



'''simple docstring'''





class 	lowerCAmelCase_:
  '''simple docstring'''




  def __init__(   self  ,__UpperCAmelCase = ""  ,__UpperCAmelCase = False				)   ->		None:
      # Mapping from the first character of the prefix of the node
      lowerCAmelCase__			:  dict[str, RadixNode]											= {}

      # A node will be a leaf if the tree contains its word
      lowerCAmelCase__			:  int											= is_leaf

      lowerCAmelCase__			:  int											= prefix




  def 							UpperCAmelCase_     (   self  ,__UpperCAmelCase				)   ->		tuple[str, str, str]:
      lowerCAmelCase__			:  int											= 0
      for q, w in zip(self.prefix  ,__UpperCAmelCase				):
          if q != w:
              break

          x += 1

      return self.prefix[:x], self.prefix[x:], word[x:]




  def 							UpperCAmelCase_     (   self  ,__UpperCAmelCase				)   ->		None:
      for word in words:
          self.insert(__UpperCAmelCase				)




  def 							UpperCAmelCase_     (   self  ,__UpperCAmelCase				)   ->		None:
      # Case 1: If the word is the prefix of the node
      # Solution: We set the current node as leaf
      if self.prefix == word:
          lowerCAmelCase__			:  int											= True

      # Case 2: The node has no edges that have a prefix to the word
      # Solution: We create an edge from the current node to a new one
      # containing the word
      elif word[0] not in self.nodes:
          lowerCAmelCase__			:  Optional[int]											= RadixNode(prefix=__UpperCAmelCase  ,is_leaf=__UpperCAmelCase				)

      else:
          lowerCAmelCase__			:  List[str]											= self.nodes[word[0]]
          lowerCAmelCase__			:  Any											= incoming_node.match(
              __UpperCAmelCase				)

          # Case 3: The node prefix is equal to the matching
          # Solution: We insert remaining word on the next node
          if remaining_prefix == "":
              self.nodes[matching_string[0]].insert(__UpperCAmelCase				)

          # Case 4: The word is greater equal to the matching
          # Solution: Create a node in between both nodes, change
          # prefixes and add the new node for the remaining word
          else:
              lowerCAmelCase__			:  Dict											= remaining_prefix

              lowerCAmelCase__			:  Tuple											= self.nodes[matching_string[0]]
              lowerCAmelCase__			:  Any											= RadixNode(__UpperCAmelCase  ,__UpperCAmelCase				)
              lowerCAmelCase__			:  str											= aux_node

              if remaining_word == "":
                  lowerCAmelCase__			:  Tuple											= True
              else:
                  self.nodes[matching_string[0]].insert(__UpperCAmelCase				)




  def 							UpperCAmelCase_     (   self  ,__UpperCAmelCase				)   ->		bool:
      lowerCAmelCase__			:  List[str]											= self.nodes.get(word[0]  ,__UpperCAmelCase				)
      if not incoming_node:
          return False
      else:
          lowerCAmelCase__			:  Any											= incoming_node.match(
              __UpperCAmelCase				)
          # If there is remaining prefix, the word can't be on the tree
          if remaining_prefix != "":
              return False
          # This applies when the word and the prefix are equal
          elif remaining_word == "":
              return incoming_node.is_leaf
          # We have word remaining so we check the next node
          else:
              return incoming_node.find(__UpperCAmelCase				)




  def 							UpperCAmelCase_     (   self  ,__UpperCAmelCase				)   ->		bool:
      lowerCAmelCase__			:  str											= self.nodes.get(word[0]  ,__UpperCAmelCase				)
      if not incoming_node:
          return False
      else:
          lowerCAmelCase__			:  List[str]											= incoming_node.match(
              __UpperCAmelCase				)
          # If there is remaining prefix, the word can't be on the tree
          if remaining_prefix != "":
              return False
          # We have word remaining so we check the next node
          elif remaining_word != "":
              return incoming_node.delete(__UpperCAmelCase				)
          else:
              # If it is not a leaf, we don't have to delete
              if not incoming_node.is_leaf:
                  return False
              else:
                  # We delete the nodes if no edges go from it
                  if len(incoming_node.nodes				) == 0:
                      del self.nodes[word[0]]
                      # We merge the current node with its only child
                      if len(self.nodes				) == 1 and not self.is_leaf:
                          lowerCAmelCase__			:  List[str]											= list(self.nodes.values()				)[0]
                          lowerCAmelCase__			:  Union[str, Any]											= merging_node.is_leaf
                          self.prefix += merging_node.prefix
                          lowerCAmelCase__			:  Union[str, Any]											= merging_node.nodes
                    # If there is more than 1 edge, we just mark it as non-leaf
                  elif len(incoming_node.nodes				) > 1:
                      lowerCAmelCase__			:  str											= False
                  # If there is 1 edge, we merge it with its child
                  else:
                      lowerCAmelCase__			:  Any											= list(incoming_node.nodes.values()				)[0]
                      lowerCAmelCase__			:  Tuple											= merging_node.is_leaf
                      incoming_node.prefix += merging_node.prefix
                      lowerCAmelCase__			:  int											= merging_node.nodes

                  return True






  def 							UpperCAmelCase_     (   self  ,__UpperCAmelCase = 0				)   ->		None:
      if self.prefix != "":
          print("""-""" * height  ,self.prefix  ,"""  (leaf)""" if self.is_leaf else """"""				)

      for value in self.nodes.values():
          value.print_tree(height + 1				)







def        _SCREAMING_SNAKE_CASE  (	):



    """simple docstring"""





    lowerCAmelCase__			:  str											= """banana bananas bandana band apple all beast""".split()
    lowerCAmelCase__			:  Tuple											= RadixNode()
    root.insert_many(UpperCamelCase      )

    assert all(root.find(UpperCamelCase      ) for word in words      )
    assert not root.find("""bandanas"""      )
    assert not root.find("""apps"""      )
    root.delete("""all"""      )
    assert not root.find("""all"""      )
    root.delete("""banana"""      )
    assert not root.find("""banana"""      )
    assert root.find("""bananas"""      )

    return True
def        _SCREAMING_SNAKE_CASE  (	):



    """simple docstring"""





    assert test_trie()
def        _SCREAMING_SNAKE_CASE  (	):



    """simple docstring"""





    lowerCAmelCase__			:  Optional[int]											= RadixNode()
    lowerCAmelCase__			:  List[Any]											= """banana bananas bandanas bandana band apple all beast""".split()
    root.insert_many(UpperCamelCase      )

    print("""Words:"""							,   UpperCamelCase      )
    print("""Tree:"""      )
    root.print_tree()


if __name__ == "__main__":
 main()











'''simple docstring'''





# 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        _SCREAMING_SNAKE_CASE  (	UpperCamelCase      ):



    """simple docstring"""





    return 1 / (1 + np.exp(-z      ))
def        _SCREAMING_SNAKE_CASE  (	UpperCamelCase							,   UpperCamelCase      ):



    """simple docstring"""





    return (-y * np.log(UpperCamelCase      ) - (1 - y) * np.log(1 - h      )).mean()
def        _SCREAMING_SNAKE_CASE  (	UpperCamelCase							,   UpperCamelCase							,   UpperCamelCase      ):



    """simple docstring"""





    lowerCAmelCase__			:  Union[str, Any]											= np.dot(UpperCamelCase							,   UpperCamelCase      )
    return np.sum(y * scores - np.log(1 + np.exp(UpperCamelCase      )      )      )
def        _SCREAMING_SNAKE_CASE  (	UpperCamelCase							,   UpperCamelCase							,   UpperCamelCase							,   UpperCamelCase=70000      ):



    """simple docstring"""





    lowerCAmelCase__			:  Union[str, Any]											= np.zeros(x.shape[1]      )

    for iterations in range(UpperCamelCase      ):
        lowerCAmelCase__			:  str											= np.dot(UpperCamelCase							,   UpperCamelCase      )
        lowerCAmelCase__			:  Tuple											= sigmoid_function(UpperCamelCase      )
        lowerCAmelCase__			:  List[str]											= np.dot(x.T							,   h - y      ) / y.size
        lowerCAmelCase__			:  List[Any]											= theta - alpha * gradient  # updating the weights
        lowerCAmelCase__			:  List[Any]											= np.dot(UpperCamelCase							,   UpperCamelCase      )
        lowerCAmelCase__			:  List[Any]											= sigmoid_function(UpperCamelCase      )
        lowerCAmelCase__			:  List[Any]											= cost_function(UpperCamelCase							,   UpperCamelCase      )
        if iterations % 100 == 0:
            print(f"""loss: {j} \t"""      )  # printing the loss after every 100 iterations
    return theta


# In[68]:

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

 _lowerCAmelCase										=     0.1
 _lowerCAmelCase										=     logistic_reg(alpha, x, y, max_iterations=7_0000)
 print('''theta: ''', theta)  # printing the theta i.e our weights vector
 def        _SCREAMING_SNAKE_CASE  (	UpperCamelCase      ):



     """simple docstring"""





     return sigmoid_function(
         np.dot(UpperCamelCase							,   UpperCamelCase      )      )  # 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''')
 ((_lowerCAmelCase)						,      (_lowerCAmelCase))										=     (x[:, 0].min(), x[:, 0].max())
 ((_lowerCAmelCase)						,      (_lowerCAmelCase))										=     (x[:, 1].min(), x[:, 1].max())
 ((_lowerCAmelCase)						,      (_lowerCAmelCase))										=     np.meshgrid(np.linspace(xa_min, xa_max), np.linspace(xa_min, xa_max))
 _lowerCAmelCase										=     np.c_[xxa.ravel(), xxa.ravel()]
 _lowerCAmelCase										=     predict_prob(grid).reshape(xxa.shape)
 plt.contour(xxa, xxa, probs, [0.5], linewidths=1, colors='''black''')

 plt.legend()




 plt.show()


