


import numpy as np
import requests







def     __magic_name__  (      ):
	'''simple docstring'''


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







def     __magic_name__  (      lowerCAmelCase_      ,					lowerCAmelCase_      ,					lowerCAmelCase_      ,					lowerCAmelCase_      ,					lowerCAmelCase_):
	'''simple docstring'''


	lowerCamelCase_			:							Tuple      =       len_data

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







def     __magic_name__  (      lowerCAmelCase_      ,					lowerCAmelCase_      ,					lowerCAmelCase_      ,					lowerCAmelCase_):
	'''simple docstring'''


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







def     __magic_name__  (      lowerCAmelCase_      ,					lowerCAmelCase_):
	'''simple docstring'''


	lowerCamelCase_			:							int      =       10_0000
	lowerCamelCase_			:							Dict      =       0.0_00_15_50

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

	lowerCamelCase_			:							List[str]      =       np.zeros((1, no_features))

	for i in range(0      ,					lowerCAmelCase_):
		lowerCamelCase_			:							List[Any]      =       run_steep_gradient_descent(lowerCAmelCase_      ,					lowerCAmelCase_      ,					lowerCAmelCase_      ,					lowerCAmelCase_      ,					lowerCAmelCase_)
		lowerCamelCase_			:							Tuple      =       sum_of_square_error(lowerCAmelCase_      ,					lowerCAmelCase_      ,					lowerCAmelCase_      ,					lowerCAmelCase_)
		print(F"""At Iteration {i + 1} - Error is {error:.5f}""")

	return theta







def     __magic_name__  (      lowerCAmelCase_      ,					lowerCAmelCase_):
	'''simple docstring'''


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







def     __magic_name__  (      ):
	'''simple docstring'''


	lowerCamelCase_			:							List[str]      =       collect_dataset()

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

	lowerCamelCase_			:							List[Any]      =       run_linear_regression(lowerCAmelCase_      ,					lowerCAmelCase_)
	lowerCamelCase_			:							List[str]      =       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, is_vision_available


__magic_name__       					=      {'''configuration_levit''': ['''LEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''LevitConfig''', '''LevitOnnxConfig''']}

try:
							if not is_vision_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__magic_name__       					=      ['''LevitFeatureExtractor''']
							__magic_name__       					=      ['''LevitImageProcessor''']

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__magic_name__       					=      [
							    '''LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''LevitForImageClassification''',
							    '''LevitForImageClassificationWithTeacher''',
							    '''LevitModel''',
							    '''LevitPreTrainedModel''',
							]


if TYPE_CHECKING:
							from .configuration_levit import LEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, LevitConfig, LevitOnnxConfig

							try:
														if not is_vision_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .feature_extraction_levit import LevitFeatureExtractor
														from .image_processing_levit import LevitImageProcessor

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_levit import (
														    LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    LevitForImageClassification,
														    LevitForImageClassificationWithTeacher,
														    LevitModel,
														    LevitPreTrainedModel,
														)
else:
							import sys

							__magic_name__       					=      _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

