[Pkg-octave-commit] r1819 - in octave-forge-pkgs/octave-nnet/trunk/debian: . patches
Ólafur Jens Sigurðsson
ojs-guest at alioth.debian.org
Tue May 6 23:26:07 UTC 2008
Author: ojs-guest
Date: 2008-05-06 23:26:06 +0000 (Tue, 06 May 2008)
New Revision: 1819
Added:
octave-forge-pkgs/octave-nnet/trunk/debian/patches/
octave-forge-pkgs/octave-nnet/trunk/debian/patches/pdfsources.diff
octave-forge-pkgs/octave-nnet/trunk/debian/patches/series
Log:
added a patch that should deliver the sources for the pdf file
Added: octave-forge-pkgs/octave-nnet/trunk/debian/patches/pdfsources.diff
===================================================================
--- octave-forge-pkgs/octave-nnet/trunk/debian/patches/pdfsources.diff (rev 0)
+++ octave-forge-pkgs/octave-nnet/trunk/debian/patches/pdfsources.diff 2008-05-06 23:26:06 UTC (rev 1819)
@@ -0,0 +1,4467 @@
+Index: nnet-0.1.7/doc/latex/asymptote/transferFunctions/logsig.asy
+===================================================================
+--- /dev/null 1970-01-01 00:00:00.000000000 +0000
++++ nnet-0.1.7/doc/latex/asymptote/transferFunctions/logsig.asy 2007-07-24 14:19:48.000000000 +0200
+@@ -0,0 +1,18 @@
++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
+Index: nnet-0.1.7/doc/latex/asymptote/transferFunctions/logsiglogo.asy
+===================================================================
+--- /dev/null 1970-01-01 00:00:00.000000000 +0000
++++ nnet-0.1.7/doc/latex/asymptote/transferFunctions/logsiglogo.asy 2007-07-24 14:19:48.000000000 +0200
+@@ -0,0 +1,17 @@
++// 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
+Index: nnet-0.1.7/doc/latex/asymptote/transferFunctions/purelin.asy
+===================================================================
+--- /dev/null 1970-01-01 00:00:00.000000000 +0000
++++ nnet-0.1.7/doc/latex/asymptote/transferFunctions/purelin.asy 2007-07-24 14:19:48.000000000 +0200
+@@ -0,0 +1,19 @@
++// 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
+Index: nnet-0.1.7/doc/latex/asymptote/transferFunctions/purelinlogo.asy
+===================================================================
+--- /dev/null 1970-01-01 00:00:00.000000000 +0000
++++ nnet-0.1.7/doc/latex/asymptote/transferFunctions/purelinlogo.asy 2007-07-24 14:19:48.000000000 +0200
+@@ -0,0 +1,17 @@
++// 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
+Index: nnet-0.1.7/doc/latex/asymptote/transferFunctions/tansig.asy
+===================================================================
+--- /dev/null 1970-01-01 00:00:00.000000000 +0000
++++ nnet-0.1.7/doc/latex/asymptote/transferFunctions/tansig.asy 2007-07-24 14:19:48.000000000 +0200
+@@ -0,0 +1,18 @@
++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
+Index: nnet-0.1.7/doc/latex/asymptote/transferFunctions/tansiglogo.asy
+===================================================================
+--- /dev/null 1970-01-01 00:00:00.000000000 +0000
++++ nnet-0.1.7/doc/latex/asymptote/transferFunctions/tansiglogo.asy 2007-07-24 14:19:48.000000000 +0200
+@@ -0,0 +1,17 @@
++// 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
+Index: nnet-0.1.7/doc/latex/common/bibliography.tex
+===================================================================
+--- /dev/null 1970-01-01 00:00:00.000000000 +0000
++++ nnet-0.1.7/doc/latex/common/bibliography.tex 2007-07-24 14:19:48.000000000 +0200
+@@ -0,0 +1,39 @@
++% 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
+Index: nnet-0.1.7/doc/latex/common/version.tex
+===================================================================
+--- /dev/null 1970-01-01 00:00:00.000000000 +0000
++++ nnet-0.1.7/doc/latex/common/version.tex 2007-07-24 14:19:48.000000000 +0200
+@@ -0,0 +1 @@
++Version: 0.1.3
+\ No newline at end of file
+Index: nnet-0.1.7/doc/latex/developers/algorithm/LevenbergMarquardt.tex
+===================================================================
+--- /dev/null 1970-01-01 00:00:00.000000000 +0000
++++ nnet-0.1.7/doc/latex/developers/algorithm/LevenbergMarquardt.tex 2007-02-18 13:44:58.000000000 +0100
+@@ -0,0 +1,39 @@
++\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 ..
++
++
++
+Index: nnet-0.1.7/doc/latex/developers/algorithm/algorithmen.tex
+===================================================================
+--- /dev/null 1970-01-01 00:00:00.000000000 +0000
++++ nnet-0.1.7/doc/latex/developers/algorithm/algorithmen.tex 2007-02-18 13:44:58.000000000 +0100
+@@ -0,0 +1,5 @@
++\chapter{Algorithm}
++Here are some general thoughts about calculating parts are used in algorithm.
++
++\input{algorithm/LevenbergMarquardt}
++
+Index: nnet-0.1.7/doc/latex/developers/analyzing/analyzingNewff.tex
+===================================================================
+--- /dev/null 1970-01-01 00:00:00.000000000 +0000
++++ nnet-0.1.7/doc/latex/developers/analyzing/analyzingNewff.tex 2007-02-18 13:44:58.000000000 +0100
+@@ -0,0 +1,232 @@
++\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.
++
++\subsection{A general net description in matlab}
++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
+Index: nnet-0.1.7/doc/latex/developers/analyzing/matlab.tex
+===================================================================
+--- /dev/null 1970-01-01 00:00:00.000000000 +0000
++++ nnet-0.1.7/doc/latex/developers/analyzing/matlab.tex 2007-02-18 13:44:58.000000000 +0100
+@@ -0,0 +1,3 @@
++\chapter{analyzing matlab functions}
++
++\input{analyzing/analyzingNewff}
+Index: nnet-0.1.7/doc/latex/developers/codeConvention.tex
+===================================================================
+--- /dev/null 1970-01-01 00:00:00.000000000 +0000
++++ nnet-0.1.7/doc/latex/developers/codeConvention.tex 2007-02-18 13:44:58.000000000 +0100
+@@ -0,0 +1,12 @@
++\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
+Index: nnet-0.1.7/doc/latex/developers/codingGuideline/codingGuideline.tex
+===================================================================
+--- /dev/null 1970-01-01 00:00:00.000000000 +0000
++++ nnet-0.1.7/doc/latex/developers/codingGuideline/codingGuideline.tex 2007-07-24 14:19:48.000000000 +0200
+@@ -0,0 +1,51 @@
++\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
+Index: nnet-0.1.7/doc/latex/developers/examples/example1.tex
+===================================================================
+--- /dev/null 1970-01-01 00:00:00.000000000 +0000
++++ nnet-0.1.7/doc/latex/developers/examples/example1.tex 2007-02-18 13:44:58.000000000 +0100
+@@ -0,0 +1,40 @@
++\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 :-(
++
+Index: nnet-0.1.7/doc/latex/developers/examples/example2.tex
+===================================================================
+--- /dev/null 1970-01-01 00:00:00.000000000 +0000
++++ nnet-0.1.7/doc/latex/developers/examples/example2.tex 2007-02-18 13:44:58.000000000 +0100
+@@ -0,0 +1,15 @@
++\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 :-(
++
+Index: nnet-0.1.7/doc/latex/developers/examples/examples.tex
+===================================================================
+--- /dev/null 1970-01-01 00:00:00.000000000 +0000
++++ nnet-0.1.7/doc/latex/developers/examples/examples.tex 2007-02-18 13:44:58.000000000 +0100
+@@ -0,0 +1,4 @@
++\chapter{Examples}
++
++
++\input{examples/example1}
+\ No newline at end of file
+Index: nnet-0.1.7/doc/latex/developers/introduction.tex
+===================================================================
+--- /dev/null 1970-01-01 00:00:00.000000000 +0000
++++ nnet-0.1.7/doc/latex/developers/introduction.tex 2007-07-24 17:13:14.000000000 +0200
+@@ -0,0 +1,27 @@
++\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}
++
++
+Index: nnet-0.1.7/doc/latex/developers/neuralNetworkToolboxForOctave.tex
+===================================================================
+--- /dev/null 1970-01-01 00:00:00.000000000 +0000
++++ nnet-0.1.7/doc/latex/developers/neuralNetworkToolboxForOctave.tex 2007-07-24 17:13:14.000000000 +0200
+@@ -0,0 +1,82 @@
++% 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{tests/tests}
++\include{analyzing/matlab}
++
++\appendix
++\include{examples/examples}
++\include{../common/bibliography}
++
++
++
++\end{document}
++
++
+Index: nnet-0.1.7/doc/latex/developers/numbering.tex
+===================================================================
+--- /dev/null 1970-01-01 00:00:00.000000000 +0000
++++ nnet-0.1.7/doc/latex/developers/numbering.tex 2007-07-24 17:13:14.000000000 +0200
+@@ -0,0 +1,9 @@
++\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
+Index: nnet-0.1.7/doc/latex/developers/octave/directorys.tex
+===================================================================
+--- /dev/null 1970-01-01 00:00:00.000000000 +0000
++++ nnet-0.1.7/doc/latex/developers/octave/directorys.tex 2007-02-18 13:57:23.000000000 +0100
+@@ -0,0 +1,59 @@
++\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
+Index: nnet-0.1.7/doc/latex/developers/octave/functions/isposintOct.tex
+===================================================================
+--- /dev/null 1970-01-01 00:00:00.000000000 +0000
++++ nnet-0.1.7/doc/latex/developers/octave/functions/isposintOct.tex 2007-07-25 09:07:19.000000000 +0200
+@@ -0,0 +1,28 @@
++\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\\
++
+Index: nnet-0.1.7/doc/latex/developers/octave/functions/min_max.tex
+===================================================================
+--- /dev/null 1970-01-01 00:00:00.000000000 +0000
++++ nnet-0.1.7/doc/latex/developers/octave/functions/min_max.tex 2007-07-25 09:07:19.000000000 +0200
+@@ -0,0 +1,14 @@
++\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
+Index: nnet-0.1.7/doc/latex/developers/octave/functions/newff.tex
+===================================================================
+--- /dev/null 1970-01-01 00:00:00.000000000 +0000
++++ nnet-0.1.7/doc/latex/developers/octave/functions/newff.tex 2007-07-25 09:07:19.000000000 +0200
+@@ -0,0 +1,14 @@
++\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
+Index: nnet-0.1.7/doc/latex/developers/octave/functions/poststd.tex
+===================================================================
+--- /dev/null 1970-01-01 00:00:00.000000000 +0000
++++ nnet-0.1.7/doc/latex/developers/octave/functions/poststd.tex 2007-07-25 09:07:19.000000000 +0200
+@@ -0,0 +1,14 @@
++\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
+Index: nnet-0.1.7/doc/latex/developers/octave/functions/prestd.tex
+===================================================================
+--- /dev/null 1970-01-01 00:00:00.000000000 +0000
++++ nnet-0.1.7/doc/latex/developers/octave/functions/prestd.tex 2007-07-25 09:07:19.000000000 +0200
+@@ -0,0 +1,14 @@
++\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
+Index: nnet-0.1.7/doc/latex/developers/octave/functions/sim.tex
+===================================================================
+--- /dev/null 1970-01-01 00:00:00.000000000 +0000
++++ nnet-0.1.7/doc/latex/developers/octave/functions/sim.tex 2007-07-25 09:07:19.000000000 +0200
+@@ -0,0 +1,14 @@
++\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
+Index: nnet-0.1.7/doc/latex/developers/octave/functions/train.tex
+===================================================================
+--- /dev/null 1970-01-01 00:00:00.000000000 +0000
++++ nnet-0.1.7/doc/latex/developers/octave/functions/train.tex 2007-07-25 09:07:19.000000000 +0200
+@@ -0,0 +1,14 @@
++\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
+Index: nnet-0.1.7/doc/latex/developers/octave/functions/trastd.tex
+===================================================================
+--- /dev/null 1970-01-01 00:00:00.000000000 +0000
++++ nnet-0.1.7/doc/latex/developers/octave/functions/trastd.tex 2007-07-25 09:07:19.000000000 +0200
+@@ -0,0 +1,14 @@
++\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
+Index: nnet-0.1.7/doc/latex/developers/octave/octave.tex
+===================================================================
+--- /dev/null 1970-01-01 00:00:00.000000000 +0000
++++ nnet-0.1.7/doc/latex/developers/octave/octave.tex 2007-07-24 17:13:14.000000000 +0200
+@@ -0,0 +1,4 @@
++\chapter{Octave's available functions}
++
++\section{Available functions}
++\input{octave/functions/min_max}
+\ No newline at end of file
+Index: nnet-0.1.7/doc/latex/developers/title.tex
+===================================================================
+--- /dev/null 1970-01-01 00:00:00.000000000 +0000
++++ nnet-0.1.7/doc/latex/developers/title.tex 2007-07-24 17:13:14.000000000 +0200
+@@ -0,0 +1,7 @@
++
++\title{A neural network toolbox for Octave\\
++ Developer's Guide\\
++ \input{../common/version}}
++
++\author{M. Schmid}
++\maketitle
+Index: nnet-0.1.7/doc/latex/developers/varietes.tex
+===================================================================
+--- /dev/null 1970-01-01 00:00:00.000000000 +0000
++++ nnet-0.1.7/doc/latex/developers/varietes.tex 2007-02-18 13:44:58.000000000 +0100
+@@ -0,0 +1,96 @@
++\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
+Index: nnet-0.1.7/doc/latex/users/bibliography.tex
+===================================================================
+--- /dev/null 1970-01-01 00:00:00.000000000 +0000
++++ nnet-0.1.7/doc/latex/users/bibliography.tex 2007-02-18 13:57:23.000000000 +0100
+@@ -0,0 +1,35 @@
++% 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
+Index: nnet-0.1.7/doc/latex/users/examples/1/1.tex
+===================================================================
+--- /dev/null 1970-01-01 00:00:00.000000000 +0000
++++ nnet-0.1.7/doc/latex/users/examples/1/1.tex 2007-07-24 17:13:14.000000000 +0200
+@@ -0,0 +1,34 @@
++\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 transfer functions, the train algorithm or the performance function. And don't change the number of hidden layers.\\
++
++\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.
++
++
+Index: nnet-0.1.7/doc/latex/users/examples/1/MLP9_1_1.m
+===================================================================
+--- /dev/null 1970-01-01 00:00:00.000000000 +0000
++++ nnet-0.1.7/doc/latex/users/examples/1/MLP9_1_1.m 2008-02-04 14:47:45.000000000 +0100
+@@ -0,0 +1,98 @@
++## Copyright (C) 2006 Michel D. Schmid <michaelschmid at users.sourceforge.net>
++##
++##
++## This program is free software; you can redistribute it and/or modify it
++## under the terms of the GNU General Public License as published by
++## the Free Software Foundation; either version 2, or (at your option)
++## any later version.
++##
++## This program is distributed in the hope that it will be useful, but
++## WITHOUT ANY WARRANTY; without even the implied warranty of
++## MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
++## General Public License for more details.
++##
++## You should have received a copy of the GNU General Public License
++## along with this program; see the file COPYING. If not, see
++## <http://www.gnu.org/licenses/>.
++
++## 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
+Index: nnet-0.1.7/doc/latex/users/examples/1/MLP9_1_1.tex
+===================================================================
+--- /dev/null 1970-01-01 00:00:00.000000000 +0000
++++ nnet-0.1.7/doc/latex/users/examples/1/MLP9_1_1.tex 2007-07-24 17:13:14.000000000 +0200
+@@ -0,0 +1,103 @@
++\noindent
++\ttfamily
++\hlstd{}\hlline{00001\ }\hlslc{\#\# Copyright (C) 2006 Michel D. Schmid}\hlstd{\ \ }\hlslc{$<$michaelschmid at users.sourceforge.net$>$}\\
++\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
+Index: nnet-0.1.7/doc/latex/users/examples/1/mData.txt
+===================================================================
+--- /dev/null 1970-01-01 00:00:00.000000000 +0000
++++ nnet-0.1.7/doc/latex/users/examples/1/mData.txt 2007-02-18 13:57:23.000000000 +0100
+@@ -0,0 +1,94 @@
++# Created by Octave 2.9.5, Wed May 24 10:33:36 2006 <sim at 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
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+Index: nnet-0.1.7/doc/latex/users/examples/examples.tex
+===================================================================
+--- /dev/null 1970-01-01 00:00:00.000000000 +0000
++++ nnet-0.1.7/doc/latex/users/examples/examples.tex 2007-02-18 13:57:23.000000000 +0100
+@@ -0,0 +1,6 @@
++\chapter{Examples}
++
++
++
++
++\input{examples/1/1}
+\ No newline at end of file
+Index: nnet-0.1.7/doc/latex/users/introduction.tex
+===================================================================
+--- /dev/null 1970-01-01 00:00:00.000000000 +0000
++++ nnet-0.1.7/doc/latex/users/introduction.tex 2007-02-18 13:57:23.000000000 +0100
+@@ -0,0 +1,16 @@
++\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
++michaelschmid at users.sourceforge.net. This will help improving this toolbox.
++
++
++\input{numbering}
++
++\input{knownIncompatibilities}
++
+Index: nnet-0.1.7/doc/latex/users/knownIncompatibilities.tex
+===================================================================
+--- /dev/null 1970-01-01 00:00:00.000000000 +0000
++++ nnet-0.1.7/doc/latex/users/knownIncompatibilities.tex 2007-02-18 13:57:23.000000000 +0100
+@@ -0,0 +1,11 @@
++\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.
++
++
+Index: nnet-0.1.7/doc/latex/users/neuralNetworkToolboxForOctaveUsersGuide.tcp
+===================================================================
+--- /dev/null 1970-01-01 00:00:00.000000000 +0000
++++ nnet-0.1.7/doc/latex/users/neuralNetworkToolboxForOctaveUsersGuide.tcp 2007-02-18 13:57:23.000000000 +0100
+@@ -0,0 +1,12 @@
++[FormatInfo]
++Type=TeXnicCenterProjectInformation
++Version=4
++
++[ProjectInfo]
++MainFile=neuralNetworkToolboxForOctaveUsersGuide.tex
++UseBibTeX=0
++UseMakeIndex=1
++ActiveProfile=LaTeX => PDF
++ProjectLanguage=de
++ProjectDialect=DE
++
+Index: nnet-0.1.7/doc/latex/users/neuralNetworkToolboxForOctaveUsersGuide.tex
+===================================================================
+--- /dev/null 1970-01-01 00:00:00.000000000 +0000
++++ nnet-0.1.7/doc/latex/users/neuralNetworkToolboxForOctaveUsersGuide.tex 2007-07-24 17:13:14.000000000 +0200
+@@ -0,0 +1,102 @@
++% 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{asymptote}
++\usepackage{fancyhdr}
++%\usepackage{supertabular}
++\usepackage{booktabs}
++%\usepackage{longtable}
++\usepackage[dvips]{rotating}
++\usepackage{multirow}
++\usepackage{multicol}
++
++\usepackage{color}
++\usepackage{amsmath}
++\usepackage{alltt}
++%\usepackage{array}
++%\usepackage{colortbl}
++
++%%%%%%%%%%%%%%%% will be used to defined a color scheme for
++%%%%%%%%%%%%%%%% latex pages converted with "Highlight"
++\newcommand{\hlstd}[1]{\textcolor[rgb]{0,0,0}{#1}}
++\newcommand{\hlnum}[1]{\textcolor[rgb]{0.75,0,0.35}{#1}}
++\newcommand{\hlesc}[1]{\textcolor[rgb]{0.42,0.35,0.8}{#1}}
++\newcommand{\hlstr}[1]{\textcolor[rgb]{0.75,0,0.35}{#1}}
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++\newcommand{\hlsym}[1]{\textcolor[rgb]{0,0,0}{#1}}
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++\newcommand{\hlkwa}[1]{\textcolor[rgb]{0.65,0.16,0.16}{\bf{#1}}}
++\newcommand{\hlkwb}[1]{\textcolor[rgb]{0.18,0.55,0.34}{\bf{#1}}}
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++\definecolor{bgcolor}{rgb}{1,0.85,0.73}
++\oddsidemargin -3mm
++\textwidth 165,2truemm
++\topmargin 0truept
++\headheight 0truept
++\headsep 0truept
++\textheight 230truemm
++%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
++
++\clubpenalty = 10000
++\widowpenalty = 10000 \displaywidowpenalty = 10000
++
++\definecolor{hellgrau}{gray}{0.95}
++\definecolor{dunkelgrau}{gray}{0.55}
++
++\definecolor{brown}{rgb}{0.75,0.004,0.3}
++
++
++\renewcommand{\headrulewidth}{0pt} % no head rule
++\renewcommand{\footrulewidth}{0pt} % no footer rule
++
++%\nointend
++%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
++% start text here!!
++
++
++\begin{document}
++\pagenumbering{roman}
++\input{title2}
++
++\tableofcontents
++\pagenumbering{arabic}
++
++\include{introduction}
++\include{octave/neuroToolbox/neuroToolbox}
++\include{examples/examples}
++
++\include{bibliography}
++
++
++
++\end{document}
++
++
+Index: nnet-0.1.7/doc/latex/users/numbering.tex
+===================================================================
+--- /dev/null 1970-01-01 00:00:00.000000000 +0000
++++ nnet-0.1.7/doc/latex/users/numbering.tex 2007-07-24 17:13:14.000000000 +0200
+@@ -0,0 +1,7 @@
++\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
+Index: nnet-0.1.7/doc/latex/users/octave/neuroToolbox/graphics/logsig.eps
+===================================================================
+--- /dev/null 1970-01-01 00:00:00.000000000 +0000
++++ nnet-0.1.7/doc/latex/users/octave/neuroToolbox/graphics/logsig.eps 2007-07-24 16:53:07.000000000 +0200
+@@ -0,0 +1,702 @@
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++0.43 setcmykcolor}DC/NavyBlue{0.94 0.54 0 0 setcmykcolor}DC/RoyalBlue{1
++0.50 0 0 setcmykcolor}DC/Blue{1 1 0 0 setcmykcolor}DC/Cerulean{0.94 0.11
++0 0 setcmykcolor}DC/Cyan{1 0 0 0 setcmykcolor}DC/ProcessBlue{0.96 0 0 0
++setcmykcolor}DC/SkyBlue{0.62 0 0.12 0 setcmykcolor}DC/Turquoise{0.85 0
++0.20 0 setcmykcolor}DC/TealBlue{0.86 0 0.34 0.02 setcmykcolor}DC
++/Aquamarine{0.82 0 0.30 0 setcmykcolor}DC/BlueGreen{0.85 0 0.33 0
++setcmykcolor}DC/Emerald{1 0 0.50 0 setcmykcolor}DC/JungleGreen{0.99 0
++0.52 0 setcmykcolor}DC/SeaGreen{0.69 0 0.50 0 setcmykcolor}DC/Green{1 0
++1 0 setcmykcolor}DC/ForestGreen{0.91 0 0.88 0.12 setcmykcolor}DC
++/PineGreen{0.92 0 0.59 0.25 setcmykcolor}DC/LimeGreen{0.50 0 1 0
++setcmykcolor}DC/YellowGreen{0.44 0 0.74 0 setcmykcolor}DC/SpringGreen{
++0.26 0 0.76 0 setcmykcolor}DC/OliveGreen{0.64 0 0.95 0.40 setcmykcolor}
++DC/RawSienna{0 0.72 1 0.45 setcmykcolor}DC/Sepia{0 0.83 1 0.70
++setcmykcolor}DC/Brown{0 0.81 1 0.60 setcmykcolor}DC/Tan{0.14 0.42 0.56 0
++setcmykcolor}DC/Gray{0 0 0 0.50 setcmykcolor}DC/Black{0 0 0 1
++setcmykcolor}DC/White{0 0 0 0 setcmykcolor}DC end
++
++%%EndProcSet
++%%BeginFont: CMSY10
++%!PS-AdobeFont-1.1: CMSY10 1.0
++%%CreationDate: 1991 Aug 15 07:20:57
++% Copyright (C) 1997 American Mathematical Society. All Rights Reserved.
++11 dict begin
++/FontInfo 7 dict dup begin
++/version (1.0) readonly def
++/Notice (Copyright (C) 1997 American Mathematical Society. All Rights Reserved) readonly def
++/FullName (CMSY10) readonly def
++/FamilyName (Computer Modern) readonly def
++/Weight (Medium) readonly def
++/ItalicAngle -14.035 def
++/isFixedPitch false def
++end readonly def
++/FontName /CMSY10 def
++/PaintType 0 def
++/FontType 1 def
++/FontMatrix [0.001 0 0 0.001 0 0] readonly def
++/Encoding 256 array
++0 1 255 {1 index exch /.notdef put} for
++dup 0 /minus put
++readonly def
++/FontBBox{-29 -960 1116 775}readonly def
++/UniqueID 5000820 def
++currentdict end
++currentfile eexec
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++cleartomark
++%%EndFont
++%%BeginFont: CMR12
++%!PS-AdobeFont-1.1: CMR12 1.0
++%%CreationDate: 1991 Aug 20 16:38:05
++% Copyright (C) 1997 American Mathematical Society. All Rights Reserved.
++11 dict begin
++/FontInfo 7 dict dup begin
++/version (1.0) readonly def
++/Notice (Copyright (C) 1997 American Mathematical Society. All Rights Reserved) readonly def
++/FullName (CMR12) readonly def
++/FamilyName (Computer Modern) readonly def
++/Weight (Medium) readonly def
++/ItalicAngle 0 def
++/isFixedPitch false def
++end readonly def
++/FontName /CMR12 def
++/PaintType 0 def
++/FontType 1 def
++/FontMatrix [0.001 0 0 0.001 0 0] readonly def
++/Encoding 256 array
++0 1 255 {1 index exch /.notdef put} for
++dup 40 /parenleft put
++dup 41 /parenright put
++dup 43 /plus put
++dup 48 /zero put
++dup 49 /one put
++dup 61 /equal put
++readonly def
++/FontBBox{-34 -251 988 750}readonly def
++/UniqueID 5000794 def
++currentdict end
++currentfile eexec
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++%%EndFont
++%%BeginFont: CMMI12
++%!PS-AdobeFont-1.1: CMMI12 1.100
++%%CreationDate: 1996 Jul 27 08:57:55
++% Copyright (C) 1997 American Mathematical Society. All Rights Reserved.
++11 dict begin
++/FontInfo 7 dict dup begin
++/version (1.100) readonly def
++/Notice (Copyright (C) 1997 American Mathematical Society. All Rights Reserved) readonly def
++/FullName (CMMI12) readonly def
++/FamilyName (Computer Modern) readonly def
++/Weight (Medium) readonly def
++/ItalicAngle -14.04 def
++/isFixedPitch false def
++end readonly def
++/FontName /CMMI12 def
++/PaintType 0 def
++/FontType 1 def
++/FontMatrix [0.001 0 0 0.001 0 0] readonly def
++/Encoding 256 array
++0 1 255 {1 index exch /.notdef put} for
++dup 97 /a put
++dup 103 /g put
++dup 105 /i put
++dup 108 /l put
++dup 110 /n put
++dup 111 /o put
++dup 115 /s put
++readonly def
++/FontBBox{-30 -250 1026 750}readonly def
++/UniqueID 5087386 def
++currentdict end
++currentfile eexec
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++ 24.6802415 19.5707779 24.7558329 19.577758 24.8314472 19.5844837 curveto
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+Index: nnet-0.1.7/doc/latex/users/octave/neuroToolbox/graphics/logsiglogo.pdf
+===================================================================
+Binary files /dev/null 1970-01-01 00:00:00.000000000 +0000 and nnet-0.1.7/doc/latex/users/octave/neuroToolbox/graphics/logsiglogo.pdf 2007-07-24 16:53:07.000000000 +0200 differ
+Index: nnet-0.1.7/doc/latex/users/octave/neuroToolbox/graphics/purelin.eps
+===================================================================
+--- /dev/null 1970-01-01 00:00:00.000000000 +0000
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+Index: nnet-0.1.7/doc/latex/users/octave/neuroToolbox/graphics/purelinlogo.pdf
+===================================================================
+Binary files /dev/null 1970-01-01 00:00:00.000000000 +0000 and nnet-0.1.7/doc/latex/users/octave/neuroToolbox/graphics/purelinlogo.pdf 2007-07-24 16:53:07.000000000 +0200 differ
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+Index: nnet-0.1.7/doc/latex/users/octave/neuroToolbox/graphics/tansiglogo.pdf
+===================================================================
+Binary files /dev/null 1970-01-01 00:00:00.000000000 +0000 and nnet-0.1.7/doc/latex/users/octave/neuroToolbox/graphics/tansiglogo.pdf 2007-07-24 16:53:07.000000000 +0200 differ
+Index: nnet-0.1.7/doc/latex/users/octave/neuroToolbox/logsig.tex
+===================================================================
+--- /dev/null 1970-01-01 00:00:00.000000000 +0000
++++ nnet-0.1.7/doc/latex/users/octave/neuroToolbox/logsig.tex 2007-07-24 17:13:14.000000000 +0200
+@@ -0,0 +1,15 @@
++\subsection{logsig}
++
++\begin{figure}[htb]
++\centering
++ \includegraphics{octave/neuroToolbox/graphics/logsig}
++\caption{Log-Sigmoid transfer function}
++\label{fig:logsigTransferFunction}
++\end{figure}
++
++\begin{figure}[htb]
++\centering
++ \includegraphics{octave/neuroToolbox/graphics/logsiglogo}
++\caption{Log-Sigmoid transfer function logo}
++\label{fig:logsigTransferFunctionLogo}
++\end{figure}
+\ No newline at end of file
+Index: nnet-0.1.7/doc/latex/users/octave/neuroToolbox/min_max.tex
+===================================================================
+--- /dev/null 1970-01-01 00:00:00.000000000 +0000
++++ nnet-0.1.7/doc/latex/users/octave/neuroToolbox/min_max.tex 2007-02-18 14:14:43.000000000 +0100
+@@ -0,0 +1,10 @@
++\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);\\
++
++
++
++
+Index: nnet-0.1.7/doc/latex/users/octave/neuroToolbox/neuroToolbox.tex
+===================================================================
+--- /dev/null 1970-01-01 00:00:00.000000000 +0000
++++ nnet-0.1.7/doc/latex/users/octave/neuroToolbox/neuroToolbox.tex 2007-07-24 17:13:14.000000000 +0200
+@@ -0,0 +1,18 @@
++\chapter{Neural Network Toolbox for Octave}
++This chapter describes all functions available in the neural network toolbox of Octave.
++
++Eventhough it will be as compatible as possible to the one of MATLAB(TM).
++
++\section{Available Functions}
++\input{octave/neuroToolbox/min_max}
++\input{octave/neuroToolbox/newff}
++\input{octave/neuroToolbox/saveMLPStruct}
++\input{octave/neuroToolbox/sim}
++\input{octave/neuroToolbox/train}
++
++
++\section{Transfer functions}
++\input{octave/neuroToolbox/logsig}
++\input{octave/neuroToolbox/purelin}
++\input{octave/neuroToolbox/tansig}
++
+Index: nnet-0.1.7/doc/latex/users/octave/neuroToolbox/newff.tex
+===================================================================
+--- /dev/null 1970-01-01 00:00:00.000000000 +0000
++++ nnet-0.1.7/doc/latex/users/octave/neuroToolbox/newff.tex 2007-07-24 17:13:14.000000000 +0200
+@@ -0,0 +1,27 @@
++\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.\\
++
++
+Index: nnet-0.1.7/doc/latex/users/octave/neuroToolbox/purelin.tex
+===================================================================
+--- /dev/null 1970-01-01 00:00:00.000000000 +0000
++++ nnet-0.1.7/doc/latex/users/octave/neuroToolbox/purelin.tex 2007-07-24 17:13:14.000000000 +0200
+@@ -0,0 +1,16 @@
++\subsection{purelin}
++
++\begin{figure}[htb]
++\centering
++ \includegraphics{octave/neuroToolbox/graphics/purelin}
++\caption{Linear transfer function}
++\label{fig:purelinTransferFunction}
++\end{figure}
++
++\begin{figure}[htb]
++\centering
++ \includegraphics{octave/neuroToolbox/graphics/purelinlogo}
++\caption{Linear transfer function logo}
++\label{fig:purelinTransferFunctionLogo}
++\end{figure}
++
+Index: nnet-0.1.7/doc/latex/users/octave/neuroToolbox/saveMLPStruct.tex
+===================================================================
+--- /dev/null 1970-01-01 00:00:00.000000000 +0000
++++ nnet-0.1.7/doc/latex/users/octave/neuroToolbox/saveMLPStruct.tex 2007-02-18 14:14:43.000000000 +0100
+@@ -0,0 +1,10 @@
++\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");\\
++
++
++
++
+Index: nnet-0.1.7/doc/latex/users/octave/neuroToolbox/sim.tex
+===================================================================
+--- /dev/null 1970-01-01 00:00:00.000000000 +0000
++++ nnet-0.1.7/doc/latex/users/octave/neuroToolbox/sim.tex 2007-02-18 14:14:43.000000000 +0100
+@@ -0,0 +1,9 @@
++\subsection{sim}
++
++\noindent \textbf{\textcolor{brown}{Syntax:}}\\
++
++\noindent simout = sim(....);\\
++
++
++
++
+Index: nnet-0.1.7/doc/latex/users/octave/neuroToolbox/tansig.tex
+===================================================================
+--- /dev/null 1970-01-01 00:00:00.000000000 +0000
++++ nnet-0.1.7/doc/latex/users/octave/neuroToolbox/tansig.tex 2007-07-24 17:13:14.000000000 +0200
+@@ -0,0 +1,29 @@
++\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}. IS THIS A COPYRIGHT PROBLEM?
++
++\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/neuroToolbox/graphics/tansig}
++\caption{Tansig transfer function}
++\label{fig:tansigTransferFunction}
++\end{figure}
++
++\begin{figure}[htb]
++\centering
++ \includegraphics{octave/neuroToolbox/graphics/tansiglogo}
++\caption{Tansig transfer function logo}
++\label{fig:tansigTransferFunctionLogo}
++\end{figure}
+Index: nnet-0.1.7/doc/latex/users/octave/neuroToolbox/train.tex
+===================================================================
+--- /dev/null 1970-01-01 00:00:00.000000000 +0000
++++ nnet-0.1.7/doc/latex/users/octave/neuroToolbox/train.tex 2007-02-18 14:14:43.000000000 +0100
+@@ -0,0 +1,9 @@
++\subsection{train}
++
++\noindent \textbf{\textcolor{brown}{Syntax:}}\\
++
++\noindent net = train(....);\\
++
++
++
++
+Index: nnet-0.1.7/doc/latex/users/title2.tex
+===================================================================
+--- /dev/null 1970-01-01 00:00:00.000000000 +0000
++++ nnet-0.1.7/doc/latex/users/title2.tex 2007-07-24 17:13:14.000000000 +0200
+@@ -0,0 +1,8 @@
++% start text here!!
++
++\title{A neural network toolbox for Octave\\
++ User's Guide \\
++ \input{../common/version}}
++
++\author{Michel D. Schmid}
++\maketitle
Added: octave-forge-pkgs/octave-nnet/trunk/debian/patches/series
===================================================================
--- octave-forge-pkgs/octave-nnet/trunk/debian/patches/series (rev 0)
+++ octave-forge-pkgs/octave-nnet/trunk/debian/patches/series 2008-05-06 23:26:06 UTC (rev 1819)
@@ -0,0 +1 @@
+pdfsources.diff
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