



'''simple docstring'''





from collections import Counter

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

A__    :   Optional[int]           =					datasets.load_iris()

A__    :   Union[str, Any]           =					np.array(data['''data'''])
A__    :   Union[str, Any]           =					np.array(data['''target'''])
A__    :   int           =					data['''target_names''']

A__     ,	A__     ,	A__     ,	A__    :   Optional[Any]           =					train_test_split(X, y)





def   a_   (		_UpperCAmelCase    :      Union[str, Any] ,_UpperCAmelCase    :      List[Any]				)    ->  List[str]:
      return np.linalg.norm(np.array(_UpperCAmelCase				) - np.array(_UpperCAmelCase				)				)





def   a_   (		_UpperCAmelCase    :      List[Any] ,_UpperCAmelCase    :      Tuple ,_UpperCAmelCase    :      Union[str, Any] ,_UpperCAmelCase    :      List[Any] ,_UpperCAmelCase    :      Union[str, Any]=5				)    ->  str:
      __snake_case :   Any													=    zip(_UpperCAmelCase ,_UpperCAmelCase				)
      # List of distances of all points from the point to be classified
      __snake_case :   str													=    []
      for data_point in data:
            __snake_case :   Union[str, Any]													=    euclidean_distance(data_point[0] ,_UpperCAmelCase				)
            distances.append((distance, data_point[1])				)
      # Choosing 'k' points with the least distances.
      __snake_case :   Tuple													=    [i[1] for i in sorted(_UpperCAmelCase				)[:k]]
      # Most commonly occurring class among them
      # is the class into which the point is classified
      __snake_case :   Optional[int]													=    Counter(_UpperCAmelCase				).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'''





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


A__    :   List[Any]           =					{
    '''configuration_luke''': ['''LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''LukeConfig'''],
    '''tokenization_luke''': ['''LukeTokenizer'''],
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    A__    :   Union[str, Any]           =					[
        '''LUKE_PRETRAINED_MODEL_ARCHIVE_LIST''',
        '''LukeForEntityClassification''',
        '''LukeForEntityPairClassification''',
        '''LukeForEntitySpanClassification''',
        '''LukeForMultipleChoice''',
        '''LukeForQuestionAnswering''',
        '''LukeForSequenceClassification''',
        '''LukeForTokenClassification''',
        '''LukeForMaskedLM''',
        '''LukeModel''',
        '''LukePreTrainedModel''',
    ]


if TYPE_CHECKING:
    from .configuration_luke import LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP, LukeConfig
    from .tokenization_luke import LukeTokenizer

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_luke import (
            LUKE_PRETRAINED_MODEL_ARCHIVE_LIST,
            LukeForEntityClassification,
            LukeForEntityPairClassification,
            LukeForEntitySpanClassification,
            LukeForMaskedLM,
            LukeForMultipleChoice,
            LukeForQuestionAnswering,
            LukeForSequenceClassification,
            LukeForTokenClassification,
            LukeModel,
            LukePreTrainedModel,
        )

else:
    import sys

    A__    :   List[str]           =					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



