jhplot
Class HBsom

java.lang.Object
  jhplot.HBsom

public class HBsom
extends Object

The Bayesian self-organizing map (BSOM). This is a method for estimating a probability distribution generating data points on the basis of a Bayesian stochastic model. It is also regarded as a learning method for a kind of neural network. The black dots in the below figure denote artificially generated data points. Based on work of: Akio Utsugi.

This class is based on: A. Utsugi (1996) ``Topology selection for self-organizing maps", Network: Computation in Neural Systems, vol. 7, no. 4, 727-740.

A. Utsugi (1997) ``Hyperparameter selection for self-organizing maps", Neural Computation, vol. 9, no. 3, pp. 623-635.

Field Summary

Bsom

bsom

Constructor Summary

HBsom()
Initialize BSOM.

Method Summary

void	doc() Show documentation
double	getAlpha() Get alpha
double	getBeta() Get beta
int	getNiterations() Get number of iterations used for fitting
P1D	getResult() Get results of training.
void	run() Run the algorithm
void	setAlphaBeta(double alpha, double beta) Set initial alpha and betai parameters.
void	setData(P1D p1d) Load data to BSOM
void	setDelta(double delta) Set calculation precision.
void	setNPoints(int units) Set number of points for fit
void	visible() Set visible frame
void	visible(boolean vis) Set frame visible or not

Methods inherited from class java.lang.Object

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

Field Detail

bsom

public Bsom bsom

Constructor Detail

HBsom

public HBsom()

Initialize BSOM.

Method Detail

setNPoints

public void setNPoints(int units)

Set number of points for fit

Parameters:

units - set number of points for fit.

setData

public void setData(P1D p1d)

Load data to BSOM

Parameters:

p1d - input data

doc

public void doc()

Show documentation

setAlphaBeta

public void setAlphaBeta(double alpha,
                         double beta)

Set initial alpha and betai parameters.

Parameters:

alpha - alpha value

beta - beta value

getResult

public P1D getResult()

Get results of training.

Returns:

P1D with results.

run

public void run()

Run the algorithm

visible

public void visible(boolean vis)

Set frame visible or not

Parameters:

vis - true if visible

setDelta

public void setDelta(double delta)

Set calculation precision. Iterations stop if (current(alpha)-previous(alpha) .lt. delta) The value should be very small for best results

Parameters:

delta - precision

getNiterations

public int getNiterations()

Get number of iterations used for fitting

getAlpha

public double getAlpha()

Get alpha

getBeta

public double getBeta()

Get beta

visible

public void visible()

Set visible frame