2014-04-09 17:37 GMT+02:00 Nathaniel Smith <n...@pobox.com>: > On Wed, Apr 9, 2014 at 4:25 PM, Björn Lindqvist <bjou...@gmail.com> wrote: >> 2014-04-08 14:52 GMT+02:00 Nathaniel Smith <n...@pobox.com>: >>> On Tue, Apr 8, 2014 at 9:58 AM, Björn Lindqvist <bjou...@gmail.com> wrote: >>>> 2014-04-07 3:41 GMT+02:00 Nathaniel Smith <n...@pobox.com>: >>>>> So, I guess as far as I'm concerned, this is ready to go. Feedback >>>>> welcome: >>>>> http://legacy.python.org/dev/peps/pep-0465/ >>>> >>>> Couldn't you please have made your motivation example actually runnable? >>>> >>>> import numpy as np >>>> from numpy.linalg import inv, solve >>>> >>>> # Using dot function: >>>> S = np.dot((np.dot(H, beta) - r).T, >>>> np.dot(inv(np.dot(np.dot(H, V), H.T)), np.dot(H, beta) - r)) >>>> >>>> # Using dot method: >>>> S = (H.dot(beta) - r).T.dot(inv(H.dot(V).dot(H.T))).dot(H.dot(beta) - r) >>>> >>>> Don't keep your reader hanging! Tell us what the magical variables H, >>>> beta, r and V are. And why import solve when you aren't using it? >>>> Curious readers that aren't very good at matrix math, like me, should >>>> still be able to follow your logic. Even if it is just random data, >>>> it's better than nothing! >>> >>> There's a footnote that explains the math in more detail and links to >>> the real code this was adapted from. And solve is used further down in >>> the section. But running it is really what you want, just insert: >>> >>> beta = np.random.randn(10) >>> H = np.random.randn(2, 10) >>> r = np.random.randn(2) >>> V = np.random.randn(10, 10) >>> >>> Does that help? ;-) >> >> Thanks! Yes it does help. Then I can see that this expression: >> >> np.dot(H, beta) - r >> >> Evaluates to a vector. And a vector transposed is the vector itself. >> So the .T part in this expression np.dot(H, beta) - r).T is >> unnecessary, isn't it? > > In univariate regressions r and beta are vectors, and the .T is a > no-op. The formula also works for multivariate regression, in which > case r and beta become matrices; in this case the .T becomes > necessary.
Then what is the shape of those variables supposed to be? The earlier definitions you gave isn't enough for this general case. -- mvh/best regards Björn Lindqvist _______________________________________________ Python-Dev mailing list Python-Dev@python.org https://mail.python.org/mailman/listinfo/python-dev Unsubscribe: https://mail.python.org/mailman/options/python-dev/archive%40mail-archive.com
