




import numpy as np
import requests






def 					snake_case					(							)							->							int:




	'''simple docstring'''


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






def 					snake_case					(							A						,   A						,   A						,   A						,   A			)							->							List[Any]:




	'''simple docstring'''


	lowerCamelCase_							:		Optional[Any]							   =							len_data

	lowerCamelCase_							:		Dict							   =							np.dot(A						,   data_x.transpose()			)
	prod -= data_y.transpose()
	lowerCamelCase_							:		Dict							   =							np.dot(A						,   A			)
	lowerCamelCase_							:		Optional[int]							   =							theta - (alpha / n) * sum_grad
	return theta






def 					snake_case					(							A						,   A						,   A						,   A			)							->							Union[str, Any]:




	'''simple docstring'''


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






def 					snake_case					(							A						,   A			)							->							Dict:




	'''simple docstring'''


	lowerCamelCase_							:		List[Any]							   =							100000
	lowerCamelCase_							:		Any							   =							0.000_1550

	lowerCamelCase_							:		Any							   =							data_x.shape[1]
	lowerCamelCase_							:		Tuple							   =							data_x.shape[0] - 1

	lowerCamelCase_							:		Tuple							   =							np.zeros((1, no_features)			)

	for i in range(0						,   A			):
		lowerCamelCase_							:		str							   =							run_steep_gradient_descent(A						,   A						,   A						,   A						,   A			)
		lowerCamelCase_							:		int							   =							sum_of_square_error(A						,   A						,   A						,   A			)
		print(f"At Iteration {i + 1} - Error is {error:.5f}"			)

	return theta






def 					snake_case					(							A						,   A			)							->							int:




	'''simple docstring'''


	lowerCamelCase_							:		Any							   =							sum(abs(y - predicted_y[i]			) for i, y in enumerate(A			)			)
	return total / len(A			)






def 					snake_case					(							)							->							str:




	'''simple docstring'''


	lowerCamelCase_							:		Tuple							   =							collect_dataset()

	lowerCamelCase_							:		Optional[Any]							   =							data.shape[0]
	lowerCamelCase_							:		Any							   =							np.c_[np.ones(A			), data[:, :-1]].astype(A			)
	lowerCamelCase_							:		List[Any]							   =							data[:, -1].astype(A			)

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


if __name__ == "__main__":
							main()






from typing import Optional

from ..utils.logging import get_logger
from .audio_classificiation import AudioClassification
from .automatic_speech_recognition import AutomaticSpeechRecognition
from .base import TaskTemplate
from .image_classification import ImageClassification
from .language_modeling import LanguageModeling
from .question_answering import QuestionAnsweringExtractive
from .summarization import Summarization
from .text_classification import TextClassification


UpperCamelCase__              =						[
    'AutomaticSpeechRecognition',
    'AudioClassification',
    'ImageClassification',
    'LanguageModeling',
    'QuestionAnsweringExtractive',
    'Summarization',
    'TaskTemplate',
    'TextClassification',
]

UpperCamelCase__              =						get_logger(__name__)


UpperCamelCase__              =						{
    AutomaticSpeechRecognition.task: AutomaticSpeechRecognition,
    AudioClassification.task: AudioClassification,
    ImageClassification.task: ImageClassification,
    LanguageModeling.task: LanguageModeling,
    QuestionAnsweringExtractive.task: QuestionAnsweringExtractive,
    Summarization.task: Summarization,
    TextClassification.task: TextClassification,
}






def 					snake_case					(							A			)							->							Optional[TaskTemplate]:




	'''simple docstring'''


	lowerCamelCase_							:		Any							   =							task_template_dict.get('task'			)
	if task_name is None:
		logger.warning(f"Couldn't find template for task '{task_name}'. Available templates: {list(A			)}"			)
		return None
	lowerCamelCase_							:		Any							   =							NAMEaTEMPLATE.get(A			)
	return template.from_dict(A			)
