package com.yahoo.labs.samoa.evaluation.measures;

/*
 * #%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.ArrayList;

import com.yahoo.labs.samoa.moa.cluster.Clustering;
import com.yahoo.labs.samoa.moa.core.DataPoint;
import com.yahoo.labs.samoa.moa.evaluation.MeasureCollection;
import com.yahoo.labs.samoa.instances.Instance;

public class SSQ extends MeasureCollection {

  public SSQ() {
    super();
  }

  @Override
  public String[] getNames() {
    return new String[] { "SSQ" };
  }

  @Override
  protected boolean[] getDefaultEnabled() {
    return new boolean[] { false };
  }

  // TODO Work on this later
  // @Override
  public void evaluateClusteringSamoa(Clustering clustering,
      Clustering trueClsutering, ArrayList<Instance> points) {
    double sum = 0.0;
    for (Instance point : points) {
      // don't include noise
      if (point.classValue() == -1) {
        continue;
      }

      double minDistance = Double.MAX_VALUE;
      for (int c = 0; c < clustering.size(); c++) {
        double distance = 0.0;
        double[] center = clustering.get(c).getCenter();
        for (int i = 0; i < center.length; i++) {
          double d = point.value(i) - center[i];
          distance += d * d;
        }
        minDistance = Math.min(distance, minDistance);
      }

      sum += minDistance;
    }

    addValue(0, sum);
  }

  @Override
  public void evaluateClustering(Clustering clustering, Clustering trueClsutering, ArrayList<DataPoint> points) {
    double sum = 0.0;
    for (int p = 0; p < points.size(); p++) {
      // don't include noise
      if (points.get(p).classValue() == -1)
        continue;

      double minDistance = Double.MAX_VALUE;
      for (int c = 0; c < clustering.size(); c++) {
        double distance = 0.0;
        double[] center = clustering.get(c).getCenter();
        for (int i = 0; i < center.length; i++) {
          double d = points.get(p).value(i) - center[i];
          distance += d * d;
        }
        minDistance = Math.min(distance, minDistance);
      }

      sum += minDistance;
    }

    addValue(0, sum);
  }
}