


'''simple docstring'''






from collections import Counter

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

lowercase							=datasets.load_iris()

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

lowercase,       lowercase,       lowercase,       lowercase							=train_test_split(X, y)





def lowerCamelCase__      (						__lowerCamelCase	:							Any	,   __lowerCamelCase	:							List[Any]							):







   '''simple docstring'''




   return np.linalg.norm(np.array(__lowerCamelCase							) - np.array(__lowerCamelCase							)							)





def lowerCamelCase__      (						__lowerCamelCase	:							int	,   __lowerCamelCase	:							Any	,   __lowerCamelCase	:							List[Any]	,   __lowerCamelCase	:							Any	,   __lowerCamelCase	:							Union[str, Any]=5							):







   '''simple docstring'''




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






# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import re

from ..utils import cached_file


# docstyle-ignore
lowercase							='\nHuman: <<task>>\n\nAssistant: '


lowercase							='huggingface-tools/default-prompts'
lowercase							={'chat': 'chat_prompt_template.txt', 'run': 'run_prompt_template.txt'}





def lowerCamelCase__      (						__lowerCamelCase	:							Union[str, Any]	,   __lowerCamelCase	:							Union[str, Any]	,   __lowerCamelCase	:							int="run"							):







   '''simple docstring'''




   if prompt_or_repo_id is None:
      _UpperCAmelCase       :       List[str]							   =DEFAULT_PROMPTS_REPO

   # prompt is considered a repo ID when it does not contain any kind of space
   if re.search('\\s'	,   __lowerCamelCase							) is not None:
      return prompt_or_repo_id

   _UpperCAmelCase       :       Dict							   =cached_file(
       __lowerCamelCase	,   PROMPT_FILES[mode]	,   repo_type='dataset'	,   user_agent={'agent': agent_name}							)
   with open(__lowerCamelCase	,   'r'	,   encoding='utf-8'							) as f:
      return f.read()

