
import random
import sys

import numpy as np
from matplotlib import pyplot as plt
from matplotlib.colors import ListedColormap

_A     =  "Usage of script: script_name <size_of_canvas:int>"

_A     =  [0] * 1_00 + [1] * 10
random.shuffle(choice)






def      lowerCamelCase__       (       __lowerCAmelCase						:						int       ):




			"""simple docstring"""


			lowerCAmelCase_      			=			[[False for i in range(__lowerCAmelCase       )] for j in range(__lowerCAmelCase       )]
			return canvas






def      lowerCamelCase__       (       __lowerCAmelCase						:						list[list[bool]]       ):




			"""simple docstring"""


			for i, row in enumerate(__lowerCAmelCase       ):
						for j, _ in enumerate(__lowerCAmelCase       ):
									lowerCAmelCase_      			=			bool(random.getrandbits(1       )       )






def      lowerCamelCase__       (       __lowerCAmelCase						:						list[list[bool]]       ):




			"""simple docstring"""


			lowerCAmelCase_      			=			np.array(__lowerCAmelCase       )
			lowerCAmelCase_      			=			np.array(create_canvas(current_canvas.shape[0]       )       )
			for r, row in enumerate(__lowerCAmelCase       ):
						for c, pt in enumerate(__lowerCAmelCase       ):
									lowerCAmelCase_      			=			__judge_point(
									    __lowerCAmelCase					,							current_canvas[r - 1 : r + 2, c - 1 : c + 2]       )

			lowerCAmelCase_      			=			next_gen_canvas
			del next_gen_canvas  # cleaning memory as we move on.
			lowerCAmelCase_      			=			current_canvas.tolist()
			return return_canvas






def      lowerCamelCase__       (       __lowerCAmelCase						:						bool					,							__lowerCAmelCase						:						list[list[bool]]       ):




			"""simple docstring"""


			lowerCAmelCase_      			=			0
			lowerCAmelCase_      			=			0
			# finding dead or alive neighbours count.
			for i in neighbours:
						for status in i:
									if status:
												alive += 1
									else:
												dead += 1

    # handling duplicate entry for focus pt.
			if pt:
						alive -= 1
			else:
						dead -= 1

			# running the rules of game here.
			lowerCAmelCase_      			=			pt
			if pt:
						if alive < 2:
									lowerCAmelCase_      			=			False
						elif alive == 2 or alive == 3:
									lowerCAmelCase_      			=			True
						elif alive > 3:
									lowerCAmelCase_      			=			False
			else:
						if alive == 3:
									lowerCAmelCase_      			=			True

			return state


if __name__ == "__main__":
			if len(sys.argv) != 2:
						raise Exception(usage_doc)

			_A     =  int(sys.argv[1])
			# main working structure of this module.
			_A     =  create_canvas(canvas_size)
			seed(c)
			_A,   _A     =  plt.subplots()
			fig.show()
			_A     =  ListedColormap(["w", "k"])
			try:
						while True:
									_A     =  run(c)
									ax.matshow(c, cmap=cmap)
									fig.canvas.draw()
									ax.cla()
			except KeyboardInterrupt:
						# do nothing.
						pass





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__       (       __lowerCAmelCase						:						Union[str, Any]					,							__lowerCAmelCase						:						Union[str, Any]       ):




			"""simple docstring"""


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






def      lowerCamelCase__       (       __lowerCAmelCase						:						List[Any]					,							__lowerCAmelCase						:						str					,							__lowerCAmelCase						:						Tuple					,							__lowerCAmelCase						:						int					,							__lowerCAmelCase						:						Any=5       ):




			"""simple docstring"""


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



