Congrats, numpy is now available on the Google App Engine:
http://googleappengine.blogspot.in/2012/02/announcing-general-availability-of.html
___
NumPy-Discussion mailing list
NumPy-Discussion@scipy.org
http://mail.scipy.org/mailman/listinfo/numpy-dis
David Cournapeau wrote:
>
>> (I'm running ancient numpy and python at work, so if this is already
>> supported in later versions, my apologies)
>
> What does ancient mean ? Could you give us the version (numpy.__version__)
>
>
1.0.4
Python 2.4.2
IPython 0.9.1
--
View this message in contex
ike to hit "control-c" and get out of this hung state.
What would it take to support this?
(I'm running ancient numpy and python at work, so if this is already
supported in later versions, my apologies)
- Tom K.
--
View this message in context:
http://old.nabble.com/interruptin
This one bit me again, and I am trying to understand it better so I can
anticipate when it will happen.
What I want to do is get rid of singleton dimensions, and index into the
last dimension with an array.
In [1]: import numpy as np
In [2]: x=np.zeros((10,1,1,1,14,1024))
In [3]: x[:,0,0,0,:
davefallest wrote:
>
> ...
> In [3]: np.arange(1.01, 1.1, 0.01)
> Out[3]: array([ 1.01, 1.02, 1.03, 1.04, 1.05, 1.06, 1.07, 1.08,
> 1.09, 1.1 ])
>
> Why does the ... np.arange command end up including my stop value?
>
>From the help for arange:
For floating point arguments,
ANNOUNCEMENT
I am pleased to announce a new release of "upfirdn" - version 0.2.0. This
package provides an efficient polyphase FIR resampler object (SWIG-ed C++)
and some python wrappers.
This release greatly improves installation with distutils relative to the
initial 0.1.0 release. 0.2.0 incl
(also posted on scipy-user)
ANNOUNCEMENT
I am pleased to announce the initial release of "upfirdn." This package
provides an efficient polyphase FIR resampler object (SWIG-ed C++) and some
python wrappers.
https://opensource.motorola.com/sf/projects/upfirdn
MOTIVATION
As a long time us
giving a knob means people are going to set it wrong.
I would also vote "+1" for an ND version of this (growing only a single
dimension). Keeping 2x for each of n dimensions, while conceivable, would
be 2**n extra memory, and hence probably too costly.
Cheers,
Tom K.
--
View th
jseabold wrote:
>
> On Mon, Jun 8, 2009 at 3:33 PM, Robert Kern wrote:
>> On Mon, Jun 8, 2009 at 14:10, Alan G Isaac wrote:
> Going back to Alan Isaac's example:
> 1) beta = (X.T*X).I * X.T * Y
> 2) beta = np.dot(np.dot(la.inv(np.dot(X.T,X)),X.T),Y)
>>>
>>>
>>> Robert Kern wrote:
bruno Piguet wrote:
>
>Can someone point me to a doc on dot product vectorisation ?
>
As I posted in the past you can try this one liner:
"numpy.array(map(numpy.dot, a, b))
that works for matrix multiply if a, b are (n, 3, 3). "
This would also work if a is (n, 3, 3) and b is (n, 3
Robert Kern-2 wrote:
>
> On Sun, Jun 7, 2009 at 07:20, Tom K. wrote:
>> Going back to Alan Isaac's example:
>> 1) beta = (X.T*X).I * X.T * Y
> ...
> 4) beta = la.lstsq(X, Y)[0]
>
> I really hate that example.
>
Understood. Maybe propose
What do we want to do? How can we convince the larger
python community that this is a good idea for them too?
Cheers,
Tom K.
--
View this message in context:
http://www.nabble.com/matrix-default-to-column-vector--tp23652920p23914277.html
Sent from the Numpy-discussion mailing list archive at Nabble.
might need a new .T that just swaps the last two
dimensions to really pull that off). But a ".I" attribute and its behavior
needn't be bundled with whatever proposal we wish to make to the python
community for a new operator of course.
Regards,
Tom K.
--
View this message in cont
t dimension of the right array,
so
shape (a0, ..., aL-1,k) @ (k, b0, ..., bM-1) --> (a0, ..., aL-1, b0, ...,
bM-1)
Does that make sense?
With this proposal, matrices go away and all our lives are sane again. :-)
Long live the numpy ndarray! Thanks to the creators for all your hard w
Jason Rennie-2 wrote:
>
> By default, it looks like a 1-dim ndarray gets converted to a row vector
> by
> the matrix constructor. This seems to lead to some odd behavior such as
> a[1] yielding the 2nd element as an ndarray and throwing an IndexError as
> a
> matrix. Is it possible to set a fl
n Numpy. I haven't looked too much "under
the hood" of numpy so I am not sure how you would do it or how hard it would
be.
Regards,
Tom K.
--
View this message in context:
http://www.nabble.com/Array-of-matrices---Inverse-and-dot-tp2166
>>> h = zeros((1, 4, 100))
>>> h[0,:,arange(14)].shape
(14, 4)
___
Numpy-discussion mailing list
Numpy-discussion@scipy.org
http://projects.scipy.org/mailman/listinfo/numpy-discussion
17 matches
Mail list logo
