nl.cwts.networkanalysis

Class GradientDescentVOSLayoutAlgorithm

java.lang.Object

nl.cwts.networkanalysis.VOSLayoutAlgorithm

nl.cwts.networkanalysis.GradientDescentVOSLayoutAlgorithm

All Implemented Interfaces:

java.lang.Cloneable, LayoutAlgorithm, QualityLayoutAlgorithm

public class GradientDescentVOSLayoutAlgorithm
extends VOSLayoutAlgorithm

Gradient descent VOS layout algorithm.

Field Summary

Fields

Modifier and Type	Field and Description
static double	DEFAULT_INITIAL_STEP_SIZE Default initial step size.
static int	DEFAULT_MAX_N_ITERATIONS Default maximum number of iterations.
static double	DEFAULT_MIN_STEP_SIZE Default minimum step size.
static int	DEFAULT_REQUIRED_N_QUALITY_VALUE_IMPROVEMENTS Default required number of quality value improvements.
static double	DEFAULT_STEP_SIZE_REDUCTION Default step size reduction.
protected double	initialStepSize Initial step size.
protected int	maxNIterations Maximum number of iterations.
protected double	minStepSize Minimum step size.
protected java.util.Random	random Random number generator.
protected int	requiredNQualityValueImprovements Required number of quality value improvements.
protected double	stepSizeReduction Step size reduction.

Fields inherited from class nl.cwts.networkanalysis.VOSLayoutAlgorithm

attraction, DEFAULT_ATTRACTION, DEFAULT_EDGE_WEIGHT_INCREMENT, DEFAULT_REPULSION, edgeWeightIncrement, repulsion

Constructor Summary

Constructors

Constructor and Description
GradientDescentVOSLayoutAlgorithm() Constructs a gradient descent VOS layout algorithm.
GradientDescentVOSLayoutAlgorithm(int attraction, int repulsion, double edgeWeightIncrement, int maxNIterations, double initialStepSize, double minStepSize, double stepSizeReduction, int requiredNQualityValueImprovements, java.util.Random random) Constructs a gradient descent VOS layout algorithm for a specified attraction parameter, repulsion parameter, edge weight increment parameter, maximum number of iterations, initial step size, minimum step size, step size reduction, and required number of quality value improvements.
GradientDescentVOSLayoutAlgorithm(int attraction, int repulsion, double edgeWeightIncrement, java.util.Random random) Constructs a gradient descent VOS layout algorithm for a specified attraction parameter, repulsion parameter, and edge weight increment parameter.
GradientDescentVOSLayoutAlgorithm(java.util.Random random) Constructs a gradient descent VOS layout algorithm.

Method Summary

Modifier and Type	Method and Description
GradientDescentVOSLayoutAlgorithm	clone() Clones the algorithm.
Layout	findLayout(Network network) Finds a layout using the gradient descent VOS layout algorithm.
double	getInitialStepSize() Returns the initial step size.
int	getMaxNIterations() Returns the maximum number of iterations.
double	getMinStepSize() Returns the minimum step size.
int	getRequiredNQualityValueImprovements() Returns the required number of quality value improvements.
double	getStepSizeReduction() Returns the step size reduction.
void	improveLayout(Network network, Layout layout) Improves a layout using the gradient descent VOS layout algorithm.
void	setInitialStepSize(double initialStepSize) Sets the initial step size.
void	setMaxNIterations(int maxNIterations) Sets the maximum number of iterations.
void	setMinStepSize(double minStepSize) Sets the minimum step size.
void	setRequiredNQualityValueImprovements(int requiredNQualityValueImprovements) Sets the required number of quality value improvements.
void	setStepSizeReduction(double stepSizeReduction) Sets the step size reduction.

Methods inherited from class nl.cwts.networkanalysis.VOSLayoutAlgorithm

calcQuality, getAttraction, getEdgeWeightIncrement, getRepulsion, setAttraction, setEdgeWeightIncrement, setRepulsion

Methods inherited from class java.lang.Object

equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Field Detail

DEFAULT_MAX_N_ITERATIONS

public static final int DEFAULT_MAX_N_ITERATIONS

Default maximum number of iterations.

See Also:

Constant Field Values

DEFAULT_INITIAL_STEP_SIZE

public static final double DEFAULT_INITIAL_STEP_SIZE

Default initial step size.

See Also:

Constant Field Values

DEFAULT_MIN_STEP_SIZE

public static final double DEFAULT_MIN_STEP_SIZE

Default minimum step size.

See Also:

Constant Field Values

DEFAULT_STEP_SIZE_REDUCTION

public static final double DEFAULT_STEP_SIZE_REDUCTION

Default step size reduction.

See Also:

Constant Field Values

DEFAULT_REQUIRED_N_QUALITY_VALUE_IMPROVEMENTS

public static final int DEFAULT_REQUIRED_N_QUALITY_VALUE_IMPROVEMENTS

Default required number of quality value improvements.

See Also:

Constant Field Values

maxNIterations

protected int maxNIterations

Maximum number of iterations.

initialStepSize

protected double initialStepSize

Initial step size.

minStepSize

protected double minStepSize

Minimum step size.

stepSizeReduction

protected double stepSizeReduction

Step size reduction.

requiredNQualityValueImprovements

protected int requiredNQualityValueImprovements

Required number of quality value improvements.

random

protected java.util.Random random

Random number generator.

Constructor Detail

GradientDescentVOSLayoutAlgorithm

public GradientDescentVOSLayoutAlgorithm()

Constructs a gradient descent VOS layout algorithm.

GradientDescentVOSLayoutAlgorithm

public GradientDescentVOSLayoutAlgorithm(java.util.Random random)

Constructs a gradient descent VOS layout algorithm.

Parameters:

random - Random number generator

GradientDescentVOSLayoutAlgorithm

public GradientDescentVOSLayoutAlgorithm(int attraction,
                                         int repulsion,
                                         double edgeWeightIncrement,
                                         java.util.Random random)

Constructs a gradient descent VOS layout algorithm for a specified attraction parameter, repulsion parameter, and edge weight increment parameter.

Parameters:

attraction - Attraction parameter

repulsion - Repulsion parameter

edgeWeightIncrement - Edge weight increment parameter

random - Random number generator

GradientDescentVOSLayoutAlgorithm

public GradientDescentVOSLayoutAlgorithm(int attraction,
                                         int repulsion,
                                         double edgeWeightIncrement,
                                         int maxNIterations,
                                         double initialStepSize,
                                         double minStepSize,
                                         double stepSizeReduction,
                                         int requiredNQualityValueImprovements,
                                         java.util.Random random)

Constructs a gradient descent VOS layout algorithm for a specified attraction parameter, repulsion parameter, edge weight increment parameter, maximum number of iterations, initial step size, minimum step size, step size reduction, and required number of quality value improvements.

Parameters:

attraction - Attraction parameter

repulsion - Repulsion parameter

edgeWeightIncrement - Edge weight increment parameter

maxNIterations - Maximum number of iterations

initialStepSize - Initial step size

minStepSize - Minimum step size

stepSizeReduction - Step size reduction

requiredNQualityValueImprovements - Required number of quality value improvements

random - Random number generator

Method Detail

clone

public GradientDescentVOSLayoutAlgorithm clone()

Clones the algorithm.

Overrides:

clone in class VOSLayoutAlgorithm

Returns:

Cloned algorithm

getMaxNIterations

public int getMaxNIterations()

Returns the maximum number of iterations.

Returns:

Maximum number of iterations

getInitialStepSize

public double getInitialStepSize()

Returns the initial step size.

Returns:

Initial step size

getMinStepSize

public double getMinStepSize()

Returns the minimum step size.

Returns:

Minimum step size

getStepSizeReduction

public double getStepSizeReduction()

Returns the step size reduction.

Returns:

Step size reduction

getRequiredNQualityValueImprovements

public int getRequiredNQualityValueImprovements()

Returns the required number of quality value improvements.

Returns:

Required number of quality value improvements

setMaxNIterations

public void setMaxNIterations(int maxNIterations)

Sets the maximum number of iterations.

Parameters:

maxNIterations - Maximum number of iterations

setInitialStepSize

public void setInitialStepSize(double initialStepSize)

Sets the initial step size.

Parameters:

initialStepSize - Initial step size

setMinStepSize

public void setMinStepSize(double minStepSize)

Sets the minimum step size.

Parameters:

minStepSize - Minimum step size

setStepSizeReduction

public void setStepSizeReduction(double stepSizeReduction)

Sets the step size reduction.

Parameters:

stepSizeReduction - Step size reduction

setRequiredNQualityValueImprovements

public void setRequiredNQualityValueImprovements(int requiredNQualityValueImprovements)

Sets the required number of quality value improvements.

Parameters:

requiredNQualityValueImprovements - Required number of quality value improvements

findLayout

public Layout findLayout(Network network)

Finds a layout using the gradient descent VOS layout algorithm.

Parameters:

network - Network

Returns:

Layout

improveLayout

public void improveLayout(Network network,
                          Layout layout)

Improves a layout using the gradient descent VOS layout algorithm.

Parameters:

network - Network

layout - Layout