




'''simple docstring'''






import matplotlib.pyplot as plt
import numpy as np

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

			return weights

def       __lowerCAmelCase (   snake_case__ ,				snake_case__ ,				snake_case__ ,				snake_case__						):
			__UpperCamelCase				:      Tuple									=       weight_matrix(snake_case__ ,				snake_case__ ,				snake_case__						)
			__UpperCamelCase				:      Optional[Any]									=       np.linalg.inv(x_train.T @ weight_mat @ x_train						) @ (
			    x_train.T @ weight_mat @ y_train.T
			)

			return weight

def       __lowerCAmelCase (   snake_case__ ,				snake_case__ ,				snake_case__						):
			__UpperCamelCase				:      List[Any]									=       np.zeros(len(snake_case__						)						)  # Initialize array of predictions
			for i, item in enumerate(snake_case__						):
						__UpperCamelCase				:      List[Any]									=       item @ local_weight(snake_case__ ,				snake_case__ ,				snake_case__ ,				snake_case__						)

			return y_pred

def       __lowerCAmelCase (   snake_case__ ,				snake_case__ ,				snake_case__						):
			import seaborn as sns

			__UpperCamelCase				:      Optional[int]									=       sns.load_dataset(snake_case__						)
			__UpperCamelCase				:      Tuple									=       np.array(data[x_name]						)
			__UpperCamelCase				:      Any									=       np.array(data[y_name]						)

			__UpperCamelCase				:      str									=       np.ones(len(snake_case__						)						)

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

			return x_train, x_data, y_data

def       __lowerCAmelCase (   snake_case__ ,				snake_case__ ,				snake_case__ ,				snake_case__ ,				snake_case__ ,				snake_case__ ,				):
			__UpperCamelCase				:      Dict									=       np.sort(snake_case__ ,				axis=0						)
			plt.scatter(snake_case__ ,				snake_case__ ,				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(snake_case__						)
			plt.ylabel(snake_case__						)
			plt.show()


if __name__ == "__main__":
				import doctest

				doctest.testmod()

				# Demo with a dataset from the seaborn module
				_lowerCAmelCase     , _lowerCAmelCase     , _lowerCAmelCase				     = load_data('''tips''', '''total_bill''', '''tip''')
				_lowerCAmelCase				     = 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


_lowerCAmelCase				     = {'''configuration_visual_bert''': ['''VISUAL_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''VisualBertConfig''']}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_lowerCAmelCase				     = [
				    '''VISUAL_BERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
				    '''VisualBertForMultipleChoice''',
				    '''VisualBertForPreTraining''',
				    '''VisualBertForQuestionAnswering''',
				    '''VisualBertForRegionToPhraseAlignment''',
				    '''VisualBertForVisualReasoning''',
				    '''VisualBertLayer''',
				    '''VisualBertModel''',
				    '''VisualBertPreTrainedModel''',
				]


if TYPE_CHECKING:
				from .configuration_visual_bert import VISUAL_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, VisualBertConfig

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_visual_bert import (
								    VISUAL_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
								    VisualBertForMultipleChoice,
								    VisualBertForPreTraining,
								    VisualBertForQuestionAnswering,
								    VisualBertForRegionToPhraseAlignment,
								    VisualBertForVisualReasoning,
								    VisualBertLayer,
								    VisualBertModel,
								    VisualBertPreTrainedModel,
								)


else:
				import sys

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



