Computer Networking: A Top-Down Approach (7th Edition)
Computer Networking: A Top-Down Approach (7th Edition)
7th Edition
ISBN: 9780133594140
Author: James Kurose, Keith Ross
Publisher: PEARSON
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Question
Using the linear_predict function that you previously wrote, write a function linear_residuals that given the learned weights in the linear_regression function calculates the residuals vector. Your functions takes the following arguments as input: 1. X:A numpy array of the shape (N,d) where N is the number of data points, and d is the data dimension. Do not assume anything about N or d other than being a positive integer. 2. beta : Anumpy array of the shape (d+1,1) where d is the data dimension Bo Ba 3. Y: A numpy array of the shape (N,) where N is the number of data points. Your function should produce the e numpy array with the shape of (N,), whose i" element is defined as el) = yl) – (Bo + Bix? + Bzx + + Bax) In [ ]: def linear_residuals (X,beta,Y): assert X.ndim==2 N = X.shape[0] х.shape[1] assert beta.shape assert Y.shape d = (d+1,1) (N,) == == #3 your code here e e.reshape(-1) %3D assert e.size return e (np.arange ( 35).reshape ( 7,5) ** 13) % 20 2.** (-np.arange (6).reshape (-1,1)) In [ ]: some_X = some beta = some_Y = np.sum(some_X, axis=1) some res linear_residuals(some_x, some_beta, some_Y) %3D assert np.array_equal (some_res.round (3), np.array([16.938, 35.844, 33. 812, 59.281, 16.938, 39.719, 16.938] )) # Checking against the pre-computed test database test results test_case_checker (linear_residuals, task_id=3) assert test_results['passed'], test_results['message']
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Transcribed Image Text:Using the linear_predict function that you previously wrote, write a function linear_residuals that given the learned weights in the linear_regression function calculates the residuals vector. Your functions takes the following arguments as input: 1. X:A numpy array of the shape (N,d) where N is the number of data points, and d is the data dimension. Do not assume anything about N or d other than being a positive integer. 2. beta : Anumpy array of the shape (d+1,1) where d is the data dimension Bo Ba 3. Y: A numpy array of the shape (N,) where N is the number of data points. Your function should produce the e numpy array with the shape of (N,), whose i" element is defined as el) = yl) – (Bo + Bix? + Bzx + + Bax) In [ ]: def linear_residuals (X,beta,Y): assert X.ndim==2 N = X.shape[0] х.shape[1] assert beta.shape assert Y.shape d = (d+1,1) (N,) == == #3 your code here e e.reshape(-1) %3D assert e.size return e (np.arange ( 35).reshape ( 7,5) ** 13) % 20 2.** (-np.arange (6).reshape (-1,1)) In [ ]: some_X = some beta = some_Y = np.sum(some_X, axis=1) some res linear_residuals(some_x, some_beta, some_Y) %3D assert np.array_equal (some_res.round (3), np.array([16.938, 35.844, 33. 812, 59.281, 16.938, 39.719, 16.938] )) # Checking against the pre-computed test database test results test_case_checker (linear_residuals, task_id=3) assert test_results['passed'], test_results['message']
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