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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.lvq.initialise;

import weka.classifiers.neural.common.RandomWrapper;
import weka.core.Instances;

/**
 * Date: 26/05/2004
 * File: RandomValues.java
 *
 * @author Jason Brownlee
 */
public class RandomValues extends CommonInitialiser {

  public RandomValues(RandomWrapper aRand, Instances aInstances) {
    super(aRand, aInstances);
  }

  public double[] getAttributes() {
    double[] attributes = new double[numAttributes];

    for (int i = 0; i < attributes.length; i++) {
      double value = 0.0;

      // check for nominal
      if (trainingInstances.attribute(i).isNominal()) {
	int range = trainingInstances.attribute(i).numValues();

	// select a random class value (0 to range-1)
	value = makeRandomSelection(range);
      }
      // generate a random value in the correct range
      else if (trainingInstances.attribute(i).isNumeric()) {
	double max = trainingInstances.attributeStats(i).numericStats.max;
	double min = trainingInstances.attributeStats(i).numericStats.min;
	// generate a random value in the range of the attribute
	value = (min + ((max - min) * (rand.getRand().nextDouble() / 1.0)));
      }

      attributes[i] = value;
    }

    return attributes;
  }
}