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Cluster
Distance
Layer
Layers
MLPs
OperatorTools
Perceptron
PerceptronInputException
SOM
SOMFileHandler
SOMInput
Tools
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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 class, interface, nested class and nested interface has its own separate page. Each of these pages has three sections consisting of a class/interface description, summary tables, and detailed member descriptions:

Each summary entry contains the first sentence from the detailed description for that item. The summary entries are alphabetical, while the detailed descriptions are in the order they appear in the source code. This preserves the logical groupings established by the programmer.

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Each annotation type has its own separate page with the following sections:

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Each enum has its own separate page with the following sections:

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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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There is a Class Hierarchy page for all packages, plus a hierarchy for each package. Each hierarchy page contains a list of classes and a list of interfaces. The classes are organized by inheritance structure starting with java.lang.Object. The interfaces do not inherit from java.lang.Object.

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The Deprecated API page lists all of the API that have been deprecated. A deprecated API is not recommended for use, generally due to improvements, and a replacement API is usually given. Deprecated APIs may be removed in future implementations.

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Constant Field Values

The Constant Field Values page lists the static final fields and their values.

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

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

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

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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

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

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

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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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PK S5bt7t7Xjavadoc/de/uos/cogsci/ai/btenbergen/bscthesis/neuralnetworks/mlp/som/SOMFileHandler.html SOMFileHandler
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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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de.uos.cogsci.ai.btenbergen.bscthesis.neuralnetworks.mlp.som.Layer

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

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SOM
PK Gp,5bYjavadoc/de/uos/cogsci/ai/btenbergen/bscthesis/neuralnetworks/mlp/som/package-summary.html de.uos.cogsci.ai.btenbergen.bscthesis.neuralnetworks.mlp.som
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Package de.uos.cogsci.ai.btenbergen.bscthesis.neuralnetworks.mlp.som

Class Summary
SOM  
 

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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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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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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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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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de.uos.cogsci.ai.btenbergen.bscthesis.tools.Distance

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

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de.uos.cogsci.ai.btenbergen.bscthesis.tools.OperatorTools

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

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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
PK Gp,5Hjavadoc/de/uos/cogsci/ai/btenbergen/bscthesis/tools/package-summary.html de.uos.cogsci.ai.btenbergen.bscthesis.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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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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Package de.uos.cogsci.ai.btenbergen.bscthesis.yale

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SOMFileHandler  
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