package com.d_HBOA;

import com.z_PEA.Individual;
import com.z_PEA.IEASolver;
import com.z_PEA.Population;
import com.z_PEA.RandomPopulation;
import com.z_PEA.SelectedSet;
import com.z_PEA.Stopper;



public class HBOASolver extends IEASolver{		
	public int N;
	private Population currentPopulation;
	private int currentGeneration = 0;			// Current generation for this solver. This is updated by each nextGeneration() call.
	private int fitnessCalls = 0;				// Number of fitness calls for this solver. This must be updated for all RandomPopulation() and replace() calls.
	private double avgFitness;					// Average fitness of the current population for this Solver. This is updated by each nextGeneration() call.
	
	private Selection selection;		 		// NOTE: Use Parameter.initializeSelector() to generate the chosen selector type.
	private BayesianNetwork bayesianNetwork;	// NOTE: Use initializeBayesianNetwork() to initialize the chosen bayesian network generator.
	private IReplacement replacement; 			// NOTE: Use Parameter.initializeReplacement() to generate the chosen replacement type.
	
	
	public HBOASolver(String paramFile, int currentN){
		this.N = currentN;
		currentPopulation = new RandomPopulation(N);	// Initial random population. Fitness and statistics are automatically computed.
		fitnessCalls      = this.N;						// NOTE: RandomPopulation() computes the fitness of all N individuals in the initial population.
		
		Parameter.initializeParameters(paramFile);  	// Initialize and validate hBOA parameters.
		selection = Parameter.initializeSelection(N);
		bayesianNetwork = Parameter.initializeBayesianNetwork(N);
		replacement = Parameter.initializeReplacement();
	}
	
	public int getN(){return N;}
	public Population getCurrentPopulation(){return currentPopulation;}
	public int getCurrentGeneration(){return currentGeneration;}
	public int getFitnessCalls(){return fitnessCalls;}
	public double getAvgFitness(){return avgFitness;}
	
	public boolean nextGeneration(){
		currentGeneration++;
		SelectedSet selectedSet = selection.select(currentPopulation);			// 1. SELECTION.
		bayesianNetwork.generateModel(selectedSet);		 						// 2. GENERATE BAYESIAN NETWORK.
		Individual[] newIndividuals = bayesianNetwork.sampleNewIndividuals();	// 3. SAMPLING.	 
		replacement.replace(currentPopulation, newIndividuals);					// 4. REPLACEMENT. NOTE: replace() computes the fitness only of the newIndividuals. 
																				//	               NOTE: This function is also responsible for updating the  
		fitnessCalls += newIndividuals.length;									//						 information about the best individual.
		currentPopulation.computeUnivariateFrequencies();						
		avgFitness = currentPopulation.computeAvgFitness();						// NOTE: Every nextGeneration() must compute the average fitness of its current Population!
																				//			 No need to update information about the best individual. Replacement is responsible for that.
		return Stopper.criteria(currentGeneration, currentPopulation);	
	}
}










