
import numpy

# List of input, output pairs
SCREAMING_SNAKE_CASE							:str							 =				(
    ((5, 2, 3), 15),
    ((6, 5, 9), 25),
    ((11, 12, 13), 41),
    ((1, 1, 1), 8),
    ((11, 12, 13), 41),
)
SCREAMING_SNAKE_CASE							:Optional[int]							 =				(((515, 22, 13), 555), ((61, 35, 49), 150))
SCREAMING_SNAKE_CASE							:str							 =				[2, 4, 1, 5]
SCREAMING_SNAKE_CASE							:List[str]							 =				len(train_data)
SCREAMING_SNAKE_CASE							:Dict							 =				0.009


def 						UpperCAmelCase			(	a_		,   a_="train"						)		->	int:







				"""simple docstring"""



				return calculate_hypothesis_value(a_		,   a_						) - output(
				    a_		,   a_						)


def 						UpperCAmelCase			(	a_						)		->	str:







				"""simple docstring"""



				__A   			= 0
				for i in range(len(a_						) - 1						):
								hyp_val += data_input_tuple[i] * parameter_vector[i + 1]
				hyp_val += parameter_vector[0]
				return hyp_val


def 						UpperCAmelCase			(	a_		,   a_						)		->	Optional[int]:







				"""simple docstring"""



				if data_set == "train":
								return train_data[example_no][1]
				elif data_set == "test":
								return test_data[example_no][1]
				return None


def 						UpperCAmelCase			(	a_		,   a_						)		->	Dict:







				"""simple docstring"""



				if data_set == "train":
								return _hypothesis_value(train_data[example_no][0]						)
				elif data_set == "test":
								return _hypothesis_value(test_data[example_no][0]						)
				return None


def 						UpperCAmelCase			(	a_		,   a_=m						)		->	int:







				"""simple docstring"""



				__A   			= 0
				for i in range(a_						):
								if index == -1:
												summation_value += _error(a_						)
								else:
												summation_value += _error(a_						) * train_data[i][0][index]
				return summation_value


def 						UpperCAmelCase			(	a_						)		->	Optional[Any]:







				"""simple docstring"""



				__A   			= summation_of_cost_derivative(a_		,   a_						) / m
				return cost_derivative_value


def 						UpperCAmelCase			(	)		->	Optional[int]:







				"""simple docstring"""



				global parameter_vector
				# Tune these values to set a tolerance value for predicted output
				__A   			= 0.000_002
				__A   			= 0
				__A   			= 0
				while True:
								j += 1
								__A   			= [0, 0, 0, 0]
								for i in range(0		,   len(a_						)						):
												__A   			= get_cost_derivative(i - 1						)
												__A   			= (
												    parameter_vector[i] - LEARNING_RATE * cost_derivative
												)
								if numpy.allclose(
								    a_		,   a_		,   atol=a_		,   rtol=a_		,   ):
												break
								__A   			= temp_parameter_vector
				print(("Number of iterations:", j)						)


def 						UpperCAmelCase			(	)		->	str:







				"""simple docstring"""



				for i in range(len(a_						)						):
								print(("Actual output value:", output(a_		,   "test"						))						)
								print(("Hypothesis output:", calculate_hypothesis_value(a_		,   "test"						))						)


if __name__ == "__main__":
					run_gradient_descent()
					print('\nTesting gradient descent for a linear hypothesis function.\n')
					test_gradient_descent()







def      _A       (		SCREAMING_SNAKE_CASE     :   Optional[Any]						):





  """simple docstring"""



  a__	:					Optional[Any]										=len(SCREAMING_SNAKE_CASE						)
  while cur > 1:
    # Find the maximum number in arr
    a__	:					List[Any]										=arr.index(max(arr[0:cur]						)						)
    # Reverse from 0 to mi
    a__	:					int										=arr[mi::-1] + arr[mi + 1 : len(SCREAMING_SNAKE_CASE						)]
    # Reverse whole list
    a__	:					List[str]										=arr[cur - 1 :: -1] + arr[cur : len(SCREAMING_SNAKE_CASE						)]
    cur -= 1
  return arr


if __name__ == "__main__":
     UpperCAmelCase	:	int		     =       input("""Enter numbers separated by a comma:\n""").strip()
     UpperCAmelCase	:	Optional[int]		     =       [int(item) for item in user_input.split(""",""")]
     print(pancake_sort(unsorted))






