/**
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   * 	contributors may be used to endorse or promote products derived from this
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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;
  import org.openimaj.util.function.Operation;
  import org.openimaj.util.parallel.Parallel;
  
  public class CachedTrainingData implements LabelledDataProvider {
  	float[][] responses;
  	boolean[] classes;
  	int[][] sortedIndices;
  	List<HaarFeature> features;
  	int width, height;
  
  	float computeWindowVarianceNorm(SummedSqTiltAreaTable sat) {
  		final int w = width - 2;
  		final int h = height - 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;
  	}
  
  	public CachedTrainingData(final List<SummedSqTiltAreaTable> positive, final List<SummedSqTiltAreaTable> negative,
  			final List<HaarFeature> features)
  	{
  		this.width = positive.get(0).sum.width - 1;
  		this.height = positive.get(0).sum.height - 1;
  
  		this.features = features;
  		final int nfeatures = features.size();
  
  		classes = new boolean[positive.size() + negative.size()];
  		responses = new float[nfeatures][classes.length];
  		sortedIndices = new int[nfeatures][];
  		// for (int f = 0; f < nfeatures; f++) {
  
  		Parallel.forIndex(0, nfeatures, 1, new Operation<Integer>() {
  
  			@Override
  			public void perform(Integer f) {
  				final HaarFeature feature = features.get(f);
  				int count = 0;
  
  				for (final SummedSqTiltAreaTable t : positive) {
  					final float wvNorm = computeWindowVarianceNorm(t);
  					responses[f][count] = feature.computeResponse(t, 0, 0) / wvNorm;
  					classes[count] = true;
  					++count;
  				}
  
  				for (final SummedSqTiltAreaTable t : negative) {
  					final float wvNorm = computeWindowVarianceNorm(t);
  					responses[f][count] = feature.computeResponse(t, 0, 0) / wvNorm;
  					classes[count] = false;
 					++count;
 				}
 
 				sortedIndices[f] = ArrayUtils.indexSort(responses[f]);
 			}
 		});
 	}
 
 	@Override
 	public float[] getFeatureResponse(int dimension) {
 		return responses[dimension];
 	}
 
 	@Override
 	public boolean[] getClasses() {
 		return classes;
 	}
 
 	@Override
 	public int numInstances() {
 		return classes.length;
 	}
 
 	@Override
 	public int numDimensions() {
 		return responses.length;
 	}
 
 	@Override
 	public float[] getInstanceFeature(int idx) {
 		final float[] feature = new float[responses.length];
 
 		for (int i = 0; i < feature.length; i++) {
 			feature[i] = responses[i][idx];
 		}
 
 		return feature;
 	}
 
 	@Override
 	public int[] getSortedResponseIndices(int d) {
 		return sortedIndices[d];
 	}
 
 	public HaarFeature getFeature(int dimension) {
 		return features.get(dimension);
 	}
 }