

"""simple docstring"""

from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier

# Load iris file
a__			:						Tuple							=			load_iris()
iris.keys()


print(f"""Target names: \n {iris.target_names} """)
print(f"""\n Features: \n {iris.feature_names}""")

# Train set e Test set
a__		,					a__		,					a__		,					a__			:						Tuple							=			train_test_split(
    iris["""data"""], iris["""target"""], random_state=4
)

# KNN

a__			:						Dict							=			KNeighborsClassifier(n_neighbors=1)
knn.fit(X_train, y_train)

# new array to test
a__			:						Optional[Any]							=			[[1, 2, 1, 4], [2, 3, 4, 5]]

a__			:						Dict							=			knn.predict(X_new)

print(
    f"""\nNew array: \n {X_new}\n\nTarget Names Prediction: \n"""
    f""" {iris["target_names"][prediction]}"""
)





"""simple docstring"""

import os





def 					__UpperCamelCase (     )					->      int:




					"""simple docstring"""



					__lowercase        =      os.path.dirname(os.path.realpath(a__      )      )
					__lowercase        =      os.path.join(a__			,						'''triangle.txt'''      )

					with open(a__      ) as f:
										__lowercase        =      f.readlines()

					__lowercase        =      [[int(a__      ) for y in x.rstrip('''\r\n'''      ).split(''' '''      )] for x in triangle]

					for i in range(1			,						len(a__      )      ):
										for j in range(len(a[i]      )      ):
															__lowercase        =      a[i - 1][j] if j != len(a[i - 1]      ) else 0
															__lowercase        =      a[i - 1][j - 1] if j > 0 else 0
															a[i][j] += max(a__			,						a__      )
					return max(a[-1]      )


if __name__ == "__main__":
							print(solution())


