module gmle.salsa.sum_of_squares;

import gmle.salsa.framework.GradientDescent;
import gmle.salsa.framework.GeneticSearch;
import gmle.salsa.framework.Simplex;
import gmle.salsa.framework.Evaluator;

behavior SumOfSquares implements Evaluator {
	int parameter_length = 10;
	double[] constants;
	int local_length, min, max;

	void init_data(int rank, int max_rank) {
		min = (int) (parameter_length * (((double)rank)/((double)max_rank)));
		max = (int) (parameter_length * (((double)(rank+1.0))/((double)max_rank)));
		local_length = max-min;

		constants = new double[local_length];
		for (int i = 0; i < local_length; i++) {
			constants[i] = Math.random() * 50.0;
		}
	}

	double[] evaluate(double[] parameters) {
		double[] sum = new double[1];

		sum[0] = 0;
		for (int i = 0; i < local_length; i++) {
			sum[0] += constants[i] * parameters[min + i] * parameters[min + i];
		}
		return sum;
	}

	double combine(double[][] values) {
		double sum = 0.0;
		for (int i = 0; i < values.length; i++) {
			sum += values[i][0];
		}
		return sum;
	}

	void printUsage() {
		System.err.println("Error: invalid arguments.");
		System.err.println("\tusage: java gmle.salsa.sum_of_squares.SumOfSquares <search method> [theaters_file]");
		System.err.println("\tsearch methods:");
		System.err.println("\t\tgd\t- gradient descent");
		System.err.println("\t\tcgd\t- conjugate gradient descent");
		System.err.println("\t\thcgd\t- hessian conjugate gradient descent");
		System.err.println("\t\tgs\t- genetic search");
		System.err.println("\t\tsimplex\t- simplex");
		System.exit(0);
	}

	void act(String[] arguments) {
		if (arguments.length != 1 && arguments.length != 2) {
			this.printUsage();
		} else {
			String theaters_file = null;
			if (arguments.length == 2) theaters_file = arguments[1];

			if (arguments[0].equals("cgd") || arguments[0].equals("gd") || arguments[0].equals("hcgd")) {
				double[] initial_parameters = new double[parameter_length];
				double[] step_sizes = new double[parameter_length];

				for (int i = 0; i < parameter_length; i++) {
					initial_parameters[i] = (Math.random() * 10) - 5.0;
					step_sizes[i] = 0.001;
				}
				GradientDescent gradient_descent = new GradientDescent();
				token init_token = null;
				if (theaters_file == null) {
					init_token = gradient_descent<-init_data("gmle.salsa.sum_of_squares.SumOfSquares", new Integer(4));
				} else {
					init_token = gradient_descent<-init_data("gmle.salsa.sum_of_squares.SumOfSquares", theaters_file);
				}

				init_token = gradient_descent<-set_evaluation_output("gd_evaluations") : waitfor(init_token);

				if (arguments[0].equals("gd")) {
					gradient_descent<-gradient_descent(initial_parameters, step_sizes) : waitfor(init_token);
				} else if (arguments[0].equals("cgd")) {
					gradient_descent<-conjugate_gradient_descent(initial_parameters, step_sizes) : waitfor(init_token);
				} else if (arguments[0].equals("hcgd")) {
					gradient_descent<-hessian_conjugate_gradient_descent(initial_parameters, step_sizes) : waitfor(init_token);
				}
			} else if (arguments[0].equals("simplex")) {
				double[][] simplex_parameters = new double[parameter_length+1][parameter_length]; 
				for (int i = 0; i < simplex_parameters.length; i++) {
					for (int j = 0; j < simplex_parameters[i].length; j++) {
						if (i == j) simplex_parameters[i][j] = 10.0 * Math.random();
						else simplex_parameters[i][j] = -10.0 * Math.random();
					}
				}

				Simplex simplex = new Simplex();
				token init_token = null;
				if (theaters_file == null) {
					init_token = simplex<-init_data("gmle.salsa.sum_of_squares.SumOfSquares", new Integer(4));
				} else {
					init_token = simplex<-init_data("gmle.salsa.sum_of_squares.SumOfSquares", theaters_file);
				}

				simplex<-set_evaluation_output("simplex_evaluations") : waitfor(init_token) @
				simplex<-simplex(simplex_parameters);
			} else if (arguments[0].equals("gs")) {
				double[] parameter_min = new double[parameter_length];
				double[] parameter_max = new double[parameter_length];

				for (int i = 0; i < parameter_length; i++) {
					parameter_min[i] = -5.0;
					parameter_max[i] = 5.0;
				}

				GeneticSearch genetic_search = new GeneticSearch();
				token init_token = null;
				if (theaters_file == null) {
					init_token = genetic_search<-init_data("gmle.salsa.sum_of_squares.SumOfSquares", new Integer(4));
				} else {
					init_token = genetic_search<-init_data("gmle.salsa.sum_of_squares.SumOfSquares", theaters_file);
				}

				genetic_search<-set_evaluation_output("genetic_evaluations") : waitfor(init_token) @
				genetic_search<-genetic_search(parameter_min, parameter_max);
			} else {
				this.printUsage();
			}
		}
	}
}