import java.util.Random;

public class ESIndividual implements Comparable{

	public int num_obj_vars; // number of object variables
	public float[] obj_var; // object variables array
	public float[] std_dev; // standard deviations array
	public float[] theta; // angles corresponding to covariances
	public float fitness; // fitness of this individual

	private static Random randg = new Random(); // Random Generator

	public ESIndividual(int size,float[] min_range,float[] max_range){
	// Creates a random individual
	// i'th obj_var must be in range min_range[i] to max_range[i]

		num_obj_vars = size;
		obj_var = new float[num_obj_vars];
		std_dev = new float[num_obj_vars];
		theta = new float[num_obj_vars*(num_obj_vars+1)/2];

		for(int i=0; i < num_obj_vars; i++){
			obj_var[i] = randg.nextFloat() * (max_range[i] - min_range[i]) + min_range[i];
			std_dev[i] = randg.nextFloat();
		}
		for(int i=0; i < theta.length; i++)
			theta[i] = randg.nextFloat() * .2f - .1f;

		evalFitness(); // evaluate fitness of this new individual
	}

	public ESIndividual(float[] obj_var,float[] std_dev,float[] theta){
	// Create an individual that its obj_var is exactly a copy of d[]
	// It is assumed min_range[i] <= obj_var[i] <= max_range[i]
		num_obj_vars = obj_var.length;
		this.obj_var = new float[num_obj_vars];
		this.std_dev = new float[num_obj_vars];
		this.theta = new float[num_obj_vars*(num_obj_vars+1)/2];

		for(int i=0; i < num_obj_vars; i++){
			this.obj_var[i] = obj_var[i];
			this.std_dev[i] = std_dev[i];
		}
		for(int i=0; i < theta.length; i++)
			this.theta[i] = theta[i];

		evalFitness(); // evaluate fitness of this new individual
	}

	public ESIndividual mutate(float[] min_range,float[] max_range){

		float beta = 0.0873f;
		float tau = 1.0f/ (float)(Math.sqrt(2*Math.sqrt(num_obj_vars)));
		float taup = 1.0f / (float)(Math.sqrt(2*num_obj_vars));

		float[] new_obj_var = new float[num_obj_vars];
		float[] delta_obj_var = new float[num_obj_vars];
		float[] new_std_dev = new float[num_obj_vars];
		float[] new_theta = new float[num_obj_vars*(num_obj_vars+1)/2];

		for(int i=0; i < num_obj_vars; i++)
			new_obj_var[i] = obj_var[i];

		float r = (float) randg.nextGaussian();
		for(int i=0; i < num_obj_vars; i++){
			float s = (float) randg.nextGaussian();
			new_std_dev[i] = std_dev[i] * (float) Math.exp(taup*r + tau*s);
		}

		for(int i=0; i < new_theta.length; i++){
			float s = (float) randg.nextGaussian();
			new_theta[i] = theta[i] + beta*s;
		}

		for(int i=0; i < num_obj_vars; i++)
			delta_obj_var[i] = new_std_dev[i] * (float) randg.nextGaussian();

		int k = num_obj_vars*(num_obj_vars+1)/2 - 1;
		for(int i = num_obj_vars - 1; i >= 0; i--){
			for(int j = num_obj_vars - 1; j >= i; j--){
				delta_obj_var[i] = delta_obj_var[i] * (float) Math.cos(theta[k]) - delta_obj_var[j] * (float) Math.sin(theta[k]);
				delta_obj_var[j] = delta_obj_var[i] * (float) Math.sin(theta[k]) + delta_obj_var[j] * (float) Math.cos(theta[k]);
				k--;
			}
		}

		for(int i=0; i < num_obj_vars; i++){
			new_obj_var[i] = delta_obj_var[i] + obj_var[i];
			new_obj_var[i] = Math.max(min_range[i],Math.min(new_obj_var[i],max_range[i]));
		}
		return new ESIndividual(new_obj_var,new_std_dev,new_theta);
	}

	public static ESIndividual xover(ESIndividual p1,ESIndividual p2){

		if( p1.num_obj_vars != p2.num_obj_vars ){
			System.err.println("error: for cross over the two parent must be of the same size");
			return null;
		}

		float[] new_obj_var = new float[p1.num_obj_vars];
		float[] new_std_dev = new float[p1.num_obj_vars];
		float[] new_theta = new float[p1.num_obj_vars*(p1.num_obj_vars+1)/2];

		for(int i=0; i < p1.num_obj_vars; i++)
			new_obj_var[i] = p1.obj_var[i] + randg.nextFloat() * (p2.obj_var[i] - p1.obj_var[i]);
		for(int i=0; i < p1.num_obj_vars; i++)
			new_std_dev[i] = p1.std_dev[i] + randg.nextFloat() * (p2.std_dev[i] - p1.std_dev[i]);
		for(int i=0; i < new_theta.length; i++)
			new_theta[i] = p1.theta[i] + randg.nextFloat() * (p2.theta[i] - p1.theta[i]);

		return new ESIndividual(new_obj_var,new_std_dev,new_theta);
	}

	private void evalFitness(){

		// De Jong's F1:
		// fitness = 1.0f / (1.0f + obj_var[0]*obj_var[0] + obj_var[1]*obj_var[1] + obj_var[2]*obj_var[2] );

		// De Jong's F2:
		// fitness = 1.0f / (1.0f + 100.0f*(obj_var[0]*obj_var[0]-obj_var[1])*(obj_var[0]*obj_var[0]-obj_var[1]) + (1.0f - obj_var[0])*(1.0f - obj_var[0]) );

//		Schaffer F6:
//		float tmp0 = obj_var[0]*obj_var[0] + obj_var[1]*obj_var[1];
//		float tmp1 = (float) Math.sin(Math.sqrt(tmp0));
//		float tmp2 = 1.0f + .001f*tmp0;
//		fitness = 1.0f / (1.0f + .5f + (tmp1*tmp1 -.5f) / (tmp2*tmp2));

		// Ackey F1 (5 Dimentional):
		float tmp0 = obj_var[0]*obj_var[0] + obj_var[1]*obj_var[1] + obj_var[2]*obj_var[2] + obj_var[3]*obj_var[3] + obj_var[4]*obj_var[4];
		float tmp1 = (float)(Math.cos(2*Math.PI*obj_var[0]) + Math.cos(2*Math.PI*obj_var[1]) + Math.cos(2*Math.PI*obj_var[2]) + Math.cos(2*Math.PI*obj_var[3]) + Math.cos(2*Math.PI*obj_var[4]) );
		fitness = 1.0f / (1.0f + 20.0f + (float)(Math.exp(1) - 20*Math.exp(-.2*Math.sqrt(tmp0/5.0f)) - Math.exp(tmp1/5.0f)));

//	  Rastrigin F1 (10 Dimentional)
//		float sum = 100;
//		for(int i=0; i < num_obj_vars; i++)
//			sum = sum + obj_var[i]*obj_var[i] - (float)(10.0*Math.cos(2*Math.PI*obj_var[i]));
//		fitness = 1.0f / (1.0f + sum);
	}

	public int compareTo(Object o){

		ESIndividual esi = (ESIndividual) o;
		int result;
		if( fitness > esi.fitness )
			result = 1;
		else if( fitness == esi.fitness )
			result = 0;
		else
			result = -1;
		return result;
	}

	public String toString(){

		String s = "[";
		for(int i=0; i < num_obj_vars - 1; i++){
			s+= obj_var[i]+", ";
		}
		s+=obj_var[num_obj_vars - 1] + "]";
		s+="  fitness = "+fitness;
		s+="  function value = "+ (1.0f / fitness - 1.0);

		return s;
	}


	public static void main(String[] args) {

  	}
}