Comments (3)
i thought both were right, and pinv is more efficient.
and i thought the third row(113) should be
V.T.dot(...)
maybe a typo?
I looked into it and you are correct. Numpy doesn't return the regular SVD values. It returns values such that A = U @ S @ V, but normal SVD is A = U @ S @ V.T, which means if you're going to use V in later calculations you need to use its transpose.
I double checked the results and they're incorrect in their current form. I submitted a pull request to fix the issue.
from ml-from-scratch.
i thought both were right, and pinv is more efficient.
and i thought the third row(113) should be
V.T.dot(...)
maybe a typo?
from ml-from-scratch.
Also, for what it's worth, I think the current solution in place is a bit more 'expressive' than just using np.linalg.pinv(X)
. They both ultimately do the same thing, but assuming the point of this repo is to communicate how ML techniques work, then the current format seems better IMO.
from ml-from-scratch.
Related Issues (20)
- Project dependencies may have API risk issues
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