

import matplotlib.pyplot as plt
import numpy as np


def SCREAMING_SNAKE_CASE			(       lowercase_:		np.ndarray						,  lowercase_:		np.ndarray						,  lowercase_:		float			):
       _UpperCamelCase       :    Any					  =					len(lowercase_			)  # Number of training samples
       _UpperCamelCase       :    Optional[int]					  =					np.eye(lowercase_			)  # Initialize weights as identity matrix
       for j in range(lowercase_			):
              _UpperCamelCase       :    str					  =					point - x_train[j]
              _UpperCamelCase       :    Optional[Any]					  =					np.exp(diff @ diff.T / (-2.0 * tau**2)			)

       return weights


def SCREAMING_SNAKE_CASE			(       lowercase_:		np.ndarray						,  lowercase_:		np.ndarray						,  lowercase_:		np.ndarray						,  lowercase_:		float			):
       _UpperCamelCase       :    Tuple					  =					weight_matrix(lowercase_						,  lowercase_						,  lowercase_			)
       _UpperCamelCase       :    Union[str, Any]					  =					np.linalg.inv(x_train.T @ weight_mat @ x_train			) @ (
           x_train.T @ weight_mat @ y_train.T
       )

       return weight


def SCREAMING_SNAKE_CASE			(       lowercase_:		np.ndarray						,  lowercase_:		np.ndarray						,  lowercase_:		float			):
       _UpperCamelCase       :    Any					  =					np.zeros(len(lowercase_			)			)  # Initialize array of predictions
       for i, item in enumerate(lowercase_			):
              _UpperCamelCase       :    Dict					  =					item @ local_weight(lowercase_						,  lowercase_						,  lowercase_						,  lowercase_			)

       return y_pred


def SCREAMING_SNAKE_CASE			(       lowercase_:		str						,  lowercase_:		str						,  lowercase_:		str			):
       import seaborn as sns

       _UpperCamelCase       :    Union[str, Any]					  =					sns.load_dataset(lowercase_			)
       _UpperCamelCase       :    Dict					  =					np.array(data[x_name]			)
       _UpperCamelCase       :    Optional[Any]					  =					np.array(data[y_name]			)

       _UpperCamelCase       :    int					  =					np.ones(len(lowercase_			)			)

       # pairing elements of one and x_data
       _UpperCamelCase       :    Any					  =					np.column_stack((one, x_data)			)

       return x_train, x_data, y_data


def SCREAMING_SNAKE_CASE			(       lowercase_:		np.ndarray						,  lowercase_:		np.ndarray						,  lowercase_:		np.ndarray						,  lowercase_:		np.ndarray						,  lowercase_:		str						,  lowercase_:		str						,  ):
       _UpperCamelCase       :    str					  =					np.sort(lowercase_						,  axis=0			)
       plt.scatter(lowercase_						,  lowercase_						,  color='blue'			)
       plt.plot(
           x_train_sorted[:, 1]						,  preds[x_train[:, 1].argsort(0			)]						,  color='yellow'						,  linewidth=5						,  )
       plt.title('Local Weighted Regression'			)
       plt.xlabel(lowercase_			)
       plt.ylabel(lowercase_			)
       plt.show()


if __name__ == "__main__":
 import doctest

 doctest.testmod()

 # Demo with a dataset from the seaborn module
 UpperCAmelCase						,   UpperCAmelCase						,   UpperCAmelCase     						=			load_data('''tips''', '''total_bill''', '''tip''')
 UpperCAmelCase     						=			local_weight_regression(training_data_x, tip, 5)
 plot_preds(training_data_x, predictions, total_bill, tip, '''total_bill''', '''tip''')




def SCREAMING_SNAKE_CASE			(       lowercase_:		int			):
       if not isinstance(lowercase_						,  lowercase_			):
              _UpperCamelCase       :    Optional[int]					  =					(
                  'operation can not be conducted on a object of type '
                  F"{type(lowercase_			).__name__}"
              )
              raise TypeError(lowercase_			)
       _UpperCamelCase       :    List[Any]					  =					''
       for _ in range(0						,  32			):
              bit_string += str(number % 2			)
              _UpperCamelCase       :    Dict					  =					number >> 1
       return bit_string


def SCREAMING_SNAKE_CASE			(       lowercase_:		int			):
       if number < 0:
              raise ValueError('the value of input must be positive'			)
       elif isinstance(lowercase_						,  lowercase_			):
              raise TypeError('Input value must be a \'int\' type'			)
       elif isinstance(lowercase_						,  lowercase_			):
              raise TypeError('\'<\' not supported between instances of \'str\' and \'int\''			)
       _UpperCamelCase       :    List[Any]					  =					0
       # iterator over [1 to 32],since we are dealing with 32 bit integer
       for _ in range(1						,  33			):
              # left shift the bits by unity
              _UpperCamelCase       :    Tuple					  =					result << 1
              # get the end bit
              _UpperCamelCase       :    List[Any]					  =					number % 2
              # right shift the bits by unity
              _UpperCamelCase       :    Dict					  =					number >> 1
              # add that bit to our ans
              _UpperCamelCase       :    int					  =					result | end_bit
       return get_reverse_bit_string(lowercase_			)


if __name__ == "__main__":
 import doctest

 doctest.testmod()



