/* XXL: The eXtensible and fleXible Library for data processing

Copyright (C) 2000-2011 Prof. Dr. Bernhard Seeger
                        Head of the Database Research Group
                        Department of Mathematics and Computer Science
                        University of Marburg
                        Germany

This library is free software; you can redistribute it and/or
modify it under the terms of the GNU Lesser General Public
License as published by the Free Software Foundation; either
version 3 of the License, or (at your option) any later version.

This library is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
Lesser General Public License for more details.

You should have received a copy of the GNU Lesser General Public
License along with this library;  If not, see <http://www.gnu.org/licenses/>. 

    http://code.google.com/p/xxl/

*/

package xxl.core.math.statistics.nonparametric.kernels;

import java.util.List;

import xxl.core.functions.AbstractFunction;
import xxl.core.functions.Function;
import xxl.core.math.functions.Integrable;
import xxl.core.math.functions.RealFunction;

/**
 * This class implements an abstract preimplementation 
 * of a <tt>kernel based cdf estimator</tt> 
 * {@link xxl.core.math.statistics.nonparametric.kernels.AbstractKernelCDF AbstractKernelCDF}
 * that doesn't use an explicit technique for avoiding boundary effects.
 * Additionally, this class provides a FACTORY for generating <tt>native kernel based cdf estimators</tt>.
 * 
 * @see xxl.core.math.statistics.nonparametric.kernels.KernelFunction
 * @see xxl.core.math.statistics.nonparametric.kernels.KernelBandwidths
 * @see xxl.core.math.functions.AbstractRealFunctionFunction
 * @see xxl.core.math.statistics.nonparametric.kernels.AbstractKernelCDF
 * @see xxl.core.math.statistics.nonparametric.kernels.ReflectionKernelCDF
 *
 */

public class NativeKernelCDF extends AbstractKernelCDF {

	/** Provides a factory for a
	 * {@link xxl.core.math.statistics.nonparametric.kernels.NativeKernelCDF native kernel cdf estimator}
	 * , i.e., an estimator
	 * without any boundary treatment technique.
	 * The parameters needed for construction are passed to the factory by an
	 * <tt>Object[]</tt> <code>o</code> containing: <BR>
	 * <code>o[0]</code>: used {@link xxl.core.math.statistics.nonparametric.kernels.KernelFunction kernel function} <BR>
	 * <code>o[1]</code>: used sample as <tt>Object[]</tt> containing numerical data <BR>
	 * <code>o[2]</code>: used bandwidth as Object of type <tt>Number</tt> <BR>
	 */
	public static Function FACTORY = new AbstractFunction<Object,NativeKernelCDF>() {
		public NativeKernelCDF invoke(List<? extends Object> list) {
			return new NativeKernelCDF(
				 (KernelFunction) list.get(0), // kernel function
				 (Object[]) list.get(1), // sample
				 ((Number) list.get(2)).doubleValue() // bandwidth
			);
		}
	};

	/**
	* Constructs a new kernel based cdf estimator.
	*
	* @param kf used {@link xxl.core.math.statistics.nonparametric.kernels.KernelFunction Kernel function} with 
	* support restricted to [-1,1]
	* @param sample sample of a data set given as <tt>Object[]</tt> containing
	* objects of type <tt>Number</tt>.
	* @param h used bandwidth for computing the estimation
	* @throws IllegalArgumentException if a kernel function without restricted support to
	* <tt>[-1,1]</tt> is given or a kernel function is given that doesn't implement the
	* interface {@link xxl.core.math.functions.Integrable Integrable}
	*
	* @see xxl.core.math.statistics.nonparametric.kernels.KernelFunction
	* @see xxl.core.math.statistics.nonparametric.kernels.KernelBandwidths
	*/
	public NativeKernelCDF(KernelFunction kf, Object[] sample, double h) throws IllegalArgumentException {
		super(kf, sample, h);
	}

	/** Evaluates the cdf estimator at the given point x.
	 * 
	 * @param x argument where to evaluate the cdf
	 * @return value of the estimated cdf at x
	 */
	protected double evalKDE(double x) {
		double r = 0.0;
		RealFunction prim = ((Integrable) kf).primitive();
		int size = sample.length;
		for (int i = 0; i < size; i++) {
			double xi = ((Number) sample[i]).doubleValue();
			if (xi <= x - h)
				r += 1.0;
			else {
				if (xi <= x + h) {
					r += prim.eval((x - xi) / h);
				}
			}
		}
		return r / size;
	}

	/** Returns an estimation of the window (interval) selectivity given.
	 * 
	 * @param left left border of the window
	 * @param right right border of the window
	 * @return an estimation of the window selectivity
	 */
	public double windowQuery(Object left, Object right) {
		if (hasChanged())
			reinit();
		double a = ((Number) left).doubleValue();
		double b = ((Number) right).doubleValue();
		double al = a; // temp var for restricted kernel support
		double bl = b; // temp var for restricted kernel support
		double r = 0.0;
		RealFunction prim = ((Integrable) kf).primitive();
		int size = sample.length;
		for (int i = 0; i < size; i++) {
			double xi = ((Number) sample[i]).doubleValue();
			if ((xi > a - h) && (xi < b + h)) {
				if ((xi >= a + h) && (xi <= b - h))
					r += 1.0;
				else {
					al = Math.max((a - xi) / h, -1.0);
					bl = Math.min((b - xi) / h, 1.0);
					r += prim.eval(bl) - prim.eval(al);
				}
			}
		}
		return r / size;
	}
}