"""simple docstring"""







UpperCamelCase_       :       str							   =		[0.33, 0.44, 0.55, 0.44, 0.33]
UpperCamelCase_       :       Optional[Any]							   =		3
UpperCamelCase_       :       List[str]							   =		5

# Buffer Space (with Parameters Space)
UpperCamelCase_       :       list[float]							   =		[]
UpperCamelCase_       :       list[float]							   =		[]

# Machine Time
UpperCamelCase_       :       Dict							   =		0


def   a__      (		UpperCAmelCase__       :			List[str]			)->	Optional[Any]:
						global buffer_space, params_space, machine_time, K, m, t

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

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

												# Control Theory: Trajectory Change
												UpperCAmelCase__						=		(params_space[key] + e) % 1 + 3

												# Modification (Transition Function) - Jumps
												UpperCAmelCase__						=		round(float(r * value * (1 - value)			)		,			1_0			)
												UpperCAmelCase__						=		r  # Saving to Parameters Space

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

						# Machine Time
						machine_time += 1


def   a__      (		)->	Any:
						global buffer_space, params_space, machine_time, K, m, t

						# PRNG (Xorshift by George Marsaglia)
						def xorshift(UpperCAmelCase__       :			List[Any]		,			UpperCAmelCase__       :			Union[str, Any]			):
												x ^= y >> 1_3
												y ^= x << 1_7
												x ^= y >> 5
												return x

						# Choosing Dynamical Systems (Increment)
						UpperCAmelCase__						=		machine_time % m

						# Evolution (Time Length)
						for _ in range(0		,			UpperCAmelCase__			):
												# Variables (Position + Parameters)
												UpperCAmelCase__						=		params_space[key]
												UpperCAmelCase__						=		buffer_space[key]

												# Modification (Transition Function) - Flow
												UpperCAmelCase__						=		round(float(r * value * (1 - value)			)		,			1_0			)
												UpperCAmelCase__						=		(machine_time * 0.01 + r * 1.01) % 1 + 3

						# Choosing Chaotic Data
						UpperCAmelCase__						=		int(buffer_space[(key + 2) % m] * (1_0**1_0)			)
						UpperCAmelCase__						=		int(buffer_space[(key - 2) % m] * (1_0**1_0)			)

						# Machine Time
						machine_time += 1

						return xorshift(UpperCAmelCase__		,			UpperCAmelCase__			) % 0Xffffffff


def   a__      (		)->	Optional[Any]:
						global buffer_space, params_space, machine_time, K, m, t

						UpperCAmelCase__						=		K
						UpperCAmelCase__						=		[0] * m
						UpperCAmelCase__						=		0


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

						# Pushing Data (Input)
						import random

						UpperCamelCase_       :       Any							   =		random.sample(range(0xff_ff_ff_ff), 100)
						for chunk in message:
												push(chunk)

						# for controlling
						UpperCamelCase_       :       Any							   =		""""""

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


"""simple docstring"""







from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier

# Load iris file
UpperCamelCase_       :       Optional[int]							   =		load_iris()
iris.keys()


print(F"""Target names: \n {iris.target_names} """)
print(F"""\n Features: \n {iris.feature_names}""")

# Train set e Test set
UpperCamelCase_   ,						UpperCamelCase_   ,						UpperCamelCase_   ,						UpperCamelCase_       :       int							   =		train_test_split(
    iris["""data"""], iris["""target"""], random_state=4
)

# KNN

UpperCamelCase_       :       str							   =		KNeighborsClassifier(n_neighbors=1)
knn.fit(X_train, y_train)

# new array to test
UpperCamelCase_       :       Union[str, Any]							   =		[[1, 2, 1, 4], [2, 3, 4, 5]]

UpperCamelCase_       :       Union[str, Any]							   =		knn.predict(X_new)

print(
    F"""\nNew array: \n {X_new}\n\nTarget Names Prediction: \n"""
    F""" {iris["target_names"][prediction]}"""
)

