[Numpy-discussion] SVD problem - matrices are not aligned
josef.pktd@gmai...
josef.pktd@gmai...
Sun Oct 24 03:29:29 CDT 2010
On Sat, Oct 23, 2010 at 11:47 PM, Daniel Wagner
<daniel.wagner.ml@googlemail.com> wrote:
>
> On Oct 23, 2010, at 11:25 PM, Charles R Harris wrote:
>
>
> On Sat, Oct 23, 2010 at 9:21 PM, Daniel Wagner
> <daniel.wagner.ml@googlemail.com> wrote:
>>
>> On Oct 23, 2010, at 10:48 PM, Charles R Harris wrote:
>>
>>
>> On Sat, Oct 23, 2010 at 8:33 PM, Daniel Wagner
>> <daniel.wagner.ml@googlemail.com> wrote:
>>>
>>> On Sat, Oct 23, 2010 at 7:00 PM, Daniel Wagner
>>> <daniel.wagner.ml@googlemail.com> wrote:
>>>>
>>>> Hi,
>>>>
>>>> I'm a new subscriber of this list. I hope to directly start with a
>>>> question is ok...
>>>>
>>>> My question or problem:
>>>> I've a matrix A which is calculated from the data b. The shapes of these
>>>> matrices are:
>>>> >>>A.shape
>>>> (954, 9)
>>>> >>>b.shape
>>>> (954,)
>>>>
>>>> I calculate the SVD of A:
>>>> >>> U, w, V = numpy.linalg.svd(A, full_matrices="True")
>>>> >>>U.shape
>>>> (954, 954)
>>>
>>> You want full_matrices set false so that U has shape (954, 9).
>>>
>>>
>>> thanks! I tried it before with "False" as a string but of course this
>>> couldn't work. omgh. (no error message?)
>>> Now I'm using:
>>> >>>U, w, V = numpy.linalg.svd(yz_matrix_by, full_matrices=False)
>>>>
>>>> >>>W.diag(w)
>>>> >>>W.shape
>>>> (9,9)
>>>> >>>V.shape
>>>> (9,9)
>>>>
>>>> If I'm doing the check of the SVD results using:
>>>> >>>numpy.allclose(A, numpy.dot(U, numpy.dot(W, V)))
>>>> I get this error:
>>>
>>> easier, allclose(A, dot(U*w, V) )
>>>
>>> That's right!
>>>
>>>> "ValueError: matrices are not aligned"
>>>>
>>>
>>> Mismatched dimensions.
>>>
>>> Yeah, this error is away. Now I get:
>>> >>>print(numpy.allclose(U_by*w_by, V_by))
>>> False
>>
>> Seems to be a missing "dot" in there.
>>
>> The following works:
>> >>>numpy.allclose(A, numpy.dot(U, numpy.dot(W, V)))
>> True
>> But now I've a new problem problem:
>> When I'm using:
>> >>> A.shape
>> (954, 9)
>> >>> b.shape
>> (954, )
>> >>> temp = numpy.linalg.pinv(A, rcond=1.0000000000000001e-15)
>> >>> temp.shape
>> (9, 954)
>> to multiply this with my data b
>> >>>x = numpy.dot(temp, b)
>> ValueError: matrices are not aligned
>> I've missmatched dimensions again....
>
> Looks like you might want to look at lstsq.
>
> It works like a charm! The next time I really should use the already
> implemented functions and if I'm unsure how they work I still can take a
> look into their source code...
> Thank you for the hint!
I'm just going the other way, using pinv and leastsq, and now switch
to decomposition directly.
>From what I can see the main difference in your example to what I
have is to use linalg.diagsvd
u,s,v = np.linalg.svd(x, full_matrices=1)
Sig = linalg.diagsvd(s,*x.shape)
>>> np.max(np.abs(np.dot(u, np.dot(Sig, v)) - x))
3.1086244689504383e-015
for the correct shapes of the sqrt of the x'x and xx':
>>> us = np.dot(u, Sig)
>>> np.max(np.abs(np.dot(us, us.T) - np.dot(x, x.T)))
1.0658141036401503e-014
>>> sv = np.dot(Sig, v)
>>> np.max(np.abs(np.dot(sv.T, sv) - np.dot(x.T, x)))
1.1368683772161603e-013
I'm still trying to collect these things into a class.
statsmodels is doing all the least square calculations with
linalg.pinv directly,
I thought your pinv example should work, you could also try with
explicit 2dimensional b
>>>x = numpy.dot(temp, b{:,None)
(scikits.learn Bayesian Ridge regression is also a good example how to
do regression with svd directly)
Josef
> Greetings,
> Daniel
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