





from collections import Counter

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

UpperCamelCase__     =	datasets.load_iris()

UpperCamelCase__     =	np.array(data["data"])
UpperCamelCase__     =	np.array(data["target"])
UpperCamelCase__     =	data["target_names"]

UpperCamelCase__   ,	UpperCamelCase__   ,	UpperCamelCase__   ,	UpperCamelCase__     =	train_test_split(X, y)





def       _UpperCamelCase       (a__				:Dict				,     a__				:Tuple    ):
							return np.linalg.norm(np.array(a__    ) - np.array(a__    )    )





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











from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


UpperCamelCase__     =	{
    "configuration_swiftformer": [
        "SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "SwiftFormerConfig",
        "SwiftFormerOnnxConfig",
    ]
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCamelCase__     =	[
					    "SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "SwiftFormerForImageClassification",
					    "SwiftFormerModel",
					    "SwiftFormerPreTrainedModel",
					]

if TYPE_CHECKING:
					from .configuration_swiftformer import (
					    SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    SwiftFormerConfig,
					    SwiftFormerOnnxConfig,
					)

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_swiftformer import (
										    SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    SwiftFormerForImageClassification,
										    SwiftFormerModel,
										    SwiftFormerPreTrainedModel,
										)

else:
					import sys

					UpperCamelCase__     =	_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




