import os
from string import ascii_letters

lowercase__      :    Tuple          =     ascii_letters + ''' \t\n'''

def        SCREAMING_SNAKE_CASE_					(						)					->     List[str]:
							lowerCAmelCase     		=		os.path.split(os.path.realpath(A_							)							)
							lowerCAmelCase     		=		{}
							with open(path[0] + '''/dictionary.txt'''							) as dictionary_file:
														for word in dictionary_file.read().split('''\n'''							):
																					lowerCAmelCase     		=		None
							return english_words


lowercase__      :    int          =     load_dictionary()

def        SCREAMING_SNAKE_CASE_					(						snake_case__ :    Optional[Any]							)					->     List[Any]:
							lowerCAmelCase     		=		message.upper()
							lowerCAmelCase     		=		remove_non_letters(A_							)
							lowerCAmelCase     		=		message.split()
							lowerCAmelCase     		=		len([word for word in possible_words if word in ENGLISH_WORDS]							)
							return float(A_							) / len(A_							)

def        SCREAMING_SNAKE_CASE_					(						snake_case__ :    Optional[int]							)					->     Union[str, Any]:
							return "".join(symbol for symbol in message if symbol in LETTERS_AND_SPACE							)

def        SCREAMING_SNAKE_CASE_					(						snake_case__ :    Union[str, Any]       ,  snake_case__ :    Any = 2_0       ,  snake_case__ :    Tuple = 8_5							)					->     Any:
							lowerCAmelCase     		=		get_english_count(A_							) * 1_0_0 >= word_percentage
							lowerCAmelCase     		=		len(remove_non_letters(A_							)							)
							lowerCAmelCase     		=		(float(A_							) / len(A_							)) * 1_0_0
							lowerCAmelCase     		=		message_letters_percentage >= letter_percentage
							return words_match and letters_match


if __name__ == "__main__":
	import doctest

	doctest.testmod()




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

# Load iris file
lowercase__      :    int          =     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
lowercase__     ,   lowercase__     ,   lowercase__     ,   lowercase__      :    Any          =     train_test_split(
    iris['''data'''], iris['''target'''], random_state=4
)

# KNN

lowercase__      :    int          =     KNeighborsClassifier(n_neighbors=1)
knn.fit(X_train, y_train)

# new array to test
lowercase__      :    List[str]          =     [[1, 2, 1, 4], [2, 3, 4, 5]]

lowercase__      :    int          =     knn.predict(X_new)

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



