



'''simple docstring'''






from collections import Counter

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

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

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

lowerCAmelCase: Dict							=					train_test_split(X, y)
def 				lowerCamelCase__	(						_A			,      _A    ):
  return np.linalg.norm(np.array(_A    ) - np.array(_A    )    )
def 				lowerCamelCase__	(						_A			,      _A			,      _A			,      _A			,      _A=5    ):
  a						: Union[str, Any]					       =					zip(_A			,      _A    )
  # List of distances of all points from the point to be classified
  a						: Union[str, Any]					       =					[]
  for data_point in data:
    a						: Dict					       =					euclidean_distance(data_point[0]			,      _A    )
    distances.append((distance, data_point[1])    )
  # Choosing 'k' points with the least distances.
  a						: int					       =					[i[1] for i in sorted(_A    )[:k]]
  # Most commonly occurring class among them
  # is the class into which the point is classified
  a						: Dict					       =					Counter(_A    ).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, is_vision_available


lowerCAmelCase: Any							=						{
    'configuration_poolformer': [
        'POOLFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'PoolFormerConfig',
        'PoolFormerOnnxConfig',
    ]
}

try:
     if not is_vision_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCAmelCase: List[str]							=						['PoolFormerFeatureExtractor']
     lowerCAmelCase: Tuple							=						['PoolFormerImageProcessor']

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCAmelCase: str							=						[
         'POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
         'PoolFormerForImageClassification',
         'PoolFormerModel',
         'PoolFormerPreTrainedModel',
     ]


if TYPE_CHECKING:
     from .configuration_poolformer import (
         POOLFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
         PoolFormerConfig,
         PoolFormerOnnxConfig,
     )

     try:
          if not is_vision_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .feature_extraction_poolformer import PoolFormerFeatureExtractor
          from .image_processing_poolformer import PoolFormerImageProcessor

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_poolformer import (
              POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
              PoolFormerForImageClassification,
              PoolFormerModel,
              PoolFormerPreTrainedModel,
          )


else:
     import sys

     lowerCAmelCase: Dict							=						_LazyModule(__name__, globals()['__file__'], _import_structure)