






'''simple docstring'''







import random






class 					a__    :
     '''simple docstring'''







     def __init__(self    :	Dict				,				lowercase__    :	list[list[float]]				,				lowercase__    :	list[int]				,				lowercase__    :	float = 0.01				,				lowercase__    :	int = 1000				,				lowercase__    :	float = -1				,				)  ->       None:
           __A		   =      sample
           if len(self.sample					) == 0:
                 raise ValueError("Sample data can not be empty"					)
           __A		   =      target
           if len(self.target					) == 0:
                 raise ValueError("Target data can not be empty"					)
           if len(self.sample					) != len(self.target					):
                 raise ValueError("Sample data and Target data do not have matching lengths"					)
           __A		   =      learning_rate
           __A		   =      epoch_number
           __A		   =      bias
           __A		   =      len(lowercase__					)
           __A		   =      len(sample[0]					)  # number of columns in dataset
           __A		   =      []






     def     _UpperCamelCase   (self    :	Union[str, Any]					)  ->       None:
           for sample in self.sample:
                 sample.insert(0				,				self.bias					)

           for _ in range(self.col_sample					):
                 self.weight.append(random.random()					)

           self.weight.insert(0				,				self.bias					)

           __A		   =      0

           while True:
                 __A		   =      False
                 for i in range(self.number_sample					):
                       __A		   =      0
                       for j in range(self.col_sample + 1					):
                             __A		   =      u + self.weight[j] * self.sample[i][j]
                       __A		   =      self.sign(lowercase__					)
                       if y != self.target[i]:
                             for j in range(self.col_sample + 1					):
                                   __A		   =      (
                                       self.weight[j]
                                       + self.learning_rate
                                       * (self.target[i] - y)
                                       * self.sample[i][j]
                                   )
                             __A		   =      True
            # print('Epoch: \n',epoch_count)
                 __A		   =      epoch_count + 1
                 # if you want control the epoch or just by error
                 if not has_misclassified:
                       print(("\nEpoch:\n", epoch_count)					)
                       print("------------------------\n"					)
                       # if epoch_count > self.epoch_number or not error:
                       break






     def     _UpperCamelCase   (self    :	Tuple				,				lowercase__    :	list[float]					)  ->       None:
           if len(self.sample					) == 0:
                 raise ValueError("Sample data can not be empty"					)
           sample.insert(0				,				self.bias					)
           __A		   =      0
           for i in range(self.col_sample + 1					):
                 __A		   =      u + self.weight[i] * sample[i]

           __A		   =      self.sign(lowercase__					)

           if y == -1:
                 print(("Sample: ", sample)					)
                 print("classification: P1"					)
           else:
                 print(("Sample: ", sample)					)
                 print("classification: P2"					)






     def     _UpperCamelCase   (self    :	List[Any]				,				lowercase__    :	float					)  ->       int:
           return 1 if u >= 0 else -1







__A      :       Dict      =	[
    [-0.6508, 0.1097, 4.0009],
    [-1.4492, 0.8896, 4.4005],
    [2.0850, 0.6876, 12.0710],
    [0.2626, 1.1476, 7.7985],
    [0.6418, 1.0234, 7.0427],
    [0.2569, 0.6730, 8.3265],
    [1.1155, 0.6043, 7.4446],
    [0.0914, 0.3399, 7.0677],
    [0.0121, 0.5256, 4.6316],
    [-0.0429, 0.4660, 5.4323],
    [0.4340, 0.6870, 8.2287],
    [0.2735, 1.0287, 7.1934],
    [0.4839, 0.4851, 7.4850],
    [0.4089, -0.1267, 5.5019],
    [1.4391, 0.1614, 8.5843],
    [-0.9115, -0.1973, 2.1962],
    [0.3654, 1.0475, 7.4858],
    [0.2144, 0.7515, 7.1699],
    [0.2013, 1.0014, 6.5489],
    [0.6483, 0.2183, 5.8991],
    [-0.1147, 0.2242, 7.2435],
    [-0.7970, 0.8795, 3.8762],
    [-1.0625, 0.6366, 2.4707],
    [0.5307, 0.1285, 5.6883],
    [-1.2200, 0.7777, 1.7252],
    [0.3957, 0.1076, 5.6623],
    [-0.1013, 0.5989, 7.1812],
    [2.4482, 0.9455, 11.2095],
    [2.0149, 0.6192, 10.9263],
    [0.2012, 0.2611, 5.4631],
]

__A      :       str      =	[
    -1,
    -1,
    -1,
    1,
    1,
    -1,
    1,
    -1,
    1,
    1,
    -1,
    1,
    -1,
    -1,
    -1,
    -1,
    1,
    1,
    1,
    1,
    -1,
    1,
    1,
    1,
    1,
    -1,
    -1,
    1,
    -1,
    1,
]


if __name__ == "__main__":
       import doctest

       doctest.testmod()

       __A      :       List[Any]      =	Perceptron(
           sample=samples, target=target, learning_rate=0.01, epoch_number=1_0_0_0, bias=-1
       )
       network.training()
       print('Finished training perceptron')
       print('Enter values to predict or q to exit')
       while True:
              __A      :       list      =	[]
              for i in range(len(samples[0])):
                     __A      :       Dict      =	input('value: ').strip()
                     if user_input == "q":
                            break
                     __A      :       str      =	float(user_input)
                     sample.insert(i, observation)
              network.sort(sample)










'''simple docstring'''







from __future__ import annotations

from queue import Queue






class 					a__    :
     '''simple docstring'''







     def __init__(self    :	Any					)  ->       None:
           __A		   =      {}






     def     _UpperCamelCase   (self    :	Optional[int]					)  ->       None:
           for i in self.vertices:
                 print(lowercase__				,				" : "				,				" -> ".join([str(lowercase__					) for j in self.vertices[i]]					)					)






     def     _UpperCamelCase   (self    :	Union[str, Any]				,				lowercase__    :	int				,				lowercase__    :	int					)  ->       None:
           if from_vertex in self.vertices:
                 self.vertices[from_vertex].append(lowercase__					)
           else:
                 __A		   =      [to_vertex]






     def     _UpperCamelCase   (self    :	Optional[int]				,				lowercase__    :	int					)  ->       set[int]:
           __A		   =      set()

           # create a first in first out queue to store all the vertices for BFS
           __A		   =      Queue()

           # mark the source node as visited and enqueue it
           visited.add(lowercase__					)
           queue.put(lowercase__					)

           while not queue.empty():
                 __A		   =      queue.get()

                 # loop through all adjacent vertex and enqueue it if not yet visited
                 for adjacent_vertex in self.vertices[vertex]:
                       if adjacent_vertex not in visited:
                             queue.put(lowercase__					)
                             visited.add(lowercase__					)
           return visited


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

       testmod(verbose=True)

       __A      :       Dict      =	Graph()
       g.add_edge(0, 1)
       g.add_edge(0, 2)
       g.add_edge(1, 2)
       g.add_edge(2, 0)
       g.add_edge(2, 3)
       g.add_edge(3, 3)

       g.print_graph()
       # 0  :  1 -> 2
       # 1  :  2
       # 2  :  0 -> 3
       # 3  :  3

       assert sorted(g.bfs(2)) == [0, 1, 2, 3]


