



import numpy as np
import requests

def 	a						(				):







		'''simple docstring'''





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

def 	a						(				_UpperCAmelCase							: List[str]			,       _UpperCAmelCase							: int			,       _UpperCAmelCase							: List[str]			,       _UpperCAmelCase							: Tuple			,       _UpperCAmelCase							: Union[str, Any]						):







		'''simple docstring'''





		__UpperCAmelCase   :					Dict													=     len_data

		__UpperCAmelCase   :					List[str]													=     np.dot(snake_case__			,       data_x.transpose()						)
		prod -= data_y.transpose()
		__UpperCAmelCase   :					Any													=     np.dot(snake_case__			,       snake_case__						)
		__UpperCAmelCase   :					Any													=     theta - (alpha / n) * sum_grad
		return theta

def 	a						(				_UpperCAmelCase							: Any			,       _UpperCAmelCase							: int			,       _UpperCAmelCase							: Any			,       _UpperCAmelCase							: Dict						):







		'''simple docstring'''





		__UpperCAmelCase   :					Any													=     np.dot(snake_case__			,       data_x.transpose()						)
		prod -= data_y.transpose()
		__UpperCAmelCase   :					Optional[int]													=     np.sum(np.square(snake_case__						)						)
		__UpperCAmelCase   :					List[str]													=     sum_elem / (2 * len_data)
		return error

def 	a						(				_UpperCAmelCase							: Optional[Any]			,       _UpperCAmelCase							: List[Any]						):







		'''simple docstring'''





		__UpperCAmelCase   :					int													=     10_00_00
		__UpperCAmelCase   :					Optional[Any]													=     0.0_00_15_50

		__UpperCAmelCase   :					Dict													=     data_x.shape[1]
		__UpperCAmelCase   :					Dict													=     data_x.shape[0] - 1

		__UpperCAmelCase   :					str													=     np.zeros((1, no_features)						)

		for i in range(0			,       snake_case__						):
				__UpperCAmelCase   :					str													=     run_steep_gradient_descent(snake_case__			,       snake_case__			,       snake_case__			,       snake_case__			,       snake_case__						)
				__UpperCAmelCase   :					str													=     sum_of_square_error(snake_case__			,       snake_case__			,       snake_case__			,       snake_case__						)
				print(f'At Iteration {i + 1} - Error is {error:.5f}'						)

		return theta

def 	a						(				_UpperCAmelCase							: List[str]			,       _UpperCAmelCase							: Optional[int]						):







		'''simple docstring'''





		__UpperCAmelCase   :					Union[str, Any]													=     sum(abs(y - predicted_y[i]						) for i, y in enumerate(snake_case__						)						)
		return total / len(snake_case__						)

def 	a						(				):







		'''simple docstring'''





		__UpperCAmelCase   :					str													=     collect_dataset()

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

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


if __name__ == "__main__":
		main()










def 	a						(				_UpperCAmelCase							: Optional[int]			,       _UpperCAmelCase							: List[Any]						):







		'''simple docstring'''





		__UpperCAmelCase   :					int													=     len(_UpperCAmelCase						)
		# Create a stack and push index of fist element to it
		__UpperCAmelCase   :					List[Any]													=     []
		st.append(0						)

		# Span value of first element is always 1
		__UpperCAmelCase   :					Any													=     1

		# Calculate span values for rest of the elements
		for i in range(1			,       _UpperCAmelCase						):
				# Pop elements from stack while stack is not
				# empty and top of stack is smaller than price[i]
				while len(_UpperCAmelCase						) > 0 and price[st[0]] <= price[i]:
						st.pop()

				# If stack becomes empty, then price[i] is greater
				# than all elements on left of it, i.e. price[0],
				# price[1], ..price[i-1]. Else the price[i]  is
				# greater than elements after top of stack
				__UpperCAmelCase   :					int													=     i + 1 if len(_UpperCAmelCase						) <= 0 else (i - st[0])

				# Push this element to stack
				st.append(_UpperCAmelCase						)

def 	a						(				_UpperCAmelCase							: Dict			,       _UpperCAmelCase							: List[str]						):







		'''simple docstring'''





		for i in range(0			,       _UpperCAmelCase						):
				print(arr[i]			,       end=''' '''						)


# Driver program to test above function
__A             =[1_0, 4, 5, 9_0, 1_2_0, 8_0]
__A             =[0 for i in range(len(price) + 1)]

# Fill the span values in array S[]
calculation_span(price, S)
print_array(S, len(price))





