



import numpy

# List of input, output pairs
UpperCamelCase__ =						(
    ((5, 2, 3), 15),
    ((6, 5, 9), 25),
    ((11, 12, 13), 41),
    ((1, 1, 1), 8),
    ((11, 12, 13), 41),
)
UpperCamelCase__ =						(((5_15, 22, 13), 5_55), ((61, 35, 49), 1_50))
UpperCamelCase__ =						[2, 4, 1, 5]
UpperCamelCase__ =						len(train_data)
UpperCamelCase__ =						0.009






def  UpperCAmelCase__    (    lowercase__						,       lowercase__="train"   ) ->      Optional[int]:
						return calculate_hypothesis_value(lowercase__						,       lowercase__   ) - output(
						    lowercase__						,       lowercase__   )






def  UpperCAmelCase__    (    lowercase__   ) ->      Union[str, Any]:
						__lowercase					=				0
						for i in range(len(lowercase__   ) - 1   ):
												hyp_val += data_input_tuple[i] * parameter_vector[i + 1]
						hyp_val += parameter_vector[0]
						return hyp_val






def  UpperCAmelCase__    (    lowercase__						,       lowercase__   ) ->      Optional[int]:
						if data_set == "train":
												return train_data[example_no][1]
						elif data_set == "test":
												return test_data[example_no][1]
						return None






def  UpperCAmelCase__    (    lowercase__						,       lowercase__   ) ->      Dict:
						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__    (    lowercase__						,       lowercase__=m   ) ->      Union[str, Any]:
						__lowercase					=				0
						for i in range(lowercase__   ):
												if index == -1:
																		summation_value += _error(lowercase__   )
												else:
																		summation_value += _error(lowercase__   ) * train_data[i][0][index]
						return summation_value






def  UpperCAmelCase__    (    lowercase__   ) ->      Optional[Any]:
						__lowercase					=				summation_of_cost_derivative(lowercase__						,       lowercase__   ) / m
						return cost_derivative_value






def  UpperCAmelCase__    (    ) ->      Tuple:
						global parameter_vector
						# Tune these values to set a tolerance value for predicted output
						__lowercase					=				0.000002
						__lowercase					=				0
						__lowercase					=				0
						while True:
												j += 1
												__lowercase					=				[0, 0, 0, 0]
												for i in range(0						,       len(lowercase__   )   ):
																		__lowercase					=				get_cost_derivative(i - 1   )
																		__lowercase					=				(
																		    parameter_vector[i] - LEARNING_RATE * cost_derivative
																		)
												if numpy.allclose(
												    lowercase__						,       lowercase__						,       atol=lowercase__						,       rtol=lowercase__						,       ):
																		break
												__lowercase					=				temp_parameter_vector
						print(("""Number of iterations:""", j)   )






def  UpperCAmelCase__    (    ) ->      Any:
						for i in range(len(lowercase__   )   ):
												print(("""Actual output value:""", output(lowercase__						,       """test"""   ))   )
												print(("""Hypothesis output:""", calculate_hypothesis_value(lowercase__						,       """test"""   ))   )


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









def  UpperCAmelCase__    (    lowercase__						,       lowercase__   ) ->      int:
						if len(lowercase__   ) != len(lowercase__   ):
												raise ValueError("""String lengths must match!"""   )

						__lowercase					=				0

						for chara, chara in zip(lowercase__						,       lowercase__   ):
												if chara != chara:
																		count += 1

						return count


if __name__ == "__main__":
			import doctest

			doctest.testmod()





