package com.yahoo.labs.samoa.moa.classifiers.functions;

/*
 * #%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 com.yahoo.labs.samoa.moa.classifiers.AbstractClassifier;
import com.yahoo.labs.samoa.moa.core.DoubleVector;
import com.yahoo.labs.samoa.moa.core.Measurement;
import com.yahoo.labs.samoa.moa.core.StringUtils;
import com.yahoo.labs.samoa.instances.Instance;

/**
 * Majority class learner. This is the simplest classifier.
 * 
 * @author Richard Kirkby (rkirkby@cs.waikato.ac.nz)
 * @version $Revision: 7 $
 */
public class MajorityClass extends AbstractClassifier {

  private static final long serialVersionUID = 1L;

  @Override
  public String getPurposeString() {
    return "Majority class classifier: always predicts the class that has been observed most frequently the in the training data.";
  }

  protected DoubleVector observedClassDistribution;

  @Override
  public void resetLearningImpl() {
    this.observedClassDistribution = new DoubleVector();
  }

  @Override
  public void trainOnInstanceImpl(Instance inst) {
    this.observedClassDistribution.addToValue((int) inst.classValue(), inst.weight());
  }

  public double[] getVotesForInstance(Instance i) {
    return this.observedClassDistribution.getArrayCopy();
  }

  @Override
  protected Measurement[] getModelMeasurementsImpl() {
    return null;
  }

  @Override
  public void getModelDescription(StringBuilder out, int indent) {
    StringUtils.appendIndented(out, indent, "Predicted majority ");
    out.append(getClassNameString());
    out.append(" = ");
    out.append(getClassLabelString(this.observedClassDistribution.maxIndex()));
    StringUtils.appendNewline(out);
    for (int i = 0; i < this.observedClassDistribution.numValues(); i++) {
      StringUtils.appendIndented(out, indent, "Observed weight of ");
      out.append(getClassLabelString(i));
      out.append(": ");
      out.append(this.observedClassDistribution.getValue(i));
      StringUtils.appendNewline(out);
    }
  }

  public boolean isRandomizable() {
    return false;
  }
}