public static class RBFNetwork.Trainer<T> extends ClassifierTrainer<T>
Constructor and Description |
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Trainer(smile.math.distance.Metric<T> distance)
Constructor.
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Modifier and Type | Method and Description |
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RBFNetwork.Trainer<T> |
setNormalized(boolean normalized)
Sets true to learn normalized RBF network.
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RBFNetwork.Trainer<T> |
setRBF(smile.math.rbf.RadialBasisFunction[] rbf)
Sets the radial basis functions.
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RBFNetwork.Trainer<T> |
setRBF(smile.math.rbf.RadialBasisFunction rbf,
int m)
Sets the radial basis function.
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RBFNetwork<T> |
train(T[] x,
int[] y)
Learns a classifier with given training data.
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RBFNetwork<T> |
train(T[] x,
int[] y,
T[] centers)
Learns a RBF network with given centers.
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setAttributes
public Trainer(smile.math.distance.Metric<T> distance)
distance
- the distance metric functor.public RBFNetwork.Trainer<T> setRBF(smile.math.rbf.RadialBasisFunction rbf, int m)
rbf
- the radial basis function.m
- the number of basis functions.public RBFNetwork.Trainer<T> setRBF(smile.math.rbf.RadialBasisFunction[] rbf)
rbf
- the radial basis functions.public RBFNetwork.Trainer<T> setNormalized(boolean normalized)
normalized
- true to learn normalized RBF network.public RBFNetwork<T> train(T[] x, int[] y)
ClassifierTrainer
train
in class ClassifierTrainer<T>
x
- the training instances.y
- the training labels.public RBFNetwork<T> train(T[] x, int[] y, T[] centers)
x
- training samples.y
- training labels in [0, k), where k is the number of classes.centers
- the centers of RBF functions.