Rbfn

java.lang.Object
- keel.Algorithms.Neural_Networks.RBFN_incremental_CL.Rbfn

```
public class Rbfn
extends java.lang.Object
```
This class codified a Radial Basis Function Network

Since:

JDK1.5

Version:

1.0

Author:

Writen by Antonio J. Rivera Rivas (University of Jaén) 03/03/2004, Modified by Víctor Rivas (University of Jaén) 23/06/2006, Modified by María Dolores Pérez Godoy (University of Jaén) 17/12/2008, Modified by Julian Luengo (University of Granada) 04/02/2013

Constructor Summary

Constructors
Constructor and Description
`Rbfn()` Creates a instance of rbnf of fixed structure just for test.
`Rbfn(double[][] X, int ndata, int nInpt, int nOutp, int nNeuro)` Creates a new instance of rbfn from a matrix of instances.
`Rbfn(int nNeuro, double[][] X, int ndata, int nInpt, int nOutpl)` Creates a new instance of rbfn from a matrix of instances.
`Rbfn(int numInpt, int numOutp)` Creates a new instance of rbfn

Method Summary

All Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`void`	`decremental(double[][] X, double[][] Y, int ndata, double percent, double alfa)` Uses a decremental algorithm to buid a net.
`double[]`	`errorRbfn(double[] realOutput, double[] netOutput)` Computes the difference between the ouput of the net and desired outpunt
`protected double`	`euclideanDist(double[] v1, double[] v2)` Computes the euclidean distance between 2 vectors
`double[]`	`evaluationRbfn(double[] _input)` Passes an input to the net obtaining its output
`java.lang.String[]`	`getIndex()` Returns the list on index of the net neurons.
`Rbf`	`getRbf(java.lang.String id)` Gets an RBF from the net given its identifier
`void`	`insertRbf(Rbf rbf)` Adds a neuron to the net
`void`	`modifyRbf(Rbf rbf, java.lang.String idRbf)` Changes a neuron in the net
`void`	`printRbfn()` Prints net on a stdout
`void`	`printRbfn(java.lang.String _fileName)` Prints net on a file.
`void`	`RAN(double[][] X, double[][] Y, int ndata, double delta, double epsilon, double alfa)` Uses RAN algorithm to build a net
`java.lang.String`	`rbfNearest(double[] v)` Returns the nearest rbf/neuron to a vector v (patron)
`void`	`removeRbf(java.lang.String idRbf)` Deletes a neuron of the net
`int`	`size()` Returns the number of neurons in the net
`void`	`testClasification(double[][] X, int ndata, int[] obtained, int max, int min)` Evaluates the net for clasification problem
`void`	`testModeling(double[][] X, int ndata, double[] obtained)` Evaluates the net for modeling problem
`void`	`trainLMS(double[][] X, double[][] Y, int ndata, int iter, double alfa)` Uses LMS to train the net.

Methods inherited from class java.lang.Object
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

- Constructor Detail
  - Rbfn
```
public Rbfn()
```
    Creates a instance of rbnf of fixed structure just for test.
  - Rbfn
```
public Rbfn(int numInpt,
            int numOutp)
```
    Creates a new instance of rbfn
    
    Parameters:
    
    numInpt - Input dimension
    
    numOutp - Ouput dimension (thus, number of ouput neurons)
  - Rbfn
```
public Rbfn(double[][] X,
            int ndata,
            int nInpt,
            int nOutp,
            int nNeuro)
```
    Creates a new instance of rbfn from a matrix of instances. Sets randomly the centres of the neurons and sets its radius taking intro account the maximun distance between centres.
    
    Parameters:
    
    X - Matrix of instances
    
    ndata - Number of instaces in X @param nInpt Number of imputs of the net
    
    nInpt - Number of imputs of the net
    
    nOutp - Number of outputs of the net
    
    nNeuro - Number of hidden neurons the net will have.
  - Rbfn
```
public Rbfn(int nNeuro,
            double[][] X,
            int ndata,
            int nInpt,
            int nOutpl)
```
    Creates a new instance of rbfn from a matrix of instances. Sets randomly the centres of the neurons and sets its radius taking intro account the average distance between centres.
    
    Parameters:
    
    nNeuro - Number of hidden neurons the net will have.
    
    X - Matrix of instances
    
    ndata - Number of instaces in X
    
    nInpt - Number of imputs of the net
    
    nOutpl - Number of outputs of the net
- Method Detail
  - euclideanDist
```
protected double euclideanDist(double[] v1,
                               double[] v2)
```
    Computes the euclidean distance between 2 vectors
    
    Parameters:
    
    v1 - A vector
    
    v2 - A vector
    
    Returns:
    
    a double with the euclidean distance between v1 and v2
  - removeRbf
```
public void removeRbf(java.lang.String idRbf)
```
    Deletes a neuron of the net
    
    Parameters:
    
    idRbf - Identifier of neuron to delete
  - insertRbf
```
public void insertRbf(Rbf rbf)
```
    Adds a neuron to the net
    
    Parameters:
    
    rbf - The neuron to insert
  - modifyRbf
```
public void modifyRbf(Rbf rbf,
                      java.lang.String idRbf)
```
    Changes a neuron in the net
    
    Parameters:
    
    idRbf - Identifier of neuron to delete
    
    rbf - Neuron to insert
  - size
```
public int size()
```
    Returns the number of neurons in the net
    
    Returns:
    
    A integer that is the number of neurons of the net
  - getRbf
```
public Rbf getRbf(java.lang.String id)
```
    Gets an RBF from the net given its identifier
    
    Parameters:
    
    id - RBF's identifier
    
    Returns:
    
    the RBF whose identified is id
  - getIndex
```
public java.lang.String[] getIndex()
```
    Returns the list on index of the net neurons.
    
    Returns:
    
    a string with the list on index of the net neurons
  - evaluationRbfn
```
public double[] evaluationRbfn(double[] _input)
```
    Passes an input to the net obtaining its output
    
    Parameters:
    
    _input - The sample
    
    Returns:
    
    The set of outputs provided by the net's output neurons.
  - errorRbfn
```
public double[] errorRbfn(double[] realOutput,
                          double[] netOutput)
```
    Computes the difference between the ouput of the net and desired outpunt
    
    Parameters:
    
    realOutput - Desired output
    
    netOutput - Outpunt of the net
    
    Returns:
    
    A vector of doubles with the difference between the output of the net and the desired output
  - rbfNearest
```
public java.lang.String rbfNearest(double[] v)
```
    Returns the nearest rbf/neuron to a vector v (patron)
    
    Parameters:
    
    v - vector
    
    Returns:
    
    the identifier of the nearest RBF to v
  - RAN
```
public void RAN(double[][] X,
                double[][] Y,
                int ndata,
                double delta,
                double epsilon,
                double alfa)
```
    Uses RAN algorithm to build a net
    
    Parameters:
    
    X - matrix of inputs instances
    
    Y - matrix of outputs instances
    
    ndata - Number of instances
    
    epsilon - minimun error to introduce a new RBF
    
    delta - minimun distance to introduce a new RBF
    
    alfa - learning factor when a new unit is not allocated
  - decremental
```
public void decremental(double[][] X,
                        double[][] Y,
                        int ndata,
                        double percent,
                        double alfa)
```
    Uses a decremental algorithm to buid a net. After initializing and training (with LMS) a net with several neurons, the algorithm in the major loop deletes the neurons with the lowest weight and train the net.
    
    Parameters:
    
    X - matrix of inputs instances
    
    Y - matrix of outputs instances
    
    ndata - Number of instances
    
    percent - Percent under the average of the weights to delete a neuron
    
    alfa - Learnig factor of LMS algorithm
  - trainLMS
```
public void trainLMS(double[][] X,
                     double[][] Y,
                     int ndata,
                     int iter,
                     double alfa)
```
    Uses LMS to train the net.
    
    Parameters:
    
    X - matrix of inputs instances
    
    Y - matrix of outputs instances
    
    ndata - Number of instances
    
    iter - Number of times the set of samples will be used.
    
    alfa - Learning factor.
  - testModeling
```
public void testModeling(double[][] X,
                         int ndata,
                         double[] obtained)
```
    Evaluates the net for modeling problem
    
    Parameters:
    
    X - matrix of inputs instances
    
    ndata - Number of instances
    
    obtained - Vector of results of the evaluation
  - testClasification
```
public void testClasification(double[][] X,
                              int ndata,
                              int[] obtained,
                              int max,
                              int min)
```
    Evaluates the net for clasification problem
    
    Parameters:
    
    X - matrix of inputs instances
    
    ndata - Number of instances
    
    obtained - Vector of results of the evaluation
    
    max - Class maximun identifier
    
    min - Class minimun identifier
  - printRbfn
```
public void printRbfn()
```
    Prints net on a stdout
  - printRbfn
```
public void printRbfn(java.lang.String _fileName)
```
    Prints net on a file.
    
    Parameters:
    
    _fileName - Name of the file.

Class Rbfn

Constructor Summary

Method Summary

Methods inherited from class java.lang.Object

Constructor Detail

Rbfn

Rbfn

Rbfn

Rbfn

Method Detail

euclideanDist

removeRbf

insertRbf

modifyRbf

size

getRbf

getIndex

evaluationRbfn

errorRbfn

rbfNearest

RAN

decremental

trainLMS

testModeling

testClasification

printRbfn

printRbfn