


from math import sqrt




def    a   (							A__							)	-> bool:




      '''simple docstring'''

      if 1 < number < 4:
            # 2 and 3 are primes
            return True
      elif number < 2 or number % 2 == 0 or number % 3 == 0:
            # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
            return False

      # All primes number are in format of 6k +/- 1
      for i in range(5      ,    int(sqrt(A__							) + 1							)      ,    6							):
            if number % i == 0 or number % (i + 2) == 0:
                  return False
      return True




def    a   (							A__ = 1_0_0_0_1							)	-> int:




      '''simple docstring'''

      SCREAMING_SNAKE_CASE__       :  Union[str, Any]						     =		0
      SCREAMING_SNAKE_CASE__       :  List[str]						     =		1
      while count != nth and number < 3:
            number += 1
            if is_prime(A__							):
                  count += 1
      while count != nth:
            number += 2
            if is_prime(A__							):
                  count += 1
      return number


if __name__ == "__main__":
    print(F'''{solution() = }''')











"""simple docstring"""







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     UpperCAmelCase_					(				__a   :			dict						):




			'''simple docstring'''



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

def     UpperCAmelCase_					(				__a   :			np.ndarray      ,							__a   :			np.ndarray						):




			'''simple docstring'''



			_lowerCamelCase					:					Dict	       =    XGBClassifier()
			classifier.fit(__a      ,							__a						)
			return classifier

def     UpperCAmelCase_					(				):




			'''simple docstring'''



			_lowerCamelCase					:					List[str]	       =    load_iris()
			_lowerCamelCase       ,							_lowerCamelCase					:					Optional[Any]	       =    data_handling(__a						)
			_lowerCamelCase       ,							_lowerCamelCase       ,							_lowerCamelCase       ,							_lowerCamelCase					:					int	       =    train_test_split(
			    __a      ,							__a      ,							test_size=0.2_5						)

			_lowerCamelCase					:					Optional[Any]	       =    iris['target_names']

			# Create an XGBoost Classifier from the training data
			_lowerCamelCase					:					Tuple	       =    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()





