import numpy as np
import requests



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



def     lowerCamelCase_    (  _lowercase					,	_lowercase					,	_lowercase					,	_lowercase					,	_lowercase				)							->       Optional[int]:
		__A							:    int         =      len_data

		__A							:    Optional[Any]         =      np.dot(__lowerCamelCase					,	data_x.transpose()				)
		prod -= data_y.transpose()
		__A							:    Tuple         =      np.dot(__lowerCamelCase					,	__lowerCamelCase				)
		__A							:    List[Any]         =      theta - (alpha / n) * sum_grad
		return theta



def     lowerCamelCase_    (  _lowercase					,	_lowercase					,	_lowercase					,	_lowercase				)							->       str:
		__A							:    Union[str, Any]         =      np.dot(__lowerCamelCase					,	data_x.transpose()				)
		prod -= data_y.transpose()
		__A							:    str         =      np.sum(np.square(__lowerCamelCase				)				)
		__A							:    List[Any]         =      sum_elem / (2 * len_data)
		return error



def     lowerCamelCase_    (  _lowercase					,	_lowercase				)							->       int:
		__A							:    Optional[Any]         =      100_000
		__A							:    Any         =      0.0_00_15_50

		__A							:    Any         =      data_x.shape[1]
		__A							:    Any         =      data_x.shape[0] - 1

		__A							:    Union[str, Any]         =      np.zeros((1, no_features)				)

		for i in range(0					,	__lowerCamelCase				):
				__A							:    Dict         =      run_steep_gradient_descent(__lowerCamelCase					,	__lowerCamelCase					,	__lowerCamelCase					,	__lowerCamelCase					,	__lowerCamelCase				)
				__A							:    Any         =      sum_of_square_error(__lowerCamelCase					,	__lowerCamelCase					,	__lowerCamelCase					,	__lowerCamelCase				)
				print(F"At Iteration {i + 1} - Error is {error:.5f}"				)

		return theta



def     lowerCamelCase_    (  _lowercase					,	_lowercase				)							->       Optional[Any]:
		__A							:    int         =      sum(abs(y - predicted_y[i]				) for i, y in enumerate(__lowerCamelCase				)				)
		return total / len(__lowerCamelCase				)



def     lowerCamelCase_    (  )							->       Any:
		__A							:    int         =      collect_dataset()

		__A							:    Tuple         =      data.shape[0]
		__A							:    Optional[int]         =      np.c_[np.ones(__lowerCamelCase				), data[:, :-1]].astype(__lowerCamelCase				)
		__A							:    Dict         =      data[:, -1].astype(__lowerCamelCase				)

		__A							:    Union[str, Any]         =      run_linear_regression(__lowerCamelCase					,	__lowerCamelCase				)
		__A							:    Any         =      theta.shape[1]
		print("Resultant Feature vector : "				)
		for i in range(0					,	__lowerCamelCase				):
				print(F"{theta[0, i]:.5f}"				)


if __name__ == "__main__":
					main()






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCamelCase_				:		int       						=       {
    """configuration_fsmt""": ["""FSMT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """FSMTConfig"""],
    """tokenization_fsmt""": ["""FSMTTokenizer"""],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCamelCase_				:		Dict       						=       ["""FSMTForConditionalGeneration""", """FSMTModel""", """PretrainedFSMTModel"""]


if TYPE_CHECKING:
	from .configuration_fsmt import FSMT_PRETRAINED_CONFIG_ARCHIVE_MAP, FSMTConfig
	from .tokenization_fsmt import FSMTTokenizer

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_fsmt import FSMTForConditionalGeneration, FSMTModel, PretrainedFSMTModel

else:
	import sys

	lowerCamelCase_				:		Dict       						=       _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




