


from __future__ import annotations

from typing import Generic, TypeVar

__snake_case     =		TypeVar('''T''')



class 					__lowerCamelCase	(Generic[T]		):







       def __init__(	self:				Union[str, Any],A_:				T			):






            '''simple docstring'''




            __UpperCamelCase				=	data  # Assign data
            __UpperCamelCase				=	None  # Initialize next as null
            __UpperCamelCase				=	None  # Initialize prev as null



class 					__lowerCamelCase	(Generic[T]		):







       def __init__(	self:				Any			):






            '''simple docstring'''




            __UpperCamelCase				=	None







       def 						snake_case_					(	self:				Dict,A_:				T			):






            '''simple docstring'''




            if self.head is None:
                 __UpperCamelCase				=	Node(A_			)
            else:
                 __UpperCamelCase				=	Node(A_			)
                 __UpperCamelCase				=	new_node
                 __UpperCamelCase				=	self.head
                 __UpperCamelCase				=	None
                 __UpperCamelCase				=	new_node







       def 						snake_case_					(	self:				Optional[int]			):






            '''simple docstring'''




            if self.head is None:
                 return None
            else:
                 assert self.head is not None
                 __UpperCamelCase				=	self.head.data
                 __UpperCamelCase				=	self.head.next
                 if self.head is not None:
                      __UpperCamelCase				=	None
                 return temp







       def 						snake_case_					(	self:				Optional[Any]			):






            '''simple docstring'''




            return self.head.data if self.head is not None else None







       def __len__(	self:				int			):






            '''simple docstring'''




            __UpperCamelCase				=	self.head
            __UpperCamelCase				=	0
            while temp is not None:
                 count += 1
                 __UpperCamelCase				=	temp.next
            return count







       def 						snake_case_					(	self:				Dict			):






            '''simple docstring'''




            return self.head is None







       def 						snake_case_					(	self:				Dict			):






            '''simple docstring'''




            print('stack elements are:'			)
            __UpperCamelCase				=	self.head
            while temp is not None:
                 print(temp.data,end='->'			)
                 __UpperCamelCase				=	temp.next


# Code execution starts here
if __name__ == "__main__":
    # Start with the empty stack
    __snake_case     =		Stack()

    # Insert 4 at the beginning. So stack becomes 4->None
    print('''Stack operations using Doubly LinkedList''')
    stack.push(4)

    # Insert 5 at the beginning. So stack becomes 4->5->None
    stack.push(5)

    # Insert 6 at the beginning. So stack becomes 4->5->6->None
    stack.push(6)

    # Insert 7 at the beginning. So stack becomes 4->5->6->7->None
    stack.push(7)

    # Print the stack
    stack.print_stack()

    # Print the top element
    print('''\nTop element is ''', stack.top())

    # Print the stack size
    print('''Size of the stack is ''', len(stack))

    # pop the top element
    stack.pop()

    # pop the top element
    stack.pop()

    # two elements have now been popped off
    stack.print_stack()

    # Print True if the stack is empty else False
    print('''\nstack is empty:''', stack.is_empty())






import matplotlib.pyplot as plt
import numpy as np






def 						_A						(						_lowercase			,       _lowercase			,       _lowercase				)			->				np.ndarray:


     """simple docstring"""


     __UpperCamelCase				=	len(_lowercase				)  # Number of training samples
     __UpperCamelCase				=	np.eye(_lowercase				)  # Initialize weights as identity matrix
     for j in range(_lowercase				):
          __UpperCamelCase				=	point - x_train[j]
          __UpperCamelCase				=	np.exp(diff @ diff.T / (-2.0 * tau**2)				)

     return weights






def 						_A						(						_lowercase			,       _lowercase			,       _lowercase			,       _lowercase				)			->				np.ndarray:


     """simple docstring"""


     __UpperCamelCase				=	weight_matrix(_lowercase			,       _lowercase			,       _lowercase				)
     __UpperCamelCase				=	np.linalg.inv(x_train.T @ weight_mat @ x_train				) @ (
         x_train.T @ weight_mat @ y_train.T
     )

     return weight






def 						_A						(						_lowercase			,       _lowercase			,       _lowercase				)			->				np.ndarray:


     """simple docstring"""


     __UpperCamelCase				=	np.zeros(len(_lowercase				)				)  # Initialize array of predictions
     for i, item in enumerate(_lowercase				):
          __UpperCamelCase				=	item @ local_weight(_lowercase			,       _lowercase			,       _lowercase			,       _lowercase				)

     return y_pred






def 						_A						(						_lowercase			,       _lowercase			,       _lowercase				)			->				tuple[np.ndarray, np.ndarray, np.ndarray]:


     """simple docstring"""


     import seaborn as sns

     __UpperCamelCase				=	sns.load_dataset(_lowercase				)
     __UpperCamelCase				=	np.array(data[x_name]				)
     __UpperCamelCase				=	np.array(data[y_name]				)

     __UpperCamelCase				=	np.ones(len(_lowercase				)				)

     # pairing elements of one and x_data
     __UpperCamelCase				=	np.column_stack((one, x_data)				)

     return x_train, x_data, y_data






def 						_A						(						_lowercase			,       _lowercase			,       _lowercase			,       _lowercase			,       _lowercase			,       _lowercase			,       )			->				None:


     """simple docstring"""


     __UpperCamelCase				=	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
    __snake_case    ,					__snake_case    ,					__snake_case     =		load_data('''tips''', '''total_bill''', '''tip''')
    __snake_case     =		local_weight_regression(training_data_x, tip, 5)
    plot_preds(training_data_x, predictions, total_bill, tip, '''total_bill''', '''tip''')


