

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 						_snake_case(   SCREAMING_SNAKE_CASE__      :     Dict   ,      SCREAMING_SNAKE_CASE__      :     Optional[Any]				)							->				Any:





    '''simple docstring'''






    return np.linalg.norm(np.array(SCREAMING_SNAKE_CASE__				) - np.array(SCREAMING_SNAKE_CASE__				)				)
def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     int   ,      SCREAMING_SNAKE_CASE__      :     Union[str, Any]   ,      SCREAMING_SNAKE_CASE__      :     List[Any]   ,      SCREAMING_SNAKE_CASE__      :     Optional[Any]   ,      SCREAMING_SNAKE_CASE__      :     List[Any]=5				)							->				Optional[Any]:





    '''simple docstring'''






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




from __future__ import annotations

from scipy.special import comb  # type: ignore


class 			A						:







    """simple docstring"""





    def __init__(				self			:	Any,lowercase_			:	list[tuple[float, float]]		)->   Optional[int]:





        '''simple docstring'''







        A__   				=       list_of_points
        # Degree determines the flexibility of the curve.
        # Degree = 1 will produce a straight line.
        A__   				=       len(lowercase_		) - 1


    def 						snake_case__					(				self			:	List[Any],lowercase_			:	float		)->   list[float]:





        '''simple docstring'''







        assert 0 <= t <= 1, "Time t must be between 0 and 1."
        A__   				=       []
        for i in range(len(self.list_of_points		)		):
            # basis function for each i
            output_values.append(
                comb(self.degree,lowercase_		) * ((1 - t) ** (self.degree - i)) * (t**i)		)
        # the basis must sum up to 1 for it to produce a valid Bezier curve.
        assert round(sum(lowercase_		),5		) == 1
        return output_values


    def 						snake_case__					(				self			:	str,lowercase_			:	float		)->   tuple[float, float]:





        '''simple docstring'''







        assert 0 <= t <= 1, "Time t must be between 0 and 1."

        A__   				=       self.basis_function(lowercase_		)
        A__   				=       0.0
        A__   				=       0.0
        for i in range(len(self.list_of_points		)		):
            # For all points, sum up the product of i-th basis function and i-th point.
            x += basis_function[i] * self.list_of_points[i][0]
            y += basis_function[i] * self.list_of_points[i][1]
        return (x, y)


    def 						snake_case__					(				self			:	str,lowercase_			:	float = 0.01		)->   str:





        '''simple docstring'''







        from matplotlib import pyplot as plt  # type: ignore

        A__   				=       []  # x coordinates of points to plot
        A__   				=       []  # y coordinates of points to plot

        A__   				=       0.0
        while t <= 1:
            A__   				=       self.bezier_curve_function(lowercase_		)
            to_plot_x.append(value[0]		)
            to_plot_y.append(value[1]		)
            t += step_size

        A__   				=       [i[0] for i in self.list_of_points]
        A__   				=       [i[1] for i in self.list_of_points]

        plt.plot(
            lowercase_,lowercase_,color='blue',label='Curve of Degree ' + str(self.degree		),)
        plt.scatter(lowercase_,lowercase_,color='red',label='Control Points'		)
        plt.legend()
        plt.show()


if __name__ == "__main__":
     import doctest

     doctest.testmod()

     BezierCurve([(1, 2), (3, 5)]).plot_curve()  # degree 1
     BezierCurve([(0, 0), (5, 5), (5, 0)]).plot_curve()  # degree 2
     BezierCurve([(0, 0), (5, 5), (5, 0), (2.5, -2.5)]).plot_curve()  # degree 3

