/**
    * 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.demos.ml.linear.data;
  
  import java.util.ArrayList;
  import java.util.List;
  
  import org.openimaj.ml.linear.data.FixedDataGenerator;
  import org.openimaj.ml.linear.kernel.LinearVectorKernel;
  import org.openimaj.ml.linear.learner.perceptron.DoubleArrayKernelPerceptron;
  import org.openimaj.ml.linear.learner.perceptron.MeanCenteredKernelPerceptron;
  import org.openimaj.ml.linear.learner.perceptron.PerceptronClass;
  import org.openimaj.ml.linear.learner.perceptron.ThresholdDoubleArrayKernelPerceptron;
  import org.openimaj.util.pair.IndependentPair;
  
  import cern.colt.Arrays;
  
  /**
   *
   * @author Sina Samangooei (ss@ecs.soton.ac.uk)
   */
  public class WikipediaPerceptronExample {
  
  	/**
  	 * @param args
  	 */
  	public static void main(String[] args) {
  		thresholded(createData());
  		centered(createData());
  	}
  
  	private static void centered(FixedDataGenerator<double[], PerceptronClass> fdg) {
  		System.out.println("CENTERED");
  		final DoubleArrayKernelPerceptron mkp = new MeanCenteredKernelPerceptron(new LinearVectorKernel());
  		for (int i = 0; i < 10; i++) {
  			System.out.println("Iteration: " + i);
  			for (int j = 0; j < 4; j++) {
  				final IndependentPair<double[], PerceptronClass> v = fdg.generate();
  				final double[] x = v.firstObject();
  				final PerceptronClass y = v.secondObject();
  				final PerceptronClass yestb = mkp.predict(x);
  				mkp.process(x, y);
  				final PerceptronClass yesta = mkp.predict(x);
  
  				System.out.println(String.format("x: %s, y: %s, ypred_b: %s, ypred_a: %s", Arrays.toString(x), y, yestb,
  						yesta));
  				// System.out.println(mkp.getWeights());
  			}
  		}
  	}
  
  	private static FixedDataGenerator<double[], PerceptronClass> createData() {
  
  		final List<IndependentPair<double[], PerceptronClass>> data = new ArrayList<IndependentPair<double[], PerceptronClass>>();
  		data.add(IndependentPair.pair(new double[] { 1, 0, 0 }, PerceptronClass.TRUE));
  		data.add(IndependentPair.pair(new double[] { 1, 0, 1 }, PerceptronClass.TRUE));
  		data.add(IndependentPair.pair(new double[] { 1, 1, 0 }, PerceptronClass.TRUE));
  		data.add(IndependentPair.pair(new double[] { 1, 1, 1 }, PerceptronClass.FALSE));
  		final FixedDataGenerator<double[], PerceptronClass> fdg = new FixedDataGenerator<double[], PerceptronClass>(data);
  		return fdg;
  	}
  
  	private static void thresholded(
  			FixedDataGenerator<double[], PerceptronClass> fdg)
  	{
  		System.out.println("Thresholded");
  		final DoubleArrayKernelPerceptron mkp = new ThresholdDoubleArrayKernelPerceptron(new LinearVectorKernel());
  		for (int i = 0; i < 10; i++) {
  			System.out.println("Iteration: " + i);
  			for (int j = 0; j < 4; j++) {
  				final IndependentPair<double[], PerceptronClass> v = fdg.generate();
  				final double[] x = v.firstObject();
 				final PerceptronClass y = v.secondObject();
 				final PerceptronClass yestb = mkp.predict(x);
 				mkp.process(x, y);
 				final PerceptronClass yesta = mkp.predict(x);
 
 				System.out.println(String.format("x: %s, y: %s, ypred_b: %s, ypred_a: %s", Arrays.toString(x), y, yestb,
 						yesta));
 				// System.out.println(mkp.getWeights());
 			}
 		}
 	}
 
 }