/**
 * Copyright (c) 2011, The University of Southampton and the individual contributors.
 * All rights reserved.
 *
 * Redistribution and use in source and binary forms, with or without modification,
 * are permitted provided that the following conditions are met:
 *
 *   *  Redistributions of source code must retain the above copyright notice,
 *      this list of conditions and the following disclaimer.
 *
 *   *  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.
 *
 *   *  Neither the name of the University of Southampton 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 OWNER 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
 * ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
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 * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
 */
package org.openimaj.ml.clustering.spectral;

import org.openimaj.ml.clustering.SpatialClusterer;
import org.openimaj.ml.clustering.SpatialClusters;
import org.openimaj.util.function.Function;
import org.openimaj.util.pair.IndependentPair;

/**
 * @author Sina Samangooei (ss@ecs.soton.ac.uk)
 * @param <DATATYPE>
 *
 */
public class SpectralClusteringConf<DATATYPE> {
        /**
         * A function which can represent itself as a string
         *
         * @param <DATATYPE>
         * @author Sina Samangooei (ss@ecs.soton.ac.uk)
         */
        public static interface ClustererProvider<DATATYPE>
                        extends
                                Function<IndependentPair<double[], double[][]>, SpatialClusterer<? extends SpatialClusters<DATATYPE>, DATATYPE>>
        {
                @Override
                public String toString();
        }

        protected static class DefaultClustererFunction<DATATYPE> implements ClustererProvider<DATATYPE> {

                private SpatialClusterer<? extends SpatialClusters<DATATYPE>, DATATYPE> internal;

                public DefaultClustererFunction(SpatialClusterer<? extends SpatialClusters<DATATYPE>, DATATYPE> internal) {
                        this.internal = internal;
                }

                @Override
                public SpatialClusterer<? extends SpatialClusters<DATATYPE>, DATATYPE> apply(
                                IndependentPair<double[], double[][]> in)
                {
                        return internal;
                }

                @Override
                public String toString() {
                        return internal.toString();
                }

        }

        /**
         * The internal clusterer
         */

        ClustererProvider<DATATYPE> internal;

        /**
         * The graph laplacian creator
         */
        public GraphLaplacian laplacian;

        /**
         * The method used to select the number of eigen vectors from the lower
         * valued eigenvalues
         */
        public EigenChooser eigenChooser;

        /**
         *
         */
        public int skipEigenVectors = 0;

        /**
         *
         */
        public boolean eigenValueScale = false;

        /**
         * @param internal
         *            the internal clusterer
         * @param eigK
         *            the value handed to {@link HardCodedEigenChooser}
         *
         */
        public SpectralClusteringConf(SpatialClusterer<? extends SpatialClusters<DATATYPE>, DATATYPE> internal, int eigK) {
                this.internal = new DefaultClustererFunction<DATATYPE>(internal);
                this.laplacian = new GraphLaplacian.Normalised();
                this.eigenChooser = new HardCodedEigenChooser(eigK);

        }

        /**
         * The underlying {@link EigenChooser} is set to an
         * {@link ChangeDetectingEigenChooser} which looks for a 100x gap between
         * eigen vectors to select number of clusters. It also insists upon a
         * maximum of 0.1 * number of data items (so 10 items per cluster)
         *
         * @param internal
         *            the internal clusterer
         *
         */
        public SpectralClusteringConf(SpatialClusterer<? extends SpatialClusters<DATATYPE>, DATATYPE> internal) {
                this.internal = new DefaultClustererFunction<DATATYPE>(internal);
                this.laplacian = new GraphLaplacian.Normalised();
                this.eigenChooser = new ChangeDetectingEigenChooser(100, 0.1);

        }

        /**
         * @param internal
         *            an internal clusterer
         * @param lap
         *            the laplacian
         * @param top
         *            the top eigen vectors
         */
        public SpectralClusteringConf(SpatialClusterer<? extends SpatialClusters<DATATYPE>, DATATYPE> internal,
                        GraphLaplacian lap, int top)
        {
                this.internal = new DefaultClustererFunction<DATATYPE>(internal);
                this.laplacian = lap;
                this.eigenChooser = new HardCodedEigenChooser(top);
        }

        /**
         * The underlying {@link EigenChooser} is set to an
         * {@link ChangeDetectingEigenChooser} which looks for a 100x gap between
         * eigen vectors to select number of clusters. It also insists upon a
         * maximum of 0.1 * number of data items (so 10 items per cluster)
         *
         * @param internal
         *            the internal clusterer
         * @param laplacian
         *            the graph laplacian
         *
         */
        public SpectralClusteringConf(SpatialClusterer<? extends SpatialClusters<DATATYPE>, DATATYPE> internal,
                        GraphLaplacian laplacian)
        {
                this.internal = new DefaultClustererFunction<DATATYPE>(internal);
                this.laplacian = laplacian;
                this.eigenChooser = new ChangeDetectingEigenChooser(100, 0.1);

        }

        /**
         * The underlying {@link EigenChooser} is set to an
         * {@link ChangeDetectingEigenChooser} which looks for a 100x gap between
         * eigen vectors to select number of clusters. It also insists upon a
         * maximum of 0.1 * number of data items (so 10 items per cluster)
         * 
         * @param internal
         *            the internal clusterer
         *
         */
        public SpectralClusteringConf(ClustererProvider<DATATYPE> internal) {
                this.internal = internal;
                this.laplacian = new GraphLaplacian.Normalised();
                this.eigenChooser = new ChangeDetectingEigenChooser(100, 0.1);

        }

}