/**
    * 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:
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    * 	this list of conditions and the following disclaimer.
   *
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   * 	this list of conditions and the following disclaimer in the documentation
   * 	and/or other materials provided with the distribution.
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   * 	contributors may be used to endorse or promote products derived from this
   * 	software without specific prior written permission.
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   * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
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  package org.openimaj.image.objectdetection.haar.training;
  
  import java.util.List;
  
  import org.openimaj.image.analysis.algorithm.SummedSqTiltAreaTable;
  import org.openimaj.image.objectdetection.haar.HaarFeature;
  import org.openimaj.ml.classification.LabelledDataProvider;
  import org.openimaj.util.array.ArrayUtils;
  
  public class BasicTrainingData implements LabelledDataProvider {
  	SummedSqTiltAreaTable[] sats;
  	boolean[] classes;
  	HaarFeature[] features;
  
  	public BasicTrainingData(List<SummedSqTiltAreaTable> positive, List<SummedSqTiltAreaTable> negative,
  			List<HaarFeature> features)
  	{
  		sats = new SummedSqTiltAreaTable[positive.size() + negative.size()];
  		classes = new boolean[sats.length];
  
  		int count = 0;
  		for (final SummedSqTiltAreaTable t : positive) {
  			sats[count] = t;
  			classes[count] = true;
  			++count;
  		}
  
  		for (final SummedSqTiltAreaTable t : negative) {
  			sats[count] = t;
  			classes[count] = false;
  			++count;
  		}
  
  		this.features = features.toArray(new HaarFeature[features.size()]);
  	}
  
  	@Override
  	public float[] getFeatureResponse(int dimension) {
  		final float[] response = new float[sats.length];
  
  		for (int i = 0; i < sats.length; i++) {
  			final float wvNorm = computeWindowVarianceNorm(sats[i]);
  
  			response[i] = features[dimension].computeResponse(sats[i], 0, 0) / wvNorm;
  		}
  
  		return response;
  	}
  
  	@Override
  	public boolean[] getClasses() {
  		return classes;
  	}
  
  	@Override
  	public int numInstances() {
  		return classes.length;
  	}
  
  	@Override
  	public int numDimensions() {
  		return features.length;
  	}
  
  	float computeWindowVarianceNorm(SummedSqTiltAreaTable sat) {
  		final int w = sat.sum.width - 1 - 2;
  		final int h = sat.sum.height - 1 - 2;
  
  		final int x = 1; // shift by 1 scaled px to centre box
  		final int y = 1;
 
 		final float sum = sat.sum.pixels[y + h][x + w] + sat.sum.pixels[y][x] -
 				sat.sum.pixels[y + h][x] - sat.sum.pixels[y][x + w];
 		final float sqSum = sat.sqSum.pixels[y + w][x + w] + sat.sqSum.pixels[y][x] -
 				sat.sqSum.pixels[y + w][x] - sat.sqSum.pixels[y][x + w];
 
 		final float cachedInvArea = 1.0f / (w * h);
 		final float mean = sum * cachedInvArea;
 		float wvNorm = sqSum * cachedInvArea - mean * mean;
 		wvNorm = (float) ((wvNorm >= 0) ? Math.sqrt(wvNorm) : 1);
 
 		return wvNorm;
 	}
 
 	@Override
 	public float[] getInstanceFeature(int idx) {
 		final float[] feature = new float[features.length];
 		final SummedSqTiltAreaTable sat = sats[idx];
 
 		final float wvNorm = computeWindowVarianceNorm(sat);
 
 		for (int i = 0; i < features.length; i++) {
 			feature[i] = features[i].computeResponse(sat, 0, 0) / wvNorm;
 		}
 
 		return feature;
 	}
 
 	@Override
 	public int[] getSortedResponseIndices(int d) {
 		return ArrayUtils.indexSort(getFeatureResponse(d));
 	}
 
 	public HaarFeature getFeature(int dimension) {
 		return features[dimension];
 	}
 }