


"""simple docstring"""
import numpy

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






def 		lowercase							(     _snake_case						:      List[Any]     ,   _snake_case						:      Optional[Any]="train"     )     ->int:
	"""simple docstring"""





	return calculate_hypothesis_value(_snake_case     ,   _snake_case     ) - output(
	    _snake_case     ,   _snake_case     )






def 		lowercase							(     _snake_case						:      Union[str, Any]     )     ->str:
	"""simple docstring"""





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






def 		lowercase							(     _snake_case						:      str     ,   _snake_case						:      str     )     ->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 		lowercase							(     _snake_case						:      str     ,   _snake_case						:      Union[str, Any]     )     ->Optional[Any]:
	"""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 		lowercase							(     _snake_case						:      Optional[Any]     ,   _snake_case						:      Tuple=m     )     ->Optional[Any]:
	"""simple docstring"""





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






def 		lowercase							(     _snake_case						:      Optional[int]     )     ->Dict:
	"""simple docstring"""





	__snake_case	:						List[Any]						=				summation_of_cost_derivative(_snake_case     ,   _snake_case     ) / m
	return cost_derivative_value






def 		lowercase							(     )     ->str:
	"""simple docstring"""





	global parameter_vector
	# Tune these values to set a tolerance value for predicted output
	__snake_case	:						Optional[Any]						=				0.000002
	__snake_case	:						int						=				0
	__snake_case	:						List[str]						=				0
	while True:
		j += 1
		__snake_case	:						List[str]						=				[0, 0, 0, 0]
		for i in range(0     ,   len(_snake_case     )     ):
			__snake_case	:						str						=				get_cost_derivative(i - 1     )
			__snake_case	:						Any						=				(
			    parameter_vector[i] - LEARNING_RATE * cost_derivative
			)
		if numpy.allclose(
		    _snake_case     ,   _snake_case     ,   atol=_snake_case     ,   rtol=_snake_case     ,   ):
			break
		__snake_case	:						Any						=				temp_parameter_vector
	print(('''Number of iterations:''', j)     )






def 		lowercase							(     )     ->int:
	"""simple docstring"""





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


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









"""simple docstring"""
from ...utils import is_torch_available, is_transformers_available


if is_transformers_available() and is_torch_available():
			from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline





