






"""simple docstring"""



import numpy as np
import requests


def   SCREAMING_SNAKE_CASE__					(     )->		Any:






     '''simple docstring'''



     UpperCAmelCase__	:						Optional[int]       =       requests.get(
         "https://raw.githubusercontent.com/yashLadha/The_Math_of_Intelligence/"
         "master/Week1/ADRvsRating.csv"							)
     UpperCAmelCase__	:						int       =       response.text.splitlines()
     UpperCAmelCase__	:						Dict       =       []
     for item in lines:
          UpperCAmelCase__	:						List[Any]       =       item.split(","							)
          data.append(snake_case							)
     data.pop(0							)  # This is for removing the labels from the list
     UpperCAmelCase__	:						int       =       np.matrix(snake_case							)
     return dataset


def   SCREAMING_SNAKE_CASE__					(     snake_case					:   str ,				snake_case					:   str ,				snake_case					:   Optional[int] ,				snake_case					:   Any ,				snake_case					:   str							)->		Optional[int]:






     '''simple docstring'''



     UpperCAmelCase__	:						Union[str, Any]       =       len_data

     UpperCAmelCase__	:						Union[str, Any]       =       np.dot(snake_case ,				data_x.transpose()							)
     prod -= data_y.transpose()
     UpperCAmelCase__	:						Optional[int]       =       np.dot(snake_case ,				snake_case							)
     UpperCAmelCase__	:						Any       =       theta - (alpha / n) * sum_grad
     return theta


def   SCREAMING_SNAKE_CASE__					(     snake_case					:   Dict ,				snake_case					:   int ,				snake_case					:   Dict ,				snake_case					:   str							)->		List[str]:






     '''simple docstring'''



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


def   SCREAMING_SNAKE_CASE__					(     snake_case					:   Tuple ,				snake_case					:   Union[str, Any]							)->		List[str]:






     '''simple docstring'''



     UpperCAmelCase__	:						str       =       10_0000
     UpperCAmelCase__	:						Dict       =       0.000_1550

     UpperCAmelCase__	:						int       =       data_x.shape[1]
     UpperCAmelCase__	:						Optional[Any]       =       data_x.shape[0] - 1

     UpperCAmelCase__	:						int       =       np.zeros((1, no_features)							)

     for i in range(0 ,				snake_case							):
          UpperCAmelCase__	:						Optional[Any]       =       run_steep_gradient_descent(snake_case ,				snake_case ,				snake_case ,				snake_case ,				snake_case							)
          UpperCAmelCase__	:						Tuple       =       sum_of_square_error(snake_case ,				snake_case ,				snake_case ,				snake_case							)
          print(f'At Iteration {i + 1} - Error is {error:.5f}'							)

     return theta


def   SCREAMING_SNAKE_CASE__					(     snake_case					:   str ,				snake_case					:   Optional[int]							)->		Any:






     '''simple docstring'''



     UpperCAmelCase__	:						str       =       sum(abs(y - predicted_y[i]							) for i, y in enumerate(snake_case							)							)
     return total / len(snake_case							)


def   SCREAMING_SNAKE_CASE__					(     )->		Optional[int]:






     '''simple docstring'''



     UpperCAmelCase__	:						Optional[int]       =       collect_dataset()

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

     UpperCAmelCase__	:						List[Any]       =       run_linear_regression(snake_case ,				snake_case							)
     UpperCAmelCase__	:						Dict       =       theta.shape[1]
     print("Resultant Feature vector : "							)
     for i in range(0 ,				snake_case							):
          print(f'{theta[0, i]:.5f}'							)


if __name__ == "__main__":
 main()










"""simple docstring"""



# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


_lowerCAmelCase  :  Optional[Any]   =			{
    """configuration_convnextv2""": [
        """CONVNEXTV2_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """ConvNextV2Config""",
    ]
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _lowerCAmelCase  :  str   =			[
     """CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """ConvNextV2ForImageClassification""",
     """ConvNextV2Model""",
     """ConvNextV2PreTrainedModel""",
     """ConvNextV2Backbone""",
 ]


if TYPE_CHECKING:
 from .configuration_convnextva import (
     CONVNEXTV2_PRETRAINED_CONFIG_ARCHIVE_MAP,
     ConvNextVaConfig,
 )

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_convnextva import (
      CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST,
      ConvNextVaBackbone,
      ConvNextVaForImageClassification,
      ConvNextVaModel,
      ConvNextVaPreTrainedModel,
  )

else:
 import sys

 _lowerCAmelCase  :  List[str]   =			_LazyModule(__name__, globals()["""__file__"""], _import_structure)


