




"""simple docstring"""



from collections import Counter

import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split

SCREAMING_SNAKE_CASE    =	datasets.load_iris()

SCREAMING_SNAKE_CASE    =	np.array(data["data"])
SCREAMING_SNAKE_CASE    =	np.array(data["target"])
SCREAMING_SNAKE_CASE    =	data["target_names"]

SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE    =	train_test_split(X, y)







def   _SCREAMING_SNAKE_CASE       (							lowercase_      ,			lowercase_						)						->		Any:
 return np.linalg.norm(np.array(lowercase_						) - np.array(lowercase_						)						)







def   _SCREAMING_SNAKE_CASE       (							lowercase_      ,			lowercase_      ,			lowercase_      ,			lowercase_      ,			lowercase_=5						)						->		str:
 A__	=    zip(lowercase_      ,			lowercase_						)
 # List of distances of all points from the point to be classified
 A__	=    []
 for data_point in data:
  A__	=    euclidean_distance(data_point[0]      ,			lowercase_						)
  distances.append((distance, data_point[1])						)
 # Choosing 'k' points with the least distances.
 A__	=    [i[1] for i in sorted(lowercase_						)[:k]]
 # Most commonly occurring class among them
 # is the class into which the point is classified
 A__	=    Counter(lowercase_						).most_common(1						)[0][0]
 return classes[result]


if __name__ == "__main__":
      print(classifier(X_train, y_train, classes, [4.4, 3.1, 1.3, 1.4]))











"""simple docstring"""



SCREAMING_SNAKE_CASE    =	"Alexander Joslin"

import operator as op

from .stack import Stack







def   _SCREAMING_SNAKE_CASE       (							lowercase_						)						->		int:
 A__	=    {"*": op.mul, "/": op.truediv, "+": op.add, "-": op.sub}

 A__	=    Stack()
 A__	=    Stack()

 for i in equation:
  if i.isdigit():
   # RULE 1
   operand_stack.push(int(lowercase_						)						)
  elif i in operators:
   # RULE 2
   operator_stack.push(lowercase_						)
  elif i == ")":
   # RULE 4
   A__	=    operator_stack.peek()
   operator_stack.pop()
   A__	=    operand_stack.peek()
   operand_stack.pop()
   A__	=    operand_stack.peek()
   operand_stack.pop()

   A__	=    operators[opr](lowercase_      ,			lowercase_						)
   operand_stack.push(lowercase_						)

    # RULE 5
 return operand_stack.peek()


if __name__ == "__main__":
      SCREAMING_SNAKE_CASE    =	"(5 + ((4 * 2) * (2 + 3)))"
      # answer = 45
      print(f'{equation} = {dijkstras_two_stack_algorithm(equation)}')





