ClustererTest.java

/**
 * Copyright 2013-2015 Pierre Merienne
 *
 * 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 com.github.pmerienne.trident.ml.clustering;

import java.util.List;

import com.github.pmerienne.trident.ml.clustering.Clusterer;
import com.github.pmerienne.trident.ml.core.Instance;
import com.github.pmerienne.trident.ml.testing.data.DatasetUtils;


public abstract class ClustererTest {

	private final static Integer FOLD_NB = 10;

	/**
	 * Cross validation with 10 folds
	 * 
	 * @param
	 * @param <F>
	 * @param clusterer
	 * @param samples
	 * @return
	 */
	protected double eval(Clusterer clusterer, List<Instance<Integer>> samples) {
		double randIndex = 0.0;

		for (int i = 0; i < FOLD_NB; i++) {
			List<Instance<Integer>> training = DatasetUtils.getTrainingFolds(i, FOLD_NB, samples);
			List<Instance<Integer>> eval = DatasetUtils.getEvalFold(i, FOLD_NB, samples);
			randIndex += this.eval(clusterer, training, eval);
		}

		return randIndex / FOLD_NB;
	}

	protected double eval(Clusterer clusterer, List<Instance<Integer>> training, List<Instance<Integer>> eval) {
		clusterer.reset();

		// Train
		for (Instance<Integer> sample : training) {
			clusterer.update(sample.features);
		}

		RandEvaluator randEvaluator = new RandEvaluator();
		double randIndex = randEvaluator.evaluate(clusterer, eval);
		return randIndex;
	}

}