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/*
* LingPipe v. 4.1.0
* Copyright (C) 2003-2011 Alias-i
*
* This program is licensed under the Alias-i Royalty Free License
* Version 1 WITHOUT ANY WARRANTY, without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the Alias-i
* Royalty Free License Version 1 for more details.
*
* You should have received a copy of the Alias-i Royalty Free License
* Version 1 along with this program; if not, visit
* http://alias-i.com/lingpipe/licenses/lingpipe-license-1.txt or contact
* Alias-i, Inc. at 181 North 11th Street, Suite 401, Brooklyn, NY 11211,
* +1 (718) 290-9170.
*/
package com.aliasi.matrix;
import com.aliasi.util.AbstractExternalizable;
import java.io.IOException;
import java.io.ObjectInput;
import java.io.ObjectOutput;
import java.io.Serializable;
/**
* A GaussianRadialBasisKernel
provides a kernel based on
* a Gaussian radial basis function with a fixed variance parameter.
* As a kernel function, it unfolds into an infinite-dimension Hilbert
* space.
*
* The radial basis kernel function of radius r
is
* defined between vectors v1
and v2
as
* follows:
*
*
* rbf(v1,v2) = exp(- r * distance(v1,v2)2)
*
*
* where distance(v1,v2)
is the Euclidean distance,
* as defined in the class documentation for {@link EuclideanDistance}.
* In this formulation, the radius r
is related to
* the variance σ2
by:
*
*
* r = 1/(2 * σ2)
*
* For more information on the Gaussian radial basis kernel
* and applications, see:
*
*
*
* @author Bob Carpenter
* @version 3.8
* @since LingPipe3.1
*/
public class GaussianRadialBasisKernel
implements KernelFunction, Serializable {
static final long serialVersionUID = -1670587197184485884L;
private final double mNegativeRadius;
/**
* Construct a Gaussian radial basis kernel with the specified
* radius of influence.
*
* @param radius The radius of influence for the kernel.
*/
public GaussianRadialBasisKernel(double radius) {
if (radius <= 0.0
|| Double.isInfinite(radius)
|| Double.isNaN(radius)) {
String msg = "Radius must be positive and finite."
+ " Found radius=" + radius;
throw new IllegalArgumentException(msg);
}
mNegativeRadius = -radius;
}
GaussianRadialBasisKernel(double negativeRadius, boolean ignore) {
mNegativeRadius = negativeRadius;
}
/**
* Returns the result of applying this Guassian radial basis
* kernel to the specified vectors. See the class documentation
* above for a full definition.
*
* @param v1 First vector.
* @param v2 Second vector.
* @return Kernel function applied to the two vectors.
* @throws IllegalArgumentException If the vectors are not of the
* same dimensionality.
*/
public double proximity(Vector v1, Vector v2) {
double dist = EuclideanDistance.DISTANCE.distance(v1,v2);
return Math.exp(mNegativeRadius * (dist * dist));
}
/**
* Returns a string-based representation of this kernel
* function, including the kernel type and radius.
*
* @return A string representing this kernel.
*/
@Override
public String toString() {
return "GaussianRadialBasedKernel(" + (-mNegativeRadius) + ")";
}
Object writeReplace() {
return new Externalizer(mNegativeRadius);
}
static class Externalizer extends AbstractExternalizable {
static final long serialVersionUID = -5223595743791099605L;
final double mNegativeRadius;
public Externalizer() {
this(1.0);
}
public Externalizer(double negativeRadius) {
mNegativeRadius = negativeRadius;
}
@Override
public void writeExternal(ObjectOutput out) throws IOException {
out.writeDouble(mNegativeRadius);
}
@Override
public Object read(ObjectInput in) throws IOException {
double negativeRadius = in.readDouble();
return new GaussianRadialBasisKernel(negativeRadius,true);
}
}
}
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