smile.regression

Class RBFNetwork.Trainer<T>

java.lang.Object

smile.regression.RegressionTrainer<T>

smile.regression.RBFNetwork.Trainer<T>

Enclosing class:

RBFNetwork<T>

public static class RBFNetwork.Trainer<T>
extends RegressionTrainer<T>

Trainer for RBF networks.

Constructor Summary

Constructors

Constructor and Description
RBFNetwork.Trainer(Metric<T> distance) Constructor.

Method Summary

Methods

Modifier and Type	Method and Description
void	setNormalized(boolean normalized) Sets true to learn normalized RBF network.
void	setNumCenters(int m) Sets the number of centers.
void	setRBF(RadialBasisFunction[] rbf) Sets the radial basis functions.
void	setRBF(RadialBasisFunction rbf, int m) Sets the radial basis function.
RBFNetwork<T>	train(T[] x, double[] y) Learns a regression model with given training data.
RBFNetwork<T>	train(T[] x, double[] y, T[] centers) Learns a RBF network with given centers.

Methods inherited from class smile.regression.RegressionTrainer

setAttributes

Methods inherited from class java.lang.Object

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

Constructor Detail

RBFNetwork.Trainer

public RBFNetwork.Trainer(Metric<T> distance)

Constructor.

Parameters:

distance - the distance metric functor.

Method Detail

setRBF

public void setRBF(RadialBasisFunction rbf,
          int m)

Sets the radial basis function.

Parameters:

rbf - the radial basis function.

m - the number of basis functions.

setRBF

public void setRBF(RadialBasisFunction[] rbf)

Sets the radial basis functions.

Parameters:

rbf - the radial basis functions.

setNumCenters

public void setNumCenters(int m)

Sets the number of centers.

Parameters:

m - the number of centers.

setNormalized

public void setNormalized(boolean normalized)

Sets true to learn normalized RBF network.

Parameters:

normalized - true to learn normalized RBF network.

train

public RBFNetwork<T> train(T[] x,
                  double[] y)

Description copied from class: RegressionTrainer

Learns a regression model with given training data.

Specified by:

train in class RegressionTrainer<T>

Parameters:

x - the training instances.

y - the training response values.

Returns:

a trained regression model.

train

public RBFNetwork<T> train(T[] x,
                  double[] y,
                  T[] centers)

Learns a RBF network with given centers.

Parameters:

x - training samples.

y - training labels in [0, k), where k is the number of classes.

centers - the centers of RBF functions.

Returns:

a trained RBF network