smile.regression

Class KernelMachine<T>

java.lang.Object

smile.base.svm.KernelMachine<T>

smile.regression.KernelMachine<T>

All Implemented Interfaces:

java.io.Serializable, java.util.function.ToDoubleFunction<T>, Regression<T>

public class KernelMachine<T>
extends KernelMachine<T>
implements Regression<T>

The learning methods building on kernels. Kernel methods owe their name to the use of kernel functions, which enable them to operate in a high-dimensional, implicit feature space without ever computing the coordinates of the data in that space, but rather by simply computing the inner products between the images of all pairs of data in the feature space.

Kernel methods can be thought of as instance-based learners: rather than learning some fixed set of parameters corresponding to the features of their inputs, they instead store (a subset of) their training set (or a new representation) and learn for it a corresponding weight. Prediction for unlabeled inputs is treated by the application of a similiarity function.

See Also:

Serialized Form

Nested Class Summary

Nested classes/interfaces inherited from interface smile.regression.Regression

Regression.Metric

Constructor Summary

Constructors

Constructor and Description
KernelMachine(smile.math.kernel.MercerKernel<T> kernel, T[] instances, double[] weight) Constructor.
KernelMachine(smile.math.kernel.MercerKernel<T> kernel, T[] instances, double[] weight, double b) Constructor.

Method Summary

Modifier and Type	Method and Description
double	predict(T x) Predicts the dependent variable of an instance.

Methods inherited from class smile.base.svm.KernelMachine

instances, intercept, kernel, score, toString, toSVM, weights

Methods inherited from class java.lang.Object

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

Methods inherited from interface smile.regression.Regression

applyAsDouble, metric, metric, predict

Constructor Detail

KernelMachine

public KernelMachine(smile.math.kernel.MercerKernel<T> kernel,
                     T[] instances,
                     double[] weight)

Constructor.

Parameters:

kernel - Kernel function.

instances - The instances in the kernel machine, e.g. support vectors.

weight - The weights of instances.

KernelMachine

public KernelMachine(smile.math.kernel.MercerKernel<T> kernel,
                     T[] instances,
                     double[] weight,
                     double b)

Constructor.

Parameters:

kernel - Kernel function.

instances - The instances in the kernel machine, e.g. support vectors.

weight - The weights of instances.

b - The intercept;

Method Detail

predict

public double predict(T x)

Description copied from interface: Regression

Predicts the dependent variable of an instance.

Specified by:

predict in interface Regression<T>

Parameters:

x - an instance.

Returns:

the predicted value of dependent variable.