






import numpy as np
import requests



def      UpperCAmelCase						( ):


		'''simple docstring'''

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



def      UpperCAmelCase						( lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__	):


		'''simple docstring'''

		__A    =							len_data

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



def      UpperCAmelCase						( lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__	):


		'''simple docstring'''

		__A    =							np.dot(lowerCAmelCase__					,     data_x.transpose()	)
		prod -= data_y.transpose()
		__A    =							np.sum(np.square(lowerCAmelCase__	)	)
		__A    =							sum_elem / (2 * len_data)
		return error



def      UpperCAmelCase						( lowerCAmelCase__					,     lowerCAmelCase__	):


		'''simple docstring'''

		__A    =							10_0000
		__A    =							0.0_001_550

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

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

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

		return theta



def      UpperCAmelCase						( lowerCAmelCase__					,     lowerCAmelCase__	):


		'''simple docstring'''

		__A    =							sum(abs(y - predicted_y[i]	) for i, y in enumerate(lowerCAmelCase__	)	)
		return total / len(lowerCAmelCase__	)



def      UpperCAmelCase						( ):


		'''simple docstring'''

		__A    =							collect_dataset()

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

		__A    =							run_linear_regression(lowerCAmelCase__					,     lowerCAmelCase__	)
		__A    =							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_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


snake_case_   :	Dict							      ={'''configuration_t5''': ['''T5_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''T5Config''', '''T5OnnxConfig''']}

try:
					if not is_sentencepiece_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					snake_case_   :	Optional[Any]							      =['''T5Tokenizer''']

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					snake_case_   :	Any							      =['''T5TokenizerFast''']

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					snake_case_   :	Optional[int]							      =[
					    '''T5_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''T5EncoderModel''',
					    '''T5ForConditionalGeneration''',
					    '''T5Model''',
					    '''T5PreTrainedModel''',
					    '''load_tf_weights_in_t5''',
					    '''T5ForQuestionAnswering''',
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					snake_case_   :	Optional[Any]							      =[
					    '''TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''TFT5EncoderModel''',
					    '''TFT5ForConditionalGeneration''',
					    '''TFT5Model''',
					    '''TFT5PreTrainedModel''',
					]

try:
					if not is_flax_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					snake_case_   :	Tuple							      =[
					    '''FlaxT5EncoderModel''',
					    '''FlaxT5ForConditionalGeneration''',
					    '''FlaxT5Model''',
					    '''FlaxT5PreTrainedModel''',
					]


if TYPE_CHECKING:
					from .configuration_ta import T5_PRETRAINED_CONFIG_ARCHIVE_MAP, TaConfig, TaOnnxConfig

					try:
										if not is_sentencepiece_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_ta import TaTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_ta_fast import TaTokenizerFast

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_ta import (
										    T5_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TaEncoderModel,
										    TaForConditionalGeneration,
										    TaForQuestionAnswering,
										    TaModel,
										    TaPreTrainedModel,
										    load_tf_weights_in_ta,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_ta import (
										    TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFTaEncoderModel,
										    TFTaForConditionalGeneration,
										    TFTaModel,
										    TFTaPreTrainedModel,
										)

					try:
										if not is_flax_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_flax_ta import (
										    FlaxTaEncoderModel,
										    FlaxTaForConditionalGeneration,
										    FlaxTaModel,
										    FlaxTaPreTrainedModel,
										)


else:
					import sys

					snake_case_   :	List[Any]							      =_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

