


'''simple docstring'''

import matplotlib.pyplot as plt
import numpy as np



def 							SCREAMING_SNAKE_CASE__(		_UpperCamelCase   :					np.ndarray     ,		_UpperCamelCase   :					np.ndarray     ,		_UpperCamelCase   :					float )		->       np.ndarray:





   '''simple docstring'''







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

   return weights



def 							SCREAMING_SNAKE_CASE__(		_UpperCamelCase   :					np.ndarray     ,		_UpperCamelCase   :					np.ndarray     ,		_UpperCamelCase   :					np.ndarray     ,		_UpperCamelCase   :					float )		->       np.ndarray:





   '''simple docstring'''







   UpperCamelCase__			    =    weight_matrix(_UpperCamelCase     ,		_UpperCamelCase     ,		_UpperCamelCase )
   UpperCamelCase__			    =    np.linalg.inv(x_train.T @ weight_mat @ x_train ) @ (
       x_train.T @ weight_mat @ y_train.T
   )

   return weight



def 							SCREAMING_SNAKE_CASE__(		_UpperCamelCase   :					np.ndarray     ,		_UpperCamelCase   :					np.ndarray     ,		_UpperCamelCase   :					float )		->       np.ndarray:





   '''simple docstring'''







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

   return y_pred



def 							SCREAMING_SNAKE_CASE__(		_UpperCamelCase   :					str     ,		_UpperCamelCase   :					str     ,		_UpperCamelCase   :					str )		->       tuple[np.ndarray, np.ndarray, np.ndarray]:





   '''simple docstring'''







   import seaborn as sns

   UpperCamelCase__			    =    sns.load_dataset(_UpperCamelCase )
   UpperCamelCase__			    =    np.array(data[x_name] )
   UpperCamelCase__			    =    np.array(data[y_name] )

   UpperCamelCase__			    =    np.ones(len(_UpperCamelCase ) )

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

   return x_train, x_data, y_data



def 							SCREAMING_SNAKE_CASE__(		_UpperCamelCase   :					np.ndarray     ,		_UpperCamelCase   :					np.ndarray     ,		_UpperCamelCase   :					np.ndarray     ,		_UpperCamelCase   :					np.ndarray     ,		_UpperCamelCase   :					str     ,		_UpperCamelCase   :					str     ,		)		->       None:





   '''simple docstring'''







   UpperCamelCase__			    =    np.sort(_UpperCamelCase     ,		axis=0 )
   plt.scatter(_UpperCamelCase     ,		_UpperCamelCase     ,		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(_UpperCamelCase )
   plt.ylabel(_UpperCamelCase )
   plt.show()


if __name__ == "__main__":
       import doctest

       doctest.testmod()

       # Demo with a dataset from the seaborn module
       __lowercase						,__lowercase						,__lowercase:		Dict    						=  load_data("tips", "total_bill", "tip")
       __lowercase:		Optional[int]    						=  local_weight_regression(training_data_x, tip, 5)
       plot_preds(training_data_x, predictions, total_bill, tip, "total_bill", "tip")





from typing import TYPE_CHECKING

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


UpperCamelCase__						=		{
    'configuration_sam': [
        'SAM_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'SamConfig',
        'SamMaskDecoderConfig',
        'SamPromptEncoderConfig',
        'SamVisionConfig',
    ],
    'processing_sam': ['SamProcessor'],
}


try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCamelCase__						=		[
							    'SAM_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'SamModel',
							    'SamPreTrainedModel',
							]
try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCamelCase__						=		[
							    'TF_SAM_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'TFSamModel',
							    'TFSamPreTrainedModel',
							]
try:
							if not is_vision_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCamelCase__						=		['SamImageProcessor']


if TYPE_CHECKING:
							from .configuration_sam import (
							    SAM_PRETRAINED_CONFIG_ARCHIVE_MAP,
							    SamConfig,
							    SamMaskDecoderConfig,
							    SamPromptEncoderConfig,
							    SamVisionConfig,
							)
							from .processing_sam import SamProcessor

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_sam import SAM_PRETRAINED_MODEL_ARCHIVE_LIST, SamModel, SamPreTrainedModel

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_sam import TF_SAM_PRETRAINED_MODEL_ARCHIVE_LIST, TFSamModel, TFSamPreTrainedModel

							try:
														if not is_vision_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .image_processing_sam import SamImageProcessor

else:
							import sys

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