


'''simple docstring'''







from __future__ import annotations

from itertools import permutations
from random import randint
from timeit import repeat




def        __lowercase						(      )				->					tuple[list[int], int]:
     """simple docstring"""




     __a       =   [randint(-1000				,  1000		) for i in range(10		)]
     __a       =   randint(-5000				,  5000		)
     return (arr, r)


SCREAMING_SNAKE_CASE_      =			make_dataset()




def        __lowercase						(      __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE		)				->					tuple[int, ...]:
     """simple docstring"""




     for triplet in permutations(__SCREAMING_SNAKE_CASE				,  3		):
          if sum(__SCREAMING_SNAKE_CASE		) == target:
               return tuple(sorted(__SCREAMING_SNAKE_CASE		)		)
     return (0, 0, 0)




def        __lowercase						(      __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE		)				->					tuple[int, int, int]:
     """simple docstring"""




     arr.sort()
     __a       =   len(__SCREAMING_SNAKE_CASE		)
     for i in range(n - 1		):
          __a ,						__a       =   i + 1, n - 1
          while left < right:
               if arr[i] + arr[left] + arr[right] == target:
                    return (arr[i], arr[left], arr[right])
               elif arr[i] + arr[left] + arr[right] < target:
                    left += 1
               elif arr[i] + arr[left] + arr[right] > target:
                    right -= 1
     return (0, 0, 0)




def        __lowercase						(      )				->					tuple[float, float]:
     """simple docstring"""




     __a       =   """
from __main__ import dataset, triplet_sum1, triplet_sum2
"""
     __a       =   """
triplet_sum1(*dataset)
"""
     __a       =   """
triplet_sum2(*dataset)
"""
     __a       =   repeat(setup=__SCREAMING_SNAKE_CASE				,  stmt=__SCREAMING_SNAKE_CASE				,  repeat=5				,  number=1_0000		)
     __a       =   repeat(setup=__SCREAMING_SNAKE_CASE				,  stmt=__SCREAMING_SNAKE_CASE				,  repeat=5				,  number=1_0000		)
     return (min(__SCREAMING_SNAKE_CASE		), min(__SCREAMING_SNAKE_CASE		))


if __name__ == "__main__":
 from doctest import testmod

 testmod()
 SCREAMING_SNAKE_CASE_      =			solution_times()
 print(f"""The time for naive implementation is {times[0]}.""")
 print(f"""The time for optimized implementation is {times[1]}.""")










'''simple docstring'''







import numpy

# List of input, output pairs
SCREAMING_SNAKE_CASE_      =			(
    ((5, 2, 3), 15),
    ((6, 5, 9), 25),
    ((11, 12, 13), 41),
    ((1, 1, 1), 8),
    ((11, 12, 13), 41),
)
SCREAMING_SNAKE_CASE_      =			(((5_15, 22, 13), 5_55), ((61, 35, 49), 1_50))
SCREAMING_SNAKE_CASE_      =			[2, 4, 1, 5]
SCREAMING_SNAKE_CASE_      =			len(train_data)
SCREAMING_SNAKE_CASE_      =			0.0_0_9




def        __lowercase						(      __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE="train"		)				->					Optional[int]:
     """simple docstring"""




     return calculate_hypothesis_value(__SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE		) - output(
         __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE		)




def        __lowercase						(      __SCREAMING_SNAKE_CASE		)				->					int:
     """simple docstring"""




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




def        __lowercase						(      __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE		)				->					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						(      __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE		)				->					Tuple:
     """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						(      __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE=m		)				->					Optional[Any]:
     """simple docstring"""




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




def        __lowercase						(      __SCREAMING_SNAKE_CASE		)				->					List[str]:
     """simple docstring"""




     __a       =   summation_of_cost_derivative(__SCREAMING_SNAKE_CASE				,  __SCREAMING_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
     __a       =   0.000_002
     __a       =   0
     __a       =   0
     while True:
          j += 1
          __a       =   [0, 0, 0, 0]
          for i in range(0				,  len(__SCREAMING_SNAKE_CASE		)		):
               __a       =   get_cost_derivative(i - 1		)
               __a       =   (
                   parameter_vector[i] - LEARNING_RATE * cost_derivative
               )
          if numpy.allclose(
              __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE				,  atol=__SCREAMING_SNAKE_CASE				,  rtol=__SCREAMING_SNAKE_CASE				,  ):
               break
          __a       =   temp_parameter_vector
     print(("""Number of iterations:""", j)		)




def        __lowercase						(      )				->					List[Any]:
     """simple docstring"""




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


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






