net.jkernelmachines.kernel.adaptative.GaussianProductKernel Maven / Gradle / Ivy

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JKernelMachines is a java library for learning with kernels. It is primary designed to deal with custom kernels that are not easily found in standard libraries, such as kernels on structured data.
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/*******************************************************************************
 * Copyright (c) 2016, David Picard.
 * All rights reserved.
 *
 * Redistribution and use in source and binary forms, with or without modification,
 * are permitted provided that the following conditions are met:
 *
 * 1. Redistributions of source code must retain the above copyright notice, this
 * list of conditions and the following disclaimer.
 *
 * 2. Redistributions in binary form must reproduce the above copyright notice,
 * this list of conditions and the following disclaimer in the documentation and/or
 * other materials provided with the distribution.
 *
 * 3. Neither the name of the copyright holder nor the names of its contributors
 * may be used to endorse or promote products derived from this software without
 * specific prior written permission.
 *
 * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
 * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
 * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
 * DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR
 * ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
 * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
 * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
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 * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
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package net.jkernelmachines.kernel.adaptative;

import java.util.Arrays;
import java.util.HashMap;
import java.util.List;
import java.util.Map;

import net.jkernelmachines.kernel.GaussianKernel;
import net.jkernelmachines.kernel.Kernel;
import net.jkernelmachines.threading.ThreadedMatrixOperator;
import net.jkernelmachines.type.TrainingSample;

/**
 * Major kernel computed as a weighted product of minor Gaussian kernels : 
 * K =  k_i^{w_i}
 * Computation of the kernel matrix is done by running a thread on sub matrices.
 * The number of threads is chosen as function of the number of available cpus.
 * @author dpicard
 *
 * @param  samples datatype
 */
public class GaussianProductKernel extends Kernel {

	/**
	 * 
	 */
	private static final long serialVersionUID = 7780445301175174296L;
	
	
	private Map, Double> kernels;
	protected int numThread = 0;
	
	public GaussianProductKernel()
	{
		kernels = new HashMap, Double>();
	}

	/**
	 * Sets the weights to h. Beware! It does not make a copy of h!
	 * @param h weights
	 */
	public GaussianProductKernel(Map, Double> h)
	{
		kernels = new HashMap, Double>();
		kernels.putAll(h);
	}
	
	/**
	 * adds a kernel to the sum with weight 1.0
	 * @param k underlying kernel
	 */
	public void addKernel(GaussianKernel k)
	{
		synchronized(kernels)
		{
			kernels.put(k, 1.0);
		}
	}
	
	/**
	 * adds a kernel to the product with weight d
	 * @param k kernel
	 * @param d corresponding weight
	 */
	public void addKernel(GaussianKernel k , double d)
	{
		synchronized(kernels)
		{
			kernels.put(k, d);
		}
	}
	
	/**
	 * removes kernel k from the product
	 * @param k kernel
	 */
	public void removeKernel(GaussianKernel k)
	{
		synchronized(kernels)
		{
			kernels.remove(k);
		}
	}
	
	/**
	 * gets the weights of kernel k
	 * @param k kernel
	 * @return the weight associated with k
	 */
	public double getWeight(GaussianKernel k)
	{
		synchronized(kernels)
		{
			Double d = kernels.get(k);
			if(d == null)
				return 0.;
			return d.doubleValue();
		}
	}
	
	/**
	 * Sets the weight of kernel k
	 * @param k kernel
	 * @param d weight
	 */
	public void setWeight(GaussianKernel k, Double d)
	{
		synchronized(kernels)
		{
			kernels.put(k, d);
		}
	}
	
	@Override
	public double valueOf(T t1, T t2) {
		double sum = 1.;
		for(GaussianKernel k : kernels.keySet())
		{
			double w = kernels.get(k);
			if(w != 0) {
				k.setGamma(w);
				sum *= k.valueOf(t1, t2);
			}
		}
		
		return sum;
	}

	@Override
	public double valueOf(T t1) {
		return valueOf(t1, t1);
	}
	
	/**
	 * get the list of kernels and associated weights.
	 * @return hashtable containing kernels as keys and weights as values.
	 */
	public Map, Double> getWeights()
	{
		return kernels;
	}
	
	@Override
	public double[][] getKernelMatrix(List> list)
	{
		final List> l = list;
		//init matrix with ones
		double matrix[][] = new double[l.size()][l.size()];
		for(double[] lines : matrix)
			Arrays.fill(lines, 1.);
		

		for(final GaussianKernel k : kernels.keySet())
		{
			final double w = kernels.get(k);
			
			//check w
			if(w == 0)
				continue;
			
			k.setGamma(w);
			final double[][] m = k.getKernelMatrix(l);
			// specific factory
			ThreadedMatrixOperator tmo = new ThreadedMatrixOperator(){
				
				@Override
				public void doLines(double[][] matrix , int from , int to) {
					
					for(int index = from ; index < to ; index++)
					{
						for(int i = 0 ; i < m[index].length ; i++)
						{
							matrix[index][i] *= m[index][i];
						}
					}
				};
				
			};
			
			matrix = tmo.getMatrix(matrix);
		}
		
		return matrix;
	}
}