




"""simple docstring"""

__A							=     [0.33, 0.44, 0.55, 0.44, 0.33]
__A							=     3
__A							=     5

# Buffer Space (with Parameters Space)
__A							=     []
__A							=     []

# Machine Time
__A							=     0



def 					UpperCamelCase__       (						lowercase__					:       int				):
		global buffer_space, params_space, machine_time, K, m, t

		# Choosing Dynamical Systems (All)
		for key, value in enumerate(lowercase__				):
				# Evolution Parameter
				snake_case						:  Dict        =			float(seed / value				)

				# Control Theory: Orbit Change
				snake_case						:  Optional[Any]        =			(buffer_space[(key + 1) % m] + e) % 1

				# Control Theory: Trajectory Change
				snake_case						:  Union[str, Any]        =			(params_space[key] + e) % 1 + 3

				# Modification (Transition Function) - Jumps
				snake_case						:  List[str]        =			round(float(r * value * (1 - value)				)    ,       10				)
				snake_case						:  str        =			r  # Saving to Parameters Space

		# Logistic Map
		assert max(lowercase__				) < 1
		assert max(lowercase__				) < 4

		# Machine Time
		machine_time += 1



def 					UpperCamelCase__       (						):
		global buffer_space, params_space, machine_time, K, m, t

		# PRNG (Xorshift by George Marsaglia)
		def xorshift(lowercase__					:       Any    ,       lowercase__					:       Optional[Any]				):
				x ^= y >> 13
				y ^= x << 17
				x ^= y >> 5
				return x

		# Choosing Dynamical Systems (Increment)
		snake_case						:  Union[str, Any]        =			machine_time % m

		# Evolution (Time Length)
		for _ in range(0    ,       lowercase__				):
				# Variables (Position + Parameters)
				snake_case						:  List[Any]        =			params_space[key]
				snake_case						:  List[str]        =			buffer_space[key]

				# Modification (Transition Function) - Flow
				snake_case						:  Optional[Any]        =			round(float(r * value * (1 - value)				)    ,       10				)
				snake_case						:  Any        =			(machine_time * 0.01 + r * 1.01) % 1 + 3

		# Choosing Chaotic Data
		snake_case						:  List[Any]        =			int(buffer_space[(key + 2) % m] * (10**10)				)
		snake_case						:  List[str]        =			int(buffer_space[(key - 2) % m] * (10**10)				)

		# Machine Time
		machine_time += 1

		return xorshift(lowercase__    ,       lowercase__				) % 0xFF_FFF_FFF



def 					UpperCamelCase__       (						):
		global buffer_space, params_space, machine_time, K, m, t

		snake_case						:  List[Any]        =			K
		snake_case						:  Any        =			[0] * m
		snake_case						:  str        =			0


if __name__ == "__main__":
							# Initialization
							reset()

							# Pushing Data (Input)
							import random

							__A							=     random.sample(range(0XFFFF_FFFF), 100)
							for chunk in message:
														push(chunk)

							# for controlling
							__A							=     ""

							# Pulling Data (Output)
							while inp in ("e", "E"):
														print(f'{format(pull(), "#04x")}')
														print(buffer_space)
														print(params_space)
														__A							=     input("(e)exit? ").strip()







"""simple docstring"""

import matplotlib.pyplot as plt
import numpy as np



def 					UpperCamelCase__       (						lowercase__					:       np.ndarray    ,       lowercase__					:       np.ndarray    ,       lowercase__					:       float				):
		snake_case						:  Any        =			len(lowercase__				)  # Number of training samples
		snake_case						:  str        =			np.eye(lowercase__				)  # Initialize weights as identity matrix
		for j in range(lowercase__				):
				snake_case						:  Any        =			point - x_train[j]
				snake_case						:  Dict        =			np.exp(diff @ diff.T / (-2.0 * tau**2)				)

		return weights



def 					UpperCamelCase__       (						lowercase__					:       np.ndarray    ,       lowercase__					:       np.ndarray    ,       lowercase__					:       np.ndarray    ,       lowercase__					:       float				):
		snake_case						:  str        =			weight_matrix(lowercase__    ,       lowercase__    ,       lowercase__				)
		snake_case						:  str        =			np.linalg.inv(x_train.T @ weight_mat @ x_train				) @ (
		    x_train.T @ weight_mat @ y_train.T
		)

		return weight



def 					UpperCamelCase__       (						lowercase__					:       np.ndarray    ,       lowercase__					:       np.ndarray    ,       lowercase__					:       float				):
		snake_case						:  Optional[int]        =			np.zeros(len(lowercase__				)				)  # Initialize array of predictions
		for i, item in enumerate(lowercase__				):
				snake_case						:  Dict        =			item @ local_weight(lowercase__    ,       lowercase__    ,       lowercase__    ,       lowercase__				)

		return y_pred



def 					UpperCamelCase__       (						lowercase__					:       str    ,       lowercase__					:       str    ,       lowercase__					:       str				):
		import seaborn as sns

		snake_case						:  Union[str, Any]        =			sns.load_dataset(lowercase__				)
		snake_case						:  Any        =			np.array(data[x_name]				)
		snake_case						:  str        =			np.array(data[y_name]				)

		snake_case						:  Any        =			np.ones(len(lowercase__				)				)

		# pairing elements of one and x_data
		snake_case						:  Optional[int]        =			np.column_stack((one, x_data)				)

		return x_train, x_data, y_data



def 					UpperCamelCase__       (						lowercase__					:       np.ndarray    ,       lowercase__					:       np.ndarray    ,       lowercase__					:       np.ndarray    ,       lowercase__					:       np.ndarray    ,       lowercase__					:       str    ,       lowercase__					:       str    ,       ):
		snake_case						:  List[str]        =			np.sort(lowercase__    ,       axis=0				)
		plt.scatter(lowercase__    ,       lowercase__    ,       color="blue"				)
		plt.plot(
		    x_train_sorted[:, 1]    ,       preds[x_train[:, 1].argsort(0				)]    ,       color="yellow"    ,       linewidth=5    ,       )
		plt.title("Local Weighted Regression"				)
		plt.xlabel(lowercase__				)
		plt.ylabel(lowercase__				)
		plt.show()


if __name__ == "__main__":
							import doctest

							doctest.testmod()

							# Demo with a dataset from the seaborn module
							__A						,    __A						,    __A							=     load_data("tips", "total_bill", "tip")
							__A							=     local_weight_regression(training_data_x, tip, 5)
							plot_preds(training_data_x, predictions, total_bill, tip, "total_bill", "tip")

