/**
    * 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
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   * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
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   * ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
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  package org.openimaj.demos;
  
  import java.io.File;
  import java.io.IOException;
  import java.util.ArrayList;
  import java.util.Collections;
  import java.util.List;
  
  import org.openimaj.feature.local.list.MemoryLocalFeatureList;
  import org.openimaj.image.feature.local.aggregate.FisherVector;
  import org.openimaj.image.feature.local.keypoints.FloatKeypoint;
  import org.openimaj.image.feature.local.keypoints.Keypoint;
  import org.openimaj.math.matrix.algorithm.pca.ThinSvdPrincipalComponentAnalysis;
  import org.openimaj.math.statistics.distribution.MixtureOfGaussians;
  import org.openimaj.ml.gmm.GaussianMixtureModelEM;
  import org.openimaj.ml.gmm.GaussianMixtureModelEM.CovarianceType;
  import org.openimaj.util.array.ArrayUtils;
  
  import Jama.Matrix;
  
  public class FisherTesting {
  	public static void main(String[] args) throws IOException {
  		final List<MemoryLocalFeatureList<FloatKeypoint>> data = new ArrayList<MemoryLocalFeatureList<FloatKeypoint>>();
  		final List<FloatKeypoint> allKeys = new ArrayList<FloatKeypoint>();
  
  		for (int i = 0; i < 100; i++) {
  			final MemoryLocalFeatureList<FloatKeypoint> tmp = FloatKeypoint.convert(MemoryLocalFeatureList.read(
  					new File(String.format("/Users/jsh2/Data/ukbench/sift/ukbench%05d.jpg", i)), Keypoint.class));
  			data.add(tmp);
  			allKeys.addAll(tmp);
  		}
  
  		Collections.shuffle(allKeys);
  
  		final double[][] sample128 = new double[1000][];
  		for (int i = 0; i < sample128.length; i++) {
  			sample128[i] = ArrayUtils.convertToDouble(allKeys.get(i).vector);
  		}
  
  		System.out.println("Performing PCA " + sample128.length);
  		final ThinSvdPrincipalComponentAnalysis pca = new ThinSvdPrincipalComponentAnalysis(64);
  		pca.learnBasis(sample128);
  		final double[][] sample64 = pca.project(new Matrix(sample128)).getArray();
  
  		System.out.println("Projecting features");
  		for (final MemoryLocalFeatureList<FloatKeypoint> kpl : data) {
  			for (final FloatKeypoint kp : kpl) {
  				kp.vector = ArrayUtils.convertToFloat(pca.project(ArrayUtils.convertToDouble(kp.vector)));
  			}
  		}
  
  		System.out.println("Learning GMM " + sample64.length);
  		final GaussianMixtureModelEM gmmem = new GaussianMixtureModelEM(512, CovarianceType.Diagonal);
  		final MixtureOfGaussians gmm = gmmem.estimate(sample64);
  
  		final double[][] v1 = gmm.logProbability(new double[][] { sample64[0] });
  		final double[][] v2 = MixtureOfGaussians.logProbability(new double[][] { sample64[0] }, gmm.gaussians);
  
  		System.out.println("Done");
  
  		// for (int i = 0; i < 512; i++) {
  		// System.out.println(gmm.gaussians[i].getMean().get(0, 0) + "," +
  		// gmm.gaussians[i].getMean().get(0, 1));
  		// }
  
  		final FisherVector<float[]> fisher = new FisherVector<float[]>(gmm, true, true);
  
  		final List<FloatKeypoint> kpl = allKeys.subList(0, 26000);
  		final long t1 = System.currentTimeMillis();
  		fisher.aggregate(kpl).asDoubleVector();
 		final long t2 = System.currentTimeMillis();
 		System.out.println(t2 - t1);
 
 		// int i = 0;
 		// final double[][] fvs = new double[5][];
 		// for (final MemoryLocalFeatureList<FloatKeypoint> kpl : data) {
 		// final long t1 = System.currentTimeMillis();
 		// fvs[i++] = fisher.aggregate(kpl).asDoubleVector();
 		// final long t2 = System.currentTimeMillis();
 		// System.out.println(t2 - t1);
 		//
 		// if (i == 5)
 		// break;
 		// }
 		//
 		// final ThinSvdPrincipalComponentAnalysis pca2 = new
 		// ThinSvdPrincipalComponentAnalysis(128);
 		// pca2.learnBasis(fvs);
 	}
 }