

"""simple docstring"""





from typing import Any




class  _lowerCAmelCase  :







 """simple docstring"""







 def __init__(      self							:      Any, UpperCAmelCase__							:      Any     ):
    __lowercase								=						data
    __lowercase								=						None







class  _lowerCAmelCase  :







 """simple docstring"""







 def __init__(      self							:      Optional[int]     ):
    __lowercase								=						None


 def  _lowercase			(      self							:      Any     ):
    __lowercase								=						self.head
    while temp is not None:
       print(temp.data, end=" "     )
       __lowercase								=						temp.next
    print()


 def  _lowercase			(      self							:      List[Any], UpperCAmelCase__							:      Any     ):
    __lowercase								=						Node(UpperCAmelCase__     )
    __lowercase								=						self.head
    __lowercase								=						new_node



 def  _lowercase			(      self							:      Dict, UpperCAmelCase__							:      str, UpperCAmelCase__							:      Tuple     ):
    if node_data_a == node_data_a:
       return
    else:
       __lowercase								=						self.head
       while node_a is not None and node_a.data != node_data_a:
          __lowercase								=						node_a.next

       __lowercase								=						self.head
       while node_a is not None and node_a.data != node_data_a:
          __lowercase								=						node_a.next

       if node_a is None or node_a is None:
          return

       __lowercase		,__lowercase								=						node_a.data, node_a.data


if __name__ == "__main__":
   _a							=							LinkedList()
   for i in range(5, 0, -1):
      ll.push(i)

   ll.print_list()

   ll.swap_nodes(1, 4)
   print('After swapping')
   ll.print_list()







"""simple docstring"""





import numpy

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




def   _A  (  UpperCamelCase_					:	str,      UpperCamelCase_					:	List[Any]="train")     ->						Optional[Any]:
   '''simple docstring'''


   return calculate_hypothesis_value(UpperCamelCase_,      UpperCamelCase_) - output(
       UpperCamelCase_,      UpperCamelCase_)




def   _A  (  UpperCamelCase_					:	List[Any])     ->						Union[str, Any]:
   '''simple docstring'''


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




def   _A  (  UpperCamelCase_					:	Dict,      UpperCamelCase_					:	Optional[int])     ->						Dict:
   '''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   _A  (  UpperCamelCase_					:	Dict,      UpperCamelCase_					:	List[str])     ->						int:
   '''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   _A  (  UpperCamelCase_					:	Any,      UpperCamelCase_					:	Tuple=m)     ->						int:
   '''simple docstring'''


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




def   _A  (  UpperCamelCase_					:	str)     ->						str:
   '''simple docstring'''


   __lowercase								=						summation_of_cost_derivative(UpperCamelCase_,      UpperCamelCase_) / m
   return cost_derivative_value




def   _A  (  )     ->						List[str]:
   '''simple docstring'''


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




def   _A  (  )     ->						int:
   '''simple docstring'''


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


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




