no
-                    # variables with only one letter!!
-  ## define identity matrix
-  muI = eye(length(xx));
-
-  startTime = clock();  # if the next some tests are OK, I can delete
-                        # startTime = clock(); 9 rows above..
-
-  ## calc performance of the actual net
-  [perf,vE,Aa,Nn] = __calcperf(net,xx,Im,Tt);
-  if (doValidation)
-    ## calc performance if validation is used
-    VV.net = net; # save the actual net in the validate
-    # structure... if no train loop will show better validate
-    # performance, this will be the returned net
-    vperf = __calcperf(net,xx,VV.Im,VV.Tt);
-    VV.perf = vperf;
-    VV.numFail = 0; # one of the stop criterias
-  endif
-
-  nLayers = net.numLayers;
-  for iEpochs = 0:epochs # longest loop & one of the stop criterias
-    ve = vE{nLayers,1};
-    ## calc jacobian
-    ## Jj is jacobian matrix
-    [Jj] = __calcjacobian(net,Im,Nn,Aa,vE);
-
-    ## rerange error vector for jacobi matrix
-    ve = ve(:);
-
-    Jjve = (Jj' * ve); # will be used to calculate the gradient
-
-    normGradX = sqrt(Jjve'*Jjve);
-
-    ## record training progress for later plotting
-    ## if requested
-    trainRec.perf(iEpochs+1) = perf;
-    trainRec.mu(iEpochs+1) = mu;
-    if (doValidation)
-      trainRec.vperf(iEpochs+1) = VV.perf;
-    endif
-
-    ## stoping criteria
-    [stop,currentTime] = stopifnecessary(stop,startTime,perf,goal,...
-                           iEpochs,epochs,time,normGradX,minGrad,mu,muMax,...
-                           doValidation,VV,maxFail);
-
-    ## show train progress
-    showtrainprogress(show,stop,iEpochs,epochs,time,currentTime,...
-		  goal,perf,minGrad,normGradX,shortStr,net);
-
-    ## show performance plot, if needed
-    if !isnan(show) # if no performance plot is needed
-      ## now make it possible to define after how much loops the
-      ## performance plot should be updated
-      if (mod(iEpochs,show)==0)
-        plot(1:length(trainRec.perf),trainRec.perf);
-	if (doValidation)
-	  hold on;
-	  plot(1:length(trainRec.vperf),trainRec.vperf,"--g");
-	endif
-      endif
-    endif # if !(strcmp(show,"NaN"))
-#    legend("Training","Validation");
-
-    ## stop if one of the criterias is reached.
-    if length(stop)
-      if (doValidation)
-        net = VV.net;
-      endif
-      break
-    endif
-
-    ## calculate DeltaX
-    while (mu <= muMax)
-      ## calculate change in x
-      ## see [4], page 12-21
-      dx = -((Jj' * Jj) + (muI*mu)) \ Jjve;
-
-      ## add changes in x to actual x values (xx)
-      x1 = xx + dx;
-      ## now add x1 to a new network to see if performance will be better
-      net1 = __setx(net,x1);
-      ## calc now new performance with the new net
-      [perf1,vE1,Aa1,N1] = __calcperf(net1,x1,Im,Tt);
-
-      if (perf1 < perf)
-        ## this means, net performance with new weight values is better...
-        ## so save the new values
-        xx = x1;
-        net = net1;
-        Nn = N1;
-        Aa = Aa1;
-        vE = vE1;
-        perf = perf1;
-
-        mu = mu * muDec;
-        if (mu < 1e-20)   # 1e-20 is properly the hard coded parameter in MATLAB(TM)
-          mu = 1e-20;
-        endif
-        break
-      endif
-      mu = mu * muInc;
-    endwhile
-
-    ## validate with DeltaX
-    if (doValidation)
-      vperf = __calcperf(net,xx,VV.Im,VV.Tt);
-      if (vperf < VV.perf)
-        VV.perf = vperf;
-    	VV.net = net;
-    	## if actual validation performance is better,
-        ## set numFail to zero again
-    	VV.numFail = 0;
-      elseif (vperf > VV.perf)
-        VV.numFail = VV.numFail + 1;
-      endif
-    endif
-
-  endfor #for iEpochs = 0:epochs
-
-#=======================================================
-#
-# additional functions
-#
-#=======================================================
-  function checkParameter(epochs,goal,maxFail,minGrad,mu,...
-	               muInc, muDec, muMax, show, time)
-    ## Parameter Checking
-
-    ## epochs must be a positive integer
-    if ( !isposint(epochs) )
-      error("Epochs is not a positive integer.")
-    endif
-
-    ## goal can be zero or a positive double
-    if ( (goal<0) || !(isa(goal,"double")) )
-      error("Goal is not zero or a positive real value.")
-    endif
-
-    ## maxFail must be also a positive integer
-    if ( !isposint(maxFail) ) # this will be used, to see if validation can
-      # break the training
-      error("maxFail is not a positive integer.")
-    endif
-
-    if (!isa(minGrad,"double")) || (!isreal(minGrad)) || (!isscalar(minGrad)) || ...
-      (minGrad < 0)
-      error("minGrad is not zero or a positive real value.")
-    end
-
-    ## mu must be a positive real value. this parameter is responsible
-    ## for moving from stepest descent to quasi newton
-    if ((!isa(mu,"double")) || (!isreal(mu)) || (any(size(mu)) != 1) || (mu <= 0))
-      error("mu is not a positive real value.")
-    endif
-
-    ## muDec defines the decrement factor
-    if ((!isa(muDec,"double")) || (!isreal(muDec)) || (any(size(muDec)) != 1) || ...
-  		 (muDec < 0) || (muDec > 1))
-      error("muDec is not a real value between 0 and 1.")
-    endif
-
-    ## muInc defines the increment factor
-    if (~isa(muInc,"double")) || (!isreal(muInc)) || (any(size(muInc)) != 1) || ...
-      (muInc < 1)
-      error("muInc is not a real value greater than 1.")
-    endif
-
-    ## muMax is the upper boundary for the mu value
-    if (!isa(muMax,"double")) || (!isreal(muMax)) || (any(size(muMax)) != 1) || ...
-      (muMax <= 0)
-      error("muMax is not a positive real value.")
-    endif
-
-    ## check for actual mu value
-    if (mu > muMax)
-      error("mu is greater than muMax.")
-    end
-
-    ## check if show is activated
-    if (!isnan(show))
-	  if (!isposint(show))
-        error(["Show is not " "NaN" " or a positive integer."])
-      endif
-    endif
-
-    ## check at last the time argument, must be zero or a positive real value
-    if (!isa(time,"double")) || (!isreal(time)) || (any(size(time)) != 1) || ...
-      (time < 0)
-      error("Time is not zero or a positive real value.")
-    end
-
-  endfunction # parameter checking
-
-#
-# -----------------------------------------------------------------------------
-#
-
-  function showtrainprogress(show,stop,iEpochs,epochs,time,currentTime, ...
-          goal,perf,minGrad,normGradX,shortStr,net)
-
-    ## check number of inputs
-    error(nargchk(12,12,nargin));
-
-    ## show progress
-    if isfinite(show) && (!rem(iEpochs,show) || length(stop))
-      fprintf(shortStr);   # outputs the training algorithm
-      if isfinite(epochs)
-        fprintf(", Epoch %g/%g",iEpochs, epochs);
-      endif
-      if isfinite(time)
-        fprintf(", Time %4.1f%%",currentTime/time*100);   # \todo: Time wird nicht ausgegeben
-      endif
-      if isfinite(goal)
-        fprintf(", %s %g/%g",upper(net.performFcn),perf,goal); # outputs the performance function
-      endif
-      if isfinite(minGrad)
-        fprintf(", Gradient %g/%g",normGradX,minGrad);
-      endif
-      fprintf("\n")
-      if length(stop)
-        fprintf("%s, %s\n\n",shortStr,stop);
-      endif
-      fflush(stdout); # writes output to stdout as soon as output messages are available
-    endif
-  endfunction
-
-#
-# -----------------------------------------------------------------------------
-#
-
-  function [stop,currentTime] = stopifnecessary(stop,startTime,perf,goal,...
-                        iEpochs,epochs,time,normGradX,minGrad,mu,muMax,...
-						doValidation,VV,maxFail)
-
-    ## check number of inputs
-    error(nargchk(14,14,nargin));
-
-    currentTime = etime(clock(),startTime);
-    if (perf <= goal)
-      stop = "Performance goal met.";
-    elseif (iEpochs == epochs)
-      stop = "Maximum epoch reached, performance goal was not met.";
-    elseif (currentTime > time)
-      stop = "Maximum time elapsed, performance goal was not met.";
-    elseif (normGradX < minGrad)
-      stop = "Minimum gradient reached, performance goal was not met.";
-    elseif (mu > muMax)
-      stop = "Maximum MU reached, performance goal was not met.";
-    elseif (doValidation)
-	  if (VV.numFail > maxFail)
-        stop = "Validation stop.";
-      endif
-    endif
-  endfunction
-
-# =====================================================================
-#
-# END additional functions
-#
-# =====================================================================
-
-endfunction
--- a/main/nnet/inst/purelin.m	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,35 +0,0 @@
-## Copyright (C) 2005 Michel D. Schmid  <michael.schmid@plexso.com>
-##
-## 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 3 of the License, 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; if not, see <http://www.gnu.org/licenses/>.
-
-## -*- texinfo -*-
-## @deftypefn {Function File} {}@var{a}= purelin (@var{n})
-## @code{purelin} is a linear transfer function used
-## by neural networks
-## @end deftypefn
-
-## Author: Michel D. Schmid
-
-function a = purelin(n)
-
-   a = n;
-
-endfunction
-
-%!assert(purelin(2),2);
-%!assert(purelin(-2),-2);
-%!assert(purelin(0),0);
-
-%!error  # this test must throw an error!
-%! assert(purelin(2),1);
--- a/main/nnet/inst/radbas.m	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,37 +0,0 @@
-## Copyright (C) 2009 Luca Favatella <slackydeb@gmail.com>
-##
-## 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 3 of the License, 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; if not, see <http://www.gnu.org/licenses/>.
-
-## -*- texinfo -*-
-## @deftypefn {Function File} {} radbas (@var{n})
-## Radial basis transfer function.
-##
-## @code{radbas(n) = exp(-n^2)}
-##
-## @end deftypefn
-
-## Author: Luca Favatella <slackydeb@gmail.com>
-## Version: 0.1
-
-function retval = radbas (n)
-
-  if (nargin != 1)
-    print_usage ();
-  else
-    retval = exp (-n^2);
-  endif
-endfunction
-
-
-%!assert (radbas (3), exp (-3^2));
--- a/main/nnet/inst/satlin.m	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,37 +0,0 @@
-## Copyright (C) 2007 Michel D. Schmid <michael.schmid@plexso.com>
-##
-## 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 3 of the License, 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; if not, see <http://www.gnu.org/licenses/>.
-
-## -*- texinfo -*-
-## @deftypefn {Function File} {}[@var{a} = satlin (@var{n})
-## A neural feed-forward network will be trained with @code{trainlm}
-##
-## @end deftypefn
-
-## @seealso{purelin,tansig,logsig}
-
-## Author: Michel D. Schmid
-
-
-function a = satlin(n)
-
-  if (n<0)
-    a = 0;
-  elseif (n>=0 && n<=1)
-    a = n;
-  else
-    a = 1; # if n>1
-  endif
-
-endfunction
--- a/main/nnet/inst/satlins.m	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,37 +0,0 @@
-## Copyright (C) 2007 Michel D. Schmid <michael.schmid@plexso.com>
-##
-## 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 3 of the License, 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; if not, see <http://www.gnu.org/licenses/>.
-
-## -*- texinfo -*-
-## @deftypefn {Function File} {}[@var{a} = satlins (@var{n})
-## A neural feed-forward network will be trained with @code{trainlm}
-##
-## @end deftypefn
-
-## @seealso{purelin,tansig,logsig,satlin,hardlim,hardlims}
-
-## Author: Michel D. Schmid
-
-
-function a = satlins(n)
-
-  if (n<-1)
-    a = -1;
-  elseif (n>=-1 && n<=1)
-    a = n;
-  else
-    a = 1; # if n>1
-  endif
-
-endfunction
--- a/main/nnet/inst/saveMLPStruct.m	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,185 +0,0 @@
-## Copyright (C) 2005 Michel D. Schmid  <michael.schmid@plexso.com>
-##
-## 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 3 of the License, 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; if not, see <http://www.gnu.org/licenses/>.
-
-## -*- texinfo -*-
-## @deftypefn {Function File} {} saveMLPStruct (@var{net},@var{strFileName})
-## @code{saveStruct} saves a neural network structure to *.txt files
-## @end deftypefn
-
-## Author: Michel D. Schmid
-
-function saveMLPStruct(net,strFileName)
-
-  ## the variable net holds the neural network structure..
-  # check if "net" is a structure type
-  if !__checknetstruct(net)
-    error("Structure doesn't seem to be a neural network")
-  endif
-
-  # open the first level file
-  fid1 = fopen(strFileName,"w+t","ieee-le");
-
-  if (fid1 < 0)
-    error ("Can not open %s", strFileName);
-  endif
-
-  ## print header
-#   try            ## wird nicht mehr ben�tigt..
-    __printMLPHeader(fid1);
-#   catch
-#     ## Add saveMLPStructure directory to the path and try again
-#     addpath ([fileparts(mfilename()),"/saveMLPStructure"]);
-#     __printMLPHeader(fid1);
-#   end_try_catch
-
-  ## check for field "networkType"
-  __printNetworkType(fid1,net);
-
-  ## check for field "numInputs"
-  __printNumInputs(fid1,net);
-
-  ## check for field "numLayers"
-  __printNumLayers(fid1,net)
-
-  ## check for field "biasConnect"
-  __printBiasConnect(fid1,net)
-
-  ## check for field "inputConnect"
-  __printInputConnect(fid1,net)
-
-  ## check for field "layerConnect"
-  __printLayerConnect(fid1,net)
-
-  ## check for field "outputConnect"
-  __printOutputConnect(fid1,net)
-
-  ## check for field "targetConnect"
-  __printTargetConnect(fid1,net)
-
-  ## print one empty line
-  fprintf(fid1,"\n");
-
-  ## check for numOutputs
-  __printNumOutputs(fid1,net);
-
-  ## check for numTargets
-  __printNumTargets(fid1,net);
-
-  ## check for numInputDelays
-  __printNumInputDelays(fid1,net);
-
-  ## check for numLayerDelays
-  __printNumLayerDelays(fid1,net);
-
-  ## print one empty line
-  fprintf(fid1,"\n");
-
-  ## print subobject structures:
-  fprintf(fid1,"  subobject structures:\n");
-
-  ## print one empty line
-  fprintf(fid1,"\n");
-
-  ## print inputs
-  __printInputs(fid1,net);
-
-  ## print layers
-  __printLayers(fid1,net);
-
-  ## print outputs
-  __printOutputs(fid1,net);
-
-  ## print targets
-  __printTargets(fid1,net);
-
-  ## print biases
-  __printBiases(fid1,net);
-
-  ## print inputweights
-  __printInputWeights(fid1,net);
-
-  ## print layerweights
-  __printLayerWeights(fid1,net);
-
-  ## print one empty line
-  fprintf(fid1,"\n");
-
-  ## print subobject structures:
-  fprintf(fid1,"  functions:\n");
-
-  ## print one empty line
-  fprintf(fid1,"\n");
-
-  ## print adaptFcn
-  __printAdaptFcn(fid1,net);
-
-  ## print initFcn
-  __printInitFcn(fid1,net);
-
-  ## print performFcn
-  __printPerformFcn(fid1,net);
-
-  ## print performFcn
-  __printTrainFcn(fid1,net);
-
-  ## print one empty line
-  fprintf(fid1,"\n");
-
-  ## print subobject structures:
-  fprintf(fid1,"  parameters:\n");
-
-  ## print one empty line
-  fprintf(fid1,"\n");
-
-  ## print adaptParam
-  __printAdaptParam(fid1,net);
-
-  ## print initParam
-  __printInitParam(fid1,net);
-
-  ## print performParam
-  __printPerformParam(fid1,net);
-
-  ## print trainParam
-  __printTrainParam(fid1,net);
-
-  ## print one empty line
-  fprintf(fid1,"\n");
-
-  ## print subobject structures:
-  fprintf(fid1,"  weight & bias values:\n");
-
-  ## print one empty line
-  fprintf(fid1,"\n");
-
-  ## print IW
-  __printIW(fid1,net);
-
-  ## print LW
-  __printLW(fid1,net);
-
-  ## print b
-  __printB(fid1,net);
-
-  ## print one empty line
-  fprintf(fid1,"\n");
-
-  ## print subobject structures:
-  fprintf(fid1,"  other:\n");
-
-
-  fclose(fid1);
-
-endfunction
--- a/main/nnet/inst/sim.m	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,82 +0,0 @@
-## Copyright (C) 2006 Michel D. Schmid  <michael.schmid@plexso.com>
-##
-## 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 3 of the License, 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; if not, see <http://www.gnu.org/licenses/>.
-
-## -*- texinfo -*-
-## @deftypefn {Function File} {@var{netoutput} =} sim (@var{net}, @var{mInput})
-## @code{sim} is usuable to simulate a before defined neural network.
-## @code{net} is created with newff(@dots{}) and @var{mInput} should be the
-## corresponding input data set!
-## @end deftypefn
-
-## Author: Michel D. Schmid
-
-
-## Comments: see in "A neural network toolbox for Octave User's Guide" [4]
-##  for variable naming... there have inputs or targets only one letter,
-## e.g. for inputs is written P. To write a program, this is stupid, you can't
-##  search for 1 letter variable... that's why it is written here like Pp, or Tt
-## instead only P or T.
-
-function [netoutput] = sim(net,mInput)
-
-  ## check range of input arguments
-  error(nargchk(2,2,nargin))
-
-  ## check input args
-  ## check "net", must be a net structure
-  if !__checknetstruct(net)
-    error("Structure doesn't seem to be a neural network")
-  endif
-  ## check "mInput", must have defined size
-  [nRows, nColumns] = size(mInput);
-  if (nRows != net.inputs{1}.size)
-    error(["Simulation input data must have: " num2str(net.inputs{1}.size) " rows."])
-  endif
-
-  ## first get weights...
-  IW = net.IW{1};
-  b1 = net.b{1};
-  b1 = repmat(b1,1,size(mInput,2));
-  nLoops = net.numLayers;
-  for i=1:nLoops
-
-    trf = net.layers{i}.transferFcn;
-    ## calculate the outputs for each layer from input to output
-
-    if i==1
-      Nn{i,1} = IW*mInput + b1;
-    else
-      LWx = net.LW{i,i-1};
-      bx = net.b{i};
-      bx = repmat(bx,1,size(Aa{i-1,1},2));
-      Nn{i,1} = LWx*Aa{i-1,1} + bx;
-    endif
-
-    switch(trf)
-      case "tansig"
-        Aa{i,1} = tansig(Nn{i,1});
-      case "purelin"
-        Aa{i,1} = purelin(Nn{i,1});
-      case "logsig"
-        Aa{i,1} = logsig(Nn{i,1});
-      otherwise
-        error(["sim:Unknown transfer fucntion: " trf "!"]);
-    endswitch
-  endfor
-
-  netoutput = Aa{i,1};
-
-endfunction
-
--- a/main/nnet/inst/subset.m	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,210 +0,0 @@
-## Copyright (C) 2008 Michel D. Schmid  <michael.schmid@plexso.com>
-##
-## 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 3 of the License, 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; if not, see <http://www.gnu.org/licenses/>.
-
-## -*- texinfo -*-
-## @deftypefn {Function File} {}[@var{mTrain}, @var{mTest}, @var{mVali}] = subset (@var{mData},@var{nTargets},@var{iOpti},@var{fTest},@var{fVali})
-## @code{subset} splits the main data matrix which contains inputs and targets into 2 or 3 subsets
-## depending on the parameters.
-##
-## The first parameter @var{mData} must be in row order. This means if the network
-## contains three inputs, the matrix must be have 3 rows and x columns to define the
-## data for the inputs. And some more rows for the outputs (targets), e.g. a neural network
-## with three inputs and two outputs must have 5 rows with x columns!
-## The second parameter @var{nTargets} defines the number or rows which contains the target values!
-## The third argument @code{iOpti} is optional and can have three status:
-## 	   0: no optimization
-##     1: will randomise the column order and order the columns containing min and max values to be in the train set
-##     2: will NOT randomise the column order, but order the columns containing min and max values to be in the train set
-##	   default value is @code{1}
-## The fourth argument @code{fTest} is also optional and defines how
-## much data sets will be in the test set. Default value is @code{1/3}
-## The fifth parameter @code{fTrain} is also optional and defines how
-## much data sets will be in the train set. Default value is @code{1/6}
-## So we have 50% of all data sets which are for training with the default values.
-##
-## @example
-##   [mTrain, mTest] = subset(mData,1)
-##   returns three subsets of the complete matrix
-##   with randomized and optimized columns!
-## @end example
-## @example
-##   [mTrain, mTest] = subset(mData,1,)
-##   returns two subsets
-## @end example
-##
-## @end deftypefn
-
-## Author: Michel D. Schmid
-
-
-function [mTrain, mTest, mVali] = subset(mData,nTargets,iOpti,fTest,fVali)
-
-  ## check range of input arguments
-  error(nargchk(2,5,nargin))
-
-  ## check the input arguments ...!
-  if (nTargets==0)
-    error("No TARGETS defined! This doesn't make any sense for feed-forward neural networks! Please define at least one row of targets")
-  endif
-
-  ## set default values
-  if (nargin==2)
-    iOpti = 1;
-    fTest = 1/3;
-    fVali = 1/6;
-  elseif (nargin==3)
-    fTest = 1/3;
-    fVali = 1/6;
-  elseif (nargin==4)
-    ## if fTest is set and nothing is set
-    ## for fVali I assume that fVali is not used!
-    fVali = 0;
-  endif
-
-  ## calculate the number of train, test and validation sets
-  fTrain = 1-fTest-fVali;
-  nTrainSets = floor(size(mData,2)*fTrain);
-  diffRestSets = size(mData,2)-nTrainSets;
-  nTestSets = floor(size(mData,2)*fTest);
-  nValiSets = size(mData,2)-nTrainSets-nTestSets;
-
-
-  ## now let's see if matrix must be optimized!
-  bOptiAgain = 1;
-  while (bOptiAgain)
-    if (iOpti == 1)
-    # check that only one optimizing run is enough!!
-    # maybe it's necessary to do it twice ..!
-    # check that all min and max values are in the train set ...!
-      mData = __optimizedatasets(mData,nTrainSets,nTargets,iOpti);
-      mTrain = mData(:,1:nTrainSets);
-      iRuns = size(mTrain,1);
-      i = 1;
-      j = 1;
-      while (i < iRuns)
-    	  if ( max(mTrain(i,:)) == max(mData(i,:)) )
-    	    j += 1;
-    	  endif
-    	  i +=1;
-      endwhile
-      if (i==j)
-        bOptiAgain = 0;
-      endif
-    elseif (iOpti == 2)
-      # check that only one optimizing run is enough!!
-      # maybe it's necessary to do it twice ..!
-      # check that all min and max values are in the train set ...!
-      mData = __optimizedatasets(mData,nTrainSets,nTargets,iOpti);
-      mTrain = mData(:,1:nTrainSets);
-      iRuns = size(mTrain,1);
-      j = 1;
-      i = 1;
-      while (i < iRuns)
-    	  if (max(mTrain(i,:))==max(mData(i,:)))
-			j += 1;
-    	  endif
-    	  i += 1;
-      endwhile
-      if (i==j)
-        bOptiAgain = 0;
-      endif
-    else
-      ## in this case, iOpti must be 0 ==> nothing todo
-      bOptiAgain = 0;
-    endif
-  endwhile #END OF while(bOptiAgain)
-
-  ## now split up
-  if (nargout==1)
-    mTrain = mData;
-  elseif (nargout==2);
-    mTrain = mData(:,1:nTrainSets);
-    mTest = mData(:,nTrainSets+1:nTrainSets+nTestSets);
-  elseif (nargout==3)
-    mTrain = mData(:,1:nTrainSets);
-    mTest = mData(:,nTrainSets+1:nTrainSets+nTestSets);
-    mVali = mData(:,nTrainSets+nTestSets+1:end);
-  endif
-
-endfunction
-
-%!shared matrix, nTargets, mTrain, mTest, mVali
-%! disp("testing subset")
-%! matrix = [1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 18 20; \
-%!			 0 2 4 1 3 5 3 4 1 -1 -2 -9 -1 10 12 20 11 11 11 11; \
-%!			-2 2 2 2 2 0 0 0 0  0 10 12 13 12 13 44 33 32 98 11; \
-%!			 0 0 0 0 1 1 1 1 0  0  1  1  1  0  0  1  1  1  0  0; \
-%!           4 4 4 4 4 4 4 4 4  4  4  4  4  4  4  4  4  4  4  4; \
-%!           1 2 3 4 5 6 7 8 9 10 11 12 13 33 44 55 66 77 88 99];
-%! nTargets = 1; # the last row is equivalent to the target values.
-%! [mTrain, mTest, mVali] = subset(matrix,nTargets);  ############################
-%!assert(size(mTrain,2)==10);# 50% of 20
-%!assert(size(mTest,2)==6);# 1/3 of 20 = 6 (floor)
-%!assert(size(mVali,2)==4);# 1/6 of 20 = 4 (floor)
-%! # It's not possible to test the column order with this call!
-%! # randomizing is used! But all max and min values should be
-%! # in the training set
-%!assert(max(mTrain(1,:))==max(matrix(1,:)));
-%!assert(min(mTrain(1,:))==min(matrix(1,:)));
-%!assert(max(mTrain(2,:))==max(matrix(2,:)));
-%!assert(min(mTrain(2,:))==min(matrix(2,:)));
-%!assert(max(mTrain(3,:))==max(matrix(3,:)));
-%!assert(min(mTrain(3,:))==min(matrix(3,:)));
-%!assert(max(mTrain(4,:))==max(matrix(4,:)));
-%!assert(min(mTrain(4,:))==min(matrix(4,:)));
-%!
-%!
-%! [mTrain, mTest, mVali] = subset(matrix,nTargets,0);  ############################
-%!assert(size(mTrain,2)==10);# 50% of 20
-%!assert(size(mTest,2)==6);# 1/3 of 20 = 6 (floor)
-%!assert(size(mVali,2)==4);# 1/6 of 20 = 4 (floor)
-%!assert(mTrain==matrix(:,1:10));
-%!assert(mTest==matrix(:,11:16));
-%!assert(mVali==matrix(:,17:20));
-%!
-%!
-%! [mTrain, mTest, mVali] = subset(matrix,nTargets,2);  ############################
-%!assert(size(mTrain,2)==10);# 50% of 20
-%!assert(size(mTest,2)==6);# 1/3 of 20 = 6 (floor)
-%!assert(size(mVali,2)==4);# 1/6 of 20 = 4 (floor)
-%!assert(max(mTrain(1,:))==max(matrix(1,:)));
-%!assert(min(mTrain(1,:))==min(matrix(1,:)));
-%!assert(max(mTrain(2,:))==max(matrix(2,:)));
-%!assert(min(mTrain(2,:))==min(matrix(2,:)));
-%!assert(max(mTrain(3,:))==max(matrix(3,:)));
-%!assert(min(mTrain(3,:))==min(matrix(3,:)));
-%!assert(max(mTrain(4,:))==max(matrix(4,:)));
-%!assert(min(mTrain(4,:))==min(matrix(4,:)));
-%!
-%!
-%! ## next test ... optimize twice
-%! matrix = [1 2 3 4 5 6 7 20 8 10 11 12 13 14 15 16 17 18 18 9; \
-%!			 0 2 4 1 3 5 3 4 1 -1 -2 -9 -1 10 12 20 11 11 11 11; \
-%!			-2 2 2 2 2 0 0 0 0  0 10 12 13 12 13 44 33 32 98 11; \
-%!			 0 0 0 0 1 1 1 1 0  0  1  1  1  0  0  1  1  1  0  0; \
-%!           4 4 4 4 4 4 4 4 4  4  4  4  4  4  4  4  4  4  4  4; \
-%!           1 2 3 4 5 6 7 8 9 10 11 12 13 33 44 55 66 77 88 99];
-%! [mTrain, mTest, mVali] = subset(matrix,nTargets,2);  ############################
-%!assert(max(mTrain(1,:))==max(matrix(1,:)));
-%!assert(min(mTrain(1,:))==min(matrix(1,:)));
-%!assert(max(mTrain(2,:))==max(matrix(2,:)));
-%!assert(min(mTrain(2,:))==min(matrix(2,:)));
-%!assert(max(mTrain(3,:))==max(matrix(3,:)));
-%!assert(min(mTrain(3,:))==min(matrix(3,:)));
-%!assert(max(mTrain(4,:))==max(matrix(4,:)));
-%!assert(min(mTrain(4,:))==min(matrix(4,:)));
-
-## \todo, a lot of tests to be sure, everything is working OK!!
-## all combinations of arguments must be testet!
--- a/main/nnet/inst/tansig.m	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,39 +0,0 @@
-## Copyright (C) 2006 Michel D. Schmid  <michael.schmid@plexso.com>
-##
-## 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 3 of the License, 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; if not, see <http://www.gnu.org/licenses/>.
-
-## -*- texinfo -*-
-## @deftypefn {Function File} {}@var{a} = tansig (@var{n})
-## @code{tansig} is a non-linear transfer function used to train
-## neural networks.
-## This function can be used in newff(...) to create a new feed forward
-## multi-layer neural network.
-##
-## @end deftypefn
-
-## @seealso{purelin, logsig}
-
-## Author: Michel D. Schmid
-
-
-function a = tansig(n)
-
-  ## see MATLAB(TM) online help
-  a = 2 ./ (1 + exp(-2*n)) - 1;
-  ## attention with critical values ==> infinite values
-  ## must be set to 1
-  i = find(!finite(a));
-  a(i) = sign(n(i));
-
-endfunction
--- a/main/nnet/inst/train.m	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,215 +0,0 @@
-## Copyright (C) 2006 Michel D. Schmid  <michael.schmid@plexso.com>
-##
-## 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 3 of the License, 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; if not, see <http://www.gnu.org/licenses/>.
-
-## -*- texinfo -*-
-## @deftypefn {Function File} {}[@var{net}] = train (@var{MLPnet},@var{mInputN},@var{mOutput},@var{[]},@var{[]},@var{VV})
-## A neural feed-forward network will be trained with @code{train}
-##
-## @example
-## [net,tr,out,E] = train(MLPnet,mInputN,mOutput,[],[],VV);
-## @end example
-## @noindent
-##
-## @example
-## left side arguments:
-##   net: the trained network of the net structure @code{MLPnet}
-## @end example
-## @noindent
-##
-## @example
-## right side arguments:
-##   MLPnet : the untrained network, created with @code{newff}
-##   mInputN: normalized input matrix
-##   mOutput: output matrix (normalized or not)
-##   []     : unused parameter
-##   []     : unused parameter
-##   VV     : validize structure
-## @end example
-## @end deftypefn
-
-## @seealso{newff,prestd,trastd}
-
-## Author: Michel D. Schmid
-
-## Comments: see in "A neural network toolbox for Octave User's Guide" [4]
-## for variable naming... there have inputs or targets only one letter,
-## e.g. for inputs is P written. To write a program, this is stupid, you can't
-## search for 1 letter variable... that's why it is written here like Pp, or Tt
-## instead only P or T.
-
-function [net] = train(net,Pp,Tt,notUsed1,notUsed2,VV)
-
-  ## check range of input arguments
-  error(nargchk(3,6,nargin))
-
-  ## set defaults
-  doValidation = 0;
-  if nargin==6
-    # doValidation=1;
-    ## check if VV is in MATLAB(TM) notation
-    [VV, doValidation] = checkVV(VV);
-  endif
-
-  ## check input args
-  checkInputArgs(net,Pp,Tt)
-
-  ## nargin ...
-  switch(nargin)
-  case 3
-    [Pp,Tt] = trainArgs(net,Pp,Tt);
-    VV = [];
-  case 6
-    [Pp,Tt] = trainArgs(net,Pp,Tt);
-    if isempty(VV)
-      VV = [];
-    else
-      if !isfield(VV,"Pp")
-        error("VV.Pp must be defined or VV must be [].")
-      endif
-      if !isfield(VV,"Tt")
-        error("VV.Tt must be defined or VV must be [].")
-      endif
-      [VV.Pp,VV.Tt] = trainArgs(net,VV.Pp,VV.Tt);
-    endif
-  otherwise
-    error("train: impossible code execution in switch(nargin)")
-  endswitch
-
-
-  ## so now, let's start training the network
-  ##===========================================
-
-  ## first let's check if a train function is defined ...
-  if isempty(net.trainFcn)
-    error("train:net.trainFcn not defined")
-  endif
-
-  ## calculate input matrix Im
-  [nRowsInputs, nColumnsInputs] = size(Pp);
-  Im = ones(nRowsInputs,nColumnsInputs).*Pp{1,1};
-
-  if (doValidation)
-    [nRowsVal, nColumnsVal] = size(VV.Pp);
-    VV.Im = ones(nRowsVal,nColumnsVal).*VV.Pp{1,1};
-  endif
-
-  ## make it MATLAB(TM) compatible
-  nLayers = net.numLayers;
-  Tt{nLayers,1} = Tt{1,1};
-  Tt{1,1} = [];
-  if (!isempty(VV))
-    VV.Tt{nLayers,1} = VV.Tt{1,1};
-    VV.Tt{1,1} = [];
-  endif
-
-  ## which training algorithm should be used
-  switch(net.trainFcn)
-    case "trainlm"
-      if !strcmp(net.performFcn,"mse")
-        error("Levenberg-Marquardt algorithm is defined with the MSE performance function, so please set MSE in NEWFF!")
-      endif
-      net = __trainlm(net,Im,Pp,Tt,VV);
-    otherwise
-      error("train algorithm argument is not valid!")
-  endswitch
-
-
-# =======================================================
-#
-# additional check functions...
-#
-# =======================================================
-
-  function checkInputArgs(net,Pp,Tt)
-
-    ## check "net", must be a net structure
-    if !__checknetstruct(net)
-      error("Structure doesn't seem to be a neural network!")
-    endif
-
-    ## check Pp (inputs)
-    nInputSize = net.inputs{1}.size; #only one exists
-    [nRowsPp, nColumnsPp] = size(Pp);
-    if ( (nColumnsPp>0) )
-      if ( nInputSize==nRowsPp )
-      # seems to be everything i.o.
-      else
-        error("Number of rows must be the same, like in net.inputs.size defined!")
-      endif
-    else
-      error("At least one column must exist")
-    endif
-
-    ## check Tt (targets)
-    [nRowsTt, nColumnsTt] = size(Tt);
-    if ( (nRowsTt | nColumnsTt)==0 )
-      error("No targets defined!")
-    elseif ( nColumnsTt!=nColumnsPp )
-      error("Inputs and targets must have the same number of data sets (columns).")
-    elseif ( net.layers{net.numLayers}.size!=nRowsTt )
-      error("Defined number of output neurons are not identically to targets data sets!")
-    endif
-
-  endfunction
-# -------------------------------------------------------
-  function [Pp,Tt] = trainArgs(net,Pp,Tt);
-
-    ## check number of arguments
-    error(nargchk(3,3,nargin));
-
-    [PpRows, PpColumns] = size(Pp);
-    Pp = mat2cell(Pp,PpRows,PpColumns);    # mat2cell is the reason
-    									   # why octave-2.9.5 doesn't work
-										   # octave-2.9.x with x>=6 should be
-										   # ok
-    [TtRows, TtColumns] = size(Tt);
-    Tt = mat2cell(Tt,TtRows,TtColumns);
-
-  endfunction
-
-# -------------------------------------------------------
-
-  function [VV, doValidation] = checkVV(VV)
-
-    ## check number of arguments
-    error(nargchk(1,1,nargin));
-
-	if (isempty(VV))
-	  doValidation = 0;
-	else
-	  doValidation = 1;
-      ## check if MATLAB(TM) naming convention is used
-      if isfield(VV,"P")
-        VV.Pp = VV.P;
-        VV.P = [];
-      elseif !isfield(VV,"Pp")
-        error("VV is defined but inside exist no VV.P or VV.Pp")
-      endif
-
-      if isfield(VV,"T")
-        VV.Tt = VV.T;
-        VV.T = [];
-      elseif !isfield(VV,"Tt")
-        error("VV is defined but inside exist no VV.TP or VV.Tt")
-      endif
-
-	endif
-
-
-  endfunction
-
-# ============================================================
-
-endfunction
--- a/main/nnet/inst/trastd.m	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,89 +0,0 @@
-## Copyright (C) 2005 Michel D. Schmid  <michael.schmid@plexso.com>
-##
-## 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 3 of the License, 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; if not, see <http://www.gnu.org/licenses/>.
-
-## -*- texinfo -*-
-## @deftypefn {Function File} {}@var{pn} = trastd (@var{p},@var{meanp},@var{stdp})
-## @code{trastd} preprocess additional data for neural network simulation.
-##
-## @example
-##   @code{p}    : test input data
-##   @code{meanp}: vector with standardization parameters of prestd(...)
-##   @code{stdp} : vector with standardization parameters of prestd(...)
-##
-##   meanp = [2.5; 6.5];
-##   stdp = [1.2910; 1.2910];
-##   p = [1 4; 2 5];
-##
-##   pn = trastd(p,meanp,stdp);
-## @end example
-## @noindent
-## @end deftypefn
-
-## @seealso{prestd, poststd}
-
-## Author: Michel D. Schmid
-
-function [Pn] = trastd(Pp,meanp,stdp)
-
-  ## check number of inputs
-  error(nargchk(3,3,nargin));
-
-
-  [nRows,nColumns]=size(Pp);
-  rowOnes = ones(1,nColumns);
-
-  ## now set all standard deviations which are zero to 1
-  [nRowsII, nColumnsII] = size(stdp);
-  rowZeros = zeros(nRowsII,1);
-  findZeros = find(stdp==0);
-  rowZeros(findZeros)=1;
-  equal = rowZeros;
-  nequal = !equal;
-  if ( sum(equal) != 0 )
-    warning("Some standard deviations are zero. Those inputs won't be transformed.");
-    meanp = meanp.*nequal;
-    stdp = stdp.*nequal + 1*equal;
-  end
-
-  Pn = (Pp-meanp*rowOnes)./(stdp*rowOnes);
-
-endfunction
-
-##
-## >> mInput = [1 2 3 4; 5 6 7 8]
-##
-## mInput =
-##
-##      1     2     3     4
-##      5     6     7     8
-##
-## >> [pn,meanp,stdp] = prestd(mInput)
-##
-## pn =
-##
-##    -1.1619   -0.3873    0.3873    1.1619
-##    -1.1619   -0.3873    0.3873    1.1619
-##
-##
-## meanp =
-##
-##     2.5000
-##     6.5000
-##
-##
-## stdp =
-##
-##     1.2910
-##     1.2910
--- a/main/nnet/inst/vec2ind.m	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,42 +0,0 @@
-## Copyright (C) 2009 Luiz Angelo Daros de Luca <luizluca@gmail.com>
-##
-## 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 3 of the License, 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; if not, see <http://www.gnu.org/licenses/>.
-
-## -*- texinfo -*-
-## @deftypefn {Function File} {@var{ind}} = vec2ind (@var{vector})
-## @code{vec2ind} convert vectors to indices
-##
-##
-## @example
-## EXAMPLE 1
-## vec = [1 2 3; 4 5 6; 7 8 9];
-##
-## ind = vec2ind(vec)
-## The prompt output will be:
-## ans =
-##    1 2 3 1 2 3 1 2 3
-## @end example
-##
-## @end deftypefn
-
-## @seealso{ind2vec}
-
-
-function [ind]=vec2ind(vector)
-  # Convert vectors to indices
-  #
-  #
-  [ind,col,vals] = find(vector);
-  ind=ind';
-endfunction
--- a/main/nnet/tests/MLP/MLPScripts.m	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,55 +0,0 @@
-## Copyright (C) 2007 Michel D. Schmid  <michael.schmid@plexso.com>
-##
-## 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 3 of the License, 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; if not, see <http://www.gnu.org/licenses/>.
-
-## author: msd
-
-
-## This file is used to call all MLP tests, but without
-## assert and so on, means if no error occure, everything
-## seems to be ok.
-## This is only a very basic test which should be run
-## from this directory
-
-tic
-cd example1;
-
-
-mlp9_1_1_tansig
-mlp9_2_1_tansig
-mlp9_2_2_1_tansig
-mlp9_2_2_tansig
-mlp9_2_3_tansig
-mlp9_5_3_tansig
-
-cd ..
-
-
-cd example2
-
-mlp9_1_1_logsig
-mlp9_2_1_logsig
-mlp9_2_2_1_logsig
-mlp9_2_2_logsig
-mlp9_2_3_logsig
-mlp9_5_3_logsig
-
-cd ..
-elapsed_time = toc;
-
-disp("Running 12 very basic tests successfully!")
-disp("Secondes needed to running all the tests: ");
-disp(elapsed_time);
-
-
--- a/main/nnet/tests/MLP/example1/mData.txt	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,94 +0,0 @@
-# Created by Octave 2.9.5, Wed May 24 10:33:36 2006     <sim@TL3124>
-# name: mData
-# type: matrix
-# rows: 89
-# columns: 13
- 306 286 2 0 12 61 2 0 3 28 4 0 2
- 368 188 1 0 6 49 0 0 3 37 0 0 1
- 511 73 0 0 40 21 0 0 16 22 0 0 1
- 418 43 0 0 34 30 1 0 9 27 5 0 1
- 299 173 1 0 8 63 1 0 1 37 3 0 1
- 312 253 0 0 2 63 2 0 0 35 3 0 1
- 492 98 0 0 7 23 0 0 13 36 0 0 2
- 506 64 0 0 32 23 0 0 13 29 0 0 3
- 476 41 0 0 32 5 0 0 19 26 0 0 3
- 483 66 0 0 17 16 0 0 10 28 2 0 3
- 429 44 0 0 37 19 0 0 23 19 0 0 1
- 521 137 0 0 16 17 0 0 7 24 0 0 2
- 340 163 1 0 16 72 3 0 3 31 1 0 1
- 323 177 0 0 8 68 4 0 0 37 2 0 1
- 344 240 2 0 4 43 1 0 4 39 6 0 2
- 459 22 0 0 46 14 0 0 27 17 0 0 1
- 487 36 0 0 34 10 0 0 19 27 0 0 3
- 331 169 0 0 19 66 1 0 0 34 1 0 1
- 541 269 1 0 12 5 0 0 6 20 0 0 3
- 475 23 0 0 37 5 0 0 26 14 0 0 3
- 475 186 1 0 15 28 0 0 5 35 0 0 2
- 496 319 0 0 2 4 0 0 2 14 0 0 3
- 525 41 0 0 38 9 0 0 37 18 0 0 3
- 484 37 0 0 50 13 0 0 29 18 0 0 2
- 511 55 0 0 32 15 0 0 23 21 0 0 2
- 515 44 0 0 29 16 0 0 19 31 0 0 2
- 478 101 0 0 15 34 0 0 11 40 0 0 2
- 429 433 3 0 8 11 0 0 5 15 0 0 3
- 471 8 0 0 58 2 0 0 25 13 0 0 1
- 303 269 3 0 10 66 2 0 1 32 7 0 2
- 445 74 0 0 19 30 0 0 4 43 0 0 1
- 488 83 0 0 34 18 0 0 15 34 0 0 3
- 264 298 4 0 7 68 10 0 1 30 7 0 2
- 489 44 0 0 21 12 0 0 29 21 0 0 1
- 475 34 0 0 38 13 0 0 28 18 0 0 3
- 492 14 0 0 44 4 0 0 40 7 0 0 1
- 454 173 1 0 14 21 0 0 2 42 0 0 2
- 306 285 5 0 5 68 3 0 1 33 1 0 2
- 508 64 0 0 31 14 0 0 17 33 0 0 2
- 477 61 0 0 36 13 0 0 22 15 0 0 2
- 349 224 1 0 3 66 3 0 2 41 1 0 1
- 447 189 0 0 11 34 0 0 5 45 0 0 2
- 496 34 0 0 49 6 0 0 31 11 0 0 3
- 484 222 0 0 16 12 0 0 9 24 0 0 2
- 412 50 0 0 25 47 0 0 11 29 0 0 1
- 316 184 0 0 13 65 3 0 5 34 3 0 1
- 345 163 1 0 17 57 2 0 2 32 1 0 1
- 285 273 2 0 7 50 13 0 1 28 10 0 1
- 317 179 0 0 11 64 0 0 4 35 0 0 1
- 500 68 0 0 23 15 0 0 18 30 0 0 1
- 495 34 0 0 39 4 0 0 21 21 0 0 3
- 387 294 1 0 10 37 0 0 4 30 5 0 2
- 258 236 2 0 2 72 7 0 0 26 8 0 1
- 423 25 0 0 26 16 0 0 16 29 0 0 3
- 501 32 0 0 40 11 0 0 24 20 0 0 3
- 459 37 0 0 46 4 0 0 32 16 0 0 3
- 511 48 0 0 35 7 0 0 27 15 0 0 2
- 295 271 3 0 6 71 5 0 3 29 4 0 1
- 502 34 0 0 25 9 0 0 23 11 0 0 3
- 458 36 0 0 12 7 0 0 24 19 0 0 2
- 470 273 1 0 9 17 0 0 2 32 0 0 3
- 477 30 0 0 24 14 0 0 18 26 0 0 3
- 406 77 1 0 15 24 2 0 6 37 1 0 1
- 291 251 2 0 8 53 2 0 0 43 2 0 1
- 407 51 0 0 28 47 0 0 5 35 0 0 1
- 390 47 0 0 17 45 1 0 3 33 2 0 1
- 347 123 1 0 7 52 7 0 4 38 3 0 1
- 300 175 0 0 8 71 2 0 0 35 0 0 1
- 473 59 0 0 44 15 0 0 22 23 0 0 1
- 487 35 0 0 34 12 0 0 29 24 0 0 3
- 532 57 0 0 19 6 0 0 28 15 0 0 2
- 286 207 1 0 5 74 2 0 1 38 4 0 1
- 405 199 0 0 13 37 0 0 6 45 0 0 2
- 337 177 0 0 11 47 1 0 4 39 3 0 1
- 527 20 0 0 32 6 0 0 23 16 0 0 1
- 326 218 1 0 4 71 2 0 1 33 1 0 1
- 486 14 0 0 41 4 0 0 40 8 0 0 1
- 420 43 0 0 26 30 9 0 9 24 4 0 1
- 286 293 3 0 7 83 9 0 1 22 5 0 1
- 395 59 0 0 30 33 0 0 4 36 0 0 1
- 506 24 0 0 34 12 0 0 26 17 0 0 3
- 396 217 0 0 16 43 2 0 6 30 0 0 2
- 457 15 0 0 44 1 0 0 30 13 0 0 1
- 472 139 0 0 16 25 0 0 11 38 0 0 2
- 493 21 0 0 33 16 0 0 22 19 0 0 3
- 311 236 0 0 6 66 4 0 2 24 9 0 1
- 490 23 0 0 34 6 0 0 30 14 0 0 1
- 485 29 0 0 33 7 0 0 14 20 0 0 3
- 481 43 0 0 38 11 0 0 21 24 0 0 2
--- a/main/nnet/tests/MLP/example1/mlp9_1_1_tansig.dat	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,5 +0,0 @@
-TRAINLM, Epoch 0/100, MSE 2.27367/0, Gradient 54.9248/1e-10
-TRAINLM, Epoch 8/100, MSE 0.570748/0, Gradient 0.0154084/1e-10
-TRAINLM, Validation stop.
-
-1.8392 1.8391 1.5589 1.5589 1.5589 1.5589 1.5589 1.8392 1.8392 1.8392 1.8392 1.8392 1.5589 1.8392 1.8392 1.5589 1.8392 1.8392 1.5589 1.8384 1.8392 1.5589 1.5589 1.8392 1.5589 1.5589 1.5589 1.8392 1.5589 1.5589
\ No newline at end of file
--- a/main/nnet/tests/MLP/example1/mlp9_1_1_tansig.dat_orig	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,16 +0,0 @@
->> TRAINLM, Epoch 0/100, MSE 2.27367/0, Gradient 54.9248/1e-010
-TRAINLM, Epoch 8/100, MSE 0.570748/0, Gradient 0.0154084/1e-010
-TRAINLM, Validation stop.
-
-
-simOut =
-
-  Columns 1 through 17
-
-    1.8392    1.8391    1.5589    1.5589    1.5589    1.5589    1.5589    1.8392    1.8392    1.8392    1.8392    1.8392    1.5589    1.8392    1.8392    1.5589    1.8392
-
-  Columns 18 through 30
-
-    1.8392    1.5589    1.8384    1.8392    1.5589    1.5589    1.8392    1.5589    1.5589    1.5589    1.8392    1.5589    1.5589
-
->>
\ No newline at end of file
--- a/main/nnet/tests/MLP/example1/mlp9_1_1_tansig.m	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,103 +0,0 @@
-## Copyright (C) 2006 Michel D. Schmid  <michael.schmid@plexso.com>
-##
-## 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 3 of the License, 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; if not, see <http://www.gnu.org/licenses/>.
-
-## author: msd
-
-##
-## This is a test to train a 9-1-1 MLP (was a real project).
-##
-
-## load data
-mData = load("mData.txt","mData");
-mData = mData.mData;
-[nRows, nColumns] = size(mData);
-# this file contains 13 columns. The first 12 columns are the inputs
-# the last column is the output
-# remove column 4, 8 and 12
-# 89 rows
-
-## this neural network example isn't a mirror of "the real life work"
-## it will be used to compare the results with MATLAB(TM) results.
-
-mOutput = mData(:,end);
-mInput = mData(:,1:end-1);
-mInput(:,[4 8 12]) = []; # delete column 4, 8 and 12
-
-## now prepare data
-mInput = mInput';
-mOutput = mOutput';
-
-# now split the data matrix in 3 pieces,
-# train data, test data and validate data
-# the proportion should be about 1/2 train,
-# 1/3 test and 1/6 validate data
-# in this neural network we have 12 weights,
-# for each weight at least 3 train sets..
-# (that's a rule of thumb like 1/2, 1/3 and 1/6)
-# 1/2 of 89 = 44.5; let's take 44 for training
-nTrainSets = floor(nRows/2);
-# now the rest of the sets are again 100%
-# ==> 2/3 for test sets and 1/3 for validate sets
-nTestSets = (nRows-nTrainSets)/3*2;
-nValiSets = nRows-nTrainSets-nTestSets;
-
-# now divide mInput & mOutput in the three sets
-mValiInput = mInput(:,1:nValiSets);
-mValliOutput = mOutput(:,1:nValiSets);
-mInput(:,1:nValiSets) = []; # delete validation set
-mOutput(:,1:nValiSets) = []; # delete validation set
-mTestInput = mInput(:,1:nTestSets);
-mTestOutput = mOutput(:,1:nTestSets);
-mInput(:,1:nTestSets) = []; # delete test set
-mOutput(:,1:nTestSets) = []; # delete test set
-mTrainInput = mInput(:,1:nTrainSets);
-mTrainOutput = mOutput(:,1:nTrainSets);
-
-[mTrainInputN,cMeanInput,cStdInput] = prestd(mTrainInput);# standardize inputs
-
-## comments: there is no reason to standardize the outputs because we have only
-# one output ...
-
-# define the max and min inputs for each row
-mMinMaxElements = min_max(mTrainInputN); # input matrix with (R x 2)...
-
-## define network
-nHiddenNeurons = 1;
-nOutputNeurons = 1;
-
-MLPnet = newff(mMinMaxElements,[nHiddenNeurons nOutputNeurons],{"tansig","purelin"},"trainlm","learngdm","mse");
-## for test purpose, define weights by hand
-MLPnet.IW{1,1}(:) = 1.5;
-MLPnet.LW{2,1}(:) = 0.5;
-MLPnet.b{1,1}(:) = 1.5;
-MLPnet.b{2,1}(:) = 0.5;
-
-## define validation data new, for MATLAB(TM) compatibility
-VV.P = mValiInput;
-VV.T = mValliOutput;
-
-## standardize also the validate data
-VV.P = trastd(VV.P,cMeanInput,cStdInput);
-
-## now train the network structure
-MLPnet.trainParam.show = NaN;
-[net] = train(MLPnet,mTrainInputN,mTrainOutput,[],[],VV);
-
-## make preparations for net test and test MLPnet
- # standardise input & output test data
-[mTestInputN] = trastd(mTestInput,cMeanInput,cStdInput);
-
-# will output the network results
-[simOut] = sim(net,mTestInputN);
--- a/main/nnet/tests/MLP/example1/mlp9_2_1_tansig.dat	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,5 +0,0 @@
-TRAINLM, Epoch 0/100, MSE 2.5516/0, Gradient 69.8821/1e-010
-TRAINLM, Epoch 16/100, MSE 0.45224/0, Gradient 0.0212947/1e-010
-TRAINLM, Validation stop.
-
-2.0386 2.0386 1.3267 2.2917 2.0386 2.0386 2.2917 2.0386 2.0386 2.0386 2.0386 1.3592 2.2917 2.0386 1.3267 1.3267 2.0386 1.3267 1.3405 2.0386 2.0386 1.3267 1.5778 2.0386 2.0386 1.3267 1.3292 2.0386 2.2917 1.3267
\ No newline at end of file
--- a/main/nnet/tests/MLP/example1/mlp9_2_1_tansig.m	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,102 +0,0 @@
-## Copyright (C) 2006 Michel D. Schmid  <michael.schmid@plexso.com>
-##
-## 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 3 of the License, 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; if not, see <http://www.gnu.org/licenses/>.
-
-## author: msd
-
-
-## load data
-mData = load("mData.txt","mData");
-mData = mData.mData;
-[nRows, nColumns] = size(mData);
-# this file contains 13 columns. The first 12 columns are the inputs
-# the last column is the output
-# remove column 4, 8 and 12
-# 89 rows
-
-## this neural network example isn't a mirror of "the real life work"
-## it will be used to compare the results with MATLAB(TM) results.
-
-mOutput = mData(:,end);
-mInput = mData(:,1:end-1);
-mInput(:,[4 8 12]) = []; # delete column 4, 8 and 12
-
-## now prepare data
-mInput = mInput';
-mOutput = mOutput';
-
-# now split the data matrix in 3 pieces,
-# train data, test data and validate data
-# the proportion should be about 1/2 train,
-# 1/3 test and 1/6 validate data
-# in this neural network we have 12 weights,
-# for each weight at least 3 train sets..
-# (that's a rule of thumb like 1/2, 1/3 and 1/6)
-# 1/2 of 89 = 44.5; let's take 44 for training
-nTrainSets = floor(nRows/2);
-# now the rest of the sets are again 100%
-# ==> 2/3 for test sets and 1/3 for validate sets
-nTestSets = (nRows-nTrainSets)/3*2;
-nValiSets = nRows-nTrainSets-nTestSets;
-
-# now divide mInput & mOutput in the three sets
-mValiInput = mInput(:,1:nValiSets);
-mValliOutput = mOutput(:,1:nValiSets);
-mInput(:,1:nValiSets) = []; # delete validation set
-mOutput(:,1:nValiSets) = []; # delete validation set
-mTestInput = mInput(:,1:nTestSets);
-mTestOutput = mOutput(:,1:nTestSets);
-mInput(:,1:nTestSets) = []; # delete test set
-mOutput(:,1:nTestSets) = []; # delete test set
-mTrainInput = mInput(:,1:nTrainSets);
-mTrainOutput = mOutput(:,1:nTrainSets);
-
-[mTrainInputN,cMeanInput,cStdInput] = prestd(mTrainInput);# standardize inputs
-
-## comments: there is no reason to standardize the outputs because we have only
-# one output ...
-
-# define the max and min inputs for each row
-mMinMaxElements = min_max(mTrainInputN); # input matrix with (R x 2)...
-
-## define network
-nHiddenNeurons = 2;
-nOutputNeurons = 1;
-
-MLPnet = newff(mMinMaxElements,[nHiddenNeurons nOutputNeurons],{"tansig","purelin"},"trainlm","learngdm","mse");
-## for test purpose, define weights by hand
-MLPnet.IW{1,1}(1,:) = 0.5;
-MLPnet.IW{1,1}(2,:) = 1.5;
-MLPnet.LW{2,1}(:) = 0.5;
-MLPnet.b{1,1}(1,:) = 0.5;
-MLPnet.b{1,1}(2,:) = 1.5;
-MLPnet.b{2,1}(:) = 0.5;
-
-## define validation data new, for MATLAB(TM) compatibility
-VV.P = mValiInput;
-VV.T = mValliOutput;
-
-## standardize also the validate data
-VV.P = trastd(VV.P,cMeanInput,cStdInput);
-
-## now train the network structure
-MLPnet.trainParam.show = NaN;
-[net] = train(MLPnet,mTrainInputN,mTrainOutput,[],[],VV);
-
-## make preparations for net test and test MLPnet
- # standardise input & output test data
-[mTestInputN] = trastd(mTestInput,cMeanInput,cStdInput);
-
-# will output the network results
-[simOut] = sim(net,mTestInputN);
--- a/main/nnet/tests/MLP/example1/mlp9_2_2_1_tansig.dat	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,5 +0,0 @@
-TRAINLM, Epoch 0/100, MSE 2.78009/0, Gradient 79.2812/1e-010
-TRAINLM, Epoch 6/100, MSE 0.202812/0, Gradient 2.34665/1e-010
-TRAINLM, Validation stop.
-
-1.4051 0.5235 1.3323 1.4051 1.4051 1.4051 1.4051 1.4051 0.1939 1.0805 1.4051 0.9002 1.4051 1.3975 1.1845 1.3363 0.5385 1.4051 1.3051 1.4051 1.4051 1.4051 1.3527 1.4021 1.4051 0.6403 1.4051 1.4029 1.4051 1.4051
\ No newline at end of file
--- a/main/nnet/tests/MLP/example1/mlp9_2_2_1_tansig.m	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,101 +0,0 @@
-## Copyright (C) 2006 Michel D. Schmid  <michael.schmid@plexso.com>
-##
-## 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 3 of the License, 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; if not, see <http://www.gnu.org/licenses/>.
-
-## author: msd
-
-
-## load data
-mData = load("mData.txt","mData");
-mData = mData.mData;
-[nRows, nColumns] = size(mData);
-# this file contains 13 columns. The first 12 columns are the inputs
-# the last column is the output
-# remove column 4, 8 and 12
-# 89 rows
-
-## this neural network example isn't a mirror of "the real life work"
-## it will be used to compare the results with MATLAB(TM) results.
-
-mOutput = mData(:,end);
-mInput = mData(:,1:end-1);
-mInput(:,[4 8 12]) = []; # delete column 4, 8 and 12
-
-## now prepare data
-mInput = mInput';
-mOutput = mOutput';
-
-
-# now split the data matrix in 3 pieces,
-# train data, test data and validate data
-# the proportion should be about 1/2 train,
-# 1/3 test and 1/6 validate data
-# in this neural network we have 12 weights,
-# for each weight at least 3 train sets..
-# (that's a rule of thumb like 1/2, 1/3 and 1/6)
-# 1/2 of 89 = 44.5; let's take 44 for training
-nTrainSets = floor(nRows/2);
-# now the rest of the sets are again 100%
-# ==> 2/3 for test sets and 1/3 for validate sets
-nTestSets = (nRows-nTrainSets)/3*2;
-nValiSets = nRows-nTrainSets-nTestSets;
-
-# now divide mInput & mOutput in the three sets
-mValiInput = mInput(:,1:nValiSets);
-mValliOutput = mOutput(:,1:nValiSets);
-mInput(:,1:nValiSets) = []; # delete validation set
-mOutput(:,1:nValiSets) = []; # delete validation set
-mTestInput = mInput(:,1:nTestSets);
-mTestOutput = mOutput(:,1:nTestSets);
-mInput(:,1:nTestSets) = []; # delete test set
-mOutput(:,1:nTestSets) = []; # delete test set
-mTrainInput = mInput(:,1:nTrainSets);
-mTrainOutput = mOutput(:,1:nTrainSets);
-
-[mTrainInputN,cMeanInput,cStdInput] = prestd(mTrainInput);# standardize inputs
-
-## comments: there is no reason to standardize the outputs because we have only
-# one output ...
-
-# define the max and min inputs for each row
-mMinMaxElements = min_max(mTrainInputN); # input matrix with (R x 2)...
-
-%% define network
-nHiddenNeurons = [2 2];
-nOutputNeurons = 1;
-
-MLPnet = newff(mMinMaxElements,[nHiddenNeurons nOutputNeurons],{'tansig','tansig','tansig','purelin'},'trainlm','learngdm','mse');
-%% for test purpose, define weights by hand
-# MLPnet.IW{1,1}(:) = 1.5;
-# MLPnet.LW{2,1}(:) = 0.5;
-# MLPnet.b{1,1}(:) = 1.5;
-# MLPnet.b{2,1}(:) = 0.5;
-
-## define validation data new, for MATLAB(TM) compatibility
-VV.P = mValiInput;
-VV.T = mValliOutput;
-
-## standardize also the validate data
-VV.P = trastd(VV.P,cMeanInput,cStdInput);
-
-## now train the network structure
-MLPnet.trainParam.show = NaN;
-[net] = train(MLPnet,mTrainInputN,mTrainOutput,[],[],VV);
-
-## make preparations for net test and test MLPnet
- # standardise input & output test data
-[mTestInputN] = trastd(mTestInput,cMeanInput,cStdInput);
-
-# will output the network results
-[simOut] = sim(net,mTestInputN);
--- a/main/nnet/tests/MLP/example1/mlp9_2_2_tansig.dat	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,6 +0,0 @@
-TRAINLM, Epoch 0/100, MSE 26.3503/0, Gradient 416.299/1e-010
-TRAINLM, Epoch 9/100, MSE 3.26824/0, Gradient 0.969955/1e-010
-TRAINLM, Validation stop.
-
-2.0431 2.0431 1.0154 2.0035 2.0431 1.0154 2.0035 2.0431 1.0154 2.0431 1.0154 1.0154 0.9758 2.0431 1.0154 1.0154 1.0154 0.9758 2.0431 2.0431 2.0431 1.0154 1.0154 1.0154 2.0431 1.0154 1.0154 2.0431 2.0037 1.0154
-8.1802 8.1802 4.0490 8.0207 8.1802 4.0490 8.0207 8.1802 4.0490 8.1802 4.0490 4.0490 3.8895 8.1802 4.0490 4.0490 4.0490 3.8895 8.1802 8.1802 8.1802 4.0490 4.0490 4.0490 8.1802 4.0490 4.0490 8.1802 8.0213 4.0490
\ No newline at end of file
--- a/main/nnet/tests/MLP/example1/mlp9_2_2_tansig.m	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,104 +0,0 @@
-## Copyright (C) 2006 Michel D. Schmid  <michael.schmid@plexso.com>
-##
-## 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 3 of the License, 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; if not, see <http://www.gnu.org/licenses/>.
-
-## author: msd
-
-
-## load data
-mData = load("mData.txt","mData");
-mData = mData.mData;
-[nRows, nColumns] = size(mData);
-# this file contains 13 columns. The first 12 columns are the inputs
-# the last column is the output
-# remove column 4, 8 and 12
-# 89 rows
-
-## this neural network example isn't a mirror of "the real life work"
-## it will be used to compare the results with MATLAB(TM) results.
-
-mOutput = mData(:,end);
-mInput = mData(:,1:end-1);
-mInput(:,[4 8 12]) = []; # delete column 4, 8 and 12
-
-## now prepare data
-mInput = mInput';
-mOutput = mOutput';
-mOutput = [mOutput; mOutput*4];
-
-# now split the data matrix in 3 pieces,
-# train data, test data and validate data
-# the proportion should be about 1/2 train,
-# 1/3 test and 1/6 validate data
-# in this neural network we have 12 weights,
-# for each weight at least 3 train sets..
-# (that's a rule of thumb like 1/2, 1/3 and 1/6)
-# 1/2 of 89 = 44.5; let's take 44 for training
-nTrainSets = floor(nRows/2);
-# now the rest of the sets are again 100%
-# ==> 2/3 for test sets and 1/3 for validate sets
-nTestSets = (nRows-nTrainSets)/3*2;
-nValiSets = nRows-nTrainSets-nTestSets;
-
-# now divide mInput & mOutput in the three sets
-mValiInput = mInput(:,1:nValiSets);
-mValliOutput = mOutput(:,1:nValiSets);
-mInput(:,1:nValiSets) = []; # delete validation set
-mOutput(:,1:nValiSets) = []; # delete validation set
-mTestInput = mInput(:,1:nTestSets);
-mTestOutput = mOutput(:,1:nTestSets);
-mInput(:,1:nTestSets) = []; # delete test set
-mOutput(:,1:nTestSets) = []; # delete test set
-mTrainInput = mInput(:,1:nTrainSets);
-mTrainOutput = mOutput(:,1:nTrainSets);
-
-[mTrainInputN,cMeanInput,cStdInput] = prestd(mTrainInput);# standardize inputs
-
-## comments: there is no reason to standardize the outputs because we have only
-# one output ...
-
-# define the max and min inputs for each row
-mMinMaxElements = min_max(mTrainInputN); # input matrix with (R x 2)...
-
-## define network
-nHiddenNeurons = 2;
-nOutputNeurons = 2;
-
-MLPnet = newff(mMinMaxElements,[nHiddenNeurons nOutputNeurons],{"tansig","purelin"},"trainlm","learngdm","mse");
-## for test purpose, define weights by hand
-MLPnet.IW{1,1}(1,:) = 1.5;
-MLPnet.IW{1,1}(2,:) = 0.5;
-MLPnet.LW{2,1}(1,:) = 1.5;
-MLPnet.LW{2,1}(2,:) = 0.5;
-MLPnet.b{1,1}(1,:) = 0.5;
-MLPnet.b{1,1}(2,:) = 1.5;
-MLPnet.b{2,1}(:) = 0.5;
-
-## define validation data new, for MATLAB(TM) compatibility
-VV.P = mValiInput;
-VV.T = mValliOutput;
-
-## standardize also the validate data
-VV.P = trastd(VV.P,cMeanInput,cStdInput);
-
-## now train the network structure
-MLPnet.trainParam.show = NaN;
-[net] = train(MLPnet,mTrainInputN,mTrainOutput,[],[],VV);
-
-## make preparations for net test and test MLPnet
- # standardise input & output test data
-[mTestInputN] = trastd(mTestInput,cMeanInput,cStdInput);
-
-# will output the network results
-[simOut] = sim(net,mTestInputN);
--- a/main/nnet/tests/MLP/example1/mlp9_2_3_tansig.dat	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,7 +0,0 @@
-TRAINLM, Epoch 0/100, MSE 134.203/0, Gradient 1028.61/1e-010
-TRAINLM, Epoch 14/100, MSE 14.8413/0, Gradient 0.138881/1e-010
-TRAINLM, Validation stop.
-
-2.1874 2.0916 1.1052 2.1874 2.1874 1.1052 1.1052 2.1874 2.1874 2.1874 1.1052 1.1931 2.1874 2.1874 1.1052 2.3418 1.1052 1.1052 2.1874 2.1874 2.1874 2.3418 1.1052 1.1052 2.1874 1.1052 2.3412 2.1874 1.1052 1.1052
-8.7496 8.3662 4.4206 8.7496 8.7496 4.4208 4.4206 8.7496 8.7496 8.7496 4.4206 4.7724 8.7496 8.7496 4.4206 9.3672 4.4206 4.4206 8.7496 8.7496 8.7496 9.3672 4.4206 4.4206 8.7496 4.4206 9.3648 8.7496 4.4206 4.4206
-21.8740 20.9156 11.0515 21.8740 21.8740 11.0520 11.0515 21.8740 21.8740 21.8740 11.0515 11.9311 21.8740 21.8740 11.0515 23.4180 11.0515 11.0515 21.8740 21.8740 21.8740 23.4180 11.0515 11.0515 21.8740 11.0515 23.4119 21.8740 11.0515 11.0515
\ No newline at end of file
--- a/main/nnet/tests/MLP/example1/mlp9_2_3_tansig.m	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,109 +0,0 @@
-## Copyright (C) 2006 Michel D. Schmid  <michael.schmid@plexso.com>
-##
-## 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 3 of the License, 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; if not, see <http://www.gnu.org/licenses/>.
-
-## author: msd
-
-
-## load data
-mData = load("mData.txt","mData");
-mData = mData.mData;
-[nRows, nColumns] = size(mData);
-# this file contains 13 columns. The first 12 columns are the inputs
-# the last column is the output
-# remove column 4, 8 and 12
-# 89 rows
-
-## this neural network example isn't a mirror of "the real life work"
-## it will be used to compare the results with MATLAB(TM) results.
-
-mOutput = mData(:,end);
-mInput = mData(:,1:end-1);
-mInput(:,[4 8 12]) = []; # delete column 4, 8 and 12
-
-## now prepare data
-mInput = mInput';
-mOutput = mOutput';
-mOutput = [mOutput; mOutput*4; mOutput*10];
-
-
-# now split the data matrix in 3 pieces,
-# train data, test data and validate data
-# the proportion should be about 1/2 train,
-# 1/3 test and 1/6 validate data
-# in this neural network we have 12 weights,
-# for each weight at least 3 train sets..
-# (that's a rule of thumb like 1/2, 1/3 and 1/6)
-# 1/2 of 89 = 44.5; let's take 44 for training
-nTrainSets = floor(nRows/2);
-# now the rest of the sets are again 100%
-# ==> 2/3 for test sets and 1/3 for validate sets
-nTestSets = (nRows-nTrainSets)/3*2;
-nValiSets = nRows-nTrainSets-nTestSets;
-
-# now divide mInput & mOutput in the three sets
-mValiInput = mInput(:,1:nValiSets);
-mValliOutput = mOutput(:,1:nValiSets);
-mInput(:,1:nValiSets) = []; # delete validation set
-mOutput(:,1:nValiSets) = []; # delete validation set
-mTestInput = mInput(:,1:nTestSets);
-mTestOutput = mOutput(:,1:nTestSets);
-mInput(:,1:nTestSets) = []; # delete test set
-mOutput(:,1:nTestSets) = []; # delete test set
-mTrainInput = mInput(:,1:nTrainSets);
-mTrainOutput = mOutput(:,1:nTrainSets);
-
-[mTrainInputN,cMeanInput,cStdInput] = prestd(mTrainInput);# standardize inputs
-
-## comments: there is no reason to standardize the outputs because we have only
-# one output ...
-
-# define the max and min inputs for each row
-mMinMaxElements = min_max(mTrainInputN); # input matrix with (R x 2)...
-
-## define network
-nHiddenNeurons = 2;
-nOutputNeurons = 3;
-
-MLPnet = newff(mMinMaxElements,[nHiddenNeurons nOutputNeurons],{"tansig","purelin"},"trainlm","learngdm","mse");
-## for test purpose, define weights by hand
-MLPnet.IW{1,1}(1,:) = 1.5;
-MLPnet.IW{1,1}(2,:) = 0.5;
-MLPnet.LW{2,1}(1,:) = 1.5;
-MLPnet.LW{2,1}(2,:) = 0.5;
-MLPnet.LW{2,1}(3,:) = 0.1;
-MLPnet.b{1,1}(1,:) = 0.5;
-MLPnet.b{1,1}(2,:) = 1.5;
-MLPnet.b{2,1}(1,:) = 0.5;
-MLPnet.b{2,1}(2,:) = 0.5;
-MLPnet.b{2,1}(3,:) = 0.1;
-
-
-## define validation data new, for MATLAB(TM) compatibility
-VV.P = mValiInput;
-VV.T = mValliOutput;
-
-## standardize also the validate data
-VV.P = trastd(VV.P,cMeanInput,cStdInput);
-
-## now train the network structure
-MLPnet.trainParam.show = NaN;
-[net] = train(MLPnet,mTrainInputN,mTrainOutput,[],[],VV);
-
-## make preparations for net test and test MLPnet
- # standardise input & output test data
-[mTestInputN] = trastd(mTestInput,cMeanInput,cStdInput);
-
-# will output the network results
-[simOut] = sim(net,mTestInputN);
--- a/main/nnet/tests/MLP/example1/mlp9_5_3_tansig.dat	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,7 +0,0 @@
-TRAINLM, Epoch 0/100, MSE 141.889/0, Gradient 1253.56/1e-010
-TRAINLM, Epoch 8/100, MSE 13.6739/0, Gradient 137.397/1e-010
-TRAINLM, Validation stop.
-
-0.8081 2.5413 2.5667 1.1749 1.3791 1.1761 1.2381 0.6851 0.6435 1.4331 2.2694 2.5965 1.0768 0.7442 0.9235 2.5681 1.8238  0.4295 1.0288 0.9430 0.4290 1.1847 1.1398 1.8846 1.7315 0.9500 2.4366 0.4475 1.2249 2.5670
-4.7533 8.7954 8.3791 6.1910 5.8888 6.1933 6.3116 4.5980 4.4590 6.6755 8.2791 8.5445 5.8721 4.4152 5.3732 8.3835 7.4296 3.7666 5.4819 5.3490 3.7647 6.2098 6.0769 7.5455 7.0122 5.3092 8.5951 3.8224 6.2864 8.3796
-12.1298 21.4848 20.4710 15.8321 14.6769 15.8372 16.0940 11.8536 11.5048 16.8779 20.3651 20.8561 15.0359 11.2229 13.7898 20.4812 18.5210 9.7772 14.1764 13.6925 9.7724 15.8731 15.5474 18.7726 17.4322 13.8107 21.0503 9.9154 16.0392 20.4721
\ No newline at end of file
--- a/main/nnet/tests/MLP/example1/mlp9_5_3_tansig.m	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,111 +0,0 @@
-## Copyright (C) 2006 Michel D. Schmid  <michael.schmid@plexso.com>
-##
-## 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 3 of the License, 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; if not, see <http://www.gnu.org/licenses/>.
-
-## author: msd
-
-
-## load data
-mData = load("mData.txt","mData");
-mData = mData.mData;
-[nRows, nColumns] = size(mData);
-# this file contains 13 columns. The first 12 columns are the inputs
-# the last column is the output
-# remove column 4, 8 and 12
-# 89 rows
-
-## this neural network example isn't a mirror of "the real life work"
-## it will be used to compare the results with MATLAB(TM) results.
-
-mOutput = mData(:,end);
-mInput = mData(:,1:end-1);
-mInput(:,[4 8 12]) = []; # delete column 4, 8 and 12
-
-## now prepare data
-mInput = mInput';
-mOutput = mOutput';
-mOutput = [mOutput; mOutput*4; mOutput*10];
-
-
-# now split the data matrix in 3 pieces,
-# train data, test data and validate data
-# the proportion should be about 1/2 train,
-# 1/3 test and 1/6 validate data
-# in this neural network we have 12 weights,
-# for each weight at least 3 train sets..
-# (that's a rule of thumb like 1/2, 1/3 and 1/6)
-# 1/2 of 89 = 44.5; let's take 44 for training
-nTrainSets = floor(nRows/2);
-# now the rest of the sets are again 100%
-# ==> 2/3 for test sets and 1/3 for validate sets
-nTestSets = (nRows-nTrainSets)/3*2;
-nValiSets = nRows-nTrainSets-nTestSets;
-
-# now divide mInput & mOutput in the three sets
-mValiInput = mInput(:,1:nValiSets);
-mValliOutput = mOutput(:,1:nValiSets);
-mInput(:,1:nValiSets) = []; # delete validation set
-mOutput(:,1:nValiSets) = []; # delete validation set
-mTestInput = mInput(:,1:nTestSets);
-mTestOutput = mOutput(:,1:nTestSets);
-mInput(:,1:nTestSets) = []; # delete test set
-mOutput(:,1:nTestSets) = []; # delete test set
-mTrainInput = mInput(:,1:nTrainSets);
-mTrainOutput = mOutput(:,1:nTrainSets);
-
-[mTrainInputN,cMeanInput,cStdInput] = prestd(mTrainInput);# standardize inputs
-
-## comments: there is no reason to standardize the outputs because we have only
-# one output ...
-
-# define the max and min inputs for each row
-mMinMaxElements = min_max(mTrainInputN); # input matrix with (R x 2)...
-
-## define network
-nHiddenNeurons = 5;
-nOutputNeurons = 3;
-
-MLPnet = newff(mMinMaxElements,[nHiddenNeurons nOutputNeurons],{"tansig","purelin"},"trainlm","learngdm","mse");
-## for test purpose, define weights by hand
-MLPnet.IW{1,1}(1,:) = 1.5;
-MLPnet.IW{1,1}(2,:) = 0.5;
-MLPnet.IW{1,1}(3:5,:) = 1;
-MLPnet.LW{2,1}(1,:) = 1.5;
-MLPnet.LW{2,1}(2,:) = 0.5;
-MLPnet.LW{2,1}(3,:) = 0.1;
-MLPnet.b{1,1}(1,:) = 0.5;
-MLPnet.b{1,1}(2,:) = 1.5;
-MLPnet.b{1,1}(3:5,:) = 1;
-MLPnet.b{2,1}(1,:) = 0.5;
-MLPnet.b{2,1}(2,:) = 0.5;
-MLPnet.b{2,1}(3,:) = 0.1;
-
-
-## define validation data new, for MATLAB(TM) compatibility
-VV.P = mValiInput;
-VV.T = mValliOutput;
-
-## standardize also the validate data
-VV.P = trastd(VV.P,cMeanInput,cStdInput);
-
-## now train the network structure
-MLPnet.trainParam.show = NaN;
-[net] = train(MLPnet,mTrainInputN,mTrainOutput,[],[],VV);
-
-## make preparations for net test and test MLPnet
- # standardise input & output test data
-[mTestInputN] = trastd(mTestInput,cMeanInput,cStdInput);
-
-# will output the network results
-[simOut] = sim(net,mTestInputN);
--- a/main/nnet/tests/MLP/example1/orig/mData.txt	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,89 +0,0 @@
-390	47	0	0	17	45	1	0	3	33	2	0	1
-395	59	0	0	30	33	0	0	4	36	0	0	1
-420	43	0	0	26	30	9	0	9	24	4	0	1
-412	50	0	0	25	47	0	0	11	29	0	0	1
-418	43	0	0	34	30	1	0	9	27	5	0	1
-406	77	1	0	15	24	2	0	6	37	1	0	1
-407	51	0	0	28	47	0	0	5	35	0	0	1
-445	74	0	0	19	30	0	0	4	43	0	0	1
-317	179	0	0	11	64	0	0	4	35	0	0	1
-300	175	0	0	8	71	2	0	0	35	0	0	1
-331	169	0	0	19	66	1	0	0	34	1	0	1
-347	123	1	0	7	52	7	0	4	38	3	0	1
-323	177	0	0	8	68	4	0	0	37	2	0	1
-337	177	0	0	11	47	1	0	4	39	3	0	1
-299	173	1	0	8	63	1	0	1	37	3	0	1
-286	207	1	0	5	74	2	0	1	38	4	0	1
-429	44	0	0	37	19	0	0	23	19	0	0	1
-459	22	0	0	46	14	0	0	27	17	0	0	1
-340	163	1	0	16	72	3	0	3	31	1	0	1
-311	236	0	0	6	66	4	0	2	24	9	0	1
-326	218	1	0	4	71	2	0	1	33	1	0	1
-316	184	0	0	13	65	3	0	5	34	3	0	1
-258	236	2	0	2	72	7	0	0	26	8	0	1
-457	15	0	0	44	1	0	0	30	13	0	0	1
-471	8	0	0	58	2	0	0	25	13	0	0	1
-285	273	2	0	7	50	13	0	1	28	10	0	1
-489	44	0	0	21	12	0	0	29	21	0	0	1
-345	163	1	0	17	57	2	0	2	32	1	0	1
-473	59	0	0	44	15	0	0	22	23	0	0	1
-486	14	0	0	41	4	0	0	40	8	0	0	1
-368	188	1	0	6	49	0	0	3	37	0	0	1
-490	23	0	0	34	6	0	0	30	14	0	0	1
-291	251	2	0	8	53	2	0	0	43	2	0	1
-492	14	0	0	44	4	0	0	40	7	0	0	1
-286	293	3	0	7	83	9	0	1	22	5	0	1
-349	224	1	0	3	66	3	0	2	41	1	0	1
-295	271	3	0	6	71	5	0	3	29	4	0	1
-500	68	0	0	23	15	0	0	18	30	0	0	1
-511	73	0	0	40	21	0	0	16	22	0	0	1
-312	253	0	0	2	63	2	0	0	35	3	0	1
-527	20	0	0	32	6	0	0	23	16	0	0	1
-264	298	4	0	7	68	10	0	1	30	7	0	2
-478	101	0	0	15	34	0	0	11	40	0	0	2
-344	240	2	0	4	43	1	0	4	39	6	0	2
-303	269	3	0	10	66	2	0	1	32	7	0	2
-306	286	2	0	12	61	2	0	3	28	4	0	2
-515	44	0	0	29	16	0	0	19	31	0	0	2
-306	285	5	0	5	68	3	0	1	33	1	0	2
-472	139	0	0	16	25	0	0	11	38	0	0	2
-454	173	1	0	14	21	0	0	2	42	0	0	2
-458	36	0	0	12	7	0	0	24	19	0	0	2
-405	199	0	0	13	37	0	0	6	45	0	0	2
-475	186	1	0	15	28	0	0	5	35	0	0	2
-447	189	0	0	11	34	0	0	5	45	0	0	2
-387	294	1	0	10	37	0	0	4	30	5	0	2
-396	217	0	0	16	43	2	0	6	30	0	0	2
-492	98	0	0	7	23	0	0	13	36	0	0	2
-521	137	0	0	16	17	0	0	7	24	0	0	2
-511	48	0	0	35	7	0	0	27	15	0	0	2
-532	57	0	0	19	6	0	0	28	15	0	0	2
-477	61	0	0	36	13	0	0	22	15	0	0	2
-481	43	0	0	38	11	0	0	21	24	0	0	2
-484	222	0	0	16	12	0	0	9	24	0	0	2
-484	37	0	0	50	13	0	0	29	18	0	0	2
-508	64	0	0	31	14	0	0	17	33	0	0	2
-511	55	0	0	32	15	0	0	23	21	0	0	2
-541	269	1	0	12	5	0	0	6	20	0	0	3
-477	30	0	0	24	14	0	0	18	26	0	0	3
-496	319	0	0	2	4	0	0	2	14	0	0	3
-470	273	1	0	9	17	0	0	2	32	0	0	3
-429	433	3	0	8	11	0	0	5	15	0	0	3
-506	64	0	0	32	23	0	0	13	29	0	0	3
-487	36	0	0	34	10	0	0	19	27	0	0	3
-475	34	0	0	38	13	0	0	28	18	0	0	3
-423	25	0	0	26	16	0	0	16	29	0	0	3
-488	83	0	0	34	18	0	0	15	34	0	0	3
-487	35	0	0	34	12	0	0	29	24	0	0	3
-475	23	0	0	37	5	0	0	26	14	0	0	3
-496	34	0	0	49	6	0	0	31	11	0	0	3
-493	21	0	0	33	16	0	0	22	19	0	0	3
-459	37	0	0	46	4	0	0	32	16	0	0	3
-525	41	0	0	38	9	0	0	37	18	0	0	3
-501	32	0	0	40	11	0	0	24	20	0	0	3
-506	24	0	0	34	12	0	0	26	17	0	0	3
-502	34	0	0	25	9	0	0	23	11	0	0	3
-485	29	0	0	33	7	0	0	14	20	0	0	3
-495	34	0	0	39	4	0	0	21	21	0	0	3
-476	41	0	0	32	5	0	0	19	26	0	0	3
-483	66	0	0	17	16	0	0	10	28	2	0	3
\ No newline at end of file
--- a/main/nnet/tests/MLP/example2/mData.txt	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,94 +0,0 @@
-# Created by Octave 2.9.5, Wed May 24 10:33:36 2006     <sim@TL3124>
-# name: mData
-# type: matrix
-# rows: 89
-# columns: 13
- 306 286 2 0 12 61 2 0 3 28 4 0 2
- 368 188 1 0 6 49 0 0 3 37 0 0 1
- 511 73 0 0 40 21 0 0 16 22 0 0 1
- 418 43 0 0 34 30 1 0 9 27 5 0 1
- 299 173 1 0 8 63 1 0 1 37 3 0 1
- 312 253 0 0 2 63 2 0 0 35 3 0 1
- 492 98 0 0 7 23 0 0 13 36 0 0 2
- 506 64 0 0 32 23 0 0 13 29 0 0 3
- 476 41 0 0 32 5 0 0 19 26 0 0 3
- 483 66 0 0 17 16 0 0 10 28 2 0 3
- 429 44 0 0 37 19 0 0 23 19 0 0 1
- 521 137 0 0 16 17 0 0 7 24 0 0 2
- 340 163 1 0 16 72 3 0 3 31 1 0 1
- 323 177 0 0 8 68 4 0 0 37 2 0 1
- 344 240 2 0 4 43 1 0 4 39 6 0 2
- 459 22 0 0 46 14 0 0 27 17 0 0 1
- 487 36 0 0 34 10 0 0 19 27 0 0 3
- 331 169 0 0 19 66 1 0 0 34 1 0 1
- 541 269 1 0 12 5 0 0 6 20 0 0 3
- 475 23 0 0 37 5 0 0 26 14 0 0 3
- 475 186 1 0 15 28 0 0 5 35 0 0 2
- 496 319 0 0 2 4 0 0 2 14 0 0 3
- 525 41 0 0 38 9 0 0 37 18 0 0 3
- 484 37 0 0 50 13 0 0 29 18 0 0 2
- 511 55 0 0 32 15 0 0 23 21 0 0 2
- 515 44 0 0 29 16 0 0 19 31 0 0 2
- 478 101 0 0 15 34 0 0 11 40 0 0 2
- 429 433 3 0 8 11 0 0 5 15 0 0 3
- 471 8 0 0 58 2 0 0 25 13 0 0 1
- 303 269 3 0 10 66 2 0 1 32 7 0 2
- 445 74 0 0 19 30 0 0 4 43 0 0 1
- 488 83 0 0 34 18 0 0 15 34 0 0 3
- 264 298 4 0 7 68 10 0 1 30 7 0 2
- 489 44 0 0 21 12 0 0 29 21 0 0 1
- 475 34 0 0 38 13 0 0 28 18 0 0 3
- 492 14 0 0 44 4 0 0 40 7 0 0 1
- 454 173 1 0 14 21 0 0 2 42 0 0 2
- 306 285 5 0 5 68 3 0 1 33 1 0 2
- 508 64 0 0 31 14 0 0 17 33 0 0 2
- 477 61 0 0 36 13 0 0 22 15 0 0 2
- 349 224 1 0 3 66 3 0 2 41 1 0 1
- 447 189 0 0 11 34 0 0 5 45 0 0 2
- 496 34 0 0 49 6 0 0 31 11 0 0 3
- 484 222 0 0 16 12 0 0 9 24 0 0 2
- 412 50 0 0 25 47 0 0 11 29 0 0 1
- 316 184 0 0 13 65 3 0 5 34 3 0 1
- 345 163 1 0 17 57 2 0 2 32 1 0 1
- 285 273 2 0 7 50 13 0 1 28 10 0 1
- 317 179 0 0 11 64 0 0 4 35 0 0 1
- 500 68 0 0 23 15 0 0 18 30 0 0 1
- 495 34 0 0 39 4 0 0 21 21 0 0 3
- 387 294 1 0 10 37 0 0 4 30 5 0 2
- 258 236 2 0 2 72 7 0 0 26 8 0 1
- 423 25 0 0 26 16 0 0 16 29 0 0 3
- 501 32 0 0 40 11 0 0 24 20 0 0 3
- 459 37 0 0 46 4 0 0 32 16 0 0 3
- 511 48 0 0 35 7 0 0 27 15 0 0 2
- 295 271 3 0 6 71 5 0 3 29 4 0 1
- 502 34 0 0 25 9 0 0 23 11 0 0 3
- 458 36 0 0 12 7 0 0 24 19 0 0 2
- 470 273 1 0 9 17 0 0 2 32 0 0 3
- 477 30 0 0 24 14 0 0 18 26 0 0 3
- 406 77 1 0 15 24 2 0 6 37 1 0 1
- 291 251 2 0 8 53 2 0 0 43 2 0 1
- 407 51 0 0 28 47 0 0 5 35 0 0 1
- 390 47 0 0 17 45 1 0 3 33 2 0 1
- 347 123 1 0 7 52 7 0 4 38 3 0 1
- 300 175 0 0 8 71 2 0 0 35 0 0 1
- 473 59 0 0 44 15 0 0 22 23 0 0 1
- 487 35 0 0 34 12 0 0 29 24 0 0 3
- 532 57 0 0 19 6 0 0 28 15 0 0 2
- 286 207 1 0 5 74 2 0 1 38 4 0 1
- 405 199 0 0 13 37 0 0 6 45 0 0 2
- 337 177 0 0 11 47 1 0 4 39 3 0 1
- 527 20 0 0 32 6 0 0 23 16 0 0 1
- 326 218 1 0 4 71 2 0 1 33 1 0 1
- 486 14 0 0 41 4 0 0 40 8 0 0 1
- 420 43 0 0 26 30 9 0 9 24 4 0 1
- 286 293 3 0 7 83 9 0 1 22 5 0 1
- 395 59 0 0 30 33 0 0 4 36 0 0 1
- 506 24 0 0 34 12 0 0 26 17 0 0 3
- 396 217 0 0 16 43 2 0 6 30 0 0 2
- 457 15 0 0 44 1 0 0 30 13 0 0 1
- 472 139 0 0 16 25 0 0 11 38 0 0 2
- 493 21 0 0 33 16 0 0 22 19 0 0 3
- 311 236 0 0 6 66 4 0 2 24 9 0 1
- 490 23 0 0 34 6 0 0 30 14 0 0 1
- 485 29 0 0 33 7 0 0 14 20 0 0 3
- 481 43 0 0 38 11 0 0 21 24 0 0 2
--- a/main/nnet/tests/MLP/example2/mlp9_1_1_logsig.dat	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,21 +0,0 @@
-TRAINLM, Epoch 0/100, MSE 3.19597/0, Gradient 87.4172/1e-010
-TRAINLM, Epoch 6/100, MSE 0.219207/0, Gradient 0.455596/1e-010
-TRAINLM, Validation stop.
-
-
-simOut =
-
-  Columns 1 through 12
-
-    1.0000    0.6866    0.8512    1.0000    1.0000    0.9998    1.0000    1.0000    0.6207    0.7705    1.0000    0.7405
-
-  Columns 13 through 24
-
-    0.9997    0.9371    0.7935    0.8539    0.6889    1.0000    0.8356    1.0000    1.0000    1.0000    0.8666    0.9578
-
-  Columns 25 through 30
-
-    0.9999    0.7036    1.0000    0.9635    1.0000    1.0000
-
->>
->>
\ No newline at end of file
--- a/main/nnet/tests/MLP/example2/mlp9_1_1_logsig.m	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,106 +0,0 @@
-## Copyright (C) 2007 Michel D. Schmid  <michael.schmid@plexso.com>
-##
-## 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 3 of the License, 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; if not, see <http://www.gnu.org/licenses/>.
-
-## author: msd
-
-
-
-## load data
-
-mData = load("mData.txt","mData");
-mData = mData.mData;
-[nRows, nColumns] = size(mData);
-# this file contains 13 columns. The first 12 columns are the inputs
-# the last column is the output
-# remove column 4, 8 and 12
-# 89 rows
-
-## first permute the whole matrix in row wise
-## this won't be used right now for debug and test purpose
-# order = randperm(nRows);
-# mData(order,:) = mData;
-
-mOutput = mData(:,end);
-mInput = mData(:,1:end-1);
-mInput(:,[4 8 12]) = []; # delete column 4, 8 and 12
-
-## now prepare data
-mInput = mInput';
-mOutput = mOutput';
-%mOutput = [mOutput; mOutput*4];
-# now split the data matrix in 3 pieces, train data, test data and validate data
-# the proportion should be about 1/2 train, 1/3 test and 1/6 validate data
-# in this neural network we have 12 weights, for each weight at least 3 train sets..
-# (that's a rule of thumb like 1/2, 1/3 and 1/6)
-# 1/2 of 89 = 44.5; let's take 44 for training
-nTrainSets = floor(nRows/2);
-# now the rest of the sets are again 100%
-# ==> 2/3 for test sets and 1/3 for validate sets
-nTestSets = (nRows-nTrainSets)/3*2;
-nValiSets = nRows-nTrainSets-nTestSets;
-
-mValiInput = mInput(:,1:nValiSets);
-mValliOutput = mOutput(:,1:nValiSets);
-mInput(:,1:nValiSets) = [];
-mOutput(:,1:nValiSets) = [];
-mTestInput = mInput(:,1:nTestSets);
-mTestOutput = mOutput(:,1:nTestSets);
-mInput(:,1:nTestSets) = [];
-mOutput(:,1:nTestSets) = [];
-mTrainInput = mInput(:,1:nTrainSets);
-mTrainOutput = mOutput(:,1:nTrainSets);
-
-[mTrainInputN,cMeanInput,cStdInput] = prestd(mTrainInput);# standardize inputs
-
-## comments: there is no reason to standardize the outputs because we have only
-# one output ...
-
-# define the max and min inputs for each row
-mMinMaxElements = min_max(mTrainInputN); % input matrix with (R x 2)...
-
-## define network
-nHiddenNeurons = 1;
-nOutputNeurons = 1;
-
-MLPnet = newff(mMinMaxElements,[nHiddenNeurons nOutputNeurons],{"logsig","purelin"},"trainlm","learngdm","mse");
-## for test purpose, define weights by hand
-MLPnet.IW{1,1}(:) = 1.5;
-MLPnet.LW{2,1}(:) = 0.5;
-MLPnet.b{1,1}(:) = 1.5;
-MLPnet.b{2,1}(:) = 0.5;
-
-#saveMLPStruct(MLPnet,"MLP3test.txt");
-#disp("network structure saved, press any key to continue...")
-#pause
-
-## define validation data new, for matlab compatibility
-VV.P = mValiInput;
-VV.T = mValliOutput;
-
-## standardize also the validate data
-VV.P = trastd(VV.P,cMeanInput,cStdInput);
-
-#[net,tr,out,E] = train(MLPnet,mInputN,mOutput,[],[],VV);
-MLPnet.trainParam.show = NaN;
-[net] = train(MLPnet,mTrainInputN,mTrainOutput,[],[],VV);
-# saveMLPStruct(net,"MLP3testNachTraining.txt");
-# disp("network structure saved, press any key to continue...")
-# pause
-
-# % make preparations for net test and test MLPnet
-#     % standardise input & output test data
-[mTestInputN] = trastd(mTestInput,cMeanInput,cStdInput);
-
-[simOut] = sim(net,mTestInputN);#%,Pi,Ai,mTestOutput);
--- a/main/nnet/tests/MLP/example2/mlp9_2_1_logsig.dat	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,20 +0,0 @@
-TRAINLM, Epoch 0/100, MSE 2.35298/0, Gradient 79.3291/1e-010
-TRAINLM, Epoch 6/100, MSE 0.154324/0, Gradient 0.341107/1e-010
-TRAINLM, Validation stop.
-
-
-simOut =
-
-  Columns 1 through 12
-
-    1.4912    0.9151    1.1368    1.4883    1.4996    1.4632    1.4907    1.5000    0.8235    1.0274    1.5000    0.9873
-
-  Columns 13 through 24
-
-    1.4608    1.2652    1.0581    1.1405    0.9181    1.4839    1.1152    1.5000    1.4901    1.4997    1.1583    1.3023
-
-  Columns 25 through 30
-
-    1.4694    0.9380    1.5000    1.3135    1.4947    1.4993
-
->>
\ No newline at end of file
--- a/main/nnet/tests/MLP/example2/mlp9_2_1_logsig.m	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,113 +0,0 @@
-## Copyright (C) 2007 Michel D. Schmid  <michael.schmid@plexso.com>
-##
-## 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 3 of the License, 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; if not, see <http://www.gnu.org/licenses/>.
-
-## author: msd
-
-
-## load data
-
-mData = load("mData.txt","mData");
-mData = mData.mData;
-[nRows, nColumns] = size(mData);
-# this file contains 13 columns. The first 12 columns are the inputs
-# the last column is the output
-# remove column 4, 8 and 12
-# 89 rows
-
-## first permute the whole matrix in row wise
-## this won't be used right now for debug and test purpose
-# order = randperm(nRows);
-# mData(order,:) = mData;
-
-mOutput = mData(:,end);
-mInput = mData(:,1:end-1);
-mInput(:,[4 8 12]) = []; # delete column 4, 8 and 12
-
-## now prepare data
-mInput = mInput';
-mOutput = mOutput';
-%mOutput = [mOutput; mOutput*4];
-# now split the data matrix in 3 pieces, train data, test data and validate data
-# the proportion should be about 1/2 train, 1/3 test and 1/6 validate data
-# in this neural network we have 12 weights, for each weight at least 3 train sets..
-# (that's a rule of thumb like 1/2, 1/3 and 1/6)
-# 1/2 of 89 = 44.5; let's take 44 for training
-nTrainSets = floor(nRows/2);
-# now the rest of the sets are again 100%
-# ==> 2/3 for test sets and 1/3 for validate sets
-nTestSets = (nRows-nTrainSets)/3*2;
-nValiSets = nRows-nTrainSets-nTestSets;
-
-mValiInput = mInput(:,1:nValiSets);
-mValliOutput = mOutput(:,1:nValiSets);
-mInput(:,1:nValiSets) = [];
-mOutput(:,1:nValiSets) = [];
-mTestInput = mInput(:,1:nTestSets);
-mTestOutput = mOutput(:,1:nTestSets);
-mInput(:,1:nTestSets) = [];
-mOutput(:,1:nTestSets) = [];
-mTrainInput = mInput(:,1:nTrainSets);
-mTrainOutput = mOutput(:,1:nTrainSets);
-
-[mTrainInputN,cMeanInput,cStdInput] = prestd(mTrainInput);# standardize inputs
-
-## comments: there is no reason to standardize the outputs because we have only
-# one output ...
-
-# define the max and min inputs for each row
-mMinMaxElements = min_max(mTrainInputN); % input matrix with (R x 2)...
-
-## define network
-nHiddenNeurons = 2;
-nOutputNeurons = 1;
-
-MLPnet = newff(mMinMaxElements,[nHiddenNeurons nOutputNeurons],{"logsig","purelin"},"trainlm","learngdm","mse");
-## for test purpose, define weights by hand
-MLPnet.IW{1,1}(1,:) = 0.5;
-MLPnet.IW{1,1}(2,:) = 1.5;
-MLPnet.LW{2,1}(:) = 0.5;
-MLPnet.b{1,1}(1,:) = 0.5;
-MLPnet.b{1,1}(2,:) = 1.5;
-MLPnet.b{2,1}(:) = 0.5;
-
-saveMLPStruct(MLPnet,"MLP3test.txt");
-#disp("network structure saved, press any key to continue...")
-#pause
-
-## define validation data new, for matlab compatibility
-VV.P = mValiInput;
-VV.T = mValliOutput;
-
-## standardize also the validate data
-VV.P = trastd(VV.P,cMeanInput,cStdInput);
-
-#[net,tr,out,E] = train(MLPnet,mInputN,mOutput,[],[],VV);
-MLPnet.trainParam.show = NaN;
-[net] = train(MLPnet,mTrainInputN,mTrainOutput,[],[],VV);
-# saveMLPStruct(net,"MLP3testNachTraining.txt");
-# disp("network structure saved, press any key to continue...")
-# pause
-# % the names in matlab help, see "train" in help for more informations
-# tr.perf(max(tr.epoch)+1);
-#
-# % make preparations for net test and test MLPnet
-#     % standardise input & output test data
-[mTestInputN] = trastd(mTestInput,cMeanInput,cStdInput);
-# % [mTestOutputN] = trastd(mTestOutput,cMeanOutput,cStdOutput);
-#     % define unused parameters to get E-variable of simulation
-# Pi = zeros(6,0);
-# Ai = zeros(6,0);
-#     % simulate net
-[simOut] = sim(net,mTestInputN);#%,Pi,Ai,mTestOutput);
--- a/main/nnet/tests/MLP/example2/mlp9_2_2_1_logsig.m	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,103 +0,0 @@
-## Copyright (C) 2006 Michel D. Schmid  <michael.schmid@plexso.com>
-##
-##
-## 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/>.
-
-
-## author: msd
-
-mData = load("mData.txt","mData");
-mData = mData.mData;
-[nRows, nColumns] = size(mData);
-# this file contains 13 columns. The first 12 columns are the inputs
-# the last column is the output
-# remove column 4, 8 and 12
-# 89 rows
-
-## first permute the whole matrix in row wise
-## this won't be used right now for debug and test purpose
-# order = randperm(nRows);
-# mData(order,:) = mData;
-
-mOutput = mData(:,end);
-mInput = mData(:,1:end-1);
-mInput(:,[4 8 12]) = []; # delete column 4, 8 and 12
-
-## now prepare data
-mInput = mInput';
-mOutput = mOutput';
-% now split the data matrix in 3 pieces, train data, test data and validate data
-% the proportion should be about 1/2 train, 1/3 test and 1/6 validate data
-% in this neural network we have 12 weights, for each weight at least 3 train sets..
-% (that's a rule of thumb like 1/2, 1/3 and 1/6)
-% 1/2 of 89 = 44.5; let's take 44 for training
-nTrainSets = floor(nRows/2);
-% now the rest of the sets are again 100%
-% ==> 2/3 for test sets and 1/3 for validate sets
-nTestSets = (nRows-nTrainSets)/3*2;
-nValiSets = nRows-nTrainSets-nTestSets;
-
-mValiInput = mInput(:,1:nValiSets);
-mValliOutput = mOutput(:,1:nValiSets);
-mInput(:,1:nValiSets) = [];
-mOutput(:,1:nValiSets) = [];
-mTestInput = mInput(:,1:nTestSets);
-mTestOutput = mOutput(:,1:nTestSets);
-mInput(:,1:nTestSets) = [];
-mOutput(:,1:nTestSets) = [];
-mTrainInput = mInput(:,1:nTrainSets);
-mTrainOutput = mOutput(:,1:nTrainSets);
-
-[mTrainInputN,cMeanInput,cStdInput] = prestd(mTrainInput);% standardize inputs
-
-%% comments: there is no reason to standardize the outputs because we have only
-% one output ...
-
-% define the max and min inputs for each row
-mMinMaxElements = min_max(mTrainInputN); % input matrix with (R x 2)...
-
-%% define network
-nHiddenNeurons = [2 2];
-nOutputNeurons = 1;
-
-MLPnet = newff(mMinMaxElements,[nHiddenNeurons nOutputNeurons],{'tansig','tansig','purelin'},'trainlm','learngdm','mse');
-%% for test purpose, define weights by hand
-# MLPnet.IW{1,1}(:) = 1.5;
-# MLPnet.LW{2,1}(:) = 0.5;
-# MLPnet.b{1,1}(:) = 1.5;
-# MLPnet.b{2,1}(:) = 0.5;
-
- saveMLPStruct(MLPnet,'MLP9-2-2-1.txt');
-# disp('network structure saved, press any key to continue...')
-
-
-## define validation data new, for matlab compatibility
-VV.P = mValiInput;
-VV.T = mValliOutput;
-
-## standardize also the validate data
-VV.P = trastd(VV.P,cMeanInput,cStdInput);
-
- %[net,tr,out,E] = train(MLPnet,mInputN,mOutput,[],[],VV);
- MLPnet.trainParam.show = NaN;
-[net] = train(MLPnet,mTrainInputN,mTrainOutput,[],[],VV);
-#
-# % % make preparations for net test and test MLPnet
-# %     % standardise input & output test data
-  [mTestInputN] = trastd(mTestInput,cMeanInput,cStdInput);
-#
-  #[simOut,Pf,Af,simE,simPerf] = sim(net,mTestInputN);
-#
-[simOut] = sim(net,mTestInputN);
\ No newline at end of file
--- a/main/nnet/tests/MLP/example2/mlp9_2_2_3_logsig.dat	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,26 +0,0 @@
-TRAINLM, Epoch 0/100, MSE 227.919/0, Gradient 1662.64/1e-010
-TRAINLM, Epoch 7/100, MSE 9.72986/0, Gradient 2.23561e-009/1e-010
-TRAINLM, Validation stop.
-
-
-simOut =
-
-  Columns 1 through 12
-
-   -3.5612   -3.5612   -3.5612   -3.5612   -3.5612   -3.5612   -3.5612   -3.5612   -3.5612   -3.5612   -3.5612   -3.5612
-    2.4161    2.4161    2.4161    2.4161    2.4161    2.4161    2.4161    2.4161    2.4161    2.4161    2.4161    2.4161
-   14.3706   14.3706   14.3706   14.3706   14.3706   14.3706   14.3706   14.3706   14.3706   14.3706   14.3706   14.3706
-
-  Columns 13 through 24
-
-   -3.5612   -3.5612   -3.5612   -3.5612   -3.5612   -3.5612   -3.5612   -3.5612   -3.5612   -3.5612   -3.5612   -3.5612
-    2.4161    2.4161    2.4161    2.4161    2.4161    2.4161    2.4161    2.4161    2.4161    2.4161    2.4161    2.4161
-   14.3706   14.3706   14.3706   14.3706   14.3706   14.3706   14.3706   14.3706   14.3706   14.3706   14.3706   14.3706
-
-  Columns 25 through 30
-
-   -3.5612   -3.5612   -3.5612   -3.5612   -3.5612   -3.5612
-    2.4161    2.4161    2.4161    2.4161    2.4161    2.4161
-   14.3706   14.3706   14.3706   14.3706   14.3706   14.3706
-
->>
\ No newline at end of file
--- a/main/nnet/tests/MLP/example2/mlp9_2_2_logsig.dat	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,23 +0,0 @@
-TRAINLM, Epoch 0/100, MSE 42.6863/0, Gradient 532.547/1e-010
-TRAINLM, Epoch 6/100, MSE 1.62318/0, Gradient 3.12953/1e-010
-TRAINLM, Validation stop.
-
-
-simOut =
-
-  Columns 1 through 12
-
-    3.4901    1.8132    2.3724    3.4869    3.4995    3.4557    3.4896    3.5000    1.6319    2.0664    3.5000    1.9704
-    1.4967    0.9377    1.1241    1.4956    1.4998    1.4852    1.4965    1.5000    0.8773    1.0221    1.5000    0.9901
-
-  Columns 13 through 24
-
-    3.4524    2.8360    2.1451    2.3844    1.8195    3.4818    2.3061    3.5000    3.4889    3.4997    2.4421    2.9859
-    1.4841    1.2787    1.0484    1.1281    0.9398    1.4939    1.1020    1.5000    1.4963    1.4999    1.1474    1.3286
-
-  Columns 25 through 30
-
-    3.4639    1.8612    3.5000    3.0314    3.4941    3.4993
-    1.4880    0.9537    1.5000    1.3438    1.4980    1.4998
-
->>
\ No newline at end of file
--- a/main/nnet/tests/MLP/example2/mlp9_2_2_logsig.m	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,111 +0,0 @@
-## Copyright (C) 2007 Michel D. Schmid  <michael.schmid@plexso.com>
-##
-## 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 3 of the License, 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; if not, see <http://www.gnu.org/licenses/>.
-
-## author: msd
-
-
-## load data
-
-mData = load("mData.txt","mData");
-# mData = mData.mData(1:10,:);
-mData = mData.mData;
-[nRows, nColumns] = size(mData);
-# this file contains 13 columns. The first 12 columns are the inputs
-# the last column is the output
-# remove column 4, 8 and 12
-# 89 rows
-
-## first permute the whole matrix in row wise
-## this won't be used right now for debug and test purpose
-# order = randperm(nRows);
-# mData(order,:) = mData;
-
-mOutput = mData(:,end);
-mInput = mData(:,1:end-1);
-mInput(:,[4 8 12]) = []; # delete column 4, 8 and 12
-
-## now prepare data
-mInput = mInput';
-mOutput = mOutput';
-mOutput = [mOutput; mOutput*4];
-# now split the data matrix in 3 pieces, train data, test data and validate data
-# the proportion should be about 1/2 train, 1/3 test and 1/6 validate data
-# in this neural network we have 12 weights, for each weight at least 3 train sets..
-# (that's a rule of thumb like 1/2, 1/3 and 1/6)
-# 1/2 of 89 = 44.5; let's take 44 for training
-nTrainSets = floor(nRows/2);
-# now the rest of the sets are again 100%
-# ==> 2/3 for test sets and 1/3 for validate sets
-nTestSets = (nRows-nTrainSets)/3*2;
-nValiSets = nRows-nTrainSets-nTestSets;
-
-mValiInput = mInput(:,1:nValiSets);
-mValliOutput = mOutput(:,1:nValiSets);
-mInput(:,1:nValiSets) = [];
-mOutput(:,1:nValiSets) = [];
-mTestInput = mInput(:,1:nTestSets);
-mTestOutput = mOutput(:,1:nTestSets);
-mInput(:,1:nTestSets) = [];
-mOutput(:,1:nTestSets) = [];
-mTrainInput = mInput(:,1:nTrainSets);
-mTrainOutput = mOutput(:,1:nTrainSets);
-
-[mTrainInputN,cMeanInput,cStdInput] = prestd(mTrainInput);# standardize inputs
-
-## comments: there is no reason to standardize the outputs because we have only
-# one output ...
-
-# define the max and min inputs for each row
-mMinMaxElements = min_max(mTrainInputN); % input matrix with (R x 2)...
-
-## define network
-nHiddenNeurons = 2;
-nOutputNeurons = 2;
-
-MLPnet = newff(mMinMaxElements,[nHiddenNeurons nOutputNeurons],{"logsig","purelin"},"trainlm","learngdm","mse");
-## for test purpose, define weights by hand
-MLPnet.IW{1,1}(1,:) = 1.5;
-MLPnet.IW{1,1}(2,:) = 0.5;
-MLPnet.LW{2,1}(1,:) = 1.5;
-MLPnet.LW{2,1}(2,:) = 0.5;
-MLPnet.b{1,1}(1,:) = 0.5;
-MLPnet.b{1,1}(2,:) = 1.5;
-MLPnet.b{2,1}(:) = 0.5;
-
-# saveMLPStruct(MLPnet,"MLP3test.txt");
-#disp("network structure saved, press any key to continue...")
-#pause
-
-## define validation data new, for MATLAB(TM) compatibility
-VV.P = mValiInput;
-VV.T = mValliOutput;
-
-## standardize also the validate data
-VV.P = trastd(VV.P,cMeanInput,cStdInput);
-
-#[net,tr,out,E] = train(MLPnet,mInputN,mOutput,[],[],VV);
-MLPnet.trainParam.show = NaN;
-[net] = train(MLPnet,mTrainInputN,mTrainOutput,[],[],VV);
-# saveMLPStruct(net,"MLP3testNachTraining.txt");
-# disp("network structure saved, press any key to continue...")
-# disp("nach Training")
-# pause
-
-# % make preparations for net test and test MLPnet
-#     % standardise input & output test data
-[mTestInputN] = trastd(mTestInput,cMeanInput,cStdInput);
-
-#     % simulate net
-[simOut] = sim(net,mTestInputN);%,Pi,Ai,mTestOutput);
--- a/main/nnet/tests/MLP/example2/mlp9_2_3_logsig.dat	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,26 +0,0 @@
-TRAINLM, Epoch 0/100, MSE 245.153/0, Gradient 1576.3/1e-010
-TRAINLM, Epoch 6/100, MSE 8.10503/0, Gradient 15.6612/1e-010
-TRAINLM, Validation stop.
-
-
-simOut =
-
-  Columns 1 through 12
-
-    3.4901    1.8132    2.3724    3.4869    3.4995    3.4557    3.4896    3.5000    1.6319    2.0664    3.5000    1.9704
-    1.4967    0.9377    1.1241    1.4956    1.4998    1.4852    1.4965    1.5000    0.8773    1.0221    1.5000    0.9901
-    0.2993    0.1875    0.2248    0.2991    0.3000    0.2970    0.2993    0.3000    0.1755    0.2044    0.3000    0.1980
-
-  Columns 13 through 24
-
-    3.4524    2.8360    2.1451    2.3844    1.8195    3.4818    2.3061    3.5000    3.4889    3.4997    2.4421    2.9859
-    1.4841    1.2787    1.0484    1.1281    0.9398    1.4939    1.1020    1.5000    1.4963    1.4999    1.1474    1.3286
-    0.2968    0.2557    0.2097    0.2256    0.1880    0.2988    0.2204    0.3000    0.2993    0.3000    0.2295    0.2657
-
-  Columns 25 through 30
-
-    3.4639    1.8612    3.5000    3.0314    3.4941    3.4993
-    1.4880    0.9537    1.5000    1.3438    1.4980    1.4998
-    0.2976    0.1907    0.3000    0.2688    0.2996    0.3000
-
->>
\ No newline at end of file
--- a/main/nnet/tests/MLP/example2/mlp9_2_3_logsig.m	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,115 +0,0 @@
-## Copyright (C) 2007 Michel D. Schmid  <michael.schmid@plexso.com>
-##
-## 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 3 of the License, 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; if not, see <http://www.gnu.org/licenses/>.
-
-## author: msd
-
-
-
-## load data
-
-mData = load("mData.txt","mData");
-# mData = mData.mData(1:10,:);
-mData = mData.mData;
-[nRows, nColumns] = size(mData);
-# this file contains 13 columns. The first 12 columns are the inputs
-# the last column is the output
-# remove column 4, 8 and 12
-# 89 rows
-
-## first permute the whole matrix in row wise
-## this won't be used right now for debug and test purpose
-# order = randperm(nRows);
-# mData(order,:) = mData;
-
-mOutput = mData(:,end);
-mInput = mData(:,1:end-1);
-mInput(:,[4 8 12]) = []; # delete column 4, 8 and 12
-
-## now prepare data
-mInput = mInput';
-mOutput = mOutput';
-mOutput = [mOutput; mOutput*4; mOutput*10];
-# now split the data matrix in 3 pieces, train data, test data and validate data
-# the proportion should be about 1/2 train, 1/3 test and 1/6 validate data
-# in this neural network we have 12 weights, for each weight at least 3 train sets..
-# (that's a rule of thumb like 1/2, 1/3 and 1/6)
-# 1/2 of 89 = 44.5; let's take 44 for training
-nTrainSets = floor(nRows/2);
-# now the rest of the sets are again 100%
-# ==> 2/3 for test sets and 1/3 for validate sets
-nTestSets = (nRows-nTrainSets)/3*2;
-nValiSets = nRows-nTrainSets-nTestSets;
-
-mValiInput = mInput(:,1:nValiSets);
-mValliOutput = mOutput(:,1:nValiSets);
-mInput(:,1:nValiSets) = [];
-mOutput(:,1:nValiSets) = [];
-mTestInput = mInput(:,1:nTestSets);
-mTestOutput = mOutput(:,1:nTestSets);
-mInput(:,1:nTestSets) = [];
-mOutput(:,1:nTestSets) = [];
-mTrainInput = mInput(:,1:nTrainSets);
-mTrainOutput = mOutput(:,1:nTrainSets);
-
-[mTrainInputN,cMeanInput,cStdInput] = prestd(mTrainInput);# standardize inputs
-
-## comments: there is no reason to standardize the outputs because we have only
-# one output ...
-
-# define the max and min inputs for each row
-mMinMaxElements = min_max(mTrainInputN); % input matrix with (R x 2)...
-
-## define network
-nHiddenNeurons = 2;
-nOutputNeurons = 3;
-
-MLPnet = newff(mMinMaxElements,[nHiddenNeurons nOutputNeurons],{"logsig","purelin"},"trainlm","learngdm","mse");
-## for test purpose, define weights by hand
-MLPnet.IW{1,1}(1,:) = 1.5;
-MLPnet.IW{1,1}(2,:) = 0.5;
-MLPnet.LW{2,1}(1,:) = 1.5;
-MLPnet.LW{2,1}(2,:) = 0.5;
-MLPnet.LW{2,1}(3,:) = 0.1;
-MLPnet.b{1,1}(1,:) = 0.5;
-MLPnet.b{1,1}(2,:) = 1.5;
-MLPnet.b{2,1}(1,:) = 0.5;
-MLPnet.b{2,1}(2,:) = 0.5;
-MLPnet.b{2,1}(3,:) = 0.1;
-
-# saveMLPStruct(MLPnet,"MLP3test.txt");
-#disp("network structure saved, press any key to continue...")
-#pause
-
-## define validation data new, for MATLAB(TM) compatibility
-VV.P = mValiInput;
-VV.T = mValliOutput;
-
-## standardize also the validate data
-VV.P = trastd(VV.P,cMeanInput,cStdInput);
-
-#[net,tr,out,E] = train(MLPnet,mInputN,mOutput,[],[],VV);
-MLPnet.trainParam.show = NaN;
-[net] = train(MLPnet,mTrainInputN,mTrainOutput,[],[],VV);
-# saveMLPStruct(net,"MLP3testNachTraining.txt");
-# disp("network structure saved, press any key to continue...")
-# disp("nach Training")
-# pause
-
-# % make preparations for net test and test MLPnet
-#     % standardise input & output test data
-[mTestInputN] = trastd(mTestInput,cMeanInput,cStdInput);
-
-#     % simulate net
-[simOut] = sim(net,mTestInputN);%,Pi,Ai,mTestOutput);
--- a/main/nnet/tests/MLP/example2/mlp9_5_3_logsig.dat	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,26 +0,0 @@
-TRAINLM, Epoch 0/100, MSE 240.504/0, Gradient 1971.95/1e-010
-TRAINLM, Epoch 6/100, MSE 7.95197/0, Gradient 203.206/1e-010
-TRAINLM, Validation stop.
-
-
-simOut =
-
-  Columns 1 through 12
-
-    7.9886    3.6782    5.2496    7.9843    7.9995    7.9278    7.9880    8.0000    3.0623    4.4398    8.0000    4.1632
-    2.9962    1.5594    2.0832    2.9948    2.9998    2.9759    2.9960    3.0000    1.3541    1.8133    3.0000    1.7211
-    0.5992    0.3119    0.4166    0.5990    0.6000    0.5952    0.5992    0.6000    0.2708    0.3627    0.6000    0.3442
-
-  Columns 13 through 24
-
-    7.9206    6.3668    4.6573    5.2794    3.6986    7.9768    5.0815    8.0000    7.9871    7.9997    5.4228    6.7236
-    2.9735    2.4556    1.8858    2.0931    1.5662    2.9923    2.0272    3.0000    2.9957    2.9999    2.1409    2.5745
-    0.5947    0.4911    0.3772    0.4186    0.3132    0.5985    0.4054    0.6000    0.5991    0.6000    0.4282    0.5149
-
-  Columns 25 through 30
-
-    7.9450    3.8310    8.0000    6.8332    7.9936    7.9993
-    2.9817    1.6103    3.0000    2.6111    2.9979    2.9998
-    0.5963    0.3221    0.6000    0.5222    0.5996    0.6000
-
->>
\ No newline at end of file
--- a/main/nnet/tests/MLP/example2/mlp9_5_3_logsig.m	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,116 +0,0 @@
-## Copyright (C) 2007 Michel D. Schmid  <michael.schmid@plexso.com>
-##
-## 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 3 of the License, 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; if not, see <http://www.gnu.org/licenses/>.
-
-## author: msd
-
-
-## load data
-
-mData = load("mData.txt","mData");
-# mData = mData.mData(1:10,:);
-mData = mData.mData;
-[nRows, nColumns] = size(mData);
-# this file contains 13 columns. The first 12 columns are the inputs
-# the last column is the output
-# remove column 4, 8 and 12
-# 89 rows
-
-## first permute the whole matrix in row wise
-## this won't be used right now for debug and test purpose
-# order = randperm(nRows);
-# mData(order,:) = mData;
-
-mOutput = mData(:,end);
-mInput = mData(:,1:end-1);
-mInput(:,[4 8 12]) = []; # delete column 4, 8 and 12
-
-## now prepare data
-mInput = mInput';
-mOutput = mOutput';
-mOutput = [mOutput; mOutput*4; mOutput*10];
-# now split the data matrix in 3 pieces, train data, test data and validate data
-# the proportion should be about 1/2 train, 1/3 test and 1/6 validate data
-# in this neural network we have 12 weights, for each weight at least 3 train sets..
-# (that's a rule of thumb like 1/2, 1/3 and 1/6)
-# 1/2 of 89 = 44.5; let's take 44 for training
-nTrainSets = floor(nRows/2);
-# now the rest of the sets are again 100%
-# ==> 2/3 for test sets and 1/3 for validate sets
-nTestSets = (nRows-nTrainSets)/3*2;
-nValiSets = nRows-nTrainSets-nTestSets;
-
-mValiInput = mInput(:,1:nValiSets);
-mValliOutput = mOutput(:,1:nValiSets);
-mInput(:,1:nValiSets) = [];
-mOutput(:,1:nValiSets) = [];
-mTestInput = mInput(:,1:nTestSets);
-mTestOutput = mOutput(:,1:nTestSets);
-mInput(:,1:nTestSets) = [];
-mOutput(:,1:nTestSets) = [];
-mTrainInput = mInput(:,1:nTrainSets);
-mTrainOutput = mOutput(:,1:nTrainSets);
-
-[mTrainInputN,cMeanInput,cStdInput] = prestd(mTrainInput);# standardize inputs
-
-## comments: there is no reason to standardize the outputs because we have only
-# one output ...
-
-# define the max and min inputs for each row
-mMinMaxElements = min_max(mTrainInputN); % input matrix with (R x 2)...
-
-## define network
-nHiddenNeurons = 5;
-nOutputNeurons = 3;
-
-MLPnet = newff(mMinMaxElements,[nHiddenNeurons nOutputNeurons],{"logsig","purelin"},"trainlm","learngdm","mse");
-## for test purpose, define weights by hand
-MLPnet.IW{1,1}(1,:) = 1.5;
-MLPnet.IW{1,1}(2,:) = 0.5;
-MLPnet.IW{1,1}(3:5,:) = 1;
-MLPnet.LW{2,1}(1,:) = 1.5;
-MLPnet.LW{2,1}(2,:) = 0.5;
-MLPnet.LW{2,1}(3,:) = 0.1;
-MLPnet.b{1,1}(1,:) = 0.5;
-MLPnet.b{1,1}(2,:) = 1.5;
-MLPnet.b{1,1}(3:5,:) = 1;
-MLPnet.b{2,1}(1,:) = 0.5;
-MLPnet.b{2,1}(2,:) = 0.5;
-MLPnet.b{2,1}(3,:) = 0.1;
-
-# saveMLPStruct(MLPnet,"MLP3test.txt");
-#disp("network structure saved, press any key to continue...")
-#pause
-
-## define validation data new, for MATLAB(TM) compatibility
-VV.P = mValiInput;
-VV.T = mValliOutput;
-
-## standardize also the validate data
-VV.P = trastd(VV.P,cMeanInput,cStdInput);
-
-#[net,tr,out,E] = train(MLPnet,mInputN,mOutput,[],[],VV);
-MLPnet.trainParam.show = NaN;
-[net] = train(MLPnet,mTrainInputN,mTrainOutput,[],[],VV);
-# saveMLPStruct(net,"MLP3testNachTraining.txt");
-# disp("network structure saved, press any key to continue...")
-# disp("nach Training")
-# pause
-
-# % make preparations for net test and test MLPnet
-#     % standardise input & output test data
-[mTestInputN] = trastd(mTestInput,cMeanInput,cStdInput);
-
-#     % simulate net
-[simOut] = sim(net,mTestInputN);%,Pi,Ai,mTestOutput);
--- a/main/nnet/tests/MLP/example2/orig/mData.txt	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,89 +0,0 @@
-390	47	0	0	17	45	1	0	3	33	2	0	1
-395	59	0	0	30	33	0	0	4	36	0	0	1
-420	43	0	0	26	30	9	0	9	24	4	0	1
-412	50	0	0	25	47	0	0	11	29	0	0	1
-418	43	0	0	34	30	1	0	9	27	5	0	1
-406	77	1	0	15	24	2	0	6	37	1	0	1
-407	51	0	0	28	47	0	0	5	35	0	0	1
-445	74	0	0	19	30	0	0	4	43	0	0	1
-317	179	0	0	11	64	0	0	4	35	0	0	1
-300	175	0	0	8	71	2	0	0	35	0	0	1
-331	169	0	0	19	66	1	0	0	34	1	0	1
-347	123	1	0	7	52	7	0	4	38	3	0	1
-323	177	0	0	8	68	4	0	0	37	2	0	1
-337	177	0	0	11	47	1	0	4	39	3	0	1
-299	173	1	0	8	63	1	0	1	37	3	0	1
-286	207	1	0	5	74	2	0	1	38	4	0	1
-429	44	0	0	37	19	0	0	23	19	0	0	1
-459	22	0	0	46	14	0	0	27	17	0	0	1
-340	163	1	0	16	72	3	0	3	31	1	0	1
-311	236	0	0	6	66	4	0	2	24	9	0	1
-326	218	1	0	4	71	2	0	1	33	1	0	1
-316	184	0	0	13	65	3	0	5	34	3	0	1
-258	236	2	0	2	72	7	0	0	26	8	0	1
-457	15	0	0	44	1	0	0	30	13	0	0	1
-471	8	0	0	58	2	0	0	25	13	0	0	1
-285	273	2	0	7	50	13	0	1	28	10	0	1
-489	44	0	0	21	12	0	0	29	21	0	0	1
-345	163	1	0	17	57	2	0	2	32	1	0	1
-473	59	0	0	44	15	0	0	22	23	0	0	1
-486	14	0	0	41	4	0	0	40	8	0	0	1
-368	188	1	0	6	49	0	0	3	37	0	0	1
-490	23	0	0	34	6	0	0	30	14	0	0	1
-291	251	2	0	8	53	2	0	0	43	2	0	1
-492	14	0	0	44	4	0	0	40	7	0	0	1
-286	293	3	0	7	83	9	0	1	22	5	0	1
-349	224	1	0	3	66	3	0	2	41	1	0	1
-295	271	3	0	6	71	5	0	3	29	4	0	1
-500	68	0	0	23	15	0	0	18	30	0	0	1
-511	73	0	0	40	21	0	0	16	22	0	0	1
-312	253	0	0	2	63	2	0	0	35	3	0	1
-527	20	0	0	32	6	0	0	23	16	0	0	1
-264	298	4	0	7	68	10	0	1	30	7	0	2
-478	101	0	0	15	34	0	0	11	40	0	0	2
-344	240	2	0	4	43	1	0	4	39	6	0	2
-303	269	3	0	10	66	2	0	1	32	7	0	2
-306	286	2	0	12	61	2	0	3	28	4	0	2
-515	44	0	0	29	16	0	0	19	31	0	0	2
-306	285	5	0	5	68	3	0	1	33	1	0	2
-472	139	0	0	16	25	0	0	11	38	0	0	2
-454	173	1	0	14	21	0	0	2	42	0	0	2
-458	36	0	0	12	7	0	0	24	19	0	0	2
-405	199	0	0	13	37	0	0	6	45	0	0	2
-475	186	1	0	15	28	0	0	5	35	0	0	2
-447	189	0	0	11	34	0	0	5	45	0	0	2
-387	294	1	0	10	37	0	0	4	30	5	0	2
-396	217	0	0	16	43	2	0	6	30	0	0	2
-492	98	0	0	7	23	0	0	13	36	0	0	2
-521	137	0	0	16	17	0	0	7	24	0	0	2
-511	48	0	0	35	7	0	0	27	15	0	0	2
-532	57	0	0	19	6	0	0	28	15	0	0	2
-477	61	0	0	36	13	0	0	22	15	0	0	2
-481	43	0	0	38	11	0	0	21	24	0	0	2
-484	222	0	0	16	12	0	0	9	24	0	0	2
-484	37	0	0	50	13	0	0	29	18	0	0	2
-508	64	0	0	31	14	0	0	17	33	0	0	2
-511	55	0	0	32	15	0	0	23	21	0	0	2
-541	269	1	0	12	5	0	0	6	20	0	0	3
-477	30	0	0	24	14	0	0	18	26	0	0	3
-496	319	0	0	2	4	0	0	2	14	0	0	3
-470	273	1	0	9	17	0	0	2	32	0	0	3
-429	433	3	0	8	11	0	0	5	15	0	0	3
-506	64	0	0	32	23	0	0	13	29	0	0	3
-487	36	0	0	34	10	0	0	19	27	0	0	3
-475	34	0	0	38	13	0	0	28	18	0	0	3
-423	25	0	0	26	16	0	0	16	29	0	0	3
-488	83	0	0	34	18	0	0	15	34	0	0	3
-487	35	0	0	34	12	0	0	29	24	0	0	3
-475	23	0	0	37	5	0	0	26	14	0	0	3
-496	34	0	0	49	6	0	0	31	11	0	0	3
-493	21	0	0	33	16	0	0	22	19	0	0	3
-459	37	0	0	46	4	0	0	32	16	0	0	3
-525	41	0	0	38	9	0	0	37	18	0	0	3
-501	32	0	0	40	11	0	0	24	20	0	0	3
-506	24	0	0	34	12	0	0	26	17	0	0	3
-502	34	0	0	25	9	0	0	23	11	0	0	3
-485	29	0	0	33	7	0	0	14	20	0	0	3
-495	34	0	0	39	4	0	0	21	21	0	0	3
-476	41	0	0	32	5	0	0	19	26	0	0	3
-483	66	0	0	17	16	0	0	10	28	2	0	3
\ No newline at end of file
--- a/main/nnet/tests/MLP/loadtestresults.m	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,54 +0,0 @@
-## Copyright (C) 2007 Michel D. Schmid  <michael.schmid@plexso.com>
-##
-## 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 3 of the License, 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; if not, see <http://www.gnu.org/licenses/>.
-
-## Author: Michel D. Schmid
-
-function [cAr mData] = loadtestresults(strFileName)
-
-  ## check range of input arguments
-  error(nargchk(1,1,nargin))
-
-  i = 1;
-  mData = [];
-  cAr = {};
-
-  fid = fopen(strFileName,"rt"); # open read only
-
-  strLine = fgetl(fid);
-  while (!feof(fid)) # this means, while not eof
-
-    [val, count] = sscanf (strLine, "%f");
-    if (count)
-      mData = [mData; val'];
-    else
-      cAr{i} = strLine;
-    endif
-
-    strLine = fgetl(fid);
-    i += 1;
-  endwhile
-
-  # here, the strLine contains the last row of a file
-  # so do the complete coding once more
-  [val, count] = sscanf (strLine, "%f");
-  if (count)
-    mData = [mData; val'];
-  else
-    cAr{i} = strLine;
-  endif
-
-  fclose(fid);
-
-endfunction
--- a/main/nnet/tests/MLP/nnetTestMLP.m	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,6 +0,0 @@
-
-test testExample1_1 # runs test for complete mlp scripts
-#test testExample1_2 # runs test for complete mlp scripts
-
-#delete('log_test1_1');
-#delete('log_test1_2');
\ No newline at end of file
--- a/main/nnet/tests/MLP/preparedata9_x_1.m	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,63 +0,0 @@
-## Copyright (C) 2006 Michel D. Schmid  <michael.schmid@plexso.com>
-##
-## 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 3 of the License, 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; if not, see <http://www.gnu.org/licenses/>.
-
-## author: msd
-
-
-## load data
-mData = load("example1/mData.txt","mData");
-mData = mData.mData;
-[nRows, nColumns] = size(mData);
-# this file contains 13 columns. The first 12 columns are the inputs
-# the last column is the output
-# remove column 4, 8 and 12
-# 89 rows
-
-## this neural network example isn't a mirror of "the real life work"
-## it will be used to compare the results with MATLAB(TM) results.
-
-mOutput = mData(:,end);
-mInput = mData(:,1:end-1);
-mInput(:,[4 8 12]) = []; # delete column 4, 8 and 12
-
-## now prepare data
-mInput = mInput';
-mOutput = mOutput';
-
-# now split the data matrix in 3 pieces,
-# train data, test data and validate data
-# the proportion should be about 1/2 train,
-# 1/3 test and 1/6 validate data
-# in this neural network we have 12 weights,
-# for each weight at least 3 train sets..
-# (that's a rule of thumb like 1/2, 1/3 and 1/6)
-# 1/2 of 89 = 44.5; let's take 44 for training
-nTrainSets = floor(nRows/2);
-# now the rest of the sets are again 100%
-# ==> 2/3 for test sets and 1/3 for validate sets
-nTestSets = (nRows-nTrainSets)/3*2;
-nValiSets = nRows-nTrainSets-nTestSets;
-
-# now divide mInput & mOutput in the three sets
-mValiInput = mInput(:,1:nValiSets);
-mValliOutput = mOutput(:,1:nValiSets);
-mInput(:,1:nValiSets) = []; # delete validation set
-mOutput(:,1:nValiSets) = []; # delete validation set
-mTestInput = mInput(:,1:nTestSets);
-mTestOutput = mOutput(:,1:nTestSets);
-mInput(:,1:nTestSets) = []; # delete test set
-mOutput(:,1:nTestSets) = []; # delete test set
-mTrainInput = mInput(:,1:nTrainSets);
-mTrainOutput = mOutput(:,1:nTrainSets);
--- a/main/nnet/tests/MLP/preparedata9_x_2.m	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,63 +0,0 @@
-## Copyright (C) 2006 Michel D. Schmid  <michael.schmid@plexso.com>
-##
-## 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 3 of the License, 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; if not, see <http://www.gnu.org/licenses/>.
-
-## author: msd
-
-
-## load data
-mData = load("example1/mData.txt","mData");
-mData = mData.mData;
-[nRows, nColumns] = size(mData);
-# this file contains 13 columns. The first 12 columns are the inputs
-# the last column is the output
-# remove column 4, 8 and 12
-# 89 rows
-
-## this neural network example isn't a mirror of "the real life work"
-## it will be used to compare the results with MATLAB(TM) results.
-
-mOutput = mData(:,end);
-mInput = mData(:,1:end-1);
-mInput(:,[4 8 12]) = []; # delete column 4, 8 and 12
-
-## now prepare data
-mInput = mInput';
-mOutput = mOutput';
-mOutput = [mOutput; mOutput*4];
-# now split the data matrix in 3 pieces,
-# train data, test data and validate data
-# the proportion should be about 1/2 train,
-# 1/3 test and 1/6 validate data
-# in this neural network we have 12 weights,
-# for each weight at least 3 train sets..
-# (that's a rule of thumb like 1/2, 1/3 and 1/6)
-# 1/2 of 89 = 44.5; let's take 44 for training
-nTrainSets = floor(nRows/2);
-# now the rest of the sets are again 100%
-# ==> 2/3 for test sets and 1/3 for validate sets
-nTestSets = (nRows-nTrainSets)/3*2;
-nValiSets = nRows-nTrainSets-nTestSets;
-
-# now divide mInput & mOutput in the three sets
-mValiInput = mInput(:,1:nValiSets);
-mValliOutput = mOutput(:,1:nValiSets);
-mInput(:,1:nValiSets) = []; # delete validation set
-mOutput(:,1:nValiSets) = []; # delete validation set
-mTestInput = mInput(:,1:nTestSets);
-mTestOutput = mOutput(:,1:nTestSets);
-mInput(:,1:nTestSets) = []; # delete test set
-mOutput(:,1:nTestSets) = []; # delete test set
-mTrainInput = mInput(:,1:nTrainSets);
-mTrainOutput = mOutput(:,1:nTrainSets);
--- a/main/nnet/tests/MLP/preparedata9_x_3.m	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,63 +0,0 @@
-## Copyright (C) 2006 Michel D. Schmid  <michael.schmid@plexso.com>
-##
-## 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 3 of the License, 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; if not, see <http://www.gnu.org/licenses/>.
-
-## author: msd
-
-
-## load data
-mData = load("example1/mData.txt","mData");
-mData = mData.mData;
-[nRows, nColumns] = size(mData);
-# this file contains 13 columns. The first 12 columns are the inputs
-# the last column is the output
-# remove column 4, 8 and 12
-# 89 rows
-
-## this neural network example isn't a mirror of "the real life work"
-## it will be used to compare the results with MATLAB(TM) results.
-
-mOutput = mData(:,end);
-mInput = mData(:,1:end-1);
-mInput(:,[4 8 12]) = []; # delete column 4, 8 and 12
-
-## now prepare data
-mInput = mInput';
-mOutput = mOutput';
-mOutput = [mOutput; mOutput*4; mOutput*10];
-# now split the data matrix in 3 pieces,
-# train data, test data and validate data
-# the proportion should be about 1/2 train,
-# 1/3 test and 1/6 validate data
-# in this neural network we have 12 weights,
-# for each weight at least 3 train sets..
-# (that's a rule of thumb like 1/2, 1/3 and 1/6)
-# 1/2 of 89 = 44.5; let's take 44 for training
-nTrainSets = floor(nRows/2);
-# now the rest of the sets are again 100%
-# ==> 2/3 for test sets and 1/3 for validate sets
-nTestSets = (nRows-nTrainSets)/3*2;
-nValiSets = nRows-nTrainSets-nTestSets;
-
-# now divide mInput & mOutput in the three sets
-mValiInput = mInput(:,1:nValiSets);
-mValliOutput = mOutput(:,1:nValiSets);
-mInput(:,1:nValiSets) = []; # delete validation set
-mOutput(:,1:nValiSets) = []; # delete validation set
-mTestInput = mInput(:,1:nTestSets);
-mTestOutput = mOutput(:,1:nTestSets);
-mInput(:,1:nTestSets) = []; # delete test set
-mOutput(:,1:nTestSets) = []; # delete test set
-mTrainInput = mInput(:,1:nTrainSets);
-mTrainOutput = mOutput(:,1:nTrainSets);
--- a/main/nnet/tests/MLP/preparedata9_x_x_1.m	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,63 +0,0 @@
-## Copyright (C) 2006 Michel D. Schmid  <michael.schmid@plexso.com>
-##
-## 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 3 of the License, 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; if not, see <http://www.gnu.org/licenses/>.
-
-## author: msd
-
-
-## load data
-mData = load("example1/mData.txt","mData");
-mData = mData.mData;
-[nRows, nColumns] = size(mData);
-# this file contains 13 columns. The first 12 columns are the inputs
-# the last column is the output
-# remove column 4, 8 and 12
-# 89 rows
-
-## this neural network example isn't a mirror of "the real life work"
-## it will be used to compare the results with MATLAB(TM) results.
-
-mOutput = mData(:,end);
-mInput = mData(:,1:end-1);
-mInput(:,[4 8 12]) = []; # delete column 4, 8 and 12
-
-## now prepare data
-mInput = mInput';
-mOutput = mOutput';
-
-# now split the data matrix in 3 pieces,
-# train data, test data and validate data
-# the proportion should be about 1/2 train,
-# 1/3 test and 1/6 validate data
-# in this neural network we have 12 weights,
-# for each weight at least 3 train sets..
-# (that's a rule of thumb like 1/2, 1/3 and 1/6)
-# 1/2 of 89 = 44.5; let's take 44 for training
-nTrainSets = floor(nRows/2);
-# now the rest of the sets are again 100%
-# ==> 2/3 for test sets and 1/3 for validate sets
-nTestSets = (nRows-nTrainSets)/3*2;
-nValiSets = nRows-nTrainSets-nTestSets;
-
-# now divide mInput & mOutput in the three sets
-mValiInput = mInput(:,1:nValiSets);
-mValliOutput = mOutput(:,1:nValiSets);
-mInput(:,1:nValiSets) = []; # delete validation set
-mOutput(:,1:nValiSets) = []; # delete validation set
-mTestInput = mInput(:,1:nTestSets);
-mTestOutput = mOutput(:,1:nTestSets);
-mInput(:,1:nTestSets) = []; # delete test set
-mOutput(:,1:nTestSets) = []; # delete test set
-mTrainInput = mInput(:,1:nTrainSets);
-mTrainOutput = mOutput(:,1:nTrainSets);
--- a/main/nnet/tests/MLP/testExample1_1.m	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,75 +0,0 @@
-## Copyright (C) 2007 Michel D. Schmid <michael.schmid@plexso.com>
-##
-## 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 3 of the License, 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; if not, see <http://www.gnu.org/licenses/>.
-
-## author: msd
-
-
-## This file is used to test all the m-files inside
-## the example1 directory.
-
-## it exist for each m-file a corresponding dat-file with
-## the numerical results of matlab
-
-## actually, following m-files will be tested:
-## A. One hidden layer
-## ==================
-## 1. mlp9_1_1_tansig
-## 2. mlp9_2_1_tansig
-## 3. mlp9_2_2_tansig
-## 4. mlp9_2_3_tansig
-## 5. mlp9_5_3_tansig
-##
-## B. Two hidden layer
-## ==================
-
-
-####### mlp9_1_1_tansig ######
-%!shared cAr, mTestResults, simOut, line, fid
-%!  diary log_test1_1
-%!  dir = "example1";
-%!  [cAr, mTestResults] = loadtestresults([dir "/mlp9_1_1_tansig.dat"]);
-%!  preparedata9_x_1;
-%!  [mTrainInputN,cMeanInput,cStdInput] = prestd(mTrainInput);# standardize inputs
-%!  mMinMaxElements = min_max(mTrainInputN); # input matrix with (R x 2)...
-%!  nHiddenNeurons = 1;
-%!  nOutputNeurons = 1;
-%!  MLPnet = newff(mMinMaxElements,[nHiddenNeurons nOutputNeurons],{"tansig","purelin"},"trainlm","learngdm","mse");
-%!  MLPnet.IW{1,1}(:) = 1.5;
-%!  MLPnet.LW{2,1}(:) = 0.5;
-%!  MLPnet.b{1,1}(:) = 1.5;
-%!  MLPnet.b{2,1}(:) = 0.5;
-%!  VV.P = mValiInput;
-%!  VV.T = mValliOutput;
-%!  VV.P = trastd(VV.P,cMeanInput,cStdInput);
-%!  [net] = train(MLPnet,mTrainInputN,mTrainOutput,[],[],VV);
-%!  [mTestInputN] = trastd(mTestInput,cMeanInput,cStdInput);
-%!  [simOut] = sim(net,mTestInputN);
-%!assert(simOut,mTestResults,0.0001)
-%!  diary off ;
-%!  fid = fopen("log_test1_1","r");
-%!  line = fgetl(fid);
-%!	if (line==-1)
-%!    error("no String in Line: Row 67");
-%!  endif
-%!assert(substr(line,16,1),substr(cAr{1,1},16,1))
-%!assert(substr(line,27,7),substr(cAr{1,1},27,7))
-%!assert(substr(line,47,7),substr(cAr{1,1},47,7))
-%!  line = fgetl(fid);
-%!assert(substr(line,16,1),substr(cAr{1,2},16,1))
-%!assert(substr(line,27,7),substr(cAr{1,2},27,7))
-%!assert(substr(line,48,6),substr(cAr{1,2},48,6))
-%!assert(strcmp("TRAINLM, Validation stop.",substr(cAr{1,3},1,25)))
-%!  fclose(fid);
-
--- a/main/nnet/tests/MLP/testExample1_2.m	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,84 +0,0 @@
-## Copyright (C) 2007 Michel D. Schmid <michael.schmid@plexso.com>
-##
-## 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 3 of the License, 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; if not, see <http://www.gnu.org/licenses/>.
-
-## author: msd
-
-
-## This file is used to test all the m-files inside
-## the example1 directory.
-
-## it exist for each m-file a corresponding dat-file with
-## the numerical results of matlab
-
-## actually, following m-files will be tested:
-## A. One hidden layer
-## ==================
-## 1. mlp9_1_1_tansig
-## 2. mlp9_2_1_tansig
-## 3. mlp9_2_2_tansig
-## 4. mlp9_2_3_tansig
-## 5. mlp9_5_3_tansig
-##
-## B. Two hidden layer
-## ==================
-
-###### mlp9_2_1_tansig ######
-%!shared cAr, mTestResults, simOut, line, fid
-%!  diary log_test1_2
-%!  dir = "example1";
-%!  [cAr, mTestResults] = loadtestresults([dir "/mlp9_2_1_tansig.dat"]);
-%!  preparedata9_x_1
-%!  [mTrainInputN,cMeanInput,cStdInput] = prestd(mTrainInput);# standardize inputs
-%!  mMinMaxElements = min_max(mTrainInputN); # input matrix with (R x 2)...
-%!  nHiddenNeurons = 2;
-%!  nOutputNeurons = 1;
-%!  MLPnet = newff(mMinMaxElements,[nHiddenNeurons nOutputNeurons],{"tansig","purelin"},"trainlm","learngdm","mse");
-%!  MLPnet.IW{1,1}(1,:) = 0.5;
-%!  MLPnet.IW{1,1}(2,:) = 1.5;
-%!  MLPnet.LW{2,1}(:) = 0.5;
-%!  MLPnet.b{1,1}(1,:) = 0.5;
-%!  MLPnet.b{1,1}(2,:) = 1.5;
-%!  MLPnet.b{2,1}(:) = 0.5;
-%!  VV.P = mValiInput;
-%!  VV.T = mValliOutput;
-%!  VV.P = trastd(VV.P,cMeanInput,cStdInput);
-%!  [net] = train(MLPnet,mTrainInputN,mTrainOutput,[],[],VV);
-%!  [mTestInputN] = trastd(mTestInput,cMeanInput,cStdInput);
-%!  [simOut] = sim(net,mTestInputN);
-%!  diary off
-%!assert(simOut,mTestResults,0.0001)
-%!  fid = fopen("log_test1_2","r");
-%!  line = fgetl(fid);
-%!assert(substr(line,16,1),substr(cAr{1,1},16,1))
-%!assert(substr(line,27,7),substr(cAr{1,1},27,7))
-%!assert(substr(line,48,7),substr(cAr{1,1},48,7))
-%!  line = fgetl(fid);
-%!assert(substr(line,16,2),substr(cAr{1,2},16,2))
-%!assert(substr(line,27,7),substr(cAr{1,2},27,7))
-%!assert(substr(line,48,6),substr(cAr{1,2},48,6))
-%!assert(strcmp("TRAINLM, Validation stop.",substr(cAr{1,3},1,25)))
-%!  fclose(fid);
-
-
-
-
-
-
-
-
-
-
-
-
--- a/main/nnet/tests/nnetTest.m	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,34 +0,0 @@
-## Copyright (C) 2008 Michel D. Schmid <michael.schmid@plexso.com>
-##
-## 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 3 of the License, 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; if not, see <http://www.gnu.org/licenses/>.
-
-## Running this file will test all function files and tests which are
-## available in the nnet package
-
-# change to directory where the function files are
-cd ../inst
-
-# now run the function files in functional order if needed
-test __analyzerows
-test __copycoltopos1
-
-test isposint
-test min_max
-test newff
-
-
-# go back to the test directory
-cd ../tests
-
-
--- a/main/nnet/tests/readme	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,33 +0,0 @@
-tests directory:
-================
-
-nnetTest.m:
------------
-Runs all the m-files containing
-test cases
-
-test_nnet_win32.pl:
--------------------
-Is a perl script. To run this, you must have
-installed a perl interpreter. I tested it with
-the ActiveState Perl for Win32 systems.
-Currently, it does nothing else as nnetTest.m does.
-
-
-
-subdirectory MLP:
-=================
-
-MLPScripts.m:
--------------
-to run some very basic tests please run
-MLPScripts. This will run all scripts
-inside the subdirectories "example1" and
-"example2".
-If no error occur, the first test is passed :-)
-
-nnetTestMLP.m
-----------
-these tests don't run on the windows version of octave-forge.
-Please use a linux/unix version. I tested it on octave-3.0.0
-compiled on ubuntu.
--- a/main/nnet/tests/test_nnet_win32.pl	Wed Feb 24 17:38:45 2016 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,82 +0,0 @@
-#!C:/perl/bin/perl.exe
-##
-## Copyright (C) 2008 Michel D. Schmid <michael.schmid@plexso.com>
-##
-## 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 3 of the License, 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; if not, see <http://www.gnu.org/licenses/>.
-
-use strict;
-use diagnostics;    # Force verbose warning diagnostics.
-use warnings;
-use English;
-
-#use vars qw( $VERSION );
-#use Getopt::Long;
-#use Win32;
-use Win32::Process;
-use Win32::Console::ANSI;
-use Term::ANSIScreen qw/:color /;
-
-
-#my $command = "D:\\programs\\programming\\octave\\bin\\octave.exe";
-my $args = "D:\\programs\\programming\\octave\\bin\\octave.exe D:\\daten\\octave\\neuroPackage\\0.1.8.1\\nnet\\tests\\nnetTest.m";
-my $numberOfFailedTests = 0;
-my $numberOfSuccessfullTests = 0;
-my $testingFile;
-my @tokens=[];
-#my $process; # Prozess-Objekt
-print "Starting with tests for the neural network package\n";
-# Win32::Process::Create($process,
-# 					   $command,
-# 					   $args,
-# 					   1,
-# 					   CREATE_NEW_CONSOLE,
-# 					   '.');
-#$process->Wait(INFINITE); # script will wait until process is finished
-
-# my $pid = $process->GetProcessID();
-# print "new process pid: $pid\n";
-open(COUNTER, "$args |")
-or die("...: $!\n");
-
-while (<COUNTER>)
-{
-   if (/^testing/)
-   {
-     chomp ($testingFile = $_);
-   }
-   if (/^!!!!!/)
-   {
-     $numberOfFailedTests += 1;
-     print colored("$testingFile $_",'red');
-   }elsif(/^PASSES/)
-   {
-     @tokens = split(/ /, $_);
-     $numberOfSuccessfullTests += $tokens[4];
-     print colored("$testingFile $_",'yellow');
-   }
-
-}
-print "\n\n";
-print colored("Summary:\n",'green');
-print colored("Number of files containing failed tests: $numberOfFailedTests!\n",'red');
-print colored("Number of successfull tests:             $numberOfSuccessfullTests!\n",'yellow');
-my $allTests = $numberOfFailedTests + $numberOfSuccessfullTests;
-print colored("\nRunning complete $allTests tests!\n",'green');
-
-close COUNTER;
-
-
-
-
-