PKX/5META-INF/MANIFEST.MFeN0EwK7`'PQHLI^ 9_N{-,W2ps)ެh7R8%eX G`l&屋5z Iט}Tbl3)PK~9PK 85de/PK 85de/uos/PK 85de/uos/cogsci/PK 85de/uos/cogsci/ai/PK 85de/uos/cogsci/ai/btenbergen/PK 85&de/uos/cogsci/ai/btenbergen/bscthesis/PK 855de/uos/cogsci/ai/btenbergen/bscthesis/neuralnetworks/PK 5w+59de/uos/cogsci/ai/btenbergen/bscthesis/neuralnetworks/mlp/PK 5w+5&.e e Dde/uos/cogsci/ai/btenbergen/bscthesis/neuralnetworks/mlp/Layer.class1m>de/uos/cogsci/ai/btenbergen/bscthesis/neuralnetworks/mlp/Layerjava/lang/Object?de/uos/cogsci/ai/btenbergen/bscthesis/neuralnetworks/mlp/LayersneuronsM[Lde/uos/cogsci/ai/btenbergen/bscthesis/neuralnetworks/perceptron/Perceptron;maxValueI(IIII)VCode()V  java/util/Random Jde/uos/cogsci/ai/btenbergen/bscthesis/neuralnetworks/perceptron/Perceptron   java/lang/Mathabs(I)I  nextInt  !ceil(D)D #$ % $ ' (IID[DZ)V ) *LineNumberTableLocalVariableTablethis@Lde/uos/cogsci/ai/btenbergen/bscthesis/neuralnetworks/mlp/Layer;rowscolsinputDimensionsrLjava/util/Random; currentNeuronijweights[DkpLLde/uos/cogsci/ai/btenbergen/bscthesis/neuralnetworks/perceptron/Perceptron;findNeuronIndex(II)Irow ? @col B Cindex findNeuronP(II)Lde/uos/cogsci/ai/btenbergen/bscthesis/neuralnetworks/perceptron/Perceptron; => HgetPerceptronsO()[Lde/uos/cogsci/ai/btenbergen/bscthesis/neuralnetworks/perceptron/Perceptron;toString()Ljava/lang/String;Njava/lang/StringBuilderP Neuon R(Ljava/lang/String;)V T QUappend(I)Ljava/lang/StringBuilder; WX QY: [-(Ljava/lang/String;)Ljava/lang/StringBuilder; W] Q^ LM ` Q`java/lang/Stringcconcat&(Ljava/lang/String;)Ljava/lang/String; ef dgsLjava/lang/String; SourceFile Layer.java!   w *Y:6*h*6i6Z: 6   *"R  Y*&(" +: * S,RCE FGI K&L,N1O7PIORSlTnUoStV}WLKZ-z ./0 1 2   345 #o6 )`7 1O89 4: t ;< => 6>6%*2A*2D>*,egi'g4l-46./6? 6B 4E /6 FG J **I2,x-  ./ ?  B JK /*,- ./LM <OL=,+QYSVZ\_*2a_bhL*+,.:- <./9ij56 klPK !5<XXCde/uos/cogsci/ai/btenbergen/bscthesis/neuralnetworks/mlp/Layer.java/* * Self-Organizing Map Plugin v1.0 * for the Yale Learning Environment (http://yale.sf.net/) * * Copyright (C) 2006 * Bastian Tenbergen * * School of Human Sciences * University of Osnabrueck * 49076 Osnabrueck, Germany * email: btenberg@uni-osnabrueck.de * web: http://www-lehre.inf.uos.de/~btenberg/ * * 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 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, write to the Free Software Foundation, Inc., * 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA. */ package de.uos.cogsci.ai.btenbergen.bscthesis.neuralnetworks.mlp; /** * This is the Layer class. * It implements a layer for a Multi-Layer Perceptron Neural Network. * The type of the layer can be either input, hidden or output. * * @version 02.05.2006 * @author Bastian Tenbergen - btenberg@uos.de */ import java.util.Random; import de.uos.cogsci.ai.btenbergen.bscthesis.neuralnetworks.perceptron.Perceptron; public class Layer implements Layers { /** * The array of neurons defining the layer. */ public Perceptron[] neurons; /** * The maximum value of weights and bias for each neuron. */ private int maxValue; /** * Constructor. A layer is defined by the number of neurons and the dimensionality of the input. * It is instantiated by invoking rows * cols Perceptrons. Each Perceptron * has as many weights as input dimensionality. The weight vectors are initialized randomly, however * limited to maxValue to prevent very large weight differences. * Neurons in Multi-Layer-Perceptrons always fire bipolar. * @param rows The number of rows in the layer * @param cols The number of cols in the layer * @param inputDimensions The dimensionality of the input vector * @param maxValue The maximum value for weights and bias for the neurons. */ public Layer(int rows, int cols, int inputDimensions, int maxValue) { Random r = new Random(); int currentNeuron = 0; neurons = new Perceptron[rows * cols]; this.maxValue = maxValue; for (int i = 0; i < rows; i++) { for (int j = 0; j < cols; j++) { double[] weights = new double[inputDimensions]; for (int k = 0; k < inputDimensions; k++) { weights[k] = (double) r.nextInt(Math.abs(this.maxValue)); }//end for-k Perceptron p = new Perceptron(i, j, r.nextInt((int) Math.abs(Math.ceil(this.maxValue))), weights, true); neurons[currentNeuron] = p; ++currentNeuron; }//end for-j }//end for-i } /** * Given a coordinate in the structure, this method returns the index of the * corresponding neuron. * @param row The X-Axis of the neuron * @param col The Y-Axis of the neuron * @return The index of the neuron. */ public int findNeuronIndex(int row, int col) { int index = -1; for (int i = 0; i < neurons.length; i++) { if (neurons[i].row == row && neurons[i].col == col) index = i; } return index; } /** * Given a coordinate in the structure, this method returns the corresponding * neuron. * @param row The X-Axis of the neuron * @param col The Y-Axis of the neuron * @return The neuron at the given position. */ public Perceptron findNeuron(int row, int col) { return this.neurons[findNeuronIndex(row, col)]; } /** * This method simply returns the neurons of the layer. * @return The neurons of the layer. */ public Perceptron[] getPerceptrons() { return neurons; } /** * Displays the neurons of the layer. */ public String toString() { String s = ""; for (int i = 0; i < this.neurons.length; i++) { s = s.concat("\n" + "Neuon " + i + ": " + this.neurons[i].toString()); } return s; } }PK 5w+5Ede/uos/cogsci/ai/btenbergen/bscthesis/neuralnetworks/mlp/Layers.class1?de/uos/cogsci/ai/btenbergen/bscthesis/neuralnetworks/mlp/Layersjava/lang/ObjectfindNeuronIndex(II)I findNeuronP(II)Lde/uos/cogsci/ai/btenbergen/bscthesis/neuralnetworks/perceptron/Perceptron;getPerceptronsO()[Lde/uos/cogsci/ai/btenbergen/bscthesis/neuralnetworks/perceptron/Perceptron;toString()Ljava/lang/String; SourceFile Layers.java   PK !5E&Dde/uos/cogsci/ai/btenbergen/bscthesis/neuralnetworks/mlp/Layers.java/* * Self-Organizing Map Plugin v1.0 * for the Yale Learning Environment (http://yale.sf.net/) * * Copyright (C) 2006 * Bastian Tenbergen * * School of Human Sciences * University of Osnabrueck * 49076 Osnabrueck, Germany * email: btenberg@uni-osnabrueck.de * web: http://www-lehre.inf.uos.de/~btenberg/ * * 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 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, write to the Free Software Foundation, Inc., * 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA. */ package de.uos.cogsci.ai.btenbergen.bscthesis.neuralnetworks.mlp; /** * This is a Layer Interface. Layers for MLPs should be constructed by implementing this interface. * * @version 01.05.2006 * @author B. Tenbergen - btenberg@uos.de */ import de.uos.cogsci.ai.btenbergen.bscthesis.neuralnetworks.perceptron.Perceptron; public interface Layers { /** * Given a coordinate in the structure, this method returns the index of the * corresponding neuron. * @param row The X-Axis of the neuron * @param col The Y-Axis of the neuron * @return The index of the neuron. */ public int findNeuronIndex(int row, int col); /** * Given a coordinate in the structure, this method returns the corresponding * neuron. * @param row The X-Axis of the neuron * @param col The Y-Axis of the neuron * @return The neuron at the given position. */ public Perceptron findNeuron(int row, int col); /** * This method simply returns the neurons of the Layer. * @return The neurons of the Layer. */ public Perceptron[] getPerceptrons(); /** * toString displays the Neurons of the Layer. */ public String toString(); }PK 5w+5. Cde/uos/cogsci/ai/btenbergen/bscthesis/neuralnetworks/mlp/MLPs.class1=de/uos/cogsci/ai/btenbergen/bscthesis/neuralnetworks/mlp/MLPsjava/lang/ObjectrunClassification()V ExceptionsXde/uos/cogsci/ai/btenbergen/bscthesis/neuralnetworks/perceptron/PerceptronInputException updateWinner([D)VupdateNeighbor findNeighborsQ([D)[Lde/uos/cogsci/ai/btenbergen/bscthesis/neuralnetworks/perceptron/Perceptron; findWinnerP([D)Lde/uos/cogsci/ai/btenbergen/bscthesis/neuralnetworks/perceptron/Perceptron;toString()Ljava/lang/String; SourceFile MLPs.java        PK 5=1MMBde/uos/cogsci/ai/btenbergen/bscthesis/neuralnetworks/mlp/MLPs.java/* * Self-Organizing Map Plugin v1.0 * for the Yale Learning Environment (http://yale.sf.net/) * * Copyright (C) 2006 * Bastian Tenbergen * * School of Human Sciences * University of Osnabrueck * 49076 Osnabrueck, Germany * email: btenberg@uni-osnabrueck.de * web: http://www-lehre.inf.uos.de/~btenberg/ * * 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 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, write to the Free Software Foundation, Inc., * 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA. */ package de.uos.cogsci.ai.btenbergen.bscthesis.neuralnetworks.mlp; /** * This is a Multi-Layer Perceptron Interface. MLPs should be constructed by implementing this interface. * * @version 01.05.2006 * @author B. Tenbergen - btenberg@uos.de */ import de.uos.cogsci.ai.btenbergen.bscthesis.neuralnetworks.perceptron.PerceptronInputException; import de.uos.cogsci.ai.btenbergen.bscthesis.neuralnetworks.perceptron.Perceptron; public interface MLPs { /** * Runs the classification process runs times. * For each pattern update(int[]) is called. * @throws If the Perceptron's weight vector is not of equal length as the input vector. */ public void runClassification() throws PerceptronInputException; /** * Updates the winner neuron. To do so, the winner neuron needs to be determined * by calling findWinner(int[]). Each weight in the winner's weight vecotr is then updated * with the following formula:
* newWeight = oldWeight + (LEARNINGRATE * (input - oldWeight))
* @param input The weight vector that determines the winning neuron. * @throws If the Perceptron's weight vector is not of equal length as the input vector. */ public void updateWinner(double[] input) throws PerceptronInputException; /** * Updates the winner's neighboring neurons. The neighboring neurons are determined and * then updated according to the following formula:
* newWeight = oldWeight + (LEARNINGRATE * (input - oldWeight))
* @param input The weight vector that determines the winning neuron. * @throws PerceptronInputException If the Perceptron's weight vector is not of equal length as the input vector. */ public void updateNeighbor(double[] input) throws PerceptronInputException; /** * This method determines the winner neuron's neighboring neurons and returns * them. This method needs to implement the topology of the MLP. * @param input The input vector determining the winning neuron. * @return The winner neuron's neighboring neurons. * @throws PerceptronInputException If the Perceptron's weight vector is not of equal length as the input vector. */ public Perceptron[] findNeighbors(double[] input) throws PerceptronInputException; /** * This method determines the winning neuron by comparing the activations given the input vector * of all neurons in the layer. This is very close to a standard SelectionSort algorithm, but since the * neurons are not sorted and only the maximum activation, this is done in O(n) time. * @param input The input vector determining the winning neuron. * @return The winning neuron. * @throws If the Perceptron's weight vector is not of equal length as the input vector. */ public Perceptron findWinner(double[] input) throws PerceptronInputException; /** * toString method. Returns the String-Representation of this MLP. * @return The String representation of this MLP. */ public String toString(); }PK 5w+5=de/uos/cogsci/ai/btenbergen/bscthesis/neuralnetworks/mlp/som/PK 5w+5YM5 5 Fde/uos/cogsci/ai/btenbergen/bscthesis/neuralnetworks/mlp/som/SOM.class1@de/uos/cogsci/ai/btenbergen/bscthesis/neuralnetworks/mlp/som/SOMjava/lang/Object=de/uos/cogsci/ai/btenbergen/bscthesis/neuralnetworks/mlp/MLPsclosed_topologyZrunsIlearningrate_winnerDlearningrate_neighborpatterns[Ljava/lang/Object;rowscolsoutput@Lde/uos/cogsci/ai/btenbergen/bscthesis/neuralnetworks/mlp/Layer;(II[Ljava/lang/Object;DDIIZ)VCode()V      java/util/Random nextInt()I !" #>de/uos/cogsci/ai/btenbergen/bscthesis/neuralnetworks/mlp/Layer%[D'(IIII)V ) &*  , . 0 2LineNumberTableLocalVariableTablethisBLde/uos/cogsci/ai/btenbergen/bscthesis/neuralnetworks/mlp/som/SOM;maxValue newMaxValuerLjava/util/Random;(II[Ljava/lang/Object;)V?  ?(II[Ljava/lang/Object;I)V computeMax([Ljava/lang/Object;)I(I)I !D E1de/uos/cogsci/ai/btenbergen/bscthesis/tools/ToolsG minMaxElement([D)[D IJ HKrdmminmaxrunClassification ExceptionsXde/uos/cogsci/ai/btenbergen/bscthesis/neuralnetworks/perceptron/PerceptronInputExceptionQ updateWinner([D)V ST UupdateNeighbor WT Xji findWinnerP([D)Lde/uos/cogsci/ai/btenbergen/bscthesis/neuralnetworks/perceptron/Perceptron; \] ^Jde/uos/cogsci/ai/btenbergen/bscthesis/neuralnetworks/perceptron/Perceptron` getWeights()[D bc ad setWeights([D)Z fg ahinputwinnerLLde/uos/cogsci/ai/btenbergen/bscthesis/neuralnetworks/perceptron/Perceptron; winnerWeights findNeighborsQ([D)[Lde/uos/cogsci/ai/btenbergen/bscthesis/neuralnetworks/perceptron/Perceptron; no p neighborsM[Lde/uos/cogsci/ai/btenbergen/bscthesis/neuralnetworks/perceptron/Perceptron;currentNeighborWeightsgetPerceptronsO()[Lde/uos/cogsci/ai/btenbergen/bscthesis/neuralnetworks/perceptron/Perceptron; uv &w  y  {row } a~col aneurons s &findNeuronIndex(II)I & winnerIndexleftrightupperlower activation([D)D amaxtoString()Ljava/lang/String; & showPatternsjava/lang/SystemoutLjava/io/PrintStream;  Patterns:java/io/PrintStreamprintln(Ljava/lang/String;)V print(D)V       logPatterns edu/udo/cs/yale/tools/LogService logMessage(Ljava/lang/String;I)V java/lang/Double(D)Ljava/lang/String;  SourceFileSOM.java!      , d**-*  &Y :  $6  6 *&Y-2( +-*/*1*346 fl mp$q-r4s7v;zQW]c5z d67d d dd d d d8 d 49 ;)9 - :; <[ *-=@4 5*67  Af *-=@4 5467   BC5Y M,+F>+2(L:,$>1 143545675-:;&M N'OPR<<1= **2(V**2(Y*݄*3ͱ4 $0;5 <679Z )[ STPR 9*+_M,eN6--1*/+1-1gkcR-,-iW4 (28549679j'3kl .m'$[ WTPR U*+qM,6>,2eN6--1*1+1-1gkcR-,2-iW,4*   7AJT5>U67Uj'Ors3t'E[ $Z noPR *+_M*-xN6-a*z*|*,R,K*-?*-,`,6*-,,`6 a:-2S- 2S,L,*|d?*-,d,6*-,,`6 a:-2S- 2S,*zdF,?*-,`,6*-,,d6a:-2S-2S,*zdL,*|d?*-,d,6*-,,d6a:-2S-2S,Z*-,`,6*-,,`6 *-,,d6a:-2S- 2S-2S,*|dZ*-,d,6*-,,`6 *-,,d6a:-2S- 2S-2S,Z*-,d,6*-,`,6*-,,`6 a:-2S-2S- 2S,*zd]*-,d,6*-,`,6*-,,d6a:-2S-2S-2Sa:,v,o*-*|d6*-,`,6*-,,`6 *-*zd6a:-2S-2S- 2S-2S,|,*|do*-,d,6*-6*-,,`6 *-*zd*|d6a:-2S-2S- 2S-2S,*zdv,o*-*zd*|d6*-,`,6*-6 *-,,d6a:-2S-2S- 2S-2S,*zd|,*|do*-,d,6*-*zd6*-*|d6 *-,,d6a:-2S-2S- 2S-2S,u*-,*|d6*-,`,6*-,,`6 *-,,d6a:-2S-2S- 2S-2S,*|dp*-,d,6*-,6*-,,`6 *-,,d6a:-2S-2S- 2S-2S,*-u*-,d,6*-,`,6*-,,`6 *-*zd,6a:-2S-2S- 2S-2S,*zdx*-,d,6*-,`,6*-*zd,6 *-,,d6a:-2S-2S- 2S-2Sa:*d`6a:-2S-dd6a:-2Ssd6`6a:-2S-2SN-d6`6$-dd66d6`6a:-2S-2S4  ,3M`sy !$%&'(!)),C-V.i/o0w1456789:;>?@A#B)C1D9EAHHI[JnKLMNORSTUVWXY\de-f@gShciijqkylmqrstuvwxyz ~2EPciqy)<Obhpx(;Nagow $*5>DJU[agoz5|Y67j'klsyrsrsrsorsrs)rsrsrs rsi rs rsi rs rsh rs rsg rs#rsrs* rsJ rsgrsrs ) V) D [D G -\ Y 2W Y )_ Z (_ b D [    `) ) D n1 4 @I N ED I <L L ;L O $ a     i  # ! c& & c& & b& & a& ) s  1 1  S6 ; P9 9 O9 9 N9 < \]PR F*-x:62+962+I( (9624*    '.15@5HF67Fj''  =s :k / &[ 2*-4!5 67W<==*2(1*2(ٲ*4* *+ ,-#.+,<0D+P2V35 W67 F[ -Z F<4=*2(1*2(ڄ*ɱ4":; <=">(<9;EA5 F67=[  ,Z PK \(5:XXEde/uos/cogsci/ai/btenbergen/bscthesis/neuralnetworks/mlp/som/SOM.java/* * Self-Organizing Map Plugin v1.0 * for the Yale Learning Environment (http://yale.sf.net/) * * Copyright (C) 2006 * Bastian Tenbergen * * School of Human Sciences * University of Osnabrueck * 49076 Osnabrueck, Germany * email: btenberg@uni-osnabrueck.de * web: http://www-lehre.inf.uos.de/~btenberg/ * * 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 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, write to the Free Software Foundation, Inc., * 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA. */ package de.uos.cogsci.ai.btenbergen.bscthesis.neuralnetworks.mlp.som; /** * This is the SOM class. * It implements a Self-Organizing Map. A SOM is a neural network * with two layers and the following structure:
* - An Inputlayer with n neurons. The activation of these neurons * is equal to the input values.
* - An Outputlayer with m neurons. Only one neuron in this layer is * active (output: 1) - all others are silent (output: 0). Winner * neurons and it's neighboring neurons are updated with a fixed * learning rate.
* - An input vector with d dimensions.
* - Every output neuron is connected to every input neuron.
* * @version 19.07.2006 * @author Bastian Tenbergen - btenberg@uos.de */ import de.uos.cogsci.ai.btenbergen.bscthesis.neuralnetworks.mlp.Layer; import de.uos.cogsci.ai.btenbergen.bscthesis.neuralnetworks.mlp.MLPs; import de.uos.cogsci.ai.btenbergen.bscthesis.neuralnetworks.perceptron.Perceptron; import de.uos.cogsci.ai.btenbergen.bscthesis.neuralnetworks.perceptron.PerceptronInputException; import de.uos.cogsci.ai.btenbergen.bscthesis.tools.Tools; import edu.udo.cs.yale.tools.LogService; import java.util.Random; public class SOM implements MLPs { /** The Topology of this Map. If true, the last neuron of each row or col is connected to the first one. */ private boolean closed_topology; /** The number of classification runs to be performed. */ private int runs; /** The learning rate. Defines how far the weight vector of the winning neuron is "pulled" into the direction of the input vector. */ private double learningrate_winner; /** The learning rate of the winner's neighboring neurons. */ private double learningrate_neighbor; /** This array contains all patterns, i.e. the input vectors. For simplicity reasons, this is an Object array that contains double arrays instead of a two dimensional matrix. Whenever a pattern is taken out of the array, it needs to be casted to a double[]. This should increase the performance on slow machines */ private Object[] patterns; /** Sets the number of rows of this SOM. */ private int rows; /** Sets the number of cols of this SOM. */ private int cols; /** The output layer. */ public Layer output; //The input layer. // private Layer input; /** * Constructor. * Creates a new Self-Organizing Map. * * @param rows The number of rows of this SOM. * @param cols The number of columns of this SOM. * @param patterns The training patterns. Must be an Object array containing * double arrays. * @param learningrate_winner Defines how far the weight vector of the winning neuron is "pulled" into * the direction of the input vector. * @param learningrate_neighbor Defines how far the weight vectors of the winner's neighbors is updated. * @param runs The number of classification runs to be performed. * @param maxValue The maximum value for weights and bias for the neurons. * @param closed_topology The Topology of this Map. If true, the last neuron of each row or col is connected to the first one. */ public SOM(int rows, int cols, Object[] patterns, double learningrate_winner, double learningrate_neighbor, int runs, int maxValue, boolean closed_topology) { this.patterns = patterns; this.closed_topology = closed_topology; int newMaxValue; switch (maxValue) { case -1: Random r = new Random(); newMaxValue = r.nextInt(); break; // case -2: newMaxValue = Math.abs(computeMax(this.patterns)); // break; default: newMaxValue = maxValue; break; } this.output = new Layer(rows, cols, ((double[]) patterns[0]).length, newMaxValue); // input = new Layer(0, 0, 0); // Since the activation of the input layer equals the input values, // the network is implemented as just an output layer. this.learningrate_winner = learningrate_winner; this.learningrate_neighbor = learningrate_neighbor; this.runs = runs; } /** * Constructor. * Creates a new Self-Organizing Map with default values for learningrates and runs. * A sensible maximum value for the neuron's weights and bias is computed automatically. * * @param rows The number of rows of this SOM. * @param cols The number of columns of this SOM. * @param patterns The training patterns. Must be an Object array containing * double arrays. */ public SOM(int rows, int cols, Object[] patterns) { this(rows, cols, patterns, 1, 0.25, 1, -1, false); } /** * Constructor. * Creates a new Self-Organizing Map with default values for learningrates. * A sensible maximum value for the neuron's weights and bias is computed automatically. * * @param rows The number of rows of this SOM. * @param cols The number of columns of this SOM. * @param patterns The training patterns. Must be an Object array containing * double arrays. * @param runs The number of runs to be performed. The more runs are performed, the more "prototypical" * the output neurons are to the input clusters. */ public SOM(int rows, int cols, Object[] patterns, int runs) { this(rows, cols, patterns, 1, 0.25, runs, -1, false); } /** * This method computues the maximum sensible bias and weight values. * @param patterns A sensible maximum is computed according to these patterns. * @return A reasonable maximum threshold. */ public int computeMax(Object[] patterns) { //We need a Random Object Random r = new Random(); //take a random pattern... int rdm = r.nextInt(patterns.length); //...and compute the minimal and maximal element //of that pattern. double[] minmax = Tools.minMaxElement(((double[]) patterns[rdm])); do { //Then, generate a random int rdm = r.nextInt(); } while (!(rdm < minmax[0]) && rdm > minmax[1]); //If the new random int is smaller than the //smallest, or bigger than the biggest //element in the pattern, generate a new one return rdm; } /** * Runs the classification process runs times. * For each pattern, the winner neuron is determined and updated. The winner neuron's * neighboring neurons are updated accordingly. * @throws If the Perceptron's weight vector is not of equal length as the input vector. */ public void runClassification() throws PerceptronInputException { for (int j = 0; j < runs; j++) { for (int i = 0; i < patterns.length; i++) { updateWinner((double[]) patterns[i]); updateNeighbor((double[]) patterns[i]); } } } /** * Updates the winner neuron of the output layer. To do so, the winner neuron needs to be determined * by calling findWinner(int[]). Each weight in the winner's weight vecotr is then updated * with the following formula:
* newWeight = oldWeight + (LEARNINGRATE * (input - oldWeight))
* @param input The weight vector that determines the winning neuron. * @throws If the Perceptron's weight vector is not of equal length as the input vector. */ public void updateWinner(double[] input) throws PerceptronInputException { Perceptron winner = findWinner(input); double[] winnerWeights = winner.getWeights(); for (int i = 0; i < winnerWeights.length; i++) { winnerWeights[i] = winnerWeights[i] + (learningrate_winner * (input[i] - winnerWeights[i])); } winner.setWeights(winnerWeights); } /** * Updates the winner neuron's neighboring neurons. To do so, the neighbors need to be determined * by calling findNeighbors(int[]). Each weight in each neighbor's weight vecotr is then updated * with the following formula:
* newWeight = oldWeight + (LEARNINGRATE * (input - oldWeight))
* @param input The weight vector that determines the winning neuron. * @throws If the Perceptron's weight vector is not of equal length as the input vector. */ public void updateNeighbor(double[] input) throws PerceptronInputException { Perceptron[] neighbors = findNeighbors(input); double[] currentNeighborWeights; if (neighbors.length == 0) return; else { for (int i = 0; i < neighbors.length; i++) { currentNeighborWeights = neighbors[i].getWeights(); for (int j = 0; j < currentNeighborWeights.length; j++) { currentNeighborWeights[j] = currentNeighborWeights[j] + (learningrate_neighbor * (input[j] - currentNeighborWeights[j])); }//end for-j neighbors[i].setWeights(currentNeighborWeights); }//end for-i }//end else } /** * This method determines the winner neuron's neighboring neurons and returns * them according to the here implemented architecture of the SOM. * @param input The input vector determining the winning neuron. * @return The winner neuron's neighboring neurons. * @throws PerceptronInputException If the Perceptron's weight vector is not of equal length as the input vector. */ public Perceptron[] findNeighbors(double[] input) throws PerceptronInputException { Perceptron winner = findWinner(input); Perceptron[] neurons = this.output.getPerceptrons(); Perceptron[] neighbors; //allocate neighbors int winnerIndex = 0; //allocate winner index int left, right, upper, lower; //allocate neighbor indexes if (neurons.length == 1) return new Perceptron[0]; //set neighbor relations for rectangle architecture if (rows > 1 && cols > 1) { //... with open topology... if (!this.closed_topology) { //winner is in upper left corner -> only right und lower neighbor if (winner.row == 0 && winner.col == 0 && this.output.neurons.length > 1) { right = this.output.findNeuronIndex(winner.row+1, winner.col); lower = this.output.findNeuronIndex(winner.row, winner.col+1); neighbors = new Perceptron[2]; neighbors[0] = neurons[right]; neighbors[1] = neurons[lower]; } //winner is in upper right corner -> only left und lower neighbor if (winner.row == 0 && winner.col == cols-1) { left = this.output.findNeuronIndex(winner.row-1, winner.col); lower = this.output.findNeuronIndex(winner.row, winner.col+1); neighbors = new Perceptron[2]; neighbors[0] = neurons[left]; neighbors[1] = neurons[lower]; } //winner is in lower left corner -> only right und upper neighbor if (winner.row == rows-1 && winner.col == 0) { right = this.output.findNeuronIndex(winner.row+1, winner.col); upper = this.output.findNeuronIndex(winner.row, winner.col-1); neighbors = new Perceptron[2]; neighbors[0] = neurons[right]; neighbors[1] = neurons[upper]; } //winner is in lower right corner -> only left und upper neighbor if (winner.row == rows-1 && winner.col == cols-1) { left = this.output.findNeuronIndex(winner.row-1, winner.col); upper = this.output.findNeuronIndex(winner.row, winner.col-1); neighbors = new Perceptron[2]; neighbors[0] = neurons[left]; neighbors[1] = neurons[upper]; } //if winner in first column -> no left neighbor if (winner.col == 0) { right = this.output.findNeuronIndex(winner.row+1, winner.col); lower = this.output.findNeuronIndex(winner.row, winner.col+1); upper = this.output.findNeuronIndex(winner.row, winner.col-1); neighbors = new Perceptron[3]; neighbors[0] = neurons[right]; neighbors[1] = neurons[lower]; neighbors[2] = neurons[upper]; } //if winner in last column -> no right neighbor if (winner.col == cols-1) { left = this.output.findNeuronIndex(winner.row-1, winner.col); lower = this.output.findNeuronIndex(winner.row, winner.col+1); upper = this.output.findNeuronIndex(winner.row, winner.col-1); neighbors = new Perceptron[3]; neighbors[0] = neurons[left]; neighbors[1] = neurons[lower]; neighbors[2] = neurons[upper]; } //if winner in first row -> no upper neighbor if (winner.row == 0) { left = this.output.findNeuronIndex(winner.row-1, winner.col); right = this.output.findNeuronIndex(winner.row+1, winner.col); lower = this.output.findNeuronIndex(winner.row, winner.col+1); neighbors = new Perceptron[3]; neighbors[0] = neurons[left]; neighbors[1] = neurons[right]; neighbors[2] = neurons[lower]; } //if winner in last row -> no lower neighbor if (winner.row == rows-1) { left = this.output.findNeuronIndex(winner.row-1, winner.col); right = this.output.findNeuronIndex(winner.row+1, winner.col); upper = this.output.findNeuronIndex(winner.row, winner.col-1); neighbors = new Perceptron[3]; neighbors[0] = neurons[left]; neighbors[1] = neurons[right]; neighbors[2] = neurons[upper]; } else { neighbors = new Perceptron[0]; } } //... with closed topology... else { //winner is in upper left corner -> left neighbor is last in first row // upper one is last in first column if (winner.row == 0 && winner.col == 0) { left = this.output.findNeuronIndex(0, cols-1); right = this.output.findNeuronIndex(winner.row+1, winner.col); lower = this.output.findNeuronIndex(winner.row, winner.col+1); upper = this.output.findNeuronIndex(rows-1, 0); neighbors = new Perceptron[4]; neighbors[0] = neurons[left]; neighbors[1] = neurons[right]; neighbors[2] = neurons[lower]; neighbors[3] = neurons[upper]; } //winner is in upper right corner -> right neighbor is first in first row // upper one is last in last col if (winner.row == 0 && winner.col == cols-1) { left = this.output.findNeuronIndex(winner.row-1, winner.col); right = this.output.findNeuronIndex(0,0); lower = this.output.findNeuronIndex(winner.row, winner.col+1); upper = this.output.findNeuronIndex(rows-1, cols-1); neighbors = new Perceptron[4]; neighbors[0] = neurons[left]; neighbors[1] = neurons[right]; neighbors[2] = neurons[lower]; neighbors[3] = neurons[upper]; } //winner is in lower left corner -> left is last in last col // lower is first in first row if (winner.row == rows-1 && winner.col == 0) { left = this.output.findNeuronIndex(rows-1, cols-1); right = this.output.findNeuronIndex(winner.row+1, winner.col); lower = this.output.findNeuronIndex(0, 0); upper = this.output.findNeuronIndex(winner.row, winner.col-1); neighbors = new Perceptron[4]; neighbors[0] = neurons[left]; neighbors[1] = neurons[right]; neighbors[2] = neurons[lower]; neighbors[3] = neurons[upper]; } //winner is in lower right corner -> right is first in last row // lower is last in first row if (winner.row == rows-1 && winner.col == cols-1) { left = this.output.findNeuronIndex(winner.row-1, winner.col); right = this.output.findNeuronIndex(rows-1, 0); lower = this.output.findNeuronIndex(0, cols-1); upper = this.output.findNeuronIndex(winner.row, winner.col-1); neighbors = new Perceptron[4]; neighbors[0] = neurons[left]; neighbors[1] = neurons[right]; neighbors[2] = neurons[lower]; neighbors[3] = neurons[upper]; } //if winner in first column -> left is last col, same row if (winner.col == 0) { left = this.output.findNeuronIndex(winner.row, cols-1); right = this.output.findNeuronIndex(winner.row+1, winner.col); lower = this.output.findNeuronIndex(winner.row, winner.col+1); upper = this.output.findNeuronIndex(winner.row, winner.col-1); neighbors = new Perceptron[4]; neighbors[0] = neurons[left]; neighbors[1] = neurons[right]; neighbors[2] = neurons[lower]; neighbors[3] = neurons[upper]; } //if winner in last column -> right is first col, same row if (winner.col == cols-1) { left = this.output.findNeuronIndex(winner.row-1, winner.col); right = this.output.findNeuronIndex(winner.row, 0); lower = this.output.findNeuronIndex(winner.row, winner.col+1); upper = this.output.findNeuronIndex(winner.row, winner.col-1); neighbors = new Perceptron[4]; neighbors[0] = neurons[left]; neighbors[1] = neurons[right]; neighbors[2] = neurons[lower]; neighbors[3] = neurons[upper]; } //if winner in first row -> upper is last row, same col if (winner.row == 0 && this.output.neurons.length > 1) { left = this.output.findNeuronIndex(winner.row-1, winner.col); right = this.output.findNeuronIndex(winner.row+1, winner.col); lower = this.output.findNeuronIndex(winner.row, winner.col+1); upper = this.output.findNeuronIndex(rows-1, winner.col); neighbors = new Perceptron[4]; neighbors[0] = neurons[left]; neighbors[1] = neurons[right]; neighbors[2] = neurons[lower]; neighbors[3] = neurons[upper]; } //if winner in last row -> lower is first row, same col if (winner.row == rows-1) { left = this.output.findNeuronIndex(winner.row-1, winner.col); right = this.output.findNeuronIndex(winner.row+1, winner.col); lower = this.output.findNeuronIndex(rows-1, winner.col); upper = this.output.findNeuronIndex(winner.row, winner.col-1); neighbors = new Perceptron[4]; neighbors[0] = neurons[left]; neighbors[1] = neurons[right]; neighbors[2] = neurons[lower]; neighbors[3] = neurons[upper]; } else { neighbors = new Perceptron[0]; } } } //set neighbor relations for chain/cirlce architecture else { //...with open topology... (chain) if (!this.closed_topology) { //if winner is leftmost neuron -> no left neighbor if (winnerIndex == 0) { right = winnerIndex+1; neighbors = new Perceptron[1]; neighbors[0] = neurons[right]; } //if winner is rightmost neuron -> no right neighbor else if (winnerIndex == neurons.length-1) { left = winnerIndex-1; neighbors = new Perceptron[1]; neighbors[0] = neurons[left]; } //if winner is in the middle -> set both neighbors else { left = winnerIndex-1; right = winnerIndex+1; neighbors = new Perceptron[2]; neighbors[0] = neurons[left]; neighbors[1] = neurons[right]; } } //...with closed topology... (circle) else {//if (this.closed_topology) { //if winner is leftmost neuron -> left neighbor is last neuron if (winnerIndex == 0) { left = neurons.length-1; right = winnerIndex+1; } //if winner is rightmost neuron -> right neighbor is first neuron else if (winnerIndex == neurons.length-1) { left = winnerIndex-1; right = 0; } //if winner is in the middle -> set both neighbors as usual else { left = winnerIndex-1; right = winnerIndex+1; } neighbors = new Perceptron[2]; neighbors[0] = neurons[left]; neighbors[1] = neurons[right]; } } return neighbors; } /** * This method determines the winning neuron by comparing the activations given the input vector * of all neurons in the layer. This is very close to a standard SelectionSort algorithm, but since the * neurons are not sorted and only the maximum activation, this is done in O(n) time. * @param input The input vector determining the winning neuron. * @return The winning neuron. * @throws PerceptronInputException If the Perceptron's weight vector is not of equal length as the input vector. */ public Perceptron findWinner(double[] input) throws PerceptronInputException { double activation; Perceptron[] neurons = this.output.getPerceptrons(); int winner = 0; double max = neurons[winner].activation(input); for (int i = 0; i < neurons.length; i++) { activation = neurons[i].activation(input); if (activation > max) { max = activation; winner = i; } } return neurons[winner]; } /** * toString method. Just calls the toString of the output layer, since this SOM just consists * of an output layer. * @return The String representation of this SOM. */ public String toString() { return this.output.toString(); } /** * This method confirms the given input vectors by prompting them. * Called usually by methods testing the functionality. */ public void showPatterns() { System.out.println("Patterns:"); for (int i = 0; i < patterns.length; i++) { for (int j = 0; j < ((double []) patterns[i]).length; j++) { System.out.print(((double[]) patterns[i])[j]); System.out.print(" "); } System.out.print(" "); } System.out.println(); } /** * This method logs the current status of the SOM by sending information * to Yale's Log-Console. The output is similar to showPatterns(). */ protected void logPatterns() { LogService.logMessage("Patterns:", LogService.STATUS); for (int i = 0; i < patterns.length; i++) { for (int j = 0; j < ((double []) patterns[i]).length; j++) { LogService.logMessage(Double.toString(((double[]) patterns[i])[j]), LogService.STATUS); LogService.logMessage(" ", LogService.STATUS); } } } }PK 5w+5@de/uos/cogsci/ai/btenbergen/bscthesis/neuralnetworks/perceptron/PK 5w+5^de/uos/cogsci/ai/btenbergen/bscthesis/neuralnetworks/perceptron/PerceptronInputException.class1$Xde/uos/cogsci/ai/btenbergen/bscthesis/neuralnetworks/perceptron/PerceptronInputExceptionjava/lang/ExceptionserialVersionUIDJ ConstantValue*msgLjava/lang/String;causeLjava/lang/Throwable;()VCode*(Ljava/lang/String;Ljava/lang/Throwable;)V  LineNumberTableLocalVariableTablethisZLde/uos/cogsci/ai/btenbergen/bscthesis/neuralnetworks/perceptron/PerceptronInputException;    (Ljava/lang/String;)V(Ljava/lang/Throwable;)V getMessage()Ljava/lang/String; SourceFilePerceptronInputException.java!  5* 45 Y**+*,=> ?@   ?*+ GH ?*+ PQ  !/*Y "#PK 5 ]de/uos/cogsci/ai/btenbergen/bscthesis/neuralnetworks/perceptron/PerceptronInputException.java/* * Self-Organizing Map Plugin v1.0 * for the Yale Learning Environment (http://yale.sf.net/) * * Copyright (C) 2006 * Bastian Tenbergen * * School of Human Sciences * University of Osnabrueck * 49076 Osnabrueck, Germany * email: btenberg@uni-osnabrueck.de * web: http://www-lehre.inf.uos.de/~btenberg/ * * 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 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, write to the Free Software Foundation, Inc., * 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA. */ package de.uos.cogsci.ai.btenbergen.bscthesis.neuralnetworks.perceptron; /** * The class PercepronInputException is thrown when the Input-Vector of a Perceptron * is not of equal length as its Weight-Vector. * Javadoc taken from java.lang.Exception class. * * * @version 01.05.2006 * @author Bastian Tenbergen - btenberg@uos.de */ public class PerceptronInputException extends Exception { private static final long serialVersionUID = 42; private String msg; protected Throwable cause; /** * Constructs a new exception with null as its detail message. * The cause is not initialized, and may subsequently be initialized by a call to Throwable.initCause(java.lang.Throwable). */ public PerceptronInputException() { this(null, null); } /** * Constructs a new exception with the specified detail message and cause. * Note that the detail message associated with cause is not automatically incorporated in this exception's detail message. * @param msg the detail message (which is saved for later retrieval by the Throwable.getMessage() method). * @param cause the cause (which is saved for later retrieval by the Throwable.getCause() method). (A null value is permitted, and indicates that the cause is nonexistent or unknown.) */ public PerceptronInputException(String msg, Throwable cause) { this.msg = msg; this.cause = cause; } /** * Constructs a new exception with the specified detail message. The cause is not initialized, and may subsequently be initialized by a call to Throwable.initCause(java.lang.Throwable). * @param msg the detail message. The detail message is saved for later retrieval by the Throwable.getMessage() method. */ public PerceptronInputException(String msg) { this(msg, null); } /** * Constructs a new exception with the specified cause and a detail message of (cause==null ? null : cause.toString()) (which typically contains the class and detail message of cause). * This constructor is useful for exceptions that are little more than wrappers for other throwables (for example, PrivilegedActionException). * @param cause the cause (which is saved for later retrieval by the Throwable.getCause() method). (A null value is permitted, and indicates that the cause is nonexistent or unknown.) */ public PerceptronInputException(Throwable cause) { this(null, cause); } /** * Just returns the detail message of the exception. * * @return the detailed message as a java.lang.String */ public String getMessage() { return msg; } }PK 5w+5B Pde/uos/cogsci/ai/btenbergen/bscthesis/neuralnetworks/perceptron/Perceptron.class1xJde/uos/cogsci/ai/btenbergen/bscthesis/neuralnetworks/perceptron/Perceptronjava/lang/ObjectBIASDweights[DbipolarZrowIcol returnPos returnNeg (IID[DZ)VCode()V          setBipolar  !LineNumberTableLocalVariableTablethisLLde/uos/cogsci/ai/btenbergen/bscthesis/neuralnetworks/perceptron/Perceptron;  '  )fire([D)I ExceptionsXde/uos/cogsci/ai/btenbergen/bscthesis/neuralnetworks/perceptron/PerceptronInputException. activation([D)D 01 2inputs6Input vector length not equal to weight vector length.5(Ljava/lang/String;)V 7 /8acti setWeights([D)Zjava/lang/System>errLjava/io/PrintStream; @A ?BVWarning: New weight vector not of the same length as the old one. Weights not updated.Djava/io/PrintStreamFprintln H7 GI newWeights getWeights()[DtoString()Ljava/lang/String;Pjava/lang/DoubleR(D)V T SU NO SWjava/lang/StringYconcat&(Ljava/lang/String;)Ljava/lang/String; [\ Z] _, The Perceptron fires bipolar.a, The Perceptron fires binary.cjava/lang/StringBuildereBias: g f8append(D)Ljava/lang/StringBuilder; jk fl , Weights: n-(Ljava/lang/String;)Ljava/lang/StringBuilder; jp fq fWsLjava/lang/String; SourceFilePerceptron.java!     $****)***"#"HJ KLMNP#Q$>$%&$ $ $$$  P*(* ****#XYZ[$ %&+,-/O*+3**(**# fg$%&401-/:+* /Y69I6(*1+1kcI*(#q ruvw+v8z$*:%&:4$:; <=-+* CEJ=*+1R+# "+$ -%&-K; LM/*#$ %&NOeQL="+SY*1VX^L+`^L** +b^L +d^LfYhi*mor+rs#"$0AH$ e%&btu+; vwPK 5NkkOde/uos/cogsci/ai/btenbergen/bscthesis/neuralnetworks/perceptron/Perceptron.java/* * Self-Organizing Map Plugin v1.0 * for the Yale Learning Environment (http://yale.sf.net/) * * Copyright (C) 2006 * Bastian Tenbergen * * School of Human Sciences * University of Osnabrueck * 49076 Osnabrueck, Germany * email: btenberg@uni-osnabrueck.de * web: http://www-lehre.inf.uos.de/~btenberg/ * * 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 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, write to the Free Software Foundation, Inc., * 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA. */ package de.uos.cogsci.ai.btenbergen.bscthesis.neuralnetworks.perceptron; /** * This is the Perceptron class. * It implements a perceptron with an arbitrary number of inputs. * * @version 01.05.2006 * @author Bastian Tenbergen - btenberg@uos.de */ public class Perceptron { /** The biad of the perceptron that needs to be exceeded to cause an activation. */ public final double BIAS; /** The weights of the perceptron that weighing the input */ public double[] weights; /** If true the perceptron fires bipolar (output: 1 or -1), else binary (output: 1 or 0).*/ public boolean bipolar; /** If the perceptron is embedded in a neural network, this indicates the row in which it is located.*/ public int row; /** If the perceptron is embedded in a neural network, this indicates the column in which it is located.*/ public int col; /** The value that is returned when the activation exceeds the threshold. */ private int returnPos; /** The value that is returned when the activation does not exceed the threshold. */ private int returnNeg; /** * Constructor. A standard Perceptron is invoked when called. * @param row The row of the structure in which this Perceptron is located. * @param col The column of the structure in which this Perceptron is located. * @param BIAS The bias (theta, threshold) of the Perceptron. Once set, never changeable. * @param weights The weight vector of the perceptron. * @param bipolar Determines, if the Perceptron is bipolar or binary. * If true, the negative output is -1. If false, the negative * output is 0. The positive output is always 1. */ public Perceptron(int row, int col, double BIAS, double[] weights, boolean bipolar) { this.row = row; this.col = col; this.BIAS = BIAS; this.weights = weights; this.bipolar = bipolar; setBipolar(); } /** * Sets the Perceptron to binary or bipolar. Called by the constructor. */ private void setBipolar() { this.returnPos = 1; if (this.bipolar) this.returnNeg = -1; else this.returnNeg = 0; } /** * Fires the perceptron. Calls activation(int[]) to compute scalar product. * @param inputs The input vector. * @return 1 if the scalar product of the input and the weights is higher than the bias. * -1 or 0 else (this depends on the Perceptron being bipolar or binary. * @throws If the Perceptron's weight vector is not of equal length as the input vector. */ public int fire(double[] inputs) throws PerceptronInputException { if (activation(inputs) >= BIAS) return returnPos; else return returnNeg; } /** * This method computes the scalar product of the input and the weights. * @param inputs The input vector. * @return The activation of the Perceptron - scalar product of weights x input. * @throws If the Perceptron's weight vector is not of equal length as the input vector. */ public double activation(double[] inputs) throws PerceptronInputException { if (inputs.length != weights.length) { throw new PerceptronInputException("Input vector length not equal to weight vector length."); } double act = 0; for (int i = 0; i < weights.length; i++) { act += weights[i] * inputs[i]; } return act; } /** * This method updates the weight vector of the Perceptron. * @param newWeights The new weight vector. * @return true if update was successful, false else. */ public boolean setWeights(double[] newWeights) { if (newWeights.length != weights.length) { System.err.println("Warning: New weight vector not of the same length as the old one. Weights not updated."); return false; } else { for (int i = 0; i < newWeights.length; i++) { weights[i] = newWeights[i]; } return true; } } /** * This method just returns the weights. * @return The weight vector of the Perceptron. */ public double[] getWeights() { return weights; } /** * toString method. Returns the String representation of the Perceptron. * @return The String representation of the Perceptron. */ public String toString() { String s = ""; for (int i = 0; i < weights.length; i++) { s = s.concat((new Double(weights[i])).toString()); s = s.concat(" "); } if (this.bipolar) s = s.concat(", The Perceptron fires bipolar."); else s = s.concat(", The Perceptron fires binary."); return "Bias: " + this.BIAS + ", Weights: " + s; } }PK 5w+5,de/uos/cogsci/ai/btenbergen/bscthesis/tools/PK 5w+5 ?de/uos/cogsci/ai/btenbergen/bscthesis/tools/OperatorTools.class19de/uos/cogsci/ai/btenbergen/bscthesis/tools/OperatorToolsjava/lang/Object()VCode  LineNumberTableLocalVariableTablethis;Lde/uos/cogsci/ai/btenbergen/bscthesis/tools/OperatorTools;convertFilename&(Ljava/lang/String;)Ljava/lang/String;java/lang/StringcharAt(I)C  java/lang/CharacterisLetterOrDigit(C)Z  replace(CC)Ljava/lang/String;  length()I ! "ö$oe& replaceAll8(Ljava/lang/String;Ljava/lang/String;)Ljava/lang/String; () *Ö,Oe.ä0ae2Ä4Ae6ü8ue:Ü<Ue>ß@ssB_0Dconcat F GfilenameLjava/lang/String;resultiIfindQueryInClusters\(Ljava/lang/String;[Lde/uos/cogsci/ai/btenbergen/bscthesis/tools/Cluster;)Ljava/lang/String;P3de/uos/cogsci/ai/btenbergen/bscthesis/tools/ClusterRcontentLjava/util/Vector; TU SVjava/util/VectorX elementAt(I)Ljava/lang/Object; Z[ Y\contains(Ljava/lang/CharSequence;)Z ^_ ` toStringln()Ljava/lang/String; bc Sdsize f! Ygequals(Ljava/lang/Object;)Z ij k`No Files found. Please check query file nameand/or lower number of neurons to improve accuracy. m hummedFileclusters6[Lde/uos/cogsci/ai/btenbergen/bscthesis/tools/Cluster;sjdisplaySongList&(Ljava/util/Vector;)Ljava/lang/String; Signature:(Ljava/util/Vector;)Ljava/lang/String;java/lang/StringBuilderx File number z(Ljava/lang/String;)V | y}append(I)Ljava/lang/StringBuilder;  y: -(Ljava/lang/String;)Ljava/lang/StringBuilder;  y toString c ysongListLocalVariableTypeTable&Ljava/util/Vector;createFileClustersL([ILjava/util/Vector;)[Lde/uos/cogsci/ai/btenbergen/bscthesis/tools/Cluster; Exceptions(java/lang/ArrayIndexOutOfBoundsException`([ILjava/util/Vector;)[Lde/uos/cogsci/ai/btenbergen/bscthesis/tools/Cluster; Y(ILjava/util/Vector;)V  S addElement(Ljava/lang/Object;)V Y positions[IshowFileClustersJ([Lde/uos/cogsci/ai/btenbergen/bscthesis/tools/Cluster;)Ljava/lang/String;isEmpty()Z Y S SourceFileOperatorTools.java!/*  -  q*L=+++_L+#+%'+L+-/+L+13+L+57+L+9;+L+=?+L+AC+L+EHL+ :<=>?=)A2B;CDDMEVF_GhHoI qIJoKJ%LM NOZQM>=6&+2W]*a,+2eHM+2Wh҄+,Ql ,nHM, * TVXZ#\.X?VH`QaXe 4ZoJZpqWrJCLM 4sM tuvw?QL=0+yY{~*]HL*h+ npq2p=t ?U<rJ8LM ? vP*SM>,SYYYS,>,*.2W+]:+h,+AA &+ACN 4PPUJpqLM(&LM P 1QL=$*2W+*2HL+HL*+ &/ 1pq.rJ*LMPK !56>de/uos/cogsci/ai/btenbergen/bscthesis/tools/OperatorTools.java/* * Self-Organizing Map Plugin v1.0 * for the Yale Learning Environment (http://yale.sf.net/) * * Copyright (C) 2006 * Bastian Tenbergen * * School of Human Sciences * University of Osnabrueck * 49076 Osnabrueck, Germany * email: btenberg@uni-osnabrueck.de * web: http://www-lehre.inf.uos.de/~btenberg/ * * 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 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, write to the Free Software Foundation, Inc., * 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA. */ package de.uos.cogsci.ai.btenbergen.bscthesis.tools; /** * This class provides some simple Tools for the SOMInput Operator for Yale. * * @version 25.08.2006 * @author Bastian Tenbergen - btenberg@uos.de */ // The methods in this class have been moved here to improve the structural layout of // the package as well as to provide an easier overview of the source-code. import java.util.Vector; import java.lang.ArrayIndexOutOfBoundsException; import de.uos.cogsci.ai.btenbergen.bscthesis.tools.Cluster; public class OperatorTools { /** * Replaces all characters which are not letters or digits by _ (underscore). Additionally all non-english characters * like umlauts or ß are replaced. The file itself is not modified, since only * the String representation of the file name is taken into conderation.
* Modified from ValueSeriesPlugin v3.2 for Yale.
*

* @author Ingo Mierswa (original author) * @author Bastian Tenbergen (modifications) * @param filename The file name to be converted. Usually the user's input file, but this works with any kind of String. * @return The converted file name. The original file is not changed, since this operator only retrieves the String representation of the user's input. * @version current: 27.08.2006, original: $Id: MusicFile.java,v 1.3 2006/03/21 15:35:26 ingomierswa Exp $ */ public static String convertFilename(String filename) { String result = filename; for (int i = 0; i < result.length(); i++) { if (!Character.isLetterOrDigit(result.charAt(i))) result = result.replace(result.charAt(i), '_'); } result = result.replaceAll("\u00f6", "oe"); result = result.replaceAll("\u00d6", "Oe"); result = result.replaceAll("\u00e4", "ae"); result = result.replaceAll("\u00c4", "Ae"); result = result.replaceAll("\u00fc", "ue"); result = result.replaceAll("\u00dc", "Ue"); result = result.replaceAll("\u00df", "ss"); result = result.concat("_0"); return result; } /** * This method checks if a String occures in the Vector of any cluster in a given Cluster-array. * If a occurrence has been found, the whole cluster is printed out. * @param hummedFile Look for this String in the clusters. * @param clusters The cluster array in which to look for the String. * @return The String representation of the cluster(s) in which the String was found. */ public static String findQueryInClusters(String hummedFile, Cluster[] clusters) { String s = ""; //for every single cluster... for (int i = 0; i < clusters.length; i++) { //...look at it's Vector's elements... for (int j = 0; j < clusters[i].content.size(); j++) { //and check, if it contains the query pattern. if (clusters[i].content.elementAt(j).contains(hummedFile)) //if it does, print out the cluster in line form and concatenate s = s.concat(clusters[i].toStringln()); }//end for-j }//end for-i //create error message if nothing was found. (s should be empty then) if (s.equals("")) { s = s.concat("No Files found. Please check query file name" + "and/or lower number of neurons to improve accuracy.\n"); } return s; } /** * This method simply shows the processed songlist. * @param songList A Vector containing the processed songs. * @return The String representation of the songlist in a ResultService-friendly form. */ public static String displaySongList(Vector songList) { String s = ""; for (int i = 0; i < songList.size(); i++) { s = s.concat("File number " + i + ": " + songList.elementAt(i) + "\n"); } return s; } /** * This method creates Cluster-Objects given an array containing the pattern's closest neurons * and a Vector containing the Song-Names. The Cluster-Objects can hence be referenced more easily. * @param positions An array containing the closest neurons to a given pattern. * @param songList A Vector containing the Song-Names. * @return A Cluster-Array containing the Clusters. */ public static Cluster[] createFileClusters(int[] positions, Vector songList) throws ArrayIndexOutOfBoundsException { Cluster[] clusters = new Cluster[positions.length]; //initialize all Clusters and assign ID. for (int i = 0; i < clusters.length; i++) clusters[i] = new Cluster(i, new Vector()); //fill Clusters with the song names. for (int i = 0; i < songList.size(); i++) { //vorher positions iteriert try { clusters[positions[i]].content.addElement(songList.elementAt(i)); } catch (ArrayIndexOutOfBoundsException aiobe) { // throw new ArrayIndexOutOfBoundsException(); } } return clusters; } /** * This method creates Cluster Objects of the processed files and shows them. Similar files occur * in the same cluster. * @param clusters The Clusters of similar songs. * @return The clusters in a ResultService-friendly form. */ public static String showFileClusters(Cluster[] clusters) { String s = ""; for (int i = 0; i < clusters.length; i++) { if (!clusters[i].content.isEmpty()) { s = s.concat(clusters[i].toString()); s = s.concat("\n"); } } return s; } }PK 5w+5;(ILjava/util/Vector;)V*(ILjava/util/Vector;)VCode()V      LineNumberTableLocalVariableTablethis5Lde/uos/cogsci/ai/btenbergen/bscthesis/tools/Cluster;LocalVariableTypeTabletoString()Ljava/lang/String;java/lang/StringBuilder Cluster # (Ljava/lang/String;)V ! "append(I)Ljava/lang/StringBuilder; $% &: (-(Ljava/lang/String;)Ljava/lang/StringBuilder; $* +java/util/Vector-  ./ / toStringln3 elementAt(I)Ljava/lang/Object; 56 .7java/lang/String9concat&(Ljava/lang/String;)Ljava/lang/String; ;< := ?size()I AB .CsLjava/lang/String;i SourceFile Cluster.java!     k***,57 89   M#Y #*'),*0,1A #2/4L=+*8:>L+@>L*D+KMOPM-R /,EF(GHIPK |5-lN 8de/uos/cogsci/ai/btenbergen/bscthesis/tools/Cluster.java/* * Self-Organizing Map Plugin v1.0 * for the Yale Learning Environment (http://yale.sf.net/) * * Copyright (C) 2006 * Bastian Tenbergen * * School of Human Sciences * University of Osnabrueck * 49076 Osnabrueck, Germany * email: btenberg@uni-osnabrueck.de * web: http://www-lehre.inf.uos.de/~btenberg/ * * 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 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, write to the Free Software Foundation, Inc., * 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA. */ package de.uos.cogsci.ai.btenbergen.bscthesis.tools; /** * This class represents a Cluster Object. A Cluster consists of an ID (positive * Integer value) and a Vector containing Strings in which the patterns are stored. * * @version 24.08.2006 * @author Bastian Tenbergen - btenberg@uos.de */ import java.util.Vector; public class Cluster { /** The ID of the Cluster. */ public int id; /** All Patterns in the Cluster. */ public Vector content; /** * Constructor. Invokes a Cluster Object Instance. * @param id The ID of the Cluster. * @param content The Patterns that belong to this cluster. */ public Cluster(int id, Vector content) { this.id = id; this.content = content; } /** * This method returns the String representation of the cluster. * @return The String representation of this cluster in form of the cluster ID and the content. */ public String toString() { return "Cluster # " + this.id + ": " + this.content.toString(); } /** * This method returns the String representation of this cluster in lines. * Only the content is printed, the ID is omitted. * @return This cluster's content in lines. */ public String toStringln() { String s = ""; for (int i = 0; i < this.content.size(); i++) { s = s.concat(this.content.elementAt(i)); s = s.concat("\n"); } return s; } }PK 5w+5!vv7de/uos/cogsci/ai/btenbergen/bscthesis/tools/Tools.class111de/uos/cogsci/ai/btenbergen/bscthesis/tools/Toolsjava/lang/Object()VCode  LineNumberTableLocalVariableTablethis3Lde/uos/cogsci/ai/btenbergen/bscthesis/tools/Tools; quicksort([DII)[Djava/lang/System arraycopy*(Ljava/lang/Object;ILjava/lang/Object;II)V  qs([DII)V  array[DonsetIoffsetsortMetmpDxypivot minMaxElement([D)[D  'minMedMaxElementpositionOfSmallest([D)Iminposi SourceFile Tools.java!/*  ,  m*N*-*--d- 9:;< *  p66*`l19*1*1*1J**1R*)R * * NIJKOQOR"S%R/T6U;VCWHXKYN[U^b_o` Hppp; !m"j#a$! %&K**d(WY*1RY**d1R h i  )&T&**d(WY*1RY**l1RY**d1R r s & *+)*1H>6*1' >*1H* " ' *)%,!#- ./0PK /(5e6de/uos/cogsci/ai/btenbergen/bscthesis/tools/Tools.java/* * Self-Organizing Map Plugin v1.0 * for the Yale Learning Environment (http://yale.sf.net/) * * Copyright (C) 2006 * Bastian Tenbergen * * School of Human Sciences * University of Osnabrueck * 49076 Osnabrueck, Germany * email: btenberg@uni-osnabrueck.de * web: http://www-lehre.inf.uos.de/~btenberg/ * * 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 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, write to the Free Software Foundation, Inc., * 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA. */ package de.uos.cogsci.ai.btenbergen.bscthesis.tools; /** * This is class providing tools for multiple purposes. * Currently implemented:
* - a generic quicksort algorithm sorting double arrays
* - a generic quicksort algorithm sorting double arrays but maintining the original array
* - a method providing the smallest and largest element of a double array
* - a method providing the smallest, the largest and the median element of a double array
* - a method returning the index of the smallest element in a given double array
* * @version 20.07.2006 * @author Bastian Tenbergen - btenberg@uos.de */ public class Tools { /** * A standard recursive QuickSort Algorithm. This algorithm takes a double array and * sorts it from the biggest to the smallest element. It takes the median element as the random pivot element. * This method calls the private method qs(double[], int, int) that does the actual job. * This method copies the given array so that the original array is maintained. * * @param array the array to be sorted * @param onset the onset element from which shall be sorted (inclusive) * @param offset the offset element until which shall be sorted (inclusive) */ public static double[] quicksort(double[] array, int onset, int offset) { double[] sortMe = new double[array.length]; System.arraycopy(array, 0, sortMe, 0, array.length); qs(sortMe, 0, sortMe.length-1); return sortMe; } /** * This method does the actual sorting. * * @param array the array to be sorted * @param onset the onset element from which shall be sorted (inclusive) * @param offset the offset element until which shall be sorted (inclusive) */ private static void qs (double[] array, int onset, int offset) { double tmp; // we need a temp variable to copy values into int x = onset; // and two temp variables serving as the upper int y = offset; // and lower marker double pivot = array[(onset + offset) / 2]; // randomly assign a pivot element // here: the median element do { while (array[x] < pivot) // as long as the lower current element x++; // is smaller as the pivot element: increase while (array[y] > pivot) // as long as the upper current element y--; // is lager as the pivot element: decrease if ( x <= y ) { // if the lower curr elem is smaller or equal to the upper curr elem tmp = array[x]; // array[x] = array[y]; // switch the elements and array[y] = tmp; // x++; // increase lower marker and y--; // decrease upper marker to continue } } while (x <= y); // continue until all elements in left half are smaller // than the ones in the right half if (onset < y) qs(array, onset, y); // sort left half if (x < offset ) qs(array, x, offset); // sort right half } /** * Returns the smallest and biggest element of a given double array in a new double array. * @param array the (unsorted) array containing one smallest and one biggest element * @return a new array with the input array's smallest and biggest as first and second element. */ public static double[] minMaxElement(double[] array) { quicksort(array, 0, array.length-1); return new double[] {array[0], array[array.length-1]}; } /** * Returns the smallest and biggest and the medium element of a given double array in a new double array. * @param array the (unsorted) array containing one smallest and one biggest element * @return a new array with the input array's smallest, biggest and the medium element. */ public static double[] minMedMaxElement(double[] array) { quicksort(array, 0, array.length-1); return new double[] {array[0], array[array.length/2], array[array.length-1]}; } /** * This method returns the position of the smallest element of a given array. * It does so in a SelectionSort-like way, but runs through array only once (since we * don't need to sort the whole array. * * @param array the array containing one smallest element, whose position is of interest. * @return the position of the smallest element in the array. */ public static int positionOfSmallest(double[] array) { double min = array[0]; //the current minimum value in array. int pos = 0; //Position of the smallest. Zero at beginning. for (int i = 1; i < array.length; i++) //Go through array, skip first position. if (array[i] < min) { //If smallere element found... pos = i; //...remember it's position... min = array[i]; //...and it's value. } return pos; //when done, return the position. } }PK 5w+5j&. . :de/uos/cogsci/ai/btenbergen/bscthesis/tools/Distance.class1`4de/uos/cogsci/ai/btenbergen/bscthesis/tools/Distancejava/lang/Object EUCLIDEANI ConstantValue MANHATTANNOMINALpatterns[Ljava/lang/Object;neuronsM[Lde/uos/cogsci/ai/btenbergen/bscthesis/neuralnetworks/perceptron/Perceptron;metricd([Ljava/lang/Object;[Lde/uos/cogsci/ai/btenbergen/bscthesis/neuralnetworks/perceptron/Perceptron;I)VCode()V        LineNumberTableLocalVariableTablethis6Lde/uos/cogsci/ai/btenbergen/bscthesis/tools/Distance;c([Ljava/lang/Object;[Lde/uos/cogsci/ai/btenbergen/bscthesis/neuralnetworks/perceptron/Perceptron;)V  #measureDistance()[I[D'Jde/uos/cogsci/ai/btenbergen/bscthesis/neuralnetworks/perceptron/Perceptron)weights +' *,euclideanDistance([D[D)D ./ 0manhattanDistance 2/ 3nominalDistance 5/ 61de/uos/cogsci/ai/btenbergen/bscthesis/tools/Tools8positionOfSmallest([D)I :; 9<closest_neurons_per_pattern[Iicurrent_pattern_distancesjjava/lang/DoubleCisNaN(D)Z EF DGjava/lang/MathIsqrt(D)D KL JMe1e2sumDcounterdabs WL JX SourceFile Distance.java!     l**+*,*FG HIJ* ! "J*+,$ RS  ! %&Q* L=*N6*s7U-**2(*2-1RZ-**2(*2-4R<-**2(*2-7R-**2(*2-1R+-=O*]*C+6 ]` bef8gVhtijoe`u4 !>? @A'B./TJ663+1H&,1H)+1,1g+1,1gkcJ+)N9O.  58BHMPHT !TQ'TR'RSTOU:@H VT2/JJ66,+1H,1H)+1,1gYcJ+)9Z.  .1;>CFHJ !JQ'JR'HSTEU3@> VT5/GJ66-+1H ,1H+1,1)cJ+)\.  +/2<AC>G !GQ'GR'ESTBU4@^_PK !5bb9de/uos/cogsci/ai/btenbergen/bscthesis/tools/Distance.java/* * Self-Organizing Map Plugin v1.0 * for the Yale Learning Environment (http://yale.sf.net/) * * Copyright (C) 2006 * Bastian Tenbergen * * School of Human Sciences * University of Osnabrueck * 49076 Osnabrueck, Germany * email: btenberg@uni-osnabrueck.de * web: http://www-lehre.inf.uos.de/~btenberg/ * * 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 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, write to the Free Software Foundation, Inc., * 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA. */ package de.uos.cogsci.ai.btenbergen.bscthesis.tools; /** * This is the Distance-Tool for the Self-Organizing-Map Operator for Yale. * It computes the distances between the a Perceptron array's weight Vector and * the input Vector. Please note that the Distance measure methods can be used in * a quasi-static way be invoking a Distance Object with null as the Perceptron array * and the patterns. * * @version 15.08.2006 * @author B. Tenbergen - btenberg@uos.de */ import de.uos.cogsci.ai.btenbergen.bscthesis.neuralnetworks.perceptron.Perceptron; import de.uos.cogsci.ai.btenbergen.bscthesis.tools.Tools; public class Distance { /** Constant for the Euclidean Distance. */ public static final int EUCLIDEAN = 0; /** Constant for the Manhattan Distance. */ public static final int MANHATTAN = 1; /** Constant for the Nominal Distance. */ public static final int NOMINAL = 2; /** The array containing the patterns. */ private Object[] patterns; /** The Perceptron array that needs to be measured against the patterns. */ private Perceptron[] neurons; /** The metric to be used. */ private int metric; /** * Constructor. A new Distance Object is created upon invokation. * @param neurons The Perceptron array of which the weight Vectors need to be measured. * @param patterns The Patterns that need to be measured. * @param metric The Metric to be used. */ public Distance(Object[] patterns, Perceptron[] neurons, int metric) { this.patterns = patterns; this.neurons = neurons; this.metric = metric; } /** * Constructor. A new Distance Object is created upon invokation. Euclidean Distance is used. * @param neurons The Perceptron array of which the weight Vectors need to be measured. * @param patterns The Patterns that need to be measured. */ public Distance(Object[] patterns, Perceptron[] neurons) { this(patterns, neurons, Distance.EUCLIDEAN); } /** * For each pattern, this method returns the closest neuron. * @return The array containing the positions of the neurons.tern. */ public int[] measureDistance() { //We need an array in which we store the neurons that are closest to the //current pattern. Needs to be as long as the patterns. int[] closest_neurons_per_pattern = new int[this.patterns.length]; //For ever pattern... for (int i = 0; i < this.patterns.length; i++) { //...we need an array that stores the distance to all neurons double[] current_pattern_distances = new double[this.neurons.length]; //For all neurons... for (int j = 0; j < this.neurons.length; j++) { switch (this.metric) { //choose metric case Distance.EUCLIDEAN: current_pattern_distances[j] = euclideanDistance((double[]) this.patterns[i], this.neurons[j].weights); break; case Distance.MANHATTAN: current_pattern_distances[j] = manhattanDistance((double[]) this.patterns[i], this.neurons[j].weights); break; case Distance.NOMINAL: current_pattern_distances[j] = nominalDistance((double[]) this.patterns[i], this.neurons[j].weights); break; default: current_pattern_distances[j] = euclideanDistance((double[]) this.patterns[i], this.neurons[j].weights); break; } //check the distance to the current pattern. //Then, find the position of the smallest distance. //The position corresponds to the neuron that is closest to the pattern. closest_neurons_per_pattern[i] = Tools.positionOfSmallest(current_pattern_distances); } } //Return the array containing the positions of the neurons. Each position //in the array corresponds to a pattern (in numerical order) and each //value at each position is the neuron closest to the pattern. return closest_neurons_per_pattern; } /** * An euclidean distance. Taken from the ClusteringPlugin v3.2 for Yale. * * @param e1 The first Vector * @param e2 The second Vector * @return The nominal Distance between both Vectors. * @author Michael Wurst * @version $Id: EuclideanDistance.java,v 1.4 2006/03/21 15:35:24 ingomierswa Exp $ */ public double euclideanDistance(double[] e1, double[] e2) { double sum = 0.0; int counter = 0; for (int i = 0; i < e1.length; i++) { if((!Double.isNaN(e1[i]))&&(!Double.isNaN(e2[i]))) { sum = sum + (e1[i] - e2[i]) * (e1[i] - e2[i]); counter++; } } double d = Math.sqrt(sum); if(counter > 0) return d; else return Double.NaN; } /** * The Manhattan distance. Taken from the ClusteringPlugin v3.2 for Yale. * * @param e1 The first Vector * @param e2 The second Vector * @return The nominal Distance between both Vectors. * @author Michael Wurst * @version $Id: ManhattanDistance.java,v 1.3 2006/03/21 15:35:24 ingomierswa Exp $ * */ public double manhattanDistance(double[] e1, double[] e2) { double sum = 0.0; int counter = 0; for (int i = 0; i < e1.length; i++) { if((!Double.isNaN(e1[i]))&&(!Double.isNaN(e2[i]))) { sum = sum + Math.abs(e1[i] - e2[i]); counter++; } } double d = sum; if(counter > 0) return d; else return Double.NaN; } /** * An euclidean distance. Taken from the ClusteringPlugin v3.2 for Yale. * * @param e1 The first Vector * @param e2 The second Vector * @return The nominal Distance between both Vectors. * @author Michael Wurst * @version $Id: NominalDistance.java,v 1.1 2006/03/29 14:24:47 mjwurst Exp $ * */ public double nominalDistance(double[] e1, double[] e2) { double sum = 0.0; int counter = 0; for (int i = 0; i < e1.length; i++) { if((!Double.isNaN(e1[i]))&&(!Double.isNaN(e2[i]))) { if(e1[i] != e2[i]) sum = sum + 1.0; counter++; } } if(counter > 0) return sum; else return Double.NaN; } }PK 5w+5+de/uos/cogsci/ai/btenbergen/bscthesis/yale/PK Q/5'uu?de/uos/cogsci/ai/btenbergen/bscthesis/yale/SOMFileHandler.class19de/uos/cogsci/ai/btenbergen/bscthesis/yale/SOMFileHandlerjava/lang/Objectpatterns[Ljava/lang/Object;songListLjava/util/Vector; Signature&Ljava/util/Vector; verbosingZfilenameLjava/io/File;(Ljava/io/File;Z)VCode()V  java/util/Vector      LineNumberTableLocalVariableTablethis;Lde/uos/cogsci/ai/btenbergen/bscthesis/yale/SOMFileHandler;(Ljava/io/File;)V  # parseFile(ZZ)[Ljava/lang/Object; Exceptionsjava/io/FileNotFoundException(java/io/IOException*java/io/BufferedReader,java/io/FileReader. " /0(Ljava/io/Reader;)V 2 -3add(Ljava/lang/Object;)Z 56 7readLine()Ljava/lang/String; 9: -;size()I => ?  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"?*+$ PQ ! %&')+QYN-Y/Y*14: -8W~I\ Q !QG QH IIJK.JKLM6NO;PQR II CD S# Y:6UY+Y[^:f`d6 g6kquy/:k{~`W*y`Wg@:6   YmR  *BS+@=*****BB***BB***BB*B:PPf !$-:PRbp| p  !IG H   !-ZR*1 RI  V+M>,+2S,+2N-:+d9o96 '6 - \1+ 2 1cR  +  -6 - \1+oR  -6 D9 6  + 2 1- 1gc9  + k9   R  -6 66 , 2 + 2 1 1oR  + 2ل  +,z  *07=CI Zdnt~ "')+) '. !*07=@. F q K ; ' = - <=2>+21 +2R+2܄++:< >#<1::A*< !<8*G=>+21+2\1kR+21˜+2\1kR+21—#+21Ę+2\1kR+21+2\1oR+21Ɨ+2\1oR+2E+7+2 JL N(OHPZQlRzUVLJY* ! Yͷ*BҶ<pY۷`ݶM> ,*B21M,`M*B2,*Y[M,M,ل*B6 bce8g;i@nSoZikq{rscu* !y;LK=.PK Q/5rq::>de/uos/cogsci/ai/btenbergen/bscthesis/yale/SOMFileHandler.java/* * Self-Organizing Map Plugin v1.0 * for the Yale Learning Environment (http://yale.sf.net/) * * Copyright (C) 2006 * Bastian Tenbergen * * School of Human Sciences * University of Osnabrueck * 49076 Osnabrueck, Germany * email: btenberg@uni-osnabrueck.de * web: http://www-lehre.inf.uos.de/~btenberg/ * * 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 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, write to the Free Software Foundation, Inc., * 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA. */ package de.uos.cogsci.ai.btenbergen.bscthesis.yale; /** * This is the SOMFileHandler class. * This class requires a String representation of a filename. The file * is then loaded and parsed. The file needs to contain double values that are * separated by a blank space (" "). Every line in the file corresponds to a * pattern and each value in each line stands for an extracted feature. * * @version 08.09.2006 * @author Bastian Tenbergen - btenberg@uos.de */ import java.io.BufferedReader; import java.io.File; import java.io.FileReader; import java.io.FileNotFoundException; import java.io.IOException; import java.util.Vector; import edu.udo.cs.yale.tools.LogService; public class SOMFileHandler { /** Each element in this Object[] is another double[] with the extracted features. */ private Object[] patterns; /** Each row in the file contains the filename of the song, the features belong to. These filenames are stored in this vector for reference. */ public Vector songList = new Vector(); /** If true, the operator will send detailed information about what is going on to Yale's Log-Console. */ private boolean verbosing; /** The file to be parsed. */ private File filename; /** * Constructor. Sets the fields filename and verbosing accordingly. * @param filename The file to be parsed. * @param verbosing If true, detailed information about what is going on is sent to Yale's Log-Console. */ public SOMFileHandler(File filename, boolean verbosing) { this.filename = filename; this.verbosing = verbosing; } /** * Constructor. Sets the field filename accordingly. Verbosing is set to true. * @param filename The file to be parsed. */ public SOMFileHandler(File filename) { this(filename, true); } /** * This Method parses a given input file. Each row in the file is a Feture Vector and is * extraced as such and stored in a java.util.Vector. The Vector will hence contain Strings * as elements whereas each String corresponds to a row in the file. The method parseFeatureVectors * is hence called with this Vector to extract the actual features out of each String. * That method's output is returned by this method. * * @param normalize If true, the input patterns will be normalized by dividing every element x(i,j) by the root * mean square deviation of the corresponding column, o(j). * @param filter If true, features with an absolute value smaller than 1 are filtered out. * @return An Object[] containing java.util.Vector as elements. Each Vector has doubles as elements. * The doubles correspond to an actual feature while the java.util.Vector corresponds to each feature Vector, * i.e. the rows in the File. * @throws FileNotFoundException iff the file cannot be found. * @throws IOException iff an error occured during file access. */ public Object[] parseFile(boolean normalize, boolean filter) throws FileNotFoundException, IOException { //need a Vector to store all rows in the file. Vector feature_vectors = new Vector(); try { String line; //temp variable for current row... BufferedReader in = new BufferedReader(new FileReader(filename)); //we need a reader to access the file... while ((line = in.readLine()) != null) { //while there are still lines left... feature_vectors.add(line); //store them in the Vector. } } //iff file is not found catch (FileNotFoundException fnfe) { throw fnfe; } //iff error occured during file access catch (IOException ioe) { //e.g. file is corrupted throw ioe; } finally { } //initialize the Object[] according to the //Vector size. Important! Otherwise there will //be not enough space for every pattern. patterns = new Object[feature_vectors.size()]; //extract the features out of each vector using //parseFeatureVectors and return it. return parseFeatureVectors(feature_vectors, normalize, filter); } /** * This method extracts double value features out of a given java.util.Vector. Each String in * the Vector is parsed and NaN values are ignored. Every numerical value is hence considered a double * and stored in a java.util.Vector. Hence every Vector corresponds to a row in the input file * parsed feature values. All rows are stored in an Object[]. * @param feature_vectors The Vector containing String representations of blank space separated double values. * @param normalize If true, the input patterns will be normalized by dividing every element x(i,j) by the root * mean square deviation of the corresponding column, o(j). * @param filter If true, features with an absolute value smaller than 1 are filtered out. * @return An Object[] containing java.util.Vector as elements. Every element is a row in the input file. */ private Object[] parseFeatureVectors(Vector feature_vectors, boolean normalize, boolean filter) { //a vector containing all the doubles of one line. //We make use of a Vector here because of two reasons: //1. we don't know how long the feature vector is, hence we cannot initialize an array properly //2. Vectors can be considered as more dynamic arrays. arrays in java are implemented as vectors //anyways so there is no effect on performance. Vector values = new Vector(); //Remember: A line is one element in 'feature_vectors' //-> One Music File //parse every line for (int i = 0; i < feature_vectors.size(); i++) { //retrieve every line as a StringBuffer StringBuffer current_pattern = new StringBuffer(feature_vectors.elementAt(i)); int end; //now, retrieve all doubles in this line; ignore NaN's. while (!(current_pattern.length() == 0)) { //Set end to the next occurance of a blank space end = current_pattern.indexOf(" "); //If end == -1, then there is no blank until the end //of the line. Hence, set end to the end of the string. if (end == -1) end = current_pattern.length(); try { //try to parse the substring from the beginning the the current next //end pisition (i.e. the index of the next blank space) //as a double and store it as an element of the Vector values.addElement(Double.parseDouble(current_pattern.substring(0, end))); } catch (NumberFormatException nfe) { //The StringBuffer does not only contain the features of the song, //but the filename plus directory name. //If the String is _no_ double, it could be either the filename, the dir name //and filename or two subsequent questionmarks "?". This is because two operators in //Yale cannot extract a feature and hence produce a ?. Seems to be a Bug in Yale's ValueSeriesPlugin. if ((current_pattern.substring(0, end)).equals("?")) { current_pattern.delete(0, end+3); //Remove the questionmarks from the string } else { //The rest should be the dir name and songname. Store both in the songList Vector. this.songList.addElement(current_pattern.toString()); } } //delete retrieved double. When line is successfully parsed, //current_pattern should be empty or contain only NaN's. current_pattern.delete(0, end+1); }//done retrieving doubles //store just retrieved doubles in a temporary double[]... double[] tmp = new double[values.size()]; for (int j = 0; j < tmp.length; j++) { tmp[j] = values.elementAt(j); } //and remove all values from Vector so that //it is empty and ready for the next String values.clear(); //store the retrieved doubles in the Object[] patterns[i] = tmp; }//END of for-LOOP //-> done parsing line. if (verbosing) doLogging(); if (normalize) patterns = normalizeFeatureVectors(patterns); if (filter) patterns = filterValues(patterns); if (true) patterns = weightFeatures(patterns); return patterns; } /** * This method normalizes the features in each vector by deviding them by the root mean square deviation. * This is necessary, since there might be a very large scope of the elements in the feature vectors. This * causes features with a very large absolute value to have a larger impact on the distance measuring than * features with a very small absolute value. 'Small features' hence might not play a significant role anymore. * To avoid that behaviour, the root mean square deviation for every column is calculated and each element x(i,j) * is devided by the rmsd o(j). The root mean square deviation is calculated as follows:
* o(j) = sqrt( (1 / ( n - 1) ) * (sum_over_vectors( x(i,j) - mean(j)) )^2 )
* with n = the number of vectors
* x(i,j) = the j-th value of the i-th vector
* mean(j) = the mean value of all j-th elements of all vectors (i.e. the j-th column)
*
* The features are hence updated as follows:
* x(i,j)_new = ( x(i,j)_old / o(j) ) * * @param patterns The patterns that need to be normalized. * @return The normalized patterns. */ private Object[] normalizeFeatureVectors(Object[] patterns) { Object[] newPatterns = new Object[patterns.length]; for (int i = 0; i < newPatterns.length; i++) { newPatterns[i] = new double[((double[]) patterns[i]).length]; } //need space to store medians double[] means = new double[((double[]) patterns[1]).length]; //need space to store root mean square deviation double[] rmsd = new double[means.length]; double tmp2 = (patterns.length - 1); double tmp3 = 1 / tmp2; //first, compute median of every column in the matrix. I.e. Every j-th element in each vector. //take every j-th element... for (int j = 0; j < means.length; j++) { //...of every vector... for (int i = 0; i < patterns.length; i++) { //and add the elements... means[j] += ((double[]) patterns[i])[j]; } } //...then... for (int j = 0; j < means.length; j++) { //...divide by the number of patterns (i.e. how many j-th elements have been added) means[j] /= patterns.length; } //second, compute root mean square deviation of every column //for every median... for (int j = 0; j < means.length; j++) { double tmp1 = 0; //...subtract the median of the j-th element in each vector from the j-th element of the i-th vector //and square the values. for (int i = 0; i < patterns.length; i++) { tmp1 = tmp1 + Math.pow((((double[]) patterns[i])[j] - means[j]), 2); } //then, multiply with 1 devided by the number of vectors minus 1... double tmp4 = tmp3 * tmp1; //...and calculate the square root of the outcome. rmsd[j] = Math.sqrt(tmp4); } //third, replace old values in every vector by the old value devided by the root mean square deviation. //run through every vector... for (int i = 0; i < patterns.length; i++) { //and replace every element by the old element devided by the root mean square deviation. for (int j = 0; j < ((double[]) patterns[i]).length; j++) { ((double[]) newPatterns[i])[j] = ((double[]) patterns[i])[j] / rmsd[j]; } } return newPatterns; } /** * This method filters potentially irrelevant features from the set of patterns. * Feautes with an absolute value smaller than 1 are set to zero. * @param patterns The set of patterns to be filtered. * @return The filtered feature vectors. */ private Object[] filterValues(Object[] patterns) { for (int i = 0; i < patterns.length; i++) { for (int j = 0; j < ((double[]) patterns[i]).length; j++) { if (Math.abs(((double[]) patterns[i])[j]) < 1) ((double[]) patterns[i])[j] = 0; } } return patterns; } /** * @param patterns * @return */ private Object[] weightFeatures(Object[] patterns) { for (int i = 0; i < patterns.length; i++) { for (int j = 0; j < ((double[]) patterns[i]).length; j++) { if (Math.abs(((double[]) patterns[i])[j]) < 1) ((double[]) patterns[i])[j] *= 1000; if (Math.abs(((double[]) patterns[i])[j]) < 10) ((double[]) patterns[i])[j] *= 100; if (Math.abs(((double[]) patterns[i])[j]) > 10) { if (Math.abs(((double[]) patterns[i])[j]) < 100) { ((double[]) patterns[i])[j] *= 10; } } if (Math.abs(((double[]) patterns[i])[j]) > 1000) ((double[]) patterns[i])[j] /= 10; if (Math.abs(((double[]) patterns[i])[j]) > 10000000) ((double[]) patterns[i])[j] /= 1000000; } } return patterns; } /** * This method just shows the retrieved features in the Object[] by sending it to Yale's Log-Console. */ private void doLogging() { LogService.logMessage("Number of patterns: " + patterns.length, LogService.STATUS); for (int i = 0; i < patterns.length; i++) { LogService.logMessage("pattern number " + (i+1) + ": ", LogService.STATUS); String s = ""; for (int j = 0; j < ((double[]) patterns[i]).length; j++) { //concat each element of each array to a string //this avoids that Yale prints out every feature in an own line //but all features of one vector in one line instead. s = s.concat(Double.toString(((double[]) patterns[i])[j])); s = s.concat(" "); //just a separator } s = s.concat(songList.elementAt(i)); s = s.concat("\n"); LogService.logMessage(s, LogService.STATUS); } } }PK /5Ka"W'W'9de/uos/cogsci/ai/btenbergen/bscthesis/yale/SOMInput.class13de/uos/cogsci/ai/btenbergen/bscthesis/yale/SOMInput!edu/udo/cs/yale/operator/Operator!edu/udo/cs/yale/operator/IOObject OUTPUT_CLASS[Ljava/lang/Class;result_neurons-Ledu/udo/cs/yale/operator/SimpleResultObject; result_filesresult_clustersresult_matchesfilenameLjava/io/File; verbosingZ numberColsI numberRowsrunsmaxValueclosed_topologylearningrate_winnerDlearningrate_neighborpatterns[Ljava/lang/Object; hummedFileLjava/lang/String;clusters6[Lde/uos/cogsci/ai/btenbergen/bscthesis/tools/Cluster;metricmetrics[Ljava/lang/String; normalizefilterclass$0Ljava/lang/Class;()VCodejava/lang/Class++edu/udo/cs/yale/operator/SimpleResultObject-  /LineNumberTableLocalVariableTable1(Ledu/udo/cs/yale/operator/OperatorDescription;)V 34 5java/lang/String7 Euclidean9 Manhattan;Nominal= "# ?this5Lde/uos/cogsci/ai/btenbergen/bscthesis/yale/SOMInput; description.Ledu/udo/cs/yale/operator/OperatorDescription;getParameterTypes()Ljava/util/List; SignatureF()Ljava/util/List; EF I4edu/udo/cs/yale/operator/parameter/ParameterTypeFileKFeature Vector FileM+The File containing the extracted Features.O:(Ljava/lang/String;Ljava/lang/String;Ljava/lang/String;Z)V 3Q LRjava/util/ListTadd(Ljava/lang/Object;)Z VW UXQuery Input FileZ4The File to which similar ones shall be queried for.\mp3^3edu/udo/cs/yale/operator/parameter/ParameterTypeInt`Number Neuron-ColumnsbTThe number of columns of the SOM. Together with the rows, this defines the topology.d*(Ljava/lang/String;Ljava/lang/String;III)V 3g ahNumber Neuron-RowsjTThe number of rows of the SOM. Together with the columns, this defines the topology.l7edu/udo/cs/yale/operator/parameter/ParameterTypeBooleannClosed TopologypChoose the topology of the Self-Organizing Map. If checked, the last neuron of each row and column is connected to the respective first one.r((Ljava/lang/String;Ljava/lang/String;Z)V 3t ou Training RunswBThe amount of training cycles in which in the neurons are trained.y6edu/udo/cs/yale/operator/parameter/ParameterTypeDouble{Learning Rate Winner}pThe Learning Rate of the SOM's Winner Neuron. Note that a Learning Rate of 0 causes the Map not to learn at all.*(Ljava/lang/String;Ljava/lang/String;DDD)V 3 |Learning Rate NeighborThe Learning Rate of the Winner's Neiboring Neurons. Not that a Learning Rate of 0 represents a WTA-Network and a Learning Rate of 1 turns the Neighbors into Winner Neurons.? Maximal ValuefThe maximum value of bias and weights the neurons may not exceed. Choose -1 for automatic computation.>edu/udo/cs/yale/operator/parameter/ParameterTypeStringCategoryMetricMThe Metric used to measure the distance between the patterns and the neurons.L(Ljava/lang/String;Ljava/lang/String;[Ljava/lang/String;Ljava/lang/String;)V 3 Filter PatternsGIf checked, features with a very small absolute value are filtered out.Normalize PatternsOIf checked, the input patterns from the Feature Vector File will be normalized. VerbosingGIf checked, the system will output information about what is happening.typesLjava/util/List;LocalVariableTypeTableDLjava/util/List; initApplyapply&()[Ledu/udo/cs/yale/operator/IOObject; Exceptions*edu/udo/cs/yale/operator/OperatorException java/io/FilegetParameterAsString&(Ljava/lang/String;)Ljava/lang/String; (Ljava/lang/String;)V 3  getParameterAsBoolean(Ljava/lang/String;)Z     getParameterAsDouble(Ljava/lang/String;)D     getParameterAsInt(Ljava/lang/String;)I         getName()Ljava/lang/String;   !  $  % equalsIgnoreCase 89de/uos/cogsci/ai/btenbergen/bscthesis/yale/SOMFileHandler(Ljava/io/File;Z)V 3  parseFile(ZZ)[Ljava/lang/Object;  File successfully parsed. edu/udo/cs/yale/tools/LogService logMessage(Ljava/lang/String;I)V Invoking Self-Organizing Map@de/uos/cogsci/ai/btenbergen/bscthesis/neuralnetworks/mlp/som/SOM(II[Ljava/lang/Object;DDIIZ)V 3 Before classification:toString Classifying...runClassification ) After Classification:&Classification completed successfully. Measuring Distances... 4de/uos/cogsci/ai/btenbergen/bscthesis/tools/Distance output@Lde/uos/cogsci/ai/btenbergen/bscthesis/neuralnetworks/mlp/Layer;  >de/uos/cogsci/ai/btenbergen/bscthesis/neuralnetworks/mlp/LayerneuronsM[Lde/uos/cogsci/ai/btenbergen/bscthesis/neuralnetworks/perceptron/Perceptron;  d([Ljava/lang/Object;[Lde/uos/cogsci/ai/btenbergen/bscthesis/neuralnetworks/perceptron/Perceptron;I)V 3 measureDistance()[I  Distance Measuring complete. Creating clusters..."songListLjava/util/Vector; $% &9de/uos/cogsci/ai/btenbergen/bscthesis/tools/OperatorTools(createFileClustersL([ILjava/util/Vector;)[Lde/uos/cogsci/ai/btenbergen/bscthesis/tools/Cluster; *+ ),  .Clusters created.0Recording Results...2%Output neurons after classification: 4#edu/udo/cs/yale/tools/ResultService6 logResult 8 79Clusters;java/lang/StringBuilder=Files displayed in clusters ? >showFileClustersJ([Lde/uos/cogsci/ai/btenbergen/bscthesis/tools/Cluster;)Ljava/lang/String; BC )Dappend-(Ljava/lang/String;)Ljava/lang/StringBuilder; FG >H >'(Ljava/lang/String;Ljava/lang/String;)V 3K .L N User InputP2Songs in Cluster that come closest to the input RconvertFilename T )UfindQueryInClusters\(Ljava/lang/String;[Lde/uos/cogsci/ai/btenbergen/bscthesis/tools/Cluster;)Ljava/lang/String; WX )Y [Output Neurons]&Output neurons after classification _ aFiles processedc+Files, features have been extracted from edisplaySongList&(Ljava/util/Vector;)Ljava/lang/String; gh )i kResult Recording complete.m(Operator "Self-Organizing Map" complete.oXde/uos/cogsci/ai/btenbergen/bscthesis/neuralnetworks/perceptron/PerceptronInputExceptionq getMessage s rt logException*(Ljava/lang/String;Ljava/lang/Throwable;)V vw x 3w z SOMInput '| 5': File not found, please specify feature vector file:edu/udo/cs/yale/operator/parameter/UndefinedParameterError tFile '-(Ljava/lang/Object;)Ljava/lang/StringBuilder; F > ' not found.Error reading file ''.java/io/FileNotFoundExceptionjava/io/IOException useMetricsomf;Lde/uos/cogsci/ai/btenbergen/bscthesis/yale/SOMFileHandler;mysomBLde/uos/cogsci/ai/btenbergen/bscthesis/neuralnetworks/mlp/som/SOM;d6Lde/uos/cogsci/ai/btenbergen/bscthesis/tools/Distance; positions[IpieZLde/uos/cogsci/ai/btenbergen/bscthesis/neuralnetworks/perceptron/PerceptronInputException;upeS@1y2ABCDEFGH* *JL+LYNPSYW+LY[]_SYW+aYcefiYW+aYkmfiYW+oYqsvYW+aYxzfdiYW+|Y~YW+|YYW+aYfdiYW+Y*@:YW+oYvYW+oYvYW+oYvYW+1>.DZm2AB )*+12 AB* <*Y*N****q**~õ**õ**˵**c˵**k˵**x˵*Y*[׵******<*:<'*<<*><<Y**M*,**߶* Y********N*--*- *  Y*-::* !* #*,'-/* 1* 35:-:*.Y<>Y@A*/EIJMO*.YQ>YSA*ٸV*/ZIJM\*.Y^>Y`A-IJMb*.Yd>YfA,'jIJMl*npL+u+yY+u+{L>Y}A*~IIJ++y*KLY>YA*IJ+{LY>YA*IJ+{Y*\SY*OSY*lSY*bSr1:N%/9CMWau%,2:AELS[bp,/ANQcsv}       2f <AB%i +##*,*1'2 AB*/,1-2 AB*.0132 ABPK /5558de/uos/cogsci/ai/btenbergen/bscthesis/yale/SOMInput.java/* * Self-Organizing Map Plugin v1.0 * for the Yale Learning Environment (http://yale.sf.net/) * * Copyright (C) 2006 * Bastian Tenbergen * * School of Human Sciences * University of Osnabrueck * 49076 Osnabrueck, Germany * email: btenberg@uni-osnabrueck.de * web: http://www-lehre.inf.uos.de/~btenberg/ * * 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 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, write to the Free Software Foundation, Inc., * 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA. */ package de.uos.cogsci.ai.btenbergen.bscthesis.yale; /** * This is the SOMInput class. This class implements the actual operator that can be used in Yale. * It takes a file as input and parses it using the SOMFileHandler class. The operator then invokes the * Self-Organizing Map and performs the classification. * * @version 22.07.2006 * @author Bastian Tenbergen - btenberg@uos.de */ import edu.udo.cs.yale.operator.Operator; import edu.udo.cs.yale.operator.OperatorException; import edu.udo.cs.yale.operator.OperatorDescription; import edu.udo.cs.yale.operator.IOObject; import edu.udo.cs.yale.operator.SimpleResultObject; import edu.udo.cs.yale.operator.parameter.ParameterType; import edu.udo.cs.yale.operator.parameter.ParameterTypeFile; import edu.udo.cs.yale.operator.parameter.ParameterTypeStringCategory; import edu.udo.cs.yale.operator.parameter.ParameterTypeInt; import edu.udo.cs.yale.operator.parameter.ParameterTypeDouble; import edu.udo.cs.yale.operator.parameter.ParameterTypeBoolean; import edu.udo.cs.yale.operator.parameter.UndefinedParameterError; import edu.udo.cs.yale.tools.LogService; import edu.udo.cs.yale.tools.ResultService; import de.uos.cogsci.ai.btenbergen.bscthesis.neuralnetworks.mlp.som.SOM; import de.uos.cogsci.ai.btenbergen.bscthesis.neuralnetworks.perceptron.PerceptronInputException; import de.uos.cogsci.ai.btenbergen.bscthesis.tools.Distance; import de.uos.cogsci.ai.btenbergen.bscthesis.tools.Cluster; import de.uos.cogsci.ai.btenbergen.bscthesis.tools.OperatorTools; import java.io.IOException; import java.io.File; import java.io.FileNotFoundException; import java.util.List; public class SOMInput extends Operator implements IOObject { /** a class array containing the Output classes. Syntactic sugar. */ private static final Class[] OUTPUT_CLASS = { SimpleResultObject.class }; /** The Results that are to be printed on Yale's GUI. Enumeration of output neurons */ private SimpleResultObject result_neurons; /** Enumeration of processed file names */ private SimpleResultObject result_files; /** Enumeration of all file clusters */ private SimpleResultObject result_clusters; /** Enumeration of all files close to the hummed input */ private SimpleResultObject result_matches; /** The file that is to be parsed. */ private File filename; /** if true, the operator will send detailed information about what is happening to Yale's Log-Console. */ private boolean verbosing; /** Sets the number of columns of the SOM. */ private int numberCols; /** Sets the number of rows of the SOM. */ private int numberRows; /** Sets the number of classification runs. The higher the number, the more acurately the neurons are trained. */ private int runs; /** Sets the maximum value, the random initialization of weights and bias of the neurons must not exceed. */ private int maxValue; /** The Topology of this Map. If true, the last neuron of each row or col is connected to the first one. */ private boolean closed_topology; /** Sets the learning rate of the winner neuron of the Self-Organizing Map. */ private double learningrate_winner; /** Sets the learning rate of the neighboring neurons of the Self-Organizing Map. */ private double learningrate_neighbor; /** The Patterns that are retrieved from the file */ private Object[] patterns; /** The user's query input */ private String hummedFile; /** The song clusters */ private Cluster[] clusters; /** The metric used to measure the distance between each pattern and each neuron */ private String metric; /** The different metrics the user can choose from. */ private String[] metrics = new String[] {"Euclidean", "Manhattan", "Nominal"}; /** If true, the input patterns from the Feature Vector File will be normalized. */ private boolean normalize; /** If true, features with an absolute value smaller than one are filtered out. */ private boolean filter; /** * Constructor. Just calls the superconstructor with the given OperatorDescription. * @param description */ public SOMInput(OperatorDescription description) { super(description); } /** * Gets the ParameterTypes as a lists and adds the parameters for this operator. * @return The appended list of Parameters */ public List getParameterTypes() { List types = super.getParameterTypes(); types.add(new ParameterTypeFile("Feature Vector File", "The File containing the extracted Features.", null, false)); types.add(new ParameterTypeFile("Query Input File", "The File to which similar ones shall be queried for.", "mp3", false)); types.add(new ParameterTypeInt("Number Neuron-Columns", "The number of columns of the SOM. Together with the rows, this defines the topology.", 1, Integer.MAX_VALUE, 5)); types.add(new ParameterTypeInt("Number Neuron-Rows", "The number of rows of the SOM. Together with the columns, this defines the topology.", 1, Integer.MAX_VALUE, 5)); types.add(new ParameterTypeBoolean("Closed Topology", "Choose the topology of the Self-Organizing Map. If checked, the last neuron of each row and column is connected to the respective first one.", false)); types.add(new ParameterTypeInt("Training Runs", "The amount of training cycles in which in the neurons are trained.", 1, Integer.MAX_VALUE, 100)); types.add(new ParameterTypeDouble("Learning Rate Winner", "The Learning Rate of the SOM's Winner Neuron. Note that a Learning Rate of 0 causes the Map not to learn at all.", 0, 1.0, 1)); types.add(new ParameterTypeDouble("Learning Rate Neighbor", "The Learning Rate of the Winner's Neiboring Neurons. Not that a Learning Rate of 0 represents a WTA-Network and a Learning Rate of 1 turns the Neighbors into Winner Neurons.", 0, 1.0, 0.25)); types.add(new ParameterTypeInt("Maximal Value", "The maximum value of bias and weights the neurons may not exceed. Choose -1 for automatic computation.", -1, Integer.MAX_VALUE, 100)); types.add(new ParameterTypeStringCategory("Metric", "The Metric used to measure the distance between the patterns and the neurons.", metrics, "Euclidean")); types.add(new ParameterTypeBoolean("Filter Patterns", "If checked, features with a very small absolute value are filtered out.", true)); types.add(new ParameterTypeBoolean("Normalize Patterns", "If checked, the input patterns from the Feature Vector File will be normalized.", false)); types.add(new ParameterTypeBoolean("Verbosing", "If checked, the system will output information about what is happening.", true)); return types; } /** * This method is called by Yale when the operator is created. Nothing is done here. */ public void initApply() { } /** * This method is called by Yale whenever the operator is applied. The file to be parsed is * obtained and the rest of the parameters are set. Then, the SOMFileHandler is invoked and the * file is parsed. * * @return Just an empty IOObject array containing SimpleResultObjects with the results of the computations. * @throws OperatorException - This is thrown when a Parameter is undefined, the file to be * parsed is not found or an error during file reading occured. */ public IOObject[] apply() throws OperatorException { try { filename = new File(getParameterAsString("Feature Vector File")); verbosing = getParameterAsBoolean("Verbosing"); closed_topology = getParameterAsBoolean("Closed Topology"); learningrate_winner = getParameterAsDouble("Learning Rate Winner"); learningrate_neighbor = getParameterAsDouble("Learning Rate Neighbor"); maxValue = getParameterAsInt("Maximal Value"); numberCols = getParameterAsInt("Number Neuron-Columns"); numberRows = getParameterAsInt("Number Neuron-Rows"); runs = getParameterAsInt("Training Runs"); hummedFile = new File(getParameterAsString("Query Input File")).getName(); metric = getParameterAsString("Metric"); normalize = getParameterAsBoolean("Normalize Patterns"); filter = getParameterAsBoolean("Filter Patterns"); int useMetric = 0; if (this.metric.equalsIgnoreCase("Euclidean")) useMetric = 0; else if (this.metric.equalsIgnoreCase("Manhattan")) useMetric = 1; else if (this.metric.equalsIgnoreCase("Nominal")) useMetric = 2; else useMetric = 0; SOMFileHandler somf = new SOMFileHandler(filename, verbosing); patterns = somf.parseFile(normalize, filter); LogService.logMessage("File successfully parsed.", LogService.INIT); if (verbosing) LogService.logMessage("Invoking Self-Organizing Map", LogService.STATUS); SOM mysom = new SOM(numberRows, numberCols, patterns, learningrate_winner, learningrate_neighbor, runs, maxValue, closed_topology); if (verbosing) { LogService.logMessage("Before classification:", LogService.STATUS); LogService.logMessage(mysom.toString(), LogService.STATUS); LogService.logMessage("Classifying...", LogService.INIT); } //THIS IS WHERE THE MAGIC IS DONE! mysom.runClassification(); //THIS IS WHERE THE MAGIC IS DONE! if (verbosing) { LogService.logMessage("After Classification:", LogService.STATUS); LogService.logMessage(mysom.toString(), LogService.STATUS); } LogService.logMessage("Classification completed successfully.", LogService.STATUS); if (verbosing) LogService.logMessage("Measuring Distances...", LogService.INIT); Distance d = new Distance(patterns, mysom.output.neurons, useMetric); int[] positions = d.measureDistance(); if (verbosing) LogService.logMessage("Distance Measuring complete.", LogService.STATUS); if (verbosing) LogService.logMessage("Creating clusters...", LogService.STATUS); this.clusters = OperatorTools.createFileClusters(positions, somf.songList); if (verbosing) LogService.logMessage("Clusters created.", LogService.STATUS); if (verbosing) LogService.logMessage("Recording Results...", LogService.INIT); ResultService.logResult("Output neurons after classification:\n"); ResultService.logResult(mysom.toString()); result_clusters = new SimpleResultObject("Clusters","Files displayed in clusters" + "\n\n\n" + OperatorTools.showFileClusters(this.clusters) ); result_matches = new SimpleResultObject("User Input","Songs in Cluster that come closest to the input" + "\n\n\n" + OperatorTools.findQueryInClusters(OperatorTools.convertFilename(hummedFile), this.clusters) ); result_neurons = new SimpleResultObject("Output Neurons", "Output neurons after classification" + "\n\n\n" + mysom.toString()); result_files = new SimpleResultObject("Files processed", "Files, features have been extracted from" + "\n\n\n" + OperatorTools.displaySongList(somf.songList) ); if (verbosing) { LogService.logMessage("Result Recording complete.", LogService.STATUS); LogService.logMessage("Operator \"Self-Organizing Map\" complete.", LogService.INIT); } } catch (PerceptronInputException pie) { LogService.logException(pie.getMessage(), pie); throw new OperatorException(pie.getMessage(), pie); } catch (UndefinedParameterError upe) { LogService.logMessage("SOMInput '" + getName() + "': File not found, please specify feature vector file", LogService.WARNING); LogService.logException(upe.getMessage(), upe); filename = null; } catch (FileNotFoundException fnfe) { throw new OperatorException( "File '" + filename + "' not found.", fnfe ); } catch (IOException ioe) { throw new OperatorException( "Error reading file '" + filename + "'.", ioe ); } finally { } return new IOObject[] { result_matches, result_clusters, result_files, result_neurons }; } /** * Returns a copy of this operator. * @return A copy of this operator. */ public IOObject copy() { return this; } /** * Returns all Input classes. * @return All Input classes. */ public Class[] getInputClasses() { return new Class[0]; } /** * Returns all Output classes. * @return All Output classes. */ public Class[] getOutputClasses() { return OUTPUT_CLASS; } }PK .5feature_vector_files/PK 75javadoc/PK Gp,5 - javadoc/allclasses-frame.html All Classes All Classes
Cluster
Distance
Layer
Layers
MLPs
OperatorTools
Perceptron
PerceptronInputException
SOM
SOMFileHandler
SOMInput
Tools
PK Gp,5^Ü0 0 javadoc/allclasses-noframe.html All Classes All Classes
Cluster
Distance
Layer
Layers
MLPs
OperatorTools
Perceptron
PerceptronInputException
SOM
SOMFileHandler
SOMInput
Tools
PK Gp,5Cpjjjavadoc/constant-values.html Constant Field Values
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Constant Field Values

Contents
de.uos.*

de.uos.cogsci.ai.btenbergen.bscthesis.neuralnetworks.perceptron.PerceptronInputException
private static final long serialVersionUID 42L

de.uos.cogsci.ai.btenbergen.bscthesis.tools.Distance
public static final int EUCLIDEAN 0
public static final int MANHATTAN 1
public static final int NOMINAL 2

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Deprecated API

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How This API Document Is Organized

This API (Application Programming Interface) document has pages corresponding to the items in the navigation bar, described as follows.

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Each documented package, class and interface has its own Use page. This page describes what packages, classes, methods, constructors and fields use any part of the given class or package. Given a class or interface A, its Use page includes subclasses of A, fields declared as A, methods that return A, and methods and constructors with parameters of type A. You can access this page by first going to the package, class or interface, then clicking on the "Use" link in the navigation bar.

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A

activation(double[]) - Method in class de.uos.cogsci.ai.btenbergen.bscthesis.neuralnetworks.perceptron.Perceptron
This method computes the scalar product of the input and the weights.
apply() - Method in class de.uos.cogsci.ai.btenbergen.bscthesis.yale.SOMInput
This method is called by Yale whenever the operator is applied.
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L

Layer - Class in de.uos.cogsci.ai.btenbergen.bscthesis.neuralnetworks.mlp
 
Layer(int, int, int, int) - Constructor for class de.uos.cogsci.ai.btenbergen.bscthesis.neuralnetworks.mlp.Layer
Constructor.
Layers - Interface in de.uos.cogsci.ai.btenbergen.bscthesis.neuralnetworks.mlp
 
learningrate_neighbor - Variable in class de.uos.cogsci.ai.btenbergen.bscthesis.neuralnetworks.mlp.som.SOM
The learning rate of the winner's neighboring neurons.
learningrate_neighbor - Variable in class de.uos.cogsci.ai.btenbergen.bscthesis.yale.SOMInput
Sets the learning rate of the neighboring neurons of the Self-Organizing Map.
learningrate_winner - Variable in class de.uos.cogsci.ai.btenbergen.bscthesis.neuralnetworks.mlp.som.SOM
The learning rate.
learningrate_winner - Variable in class de.uos.cogsci.ai.btenbergen.bscthesis.yale.SOMInput
Sets the learning rate of the winner neuron of the Self-Organizing Map.
logPatterns() - Method in class de.uos.cogsci.ai.btenbergen.bscthesis.neuralnetworks.mlp.som.SOM
This method logs the current status of the SOM by sending information to Yale's Log-Console.
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M

MANHATTAN - Static variable in class de.uos.cogsci.ai.btenbergen.bscthesis.tools.Distance
Constant for the Manhattan Distance.
manhattanDistance(double[], double[]) - Method in class de.uos.cogsci.ai.btenbergen.bscthesis.tools.Distance
The Manhattan distance.
maxValue - Variable in class de.uos.cogsci.ai.btenbergen.bscthesis.neuralnetworks.mlp.Layer
The maximum value of weights and bias for each neuron.
maxValue - Variable in class de.uos.cogsci.ai.btenbergen.bscthesis.yale.SOMInput
Sets the maximum value, the random initialization of weights and bias of the neurons must not exceed.
measureDistance() - Method in class de.uos.cogsci.ai.btenbergen.bscthesis.tools.Distance
For each pattern, this method returns the closest neuron.
metric - Variable in class de.uos.cogsci.ai.btenbergen.bscthesis.tools.Distance
The metric to be used.
metric - Variable in class de.uos.cogsci.ai.btenbergen.bscthesis.yale.SOMInput
The metric used to measure the distance between each pattern and each neuron
metrics - Variable in class de.uos.cogsci.ai.btenbergen.bscthesis.yale.SOMInput
The different metrics the user can choose from.
minMaxElement(double[]) - Static method in class de.uos.cogsci.ai.btenbergen.bscthesis.tools.Tools
Returns the smallest and biggest element of a given double array in a new double array.
minMedMaxElement(double[]) - Static method in class de.uos.cogsci.ai.btenbergen.bscthesis.tools.Tools
Returns the smallest and biggest and the medium element of a given double array in a new double array.
MLPs - Interface in de.uos.cogsci.ai.btenbergen.bscthesis.neuralnetworks.mlp
 
msg - Variable in exception de.uos.cogsci.ai.btenbergen.bscthesis.neuralnetworks.perceptron.PerceptronInputException
 
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N

neurons - Variable in class de.uos.cogsci.ai.btenbergen.bscthesis.neuralnetworks.mlp.Layer
The array of neurons defining the layer.
neurons - Variable in class de.uos.cogsci.ai.btenbergen.bscthesis.tools.Distance
The Perceptron array that needs to be measured against the patterns.
NOMINAL - Static variable in class de.uos.cogsci.ai.btenbergen.bscthesis.tools.Distance
Constant for the Nominal Distance.
nominalDistance(double[], double[]) - Method in class de.uos.cogsci.ai.btenbergen.bscthesis.tools.Distance
An euclidean distance.
normalize - Variable in class de.uos.cogsci.ai.btenbergen.bscthesis.yale.SOMInput
If true, the input patterns from the Feature Vector File will be normalized.
normalizeFeatureVectors(Object[]) - Method in class de.uos.cogsci.ai.btenbergen.bscthesis.yale.SOMFileHandler
This method normalizes the features in each vector by deviding them by the root mean square deviation.
numberCols - Variable in class de.uos.cogsci.ai.btenbergen.bscthesis.yale.SOMInput
Sets the number of columns of the SOM.
numberRows - Variable in class de.uos.cogsci.ai.btenbergen.bscthesis.yale.SOMInput
Sets the number of rows of the SOM.
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O

OperatorTools - Class in de.uos.cogsci.ai.btenbergen.bscthesis.tools
 
OperatorTools() - Constructor for class de.uos.cogsci.ai.btenbergen.bscthesis.tools.OperatorTools
 
output - Variable in class de.uos.cogsci.ai.btenbergen.bscthesis.neuralnetworks.mlp.som.SOM
The output layer.
OUTPUT_CLASS - Static variable in class de.uos.cogsci.ai.btenbergen.bscthesis.yale.SOMInput
a class array containing the Output classes.
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P

parseFeatureVectors(Vector<String>, boolean) - Method in class de.uos.cogsci.ai.btenbergen.bscthesis.yale.SOMFileHandler
This method extracts double value features out of a given java.util.Vector.
parseFile(boolean) - Method in class de.uos.cogsci.ai.btenbergen.bscthesis.yale.SOMFileHandler
This Method parses a given input file.
patterns - Variable in class de.uos.cogsci.ai.btenbergen.bscthesis.neuralnetworks.mlp.som.SOM
This array contains all patterns, i.e. the input vectors.
patterns - Variable in class de.uos.cogsci.ai.btenbergen.bscthesis.tools.Distance
The array containing the patterns.
patterns - Variable in class de.uos.cogsci.ai.btenbergen.bscthesis.yale.SOMFileHandler
Each element in this Object[] is another double[] with the extracted features.
patterns - Variable in class de.uos.cogsci.ai.btenbergen.bscthesis.yale.SOMInput
The Patterns that are retrieved from the file
Perceptron - Class in de.uos.cogsci.ai.btenbergen.bscthesis.neuralnetworks.perceptron
This is the Perceptron class.
Perceptron(int, int, double, double[], boolean) - Constructor for class de.uos.cogsci.ai.btenbergen.bscthesis.neuralnetworks.perceptron.Perceptron
Constructor.
PerceptronInputException - Exception in de.uos.cogsci.ai.btenbergen.bscthesis.neuralnetworks.perceptron
The class PercepronInputException is thrown when the Input-Vector of a Perceptron is not of equal length as its Weight-Vector.
PerceptronInputException() - Constructor for exception de.uos.cogsci.ai.btenbergen.bscthesis.neuralnetworks.perceptron.PerceptronInputException
Constructs a new exception with null as its detail message.
PerceptronInputException(String, Throwable) - Constructor for exception de.uos.cogsci.ai.btenbergen.bscthesis.neuralnetworks.perceptron.PerceptronInputException
Constructs a new exception with the specified detail message and cause.
PerceptronInputException(String) - Constructor for exception de.uos.cogsci.ai.btenbergen.bscthesis.neuralnetworks.perceptron.PerceptronInputException
Constructs a new exception with the specified detail message.
PerceptronInputException(Throwable) - Constructor for exception de.uos.cogsci.ai.btenbergen.bscthesis.neuralnetworks.perceptron.PerceptronInputException
Constructs a new exception with the specified cause and a detail message of (cause==null ?
positionOfSmallest(double[]) - Static method in class de.uos.cogsci.ai.btenbergen.bscthesis.tools.Tools
This method returns the position of the smallest element of a given array.
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Q

qs(double[], int, int) - Static method in class de.uos.cogsci.ai.btenbergen.bscthesis.tools.Tools
This method does the actual sorting.
quicksort(double[], int, int) - Static method in class de.uos.cogsci.ai.btenbergen.bscthesis.tools.Tools
A standard recursive QuickSort Algorithm.
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R

result_clusters - Variable in class de.uos.cogsci.ai.btenbergen.bscthesis.yale.SOMInput
Enumeration of all file clusters
result_files - Variable in class de.uos.cogsci.ai.btenbergen.bscthesis.yale.SOMInput
Enumeration of processed file names
result_matches - Variable in class de.uos.cogsci.ai.btenbergen.bscthesis.yale.SOMInput
Enumeration of all files close to the hummed input
result_neurons - Variable in class de.uos.cogsci.ai.btenbergen.bscthesis.yale.SOMInput
The Results that are to be printed on Yale's GUI.
returnNeg - Variable in class de.uos.cogsci.ai.btenbergen.bscthesis.neuralnetworks.perceptron.Perceptron
The value that is returned when the activation does not exceed the threshold.
returnPos - Variable in class de.uos.cogsci.ai.btenbergen.bscthesis.neuralnetworks.perceptron.Perceptron
The value that is returned when the activation exceeds the threshold.
row - Variable in class de.uos.cogsci.ai.btenbergen.bscthesis.neuralnetworks.perceptron.Perceptron
If the perceptron is embedded in a neural network, this indicates the row in which it is located.
rows - Variable in class de.uos.cogsci.ai.btenbergen.bscthesis.neuralnetworks.mlp.som.SOM
Sets the number of rows of this SOM.
runClassification() - Method in interface de.uos.cogsci.ai.btenbergen.bscthesis.neuralnetworks.mlp.MLPs
Runs the classification process runs times.
runClassification() - Method in class de.uos.cogsci.ai.btenbergen.bscthesis.neuralnetworks.mlp.som.SOM
Runs the classification process runs times.
runs - Variable in class de.uos.cogsci.ai.btenbergen.bscthesis.neuralnetworks.mlp.som.SOM
The number of classification runs to be performed.
runs - Variable in class de.uos.cogsci.ai.btenbergen.bscthesis.yale.SOMInput
Sets the number of classification runs.
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S

serialVersionUID - Static variable in exception de.uos.cogsci.ai.btenbergen.bscthesis.neuralnetworks.perceptron.PerceptronInputException
 
setBipolar() - Method in class de.uos.cogsci.ai.btenbergen.bscthesis.neuralnetworks.perceptron.Perceptron
Sets the Perceptron to binary or bipolar.
setWeights(double[]) - Method in class de.uos.cogsci.ai.btenbergen.bscthesis.neuralnetworks.perceptron.Perceptron
This method updates the weight vector of the Perceptron.
showFileClusters(Cluster[]) - Static method in class de.uos.cogsci.ai.btenbergen.bscthesis.tools.OperatorTools
This method creates Cluster Objects of the processed files and shows them.
showPatterns() - Method in class de.uos.cogsci.ai.btenbergen.bscthesis.neuralnetworks.mlp.som.SOM
This method confirms the given input vectors by prompting them.
SOM - Class in de.uos.cogsci.ai.btenbergen.bscthesis.neuralnetworks.mlp.som
 
SOM(int, int, Object[], double, double, int, int, boolean) - Constructor for class de.uos.cogsci.ai.btenbergen.bscthesis.neuralnetworks.mlp.som.SOM
Constructor.
SOM(int, int, Object[]) - Constructor for class de.uos.cogsci.ai.btenbergen.bscthesis.neuralnetworks.mlp.som.SOM
Constructor.
SOM(int, int, Object[], int) - Constructor for class de.uos.cogsci.ai.btenbergen.bscthesis.neuralnetworks.mlp.som.SOM
Constructor.
SOMFileHandler - Class in de.uos.cogsci.ai.btenbergen.bscthesis.yale
 
SOMFileHandler(File, boolean) - Constructor for class de.uos.cogsci.ai.btenbergen.bscthesis.yale.SOMFileHandler
Constructor.
SOMFileHandler(File) - Constructor for class de.uos.cogsci.ai.btenbergen.bscthesis.yale.SOMFileHandler
Constructor.
SOMInput - Class in de.uos.cogsci.ai.btenbergen.bscthesis.yale
 
SOMInput(OperatorDescription) - Constructor for class de.uos.cogsci.ai.btenbergen.bscthesis.yale.SOMInput
Constructor.
songList - Variable in class de.uos.cogsci.ai.btenbergen.bscthesis.yale.SOMFileHandler
Each row in the file contains the filename of the song, the features belong to.
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T

Tools - Class in de.uos.cogsci.ai.btenbergen.bscthesis.tools
This is class providing tools for multiple purposes.
Tools() - Constructor for class de.uos.cogsci.ai.btenbergen.bscthesis.tools.Tools
 
toString() - Method in class de.uos.cogsci.ai.btenbergen.bscthesis.neuralnetworks.mlp.Layer
Displays the neurons of the layer.
toString() - Method in interface de.uos.cogsci.ai.btenbergen.bscthesis.neuralnetworks.mlp.Layers
toString displays the Neurons of the Layer.
toString() - Method in interface de.uos.cogsci.ai.btenbergen.bscthesis.neuralnetworks.mlp.MLPs
toString method.
toString() - Method in class de.uos.cogsci.ai.btenbergen.bscthesis.neuralnetworks.mlp.som.SOM
toString method.
toString() - Method in class de.uos.cogsci.ai.btenbergen.bscthesis.neuralnetworks.perceptron.Perceptron
toString method.
toString() - Method in class de.uos.cogsci.ai.btenbergen.bscthesis.tools.Cluster
This method returns the String representation of the cluster.
toStringln() - Method in class de.uos.cogsci.ai.btenbergen.bscthesis.tools.Cluster
This method returns the String representation of this cluster in lines.
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U

updateNeighbor(double[]) - Method in interface de.uos.cogsci.ai.btenbergen.bscthesis.neuralnetworks.mlp.MLPs
Updates the winner's neighboring neurons.
updateNeighbor(double[]) - Method in class de.uos.cogsci.ai.btenbergen.bscthesis.neuralnetworks.mlp.som.SOM
Updates the winner neuron's neighboring neurons.
updateWinner(double[]) - Method in interface de.uos.cogsci.ai.btenbergen.bscthesis.neuralnetworks.mlp.MLPs
Updates the winner neuron.
updateWinner(double[]) - Method in class de.uos.cogsci.ai.btenbergen.bscthesis.neuralnetworks.mlp.som.SOM
Updates the winner neuron of the output layer.
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B

BIAS - Variable in class de.uos.cogsci.ai.btenbergen.bscthesis.neuralnetworks.perceptron.Perceptron
The biad of the perceptron that needs to be exceeded to cause an activation.
bipolar - Variable in class de.uos.cogsci.ai.btenbergen.bscthesis.neuralnetworks.perceptron.Perceptron
If true the perceptron fires bipolar (output: 1 or -1), else binary (output: 1 or 0).
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V

verbosing - Variable in class de.uos.cogsci.ai.btenbergen.bscthesis.yale.SOMFileHandler
If true, the operator will send detailed information about what is going on to Yale's Log-Console.
verbosing - Variable in class de.uos.cogsci.ai.btenbergen.bscthesis.yale.SOMInput
if true, the operator will send detailed information about what is happening to Yale's Log-Console.
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W

weights - Variable in class de.uos.cogsci.ai.btenbergen.bscthesis.neuralnetworks.perceptron.Perceptron
The weights of the perceptron that weighing the input
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PK Gp,5О.. javadoc/index-files/index-3.html C-Index
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C

cause - Variable in exception de.uos.cogsci.ai.btenbergen.bscthesis.neuralnetworks.perceptron.PerceptronInputException
 
closed_topology - Variable in class de.uos.cogsci.ai.btenbergen.bscthesis.neuralnetworks.mlp.som.SOM
The Topology of this Map.
closed_topology - Variable in class de.uos.cogsci.ai.btenbergen.bscthesis.yale.SOMInput
The Topology of this Map.
Cluster - Class in de.uos.cogsci.ai.btenbergen.bscthesis.tools
 
Cluster(int, Vector<String>) - Constructor for class de.uos.cogsci.ai.btenbergen.bscthesis.tools.Cluster
Constructor.
clusters - Variable in class de.uos.cogsci.ai.btenbergen.bscthesis.yale.SOMInput
The song clusters
col - Variable in class de.uos.cogsci.ai.btenbergen.bscthesis.neuralnetworks.perceptron.Perceptron
If the perceptron is embedded in a neural network, this indicates the column in which it is located.
cols - Variable in class de.uos.cogsci.ai.btenbergen.bscthesis.neuralnetworks.mlp.som.SOM
Sets the number of cols of this SOM.
computeMax(Object[]) - Method in class de.uos.cogsci.ai.btenbergen.bscthesis.neuralnetworks.mlp.som.SOM
This method computues the maximum sensible bias and weight values.
content - Variable in class de.uos.cogsci.ai.btenbergen.bscthesis.tools.Cluster
All Patterns in the Cluster.
convertFilename(String) - Static method in class de.uos.cogsci.ai.btenbergen.bscthesis.tools.OperatorTools
Replaces all characters which are not letters or digits by _ (underscore).
copy() - Method in class de.uos.cogsci.ai.btenbergen.bscthesis.yale.SOMInput
Returns a copy of this operator.
createFileClusters(int[], Vector<String>) - Static method in class de.uos.cogsci.ai.btenbergen.bscthesis.tools.OperatorTools
This method creates Cluster-Objects given an array containing the pattern's closest neurons and a Vector containing the Song-Names.
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D

de.uos.cogsci.ai.btenbergen.bscthesis.neuralnetworks.mlp - package de.uos.cogsci.ai.btenbergen.bscthesis.neuralnetworks.mlp
 
de.uos.cogsci.ai.btenbergen.bscthesis.neuralnetworks.mlp.som - package de.uos.cogsci.ai.btenbergen.bscthesis.neuralnetworks.mlp.som
 
de.uos.cogsci.ai.btenbergen.bscthesis.neuralnetworks.perceptron - package de.uos.cogsci.ai.btenbergen.bscthesis.neuralnetworks.perceptron
 
de.uos.cogsci.ai.btenbergen.bscthesis.tools - package de.uos.cogsci.ai.btenbergen.bscthesis.tools
 
de.uos.cogsci.ai.btenbergen.bscthesis.yale - package de.uos.cogsci.ai.btenbergen.bscthesis.yale
 
displaySongList(Vector<String>) - Static method in class de.uos.cogsci.ai.btenbergen.bscthesis.tools.OperatorTools
This method simply shows the processed songlist.
Distance - Class in de.uos.cogsci.ai.btenbergen.bscthesis.tools
 
Distance(Object[], Perceptron[], int) - Constructor for class de.uos.cogsci.ai.btenbergen.bscthesis.tools.Distance
Constructor.
Distance(Object[], Perceptron[]) - Constructor for class de.uos.cogsci.ai.btenbergen.bscthesis.tools.Distance
Constructor.
doLogging() - Method in class de.uos.cogsci.ai.btenbergen.bscthesis.yale.SOMFileHandler
This method just shows the retrieved features in the Object[] by sending it to Yale's Log-Console.
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PK Gp,5[[ javadoc/index-files/index-5.html E-Index
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E

EUCLIDEAN - Static variable in class de.uos.cogsci.ai.btenbergen.bscthesis.tools.Distance
Constant for the Euclidean Distance.
euclideanDistance(double[], double[]) - Method in class de.uos.cogsci.ai.btenbergen.bscthesis.tools.Distance
An euclidean distance.
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F

filename - Variable in class de.uos.cogsci.ai.btenbergen.bscthesis.yale.SOMFileHandler
The file to be parsed.
filename - Variable in class de.uos.cogsci.ai.btenbergen.bscthesis.yale.SOMInput
The file that is to be parsed.
findNeighbors(double[]) - Method in interface de.uos.cogsci.ai.btenbergen.bscthesis.neuralnetworks.mlp.MLPs
This method determines the winner neuron's neighboring neurons and returns them.
findNeighbors(double[]) - Method in class de.uos.cogsci.ai.btenbergen.bscthesis.neuralnetworks.mlp.som.SOM
This method determines the winner neuron's neighboring neurons and returns them according to the here implemented architecture of the SOM.
findNeuron(int, int) - Method in class de.uos.cogsci.ai.btenbergen.bscthesis.neuralnetworks.mlp.Layer
Given a coordinate in the structure, this method returns the corresponding neuron.
findNeuron(int, int) - Method in interface de.uos.cogsci.ai.btenbergen.bscthesis.neuralnetworks.mlp.Layers
Given a coordinate in the structure, this method returns the corresponding neuron.
findNeuronIndex(int, int) - Method in class de.uos.cogsci.ai.btenbergen.bscthesis.neuralnetworks.mlp.Layer
Given a coordinate in the structure, this method returns the index of the corresponding neuron.
findNeuronIndex(int, int) - Method in interface de.uos.cogsci.ai.btenbergen.bscthesis.neuralnetworks.mlp.Layers
Given a coordinate in the structure, this method returns the index of the corresponding neuron.
findQueryInClusters(String, Cluster[]) - Static method in class de.uos.cogsci.ai.btenbergen.bscthesis.tools.OperatorTools
This method checks if a String occures in the Vector of any cluster in a given Cluster-array.
findWinner(double[]) - Method in interface de.uos.cogsci.ai.btenbergen.bscthesis.neuralnetworks.mlp.MLPs
This method determines the winning neuron by comparing the activations given the input vector of all neurons in the layer.
findWinner(double[]) - Method in class de.uos.cogsci.ai.btenbergen.bscthesis.neuralnetworks.mlp.som.SOM
This method determines the winning neuron by comparing the activations given the input vector of all neurons in the layer.
fire(double[]) - Method in class de.uos.cogsci.ai.btenbergen.bscthesis.neuralnetworks.perceptron.Perceptron
Fires the perceptron.
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PK Gp,5$$ javadoc/index-files/index-7.html G-Index
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G

getInputClasses() - Method in class de.uos.cogsci.ai.btenbergen.bscthesis.yale.SOMInput
Returns all Input classes.
getMessage() - Method in exception de.uos.cogsci.ai.btenbergen.bscthesis.neuralnetworks.perceptron.PerceptronInputException
Just returns the detail message of the exception.
getOutputClasses() - Method in class de.uos.cogsci.ai.btenbergen.bscthesis.yale.SOMInput
Returns all Output classes.
getParameterTypes() - Method in class de.uos.cogsci.ai.btenbergen.bscthesis.yale.SOMInput
Gets the ParameterTypes as a lists and adds the parameters for this operator.
getPerceptrons() - Method in class de.uos.cogsci.ai.btenbergen.bscthesis.neuralnetworks.mlp.Layer
This method simply returns the neurons of the layer.
getPerceptrons() - Method in interface de.uos.cogsci.ai.btenbergen.bscthesis.neuralnetworks.mlp.Layers
This method simply returns the neurons of the Layer.
getWeights() - Method in class de.uos.cogsci.ai.btenbergen.bscthesis.neuralnetworks.perceptron.Perceptron
This method just returns the weights.
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H

hummedFile - Variable in class de.uos.cogsci.ai.btenbergen.bscthesis.yale.SOMInput
The user's query input
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I

id - Variable in class de.uos.cogsci.ai.btenbergen.bscthesis.tools.Cluster
The ID of the Cluster.
initApply() - Method in class de.uos.cogsci.ai.btenbergen.bscthesis.yale.SOMInput
This method is called by Yale when the operator is created.
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de.uos.cogsci.ai.btenbergen.bscthesis.neuralnetworks.mlp
de.uos.cogsci.ai.btenbergen.bscthesis.neuralnetworks.mlp.som
de.uos.cogsci.ai.btenbergen.bscthesis.neuralnetworks.perceptron
de.uos.cogsci.ai.btenbergen.bscthesis.tools
de.uos.cogsci.ai.btenbergen.bscthesis.yale

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de.uos.cogsci.ai.btenbergen.bscthesis.neuralnetworks.mlp  
de.uos.cogsci.ai.btenbergen.bscthesis.neuralnetworks.mlp.som  
de.uos.cogsci.ai.btenbergen.bscthesis.neuralnetworks.perceptron  
de.uos.cogsci.ai.btenbergen.bscthesis.tools  
de.uos.cogsci.ai.btenbergen.bscthesis.yale  

 

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de.uos.cogsci.ai.btenbergen.bscthesis.neuralnetworks.mlp, de.uos.cogsci.ai.btenbergen.bscthesis.neuralnetworks.mlp.som, de.uos.cogsci.ai.btenbergen.bscthesis.neuralnetworks.perceptron, de.uos.cogsci.ai.btenbergen.bscthesis.tools, de.uos.cogsci.ai.btenbergen.bscthesis.yale

Class Hierarchy

  • java.lang.Object
    • de.uos.cogsci.ai.btenbergen.bscthesis.tools.Cluster
    • de.uos.cogsci.ai.btenbergen.bscthesis.tools.Distance
    • de.uos.cogsci.ai.btenbergen.bscthesis.neuralnetworks.mlp.Layer (implements de.uos.cogsci.ai.btenbergen.bscthesis.neuralnetworks.mlp.Layers)
    • edu.udo.cs.yale.operator.Operator
      • de.uos.cogsci.ai.btenbergen.bscthesis.yale.SOMInput (implements edu.udo.cs.yale.operator.IOObject)
    • de.uos.cogsci.ai.btenbergen.bscthesis.tools.OperatorTools
    • de.uos.cogsci.ai.btenbergen.bscthesis.neuralnetworks.perceptron.Perceptron
    • de.uos.cogsci.ai.btenbergen.bscthesis.neuralnetworks.mlp.som.SOM (implements de.uos.cogsci.ai.btenbergen.bscthesis.neuralnetworks.mlp.MLPs)
    • de.uos.cogsci.ai.btenbergen.bscthesis.yale.SOMFileHandler
    • java.lang.Throwable (implements java.io.Serializable)
    • de.uos.cogsci.ai.btenbergen.bscthesis.tools.Tools

Interface Hierarchy

  • de.uos.cogsci.ai.btenbergen.bscthesis.neuralnetworks.mlp.Layers
  • de.uos.cogsci.ai.btenbergen.bscthesis.neuralnetworks.mlp.MLPs
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Serialized Form

Package de.uos.cogsci.ai.btenbergen.bscthesis.neuralnetworks.perceptron

Class de.uos.cogsci.ai.btenbergen.bscthesis.neuralnetworks.perceptron.PerceptronInputException extends java.lang.Exception implements Serializable

serialVersionUID: 42L

Serialized Fields

msg

java.lang.String msg

cause

java.lang.Throwable cause

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de.uos.cogsci.ai.btenbergen.bscthesis.neuralnetworks.mlp
Class Layer

java.lang.Object
  extended by de.uos.cogsci.ai.btenbergen.bscthesis.neuralnetworks.mlp.Layer
All Implemented Interfaces:
Layers
public class Layer
extends java.lang.Object
implements Layers

Field Summary
private  int maxValue
          The maximum value of weights and bias for each neuron.
 Perceptron[] neurons
          The array of neurons defining the layer.
 
Constructor Summary
Layer(int rows, int cols, int inputDimensions, int maxValue)
          Constructor.
 
Method Summary
 Perceptron findNeuron(int row, int col)
          Given a coordinate in the structure, this method returns the corresponding neuron.
 int findNeuronIndex(int row, int col)
          Given a coordinate in the structure, this method returns the index of the corresponding neuron.
 Perceptron[] getPerceptrons()
          This method simply returns the neurons of the layer.
 java.lang.String toString()
          Displays the neurons of the layer.
 
Methods inherited from class java.lang.Object
clone, equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait
 

Field Detail

neurons

public Perceptron[] neurons
The array of neurons defining the layer.

maxValue

private int maxValue
The maximum value of weights and bias for each neuron.

Constructor Detail

Layer

public Layer(int rows,
             int cols,
             int inputDimensions,
             int maxValue)
Constructor. A layer is defined by the number of neurons and the dimensionality of the input. It is instantiated by invoking rows * cols Perceptrons. Each Perceptron has as many weights as input dimensionality. The weight vectors are initialized randomly, however limited to maxValue to prevent very large weight differences. Neurons in Multi-Layer-Perceptrons always fire bipolar.

Parameters:
rows - The number of rows in the layer
cols - The number of cols in the layer
inputDimensions - The dimensionality of the input vector
maxValue - The maximum value for weights and bias for the neurons.
Method Detail

findNeuronIndex

public int findNeuronIndex(int row,
                           int col)
Given a coordinate in the structure, this method returns the index of the corresponding neuron.

Specified by:
findNeuronIndex in interface Layers
Parameters:
row - The X-Axis of the neuron
col - The Y-Axis of the neuron
Returns:
The index of the neuron.

findNeuron

public Perceptron findNeuron(int row,
                             int col)
Given a coordinate in the structure, this method returns the corresponding neuron.

Specified by:
findNeuron in interface Layers
Parameters:
row - The X-Axis of the neuron
col - The Y-Axis of the neuron
Returns:
The neuron at the given position.

getPerceptrons

public Perceptron[] getPerceptrons()
This method simply returns the neurons of the layer.

Specified by:
getPerceptrons in interface Layers
Returns:
The neurons of the layer.

toString

public java.lang.String toString()
Displays the neurons of the layer.

Specified by:
toString in interface Layers
Overrides:
toString in class java.lang.Object
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PK Gp,5MI11Ljavadoc/de/uos/cogsci/ai/btenbergen/bscthesis/neuralnetworks/mlp/Layers.html Layers
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de.uos.cogsci.ai.btenbergen.bscthesis.neuralnetworks.mlp
Interface Layers

All Known Implementing Classes:
Layer
public interface Layers

Method Summary
 Perceptron findNeuron(int row, int col)
          Given a coordinate in the structure, this method returns the corresponding neuron.
 int findNeuronIndex(int row, int col)
          Given a coordinate in the structure, this method returns the index of the corresponding neuron.
 Perceptron[] getPerceptrons()
          This method simply returns the neurons of the Layer.
 java.lang.String toString()
          toString displays the Neurons of the Layer.
 

Method Detail

findNeuronIndex

int findNeuronIndex(int row,
                    int col)
Given a coordinate in the structure, this method returns the index of the corresponding neuron.

Parameters:
row - The X-Axis of the neuron
col - The Y-Axis of the neuron
Returns:
The index of the neuron.

findNeuron

Perceptron findNeuron(int row,
                      int col)
Given a coordinate in the structure, this method returns the corresponding neuron.

Parameters:
row - The X-Axis of the neuron
col - The Y-Axis of the neuron
Returns:
The neuron at the given position.

getPerceptrons

Perceptron[] getPerceptrons()
This method simply returns the neurons of the Layer.

Returns:
The neurons of the Layer.

toString

java.lang.String toString()
toString displays the Neurons of the Layer.

Overrides:
toString in class java.lang.Object
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PK Gp,5y7 E EJjavadoc/de/uos/cogsci/ai/btenbergen/bscthesis/neuralnetworks/mlp/MLPs.html MLPs
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de.uos.cogsci.ai.btenbergen.bscthesis.neuralnetworks.mlp
Interface MLPs

All Known Implementing Classes:
SOM
public interface MLPs

Method Summary
 Perceptron[] findNeighbors(double[] input)
          This method determines the winner neuron's neighboring neurons and returns them.
 Perceptron findWinner(double[] input)
          This method determines the winning neuron by comparing the activations given the input vector of all neurons in the layer.
 void runClassification()
          Runs the classification process runs times.
 java.lang.String toString()
          toString method.
 void updateNeighbor(double[] input)
          Updates the winner's neighboring neurons.
 void updateWinner(double[] input)
          Updates the winner neuron.
 

Method Detail

runClassification

void runClassification()
                       throws PerceptronInputException
Runs the classification process runs times. For each pattern update(int[]) is called.

Throws:
If - the Perceptron's weight vector is not of equal length as the input vector.
PerceptronInputException

updateWinner

void updateWinner(double[] input)
                  throws PerceptronInputException
Updates the winner neuron. To do so, the winner neuron needs to be determined by calling findWinner(int[]). Each weight in the winner's weight vecotr is then updated with the following formula:
newWeight = oldWeight + (LEARNINGRATE * (input - oldWeight))

Parameters:
input - The weight vector that determines the winning neuron.
Throws:
If - the Perceptron's weight vector is not of equal length as the input vector.
PerceptronInputException

updateNeighbor

void updateNeighbor(double[] input)
                    throws PerceptronInputException
Updates the winner's neighboring neurons. The neighboring neurons are determined and then updated according to the following formula:
newWeight = oldWeight + (LEARNINGRATE * (input - oldWeight))

Parameters:
input - The weight vector that determines the winning neuron.
Throws:
PerceptronInputException - If the Perceptron's weight vector is not of equal length as the input vector.

findNeighbors

Perceptron[] findNeighbors(double[] input)
                           throws PerceptronInputException
This method determines the winner neuron's neighboring neurons and returns them. This method needs to implement the topology of the MLP.

Parameters:
input - The input vector determining the winning neuron.
Returns:
The winner neuron's neighboring neurons.
Throws:
PerceptronInputException - If the Perceptron's weight vector is not of equal length as the input vector.

findWinner

Perceptron findWinner(double[] input)
                      throws PerceptronInputException
This method determines the winning neuron by comparing the activations given the input vector of all neurons in the layer. This is very close to a standard SelectionSort algorithm, but since the neurons are not sorted and only the maximum activation, this is done in O(n) time.

Parameters:
input - The input vector determining the winning neuron.
Returns:
The winning neuron.
Throws:
If - the Perceptron's weight vector is not of equal length as the input vector.
PerceptronInputException

toString

java.lang.String toString()
toString method. Returns the String-Representation of this MLP.

Overrides:
toString in class java.lang.Object
Returns:
The String representation of this MLP.
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Uses of Class
de.uos.cogsci.ai.btenbergen.bscthesis.neuralnetworks.mlp.Layer

Packages that use Layer
de.uos.cogsci.ai.btenbergen.bscthesis.neuralnetworks.mlp.som   
 

Uses of Layer in de.uos.cogsci.ai.btenbergen.bscthesis.neuralnetworks.mlp.som
 

Fields in de.uos.cogsci.ai.btenbergen.bscthesis.neuralnetworks.mlp.som declared as Layer
 Layer SOM.output
          The output layer.
 

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PK Gp,55% " "Vjavadoc/de/uos/cogsci/ai/btenbergen/bscthesis/neuralnetworks/mlp/class-use/Layers.html Uses of Interface de.uos.cogsci.ai.btenbergen.bscthesis.neuralnetworks.mlp.Layers
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Uses of Interface
de.uos.cogsci.ai.btenbergen.bscthesis.neuralnetworks.mlp.Layers

Packages that use Layers
de.uos.cogsci.ai.btenbergen.bscthesis.neuralnetworks.mlp   
 

Uses of Layers in de.uos.cogsci.ai.btenbergen.bscthesis.neuralnetworks.mlp
 

Classes in de.uos.cogsci.ai.btenbergen.bscthesis.neuralnetworks.mlp that implement Layers
 class Layer
           
 

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PK Gp,5)ŭ""Tjavadoc/de/uos/cogsci/ai/btenbergen/bscthesis/neuralnetworks/mlp/class-use/MLPs.html Uses of Interface de.uos.cogsci.ai.btenbergen.bscthesis.neuralnetworks.mlp.MLPs
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de.uos.cogsci.ai.btenbergen.bscthesis.neuralnetworks.mlp.MLPs

Packages that use MLPs
de.uos.cogsci.ai.btenbergen.bscthesis.neuralnetworks.mlp.som   
 

Uses of MLPs in de.uos.cogsci.ai.btenbergen.bscthesis.neuralnetworks.mlp.som
 

Classes in de.uos.cogsci.ai.btenbergen.bscthesis.neuralnetworks.mlp.som that implement MLPs
 class SOM
           
 

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Interfaces 
Layers
MLPs
Classes 
Layer
PK Gp,5kL}}Ujavadoc/de/uos/cogsci/ai/btenbergen/bscthesis/neuralnetworks/mlp/package-summary.html de.uos.cogsci.ai.btenbergen.bscthesis.neuralnetworks.mlp
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Package de.uos.cogsci.ai.btenbergen.bscthesis.neuralnetworks.mlp

Interface Summary
Layers  
MLPs  
 

Class Summary
Layer  
 

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Package Hierarchies:
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Class Hierarchy

  • java.lang.Object
    • de.uos.cogsci.ai.btenbergen.bscthesis.neuralnetworks.mlp.Layer (implements de.uos.cogsci.ai.btenbergen.bscthesis.neuralnetworks.mlp.Layers)

Interface Hierarchy

  • de.uos.cogsci.ai.btenbergen.bscthesis.neuralnetworks.mlp.Layers
  • de.uos.cogsci.ai.btenbergen.bscthesis.neuralnetworks.mlp.MLPs
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de.uos.cogsci.ai.btenbergen.bscthesis.neuralnetworks.mlp

Packages that use de.uos.cogsci.ai.btenbergen.bscthesis.neuralnetworks.mlp
de.uos.cogsci.ai.btenbergen.bscthesis.neuralnetworks.mlp   
de.uos.cogsci.ai.btenbergen.bscthesis.neuralnetworks.mlp.som   
 

Classes in de.uos.cogsci.ai.btenbergen.bscthesis.neuralnetworks.mlp used by de.uos.cogsci.ai.btenbergen.bscthesis.neuralnetworks.mlp
Layers
           
 

Classes in de.uos.cogsci.ai.btenbergen.bscthesis.neuralnetworks.mlp used by de.uos.cogsci.ai.btenbergen.bscthesis.neuralnetworks.mlp.som
Layer
           
MLPs
           
 

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PK 75Ejavadoc/de/uos/cogsci/ai/btenbergen/bscthesis/neuralnetworks/mlp/som/PK R5mIIOjavadoc/de/uos/cogsci/ai/btenbergen/bscthesis/neuralnetworks/mlp/som/Layer.html Layer
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de.uos.cogsci.ai.btenbergen.bscthesis.neuralnetworks.mlp.som
Class Layer

java.lang.Object
  extended by de.uos.cogsci.ai.btenbergen.bscthesis.neuralnetworks.mlp.som.Layer
All Implemented Interfaces:
Layers
public class Layer
extends java.lang.Object
implements Layers

Field Summary
 Perceptron[] neurons
          The array of neurons defining the layer.
 
Constructor Summary
Layer(int rows, int cols, int inputDimensions, int maxValue)
          Constructor.
 
Method Summary
 Perceptron findNeuron(int row, int col)
          Given a coordinate in the structure, this method returns the corresponding neuron.
 int findNeuronIndex(int row, int col)
          Given a coordinate in the structure, this method returns the index of the corresponding neuron.
 Perceptron[] getPerceptrons()
          This method simply returns the neurons of the layer.
 java.lang.String toString()
          Displays the neurons of the layer.
 
Methods inherited from class java.lang.Object
equals, getClass, hashCode, notify, notifyAll, wait, wait, wait
 

Field Detail

neurons

public Perceptron[] neurons
The array of neurons defining the layer.

Constructor Detail

Layer

public Layer(int rows,
             int cols,
             int inputDimensions,
             int maxValue)
Constructor. A layer is defined by the number of neurons and the dimensionality of the input. It is instantiated by invoking rows * cols Perceptrons. Each Perceptron has as many weights as input dimensionality. The weight vectors are initialized randomly, however limited to maxValue to prevent very large weight differences. Neurons in Multi-Layer-Perceptrons always fire bipolar.

Parameters:
rows - The number of rows in the layer
cols - The number of cols in the layer
inputDimensions - The dimensionality of the input vector
The - maximum value for weights and bias for the neurons.
Method Detail

findNeuronIndex

public int findNeuronIndex(int row,
                           int col)
Given a coordinate in the structure, this method returns the index of the corresponding neuron.

Specified by:
findNeuronIndex in interface Layers
Parameters:
row - The X-Axis of the neuron
col - The Y-Axis of the neuron
Returns:
The index of the neuron.

findNeuron

public Perceptron findNeuron(int row,
                             int col)
Given a coordinate in the structure, this method returns the corresponding neuron.

Specified by:
findNeuron in interface Layers
Parameters:
row - The X-Axis of the neuron
col - The Y-Axis of the neuron
Returns:
The neuron at the given position.

getPerceptrons

public Perceptron[] getPerceptrons()
This method simply returns the neurons of the layer.

Specified by:
getPerceptrons in interface Layers
Returns:
The neurons of the layer.

toString

public java.lang.String toString()
Displays the neurons of the layer.

Specified by:
toString in interface Layers
Overrides:
toString in class java.lang.Object
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PK Gp,5GEzzMjavadoc/de/uos/cogsci/ai/btenbergen/bscthesis/neuralnetworks/mlp/som/SOM.html SOM
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de.uos.cogsci.ai.btenbergen.bscthesis.neuralnetworks.mlp.som
Class SOM

java.lang.Object
  extended by de.uos.cogsci.ai.btenbergen.bscthesis.neuralnetworks.mlp.som.SOM
All Implemented Interfaces:
MLPs
public class SOM
extends java.lang.Object
implements MLPs

Field Summary
private  boolean closed_topology
          The Topology of this Map.
private  int cols
          Sets the number of cols of this SOM.
private  double learningrate_neighbor
          The learning rate of the winner's neighboring neurons.
private  double learningrate_winner
          The learning rate.
 Layer output
          The output layer.
private  java.lang.Object[] patterns
          This array contains all patterns, i.e. the input vectors.
private  int rows
          Sets the number of rows of this SOM.
private  int runs
          The number of classification runs to be performed.
 
Constructor Summary
SOM(int rows, int cols, java.lang.Object[] patterns)
          Constructor.
SOM(int rows, int cols, java.lang.Object[] patterns, double learningrate_winner, double learningrate_neighbor, int runs, int maxValue, boolean closed_topology)
          Constructor.
SOM(int rows, int cols, java.lang.Object[] patterns, int runs)
          Constructor.
 
Method Summary
 int computeMax(java.lang.Object[] patterns)
          This method computues the maximum sensible bias and weight values.
 Perceptron[] findNeighbors(double[] input)
          This method determines the winner neuron's neighboring neurons and returns them according to the here implemented architecture of the SOM.
 Perceptron findWinner(double[] input)
          This method determines the winning neuron by comparing the activations given the input vector of all neurons in the layer.
protected  void logPatterns()
          This method logs the current status of the SOM by sending information to Yale's Log-Console.
 void runClassification()
          Runs the classification process runs times.
 void showPatterns()
          This method confirms the given input vectors by prompting them.
 java.lang.String toString()
          toString method.
 void updateNeighbor(double[] input)
          Updates the winner neuron's neighboring neurons.
 void updateWinner(double[] input)
          Updates the winner neuron of the output layer.
 
Methods inherited from class java.lang.Object
clone, equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait
 

Field Detail

closed_topology

private boolean closed_topology
The Topology of this Map. If true, the last neuron of each row or col is connected to the first one.

runs

private int runs
The number of classification runs to be performed.

learningrate_winner

private double learningrate_winner
The learning rate. Defines how far the weight vector of the winning neuron is "pulled" into the direction of the input vector.

learningrate_neighbor

private double learningrate_neighbor
The learning rate of the winner's neighboring neurons.

patterns

private java.lang.Object[] patterns
This array contains all patterns, i.e. the input vectors. For simplicity reasons, this is an Object array that contains double arrays instead of a two dimensional matrix. Whenever a pattern is taken out of the array, it needs to be casted to a double[]. This should increase the performance on slow machines

rows

private int rows
Sets the number of rows of this SOM.

cols

private int cols
Sets the number of cols of this SOM.

output

public Layer output
The output layer.

Constructor Detail

SOM

public SOM(int rows,
           int cols,
           java.lang.Object[] patterns,
           double learningrate_winner,
           double learningrate_neighbor,
           int runs,
           int maxValue,
           boolean closed_topology)
Constructor. Creates a new Self-Organizing Map.

Parameters:
rows - The number of rows of this SOM.
cols - The number of columns of this SOM.
patterns - The training patterns. Must be an Object array containing double arrays.
learningrate_winner - Defines how far the weight vector of the winning neuron is "pulled" into the direction of the input vector.
learningrate_neighbor - Defines how far the weight vectors of the winner's neighbors is updated.
runs - The number of classification runs to be performed.
maxValue - The maximum value for weights and bias for the neurons.
closed_topology - The Topology of this Map. If true, the last neuron of each row or col is connected to the first one.

SOM

public SOM(int rows,
           int cols,
           java.lang.Object[] patterns)
Constructor. Creates a new Self-Organizing Map with default values for learningrates and runs. A sensible maximum value for the neuron's weights and bias is computed automatically.

Parameters:
rows - The number of rows of this SOM.
cols - The number of columns of this SOM.
patterns - The training patterns. Must be an Object array containing double arrays.

SOM

public SOM(int rows,
           int cols,
           java.lang.Object[] patterns,
           int runs)
Constructor. Creates a new Self-Organizing Map with default values for learningrates. A sensible maximum value for the neuron's weights and bias is computed automatically.

Parameters:
rows - The number of rows of this SOM.
cols - The number of columns of this SOM.
patterns - The training patterns. Must be an Object array containing double arrays.
runs - The number of runs to be performed. The more runs are performed, the more "prototypical" the output neurons are to the input clusters.
Method Detail

computeMax

public int computeMax(java.lang.Object[] patterns)
This method computues the maximum sensible bias and weight values.

Parameters:
patterns - A sensible maximum is computed according to these patterns.
Returns:
A reasonable maximum threshold.

runClassification

public void runClassification()
                       throws PerceptronInputException
Runs the classification process runs times. For each pattern, the winner neuron is determined and updated. The winner neuron's neighboring neurons are updated accordingly.

Specified by:
runClassification in interface MLPs
Throws:
If - the Perceptron's weight vector is not of equal length as the input vector.
PerceptronInputException

updateWinner

public void updateWinner(double[] input)
                  throws PerceptronInputException
Updates the winner neuron of the output layer. To do so, the winner neuron needs to be determined by calling findWinner(int[]). Each weight in the winner's weight vecotr is then updated with the following formula:
newWeight = oldWeight + (LEARNINGRATE * (input - oldWeight))

Specified by:
updateWinner in interface MLPs
Parameters:
input - The weight vector that determines the winning neuron.
Throws:
If - the Perceptron's weight vector is not of equal length as the input vector.
PerceptronInputException

updateNeighbor

public void updateNeighbor(double[] input)
                    throws PerceptronInputException
Updates the winner neuron's neighboring neurons. To do so, the neighbors need to be determined by calling findNeighbors(int[]). Each weight in each neighbor's weight vecotr is then updated with the following formula:
newWeight = oldWeight + (LEARNINGRATE * (input - oldWeight))

Specified by:
updateNeighbor in interface MLPs
Parameters:
input - The weight vector that determines the winning neuron.
Throws:
If - the Perceptron's weight vector is not of equal length as the input vector.
PerceptronInputException - If the Perceptron's weight vector is not of equal length as the input vector.

findNeighbors

public Perceptron[] findNeighbors(double[] input)
                           throws PerceptronInputException
This method determines the winner neuron's neighboring neurons and returns them according to the here implemented architecture of the SOM.

Specified by:
findNeighbors in interface MLPs
Parameters:
input - The input vector determining the winning neuron.
Returns:
The winner neuron's neighboring neurons.
Throws:
PerceptronInputException - If the Perceptron's weight vector is not of equal length as the input vector.

findWinner

public Perceptron findWinner(double[] input)
                      throws PerceptronInputException
This method determines the winning neuron by comparing the activations given the input vector of all neurons in the layer. This is very close to a standard SelectionSort algorithm, but since the neurons are not sorted and only the maximum activation, this is done in O(n) time.

Specified by:
findWinner in interface MLPs
Parameters:
input - The input vector determining the winning neuron.
Returns:
The winning neuron.
Throws:
PerceptronInputException - If the Perceptron's weight vector is not of equal length as the input vector.

toString

public java.lang.String toString()
toString method. Just calls the toString of the output layer, since this SOM just consists of an output layer.

Specified by:
toString in interface MLPs
Overrides:
toString in class java.lang.Object
Returns:
The String representation of this SOM.

showPatterns

public void showPatterns()
This method confirms the given input vectors by prompting them. Called usually by methods testing the functionality.

logPatterns

protected void logPatterns()
This method logs the current status of the SOM by sending information to Yale's Log-Console. The output is similar to showPatterns().

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de.uos.cogsci.ai.btenbergen.bscthesis.neuralnetworks.mlp.som
Class SOMFileHandler

java.lang.Object
  extended by de.uos.cogsci.ai.btenbergen.bscthesis.neuralnetworks.mlp.som.SOMFileHandler

public class SOMFileHandler
extends java.lang.Object

Field Summary
 java.util.Vector<java.lang.String> songList
          Each row in the file contains the filename of the song, the features belong to.
 
Constructor Summary
SOMFileHandler(java.io.File filename)
          Constructor.
SOMFileHandler(java.io.File filename, boolean verbosing)
          Constructor.
 
Method Summary
 java.lang.Object[] parseFile()
          This Method parses a given input file.
 
Methods inherited from class java.lang.Object
equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait
 

Field Detail

songList

public java.util.Vector<java.lang.String> songList
Each row in the file contains the filename of the song, the features belong to. These filenames are stored in this vector for reference.

Constructor Detail

SOMFileHandler

public SOMFileHandler(java.io.File filename,
                      boolean verbosing)
Constructor. Sets the fields filename and verbosing accordingly.

Parameters:
filename - The file to be parsed.
verbosing - If true, detailed information about what is going on is sent to Yale's Log-Console.

SOMFileHandler

public SOMFileHandler(java.io.File filename)
Constructor. Sets the field filename accordingly. Verbosing is set to true.

Parameters:
filename - The file to be parsed.
Method Detail

parseFile

public java.lang.Object[] parseFile()
                             throws java.io.FileNotFoundException,
                                    java.io.IOException
This Method parses a given input file. Each row in the file is a Feture Vector and is extraced as such and stored in a java.util.Vector. The Vector will hence contain Strings as elements whereas each String corresponds to a row in the file. The method parseFeatureVectors is hence called with this Vector to extract the actual features out of each String. That method's output is returned by this method.

Returns:
An Object[] containing java.util.Vector as elements. Each Vector has doubles as elements. The doubles correspond to an actual feature while the java.util.Vector corresponds to each feature Vector, i.e. the rows in the File.
Throws:
java.io.FileNotFoundException - iff the file cannot be found.
java.io.IOException - iff an error occured during file access.
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Uses of Class
de.uos.cogsci.ai.btenbergen.bscthesis.neuralnetworks.mlp.som.Layer

Packages that use Layer
de.uos.cogsci.ai.btenbergen.bscthesis.neuralnetworks.mlp.som   
 

Uses of Layer in de.uos.cogsci.ai.btenbergen.bscthesis.neuralnetworks.mlp.som
 

Fields in de.uos.cogsci.ai.btenbergen.bscthesis.neuralnetworks.mlp.som declared as Layer
 Layer SOM.output
          The output layer.
 

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de.uos.cogsci.ai.btenbergen.bscthesis.neuralnetworks.mlp.som.SOM

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Classes 
SOM
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Package de.uos.cogsci.ai.btenbergen.bscthesis.neuralnetworks.mlp.som

Class Summary
SOM  
 

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Hierarchy For Package de.uos.cogsci.ai.btenbergen.bscthesis.neuralnetworks.mlp.som

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  • java.lang.Object
    • de.uos.cogsci.ai.btenbergen.bscthesis.neuralnetworks.mlp.som.SOM (implements de.uos.cogsci.ai.btenbergen.bscthesis.neuralnetworks.mlp.MLPs)
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de.uos.cogsci.ai.btenbergen.bscthesis.neuralnetworks.mlp.som

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de.uos.cogsci.ai.btenbergen.bscthesis.neuralnetworks.perceptron
Class Perceptron

java.lang.Object
  extended by de.uos.cogsci.ai.btenbergen.bscthesis.neuralnetworks.perceptron.Perceptron

public class Perceptron
extends java.lang.Object

This is the Perceptron class. It implements a perceptron with an arbitrary number of inputs.

Version:
01.05.2006
Author:
Bastian Tenbergen - btenberg@uos.de

Field Summary
 double BIAS
          The biad of the perceptron that needs to be exceeded to cause an activation.
 boolean bipolar
          If true the perceptron fires bipolar (output: 1 or -1), else binary (output: 1 or 0).
 int col
          If the perceptron is embedded in a neural network, this indicates the column in which it is located.
private  int returnNeg
          The value that is returned when the activation does not exceed the threshold.
private  int returnPos
          The value that is returned when the activation exceeds the threshold.
 int row
          If the perceptron is embedded in a neural network, this indicates the row in which it is located.
 double[] weights
          The weights of the perceptron that weighing the input
 
Constructor Summary
Perceptron(int row, int col, double BIAS, double[] weights, boolean bipolar)
          Constructor.
 
Method Summary
 double activation(double[] inputs)
          This method computes the scalar product of the input and the weights.
 int fire(double[] inputs)
          Fires the perceptron.
 double[] getWeights()
          This method just returns the weights.
private  void setBipolar()
          Sets the Perceptron to binary or bipolar.
 boolean setWeights(double[] newWeights)
          This method updates the weight vector of the Perceptron.
 java.lang.String toString()
          toString method.
 
Methods inherited from class java.lang.Object
clone, equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait
 

Field Detail

BIAS

public final double BIAS
The biad of the perceptron that needs to be exceeded to cause an activation.

weights

public double[] weights
The weights of the perceptron that weighing the input

bipolar

public boolean bipolar
If true the perceptron fires bipolar (output: 1 or -1), else binary (output: 1 or 0).

row

public int row
If the perceptron is embedded in a neural network, this indicates the row in which it is located.

col

public int col
If the perceptron is embedded in a neural network, this indicates the column in which it is located.

returnPos

private int returnPos
The value that is returned when the activation exceeds the threshold.

returnNeg

private int returnNeg
The value that is returned when the activation does not exceed the threshold.

Constructor Detail

Perceptron

public Perceptron(int row,
                  int col,
                  double BIAS,
                  double[] weights,
                  boolean bipolar)
Constructor. A standard Perceptron is invoked when called.

Parameters:
row - The row of the structure in which this Perceptron is located.
col - The column of the structure in which this Perceptron is located.
BIAS - The bias (theta, threshold) of the Perceptron. Once set, never changeable.
weights - The weight vector of the perceptron.
bipolar - Determines, if the Perceptron is bipolar or binary. If true, the negative output is -1. If false, the negative output is 0. The positive output is always 1.
Method Detail

setBipolar

private void setBipolar()
Sets the Perceptron to binary or bipolar. Called by the constructor.

fire

public int fire(double[] inputs)
         throws PerceptronInputException
Fires the perceptron. Calls activation(int[]) to compute scalar product.

Parameters:
inputs - The input vector.
Returns:
1 if the scalar product of the input and the weights is higher than the bias. -1 or 0 else (this depends on the Perceptron being bipolar or binary.
Throws:
If - the Perceptron's weight vector is not of equal length as the input vector.
PerceptronInputException

activation

public double activation(double[] inputs)
                  throws PerceptronInputException
This method computes the scalar product of the input and the weights.

Parameters:
inputs - The input vector.
Returns:
The activation of the Perceptron - scalar product of weights x input.
Throws:
If - the Perceptron's weight vector is not of equal length as the input vector.
PerceptronInputException

setWeights

public boolean setWeights(double[] newWeights)
This method updates the weight vector of the Perceptron.

Parameters:
newWeights - The new weight vector.
Returns:
true if update was successful, false else.

getWeights

public double[] getWeights()
This method just returns the weights.

Returns:
The weight vector of the Perceptron.

toString

public java.lang.String toString()
toString method. Returns the String representation of the Perceptron.

Overrides:
toString in class java.lang.Object
Returns:
The String representation of the Perceptron.
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PK Fp,5ZmImIejavadoc/de/uos/cogsci/ai/btenbergen/bscthesis/neuralnetworks/perceptron/PerceptronInputException.html PerceptronInputException
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de.uos.cogsci.ai.btenbergen.bscthesis.neuralnetworks.perceptron
Class PerceptronInputException

java.lang.Object
  extended by java.lang.Throwable
      extended by java.lang.Exception
          extended by de.uos.cogsci.ai.btenbergen.bscthesis.neuralnetworks.perceptron.PerceptronInputException
All Implemented Interfaces:
java.io.Serializable
public class PerceptronInputException
extends java.lang.Exception

The class PercepronInputException is thrown when the Input-Vector of a Perceptron is not of equal length as its Weight-Vector. Javadoc taken from java.lang.Exception class.

Version:
01.05.2006
Author:
Bastian Tenbergen - btenberg@uos.de
See Also:
Serialized Form

Field Summary
protected  java.lang.Throwable cause
           
private  java.lang.String msg
           
private static long serialVersionUID
           
 
Constructor Summary
PerceptronInputException()
          Constructs a new exception with null as its detail message.
PerceptronInputException(java.lang.String msg)
          Constructs a new exception with the specified detail message.
PerceptronInputException(java.lang.String msg, java.lang.Throwable cause)
          Constructs a new exception with the specified detail message and cause.
PerceptronInputException(java.lang.Throwable cause)
          Constructs a new exception with the specified cause and a detail message of (cause==null ?
 
Method Summary
 java.lang.String getMessage()
          Just returns the detail message of the exception.
 
Methods inherited from class java.lang.Throwable
fillInStackTrace, getCause, getLocalizedMessage, getStackTrace, initCause, printStackTrace, printStackTrace, printStackTrace, setStackTrace, toString
 
Methods inherited from class java.lang.Object
clone, equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait
 

Field Detail

serialVersionUID

private static final long serialVersionUID
See Also:
Constant Field Values

msg

private java.lang.String msg

cause

protected java.lang.Throwable cause
Constructor Detail

PerceptronInputException

public PerceptronInputException()
Constructs a new exception with null as its detail message. The cause is not initialized, and may subsequently be initialized by a call to Throwable.initCause(java.lang.Throwable).

PerceptronInputException

public PerceptronInputException(java.lang.String msg,
                                java.lang.Throwable cause)
Constructs a new exception with the specified detail message and cause. Note that the detail message associated with cause is not automatically incorporated in this exception's detail message.

Parameters:
msg - the detail message (which is saved for later retrieval by the Throwable.getMessage() method).
cause - the cause (which is saved for later retrieval by the Throwable.getCause() method). (A null value is permitted, and indicates that the cause is nonexistent or unknown.)

PerceptronInputException

public PerceptronInputException(java.lang.String msg)
Constructs a new exception with the specified detail message. The cause is not initialized, and may subsequently be initialized by a call to Throwable.initCause(java.lang.Throwable).

Parameters:
msg - the detail message. The detail message is saved for later retrieval by the Throwable.getMessage() method.

PerceptronInputException

public PerceptronInputException(java.lang.Throwable cause)
Constructs a new exception with the specified cause and a detail message of (cause==null ? null : cause.toString()) (which typically contains the class and detail message of cause). This constructor is useful for exceptions that are little more than wrappers for other throwables (for example, PrivilegedActionException).

Parameters:
cause - the cause (which is saved for later retrieval by the Throwable.getCause() method). (A null value is permitted, and indicates that the cause is nonexistent or unknown.)
Method Detail

getMessage

public java.lang.String getMessage()
Just returns the detail message of the exception.

Overrides:
getMessage in class java.lang.Throwable
Returns:
the detailed message as a java.lang.String
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Uses of Class
de.uos.cogsci.ai.btenbergen.bscthesis.neuralnetworks.perceptron.Perceptron

Packages that use Perceptron
de.uos.cogsci.ai.btenbergen.bscthesis.neuralnetworks.mlp   
de.uos.cogsci.ai.btenbergen.bscthesis.neuralnetworks.mlp.som   
de.uos.cogsci.ai.btenbergen.bscthesis.tools   
 

Uses of Perceptron in de.uos.cogsci.ai.btenbergen.bscthesis.neuralnetworks.mlp
 

Fields in de.uos.cogsci.ai.btenbergen.bscthesis.neuralnetworks.mlp declared as Perceptron
 Perceptron[] Layer.neurons
          The array of neurons defining the layer.
 

Methods in de.uos.cogsci.ai.btenbergen.bscthesis.neuralnetworks.mlp that return Perceptron
 Perceptron[] MLPs.findNeighbors(double[] input)
          This method determines the winner neuron's neighboring neurons and returns them.
 Perceptron Layers.findNeuron(int row, int col)
          Given a coordinate in the structure, this method returns the corresponding neuron.
 Perceptron Layer.findNeuron(int row, int col)
          Given a coordinate in the structure, this method returns the corresponding neuron.
 Perceptron MLPs.findWinner(double[] input)
          This method determines the winning neuron by comparing the activations given the input vector of all neurons in the layer.
 Perceptron[] Layers.getPerceptrons()
          This method simply returns the neurons of the Layer.
 Perceptron[] Layer.getPerceptrons()
          This method simply returns the neurons of the layer.
 

Uses of Perceptron in de.uos.cogsci.ai.btenbergen.bscthesis.neuralnetworks.mlp.som
 

Methods in de.uos.cogsci.ai.btenbergen.bscthesis.neuralnetworks.mlp.som that return Perceptron
 Perceptron[] SOM.findNeighbors(double[] input)
          This method determines the winner neuron's neighboring neurons and returns them according to the here implemented architecture of the SOM.
 Perceptron SOM.findWinner(double[] input)
          This method determines the winning neuron by comparing the activations given the input vector of all neurons in the layer.
 

Uses of Perceptron in de.uos.cogsci.ai.btenbergen.bscthesis.tools
 

Fields in de.uos.cogsci.ai.btenbergen.bscthesis.tools declared as Perceptron
private  Perceptron[] Distance.neurons
          The Perceptron array that needs to be measured against the patterns.
 

Constructors in de.uos.cogsci.ai.btenbergen.bscthesis.tools with parameters of type Perceptron
Distance(java.lang.Object[] patterns, Perceptron[] neurons)
          Constructor.
Distance(java.lang.Object[] patterns, Perceptron[] neurons, int metric)
          Constructor.
 

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Uses of Class
de.uos.cogsci.ai.btenbergen.bscthesis.neuralnetworks.perceptron.PerceptronInputException

Packages that use PerceptronInputException
de.uos.cogsci.ai.btenbergen.bscthesis.neuralnetworks.mlp   
de.uos.cogsci.ai.btenbergen.bscthesis.neuralnetworks.mlp.som   
de.uos.cogsci.ai.btenbergen.bscthesis.neuralnetworks.perceptron   
 

Uses of PerceptronInputException in de.uos.cogsci.ai.btenbergen.bscthesis.neuralnetworks.mlp
 

Methods in de.uos.cogsci.ai.btenbergen.bscthesis.neuralnetworks.mlp that throw PerceptronInputException
 Perceptron[] MLPs.findNeighbors(double[] input)
          This method determines the winner neuron's neighboring neurons and returns them.
 Perceptron MLPs.findWinner(double[] input)
          This method determines the winning neuron by comparing the activations given the input vector of all neurons in the layer.
 void MLPs.runClassification()
          Runs the classification process runs times.
 void MLPs.updateNeighbor(double[] input)
          Updates the winner's neighboring neurons.
 void MLPs.updateWinner(double[] input)
          Updates the winner neuron.
 

Uses of PerceptronInputException in de.uos.cogsci.ai.btenbergen.bscthesis.neuralnetworks.mlp.som
 

Methods in de.uos.cogsci.ai.btenbergen.bscthesis.neuralnetworks.mlp.som that throw PerceptronInputException
 Perceptron[] SOM.findNeighbors(double[] input)
          This method determines the winner neuron's neighboring neurons and returns them according to the here implemented architecture of the SOM.
 Perceptron SOM.findWinner(double[] input)
          This method determines the winning neuron by comparing the activations given the input vector of all neurons in the layer.
 void SOM.runClassification()
          Runs the classification process runs times.
 void SOM.updateNeighbor(double[] input)
          Updates the winner neuron's neighboring neurons.
 void SOM.updateWinner(double[] input)
          Updates the winner neuron of the output layer.
 

Uses of PerceptronInputException in de.uos.cogsci.ai.btenbergen.bscthesis.neuralnetworks.perceptron
 

Methods in de.uos.cogsci.ai.btenbergen.bscthesis.neuralnetworks.perceptron that throw PerceptronInputException
 double Perceptron.activation(double[] inputs)
          This method computes the scalar product of the input and the weights.
 int Perceptron.fire(double[] inputs)
          Fires the perceptron.
 

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Classes 
Perceptron
Exceptions 
PerceptronInputException
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Package de.uos.cogsci.ai.btenbergen.bscthesis.neuralnetworks.perceptron

Class Summary
Perceptron This is the Perceptron class.
 

Exception Summary
PerceptronInputException The class PercepronInputException is thrown when the Input-Vector of a Perceptron is not of equal length as its Weight-Vector.
 

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Hierarchy For Package de.uos.cogsci.ai.btenbergen.bscthesis.neuralnetworks.perceptron

Package Hierarchies:
All Packages

Class Hierarchy

  • java.lang.Object
    • de.uos.cogsci.ai.btenbergen.bscthesis.neuralnetworks.perceptron.Perceptron
    • java.lang.Throwable (implements java.io.Serializable)
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Uses of Package
de.uos.cogsci.ai.btenbergen.bscthesis.neuralnetworks.perceptron

Packages that use de.uos.cogsci.ai.btenbergen.bscthesis.neuralnetworks.perceptron
de.uos.cogsci.ai.btenbergen.bscthesis.neuralnetworks.mlp   
de.uos.cogsci.ai.btenbergen.bscthesis.neuralnetworks.mlp.som   
de.uos.cogsci.ai.btenbergen.bscthesis.neuralnetworks.perceptron   
de.uos.cogsci.ai.btenbergen.bscthesis.tools   
 

Classes in de.uos.cogsci.ai.btenbergen.bscthesis.neuralnetworks.perceptron used by de.uos.cogsci.ai.btenbergen.bscthesis.neuralnetworks.mlp
Perceptron
          This is the Perceptron class.
PerceptronInputException
          The class PercepronInputException is thrown when the Input-Vector of a Perceptron is not of equal length as its Weight-Vector.
 

Classes in de.uos.cogsci.ai.btenbergen.bscthesis.neuralnetworks.perceptron used by de.uos.cogsci.ai.btenbergen.bscthesis.neuralnetworks.mlp.som
Perceptron
          This is the Perceptron class.
PerceptronInputException
          The class PercepronInputException is thrown when the Input-Vector of a Perceptron is not of equal length as its Weight-Vector.
 

Classes in de.uos.cogsci.ai.btenbergen.bscthesis.neuralnetworks.perceptron used by de.uos.cogsci.ai.btenbergen.bscthesis.neuralnetworks.perceptron
PerceptronInputException
          The class PercepronInputException is thrown when the Input-Vector of a Perceptron is not of equal length as its Weight-Vector.
 

Classes in de.uos.cogsci.ai.btenbergen.bscthesis.neuralnetworks.perceptron used by de.uos.cogsci.ai.btenbergen.bscthesis.tools
Perceptron
          This is the Perceptron class.
 

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PK 754javadoc/de/uos/cogsci/ai/btenbergen/bscthesis/tools/PK Gp,5FaX44@javadoc/de/uos/cogsci/ai/btenbergen/bscthesis/tools/Cluster.html Cluster
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de.uos.cogsci.ai.btenbergen.bscthesis.tools
Class Cluster

java.lang.Object
  extended by de.uos.cogsci.ai.btenbergen.bscthesis.tools.Cluster

public class Cluster
extends java.lang.Object

Field Summary
 java.util.Vector<java.lang.String> content
          All Patterns in the Cluster.
 int id
          The ID of the Cluster.
 
Constructor Summary
Cluster(int id, java.util.Vector<java.lang.String> content)
          Constructor.
 
Method Summary
 java.lang.String toString()
          This method returns the String representation of the cluster.
 java.lang.String toStringln()
          This method returns the String representation of this cluster in lines.
 
Methods inherited from class java.lang.Object
clone, equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait
 

Field Detail

id

public int id
The ID of the Cluster.

content

public java.util.Vector<java.lang.String> content
All Patterns in the Cluster.

Constructor Detail

Cluster

public Cluster(int id,
               java.util.Vector<java.lang.String> content)
Constructor. Invokes a Cluster Object Instance.

Parameters:
id - The ID of the Cluster.
content - The Patterns that belong to this cluster.
Method Detail

toString

public java.lang.String toString()
This method returns the String representation of the cluster.

Overrides:
toString in class java.lang.Object
Returns:
The String representation of this cluster in form of the cluster ID and the content.

toStringln

public java.lang.String toStringln()
This method returns the String representation of this cluster in lines. Only the content is printed, the ID is omitted.

Returns:
This cluster's content in lines.
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PK Gp,5%_aRRAjavadoc/de/uos/cogsci/ai/btenbergen/bscthesis/tools/Distance.html Distance
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de.uos.cogsci.ai.btenbergen.bscthesis.tools
Class Distance

java.lang.Object
  extended by de.uos.cogsci.ai.btenbergen.bscthesis.tools.Distance

public class Distance
extends java.lang.Object

Field Summary
static int EUCLIDEAN
          Constant for the Euclidean Distance.
static int MANHATTAN
          Constant for the Manhattan Distance.
private  int metric
          The metric to be used.
private  Perceptron[] neurons
          The Perceptron array that needs to be measured against the patterns.
static int NOMINAL
          Constant for the Nominal Distance.
private  java.lang.Object[] patterns
          The array containing the patterns.
 
Constructor Summary
Distance(java.lang.Object[] patterns, Perceptron[] neurons)
          Constructor.
Distance(java.lang.Object[] patterns, Perceptron[] neurons, int metric)
          Constructor.
 
Method Summary
 double euclideanDistance(double[] e1, double[] e2)
          An euclidean distance.
 double manhattanDistance(double[] e1, double[] e2)
          The Manhattan distance.
 int[] measureDistance()
          For each pattern, this method returns the closest neuron.
 double nominalDistance(double[] e1, double[] e2)
          An euclidean distance.
 
Methods inherited from class java.lang.Object
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait
 

Field Detail

EUCLIDEAN

public static final int EUCLIDEAN
Constant for the Euclidean Distance.

See Also:
Constant Field Values

MANHATTAN

public static final int MANHATTAN
Constant for the Manhattan Distance.

See Also:
Constant Field Values

NOMINAL

public static final int NOMINAL
Constant for the Nominal Distance.

See Also:
Constant Field Values

patterns

private java.lang.Object[] patterns
The array containing the patterns.

neurons

private Perceptron[] neurons
The Perceptron array that needs to be measured against the patterns.

metric

private int metric
The metric to be used.

Constructor Detail

Distance

public Distance(java.lang.Object[] patterns,
                Perceptron[] neurons,
                int metric)
Constructor. A new Distance Object is created upon invokation.

Parameters:
neurons - The Perceptron array of which the weight Vectors need to be measured.
patterns - The Patterns that need to be measured.
metric - The Metric to be used.

Distance

public Distance(java.lang.Object[] patterns,
                Perceptron[] neurons)
Constructor. A new Distance Object is created upon invokation. Euclidean Distance is used.

Parameters:
neurons - The Perceptron array of which the weight Vectors need to be measured.
patterns - The Patterns that need to be measured.
Method Detail

measureDistance

public int[] measureDistance()
For each pattern, this method returns the closest neuron.

Returns:
The array containing the positions of the neurons.tern.

euclideanDistance

public double euclideanDistance(double[] e1,
                                double[] e2)
An euclidean distance. Taken from the ClusteringPlugin v3.2 for Yale.

Parameters:
e1 - The first Vector
e2 - The second Vector
Returns:
The nominal Distance between both Vectors.

manhattanDistance

public double manhattanDistance(double[] e1,
                                double[] e2)
The Manhattan distance. Taken from the ClusteringPlugin v3.2 for Yale.

Parameters:
e1 - The first Vector
e2 - The second Vector
Returns:
The nominal Distance between both Vectors.

nominalDistance

public double nominalDistance(double[] e1,
                              double[] e2)
An euclidean distance. Taken from the ClusteringPlugin v3.2 for Yale.

Parameters:
e1 - The first Vector
e2 - The second Vector
Returns:
The nominal Distance between both Vectors.
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PK Gp,5 CCFjavadoc/de/uos/cogsci/ai/btenbergen/bscthesis/tools/OperatorTools.html OperatorTools
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de.uos.cogsci.ai.btenbergen.bscthesis.tools
Class OperatorTools

java.lang.Object
  extended by de.uos.cogsci.ai.btenbergen.bscthesis.tools.OperatorTools

public class OperatorTools
extends java.lang.Object

Constructor Summary
OperatorTools()
           
 
Method Summary
static java.lang.String convertFilename(java.lang.String filename)
          Replaces all characters which are not letters or digits by _ (underscore).
static Cluster[] createFileClusters(int[] positions, java.util.Vector<java.lang.String> songList)
          This method creates Cluster-Objects given an array containing the pattern's closest neurons and a Vector containing the Song-Names.
static java.lang.String displaySongList(java.util.Vector<java.lang.String> songList)
          This method simply shows the processed songlist.
static java.lang.String findQueryInClusters(java.lang.String hummedFile, Cluster[] clusters)
          This method checks if a String occures in the Vector of any cluster in a given Cluster-array.
static java.lang.String showFileClusters(Cluster[] clusters)
          This method creates Cluster Objects of the processed files and shows them.
 
Methods inherited from class java.lang.Object
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait
 

Constructor Detail

OperatorTools

public OperatorTools()
Method Detail

convertFilename

public static java.lang.String convertFilename(java.lang.String filename)
Replaces all characters which are not letters or digits by _ (underscore). Additionally all non-english characters like umlauts or ß are replaced. The file itself is not modified, since only the String representation of the file name is taken into conderation.
Modified from ValueSeriesPlugin v3.2 for Yale.

Parameters:
filename - The file name to be converted. Usually the user's input file, but this works with any kind of String.
Returns:
The converted file name. The original file is not changed, since this operator only retrieves the String representation of the user's input.

findQueryInClusters

public static java.lang.String findQueryInClusters(java.lang.String hummedFile,
                                                   Cluster[] clusters)
This method checks if a String occures in the Vector of any cluster in a given Cluster-array. If a occurrence has been found, the whole cluster is printed out.

Parameters:
hummedFile - Look for this String in the clusters.
clusters - The cluster array in which to look for the String.
Returns:
The String representation of the cluster(s) in which the String was found.

displaySongList

public static java.lang.String displaySongList(java.util.Vector<java.lang.String> songList)
This method simply shows the processed songlist.

Parameters:
songList - A Vector containing the processed songs.
Returns:
The String representation of the songlist in a ResultService-friendly form.

createFileClusters

public static Cluster[] createFileClusters(int[] positions,
                                           java.util.Vector<java.lang.String> songList)
                                    throws java.lang.ArrayIndexOutOfBoundsException
This method creates Cluster-Objects given an array containing the pattern's closest neurons and a Vector containing the Song-Names. The Cluster-Objects can hence be referenced more easily.

Parameters:
positions - An array containing the closest neurons to a given pattern.
songList - A Vector containing the Song-Names.
Returns:
A Cluster-Array containing the Clusters.
Throws:
java.lang.ArrayIndexOutOfBoundsException

showFileClusters

public static java.lang.String showFileClusters(Cluster[] clusters)
This method creates Cluster Objects of the processed files and shows them. Similar files occur in the same cluster.

Parameters:
clusters - The Clusters of similar songs.
Returns:
The clusters in a ResultService-friendly form.
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PK Gp,528=8=>javadoc/de/uos/cogsci/ai/btenbergen/bscthesis/tools/Tools.html Tools
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de.uos.cogsci.ai.btenbergen.bscthesis.tools
Class Tools

java.lang.Object
  extended by de.uos.cogsci.ai.btenbergen.bscthesis.tools.Tools

public class Tools
extends java.lang.Object

This is class providing tools for multiple purposes. Currently implemented:
- a generic quicksort algorithm sorting double arrays
- a generic quicksort algorithm sorting double arrays but maintining the original array
- a method providing the smallest and largest element of a double array
- a method providing the smallest, the largest and the median element of a double array
- a method returning the index of the smallest element in a given double array

Version:
20.07.2006
Author:
Bastian Tenbergen - btenberg@uos.de

Constructor Summary
Tools()
           
 
Method Summary
static double[] minMaxElement(double[] array)
          Returns the smallest and biggest element of a given double array in a new double array.
static double[] minMedMaxElement(double[] array)
          Returns the smallest and biggest and the medium element of a given double array in a new double array.
static int positionOfSmallest(double[] array)
          This method returns the position of the smallest element of a given array.
private static void qs(double[] array, int onset, int offset)
          This method does the actual sorting.
static double[] quicksort(double[] array, int onset, int offset)
          A standard recursive QuickSort Algorithm.
 
Methods inherited from class java.lang.Object
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait
 

Constructor Detail

Tools

public Tools()
Method Detail

quicksort

public static double[] quicksort(double[] array,
                                 int onset,
                                 int offset)
A standard recursive QuickSort Algorithm. This algorithm takes a double array and sorts it from the biggest to the smallest element. It takes the median element as the random pivot element. This method calls the private method qs(double[], int, int) that does the actual job. This method copies the given array so that the original array is maintained.

Parameters:
array - the array to be sorted
onset - the onset element from which shall be sorted (inclusive)
offset - the offset element until which shall be sorted (inclusive)

qs

private static void qs(double[] array,
                       int onset,
                       int offset)
This method does the actual sorting.

Parameters:
array - the array to be sorted
onset - the onset element from which shall be sorted (inclusive)
offset - the offset element until which shall be sorted (inclusive)

minMaxElement

public static double[] minMaxElement(double[] array)
Returns the smallest and biggest element of a given double array in a new double array.

Parameters:
array - the (unsorted) array containing one smallest and one biggest element
Returns:
a new array with the input array's smallest and biggest as first and second element.

minMedMaxElement

public static double[] minMedMaxElement(double[] array)
Returns the smallest and biggest and the medium element of a given double array in a new double array.

Parameters:
array - the (unsorted) array containing one smallest and one biggest element
Returns:
a new array with the input array's smallest, biggest and the medium element.

positionOfSmallest

public static int positionOfSmallest(double[] array)
This method returns the position of the smallest element of a given array. It does so in a SelectionSort-like way, but runs through array only once (since we don't need to sort the whole array.

Parameters:
array - the array containing one smallest element, whose position is of interest.
Returns:
the position of the smallest element in the array.
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Uses of Class
de.uos.cogsci.ai.btenbergen.bscthesis.tools.Cluster

Packages that use Cluster
de.uos.cogsci.ai.btenbergen.bscthesis.tools   
de.uos.cogsci.ai.btenbergen.bscthesis.yale   
 

Uses of Cluster in de.uos.cogsci.ai.btenbergen.bscthesis.tools
 

Methods in de.uos.cogsci.ai.btenbergen.bscthesis.tools that return Cluster
static Cluster[] OperatorTools.createFileClusters(int[] positions, java.util.Vector<java.lang.String> songList)
          This method creates Cluster-Objects given an array containing the pattern's closest neurons and a Vector containing the Song-Names.
 

Methods in de.uos.cogsci.ai.btenbergen.bscthesis.tools with parameters of type Cluster
static java.lang.String OperatorTools.findQueryInClusters(java.lang.String hummedFile, Cluster[] clusters)
          This method checks if a String occures in the Vector of any cluster in a given Cluster-array.
static java.lang.String OperatorTools.showFileClusters(Cluster[] clusters)
          This method creates Cluster Objects of the processed files and shows them.
 

Uses of Cluster in de.uos.cogsci.ai.btenbergen.bscthesis.yale
 

Fields in de.uos.cogsci.ai.btenbergen.bscthesis.yale declared as Cluster
private  Cluster[] SOMInput.clusters
          The song clusters
 

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Uses of Class
de.uos.cogsci.ai.btenbergen.bscthesis.tools.Distance

No usage of de.uos.cogsci.ai.btenbergen.bscthesis.tools.Distance

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Uses of Class
de.uos.cogsci.ai.btenbergen.bscthesis.tools.OperatorTools

No usage of de.uos.cogsci.ai.btenbergen.bscthesis.tools.OperatorTools

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Uses of Class
de.uos.cogsci.ai.btenbergen.bscthesis.tools.Tools

No usage of de.uos.cogsci.ai.btenbergen.bscthesis.tools.Tools

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Classes 
Cluster
Distance
OperatorTools
Tools
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Package de.uos.cogsci.ai.btenbergen.bscthesis.tools

Class Summary
Cluster  
Distance  
OperatorTools  
Tools This is class providing tools for multiple purposes.
 

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Hierarchy For Package de.uos.cogsci.ai.btenbergen.bscthesis.tools

Package Hierarchies:
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Class Hierarchy

  • java.lang.Object
    • de.uos.cogsci.ai.btenbergen.bscthesis.tools.Cluster
    • de.uos.cogsci.ai.btenbergen.bscthesis.tools.Distance
    • de.uos.cogsci.ai.btenbergen.bscthesis.tools.OperatorTools
    • de.uos.cogsci.ai.btenbergen.bscthesis.tools.Tools
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de.uos.cogsci.ai.btenbergen.bscthesis.tools

Packages that use de.uos.cogsci.ai.btenbergen.bscthesis.tools
de.uos.cogsci.ai.btenbergen.bscthesis.tools   
de.uos.cogsci.ai.btenbergen.bscthesis.yale   
 

Classes in de.uos.cogsci.ai.btenbergen.bscthesis.tools used by de.uos.cogsci.ai.btenbergen.bscthesis.tools
Cluster
           
 

Classes in de.uos.cogsci.ai.btenbergen.bscthesis.tools used by de.uos.cogsci.ai.btenbergen.bscthesis.yale
Cluster
           
 

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PK 753javadoc/de/uos/cogsci/ai/btenbergen/bscthesis/yale/PK Fp,5cgPgPFjavadoc/de/uos/cogsci/ai/btenbergen/bscthesis/yale/SOMFileHandler.html SOMFileHandler
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de.uos.cogsci.ai.btenbergen.bscthesis.yale
Class SOMFileHandler

java.lang.Object
  extended by de.uos.cogsci.ai.btenbergen.bscthesis.yale.SOMFileHandler

public class SOMFileHandler
extends java.lang.Object

Field Summary
private  java.io.File filename
          The file to be parsed.
private  java.lang.Object[] patterns
          Each element in this Object[] is another double[] with the extracted features.
 java.util.Vector<java.lang.String> songList
          Each row in the file contains the filename of the song, the features belong to.
private  boolean verbosing
          If true, the operator will send detailed information about what is going on to Yale's Log-Console.
 
Constructor Summary
SOMFileHandler(java.io.File filename)
          Constructor.
SOMFileHandler(java.io.File filename, boolean verbosing)
          Constructor.
 
Method Summary
private  void doLogging()
          This method just shows the retrieved features in the Object[] by sending it to Yale's Log-Console.
private  java.lang.Object[] normalizeFeatureVectors(java.lang.Object[] patterns)
          This method normalizes the features in each vector by deviding them by the root mean square deviation.
private  java.lang.Object[] parseFeatureVectors(java.util.Vector<java.lang.String> feature_vectors, boolean normalize)
          This method extracts double value features out of a given java.util.Vector.
 java.lang.Object[] parseFile(boolean normalize)
          This Method parses a given input file.
 
Methods inherited from class java.lang.Object
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait
 

Field Detail

patterns

private java.lang.Object[] patterns
Each element in this Object[] is another double[] with the extracted features.

songList

public java.util.Vector<java.lang.String> songList
Each row in the file contains the filename of the song, the features belong to. These filenames are stored in this vector for reference.

verbosing

private boolean verbosing
If true, the operator will send detailed information about what is going on to Yale's Log-Console.

filename

private java.io.File filename
The file to be parsed.

Constructor Detail

SOMFileHandler

public SOMFileHandler(java.io.File filename,
                      boolean verbosing)
Constructor. Sets the fields filename and verbosing accordingly.

Parameters:
filename - The file to be parsed.
verbosing - If true, detailed information about what is going on is sent to Yale's Log-Console.

SOMFileHandler

public SOMFileHandler(java.io.File filename)
Constructor. Sets the field filename accordingly. Verbosing is set to true.

Parameters:
filename - The file to be parsed.
Method Detail

parseFile

public java.lang.Object[] parseFile(boolean normalize)
                             throws java.io.FileNotFoundException,
                                    java.io.IOException
This Method parses a given input file. Each row in the file is a Feture Vector and is extraced as such and stored in a java.util.Vector. The Vector will hence contain Strings as elements whereas each String corresponds to a row in the file. The method parseFeatureVectors is hence called with this Vector to extract the actual features out of each String. That method's output is returned by this method.

Parameters:
normalize - If true, the input patterns will be normalized by dividing every element x(i,j) by the root mean square deviation of the corresponding column, o(j).
Returns:
An Object[] containing java.util.Vector as elements. Each Vector has doubles as elements. The doubles correspond to an actual feature while the java.util.Vector corresponds to each feature Vector, i.e. the rows in the File.
Throws:
java.io.FileNotFoundException - iff the file cannot be found.
java.io.IOException - iff an error occured during file access.

parseFeatureVectors

private java.lang.Object[] parseFeatureVectors(java.util.Vector<java.lang.String> feature_vectors,
                                               boolean normalize)
This method extracts double value features out of a given java.util.Vector. Each String in the Vector is parsed and NaN values are ignored. Every numerical value is hence considered a double and stored in a java.util.Vector. Hence every Vector corresponds to a row in the input file parsed feature values. All rows are stored in an Object[].

Parameters:
feature_vectors - The Vector containing String representations of blank space separated double values.
normalize - If true, the input patterns will be normalized by dividing every element x(i,j) by the root mean square deviation of the corresponding column, o(j).
Returns:
An Object[] containing java.util.Vector as elements. Every element is a row in the input file.

normalizeFeatureVectors

private java.lang.Object[] normalizeFeatureVectors(java.lang.Object[] patterns)
This method normalizes the features in each vector by deviding them by the root mean square deviation. This is necessary, since there might be a very large scope of the elements in the feature vectors. This causes features with a very large absolute value to have a larger impact on the distance measuring than features with a very small absolute value. 'Small features' hence might not play a significant role anymore. To avoid that behaviour, the root mean square deviation for every column is calculated and each element x(i,j) is devided by the rmsd o(j). The root mean square deviation is calculated as follows:
o(j) = sqrt( (1 / ( n - 1) ) * (sum_over_vectors( x(i,j) - mean(j)) )^2 )
with n = the number of vectors
x(i,j) = the j-th value of the i-th vector
mean(j) = the mean value of all j-th elements of all vectors (i.e. the j-th column)

The features are hence updated as follows:
x(i,j)_new = ( x(i,j)_old / o(j) )

Parameters:
patterns - The patterns that need to be normalized.
Returns:
The normalized patterns.

doLogging

private void doLogging()
This method just shows the retrieved features in the Object[] by sending it to Yale's Log-Console.

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PK Gp,5t uPzz@javadoc/de/uos/cogsci/ai/btenbergen/bscthesis/yale/SOMInput.html SOMInput
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de.uos.cogsci.ai.btenbergen.bscthesis.yale
Class SOMInput

java.lang.Object
  extended by edu.udo.cs.yale.operator.Operator
      extended by de.uos.cogsci.ai.btenbergen.bscthesis.yale.SOMInput
All Implemented Interfaces:
edu.udo.cs.yale.operator.IOObject
public class SOMInput
extends edu.udo.cs.yale.operator.Operator
implements edu.udo.cs.yale.operator.IOObject

Field Summary
private  boolean closed_topology
          The Topology of this Map.
private  Cluster[] clusters
          The song clusters
private  java.io.File filename
          The file that is to be parsed.
private  java.lang.String hummedFile
          The user's query input
private  double learningrate_neighbor
          Sets the learning rate of the neighboring neurons of the Self-Organizing Map.
private  double learningrate_winner
          Sets the learning rate of the winner neuron of the Self-Organizing Map.
private  int maxValue
          Sets the maximum value, the random initialization of weights and bias of the neurons must not exceed.
private  int metric
          The metric used to measure the distance between each pattern and each neuron
private  java.lang.String[] metrics
          The different metrics the user can choose from.
private  boolean normalize
          If true, the input patterns from the Feature Vector File will be normalized.
private  int numberCols
          Sets the number of columns of the SOM.
private  int numberRows
          Sets the number of rows of the SOM.
private static java.lang.Class[] OUTPUT_CLASS
          a class array containing the Output classes.
private  java.lang.Object[] patterns
          The Patterns that are retrieved from the file
private  edu.udo.cs.yale.operator.SimpleResultObject result_clusters
          Enumeration of all file clusters
private  edu.udo.cs.yale.operator.SimpleResultObject result_files
          Enumeration of processed file names
private  edu.udo.cs.yale.operator.SimpleResultObject result_matches
          Enumeration of all files close to the hummed input
private  edu.udo.cs.yale.operator.SimpleResultObject result_neurons
          The Results that are to be printed on Yale's GUI.
private  int runs
          Sets the number of classification runs.
private  boolean verbosing
          if true, the operator will send detailed information about what is happening to Yale's Log-Console.
 
Constructor Summary
SOMInput(edu.udo.cs.yale.operator.OperatorDescription description)
          Constructor.
 
Method Summary
 edu.udo.cs.yale.operator.IOObject[] apply()
          This method is called by Yale whenever the operator is applied.
 edu.udo.cs.yale.operator.IOObject copy()
          Returns a copy of this operator.
 java.lang.Class[] getInputClasses()
          Returns all Input classes.
 java.lang.Class[] getOutputClasses()
          Returns all Output classes.
 java.util.List<edu.udo.cs.yale.operator.parameter.ParameterType> getParameterTypes()
          Gets the ParameterTypes as a lists and adds the parameters for this operator.
 void initApply()
          This method is called by Yale when the operator is created.
 
Methods inherited from class edu.udo.cs.yale.operator.Operator
addError, addValue, addWarning, apply, checkDeprecations, checkIO, checkProperties, clearErrorList, cloneOperator, createExperimentTree, createExperimentTree, createFromXML, createMarkedExperimentTree, delete, experimentFinished, experimentStarts, getAddOnlyAdditionalOutput, getApplyCount, getDeliveredOutputClasses, getDeprecationInfo, getDesiredInputClasses, getErrorList, getExperiment, getInnerOperatorsXML, getInput, getInput, getInput, getInputDescription, getIOContainerForInApplyLoopBreakpoint, getName, getNumberOfSteps, getOperatorClassName, getOperatorDescription, getParameter, getParameterAsBoolean, getParameterAsColor, getParameterAsDouble, getParameterAsFile, getParameterAsInt, getParameterAsString, getParameterList, getParameters, getParameterType, getParent, getStartTime, getStatus, getUserDescription, getValue, getValues, getXML, hasBreakpoint, hasBreakpoint, hasInput, inApplyLoop, indent, isEnabled, isParameterSet, logMessage, performAdditionalChecks, register, remove, rename, resume, setBreakpoint, setEnabled, setExperiment, setInput, setListParameter, setOperatorParameters, setParameter, setParent, setUserDescription, toString, writeXML
 
Methods inherited from class java.lang.Object
clone, equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait
 

Field Detail

OUTPUT_CLASS

private static final java.lang.Class[] OUTPUT_CLASS
a class array containing the Output classes. Syntactic sugar.

result_neurons

private edu.udo.cs.yale.operator.SimpleResultObject result_neurons
The Results that are to be printed on Yale's GUI. Enumeration of output neurons

result_files

private edu.udo.cs.yale.operator.SimpleResultObject result_files
Enumeration of processed file names

result_clusters

private edu.udo.cs.yale.operator.SimpleResultObject result_clusters
Enumeration of all file clusters

result_matches

private edu.udo.cs.yale.operator.SimpleResultObject result_matches
Enumeration of all files close to the hummed input

filename

private java.io.File filename
The file that is to be parsed.

verbosing

private boolean verbosing
if true, the operator will send detailed information about what is happening to Yale's Log-Console.

numberCols

private int numberCols
Sets the number of columns of the SOM.

numberRows

private int numberRows
Sets the number of rows of the SOM.

runs

private int runs
Sets the number of classification runs. The higher the number, the more acurately the neurons are trained.

maxValue

private int maxValue
Sets the maximum value, the random initialization of weights and bias of the neurons must not exceed.

closed_topology

private boolean closed_topology
The Topology of this Map. If true, the last neuron of each row or col is connected to the first one.

learningrate_winner

private double learningrate_winner
Sets the learning rate of the winner neuron of the Self-Organizing Map.

learningrate_neighbor

private double learningrate_neighbor
Sets the learning rate of the neighboring neurons of the Self-Organizing Map.

patterns

private java.lang.Object[] patterns
The Patterns that are retrieved from the file

hummedFile

private java.lang.String hummedFile
The user's query input

clusters

private Cluster[] clusters
The song clusters

metric

private int metric
The metric used to measure the distance between each pattern and each neuron

metrics

private java.lang.String[] metrics
The different metrics the user can choose from.

normalize

private boolean normalize
If true, the input patterns from the Feature Vector File will be normalized.

Constructor Detail

SOMInput

public SOMInput(edu.udo.cs.yale.operator.OperatorDescription description)
Constructor. Just calls the superconstructor with the given OperatorDescription.

Parameters:
description -
Method Detail

getParameterTypes

public java.util.List<edu.udo.cs.yale.operator.parameter.ParameterType> getParameterTypes()
Gets the ParameterTypes as a lists and adds the parameters for this operator.

Overrides:
getParameterTypes in class edu.udo.cs.yale.operator.Operator
Returns:
The appended list of Parameters

initApply

public void initApply()
This method is called by Yale when the operator is created. Nothing is done here.

apply

public edu.udo.cs.yale.operator.IOObject[] apply()
                                          throws edu.udo.cs.yale.operator.OperatorException
This method is called by Yale whenever the operator is applied. The file to be parsed is obtained and the rest of the parameters are set. Then, the SOMFileHandler is invoked and the file is parsed.

Specified by:
apply in class edu.udo.cs.yale.operator.Operator
Returns:
Just an empty IOObject array containing SimpleResultObjects with the results of the computations.
Throws:
edu.udo.cs.yale.operator.OperatorException - - This is thrown when a Parameter is undefined, the file to be parsed is not found or an error during file reading occured.

copy

public edu.udo.cs.yale.operator.IOObject copy()
Returns a copy of this operator.

Specified by:
copy in interface edu.udo.cs.yale.operator.IOObject
Returns:
A copy of this operator.

getInputClasses

public java.lang.Class[] getInputClasses()
Returns all Input classes.

Specified by:
getInputClasses in class edu.udo.cs.yale.operator.Operator
Returns:
All Input classes.

getOutputClasses

public java.lang.Class[] getOutputClasses()
Returns all Output classes.

Specified by:
getOutputClasses in class edu.udo.cs.yale.operator.Operator
Returns:
All Output classes.
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de.uos.cogsci.ai.btenbergen.bscthesis.yale.SOMFileHandler

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Uses of Class
de.uos.cogsci.ai.btenbergen.bscthesis.yale.SOMInput

No usage of de.uos.cogsci.ai.btenbergen.bscthesis.yale.SOMInput

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Classes 
SOMFileHandler
SOMInput
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Package de.uos.cogsci.ai.btenbergen.bscthesis.yale

Class Summary
SOMFileHandler  
SOMInput  
 

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Hierarchy For Package de.uos.cogsci.ai.btenbergen.bscthesis.yale

Package Hierarchies:
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Class Hierarchy

  • java.lang.Object
    • edu.udo.cs.yale.operator.Operator
      • de.uos.cogsci.ai.btenbergen.bscthesis.yale.SOMInput (implements edu.udo.cs.yale.operator.IOObject)
    • de.uos.cogsci.ai.btenbergen.bscthesis.yale.SOMFileHandler
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de.uos.cogsci.ai.btenbergen.bscthesis.yale

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PK 75javadoc/resources/PK S5M99javadoc/resources/inherit.gifGIF89a, DrjԐ;߀Q@N;PK S/5sample_experiments/PK 5+[GG LICENSE.txt GNU GENERAL PUBLIC LICENSE Version 2, June 1991 Copyright (C) 1989, 1991 Free Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA Everyone is permitted to copy and distribute verbatim copies of this license document, but changing it is not allowed. Preamble The licenses for most software are designed to take away your freedom to share and change it. By contrast, the GNU General Public License is intended to guarantee your freedom to share and change free software--to make sure the software is free for all its users. This General Public License applies to most of the Free Software Foundation's software and to any other program whose authors commit to using it. (Some other Free Software Foundation software is covered by the GNU Lesser General Public License instead.) You can apply it to your programs, too. When we speak of free software, we are referring to freedom, not price. Our General Public Licenses are designed to make sure that you have the freedom to distribute copies of free software (and charge for this service if you wish), that you receive source code or can get it if you want it, that you can change the software or use pieces of it in new free programs; and that you know you can do these things. To protect your rights, we need to make restrictions that forbid anyone to deny you these rights or to ask you to surrender the rights. These restrictions translate to certain responsibilities for you if you distribute copies of the software, or if you modify it. For example, if you distribute copies of such a program, whether gratis or for a fee, you must give the recipients all the rights that you have. You must make sure that they, too, receive or can get the source code. And you must show them these terms so they know their rights. We protect your rights with two steps: (1) copyright the software, and (2) offer you this license which gives you legal permission to copy, distribute and/or modify the software. Also, for each author's protection and ours, we want to make certain that everyone understands that there is no warranty for this free software. If the software is modified by someone else and passed on, we want its recipients to know that what they have is not the original, so that any problems introduced by others will not reflect on the original authors' reputations. Finally, any free program is threatened constantly by software patents. We wish to avoid the danger that redistributors of a free program will individually obtain patent licenses, in effect making the program proprietary. To prevent this, we have made it clear that any patent must be licensed for everyone's free use or not licensed at all. The precise terms and conditions for copying, distribution and modification follow. GNU GENERAL PUBLIC LICENSE TERMS AND CONDITIONS FOR COPYING, DISTRIBUTION AND MODIFICATION 0. This License applies to any program or other work which contains a notice placed by the copyright holder saying it may be distributed under the terms of this General Public License. The "Program", below, refers to any such program or work, and a "work based on the Program" means either the Program or any derivative work under copyright law: that is to say, a work containing the Program or a portion of it, either verbatim or with modifications and/or translated into another language. (Hereinafter, translation is included without limitation in the term "modification".) Each licensee is addressed as "you". Activities other than copying, distribution and modification are not covered by this License; they are outside its scope. The act of running the Program is not restricted, and the output from the Program is covered only if its contents constitute a work based on the Program (independent of having been made by running the Program). Whether that is true depends on what the Program does. 1. You may copy and distribute verbatim copies of the Program's source code as you receive it, in any medium, provided that you conspicuously and appropriately publish on each copy an appropriate copyright notice and disclaimer of warranty; keep intact all the notices that refer to this License and to the absence of any warranty; and give any other recipients of the Program a copy of this License along with the Program. You may charge a fee for the physical act of transferring a copy, and you may at your option offer warranty protection in exchange for a fee. 2. You may modify your copy or copies of the Program or any portion of it, thus forming a work based on the Program, and copy and distribute such modifications or work under the terms of Section 1 above, provided that you also meet all of these conditions: a) You must cause the modified files to carry prominent notices stating that you changed the files and the date of any change. b) You must cause any work that you distribute or publish, that in whole or in part contains or is derived from the Program or any part thereof, to be licensed as a whole at no charge to all third parties under the terms of this License. c) If the modified program normally reads commands interactively when run, you must cause it, when started running for such interactive use in the most ordinary way, to print or display an announcement including an appropriate copyright notice and a notice that there is no warranty (or else, saying that you provide a warranty) and that users may redistribute the program under these conditions, and telling the user how to view a copy of this License. (Exception: if the Program itself is interactive but does not normally print such an announcement, your work based on the Program is not required to print an announcement.) These requirements apply to the modified work as a whole. If identifiable sections of that work are not derived from the Program, and can be reasonably considered independent and separate works in themselves, then this License, and its terms, do not apply to those sections when you distribute them as separate works. But when you distribute the same sections as part of a whole which is a work based on the Program, the distribution of the whole must be on the terms of this License, whose permissions for other licensees extend to the entire whole, and thus to each and every part regardless of who wrote it. Thus, it is not the intent of this section to claim rights or contest your rights to work written entirely by you; rather, the intent is to exercise the right to control the distribution of derivative or collective works based on the Program. In addition, mere aggregation of another work not based on the Program with the Program (or with a work based on the Program) on a volume of a storage or distribution medium does not bring the other work under the scope of this License. 3. You may copy and distribute the Program (or a work based on it, under Section 2) in object code or executable form under the terms of Sections 1 and 2 above provided that you also do one of the following: a) Accompany it with the complete corresponding machine-readable source code, which must be distributed under the terms of Sections 1 and 2 above on a medium customarily used for software interchange; or, b) Accompany it with a written offer, valid for at least three years, to give any third party, for a charge no more than your cost of physically performing source distribution, a complete machine-readable copy of the corresponding source code, to be distributed under the terms of Sections 1 and 2 above on a medium customarily used for software interchange; or, c) Accompany it with the information you received as to the offer to distribute corresponding source code. (This alternative is allowed only for noncommercial distribution and only if you received the program in object code or executable form with such an offer, in accord with Subsection b above.) The source code for a work means the preferred form of the work for making modifications to it. For an executable work, complete source code means all the source code for all modules it contains, plus any associated interface definition files, plus the scripts used to control compilation and installation of the executable. However, as a special exception, the source code distributed need not include anything that is normally distributed (in either source or binary form) with the major components (compiler, kernel, and so on) of the operating system on which the executable runs, unless that component itself accompanies the executable. If distribution of executable or object code is made by offering access to copy from a designated place, then offering equivalent access to copy the source code from the same place counts as distribution of the source code, even though third parties are not compelled to copy the source along with the object code. 4. You may not copy, modify, sublicense, or distribute the Program except as expressly provided under this License. Any attempt otherwise to copy, modify, sublicense or distribute the Program is void, and will automatically terminate your rights under this License. However, parties who have received copies, or rights, from you under this License will not have their licenses terminated so long as such parties remain in full compliance. 5. You are not required to accept this License, since you have not signed it. However, nothing else grants you permission to modify or distribute the Program or its derivative works. These actions are prohibited by law if you do not accept this License. Therefore, by modifying or distributing the Program (or any work based on the Program), you indicate your acceptance of this License to do so, and all its terms and conditions for copying, distributing or modifying the Program or works based on it. 6. Each time you redistribute the Program (or any work based on the Program), the recipient automatically receives a license from the original licensor to copy, distribute or modify the Program subject to these terms and conditions. You may not impose any further restrictions on the recipients' exercise of the rights granted herein. You are not responsible for enforcing compliance by third parties to this License. 7. If, as a consequence of a court judgment or allegation of patent infringement or for any other reason (not limited to patent issues), conditions are imposed on you (whether by court order, agreement or otherwise) that contradict the conditions of this License, they do not excuse you from the conditions of this License. If you cannot distribute so as to satisfy simultaneously your obligations under this License and any other pertinent obligations, then as a consequence you may not distribute the Program at all. For example, if a patent license would not permit royalty-free redistribution of the Program by all those who receive copies directly or indirectly through you, then the only way you could satisfy both it and this License would be to refrain entirely from distribution of the Program. If any portion of this section is held invalid or unenforceable under any particular circumstance, the balance of the section is intended to apply and the section as a whole is intended to apply in other circumstances. It is not the purpose of this section to induce you to infringe any patents or other property right claims or to contest validity of any such claims; this section has the sole purpose of protecting the integrity of the free software distribution system, which is implemented by public license practices. Many people have made generous contributions to the wide range of software distributed through that system in reliance on consistent application of that system; it is up to the author/donor to decide if he or she is willing to distribute software through any other system and a licensee cannot impose that choice. This section is intended to make thoroughly clear what is believed to be a consequence of the rest of this License. 8. If the distribution and/or use of the Program is restricted in certain countries either by patents or by copyrighted interfaces, the original copyright holder who places the Program under this License may add an explicit geographical distribution limitation excluding those countries, so that distribution is permitted only in or among countries not thus excluded. In such case, this License incorporates the limitation as if written in the body of this License. 9. The Free Software Foundation may publish revised and/or new versions of the General Public License from time to time. Such new versions will be similar in spirit to the present version, but may differ in detail to address new problems or concerns. Each version is given a distinguishing version number. If the Program specifies a version number of this License which applies to it and "any later version", you have the option of following the terms and conditions either of that version or of any later version published by the Free Software Foundation. If the Program does not specify a version number of this License, you may choose any version ever published by the Free Software Foundation. 10. If you wish to incorporate parts of the Program into other free programs whose distribution conditions are different, write to the author to ask for permission. For software which is copyrighted by the Free Software Foundation, write to the Free Software Foundation; we sometimes make exceptions for this. Our decision will be guided by the two goals of preserving the free status of all derivatives of our free software and of promoting the sharing and reuse of software generally. NO WARRANTY 11. BECAUSE THE PROGRAM IS LICENSED FREE OF CHARGE, THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY APPLICABLE LAW. EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE. THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM IS WITH YOU. SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF ALL NECESSARY SERVICING, REPAIR OR CORRECTION. 12. IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MAY MODIFY AND/OR REDISTRIBUTE THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS), EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF SUCH DAMAGES. END OF TERMS AND CONDITIONS How to Apply These Terms to Your New Programs If you develop a new program, and you want it to be of the greatest possible use to the public, the best way to achieve this is to make it free software which everyone can redistribute and change under these terms. To do so, attach the following notices to the program. It is safest to attach them to the start of each source file to most effectively convey the exclusion of warranty; and each file should have at least the "copyright" line and a pointer to where the full notice is found. Copyright (C) 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 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, write to the Free Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA. Also add information on how to contact you by electronic and paper mail. If the program is interactive, make it output a short notice like this when it starts in an interactive mode: Gnomovision version 69, Copyright (C) year name of author Gnomovision comes with ABSOLUTELY NO WARRANTY; for details type `show w'. This is free software, and you are welcome to redistribute it under certain conditions; type `show c' for details. The hypothetical commands `show w' and `show c' should show the appropriate parts of the General Public License. Of course, the commands you use may be called something other than `show w' and `show c'; they could even be mouse-clicks or menu items--whatever suits your program. You should also get your employer (if you work as a programmer) or your school, if any, to sign a "copyright disclaimer" for the program, if necessary. Here is a sample; alter the names: Yoyodyne, Inc., hereby disclaims all copyright interest in the program `Gnomovision' (which makes passes at compilers) written by James Hacker. , 1 April 1989 Ty Coon, President of Vice This General Public License does not permit incorporating your program into proprietary programs. If your program is a subroutine library, you may consider it more useful to permit linking proprietary applications with the library. If this is what you want to do, use the GNU Lesser General Public License instead of this License. PK L!5 META-INF/PK 5XznnMETA-INF/ABOUT.NFOThis plugin provides an operator for the classification of large feature vectors with an Self-Organizing Map. 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