[Numpy-discussion] Matrix times scalar is wacky

[Bill Baxter]

Workarounds I know of are:

    asmatrix(scalar * m.A)

or I noticed the other day that scalar division works ok, so if you know
scalar is != 0,

    m / (1.0/scalar)

where m is a numpy.matrix, of course.

I find myself repeatedly getting bitten by this, since I'm writing code that
does lots of lerping between vectors.
This is pretty dang ugly to see all over the place:
   vlerp = asmatrix((1-t) * v1.A + t * v2.A)
when it should just be
   vlerp = (1-t)*v1 + t*v2
yeh yeh, I should write a function...
--bb


On 3/9/06, Sven Schreiber <svetosch at gmx.net> wrote:
>
> Travis Oliphant schrieb:
> > Bill Baxter wrote:
> >
> >> Multiplying a matrix times a scalar seems to return junk for some
> reason:
> >>
> >> >>> A = numpy.asmatrix(numpy.rand(1,2))
> >> >>> A
> >> matrix([[ 0.30604211,  0.98475225]])
> >> >>> A * 0.2
> >> matrix([[  6.12084210e-002,   7.18482614e-290]])
> >> >>> 0.2 * A
> >> matrix([[  6.12084210e-002,   7.18482614e-290]])
> >> >>> numpy.__version__
> >> '0.9.5'
> >>
> > This should be fixed in SVN.
> >
> >
> I have just been bitten by this bug, so I would like to ask when to
> expect the next release. And/or are there any workarounds?
>
> Thanks,
> Sven
>



--
William V. Baxter III
OLM Digital
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1-8-8 Wakabayashi Setagaya-ku
Tokyo, Japan  154-0023
+81 (3) 3422-3380
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