ART1 (encog-java-core 3.4 API)

java.lang.Object
- org.encog.ml.BasicML
- - org.encog.neural.art.ART
  - - org.encog.neural.art.ART1

All Implemented Interfaces:

java.io.Serializable, MLClassification, MLInput, MLInputOutput, MLMethod, MLOutput, MLProperties, MLResettable
```
public class ART1
extends ART
implements MLResettable, MLClassification
```
Implements an ART1 neural network. An ART1 neural network is trained to recognize bipolar patterns as it is presented data. There is no distinct learning phase, like there is with other neural network types. The ART1 neural network is a type of Adaptive Resonance Theory (ART) neural network. ART1 was developed by Stephen Grossberg and Gail Carpenter. This neural network type supports only bipolar input. The ART1 neural network is trained as it is used. New patterns are presented to the ART1 network, and they are classified into either new, or existing, classes. Once the maximum number of classes have been used the network will report that it is out of classes. ART1 neural networks are used for classification. There are essentially 2 layers in an ART1 network. The first, named the F1 layer, acts as the input. The F1 layer receives bipolar patterns that the network is to classify. The F2 layer specifies the maximum number classes that the ART1 network can recognize. Plasticity is an important part for all Adaptive Resonance Theory (ART) neural networks. Unlike most neural networks, ART1 does not have a distinct training and usage stage. The ART1 network will learn as it is used.

See Also:

Serialized Form

Field Summary
- Fields inherited from class org.encog.neural.art.ART
  PROPERTY_A1, PROPERTY_B1, PROPERTY_C1, PROPERTY_D1, PROPERTY_L, PROPERTY_NO_WINNER, PROPERTY_VIGILANCE

Constructor Summary

Constructors
Constructor and Description

ART1()
Default constructor, used mainly for persistence.

ART1(int theF1Count, int theF2Count)
Construct the ART1 network.

Constructors
Constructor and Description
`ART1()` Default constructor, used mainly for persistence.
`ART1(int theF1Count, int theF2Count)` Construct the ART1 network.

Method Summary

All Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`void`	`adjustWeights()` Adjust the weights for the pattern just presented.
`int`	`classify(MLData input)` Classify the input data to a class number.
`void`	`compute(BiPolarNeuralData input, BiPolarNeuralData output)` Compute the output from the ART1 network.
`MLData`	`compute(MLData input)` Compute the output for the BasicNetwork class.
`double`	`getA1()`
`double`	`getB1()`
`double`	`getC1()`
`double`	`getD1()`
`int`	`getF1Count()`
`int`	`getF2Count()`
`int`	`getInputCount()`
`double`	`getL()`
`int`	`getNoWinner()`
`int`	`getOutputCount()`
`double`	`getVigilance()`
`Matrix`	`getWeightsF1toF2()`
`Matrix`	`getWeightsF2toF1()`
`int`	`getWinner()`
`boolean`	`hasWinner()`
`double`	`magnitude(BiPolarNeuralData input)` Get the magnitude of the specified input.
`void`	`reset()` Reset the weight matrix back to starting values.
`void`	`reset(int seed)` Reset with a specic seed.
`void`	`setA1(double theA1)` Set the A1 parameter.
`void`	`setB1(double theB1)` Set the B1 parameter.
`void`	`setC1(double theC1)` Set the C1 parameter.
`void`	`setD1(double theD1)` Set the D1 parameter.
`void`	`setF1Count(int i)` Set the F1 count.
`void`	`setF2Count(int i)` Set the F2 count.
`void`	`setL(double theL)` Set the L parameter.
`void`	`setNoWinner(int i)` Set the i parameter.
`void`	`setVigilance(double theVigilance)` Set the vigilance.
`void`	`setWeightsF1toF2(Matrix matrix)` Set the f1 to f2 matrix.
`void`	`setWeightsF2toF1(Matrix matrix)` Set the f2 to f1 matrix.

Methods inherited from class org.encog.neural.art.ART
updateProperties

Methods inherited from class org.encog.ml.BasicML
getProperties, getPropertyDouble, getPropertyLong, getPropertyString, setProperty, setProperty, setProperty

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

- Constructor Detail
  - ART1
```
public ART1()
```
    Default constructor, used mainly for persistence.
  - ART1
```
public ART1(int theF1Count,
            int theF2Count)
```
    Construct the ART1 network.
    
    Parameters:
    
    theF1Count - The neuron count for the f1 layer.
    
    theF2Count - The neuron count for the f2 layer.
- Method Detail
  - adjustWeights
```
public void adjustWeights()
```
    Adjust the weights for the pattern just presented.
  - classify
```
public int classify(MLData input)
```
    Classify the input data to a class number.
    
    Specified by:
    
    classify in interface MLClassification
    
    Parameters:
    
    input - The input data.
    
    Returns:
    
    The class that the data belongs to.
  - compute
```
public void compute(BiPolarNeuralData input,
                    BiPolarNeuralData output)
```
    Compute the output from the ART1 network. This can be called directly or used by the BasicNetwork class. Both input and output should be bipolar numbers.
    
    Parameters:
    
    input - The input to the network.
    
    output - The output from the network.
  - compute
```
public MLData compute(MLData input)
```
    Compute the output for the BasicNetwork class.
    
    Parameters:
    
    input - The input to the network.
    
    Returns:
    
    The output from the network.
  - getA1
```
public double getA1()
```
    Returns:
    
    The A1 parameter.
  - getB1
```
public double getB1()
```
    Returns:
    
    The B1 parameter.
  - getC1
```
public double getC1()
```
    Returns:
    
    The C1 parameter.
  - getD1
```
public double getD1()
```
    Returns:
    
    The D1 parameter.
  - getF1Count
```
public int getF1Count()
```
    Returns:
    
    the f1Count
  - getF2Count
```
public int getF2Count()
```
    Returns:
    
    the f2Count
  - getInputCount
```
public int getInputCount()
```
    Specified by:
    
    getInputCount in interface MLInput
    
    Returns:
    
    The input.
  - getL
```
public double getL()
```
    Returns:
    
    The L parameter.
  - getNoWinner
```
public int getNoWinner()
```
    Returns:
    
    This is the value that is returned if there is no winner. This value is generally set to the index of the last classes, plus 1. For example, if there were 3 classes, the network would return 0-2 to represent what class was found, in this case the no winner property would be set to 3.
  - getOutputCount
```
public int getOutputCount()
```
    Specified by:
    
    getOutputCount in interface MLOutput
    
    Returns:
    
    The number of neurons in the output count, which is the f2 layer count.
  - getVigilance
```
public double getVigilance()
```
    Returns:
    
    The vigilance parameter.
  - getWeightsF1toF2
```
public Matrix getWeightsF1toF2()
```
    Returns:
    
    the weightsF1toF2
  - getWeightsF2toF1
```
public Matrix getWeightsF2toF1()
```
    Returns:
    
    the weightsF2toF1
  - getWinner
```
public int getWinner()
```
    Returns:
    
    The winning neuron.
  - hasWinner
```
public boolean hasWinner()
```
    Returns:
    
    Does this network have a "winner"?
  - magnitude
```
public double magnitude(BiPolarNeuralData input)
```
    Get the magnitude of the specified input.
    
    Parameters:
    
    input - The input to calculate the magnitude for.
    
    Returns:
    
    The magnitude of the specified pattern.
  - reset
```
public void reset()
```
    Reset the weight matrix back to starting values.
    
    Specified by:
    
    reset in interface MLResettable
  - reset
```
public void reset(int seed)
```
    Reset with a specic seed.
    
    Specified by:
    
    reset in interface MLResettable
    
    Parameters:
    
    seed - The seed to reset with.
  - setA1
```
public void setA1(double theA1)
```
    Set the A1 parameter.
    
    Parameters:
    
    theA1 - The new value.
  - setB1
```
public void setB1(double theB1)
```
    Set the B1 parameter.
    
    Parameters:
    
    theB1 - The new value.
  - setC1
```
public void setC1(double theC1)
```
    Set the C1 parameter.
    
    Parameters:
    
    theC1 - The new value.
  - setD1
```
public void setD1(double theD1)
```
    Set the D1 parameter.
    
    Parameters:
    
    theD1 - The new value.
  - setF1Count
```
public void setF1Count(int i)
```
    Set the F1 count. The F1 layer is the input layer.
    
    Parameters:
    
    i - The count.
  - setF2Count
```
public void setF2Count(int i)
```
    Set the F2 count. The F2 layer is the output layer.
    
    Parameters:
    
    i - The count.
  - setL
```
public void setL(double theL)
```
    Set the L parameter.
    
    Parameters:
    
    theL - The new value.
  - setNoWinner
```
public void setNoWinner(int i)
```
    Set the i parameter.
    
    Parameters:
    
    i - The new value.
  - setVigilance
```
public void setVigilance(double theVigilance)
```
    Set the vigilance.
    
    Parameters:
    
    theVigilance - The new value.
  - setWeightsF1toF2
```
public void setWeightsF1toF2(Matrix matrix)
```
    Set the f1 to f2 matrix.
    
    Parameters:
    
    matrix - The new matrix.
  - setWeightsF2toF1
```
public void setWeightsF2toF1(Matrix matrix)
```
    Set the f2 to f1 matrix.
    
    Parameters:
    
    matrix - The new matrix.

Class ART1

Field Summary

Fields inherited from class org.encog.neural.art.ART

Constructor Summary

Method Summary

Methods inherited from class org.encog.neural.art.ART

Methods inherited from class org.encog.ml.BasicML

Methods inherited from class java.lang.Object

Constructor Detail

ART1

ART1

Method Detail

adjustWeights

classify

compute

compute

getA1

getB1

getC1

getD1

getF1Count

getF2Count

getInputCount

getL

getNoWinner

getOutputCount

getVigilance

getWeightsF1toF2

getWeightsF2toF1

getWinner

hasWinner

magnitude

reset

reset

setA1

setB1

setC1

setD1

setF1Count

setF2Count

setL

setNoWinner

setVigilance

setWeightsF1toF2

setWeightsF2toF1