/*******************************************************************************
 * Copyright 2012 Yuriy Lagodiuk
 * 
 * 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.lagodiuk.gp.symbolic;

import java.util.Collection;
import java.util.List;

import com.lagodiuk.ga.GeneticAlgorithm;
import com.lagodiuk.ga.Fitness;
import com.lagodiuk.ga.IterartionListener;
import com.lagodiuk.ga.Population;
import com.lagodiuk.gp.symbolic.interpreter.Context;
import com.lagodiuk.gp.symbolic.interpreter.Expression;
import com.lagodiuk.gp.symbolic.interpreter.Function;
import com.lagodiuk.gp.symbolic.interpreter.SyntaxTreeUtils;

public class SymbolicRegressionEngine {

	private static final int INITIAL_PARENT_CHROMOSOMES_SURVIVE_COUNT = 1;

	private static final int DEFAULT_POPULATION_SIZE = 5;

	private static final int MAX_INITIAL_TREE_DEPTH = 1;

	private GeneticAlgorithm environment;

	private Context context;

	private ExpressionFitness expressionFitness;

	public SymbolicRegressionEngine(ExpressionFitness expressionFitness, Collection variables, List baseFunctions) {
		this.context = new Context(baseFunctions, variables);
		this.expressionFitness = expressionFitness;
		SymbolicRegressionFitness fitnessFunction = new SymbolicRegressionFitness(this.expressionFitness);
		Population population = this.createPopulation(this.context, fitnessFunction, DEFAULT_POPULATION_SIZE);
		this.environment = new GeneticAlgorithm(population, fitnessFunction);
		this.environment.setParentChromosomesSurviveCount(INITIAL_PARENT_CHROMOSOMES_SURVIVE_COUNT);
	}

	private Population createPopulation(Context context, Fitness fitnessFunction, int populationSize) {
		Population population = new Population();
		for (int i = 0; i < populationSize; i++) {
			GpChromosome chromosome =
					new GpChromosome(context, fitnessFunction, SyntaxTreeUtils.createTree(MAX_INITIAL_TREE_DEPTH, context));
			population.addChromosome(chromosome);
		}
		return population;
	}

	public void addIterationListener(final SymbolicRegressionIterationListener listener) {
		this.environment.addIterationListener(new IterartionListener() {
			@Override
			public void update(GeneticAlgorithm environment) {
				listener.update(SymbolicRegressionEngine.this);
			}
		});
	}

	public void evolve(int itrationsCount) {
		this.environment.evolve(itrationsCount);
	}

	public Context getContext() {
		return this.context;
	}

	public Expression getBestSyntaxTree() {
		return this.environment.getBest().getSyntaxTree();
	}

	public double fitness(Expression expression) {
		return this.expressionFitness.fitness(expression, this.context);
	}

	public void terminate() {
		this.environment.terminate();
	}

	public int getIteration() {
		return this.environment.getIteration();
	}

	public void setParentsSurviveCount(int n) {
		this.environment.setParentChromosomesSurviveCount(n);
	}

}