



from math import sqrt

import numpy as np
from sympy import symbols

# Coefficient
# Speed of light (m/s)
_lowercase: Dict      			=		299792458

# Symbols
_lowercase			,				_lowercase			,				_lowercase			,				_lowercase: Dict      			=		symbols("ct x y z")






def     a(      A       :    float       )		->       float:
					"""simple docstring"""





					if velocity > c:
										raise ValueError("Speed must not exceed light speed 299,792,458 [m/s]!"       )
					elif velocity < 1:
										# Usually the speed should be much higher than 1 (c order of magnitude)
										raise ValueError("Speed must be greater than or equal to 1!"       )

					return velocity / c






def     a(      A       :    float       )		->       float:
					"""simple docstring"""





					return 1 / sqrt(1 - beta(A       ) ** 2       )






def     a(      A       :    float       )		->       np.ndarray:
					"""simple docstring"""





					return np.array(
					    [
					        [gamma(A       ), -gamma(A       ) * beta(A       ), 0, 0],
					        [-gamma(A       ) * beta(A       ), gamma(A       ), 0, 0],
					        [0, 0, 1, 0],
					        [0, 0, 0, 1],
					    ]       )






def     a(      A       :    float ,     A       :    np.ndarray | None = None       )		->       np.ndarray:
					"""simple docstring"""





					if event is None:
										a					     =   np.array([ct, x, y, z]       )  # Symbolic four vector
					else:
										event[0] *= c  # x0 is ct (speed of light * time)

					return transformation_matrix(A       ) @ event


if __name__ == "__main__":
				import doctest

				doctest.testmod()

				# Example of symbolic vector:
				_lowercase: Union[str, Any]      			=		transform(29979245)
				print("Example of four vector: ")
				print(F"""ct' = {four_vector[0]}""")
				print(F"""x' = {four_vector[1]}""")
				print(F"""y' = {four_vector[2]}""")
				print(F"""z' = {four_vector[3]}""")

				# Substitute symbols with numerical values
				_lowercase: Tuple      			=		{ct: c, x: 1, y: 1, z: 1}
				_lowercase: Optional[int]      			=		[four_vector[i].subs(sub_dict) for i in range(4)]

				print(F"""\n{numerical_vector}""")





import numpy as np
from matplotlib import pyplot as plt
from sklearn.datasets import load_iris
from sklearn.metrics import ConfusionMatrixDisplay
from sklearn.model_selection import train_test_split
from xgboost import XGBClassifier






def     a(      A       :    dict       )		->       tuple:
					"""simple docstring"""





					return (data["data"], data["target"])






def     a(      A       :    np.ndarray ,     A       :    np.ndarray       )		->       XGBClassifier:
					"""simple docstring"""





					a					     =   XGBClassifier()
					classifier.fit(A ,     A       )
					return classifier






def     a(      )		->       None:
					"""simple docstring"""





					a					     =   load_iris()
					a     ,				a					     =   data_handling(A       )
					a     ,				a     ,				a     ,				a					     =   train_test_split(
					    A ,     A ,     test_size=0.25       )

					a					     =   iris["target_names"]

					# Create an XGBoost Classifier from the training data
					a					     =   xgboost(A ,     A       )

					# Display the confusion matrix of the classifier with both training and test sets
					ConfusionMatrixDisplay.from_estimator(
					    A ,     A ,     A ,     display_labels=A ,     cmap="Blues" ,     normalize="true" ,     )
					plt.title("Normalized Confusion Matrix - IRIS Dataset"       )
					plt.show()


if __name__ == "__main__":
				import doctest

				doctest.testmod(verbose=True)
				main()
