import numpy

# List of input, output pairs
lowerCamelCase_    :		Dict							=	(
    ((5, 2, 3), 15),
    ((6, 5, 9), 25),
    ((11, 12, 13), 41),
    ((1, 1, 1), 8),
    ((11, 12, 13), 41),
)
lowerCamelCase_    :		List[str]							=	(((515, 22, 13), 555), ((61, 35, 49), 150))
lowerCamelCase_    :		Optional[int]							=	[2, 4, 1, 5]
lowerCamelCase_    :		Dict							=	len(train_data)
lowerCamelCase_    :		Tuple							=	0.0_0_9
def 							lowerCAmelCase(     __lowerCamelCase				,    __lowerCamelCase="train"						):
     return calculate_hypothesis_value(__lowerCamelCase				,    __lowerCamelCase						) - output(
         __lowerCamelCase				,    __lowerCamelCase						)
def 							lowerCAmelCase(     __lowerCamelCase						):
     __a		 =       0
     for i in range(len(__lowerCamelCase						) - 1						):
          hyp_val += data_input_tuple[i] * parameter_vector[i + 1]
     hyp_val += parameter_vector[0]
     return hyp_val
def 							lowerCAmelCase(     __lowerCamelCase				,    __lowerCamelCase						):
     if data_set == "train":
          return train_data[example_no][1]
     elif data_set == "test":
          return test_data[example_no][1]
     return None
def 							lowerCAmelCase(     __lowerCamelCase				,    __lowerCamelCase						):
     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(     __lowerCamelCase				,    __lowerCamelCase=m						):
     __a		 =       0
     for i in range(__lowerCamelCase						):
          if index == -1:
               summation_value += _error(__lowerCamelCase						)
          else:
               summation_value += _error(__lowerCamelCase						) * train_data[i][0][index]
     return summation_value
def 							lowerCAmelCase(     __lowerCamelCase						):
     __a		 =       summation_of_cost_derivative(__lowerCamelCase				,    __lowerCamelCase						) / m
     return cost_derivative_value
def 							lowerCAmelCase(     ):
     global parameter_vector
     # Tune these values to set a tolerance value for predicted output
     __a		 =       0.00_00_02
     __a		 =       0
     __a		 =       0
     while True:
          j += 1
          __a		 =       [0, 0, 0, 0]
          for i in range(0				,    len(__lowerCamelCase						)						):
               __a		 =       get_cost_derivative(i - 1						)
               __a		 =       (
                   parameter_vector[i] - LEARNING_RATE * cost_derivative
               )
          if numpy.allclose(
              __lowerCamelCase				,    __lowerCamelCase				,    atol=__lowerCamelCase				,    rtol=__lowerCamelCase				,    ):
               break
          __a		 =       temp_parameter_vector
     print(('Number of iterations:', j)						)
def 							lowerCAmelCase(     ):
     for i in range(len(__lowerCamelCase						)						):
          print(('Actual output value:', output(__lowerCamelCase				,    'test'						))						)
          print(('Hypothesis output:', calculate_hypothesis_value(__lowerCamelCase				,    'test'						))						)


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





from __future__ import annotations

import numpy as np
def 							lowerCAmelCase(     __lowerCamelCase						):
					return np.maximum(0				,    __lowerCamelCase						)


if __name__ == "__main__":
	print(np.array(relu([-1, 0, 5])))  # --> [0, 0, 5]




