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) 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: "ValueError: matrices are not aligned"
I'just don't get where the error does come from. There seems to be a general problem with my data or how I'm using the svd function. I hope anybody has an idea what I'm doing wrong or where my problem comes from. I really appreciate any suggestions! Many thanks! Greetings, Daniel Wagner
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).
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) ) "ValueError: matrices are not aligned"
Mismatched dimensions. Chuck
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
I have to think about it if this makes sense ... (for me and prob. my my data) Thanks and Greetings, Daniel
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. Chuck
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.... Greetings, Daniel
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. Chuck
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.
right, I want to compute the least square..... Does lstsq() use svd for it's computations? Is it possible to take a look how it calculates the values? Thanks. Greetings, Daniel
Chuck
_______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
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! Greetings, Daniel
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 _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
On Oct 24, 2010, at 4:29 AM, josef.pktd@gmail.com wrote:
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) Thanks, this works: x2 = x = numpy.dot(temp, b[:,None])
Greetings, Daniel
participants (3)
-
Charles R Harris -
Daniel Wagner -
josef.pktd＠gmail.com