/**
    * 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.
   *
   *   *	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
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   * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
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  package org.openimaj.workinprogress.featlearn;
  
  import no.uib.cipr.matrix.DenseMatrix;
  import no.uib.cipr.matrix.DenseVector;
  import no.uib.cipr.matrix.Matrices;
  import no.uib.cipr.matrix.Matrix.Norm;
  import no.uib.cipr.matrix.Vector;
  
  import org.openimaj.workinprogress.optimisation.DifferentiableObjectiveFunction;
  import org.openimaj.workinprogress.optimisation.params.VectorParameters;
  
  public class SparseAutoencoder
  		implements
  		DifferentiableObjectiveFunction<SparseAutoencoder.Model, double[], VectorParameters>
  {
  	public static class Model {
  		DenseMatrix w1;
  		DenseMatrix w2;
  		DenseVector b1;
  		DenseVector b2;
  
  		public Model(int nvisible, int nhidden) {
  			w1 = (DenseMatrix) Matrices.random(nhidden, nhidden);
  			w2 = (DenseMatrix) Matrices.random(nvisible, nhidden);
  			b1 = (DenseVector) Matrices.random(nhidden);
  			b2 = (DenseVector) Matrices.random(nvisible);
  		}
  
  		double[] feedforward(double[] input) {
  			final DenseVector iv = new DenseVector(input, false);
  			final DenseVector a1 = sigmoid(w1.multAdd(iv, b1.copy()));
  			final DenseVector a2 = sigmoid(w2.multAdd(a1, b2.copy()));
  
  			return a2.getData();
  		}
  
  		double[][] getLayerActivations(double[] input) {
  			final DenseVector iv = new DenseVector(input, false);
  			final DenseVector a1 = sigmoid(w1.multAdd(iv, b1.copy()));
  			final DenseVector a2 = sigmoid(w2.multAdd(a1, b2.copy()));
  			return new double[][] { a1.getData(), a2.getData() };
  		}
  
  		private DenseVector sigmoid(Vector vector) {
  			final DenseVector out = (DenseVector) vector.copy();
  
  			final double[] xd = out.getData();
  
  			for (int i = 0; i < xd.length; i++)
  				xd[i] = 1 / (1 + Math.exp(-xd[i]));
  
  			return out;
  		}
  	}
  
  	private double lamda; // regularization for weight decay
  
  	@Override
  	public double value(Model model, double[] data) {
  		final double[] predict = model.feedforward(data);
  
  		double err = 0;
  		for (int i = 0; i < predict.length; i++) {
  			final double diff = predict[i] - data[i];
  			err += (diff * diff);
  		}
  
  		// Note that this is rather expensive each iter... can it be improved?
  		final double n1 = model.w1.norm(Norm.Frobenius);
  		final double n2 = model.w2.norm(Norm.Frobenius);
 		final double reg = n1 * n1 + n2 * n2;
 
 		return 0.5 * (err + lamda * reg);
 	}
 
 	@Override
 	public VectorParameters derivative(Model model, double[] data) {
 		final double[][] as = model.getLayerActivations(data);
 
 		final double[] d2 = new double[as[1].length];
 		for (int i = 0; i < d2.length; i++) {
 			d2[i] = -(data[i] - as[1][i]) * (as[1][i] * (1 - as[1][i]));
 		}
 
 		final DenseVector wd = (DenseVector) model.w1.transMult(new DenseVector(d2),
 				new DenseVector(model.w1.numColumns()));
 		final double[] d1 = wd.getData();
 		for (int i = 0; i < d1.length; i++) {
 			d1[i] *= (as[0][i] * (1 - as[0][i]));
 		}
 
 		return null;
 	}
 
 	@Override
 	public void updateModel(Model model, VectorParameters weights) {
 		// TODO Auto-generated method stub
 
 	}
 }