package org.apache.samoa.streams.generators;

/*
 * #%L
 * SAMOA
 * %%
 * Copyright (C) 2014 - 2015 Apache Software Foundation
 * %%
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 * 
 *      http://www.apache.org/licenses/LICENSE-2.0
 * 
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * #L%
 */

import java.util.Random;

import org.apache.samoa.instances.Attribute;
import org.apache.samoa.instances.DenseInstance;
import org.apache.samoa.instances.Instance;
import org.apache.samoa.instances.Instances;
import org.apache.samoa.instances.InstancesHeader;
import org.apache.samoa.moa.core.Example;
import org.apache.samoa.moa.core.FastVector;
import org.apache.samoa.moa.core.InstanceExample;
import org.apache.samoa.moa.core.ObjectRepository;
import org.apache.samoa.moa.options.AbstractOptionHandler;
import org.apache.samoa.moa.tasks.TaskMonitor;
import org.apache.samoa.streams.InstanceStream;

import com.github.javacliparser.FloatOption;
import com.github.javacliparser.IntOption;

/**
 * Stream generator for Hyperplane data stream.
 * 
 * @author Albert Bifet (abifet at cs dot waikato dot ac dot nz)
 * @version $Revision: 7 $
 */
public class HyperplaneGenerator extends AbstractOptionHandler implements InstanceStream {

  @Override
  public String getPurposeString() {
    return "Generates a problem of predicting class of a rotating hyperplane.";
  }

  private static final long serialVersionUID = 1L;

  public IntOption instanceRandomSeedOption = new IntOption("instanceRandomSeed", 'i',
      "Seed for random generation of instances.", 1);

  public IntOption numClassesOption = new IntOption("numClasses", 'c', "The number of classes to generate.", 2, 2,
      Integer.MAX_VALUE);

  public IntOption numAttsOption = new IntOption("numAtts", 'a', "The number of attributes to generate.", 10, 0,
      Integer.MAX_VALUE);

  public IntOption numDriftAttsOption = new IntOption("numDriftAtts", 'k', "The number of attributes with drift.", 2,
      0, Integer.MAX_VALUE);

  public FloatOption magChangeOption = new FloatOption("magChange", 't', "Magnitude of the change for every example",
      0.0, 0.0, 1.0);

  public IntOption noisePercentageOption = new IntOption("noisePercentage", 'n',
      "Percentage of noise to add to the data.", 5, 0, 100);

  public IntOption sigmaPercentageOption = new IntOption("sigmaPercentage", 's',
      "Percentage of probability that the direction of change is reversed.", 10,
      0, 100);

  protected InstancesHeader streamHeader;

  protected Random instanceRandom;

  protected double[] weights;

  protected int[] sigma;

  public int numberInstance;

  @Override
  protected void prepareForUseImpl(TaskMonitor monitor, ObjectRepository repository) {
    monitor.setCurrentActivity("Preparing hyperplane...", -1.0);
    generateHeader();
    restart();
  }

  @SuppressWarnings({ "rawtypes", "unchecked" })
  protected void generateHeader() {
    FastVector attributes = new FastVector();
    for (int i = 0; i < this.numAttsOption.getValue(); i++) {
      attributes.addElement(new Attribute("att" + (i + 1)));
    }

    FastVector classLabels = new FastVector();
    for (int i = 0; i < this.numClassesOption.getValue(); i++) {
      classLabels.addElement("class" + (i + 1));
    }
    attributes.addElement(new Attribute("class", classLabels));
    this.streamHeader = new InstancesHeader(new Instances(getCLICreationString(InstanceStream.class), attributes, 0));
    this.streamHeader.setClassIndex(this.streamHeader.numAttributes() - 1);
  }

  @Override
  public long estimatedRemainingInstances() {
    return -1;
  }

  @Override
  public InstancesHeader getHeader() {
    return this.streamHeader;
  }

  @Override
  public boolean hasMoreInstances() {
    return true;
  }

  @Override
  public boolean isRestartable() {
    return true;
  }

  @Override
  public Example<Instance> nextInstance() {

    int numAtts = this.numAttsOption.getValue();
    double[] attVals = new double[numAtts + 1];
    double sum = 0.0;
    double sumWeights = 0.0;
    for (int i = 0; i < numAtts; i++) {
      attVals[i] = this.instanceRandom.nextDouble();
      sum += this.weights[i] * attVals[i];
      sumWeights += this.weights[i];
    }
    int classLabel;
    if (sum >= sumWeights * 0.5) {
      classLabel = 1;
    } else {
      classLabel = 0;
    }
    // Add Noise
    if ((1 + (this.instanceRandom.nextInt(100))) <= this.noisePercentageOption.getValue()) {
      classLabel = (classLabel == 0 ? 1 : 0);
    }

    Instance inst = new DenseInstance(1.0, attVals);
    inst.setDataset(getHeader());
    inst.setClassValue(classLabel);
    addDrift();
    return new InstanceExample(inst);
  }

  private void addDrift() {
    for (int i = 0; i < this.numDriftAttsOption.getValue(); i++) {
      this.weights[i] += (double) ((double) sigma[i]) * ((double) this.magChangeOption.getValue());
      if (// this.weights[i] >= 1.0 || this.weights[i] <= 0.0 ||
      (1 + (this.instanceRandom.nextInt(100))) <= this.sigmaPercentageOption.getValue()) {
        this.sigma[i] *= -1;
      }
    }
  }

  @Override
  public void restart() {
    this.instanceRandom = new Random(this.instanceRandomSeedOption.getValue());
    this.weights = new double[this.numAttsOption.getValue()];
    this.sigma = new int[this.numAttsOption.getValue()];
    for (int i = 0; i < this.numAttsOption.getValue(); i++) {
      this.weights[i] = this.instanceRandom.nextDouble();
      this.sigma[i] = (i < this.numDriftAttsOption.getValue() ? 1 : 0);
    }
  }

  @Override
  public void getDescription(StringBuilder sb, int indent) {
    // TODO Auto-generated method stub
  }
}