RBFNet (Java Statistical Analysis Tool 0.0.8 API)

java.lang.Object
- jsat.classifiers.neuralnetwork.RBFNet

All Implemented Interfaces:

Serializable, Cloneable, Classifier, DataTransform, Parameterized, Regressor
```
public class RBFNet
extends Object
implements Classifier, Regressor, DataTransform, Parameterized
```
This provides a highly configurable implementation of a Radial Basis Function Neural Network. A RBF network is a type of neural network that contains one hidden layer, and is related to the LVQ algorithm. In a classical RBF Network, the distance between two data points is generally the EuclideanDistance or MahalanobisDistance. This implementation allows the specification of any arbitrary distance metric.

Another restriction on classical RBF Nets is that a weighted sum of the output of the hidden units be used to make the final decision. Instead this implementation allows the specification of an arbitrary Classifier or Regressor to estimate the outputs based on the hidden unit activations. Whether or not the predictor supports Classification, Regression, and what classification features it supports - will determine what the RBF Network supports. This allows for models technically more complicated and powerful than the standard RBF network.

The initial phases of a RBF Network is to learn the neuron locations and activations. This part can also be seen as learning a data transformation. As such, the RBF Network can be used as a DataTransform itself.
The last phase of the network is to learn the model based on the data point activations.

It is highly recommended to use a base learning method that can efficiently use sparse vectors.

Author:

Edward Raff

See Also:

Serialized Form

Nested Class Summary

Nested Classes
Modifier and Type	Class and Description
`static class`	`RBFNet.Phase1Learner` The first phase of learning a RBF Neural Network is to determine the neuron locations.
`static class`	`RBFNet.Phase2Learner` The second phase of learning a RBF Neural Network is to determine how the neurons are activated to produce the output of the hidden layer.

Constructor Summary

Constructors
Constructor and Description
`RBFNet()` Creates a new RBF Network suitable for binary classification or regression and uses 100 hidden nodes.
`RBFNet(int numCentroids)` Creates a new RBF Network suitable for binary classification or regression.
`RBFNet(int numCentroids, RBFNet.Phase1Learner cl, RBFNet.Phase2Learner bl, double alpha, int p, DistanceMetric dm, Classifier baseClassifier)` Creates a new RBF Network for classification tasks.
`RBFNet(int numCentroids, RBFNet.Phase1Learner cl, RBFNet.Phase2Learner bl, double alpha, int p, DistanceMetric dm, Regressor baseRegressor)` Creates a new RBF Network for regression tasks.
`RBFNet(RBFNet toCopy)` Copy constructor

Method Summary

All Methods Static Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`CategoricalResults`	`classify(DataPoint data)` Performs classification on the given data point.
`RBFNet`	`clone()`
`void`	`fit(DataSet data)` Fits this transform to the given dataset.
`double`	`getAlpha()` Returns the alpha bandwidth learning parameter
`DistanceMetric`	`getDistanceMetric()` Returns the distance metric in use
`int`	`getNumCentroids()` Returns the number of centroids to use when training
`int`	`getP()` Returns the nearest neighbors parameter.
`Parameter`	`getParameter(String paramName)` Returns the parameter with the given name.
`List<Parameter>`	`getParameters()` Returns the list of parameters that can be altered for this learner.
`RBFNet.Phase1Learner`	`getPhase1Learner()` Returns the method to use for learning the centroids of the network.
`RBFNet.Phase2Learner`	`getPhase2Learner()` Returns the learning method to use for determining the bandwidths of each center in the network.
`static Distribution`	`guessAlpha(DataSet data)` Guesses the distribution for the `setAlpha(double)` parameter
`static Distribution`	`guessNumCentroids(DataSet data)` Guesses the distribution for the `setNumCentroids(int)` parameter
`static Distribution`	`guessP(DataSet data)` Guesses the distribution for the `setP(int)` parameter
`boolean`	`isNormalize()` Returns whether or not the network is currently normalizing its neuron outputs.
`double`	`regress(DataPoint data)`
`void`	`setAlpha(double alpha)` Sets the alpha parameter.
`void`	`setDistanceMetric(DistanceMetric dm)` Sets the distance metric used to determine neuron activations.
`void`	`setNormalize(boolean normalize)` Sets whether or not to normalize the outputs of the neurons in the network so that the activations sum to one.
`void`	`setNumCentroids(int numCentroids)` Sets the number of centroids to learn for this model.
`void`	`setP(int p)` Sets the nearest neighbor parameter.
`void`	`setPhase1Learner(RBFNet.Phase1Learner p1l)` Sets the method used for learning the centroids (or hidden units) of the network.
`void`	`setPhase2Learner(RBFNet.Phase2Learner p2l)` Sets the method used for learning the bandwidths for each centroid in the network.
`boolean`	`supportsWeightedData()` Indicates whether the model knows how to train using weighted data points.
`void`	`train(RegressionDataSet dataSet)`
`void`	`train(RegressionDataSet dataSet, ExecutorService threadPool)`
`void`	`trainC(ClassificationDataSet dataSet)` Trains the classifier and constructs a model for classification using the given data set.
`void`	`trainC(ClassificationDataSet dataSet, ExecutorService threadPool)` Trains the classifier and constructs a model for classification using the given data set.
`DataPoint`	`transform(DataPoint dp)` Returns a new data point that is a transformation of the original data point.

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

- Constructor Detail
  - RBFNet
```
public RBFNet()
```
    Creates a new RBF Network suitable for binary classification or regression and uses 100 hidden nodes. One of the other constructors should be used if you need classification for multi-class or if you need probability outputs.
    
    This will use RBFNet.Phase1Learner.K_MEANS for neuron selection and RBFNet.Phase2Learner.NEAREST_OTHER_CENTROID_AVERAGE for activation tuning. The EuclideanDistance will be use as the metric.
  - RBFNet
```
public RBFNet(int numCentroids)
```
    Creates a new RBF Network suitable for binary classification or regression. One of the other constructors should be used if you need classification for multi-class or if you need probability outputs.
    
    This will use RBFNet.Phase1Learner.K_MEANS for neuron selection and RBFNet.Phase2Learner.NEAREST_OTHER_CENTROID_AVERAGE for activation tuning. The EuclideanDistance will be use as the metric.
    
    Parameters:
    
    numCentroids - the number of centroids or neurons to use in the network's hidden layer
  - RBFNet
```
public RBFNet(int numCentroids,
              RBFNet.Phase1Learner cl,
              RBFNet.Phase2Learner bl,
              double alpha,
              int p,
              DistanceMetric dm,
              Classifier baseClassifier)
```
    Creates a new RBF Network for classification tasks. If the classifier can also perform regression, then the network will be able to perform both.
    
    Parameters:
    
    numCentroids - the number of centroids or neurons to use in the network's hidden layer
    
    cl - the method to learn the neuron locations
    
    bl - the method to learn the neuron activations
    
    alpha - a parameter that may have an effect on the neuron activation learning method.
    
    p - a parameter that may have an effect on the neuron activation learning method
    
    dm - the distance metric to use
    
    baseClassifier - the base classifier to learn on top of the hidden layer activations.
  - RBFNet
```
public RBFNet(int numCentroids,
              RBFNet.Phase1Learner cl,
              RBFNet.Phase2Learner bl,
              double alpha,
              int p,
              DistanceMetric dm,
              Regressor baseRegressor)
```
    Creates a new RBF Network for regression tasks. If the regressor can also perform classification, then the network will be able to perform both.
    
    Parameters:
    
    numCentroids - the number of centroids or neurons to use in the network's hidden layer
    
    cl - the method to learn the neuron locations
    
    bl - the method to learn the neuron activations
    
    alpha - a parameter that may have an effect on the neuron activation learning method.
    
    p - a parameter that may have an effect on the neuron activation learning method
    
    dm - the distance metric to use
    
    baseRegressor - the base regressor to learn on op of the hidden layer activations.
  - RBFNet
```
public RBFNet(RBFNet toCopy)
```
    Copy constructor
    
    Parameters:
    
    toCopy - the network to copy
- Method Detail
  - transform
```
public DataPoint transform(DataPoint dp)
```
    Description copied from interface: DataTransform
    
    Returns a new data point that is a transformation of the original data point. This new data point is a different object, but may contain the same references as the original data point. It is not guaranteed that you can mutate the transformed point without having a side effect on the original point.
    
    Specified by:
    
    transform in interface DataTransform
    
    Parameters:
    
    dp - the data point to apply a transformation to
    
    Returns:
    
    a transformed data point
  - setAlpha
```
public void setAlpha(double alpha)
```
    Sets the alpha parameter. This value is used for certain RBFNet.Phase2Learner learners as a parameter. A good default value for most methods is often 1 or 3. However the parameter must always be a non-negative value.
    
    Parameters:
    
    alpha - a non negative value that controls the width of the learned bandwidths.
  - getAlpha
```
public double getAlpha()
```
    Returns the alpha bandwidth learning parameter
    
    Returns:
    
    the alpha bandwidth learning parameter
    
    See Also:
    
    setAlpha(double)
  - guessAlpha
```
public static Distribution guessAlpha(DataSet data)
```
    Guesses the distribution for the setAlpha(double) parameter
    
    Parameters:
    
    data - the data to create a guess for
    
    Returns:
    
    a guess for the distribution of the Alpha parameter
  - setP
```
public void setP(int p)
```
    Sets the nearest neighbor parameter. This value is used for certain RBFNet.Phase2Learner learners as a parameter. It is used to control the number of neighbors taken into account in learning the parameter value. It must always be a positive value. 3 is usually a good value for this parameter.
    
    Parameters:
    
    p - the positive integer used that controls the width of the learned bandwidths
  - getP
```
public int getP()
```
    Returns the nearest neighbors parameter.
    
    Returns:
    
    the nearest neighbors parameter.
    
    See Also:
    
    setP(int)
  - guessP
```
public static Distribution guessP(DataSet data)
```
    Guesses the distribution for the setP(int) parameter
    
    Parameters:
    
    data - the data to create a guess for
    
    Returns:
    
    a guess for the distribution of the P parameter
  - setNumCentroids
```
public void setNumCentroids(int numCentroids)
```
    Sets the number of centroids to learn for this model. Increasing the number of centroids increases the complexity of the model as well as training and evaluation time. The centroids serve as the hidden units in the network.
    
    The centroids learned are controlled via the setPhase1Learner(jsat.classifiers.neuralnetwork.RBFNet.Phase1Learner) method
    
    Parameters:
    
    numCentroids - the number of centroids to use in the model
  - getNumCentroids
```
public int getNumCentroids()
```
    Returns the number of centroids to use when training
    
    Returns:
    
    * Returns the number of centroids to use when training
  - guessNumCentroids
```
public static Distribution guessNumCentroids(DataSet data)
```
    Guesses the distribution for the setNumCentroids(int) parameter
    
    Parameters:
    
    data - the data to create a guess for
    
    Returns:
    
    a guess for the distribution of the number of centroids to use
  - setDistanceMetric
```
public void setDistanceMetric(DistanceMetric dm)
```
    Sets the distance metric used to determine neuron activations.
    
    Parameters:
    
    dm - the distance metric to use
  - getDistanceMetric
```
public DistanceMetric getDistanceMetric()
```
    Returns the distance metric in use
    
    Returns:
    
    the distance metric in use
  - setPhase1Learner
```
public void setPhase1Learner(RBFNet.Phase1Learner p1l)
```
    Sets the method used for learning the centroids (or hidden units) of the network.
    
    Parameters:
    
    p1l - the learning method to use
  - getPhase1Learner
```
public RBFNet.Phase1Learner getPhase1Learner()
```
    Returns the method to use for learning the centroids of the network.
    
    Returns:
    
    the method to use for learning the centroids of the network.
  - setPhase2Learner
```
public void setPhase2Learner(RBFNet.Phase2Learner p2l)
```
    Sets the method used for learning the bandwidths for each centroid in the network. Depending on the method used, setAlpha(double) or setP(int) may impact the learned bandwidths.
    
    Parameters:
    
    p2l - the learning method to use
  - getPhase2Learner
```
public RBFNet.Phase2Learner getPhase2Learner()
```
    Returns the learning method to use for determining the bandwidths of each center in the network.
    
    Returns:
    
    the learning method to use for the bandwidths
  - setNormalize
```
public void setNormalize(boolean normalize)
```
    Sets whether or not to normalize the outputs of the neurons in the network so that the activations sum to one. Normalizing the outputs can increase the generalization ability of the network. By default this is set to true
    
    Parameters:
    
    normalize - true to normalize the neuron outputs, false to use the raw activation values.
  - isNormalize
```
public boolean isNormalize()
```
    Returns whether or not the network is currently normalizing its neuron outputs.
    
    Returns:
    
    whether or not the neuron outputs are normalized
  - classify
```
public CategoricalResults classify(DataPoint data)
```
    Description copied from interface: Classifier
    
    Performs classification on the given data point.
    
    Specified by:
    
    classify in interface Classifier
    
    Parameters:
    
    data - the data point to classify
    
    Returns:
    
    the results of the classification.
  - trainC
```
public void trainC(ClassificationDataSet dataSet,
                   ExecutorService threadPool)
```
    Description copied from interface: Classifier
    
    Trains the classifier and constructs a model for classification using the given data set. If the training method knows how, it will used the threadPool to conduct training in parallel. This method will block until the training has completed.
    
    Specified by:
    
    trainC in interface Classifier
    
    Parameters:
    
    dataSet - the data set to train on
    
    threadPool - the source of threads to use.
  - trainC
```
public void trainC(ClassificationDataSet dataSet)
```
    Description copied from interface: Classifier
    
    Trains the classifier and constructs a model for classification using the given data set.
    
    Specified by:
    
    trainC in interface Classifier
    
    Parameters:
    
    dataSet - the data set to train on
  - supportsWeightedData
```
public boolean supportsWeightedData()
```
    Description copied from interface: Classifier
    
    Indicates whether the model knows how to train using weighted data points. If it does, the model will train assuming the weights. The values returned by this method may change depending on the parameters set for the model.
    
    Specified by:
    
    supportsWeightedData in interface Classifier
    
    Specified by:
    
    supportsWeightedData in interface Regressor
    
    Returns:
    
    true if the model supports weighted data, false otherwise
  - regress
```
public double regress(DataPoint data)
```
    Specified by:
    
    regress in interface Regressor
  - fit
```
public void fit(DataSet data)
```
    Description copied from interface: DataTransform
    
    Fits this transform to the given dataset. Some transforms can only be learned from classification or regression datasets. If an incompatible dataset type is given, a FailedToFitException exception may be thrown.
    
    Specified by:
    
    fit in interface DataTransform
    
    Parameters:
    
    data - the dataset to fir this transform to
  - train
```
public void train(RegressionDataSet dataSet,
                  ExecutorService threadPool)
```
    Specified by:
    
    train in interface Regressor
  - train
```
public void train(RegressionDataSet dataSet)
```
    Specified by:
    
    train in interface Regressor
  - clone
```
public RBFNet clone()
```
    Specified by:
    
    clone in interface Classifier
    
    Specified by:
    
    clone in interface DataTransform
    
    Specified by:
    
    clone in interface Regressor
    
    Overrides:
    
    clone in class Object
  - getParameters
```
public List<Parameter> getParameters()
```
    Description copied from interface: Parameterized
    
    Returns the list of parameters that can be altered for this learner.
    
    Specified by:
    
    getParameters in interface Parameterized
    
    Returns:
    
    the list of parameters that can be altered for this learner.
  - getParameter
```
public Parameter getParameter(String paramName)
```
    Description copied from interface: Parameterized
    
    Returns the parameter with the given name. Two different strings may map to a single Parameter object. An ASCII only string, and a Unicode style string.
    
    Specified by:
    
    getParameter in interface Parameterized
    
    Parameters:
    
    paramName - the name of the parameter to obtain
    
    Returns:
    
    the Parameter in question, or null if no such named Parameter exists.

Class RBFNet

Nested Class Summary

Constructor Summary

Method Summary

Methods inherited from class java.lang.Object

Constructor Detail

RBFNet

RBFNet

RBFNet

RBFNet

RBFNet

Method Detail

transform

setAlpha

getAlpha

guessAlpha

setP

getP

guessP

setNumCentroids

getNumCentroids

guessNumCentroids

setDistanceMetric

getDistanceMetric

setPhase1Learner

getPhase1Learner

setPhase2Learner

getPhase2Learner

setNormalize

isNormalize

classify

trainC

trainC

supportsWeightedData

regress

fit

train

train

clone

getParameters

getParameter