smile.math.kernel

Class PearsonKernel

java.lang.Object

smile.math.kernel.PearsonKernel

All Implemented Interfaces:

java.io.Serializable, java.util.function.ToDoubleBiFunction<double[],double[]>, MercerKernel<double[]>

public class PearsonKernel
extends java.lang.Object
implements MercerKernel<double[]>

Pearson VII universal kernel. The Pearson VII function is often used for curve fitting of X-ray diffraction scans and single bands in infrared spectra.

References

1. B. Üstün, W.J. Melssen, and L. Buydens. Facilitating the Application of Support Vector Regression by Using a Universal Pearson VII Function Based Kernel, 2006.

See Also:

Serialized Form

Constructor Summary

Constructors

Constructor and Description
PearsonKernel(double sigma, double omega) Constructor.
PearsonKernel(double sigma, double omega, double lo, double hi) Constructor.

Method Summary

Modifier and Type	Method and Description
double[]	hi() Returns the upper bound of hyperparameters.
double[]	hyperparameters() Returns the hyperparameters for tuning.
double	k(double[] x, double[] y) Kernel function.
double[]	kg(double[] x, double[] y) Computes the kernel and its gradient over hyperparameters.
double[]	lo() Returns the lower bound of hyperparameters.
PearsonKernel	of(double[] params) Returns the same kind kernel with the new hyperparameters.
double	omega() Returns the tailing factor of the peak.
double	sigma() Returns Pearson width.
java.lang.String	toString()

Methods inherited from class java.lang.Object

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

Methods inherited from interface smile.math.kernel.MercerKernel

apply, applyAsDouble, K, K, KG

Constructor Detail

PearsonKernel

public PearsonKernel(double sigma,
                     double omega)

Constructor.

Parameters:

sigma - Pearson width.

omega - The tailing factor of the peak.

PearsonKernel

public PearsonKernel(double sigma,
                     double omega,
                     double lo,
                     double hi)

Constructor.

Parameters:

sigma - Pearson width.

omega - The tailing factor of the peak. The tailing factor is fixed during hyperparameter tuning.

lo - The lower bound of length scale for hyperparameter tuning.

hi - The upper bound of length scale for hyperparameter tuning.

Method Detail

sigma

public double sigma()

Returns Pearson width.

omega

public double omega()

Returns the tailing factor of the peak.

toString

public java.lang.String toString()

Overrides:

toString in class java.lang.Object

k

public double k(double[] x,
                double[] y)

Description copied from interface: MercerKernel

Kernel function.

Specified by:

k in interface MercerKernel<double[]>

kg

public double[] kg(double[] x,
                   double[] y)

Description copied from interface: MercerKernel

Computes the kernel and its gradient over hyperparameters.

Specified by:

kg in interface MercerKernel<double[]>

of

public PearsonKernel of(double[] params)

Description copied from interface: MercerKernel

Returns the same kind kernel with the new hyperparameters.

Specified by:

of in interface MercerKernel<double[]>

hyperparameters

public double[] hyperparameters()

Description copied from interface: MercerKernel

Returns the hyperparameters for tuning.

Specified by:

hyperparameters in interface MercerKernel<double[]>

lo

public double[] lo()

Description copied from interface: MercerKernel

Returns the lower bound of hyperparameters.

Specified by:

lo in interface MercerKernel<double[]>

hi

public double[] hi()

Description copied from interface: MercerKernel

Returns the upper bound of hyperparameters.

Specified by:

hi in interface MercerKernel<double[]>