import numpy as np
import requests
def 							lowerCAmelCase(	):
					__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 							lowerCAmelCase(	__lowerCamelCase      ,				__lowerCamelCase      ,				__lowerCamelCase      ,				__lowerCamelCase      ,				__lowerCamelCase						):
					__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 							lowerCAmelCase(	__lowerCamelCase      ,				__lowerCamelCase      ,				__lowerCamelCase      ,				__lowerCamelCase						):
					__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 							lowerCAmelCase(	__lowerCamelCase      ,				__lowerCamelCase						):
					__a							=   10_0000
					__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      ,				__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 							lowerCAmelCase(	__lowerCamelCase      ,				__lowerCamelCase						):
					__a							=   sum(abs(y - predicted_y[i]						) for i, y in enumerate(__lowerCamelCase						)						)
					return total / len(__lowerCamelCase						)
def 							lowerCAmelCase(	):
					__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()








import doctest

import projectq
from projectq.ops import H, Measure
def       UpperCamelCase__     (  SCREAMING_SNAKE_CASE__						):
 __lowerCamelCase		:							int							=						quantum_engine.allocate_qubit()
 H | qubit
 Measure | qubit
 return int(SCREAMING_SNAKE_CASE__						)


if __name__ == "__main__":
       doctest.testmod()

       # initialises a new quantum backend
       lowercase_           =	projectq.MainEngine()

       # Generate a list of 10 random numbers
       lowercase_           =	[get_random_number(quantum_engine) for _ in range(1_0)]

       # Flushes the quantum engine from memory
       quantum_engine.flush()

       print('Random numbers', random_numbers_list)

