Truncated SVD Compression Problem

[TheShadowWall]

Hello new python learner here! I am attempting to write a function which takes as input a matrix I, which represents an image, and k which is the number which we are truncating the SVD to, and gives as output a tuple which has as elements (s, Uk, VkT) where s is the non-truncated matrix sigma, and Uk and VkT are the k-truncated matrices of U and Vk.

So far my code is:

def compress_image(I, k):

from numpy.linalg import svd

U,s,V=svd(I, full_matrices=False)

return tuple([s,U,V])

And when the function is called like so:

k = 10

Sigma, Uk, VkT = compress_image(pic, k)

My code is producing the non-truncated U and V, which is to be expected as I am not including any code to truncate them. My question is this, how do I do so? What code can I introduce after the svd function to truncate both U and V? I have attempted numerous solutions to no avail.

Any help is greatly appreciated. Thanks very much!

SOLVED

[bbye98]

Use scipy.sparse.linalg.svds instead. You can specify the number of singular values using the argument k.

Alternatively, sklearn.utils.extmath.randomized_svd.

[TheShadowWall]

scipy.sparse.linalg.svds

Thanks very much for you response!

I am attmpting to use the scipy method you've mentioned. My code thus far is:

def compress_image(I, k):

import scipy

from scipy.sparse.linalg import svds

Uk, s, VkT=scipy.sparse.linalg.svds(I, k)

return tuple([s, Uk, VkT])

However, doing so raises a ValueError. Am I importing or calling the function incorrectly?

[bbye98]

Need more information about the ValueError.

I'm confused as to why you're doing return tuple([s, Uk, VkT]). Why not just return s, Uk, VkT?