

from collections import Counter

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

_a:    int 							=			datasets.load_iris()

_a:    Union[str, Any] 							=			np.array(data["""data"""])
_a:    Any 							=			np.array(data["""target"""])
_a:    Tuple 							=			data["target_names"]

_a:    Union[str, Any] 							=			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    ):
	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 __future__ import annotations

from collections import Counter
from random import random

class 						__UpperCamelCase    :




	def __init__(		self	:						Any							):


		'''simple docstring'''







		UpperCAmelCase_       				=			{}




	def       __A					(		self	:						List[str]		,	lowerCAmelCase	:						str							):


		'''simple docstring'''







		UpperCAmelCase_       				=			{}




	def       __A					(		self	:						Union[str, Any]		,	lowerCAmelCase	:						str		,	lowerCAmelCase	:						str		,	lowerCAmelCase	:						float							):


		'''simple docstring'''







		if nodea not in self.connections:
			self.add_node(lowerCAmelCase							)
		if nodea not in self.connections:
			self.add_node(lowerCAmelCase							)
		UpperCAmelCase_       				=			probability




	def       __A					(		self	:						Tuple							):


		'''simple docstring'''







		return list(self.connections							)




	def       __A					(		self	:						str		,	lowerCAmelCase	:						str							):


		'''simple docstring'''







		UpperCAmelCase_       				=			0
		UpperCAmelCase_       				=			random()

		for dest in self.connections[node]:
			current_probability += self.connections[node][dest]
			if current_probability > random_value:
				return dest
		return ""



def       __lowerCAmelCase      (						A       ,     A       ,     A    ):
	UpperCAmelCase_       				=			MarkovChainGraphUndirectedUnweighted()

	for nodea, nodea, probability in transitions:
		graph.add_transition_probability(A       ,     A       ,     A    )

	UpperCAmelCase_       				=			Counter(graph.get_nodes()    )
	UpperCAmelCase_       				=			start

	for _ in range(A    ):
		UpperCAmelCase_       				=			graph.transition(A    )
		visited[node] += 1

	return visited


if __name__ == "__main__":
				import doctest

				doctest.testmod()