
'''simple docstring'''



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

# Load iris file
UpperCamelCase__ :						str    							=					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
UpperCamelCase__ :						Union[str, Any]    							=					train_test_split(
    iris["data"], iris["target"], random_state=4
)

# KNN

UpperCamelCase__ :						str    							=					KNeighborsClassifier(n_neighbors=1)
knn.fit(X_train, y_train)

# new array to test
UpperCamelCase__ :						Union[str, Any]    							=					[[1, 2, 1, 4], [2, 3, 4, 5]]

UpperCamelCase__ :						Dict    							=					knn.predict(X_new)

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








'''simple docstring'''



from cva import destroyAllWindows, imread, imshow, waitKey
def 				lowerCAmelCase_		(			SCREAMING_SNAKE_CASE_      , SCREAMING_SNAKE_CASE_  )       ->       Dict:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE	,							_SCREAMING_SNAKE_CASE       	=			img.shape[0], img.shape[1]

   def to_grayscale(SCREAMING_SNAKE_CASE_      , SCREAMING_SNAKE_CASE_      , SCREAMING_SNAKE_CASE_  ):
      return 0.2126 * red + 0.587 * green + 0.114 * blue

   def normalize(SCREAMING_SNAKE_CASE_  ):
      return min(SCREAMING_SNAKE_CASE_      , 2_55  )

   for i in range(SCREAMING_SNAKE_CASE_  ):
      for j in range(SCREAMING_SNAKE_CASE_  ):
         _SCREAMING_SNAKE_CASE       	=			int(to_grayscale(*img[i][j]  )  )
         _SCREAMING_SNAKE_CASE       	=			[
             normalize(SCREAMING_SNAKE_CASE_  ),
             normalize(greyscale + factor  ),
             normalize(greyscale + 2 * factor  ),
         ]

   return img


if __name__ == "__main__":
     # read original image
     UpperCamelCase__ :						Optional[int]    							=					{
         percentage: imread("image_data/lena.jpg", 1) for percentage in (10, 20, 30, 40)
     }

     for percentage, img in images.items():
          make_sepia(img, percentage)

     for percentage, img in images.items():
          imshow(f"""Original image with sepia (factor: {percentage})""", img)

     waitKey(0)
     destroyAllWindows()






