
"""simple docstring"""






def 						_snake_case       (	lowercase__		:  int      ,     lowercase__		:  int		)  ->				int:


			'''simple docstring'''





			return int((input_a, input_a).count(1		) != 0		)






def 						_snake_case       (	)  ->				None:


			'''simple docstring'''





			assert or_gate(0      ,     0		) == 0
			assert or_gate(0      ,     1		) == 1
			assert or_gate(1      ,     0		) == 1
			assert or_gate(1      ,     1		) == 1


if __name__ == "__main__":
				print(or_gate(0, 1))
				print(or_gate(1, 0))
				print(or_gate(0, 0))
				print(or_gate(1, 1))










"""simple docstring"""


import numpy

# List of input, output pairs
A     :   Any			    =			(
    ((5, 2, 3), 1_5),
    ((6, 5, 9), 2_5),
    ((1_1, 1_2, 1_3), 4_1),
    ((1, 1, 1), 8),
    ((1_1, 1_2, 1_3), 4_1),
)
A     :   str			    =			(((5_1_5, 2_2, 1_3), 5_5_5), ((6_1, 3_5, 4_9), 1_5_0))
A     :   Union[str, Any]			    =			[2, 4, 1, 5]
A     :   int			    =			len(train_data)
A     :   Dict			    =			0.009


def _lowerCamelCase	(		_UpperCamelCase    , _UpperCamelCase="train"      ):

			'''simple docstring'''




			return calculate_hypothesis_value(_UpperCamelCase    , _UpperCamelCase      ) - output(
			    _UpperCamelCase    , _UpperCamelCase      )


def _lowerCamelCase	(		_UpperCamelCase      ):

			'''simple docstring'''




			__lowerCAmelCase				    =			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 _lowerCamelCase	(		_UpperCamelCase    , _UpperCamelCase      ):

			'''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 _lowerCamelCase	(		_UpperCamelCase    , _UpperCamelCase      ):

			'''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 _lowerCamelCase	(		_UpperCamelCase    , _UpperCamelCase=m      ):

			'''simple docstring'''




			__lowerCAmelCase				    =			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 _lowerCamelCase	(		_UpperCamelCase      ):

			'''simple docstring'''




			__lowerCAmelCase				    =			summation_of_cost_derivative(_UpperCamelCase    , _UpperCamelCase      ) / m
			return cost_derivative_value


def _lowerCamelCase	(		):

			'''simple docstring'''




			global parameter_vector
			# Tune these values to set a tolerance value for predicted output
			__lowerCAmelCase				    =			0.00_00_02
			__lowerCAmelCase				    =			0
			__lowerCAmelCase				    =			0
			while True:
						j += 1
						__lowerCAmelCase				    =			[0, 0, 0, 0]
						for i in range(0    , len(_UpperCamelCase      )      ):
									__lowerCAmelCase				    =			get_cost_derivative(i - 1      )
									__lowerCAmelCase				    =			(
									    parameter_vector[i] - LEARNING_RATE * cost_derivative
									)
						if numpy.allclose(
						    _UpperCamelCase    , _UpperCamelCase    , atol=_UpperCamelCase    , rtol=_UpperCamelCase    , ):
									break
						__lowerCAmelCase				    =			temp_parameter_vector
			print(("Number of iterations:", j)      )


def _lowerCamelCase	(		):

			'''simple docstring'''




			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()




