'''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 							UpperCamelCase__			(			a__			,      a__="train"      ):


						'''simple docstring'''






						return calculate_hypothesis_value(a__			,      a__      ) - output(
						    a__			,      a__      )



def 							UpperCamelCase__			(			a__      ):


						'''simple docstring'''






						_lowerCAmelCase										=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__      ):


						'''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__      ):


						'''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      ):


						'''simple docstring'''






						_lowerCAmelCase										=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__      ):


						'''simple docstring'''






						_lowerCAmelCase										=summation_of_cost_derivative(a__			,      a__      ) / m
						return cost_derivative_value



def 							UpperCamelCase__			(			):


						'''simple docstring'''






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



def 							UpperCamelCase__			(			):


						'''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()






'''simple docstring'''

lowercase_     		= '''
# Installazione di Transformers
! pip install transformers datasets
# Per installare dalla fonte invece dell\'ultima versione rilasciata, commenta il comando sopra e
# rimuovi la modalità commento al comando seguente.
# ! pip install git+https://github.com/huggingface/transformers.git
'''

lowercase_     		= [{'''type''': '''code''', '''content''': INSTALL_CONTENT}]
lowercase_     		= {
    '''{processor_class}''': '''FakeProcessorClass''',
    '''{model_class}''': '''FakeModelClass''',
    '''{object_class}''': '''FakeObjectClass''',
}





