






'''simple docstring'''

from collections import Counter

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

SCREAMING_SNAKE_CASE__           =  datasets.load_iris()

SCREAMING_SNAKE_CASE__           =  np.array(data['data'])
SCREAMING_SNAKE_CASE__           =  np.array(data['target'])
SCREAMING_SNAKE_CASE__           =  data['target_names']

SCREAMING_SNAKE_CASE__       , SCREAMING_SNAKE_CASE__       , SCREAMING_SNAKE_CASE__       , SCREAMING_SNAKE_CASE__           =  train_test_split(X, y)







def        lowercase__					( __UpperCamelCase						,		__UpperCamelCase       )->					Any:
      return np.linalg.norm(np.array(__UpperCamelCase       ) - np.array(__UpperCamelCase       )       )







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






# flake8: noqa
# Lint as: python3

from typing import Dict, List, Optional, Type

from .. import config
from ..utils import logging
from .formatting import (
    ArrowFormatter,
    CustomFormatter,
    Formatter,
    PandasFormatter,
    PythonFormatter,
    TensorFormatter,
    format_table,
    query_table,
)
from .np_formatter import NumpyFormatter


a__		     =     logging.get_logger(__name__)

a__		     =     {}
a__		     =     {}
a__		     =     {}





def 				A__ (snake_case   :		type		,	snake_case   :		Optional[str]		,	snake_case   :		Optional[List[str]] = None		,	)     ->						str:
					__UpperCamelCase					:	int             =      aliases if aliases is not None else []
					if format_type in _FORMAT_TYPES:
										logger.warning(
										    F'''Overwriting format type \'{format_type}\' ({_FORMAT_TYPES[format_type].__name__} -> {formatter_cls.__name__})'''				)
					__UpperCamelCase					:	Optional[Any]             =      formatter_cls
					for alias in set(aliases + [format_type]				):
										if alias in _FORMAT_TYPES_ALIASES:
															logger.warning(
															    F'''Overwriting format type alias \'{alias}\' ({_FORMAT_TYPES_ALIASES[alias]} -> {format_type})'''				)
										__UpperCamelCase					:	Optional[int]             =      format_type





def 				A__ (snake_case   :		Exception		,	snake_case   :		Optional[str]		,	snake_case   :		Optional[List[str]] = None				)     ->						Tuple:
					__UpperCamelCase					:	List[Any]             =      aliases if aliases is not None else []
					for alias in set(aliases + [format_type]				):
										__UpperCamelCase					:	Optional[int]             =      unavailable_error


# Here we define all the available formatting functions that can be used by `Dataset.set_format`
_register_formatter(PythonFormatter, None, aliases=['''python'''])
_register_formatter(ArrowFormatter, '''arrow''', aliases=['''pa''', '''pyarrow'''])
_register_formatter(NumpyFormatter, '''numpy''', aliases=['''np'''])
_register_formatter(PandasFormatter, '''pandas''', aliases=['''pd'''])
_register_formatter(CustomFormatter, '''custom''')

if config.TORCH_AVAILABLE:
	from .torch_formatter import TorchFormatter

	_register_formatter(TorchFormatter, '''torch''', aliases=['''pt''', '''pytorch'''])
else:
	a__		     =     ValueError('''PyTorch needs to be installed to be able to return PyTorch tensors.''')
	_register_unavailable_formatter(_torch_error, '''torch''', aliases=['''pt''', '''pytorch'''])

if config.TF_AVAILABLE:
	from .tf_formatter import TFFormatter

	_register_formatter(TFFormatter, '''tensorflow''', aliases=['''tf'''])
else:
	a__		     =     ValueError('''Tensorflow needs to be installed to be able to return Tensorflow tensors.''')
	_register_unavailable_formatter(_tf_error, '''tensorflow''', aliases=['''tf'''])

if config.JAX_AVAILABLE:
	from .jax_formatter import JaxFormatter

	_register_formatter(JaxFormatter, '''jax''', aliases=[])
else:
	a__		     =     ValueError('''JAX needs to be installed to be able to return JAX arrays.''')
	_register_unavailable_formatter(_jax_error, '''jax''', aliases=[])





def 				A__ (snake_case   :		Optional[str]				)     ->						Optional[str]:
					if format_type in _FORMAT_TYPES_ALIASES:
										return _FORMAT_TYPES_ALIASES[format_type]
					else:
										return format_type







def 				A__ (snake_case   :		Optional[str]		,	**snake_case   :		List[str]				)     ->						Formatter:
					__UpperCamelCase					:	int             =      get_format_type_from_alias(snake_case				)
					if format_type in _FORMAT_TYPES:
										return _FORMAT_TYPES[format_type](**snake_case				)
					if format_type in _FORMAT_TYPES_ALIASES_UNAVAILABLE:
										raise _FORMAT_TYPES_ALIASES_UNAVAILABLE[format_type]
					else:
										raise ValueError(
										    F'''Return type should be None or selected in {list(type for type in _FORMAT_TYPES.keys() if type != None				)}, but got \'{format_type}\''''				)






