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/*
 *   This program is free software: you can redistribute it and/or modify
 *   it under the terms of the GNU General Public License as published by
 *   the Free Software Foundation, either version 3 of the License, or
 *   (at your option) any later version.
 *
 *   This program is distributed in the hope that it will be useful,
 *   but WITHOUT ANY WARRANTY; without even the implied warranty of
 *   MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 *   GNU General Public License for more details.
 *
 *   You should have received a copy of the GNU General Public License
 *   along with this program.  If not, see .
 */

package weka.classifiers.neural.singlelayerperceptron;

import weka.classifiers.neural.common.NeuralModel;
import weka.classifiers.neural.common.SimpleNeuron;
import weka.classifiers.neural.common.WekaAlgorithmAncestor;
import weka.classifiers.neural.common.learning.LearningKernelFactory;
import weka.classifiers.neural.common.learning.LearningRateKernel;
import weka.classifiers.neural.common.training.TrainerFactory;
import weka.classifiers.neural.common.transfer.TransferFunction;
import weka.classifiers.neural.common.transfer.TransferFunctionFactory;
import weka.classifiers.neural.singlelayerperceptron.algorithm.PerceptronAlgorithm;
import weka.core.Instances;
import weka.core.Option;
import weka.core.SelectedTag;

import java.util.ArrayList;
import java.util.Collection;

/**
 * Title: Weka Neural Implementation
 * Description: ...
 * Copyright: Copyright (c) 2003
 * Company: N/A
 *
 * @author Jason Brownlee
 * @version 1.0
 */

public class Perceptron extends WekaAlgorithmAncestor {

  private final static int EXTRA_PARAM_LEARNING_RATE_FUNCTION = 0;

  private final static String[] EXTRA_PARAMETERS =
    {
      "M" // learning rate function
    };

  private final static String[] EXTRA_PARAMETER_NOTES =
    {
      "" // learning rate function
    };

  // descriptions for all parameters
  private final static String[] EXTRA_PARAM_DESCRIPTIONS =
    {
      "Learning rate function to use while training, static is typically better " + LearningKernelFactory.DESCRIPTION
    };


  public Perceptron() {
    // set static values
    transferFunction = TransferFunctionFactory.TRANSFER_SIGN;
    trainingMode = TrainerFactory.TRAINER_ONLINE;

    // set good initial values
    trainingIterations = 500;
    biasInput = SimpleNeuron.DEFAULT_BIAS_VALUE;
    learningRate = 0.1;
    learningRateFunction = LearningKernelFactory.LEARNING_FUNCTION_STATIC;
    randomNumberSeed = 0;
  }


  protected Collection getAlgorithmOptions() {
    ArrayList list = new ArrayList(2);

    list.add("-" + EXTRA_PARAMETERS[EXTRA_PARAM_LEARNING_RATE_FUNCTION]);
    list.add(Integer.toString(learningRateFunction));

    return list;

  }

  protected Collection getListOptions() {
    ArrayList list = new ArrayList(1);

    for (int i = 0; i < EXTRA_PARAMETERS.length; i++) {
      String param = "-" + EXTRA_PARAMETERS[i] + " " + EXTRA_PARAMETER_NOTES[i];
      list.add(new Option("\t" + EXTRA_PARAM_DESCRIPTIONS[i], EXTRA_PARAMETERS[i], 1, param));
    }

    return list;
  }

  public String globalInfo() {
    StringBuffer buffer = new StringBuffer();

    buffer.append("Single Layer Perceptron : Perceptron Learning Rule, Binary inputs, Sign transfer function");

    return buffer.toString();
  }

  protected NeuralModel prepareAlgorithm(Instances instances) throws java.lang.Exception {
    // prepare the transfer function
    TransferFunction transferFunc = TransferFunctionFactory.factory(transferFunction);
    // prepare the learning rate function
    LearningRateKernel learningFunction = LearningKernelFactory.factory(learningRateFunction, learningRate, trainingIterations);

    // construct the algorithm
    PerceptronAlgorithm algorithm = new PerceptronAlgorithm(transferFunc, biasInput, rand, learningFunction, instances);

    return algorithm;
  }

  protected void validateArguments() throws java.lang.Exception {
    // do nothing
  }

  protected void setArguments(String[] options)
    throws Exception {
    for (int i = 0; i < EXTRA_PARAMETERS.length; i++) {
      String data = weka.core.Utils.getOption(EXTRA_PARAMETERS[i].charAt(0), options);

      if (data == null || data.length() == 0) {
	continue;
      }

      switch (i) {
	case EXTRA_PARAM_LEARNING_RATE_FUNCTION: {
	  learningRateFunction = Integer.parseInt(data);
	  break;
	}
	default: {
	  throw new Exception("Invalid option offset: " + i);
	}
      }
    }
  }

  public String learningRateFunctionTipText() {
    return EXTRA_PARAM_DESCRIPTIONS[EXTRA_PARAM_LEARNING_RATE_FUNCTION];
  }

  public void setLearningRateFunction(SelectedTag l) {
    if (l.getTags() == LearningKernelFactory.TAGS_LEARNING_FUNCTION) {
      learningRateFunction = l.getSelectedTag().getID();
    }
  }

  public SelectedTag getLearningRateFunction() {
    return new SelectedTag(learningRateFunction, LearningKernelFactory.TAGS_LEARNING_FUNCTION);
  }

  /**
   * Entry point into the algorithm for direct usage
   *
   * @param args
   */
  public static void main(String[] args) {
   runClassifier(new Perceptron(), args);
  }
}