





"""simple docstring"""
import itertools
from dataclasses import dataclass
from typing import Optional

import pyarrow as pa

import datasets
from datasets.table import table_cast


_snake_case					:		Union[str, Any]							=			datasets.utils.logging.get_logger(__name__)






@dataclass
class  _UpperCAmelCase       (     datasets.BuilderConfig			):
					UpperCamelCase								=							None







class  _UpperCAmelCase       (     datasets.ArrowBasedBuilder			):
					UpperCamelCase								=							ArrowConfig


					def      lowerCamelCase	( self      :Optional[Any]		):
												return datasets.DatasetInfo(features=self.config.features		)


					def      lowerCamelCase	( self      :Any					,	__UpperCamelCase      :Tuple		):
												if not self.config.data_files:
																			raise ValueError(f"At least one data file must be specified, but got data_files={self.config.data_files}"		)
												A		=  dl_manager.download_and_extract(self.config.data_files		)
												if isinstance(__UpperCamelCase					,	(str, list, tuple)		):
																			A		=  data_files
																			if isinstance(__UpperCamelCase					,	__UpperCamelCase		):
																										A		=  [files]
																			# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
																			A		=  [dl_manager.iter_files(__UpperCamelCase		) for file in files]
																			return [datasets.SplitGenerator(name=datasets.Split.TRAIN					,	gen_kwargs={"files": files}		)]
												A		=  []
												for split_name, files in data_files.items():
																			if isinstance(__UpperCamelCase					,	__UpperCamelCase		):
																										A		=  [files]
																			# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
																			A		=  [dl_manager.iter_files(__UpperCamelCase		) for file in files]
																			# Infer features is they are stoed in the arrow schema
																			if self.info.features is None:
																										for file in itertools.chain.from_iterable(__UpperCamelCase		):
																																	with open(__UpperCamelCase					,	"rb"		) as f:
																																								A		=  datasets.Features.from_arrow_schema(pa.ipc.open_stream(__UpperCamelCase		).schema		)
																																	break
																			splits.append(datasets.SplitGenerator(name=__UpperCamelCase					,	gen_kwargs={"files": files}		)		)
												return splits


					def      lowerCamelCase	( self      :Tuple					,	__UpperCamelCase      :pa.Table		):
												if self.info.features is not None:
																			# more expensive cast to support nested features with keys in a different order
																			# allows str <-> int/float or str to Audio for example
																			A		=  table_cast(__UpperCamelCase					,	self.info.features.arrow_schema		)
												return pa_table


					def      lowerCamelCase	( self      :str					,	__UpperCamelCase      :Tuple		):
												for file_idx, file in enumerate(itertools.chain.from_iterable(__UpperCamelCase		)		):
																			with open(__UpperCamelCase					,	"rb"		) as f:
																										try:
																																	for batch_idx, record_batch in enumerate(pa.ipc.open_stream(__UpperCamelCase		)		):
																																								A		=  pa.Table.from_batches([record_batch]		)
																																								# Uncomment for debugging (will print the Arrow table size and elements)
																																								# logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
																																								# logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
																																								yield f"{file_idx}_{batch_idx}", self._cast_table(__UpperCamelCase		)
																										except ValueError as e:
																																	logger.error(f"Failed to read file '{file}' with error {type(__UpperCamelCase		)}: {e}"		)
																																	raise









"""simple docstring"""
import numpy as np
import requests






def  A__    (      ):
							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(UpperCamelCase	)
							data.pop(0	)  # This is for removing the labels from the list
							A		=  np.matrix(UpperCamelCase	)
							return dataset






def  A__    (      UpperCamelCase ,  UpperCamelCase ,  UpperCamelCase ,  UpperCamelCase ,  UpperCamelCase	):
							A		=  len_data

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






def  A__    (      UpperCamelCase ,  UpperCamelCase ,  UpperCamelCase ,  UpperCamelCase	):
							A		=  np.dot(UpperCamelCase ,  data_x.transpose()	)
							prod -= data_y.transpose()
							A		=  np.sum(np.square(UpperCamelCase	)	)
							A		=  sum_elem / (2 * len_data)
							return error






def  A__    (      UpperCamelCase ,  UpperCamelCase	):
							A		=  100_000
							A		=  0.0_00_15_50

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

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

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

							return theta






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






def  A__    (      ):
							A		=  collect_dataset()

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

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


if __name__ == "__main__":
		main()


