
from __future__ import annotations

from collections.abc import Callable






def      lowerCamelCase__       (       __lowerCAmelCase						:						Callable[[int | float], int | float]					,							__lowerCAmelCase						:						int | float					,							__lowerCAmelCase						:						int | float					,							__lowerCAmelCase						:						int = 100					,							):




			"""simple docstring"""


			lowerCAmelCase_      			=			x_start
			lowerCAmelCase_      			=			fnc(__lowerCAmelCase       )
			lowerCAmelCase_      			=			0.0
			for _ in range(__lowerCAmelCase       ):
						# Approximates small segments of curve as linear and solve
						# for trapezoidal area
						lowerCAmelCase_      			=			(x_end - x_start) / steps + xa
						lowerCAmelCase_      			=			fnc(__lowerCAmelCase       )
						area += abs(fxa + fxa       ) * (xa - xa) / 2
						# Increment step
						lowerCAmelCase_      			=			xa
						lowerCAmelCase_      			=			fxa
			return area


if __name__ == "__main__":






			def      lowerCamelCase__       (       __lowerCAmelCase						:						str       ):




						"""simple docstring"""


						return x**3 + x**2

			print("f(x) = x^3 + x^2")
			print("The area between the curve, x = -5, x = 5 and the x axis is:")
			_A     =  10
			while i <= 10_00_00:
						print(f"""with {i} steps: {trapezoidal_area(f, -5, 5, i)}""")
						i *= 10





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]))



