



import numpy as np
import requests




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




def 							__SCREAMING_SNAKE_CASE       (		SCREAMING_SNAKE_CASE	:				Optional[int]   ,							SCREAMING_SNAKE_CASE	:				Tuple   ,							SCREAMING_SNAKE_CASE	:				str   ,							SCREAMING_SNAKE_CASE	:				int   ,							SCREAMING_SNAKE_CASE	:				Optional[int]      )					->						Tuple:
				__lowercase								= len_data

				__lowercase								= np.dot(SCREAMING_SNAKE_CASE   ,							data_x.transpose()      )
				prod -= data_y.transpose()
				__lowercase								= np.dot(SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE      )
				__lowercase								= theta - (alpha / n) * sum_grad
				return theta




def 							__SCREAMING_SNAKE_CASE       (		SCREAMING_SNAKE_CASE	:				Dict   ,							SCREAMING_SNAKE_CASE	:				List[Any]   ,							SCREAMING_SNAKE_CASE	:				List[Any]   ,							SCREAMING_SNAKE_CASE	:				int      )					->						Optional[int]:
				__lowercase								= np.dot(SCREAMING_SNAKE_CASE   ,							data_x.transpose()      )
				prod -= data_y.transpose()
				__lowercase								= np.sum(np.square(SCREAMING_SNAKE_CASE      )      )
				__lowercase								= sum_elem / (2 * len_data)
				return error




def 							__SCREAMING_SNAKE_CASE       (		SCREAMING_SNAKE_CASE	:				str   ,							SCREAMING_SNAKE_CASE	:				List[Any]      )					->						int:
				__lowercase								= 100000
				__lowercase								= 0.0_001_550

				__lowercase								= data_x.shape[1]
				__lowercase								= data_x.shape[0] - 1

				__lowercase								= np.zeros((1, no_features)      )

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

				return theta




def 							__SCREAMING_SNAKE_CASE       (		SCREAMING_SNAKE_CASE	:				Tuple   ,							SCREAMING_SNAKE_CASE	:				Optional[int]      )					->						Any:
				__lowercase								= sum(abs(y - predicted_y[i]      ) for i, y in enumerate(SCREAMING_SNAKE_CASE      )      )
				return total / len(SCREAMING_SNAKE_CASE      )




def 							__SCREAMING_SNAKE_CASE       (		)					->						List[str]:
				__lowercase								= collect_dataset()

				__lowercase								= data.shape[0]
				__lowercase								= np.c_[np.ones(SCREAMING_SNAKE_CASE      ), data[:, :-1]].astype(SCREAMING_SNAKE_CASE      )
				__lowercase								= data[:, -1].astype(SCREAMING_SNAKE_CASE      )

				__lowercase								= run_linear_regression(SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE      )
				__lowercase								= theta.shape[1]
				print('Resultant Feature vector : '      )
				for i in range(0   ,							SCREAMING_SNAKE_CASE      ):
								print(F"""{theta[0, i]:.5f}"""      )


if __name__ == "__main__":
							main()







from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE__							=    {"""configuration_hubert""": ["""HUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """HubertConfig"""]}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							SCREAMING_SNAKE_CASE__							=    [
							    """HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """HubertForCTC""",
							    """HubertForSequenceClassification""",
							    """HubertModel""",
							    """HubertPreTrainedModel""",
							]


try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							SCREAMING_SNAKE_CASE__							=    [
							    """TF_HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """TFHubertForCTC""",
							    """TFHubertModel""",
							    """TFHubertPreTrainedModel""",
							]

if TYPE_CHECKING:
							from .configuration_hubert import HUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, HubertConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_hubert import (
														    HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    HubertForCTC,
														    HubertForSequenceClassification,
														    HubertModel,
														    HubertPreTrainedModel,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_hubert import (
														    TF_HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFHubertForCTC,
														    TFHubertModel,
														    TFHubertPreTrainedModel,
														)


else:
							import sys

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





