Hi,
I have an NxM array, which I am indexing with a 1-d, length N boolean
array. For example, with a 3x5 array:
In [1]: import numpy
In [2]: data = numpy.arange(15)
In [3]: data.shape = 3, 5
Now, I want to select rows 0 and 2, so I can do:
In [4]: mask = numpy.array([True, False, True])
In [5]
Tue, 08 Feb 2011 11:35:12 -0700, Charles R Harris wrote:
> Permission to close ticket for Mark Wiebe
>
> I don't know who handles these permissions, I didn't see a way to do it
> myself.
Granted. (You'd need a shell account to do these changes.)
--
Pauli Virtanen
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I don't know who handles these permissions, I didn't see a way to do it
myself.
Chuck
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On Tue, 08 Feb 2011 18:06:48 +, Andrew Jaffe wrote:
> For this shape=(N,3) vector, this is not what you mean: as Robert Kern
> also has it you want axis=1, which produces a shape=(N,) (or the
> [:,newaxis] version which produces shape=(N,1).
>
> But what is the point of the ones(3)? I think
On 08/02/2011 16:44, Ben Gamari wrote:
> I have an array of (say, row) vectors,
>
>v = [ [ a1, a2, a3 ],
> [ b1, b2, b3 ],
> [ c1, c2, c3 ],
> ...
>]
>
> What is the optimal way to compute the norm of each vector,
>norm(v)**2 = [
>[ a1**2 + a2**2 +
On Tue, Feb 8, 2011 at 11:55, Ben Gamari wrote:
> On Tue, 8 Feb 2011 10:46:34 -0600, Robert Kern wrote:
>> (v*v).sum(axis=1)[:,np.newaxis]
>>
>> You can leave off the newaxis bit if you don't really need a column vector.
>>
> Fair enough, I unfortunately neglected to mention that I ultimately wan
On Tue, 8 Feb 2011 10:46:34 -0600, Robert Kern wrote:
> (v*v).sum(axis=1)[:,np.newaxis]
>
> You can leave off the newaxis bit if you don't really need a column vector.
>
Fair enough, I unfortunately neglected to mention that I ultimately want
to normalize these vectors, hence the *ones(3) in my
On Tue, Feb 8, 2011 at 10:44, Ben Gamari wrote:
> I have an array of (say, row) vectors,
>
> v = [ [ a1, a2, a3 ],
> [ b1, b2, b3 ],
> [ c1, c2, c3 ],
> ...
> ]
>
> What is the optimal way to compute the norm of each vector,
> norm(v)**2 = [
> [ a1**2 + a2**2 + a3*
I have an array of (say, row) vectors,
v = [ [ a1, a2, a3 ],
[ b1, b2, b3 ],
[ c1, c2, c3 ],
...
]
What is the optimal way to compute the norm of each vector,
norm(v)**2 = [
[ a1**2 + a2**2 + a3**2 ],
[ b1**2 + b2**2 + b3**2 ],
...
]
It see
Tue, 08 Feb 2011 15:24:10 +, EMMEL Thomas wrote:
[clip]
> n = 100 # for test otherwise ~30
> a1 = reshape(zeros(3*n).astype(float), (n,3))
>
> a2 = zeros(n).astype(int)
>
> for indices, data in [...]:
> #data = array((1.,2.,3.))
> #indices = (1,5,60)
> for index in indices:
>
On Tue, Feb 8, 2011 at 09:24, EMMEL Thomas wrote:
> Hi,
>
> here is something I am thinking about for some time and I am wondering
> whether there is a better solution
> within numpy.
>
> The task is:
> I have an array (30+ entries) with arrays each with length == 3, that is
> initially empty
Hi,
here is something I am thinking about for some time and I am wondering whether
there is a better solution
within numpy.
The task is:
I have an array (30+ entries) with arrays each with length == 3, that is
initially empty like this:
n = 100 # for test otherwise ~30
a1 = reshape(zer
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