"""simple docstring"""






from __future__ import annotations



def   a_						(					lowerCamelCase					,			lowerCamelCase       ):
				if nth_term == "":
								return [""]
				UpperCAmelCase__             =							int(lowerCamelCase       )
				UpperCAmelCase__             =							int(lowerCamelCase       )
				UpperCAmelCase__             =							[]
				for temp in range(int(lowerCamelCase       )       ):
								series.append(f'''1 / {pow(temp + 1					,			int(lowerCamelCase       )       )}''' if series else '1'       )
				return series


if __name__ == "__main__":
						import doctest

						doctest.testmod()

						lowerCAmelCase__  :  Optional[int]						     =	int(input('Enter the last number (nth term) of the P-Series'))
						lowerCAmelCase__  :  Optional[Any]						     =	int(input('Enter the power for  P-Series'))
						print('Formula of P-Series => 1+1/2^p+1/3^p ..... 1/n^p')
						print(p_series(nth_term, power))








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()
