On Fri, Feb 26, 2010 at 11:30 PM, Rob Speer <rs...@mi...> wrote:
> It's hard to tell from the Web, but it sounds like the right thing to
> do when I encounter unexpected behavior in PySparse is to e-mail this
> list, right?
>
> I'm trying to assign to a slice of an ll_mat from another matrix. If
> this other matrix is also an ll_mat, this works fine. However, it acts
> very strange if the other matrix is a standard NumPy ndarray:
>
> ----------
> In [1]: from pysparse.spmatrix import *
>
> In [2]: sq = ll_mat(5,5)
>
> In [3]: sq
> Out[3]: <ll_mat object at 0x1d26bf0>
>
> In [4]: print sq
> ------> print(sq)
> ll_mat(general, [5,5]):
> -------- -------- -------- -------- --------
> -------- -------- -------- -------- --------
> -------- -------- -------- -------- --------
> -------- -------- -------- -------- --------
> -------- -------- -------- -------- --------
>
> In [6]: import numpy as np
>
> In [7]: sq[:,:] = np.arange(25).reshape((5,5))
> In LHS=RHS, RHS has shape (2,27483600), LHS has shape (5,5)
> ---------------------------------------------------------------------------
> ValueError Traceback (most recent call last)
>
> /Users/rspeer/python/src/pysparse/<ipython console> in <module>()
>
> ValueError: Matrix shapes are different
> -----------
>
> There seems to be no other straightforward way to convert a small
> dense matrix to a sparse one, either. Am I missing how to do this, or
> is this a bug that should be fixed?
Rob,
Yes, e-mailing this list is the right thing to do. Welcome to Pysparse!
Indeed, Pysparse expects the right-hand side of an assignment to be a
ll_mat object. Currently, you can't assign from a dense matrix or
easily convert a dense matrix to a ll_mat object. I agree that this
functionality would be useful. I might not have a chance to code that
before a few weeks though. If anyone wants to have a stab at it, I can
get them started.
--
Dominique
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