




"""simple docstring"""






import numpy

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







def      _snake_case			(				lowercase__							,    lowercase__="train"		):
				return calculate_hypothesis_value(_UpperCamelCase							,    _UpperCamelCase		) - output(
				    _UpperCamelCase							,    _UpperCamelCase		)







def      _snake_case			(				lowercase__		):
				_lowerCamelCase     :			str    =    0
				for i in range(len(_UpperCamelCase		) - 1		):
								hyp_val += data_input_tuple[i] * parameter_vector[i + 1]
				hyp_val += parameter_vector[0]
				return hyp_val







def      _snake_case			(				lowercase__							,    lowercase__		):




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







def      _snake_case			(				lowercase__							,    lowercase__		):




				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      _snake_case			(				lowercase__							,    lowercase__=m		):
				_lowerCamelCase     :			Optional[Any]    =    0
				for i in range(_UpperCamelCase		):
								if index == -1:
												summation_value += _error(_UpperCamelCase		)
								else:
												summation_value += _error(_UpperCamelCase		) * train_data[i][0][index]
				return summation_value







def      _snake_case			(				lowercase__		):
				_lowerCamelCase     :			Dict    =    summation_of_cost_derivative(_UpperCamelCase							,    _UpperCamelCase		) / m
				return cost_derivative_value







def      _snake_case			(				):
				global parameter_vector
				# Tune these values to set a tolerance value for predicted output
				_lowerCamelCase     :			int    =    0.0_0_0_0_0_2
				_lowerCamelCase     :			Optional[int]    =    0
				_lowerCamelCase     :			List[str]    =    0
				while True:
								j += 1
								_lowerCamelCase     :			List[Any]    =    [0, 0, 0, 0]
								for i in range(0							,    len(_UpperCamelCase		)		):
												_lowerCamelCase     :			Any    =    get_cost_derivative(i - 1		)
												_lowerCamelCase     :			Dict    =    (
												    parameter_vector[i] - LEARNING_RATE * cost_derivative
												)
								if numpy.allclose(
								    _UpperCamelCase							,    _UpperCamelCase							,    atol=_UpperCamelCase							,    rtol=_UpperCamelCase							,    ):
												break
								_lowerCamelCase     :			List[Any]    =    temp_parameter_vector
				print(('Number of iterations:', j)		)







def      _snake_case			(				):




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


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



"""simple docstring"""


class   _UpperCamelCase  (   lowerCAmelCase__		):
							'''simple docstring'''







							pass
class   _UpperCamelCase  (   lowerCAmelCase__		):
							'''simple docstring'''







							pass
class   _UpperCamelCase  :
							'''simple docstring'''







							def __init__(     self	):
										__lowerCAmelCase				    =			[
										    [],
										    [],
										    [],
										]

							def 			snake_case				(     self    ,   __a    ,   __a	):
										try:
													if len(self.queues[priority]	) >= 1_00:
																raise OverflowError("Maximum queue size is 100"	)
													self.queues[priority].append(__a	)
										except IndexError:
													raise ValueError("Valid priorities are 0, 1, and 2"	)

							def 			snake_case				(     self	):
										for queue in self.queues:
													if queue:
																return queue.pop(0	)
										raise UnderFlowError("All queues are empty"	)



							def __str__(     self	):
										return "\n".join(f"Priority {i}: {q}" for i, q in enumerate(self.queues	)	)
class   _UpperCamelCase  :
							'''simple docstring'''







							def __init__(     self	):
										__lowerCAmelCase				    =			[]

							def 			snake_case				(     self    ,   __a	):
										if len(self.queue	) == 1_00:
													raise OverFlowError("Maximum queue size is 100"	)
										self.queue.append(__a	)

							def 			snake_case				(     self	):
										if not self.queue:
													raise UnderFlowError("The queue is empty"	)
										else:
													__lowerCAmelCase				    =			min(self.queue	)
													self.queue.remove(__a	)
													return data



							def __str__(     self	):
										return str(self.queue	)


def _lowerCamelCase	(		):

			'''simple docstring'''




			__lowerCAmelCase				    =			FixedPriorityQueue()
			fpq.enqueue(0    , 10      )
			fpq.enqueue(1    , 70      )
			fpq.enqueue(0    , 100      )
			fpq.enqueue(2    , 1      )
			fpq.enqueue(2    , 5      )
			fpq.enqueue(1    , 7      )
			fpq.enqueue(2    , 4      )
			fpq.enqueue(1    , 64      )
			fpq.enqueue(0    , 128      )
			print(_UpperCamelCase      )
			print(fpq.dequeue()      )
			print(fpq.dequeue()      )
			print(fpq.dequeue()      )
			print(fpq.dequeue()      )
			print(fpq.dequeue()      )
			print(_UpperCamelCase      )
			print(fpq.dequeue()      )
			print(fpq.dequeue()      )
			print(fpq.dequeue()      )
			print(fpq.dequeue()      )
			print(fpq.dequeue()      )


def _lowerCamelCase	(		):

			'''simple docstring'''




			__lowerCAmelCase				    =			ElementPriorityQueue()
			epq.enqueue(10      )
			epq.enqueue(70      )
			epq.enqueue(100      )
			epq.enqueue(1      )
			epq.enqueue(5      )
			epq.enqueue(7      )
			epq.enqueue(4      )
			epq.enqueue(64      )
			epq.enqueue(128      )
			print(_UpperCamelCase      )
			print(epq.dequeue()      )
			print(epq.dequeue()      )
			print(epq.dequeue()      )
			print(epq.dequeue()      )
			print(epq.dequeue()      )
			print(_UpperCamelCase      )
			print(epq.dequeue()      )
			print(epq.dequeue()      )
			print(epq.dequeue()      )
			print(epq.dequeue()      )
			print(epq.dequeue()      )


if __name__ == "__main__":
	fixed_priority_queue()
	element_priority_queue()




