





"""simple docstring"""
import json
import os
import tempfile

import datasets
from utils import generate_example_dataset, get_duration


_snake_case					:		List[Any]							=			50_0000

_snake_case						,						_snake_case					:		str							=			os.path.split(__file__)
_snake_case					:		Optional[int]							=			os.path.join(RESULTS_BASEPATH, 'results', RESULTS_FILENAME.replace('.py', '.json'))






@get_duration
def  A__    (      UpperCamelCase	):
							A		=  dataset.select(range(0 ,  len(UpperCamelCase	) ,  2	)	)






@get_duration
def  A__    (      UpperCamelCase	):
							A		=  dataset.sort("numbers"	)






@get_duration
def  A__    (      UpperCamelCase	):
							A		=  dataset.shuffle()






@get_duration
def  A__    (      UpperCamelCase	):
							A		=  dataset.train_test_split(0.1	)






@get_duration
def  A__    (      UpperCamelCase ,  UpperCamelCase=10	):




							for shard_id in range(UpperCamelCase	):
														A		=  dataset.shard(UpperCamelCase ,  UpperCamelCase	)






def  A__    (      ):
							A		=  {"num examples": SPEED_TEST_N_EXAMPLES}
							A		=  (select, sort, shuffle, train_test_split, shard)
							with tempfile.TemporaryDirectory() as tmp_dir:
														print("generating dataset"	)
														A		=  datasets.Features({"text": datasets.Value("string"	), "numbers": datasets.Value("float32"	)}	)
														A		=  generate_example_dataset(
														    os.path.join(UpperCamelCase ,  "dataset.arrow"	) ,  UpperCamelCase ,  num_examples=UpperCamelCase	)
														print("Functions"	)
														for func in functions:
																					print(func.__name__	)
																					A		=  func(UpperCamelCase	)

							with open(UpperCamelCase ,  "wb"	) as f:
														f.write(json.dumps(UpperCamelCase	).encode("utf-8"	)	)


if __name__ == "__main__":  # useful to run the profiler
					benchmark_indices_mapping()









"""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()


