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@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 Kono Dens Building Rm 302 1-8-8 Wakabayashi Setagaya-ku Tokyo, Japan 154-0023 +81 (3) 3422-3380