


from .balanced_parentheses import balanced_parentheses
from .stack import Stack
def  __SCREAMING_SNAKE_CASE       (				lowercase__  :str				)       ->  int:
						return {"+": 1, "-": 1, "*": 2, "/": 2, "^": 3}.get(lowercase__      ,						-1				)
def  __SCREAMING_SNAKE_CASE       (				lowercase__  :str				)       ->  str:
						if not balanced_parentheses(lowercase__				):
												raise ValueError('Mismatched parentheses'				)
						__magic_name__				:							Stack[str]				=      Stack()
						__magic_name__				:							Dict				=      []
						for char in expression_str:
												if char.isalpha() or char.isdigit():
																		postfix.append(lowercase__				)
												elif char == "(":
																		stack.push(lowercase__				)
												elif char == ")":
																		while not stack.is_empty() and stack.peek() != "(":
																								postfix.append(stack.pop()				)
																		stack.pop()
												else:
																		while not stack.is_empty() and precedence(lowercase__				) <= precedence(stack.peek()				):
																								postfix.append(stack.pop()				)
																		stack.push(lowercase__				)
						while not stack.is_empty():
												postfix.append(stack.pop()				)
						return " ".join(lowercase__				)


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

			testmod()
			snake_case__     							= 'a+b*(c^d-e)^(f+g*h)-i'

			print('Infix to Postfix Notation demonstration:\n')
			print('Infix notation: ' + expression)
			print('Postfix notation: ' + infix_to_postfix(expression))






from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier

# Load iris file
snake_case__     							= load_iris()
iris.keys()


print(F"""Target names: \n {iris.target_names} """)
print(F"""\n Features: \n {iris.feature_names}""")

# Train set e Test set
snake_case__							,     snake_case__							,     snake_case__							,     snake_case__     							= train_test_split(
    iris['data'], iris['target'], random_state=4
)

# KNN

snake_case__     							= KNeighborsClassifier(n_neighbors=1)
knn.fit(X_train, y_train)

# new array to test
snake_case__     							= [[1, 2, 1, 4], [2, 3, 4, 5]]

snake_case__     							= knn.predict(X_new)

print(
    F"""\nNew array: \n {X_new}\n\nTarget Names Prediction: \n"""
    F""" {iris['target_names'][prediction]}"""
)


