I'm trying to do the following with numpy (python newbie here)
num_rows = 80
num_cols = 23
A = numpy.zeros(shape=(num_rows, num_cols))
k = 5
numpy.transpose(A)
U,s,V = linalg.svd(A)
sk = s[0:(k-1), 0:(k-1)]
Traceback (most recent call last):
File "tdm2svd.py", line 40, in <module>
sk = s[0:(k-1), 0:(k-1)]
IndexError: too many indices
What am I doing wrong?
to answer your question s is only a 1d array ... (even if you did actually transpose it ... which you did not)
>>> u,s,v = linalg.svd(A)
>>> s
array([ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])
>>>
for selecting a submatrix I think this does what you want ... there may be a better way
>>> rows = range(10,15)
>>> cols = range(5,8)
>>> A[rows][:,cols]
array([[ 0., 0., 0.],
[ 0., 0., 0.],
[ 0., 0., 0.],
[ 0., 0., 0.],
[ 0., 0., 0.]])
or probably better
>>> A[15:32, 2:7]
array([[ 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0.]])
numpy.linalg.svd. You'll see thatsis a 1d array holding the singular values. Also,numpy.transposedoes not change an array in-place, so the linenumpy.transpose(A)has no effect.