



"""simple docstring"""







import matplotlib.pyplot as plt
import numpy as np

def    __SCREAMING_SNAKE_CASE		( lowerCamelCase_:       np.ndarray    ,		lowerCamelCase_:       np.ndarray    ,		lowerCamelCase_:       float   ):
						"""simple docstring"""






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

						return weights

def    __SCREAMING_SNAKE_CASE		( lowerCamelCase_:       np.ndarray    ,		lowerCamelCase_:       np.ndarray    ,		lowerCamelCase_:       np.ndarray    ,		lowerCamelCase_:       float   ):
						"""simple docstring"""






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

						return weight

def    __SCREAMING_SNAKE_CASE		( lowerCamelCase_:       np.ndarray    ,		lowerCamelCase_:       np.ndarray    ,		lowerCamelCase_:       float   ):
						"""simple docstring"""






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

						return y_pred

def    __SCREAMING_SNAKE_CASE		( lowerCamelCase_:       str    ,		lowerCamelCase_:       str    ,		lowerCamelCase_:       str   ):
						"""simple docstring"""






						import seaborn as sns

						snake_case      : int							=   sns.load_dataset(lowerCamelCase_   )
						snake_case      : Union[str, Any]							=   np.array(data[x_name]   )
						snake_case      : Union[str, Any]							=   np.array(data[y_name]   )

						snake_case      : List[str]							=   np.ones(len(lowerCamelCase_   )   )

						# pairing elements of one and x_data
						snake_case      : Dict							=   np.column_stack((one, x_data)   )

						return x_train, x_data, y_data

def    __SCREAMING_SNAKE_CASE		( lowerCamelCase_:       np.ndarray    ,		lowerCamelCase_:       np.ndarray    ,		lowerCamelCase_:       np.ndarray    ,		lowerCamelCase_:       np.ndarray    ,		lowerCamelCase_:       str    ,		lowerCamelCase_:       str    ,		):
						"""simple docstring"""






						snake_case      : Dict							=   np.sort(lowerCamelCase_    ,		axis=0   )
						plt.scatter(lowerCamelCase_    ,		lowerCamelCase_    ,		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(lowerCamelCase_   )
						plt.ylabel(lowerCamelCase_   )
						plt.show()


if __name__ == "__main__":
			import doctest

			doctest.testmod()

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








"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
    is_vision_available,
)


A    =	{
    'configuration_mobilevitv2': [
        'MOBILEVITV2_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'MobileViTV2Config',
        'MobileViTV2OnnxConfig',
    ],
}


try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			A    =	[
			    'MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST',
			    'MobileViTV2ForImageClassification',
			    'MobileViTV2ForSemanticSegmentation',
			    'MobileViTV2Model',
			    'MobileViTV2PreTrainedModel',
			]

if TYPE_CHECKING:
			from .configuration_mobilevitva import (
			    MOBILEVITV2_PRETRAINED_CONFIG_ARCHIVE_MAP,
			    MobileViTVaConfig,
			    MobileViTVaOnnxConfig,
			)

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_mobilevitva import (
						    MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST,
						    MobileViTVaForImageClassification,
						    MobileViTVaForSemanticSegmentation,
						    MobileViTVaModel,
						    MobileViTVaPreTrainedModel,
						)

else:
			import sys

			A    =	_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



