





import matplotlib.pyplot as plt
import numpy as np


def 					__SCREAMING_SNAKE_CASE  (							__lowercase:		np.ndarray     ,							__lowercase:		np.ndarray     ,							__lowercase:		float):



  '''simple docstring'''






  __lowerCAmelCase			=				len(__lowercase)  # Number of training samples
  __lowerCAmelCase			=				np.eye(__lowercase)  # Initialize weights as identity matrix
  for j in range(__lowercase):
    __lowerCAmelCase			=				point - x_train[j]
    __lowerCAmelCase			=				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):



  '''simple docstring'''






  __lowerCAmelCase			=				weight_matrix(__lowercase     ,							__lowercase     ,							__lowercase)
  __lowerCAmelCase			=				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):



  '''simple docstring'''






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

  return y_pred


def 					__SCREAMING_SNAKE_CASE  (							__lowercase:		str     ,							__lowercase:		str     ,							__lowercase:		str):



  '''simple docstring'''






  import seaborn as sns

  __lowerCAmelCase			=				sns.load_dataset(__lowercase)
  __lowerCAmelCase			=				np.array(data[x_name])
  __lowerCAmelCase			=				np.array(data[y_name])

  __lowerCAmelCase			=				np.ones(len(__lowercase))

  # pairing elements of one and x_data
  __lowerCAmelCase			=				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     ,							):



  '''simple docstring'''






  __lowerCAmelCase			=				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
  __lowercase						,       __lowercase						,       __lowercase													=			load_data("""tips""", """total_bill""", """tip""")
  __lowercase													=			local_weight_regression(training_data_x, tip, 5)
  plot_preds(training_data_x, predictions, total_bill, tip, """total_bill""", """tip""")







class   __snake_case				:






    '''simple docstring'''





    def __init__(   self						:		Any							):

      """simple docstring"""





      __lowerCAmelCase			=				{}
      # indexmap keys are an index and it's values are the number at that index
      __lowerCAmelCase			=				{}





    def 	A		(   self						:		List[Any]			,						snake_case						:		list | str | range			,						snake_case						:		int							):

      """simple docstring"""





      if isinstance(snake_case			,						(range, str)							):
        __lowerCAmelCase			=				list(snake_case							)
      elif not isinstance(snake_case			,						snake_case							):
        raise TypeError(
            '''binary_search_delete() only accepts either a list, range or str'''							)

      __lowerCAmelCase			=				0
      __lowerCAmelCase			=				len(snake_case							) - 1

      while low <= high:
        __lowerCAmelCase			=				(low + high) // 2
        if array[mid] == item:
          array.pop(snake_case							)
          return array
        elif array[mid] < item:
          __lowerCAmelCase			=				mid + 1
        else:
          __lowerCAmelCase			=				mid - 1
      raise ValueError(
          '''Either the item is not in the array or the array was unsorted'''							)





    def 	A		(   self						:		Tuple			,						snake_case						:		list | str | range			,						snake_case						:		int							):

      """simple docstring"""





      if isinstance(snake_case			,						(range, str)							):
        __lowerCAmelCase			=				list(snake_case							)
      elif not isinstance(snake_case			,						snake_case							):
        raise TypeError(
            '''binary_search_insert() only accepts either a list, range or str'''							)

      __lowerCAmelCase			=				0
      __lowerCAmelCase			=				len(snake_case							) - 1

      while low <= high:
        __lowerCAmelCase			=				(low + high) // 2
        if array[mid] == index:
          # If the item already exists in the array,
          # insert it after the existing item
          array.insert(mid + 1			,						snake_case							)
          return array
        elif array[mid] < index:
          __lowerCAmelCase			=				mid + 1
        else:
          __lowerCAmelCase			=				mid - 1

        # If the item doesn't exist in the array, insert it at the appropriate position
      array.insert(snake_case			,						snake_case							)
      return array





    def 	A		(   self						:		Tuple			,						snake_case						:		int			,						snake_case						:		int							):

      """simple docstring"""





      if index in self.indexmap:
        __lowerCAmelCase			=				self.indexmap[index]
        if len(self.numbermap[n]							) == 1:
          del self.numbermap[n]
        else:
          __lowerCAmelCase			=				self.binary_search_delete(self.numbermap[n]			,						snake_case							)

        # Set new index
      __lowerCAmelCase			=				number

      # Number not seen before or empty so insert number value
      if number not in self.numbermap:
        __lowerCAmelCase			=				[index]

      # Here we need to perform a binary search insertion in order to insert
      # The item in the correct place
      else:
        __lowerCAmelCase			=				self.binary_search_insert(
            self.numbermap[number]			,						snake_case							)





    def 	A		(   self						:		List[str]			,						snake_case						:		int							):

      """simple docstring"""





      return self.numbermap.get(snake_case			,						[-1]							)[0]


if __name__ == "__main__":
      import doctest

      doctest.testmod()
