class LinearRegression < Object::Base

	def initialize(options)
		@training_set = options['training_set']
		@theta0 = options['theta0start']
		@theta1 = options['theta1start']
		@alpha = options['alpha']
	end
	
	def minimize_theta
		isconverged = false
		while !isconverged do
			temp_theta0 = perform_gradient_descent_for_theta0(@theta0)
			temp_theta1 = perform_gradient_descent_for_theta1(@theta1)
			if (temp_theta0 - @theta0 == 0 && temp_theta1 - @theta1 == 0)
				isconverged = true
			end
			@theta0 = temp_theta0
			@theta1 = temp_theta1
		end
		return [@theta0,@theta1]
	end
	
	def perform_gradient_descent_for_theta0(theta)
		return theta - @alpha * calculate_deritive(theta) * cost_function(theta, @theta1)
	end
	
	def perform_gradient_descent_for_theta1(theta)
		result = theta - @alpha  calculate_deritive(theta) * cost_function(@theta0, theta)
	end
	
	def calculate_deritive(theta)
		#calculate deritive for theta
	end
	
	def cost_function(theta0, theta1)
		#calculate cost function
		sum_of_squared_errors = 0
		@training_set.each do |training_pair|
			squared_error = (theta0 * taining_pair['x']) - (theta1 * taining_pair['y']) ^ 2
			sum_of_squared_errors += squared_error
		end
		return sum_of_squared_errors
	end
end