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
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