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/***************************************************************************
 * Copyright (C) 2017 iObserve Project (https://www.iobserve-devops.net)
 *
 * 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.
 ***************************************************************************/
package kieker.analysis.generic.clustering.mtree;

/**
 * An implementation of the trimmed algorithm. The algorithm is proposed in the paper "A
 * Sub-Quadratic Exact Medoid Algorithm". It seems to try to find the model which has the
 * smallest distance to all other models.
 *
 * @param 
 *            the type of the Clustered Elements
 *
 * @author Lars Jürgensen
 * @since 2.0.0
 */
public class TrimmedAlgorithm {

	private final double[] lowerBounds;

	private double lowestEnergy = Double.MAX_VALUE;
	private T bestCandidate = null; // NOPMD documentation

	private final T[] models;

	private final IDistanceFunction distanceFunction;

	public TrimmedAlgorithm(final T[] models, final IDistanceFunction distanceFunction) {
		if (models.length == 0) {
			throw new IllegalArgumentException("Amount of models has to be larger than 0.");
		}
		this.lowerBounds = new double[models.length];
		this.models = models;
		this.distanceFunction = distanceFunction;
	}

	public T calculate() {
		this.bestCandidate = this.models[0];
		for (int i = 0; i < this.models.length; i++) {
			if (this.lowerBounds[i] < this.lowestEnergy) {

				final double[] distances = new double[this.models.length];

				double distanceSum = 0;
				for (int j = 0; j < this.models.length; j++) { // NOPMD no arrays to be copied
					distances[j] = this.distanceFunction.calculate(this.models[i], this.models[j]);
					distanceSum += distances[j];
				}

				this.lowerBounds[i] = distanceSum / (this.models.length - 1);

				if (this.lowerBounds[i] < this.lowestEnergy) {
					this.lowestEnergy = this.lowerBounds[i];
					this.bestCandidate = this.models[i];
				}

				for (int j = 0; j < this.models.length; j++) {
					Math.max(this.lowerBounds[j], Math.abs(this.lowerBounds[i] - distances[j]));
				}
			}
		}
		return this.bestCandidate;
	}
}