


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    lowercase_	(	_A    :					dict		):

						"""simple docstring"""




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

def    lowercase_	(	_A    :					np.ndarray					,      _A    :					np.ndarray		):

						"""simple docstring"""




						lowerCamelCase__	:       Union[str, Any]					=						XGBClassifier()
						classifier.fit(_A					,      _A		)
						return classifier

def    lowercase_	(	):

						"""simple docstring"""




						lowerCamelCase__	:       int					=						load_iris()
						lowerCamelCase__	:       List[str]					=						data_handling(_A		)
						lowerCamelCase__	:       List[Any]					=						train_test_split(
						    _A					,      _A					,      test_size=0.25		)

						lowerCamelCase__	:       List[Any]					=						iris["target_names"]

						# Create an XGBoost Classifier from the training data
						lowerCamelCase__	:       str					=						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()






import os

def    lowercase_	(	_A    :					str = "input.txt"		):

      """simple docstring"""




      with open(os.path.join(os.path.dirname(_A		)					,      _A		)		) as input_file:
            lowerCamelCase__	:       List[Any]					=						[
                [int(_A		) for element in line.split(","		)]
                for line in input_file.readlines()
            ]

      lowerCamelCase__	:       Optional[Any]					=						len(_A		)
      lowerCamelCase__	:       Union[str, Any]					=						len(matrix[0]		)

      lowerCamelCase__	:       Union[str, Any]					=						[[-1 for _ in range(_A		)] for _ in range(_A		)]
      for i in range(_A		):
            lowerCamelCase__	:       Optional[Any]					=						matrix[i][0]

      for j in range(1					,      _A		):
            for i in range(_A		):
                  lowerCamelCase__	:       int					=						minimal_path_sums[i][j - 1] + matrix[i][j]

            for i in range(1					,      _A		):
                  lowerCamelCase__	:       Tuple					=						min(
                      minimal_path_sums[i][j]					,      minimal_path_sums[i - 1][j] + matrix[i][j]		)

            for i in range(rows - 2					,      -1					,      -1		):
                  lowerCamelCase__	:       str					=						min(
                      minimal_path_sums[i][j]					,      minimal_path_sums[i + 1][j] + matrix[i][j]		)

      return min(minimal_path_sums_row[-1] for minimal_path_sums_row in minimal_path_sums		)


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


