





from __future__ import annotations






def 					lowerCamelCase_						(    _UpperCamelCase			,    _UpperCamelCase			)		->						list[str]:






				"""simple docstring"""


				if nth_term == "":
								return [""]
				snake_case_						:   str           =  int(_UpperCamelCase			)
				snake_case_						:   Dict           =  int(_UpperCamelCase			)
				snake_case_						:   list[str]           =  []
				for temp in range(int(_UpperCamelCase			)			):
								series.append(f'''1 / {pow(temp + 1			,    int(_UpperCamelCase			)			)}''' if series else '''1'''			)
				return series


if __name__ == "__main__":
	import doctest

	doctest.testmod()

	lowerCAmelCase_    =    int(input('''Enter the last number (nth term) of the P-Series'''))
	lowerCAmelCase_    =    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 					lowerCamelCase_						(    _UpperCamelCase			)		->						tuple:






				"""simple docstring"""


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






def 					lowerCamelCase_						(    _UpperCamelCase			,    _UpperCamelCase			)		->						XGBClassifier:






				"""simple docstring"""


				snake_case_						:   Optional[Any]           =  XGBClassifier()
				classifier.fit(_UpperCamelCase			,    _UpperCamelCase			)
				return classifier






def 					lowerCamelCase_						(    )		->						None:






				"""simple docstring"""


				snake_case_						:   Optional[Any]           =  load_iris()
				snake_case_      ,							snake_case_						:   str           =  data_handling(_UpperCamelCase			)
				snake_case_      ,							snake_case_      ,							snake_case_      ,							snake_case_						:   Dict           =  train_test_split(
				    _UpperCamelCase			,    _UpperCamelCase			,    test_size=0.25			)

				snake_case_						:   List[str]           =  iris['''target_names''']

				# Create an XGBoost Classifier from the training data
				snake_case_						:   int           =  xgboost(_UpperCamelCase			,    _UpperCamelCase			)

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


if __name__ == "__main__":
	import doctest

	doctest.testmod(verbose=True)
	main()
