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 :A numpy array of the shape (d+1,1) where d is the data dimension Bo ... L 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'h element is defined as e) = y) – (Bo + Bix + B2x + …· + Baxg)
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 :A numpy array of the shape (d+1,1) where d is the data dimension Bo ... L 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'h element is defined as e) = y) – (Bo + Bix + B2x + …· + Baxg)
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![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']](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2F4902cbb3-0fbf-4391-90d0-15ad68ae7505%2F861e7b06-e437-4ef8-9567-c3d6c88f0531%2F4vmfqb_processed.png&w=3840&q=75)
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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