class LearnedDog
	def self.prediction(training_command)
        x_data = []
		y_data = []
        # Start training:
		15.times do
			command = rand(0..3)
            # If the command is guessed, the dog receives a sugar:)
			if command != training_command
				sugar = 0
			else
				sugar = 1
			end
            # The list of commands and the number of sugar are written in two arrays:
			x_data += [[command]]
			y_data += [[sugar]]
		end
		# Setup training data model:
		train = RubyFann::TrainData.new(:inputs=>x_data, :desired_outputs=>y_data)
		# Setup model and train using training data:
		model = RubyFann::Standard.new(:num_inputs=>1, :hidden_neurons=>[6], :num_outputs=>1)
		# 500 max_epochs, 10 errors between reports and 0.01 desired MSE (mean-squared-error):
		model.train_on_data(train, 500, 10, 0.01) 
		#Predicted results:
		arr = [model.run([0]), model.run([1]), model.run([2]), model.run([3])]
		#Calculating the highest probability:
		actions_array = ['Sports','Money','Books','Pleased']
		result_command = actions_array[arr.index(arr.compact.max)]
		#Distance traveled:
		result_array = []
		x_data.flatten.each do |i|
			result_array << actions_array[i]
		end
		[result_command, result_array]
	end
end