

"""simple docstring"""
import numpy as np
import requests







def       UpperCamelCase_					(     )						->     Tuple:



       """simple docstring"""







       __magic_name__			:		Optional[Any]        =     requests.get(
           '''https://raw.githubusercontent.com/yashLadha/The_Math_of_Intelligence/'''
           '''master/Week1/ADRvsRating.csv'''   )
       __magic_name__			:		Union[str, Any]        =     response.text.splitlines()
       __magic_name__			:		Union[str, Any]        =     []
       for item in lines:
              __magic_name__			:		Tuple        =     item.split(''','''   )
              data.append(lowerCamelCase   )
       data.pop(0   )  # This is for removing the labels from the list
       __magic_name__			:		Union[str, Any]        =     np.matrix(lowerCamelCase   )
       return dataset







def       UpperCamelCase_					(     lowerCamelCase							:					int     , lowerCamelCase							:					List[Any]     , lowerCamelCase							:					Tuple     , lowerCamelCase							:					Optional[Any]     , lowerCamelCase							:					Tuple   )						->     Optional[Any]:



       """simple docstring"""







       __magic_name__			:		Optional[int]        =     len_data

       __magic_name__			:		Tuple        =     np.dot(lowerCamelCase     , data_x.transpose()   )
       prod -= data_y.transpose()
       __magic_name__			:		Tuple        =     np.dot(lowerCamelCase     , lowerCamelCase   )
       __magic_name__			:		int        =     theta - (alpha / n) * sum_grad
       return theta







def       UpperCamelCase_					(     lowerCamelCase							:					Tuple     , lowerCamelCase							:					Any     , lowerCamelCase							:					Optional[Any]     , lowerCamelCase							:					Optional[Any]   )						->     int:



       """simple docstring"""







       __magic_name__			:		Optional[Any]        =     np.dot(lowerCamelCase     , data_x.transpose()   )
       prod -= data_y.transpose()
       __magic_name__			:		Union[str, Any]        =     np.sum(np.square(lowerCamelCase   )   )
       __magic_name__			:		Dict        =     sum_elem / (2 * len_data)
       return error







def       UpperCamelCase_					(     lowerCamelCase							:					List[str]     , lowerCamelCase							:					Dict   )						->     Tuple:



       """simple docstring"""







       __magic_name__			:		Dict        =     10_0000
       __magic_name__			:		Any        =     0.0_0_0_1_5_5_0

       __magic_name__			:		str        =     data_x.shape[1]
       __magic_name__			:		List[str]        =     data_x.shape[0] - 1

       __magic_name__			:		List[str]        =     np.zeros((1, no_features)   )

       for i in range(0     , lowerCamelCase   ):
              __magic_name__			:		Optional[int]        =     run_steep_gradient_descent(lowerCamelCase     , lowerCamelCase     , lowerCamelCase     , lowerCamelCase     , lowerCamelCase   )
              __magic_name__			:		Any        =     sum_of_square_error(lowerCamelCase     , lowerCamelCase     , lowerCamelCase     , lowerCamelCase   )
              print(f"""At Iteration {i + 1} - Error is {error:.5f}"""   )

       return theta







def       UpperCamelCase_					(     lowerCamelCase							:					Optional[int]     , lowerCamelCase							:					int   )						->     int:



       """simple docstring"""







       __magic_name__			:		Union[str, Any]        =     sum(abs(y - predicted_y[i]   ) for i, y in enumerate(lowerCamelCase   )   )
       return total / len(lowerCamelCase   )







def       UpperCamelCase_					(     )						->     Dict:



       """simple docstring"""







       __magic_name__			:		Union[str, Any]        =     collect_dataset()

       __magic_name__			:		int        =     data.shape[0]
       __magic_name__			:		List[str]        =     np.c_[np.ones(lowerCamelCase   ), data[:, :-1]].astype(lowerCamelCase   )
       __magic_name__			:		Dict        =     data[:, -1].astype(lowerCamelCase   )

       __magic_name__			:		Optional[int]        =     run_linear_regression(lowerCamelCase     , lowerCamelCase   )
       __magic_name__			:		Optional[int]        =     theta.shape[1]
       print('''Resultant Feature vector : '''   )
       for i in range(0     , lowerCamelCase   ):
              print(f"""{theta[0, i]:.5f}"""   )


if __name__ == "__main__":
 main()



"""simple docstring"""
from typing import TYPE_CHECKING

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


A									=						{
    """configuration_imagegpt""": ["""IMAGEGPT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ImageGPTConfig""", """ImageGPTOnnxConfig"""]
}

try:
 if not is_vision_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 A									=						["""ImageGPTFeatureExtractor"""]
 A									=						["""ImageGPTImageProcessor"""]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 A									=						[
     """IMAGEGPT_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """ImageGPTForCausalImageModeling""",
     """ImageGPTForImageClassification""",
     """ImageGPTModel""",
     """ImageGPTPreTrainedModel""",
     """load_tf_weights_in_imagegpt""",
 ]


if TYPE_CHECKING:
 from .configuration_imagegpt import IMAGEGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, ImageGPTConfig, ImageGPTOnnxConfig

 try:
  if not is_vision_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .feature_extraction_imagegpt import ImageGPTFeatureExtractor
  from .image_processing_imagegpt import ImageGPTImageProcessor

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_imagegpt import (
      IMAGEGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
      ImageGPTForCausalImageModeling,
      ImageGPTForImageClassification,
      ImageGPTModel,
      ImageGPTPreTrainedModel,
      load_tf_weights_in_imagegpt,
  )

else:
 import sys

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