/**
 * 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
 * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
 * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
 */
package org.openimaj.ml.annotation.basic;

import gnu.trove.list.array.TDoubleArrayList;
import gnu.trove.map.hash.TIntIntHashMap;
import gnu.trove.map.hash.TObjectIntHashMap;
import gnu.trove.procedure.TObjectIntProcedure;

import java.util.ArrayList;
import java.util.Collection;
import java.util.HashSet;
import java.util.List;
import java.util.Set;

import org.openimaj.ml.annotation.Annotated;
import org.openimaj.ml.annotation.BatchAnnotator;
import org.openimaj.ml.annotation.ScoredAnnotation;
import org.openimaj.ml.annotation.basic.util.NumAnnotationsChooser;

import cern.jet.random.Empirical;
import cern.jet.random.EmpiricalWalker;
import cern.jet.random.engine.MersenneTwister;

/**
 * Annotator that randomly assigns annotations, but takes account of the prior
 * probability of each annotation based on the proportion of times it occurred
 * in training. Annotations that occurred less in training are less likely to be
 * picked. The number of annotations produced is set by the type of
 * {@link NumAnnotationsChooser} used.
 * 
 * @author Jonathon Hare (jsh2@ecs.soton.ac.uk)
 * 
 * @param <OBJECT>
 *            Type of object being annotated
 * @param <ANNOTATION>
 *            Type of annotation.
 */
public class IndependentPriorRandomAnnotator<OBJECT, ANNOTATION> extends BatchAnnotator<OBJECT, ANNOTATION> {
        protected List<ANNOTATION> annotations;
        protected NumAnnotationsChooser numAnnotations;
        protected EmpiricalWalker annotationProbability;

        /**
         * Construct with the given {@link NumAnnotationsChooser} to determine how
         * many annotations are produced by calls to {@link #annotate(Object)}.
         * 
         * @param chooser
         *            the {@link NumAnnotationsChooser} to use.
         */
        public IndependentPriorRandomAnnotator(NumAnnotationsChooser chooser) {
                this.numAnnotations = chooser;
        }

        @Override
        public void train(List<? extends Annotated<OBJECT, ANNOTATION>> data) {
                final TIntIntHashMap nAnnotationCounts = new TIntIntHashMap();
                final TObjectIntHashMap<ANNOTATION> annotationCounts = new TObjectIntHashMap<ANNOTATION>();
                int maxVal = 0;

                for (final Annotated<OBJECT, ANNOTATION> sample : data) {
                        final Collection<ANNOTATION> annos = sample.getAnnotations();

                        for (final ANNOTATION s : annos) {
                                annotationCounts.adjustOrPutValue(s, 1, 1);
                        }

                        nAnnotationCounts.adjustOrPutValue(annos.size(), 1, 1);

                        if (annos.size() > maxVal)
                                maxVal = annos.size();
                }

                // build distribution and rng for each annotation
                annotations = new ArrayList<ANNOTATION>();
                final TDoubleArrayList probs = new TDoubleArrayList();
                annotationCounts.forEachEntry(new TObjectIntProcedure<ANNOTATION>() {
                        @Override
                        public boolean execute(ANNOTATION a, int b) {
                                annotations.add(a);
                                probs.add(b);
                                return true;
                        }
                });
                annotationProbability = new EmpiricalWalker(probs.toArray(), Empirical.NO_INTERPOLATION, new MersenneTwister());

                numAnnotations.train(data);
        }

        @Override
        public List<ScoredAnnotation<ANNOTATION>> annotate(OBJECT image) {
                final int nAnnotations = numAnnotations.numAnnotations();

                final List<ScoredAnnotation<ANNOTATION>> annos = new ArrayList<ScoredAnnotation<ANNOTATION>>();

                for (int i = 0; i < nAnnotations; i++) {
                        final int annotationIdx = annotationProbability.nextInt();
                        annos.add(new ScoredAnnotation<ANNOTATION>(annotations.get(annotationIdx), (float) annotationProbability
                                        .pdf(annotationIdx + 1)));
                }

                return annos;
        }

        @Override
        public Set<ANNOTATION> getAnnotations() {
                return new HashSet<ANNOTATION>(annotations);
        }
}