


"""simple docstring"""






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

# Load iris file
SCREAMING_SNAKE_CASE__				:  Union[str, Any]						= 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
SCREAMING_SNAKE_CASE__				:  Union[str, Any]						= train_test_split(
    iris["data"], iris["target"], random_state=4
)

# KNN

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

# new array to test
SCREAMING_SNAKE_CASE__				:  Optional[int]						= [[1, 2, 1, 4], [2, 3, 4, 5]]

SCREAMING_SNAKE_CASE__				:  int						= knn.predict(X_new)

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







"""simple docstring"""
def 					a__ ( a :					str		):

  """simple docstring"""







  _snake_case  :    Tuple            = word.upper()
  _snake_case  :    List[Any]            = 0
  _snake_case  :    Any            = {}
  _snake_case  :    List[str]            = []

  for letter in word:
    if letter not in letter_nums:
      _snake_case  :    Optional[int]            = str(a		)
      next_num += 1
    word_pattern.append(letter_nums[letter]		)
  return ".".join(a		)


if __name__ == "__main__":
    import pprint
    import time

    _a   :      str       =				time.time()
    with open("""dictionary.txt""") as in_file:
        _a   :      Optional[int]       =				in_file.read().splitlines()

    _a   :      dict       =				{}
    for word in word_list:
        _a   :      Optional[Any]       =				get_word_pattern(word)
        if pattern in all_patterns:
            all_patterns[pattern].append(word)
        else:
            _a   :      List[Any]       =				[word]

    with open("""word_patterns.txt""", """w""") as out_file:
        out_file.write(pprint.pformat(all_patterns))

    _a   :      List[Any]       =				round(time.time() - start_time, 2)
    print(f'Done!  {len(all_patterns):,} word patterns found in {total_time} seconds.')
    # Done!  9,581 word patterns found in 0.58 seconds.

