CodeKiwi · June 24, 2014 04:37 · Jun 24, 2014
diff --git a/gradientdescentforlinearregression b/gradientdescentforlinearregression
@@ -0,0 +1,45 @@
+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