






class 							A_    :






 '''simple docstring'''




 def __init__(	self							,  _A							,  _A) ->		Any:

     """simple docstring"""



     _UpperCAmelCase				:      List[str]			  =				name
     _UpperCAmelCase				:      Tuple			  =				val


 def __str__(	self) ->		Optional[int]:

     """simple docstring"""



     return f'''{self.__class__.__name__}({self.name}, {self.val})'''


 def __lt__(	self							,  _A) ->		Optional[int]:

     """simple docstring"""



     return self.val < other.val



class 							A_    :






 '''simple docstring'''




 def __init__(	self							,  _A) ->		Dict:

     """simple docstring"""



     _UpperCAmelCase				:      List[Any]			  =				{}
     _UpperCAmelCase				:      Tuple			  =				{}
     _UpperCAmelCase				:      str			  =				self.build_heap(_A)


 def __getitem__(	self							,  _A) ->		Tuple:

     """simple docstring"""



     return self.get_value(_A)


 def  snake_case__				(	self							,  _A) ->		Union[str, Any]:

     """simple docstring"""



     return (idx - 1) // 2


 def  snake_case__				(	self							,  _A) ->		Tuple:

     """simple docstring"""



     return idx * 2 + 1


 def  snake_case__				(	self							,  _A) ->		Dict:

     """simple docstring"""



     return idx * 2 + 2


 def  snake_case__				(	self							,  _A) ->		Optional[Any]:

     """simple docstring"""



     return self.heap_dict[key]


 def  snake_case__				(	self							,  _A) ->		Optional[int]:

     """simple docstring"""



     _UpperCAmelCase				:      Union[str, Any]			  =				len(_A) - 1
     _UpperCAmelCase				:      str			  =				self.get_parent_idx(_A)

     for idx, i in enumerate(_A):
         _UpperCAmelCase				:      int			  =				idx
         _UpperCAmelCase				:      Any			  =				i.val

     for i in range(_A							,  -1							,  -1):
         self.sift_down(_A							,  _A)
     return array


 def  snake_case__				(	self							,  _A							,  _A) ->		str:

     """simple docstring"""



     while True:
         _UpperCAmelCase				:      Optional[Any]			  =				self.get_left_child_idx(_A)  # noqa: E741
         _UpperCAmelCase				:      List[str]			  =				self.get_right_child_idx(_A)

         _UpperCAmelCase				:      List[Any]			  =				idx
         if l < len(_A) and array[l] < array[idx]:
             _UpperCAmelCase				:      str			  =				l
         if r < len(_A) and array[r] < array[smallest]:
             _UpperCAmelCase				:      Union[str, Any]			  =				r

         if smallest != idx:
             _UpperCAmelCase  , _UpperCAmelCase				:      Dict			  =				array[smallest], array[idx]
             (
                 (
                 _UpperCAmelCase
             )  , (
                 _UpperCAmelCase
             )  , 
             )				:      Dict			  =				(
                 self.idx_of_element[array[smallest]],
                 self.idx_of_element[array[idx]],
             )
             _UpperCAmelCase				:      Dict			  =				smallest
         else:
             break


 def  snake_case__				(	self							,  _A) ->		str:

     """simple docstring"""



     _UpperCAmelCase				:      Dict			  =				self.get_parent_idx(_A)
     while p >= 0 and self.heap[p] > self.heap[idx]:
         _UpperCAmelCase  , _UpperCAmelCase				:      str			  =				self.heap[idx], self.heap[p]
         _UpperCAmelCase  , _UpperCAmelCase				:      Dict			  =				(
             self.idx_of_element[self.heap[idx]],
             self.idx_of_element[self.heap[p]],
         )
         _UpperCAmelCase				:      str			  =				p
         _UpperCAmelCase				:      Tuple			  =				self.get_parent_idx(_A)


 def  snake_case__				(	self) ->		Union[str, Any]:

     """simple docstring"""



     return self.heap[0]


 def  snake_case__				(	self) ->		Union[str, Any]:

     """simple docstring"""



     _UpperCAmelCase  , _UpperCAmelCase				:      str			  =				self.heap[-1], self.heap[0]
     _UpperCAmelCase  , _UpperCAmelCase				:      Optional[Any]			  =				(
         self.idx_of_element[self.heap[-1]],
         self.idx_of_element[self.heap[0]],
     )

     _UpperCAmelCase				:      int			  =				self.heap.pop()
     del self.idx_of_element[x]
     self.sift_down(0							,  self.heap)
     return x


 def  snake_case__				(	self							,  _A) ->		Union[str, Any]:

     """simple docstring"""



     self.heap.append(_A)
     _UpperCAmelCase				:      int			  =				len(self.heap) - 1
     _UpperCAmelCase				:      List[str]			  =				node.val
     self.sift_up(len(self.heap) - 1)


 def  snake_case__				(	self) ->		Union[str, Any]:

     """simple docstring"""



     return len(self.heap) == 0


 def  snake_case__				(	self							,  _A							,  _A) ->		List[Any]:

     """simple docstring"""



     assert (
         self.heap[self.idx_of_element[node]].val > new_value
     ), "newValue must be less that current value"
     _UpperCAmelCase				:      List[str]			  =				new_value
     _UpperCAmelCase				:      int			  =				new_value
     self.sift_up(self.idx_of_element[node])



SCREAMING_SNAKE_CASE 							=						Node('R', -1)
SCREAMING_SNAKE_CASE 							=						Node('B', 6)
SCREAMING_SNAKE_CASE 							=						Node('A', 3)
SCREAMING_SNAKE_CASE 							=						Node('X', 1)
SCREAMING_SNAKE_CASE 							=						Node('E', 4)

# Use one of these two ways to generate Min-Heap

# Generating Min-Heap from array
SCREAMING_SNAKE_CASE 							=						MinHeap([r, b, a, x, e])

# Generating Min-Heap by Insert method
# myMinHeap.insert(a)
# myMinHeap.insert(b)
# myMinHeap.insert(x)
# myMinHeap.insert(r)
# myMinHeap.insert(e)

# Before
print('Min Heap - before decrease key')
for i in my_min_heap.heap:
  print(i)

print('Min Heap - After decrease key of node [B -> -17]')
my_min_heap.decrease_key(b, -17)

# After
for i in my_min_heap.heap:
  print(i)

if __name__ == "__main__":
  import doctest

  doctest.testmod()












# Logistic Regression from scratch

# In[62]:

# In[63]:

# importing all the required libraries

import numpy as np
from matplotlib import pyplot as plt
from sklearn import datasets

def      _lowerCamelCase	(						__A    :							Optional[int] )      ->					str:
    return 1 / (1 + np.exp(-z ))

def      _lowerCamelCase	(						__A    :							Tuple		,		__A    :							List[Any] )      ->					Optional[int]:
    return (-y * np.log(__A ) - (1 - y) * np.log(1 - h )).mean()

def      _lowerCamelCase	(						__A    :							Tuple		,		__A    :							List[Any]		,		__A    :							int )      ->					Any:
    _UpperCAmelCase				:      int			  =				np.dot(__A		,		__A )
    return np.sum(y * scores - np.log(1 + np.exp(__A ) ) )

def      _lowerCamelCase	(						__A    :							List[Any]		,		__A    :							Union[str, Any]		,		__A    :							str		,		__A    :							Dict=70_000 )      ->					Optional[Any]:
    _UpperCAmelCase				:      Union[str, Any]			  =				np.zeros(x.shape[1] )

    for iterations in range(__A ):
        _UpperCAmelCase				:      List[Any]			  =				np.dot(__A		,		__A )
        _UpperCAmelCase				:      Union[str, Any]			  =				sigmoid_function(__A )
        _UpperCAmelCase				:      Optional[Any]			  =				np.dot(x.T		,		h - y ) / y.size
        _UpperCAmelCase				:      Optional[Any]			  =				theta - alpha * gradient  # updating the weights
        _UpperCAmelCase				:      List[Any]			  =				np.dot(__A		,		__A )
        _UpperCAmelCase				:      Any			  =				sigmoid_function(__A )
        _UpperCAmelCase				:      Union[str, Any]			  =				cost_function(__A		,		__A )
        if iterations % 100 == 0:
            print(f'''loss: {j} \t''' )  # printing the loss after every 100 iterations
    return theta


# In[68]:

if __name__ == "__main__":
  SCREAMING_SNAKE_CASE 							=						datasets.load_iris()
  SCREAMING_SNAKE_CASE 							=						iris.data[:, :2]
  SCREAMING_SNAKE_CASE 							=						(iris.target != 0) * 1

  SCREAMING_SNAKE_CASE 							=						0.1
  SCREAMING_SNAKE_CASE 							=						logistic_reg(alpha, x, y, max_iterations=70000)
  print('theta: ', theta)  # printing the theta i.e our weights vector

  def      _lowerCamelCase	(						__A    :							Any )      ->					int:
      return sigmoid_function(
          np.dot(__A		,		__A ) )  # predicting the value of probability from the logistic regression algorithm

  plt.figure(figsize=(10, 6))
  plt.scatter(x[y == 0][:, 0], x[y == 0][:, 1], color='b', label='0')
  plt.scatter(x[y == 1][:, 0], x[y == 1][:, 1], color='r', label='1')
  ((SCREAMING_SNAKE_CASE)							,			(SCREAMING_SNAKE_CASE)) 							=						(x[:, 0].min(), x[:, 0].max())
  ((SCREAMING_SNAKE_CASE)							,			(SCREAMING_SNAKE_CASE)) 							=						(x[:, 1].min(), x[:, 1].max())
  ((SCREAMING_SNAKE_CASE)							,			(SCREAMING_SNAKE_CASE)) 							=						np.meshgrid(np.linspace(xa_min, xa_max), np.linspace(xa_min, xa_max))
  SCREAMING_SNAKE_CASE 							=						np.c_[xxa.ravel(), xxa.ravel()]
  SCREAMING_SNAKE_CASE 							=						predict_prob(grid).reshape(xxa.shape)
  plt.contour(xxa, xxa, probs, [0.5], linewidths=1, colors='black')

  plt.legend()


  plt.show()




