
"""simple docstring"""





from collections import Counter

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

_a								=						datasets.load_iris()

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

_a				,				_a				,				_a				,				_a								=						train_test_split(X, y)



def  lowerCamelCase__						(     __snake_case,	__snake_case      )				->						List[str]:


					"""simple docstring"""





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



def  lowerCamelCase__						(     __snake_case,	__snake_case,	__snake_case,	__snake_case,	__snake_case=5      )				->						str:


					"""simple docstring"""





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

import requests

def 		_SCREAMING_SNAKE_CASE			(_UpperCAmelCase					:    str			):
 lowerCAmelCase    				= F'https://hacker-news.firebaseio.com/v0/item/{story_id}.json?print=pretty'
 return requests.get(_UpperCAmelCase			).json()

def 		_SCREAMING_SNAKE_CASE			(_UpperCAmelCase					:    int = 10			):
 lowerCAmelCase    				= 'https://hacker-news.firebaseio.com/v0/topstories.json?print=pretty'
 lowerCAmelCase    				= requests.get(_UpperCAmelCase			).json()[:max_stories]
 return [get_hackernews_story(_UpperCAmelCase			) for story_id in story_ids]

def 		_SCREAMING_SNAKE_CASE			(_UpperCAmelCase					:    int = 10			):
 lowerCAmelCase    				= hackernews_top_stories(_UpperCAmelCase			)
 return "\n".join('* [{title}]({url})'.format(**_UpperCAmelCase			) for story in stories			)


if __name__ == "__main__":
       print(hackernews_top_stories_as_markdown())





