



import matplotlib.pyplot as plt
import numpy as np
def 						snake_case__							(       __lowercase				,  __lowercase				,  __lowercase							)		->		List[str]:







						"""simple docstring"""

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

						return weights
def 						snake_case__							(       __lowercase				,  __lowercase				,  __lowercase				,  __lowercase							)		->		Union[str, Any]:







						"""simple docstring"""

						A__						:				List[str]         =			weight_matrix(_a				,  _a				,  _a							)
						A__						:				Optional[int]         =			np.linalg.inv(x_train.T @ weight_mat @ x_train							) @ (
						    x_train.T @ weight_mat @ y_train.T
						)

						return weight
def 						snake_case__							(       __lowercase				,  __lowercase				,  __lowercase							)		->		Tuple:







						"""simple docstring"""

						A__						:				Union[str, Any]         =			np.zeros(len(_a							)							)  # Initialize array of predictions
						for i, item in enumerate(_a							):
												A__						:				int         =			item @ local_weight(_a				,  _a				,  _a				,  _a							)

						return y_pred
def 						snake_case__							(       __lowercase				,  __lowercase				,  __lowercase							)		->		List[str]:







						"""simple docstring"""

						import seaborn as sns

						A__						:				Union[str, Any]         =			sns.load_dataset(_a							)
						A__						:				Optional[Any]         =			np.array(data[x_name]							)
						A__						:				List[str]         =			np.array(data[y_name]							)

						A__						:				Optional[int]         =			np.ones(len(_a							)							)

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

						return x_train, x_data, y_data
def 						snake_case__							(       __lowercase				,  __lowercase				,  __lowercase				,  __lowercase				,  __lowercase				,  __lowercase				,  )		->		Any:







						"""simple docstring"""

						A__						:				Dict         =			np.sort(_a				,  axis=0							)
						plt.scatter(_a				,  _a				,  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(_a							)
						plt.ylabel(_a							)
						plt.show()


if __name__ == "__main__":
		import doctest

		doctest.testmod()

		# Demo with a dataset from the seaborn module
		snake_case			,							snake_case			,							snake_case     :    Union[str, Any]						=    load_data('tips', 'total_bill', 'tip')
		snake_case     :    Optional[int]						=    local_weight_regression(training_data_x, tip, 5)
		plot_preds(training_data_x, predictions, total_bill, tip, 'total_bill', 'tip')
'''simple docstring'''
import numpy as np







def 			_UpperCamelCase			(  _a						:						Union[str, Any]					,  _a						:						Any					,  _a						:						Dict      ):




    """simple docstring"""



    __UpperCamelCase						:						Tuple		     =	n_draws * class_counts / class_counts.sum()
    # floored means we don't overshoot n_samples, but probably undershoot
    __UpperCamelCase						:						Optional[Any]		     =	np.floor(_a      )
    # we add samples according to how much "left over" probability
    # they had, until we arrive at n_samples
    __UpperCamelCase						:						Tuple		     =	int(n_draws - floored.sum()      )
    if need_to_add > 0:
        __UpperCamelCase						:						Any		     =	continuous - floored
        __UpperCamelCase						:						Any		     =	np.sort(np.unique(_a      )      )[::-1]
        # add according to remainder, but break ties
        # randomly to avoid biases
        for value in values:
            ((__UpperCamelCase)     ,       )						:						Any		     =	np.where(remainder == value      )
            # if we need_to_add less than what's in inds
            # we draw randomly from them.
            # if we need to add more, we add them all and
            # go to the next value
            __UpperCamelCase						:						List[Any]		     =	min(len(_a      )					,  _a      )
            __UpperCamelCase						:						Tuple		     =	rng.choice(_a					,  size=_a					,  replace=_a      )
            floored[inds] += 1
            need_to_add -= add_now
            if need_to_add == 0:
                break
    return floored.astype(np.intaa      )







def 			_UpperCamelCase			(  _a						:						Optional[int]					,  _a						:						Union[str, Any]					,  _a						:						int					,  _a						:						Union[str, Any]					,  _a						:						str=1_0      ):




    """simple docstring"""



    __UpperCamelCase     ,       __UpperCamelCase						:						int		     =	np.unique(_a					,  return_inverse=_a      )
    __UpperCamelCase						:						str		     =	classes.shape[0]
    __UpperCamelCase						:						str		     =	np.bincount(_a      )
    if np.min(_a      ) < 2:
        raise ValueError('Minimum class count error'      )
    if n_train < n_classes:
        raise ValueError(
            'The train_size = %d should be greater or ' 'equal to the number of classes = %d' % (n_train, n_classes)      )
    if n_test < n_classes:
        raise ValueError(
            'The test_size = %d should be greater or ' 'equal to the number of classes = %d' % (n_test, n_classes)      )
    __UpperCamelCase						:						Optional[int]		     =	np.split(np.argsort(_a					,  kind='mergesort'      )					,  np.cumsum(_a      )[:-1]      )
    for _ in range(_a      ):
        __UpperCamelCase						:						List[str]		     =	approximate_mode(_a					,  _a					,  _a      )
        __UpperCamelCase						:						Tuple		     =	class_counts - n_i
        __UpperCamelCase						:						int		     =	approximate_mode(_a					,  _a					,  _a      )

        __UpperCamelCase						:						List[Any]		     =	[]
        __UpperCamelCase						:						str		     =	[]

        for i in range(_a      ):
            __UpperCamelCase						:						Optional[Any]		     =	rng.permutation(class_counts[i]      )
            __UpperCamelCase						:						List[Any]		     =	class_indices[i].take(_a					,  mode='clip'      )
            train.extend(perm_indices_class_i[: n_i[i]]      )
            test.extend(perm_indices_class_i[n_i[i] : n_i[i] + t_i[i]]      )
        __UpperCamelCase						:						int		     =	rng.permutation(_a      )
        __UpperCamelCase						:						int		     =	rng.permutation(_a      )

        yield train, test



