> -Original Message-
> From: numpy-discussion-boun...@scipy.org [mailto:numpy-discussion-
> boun...@scipy.org] On Behalf Of Robin
> Sent: 15 Oct 2009 6:54 AM
> To: numpy-discussion@scipy.org
> Subject: Re: [Numpy-discussion] extension questions: f2py and cython
>
> Hi,
>
> I have anothe
> -Original Message-
> From: numpy-discussion-boun...@scipy.org [mailto:numpy-discussion-
> boun...@scipy.org] On Behalf Of Robin
> Sent: 15 Oct 2009 4:37 AM
> To: numpy-discussion@scipy.org
> Subject: [Numpy-discussion] extension questions: f2py and cython
>
> Hi,
>
> Sent this last we
Hmm ... good point.
It appears to give a probability distribution proportional to x**(a-1),
but I see no good reason why the domain should be limited to [0,1].
def test(a):
nums =
plt.hist(np.random.power(a,10),bins=100,ec='none',fc='#dd')
x = np.linspace(0,1,200)
plt.plot(x,nu
You might get better results for 'power-law distribution'
http://en.wikipedia.org/wiki/Power_law
Andrew
> -Original Message-
> From: numpy-discussion-boun...@scipy.org [mailto:numpy-discussion-
> boun...@scipy.org] On Behalf Of a...@ajackson.org
> Sent: 7 Aug 2009 11:45 AM
> To: Discussio
> -Original Message-
> From: numpy-discussion-boun...@scipy.org [mailto:numpy-discussion-
> boun...@scipy.org] On Behalf Of David Cournapeau
> Sent: 9 Jun 2009 8:43 PM
> To: Discussion of Numerical Python
> Subject: Re: [Numpy-discussion] Inquiry Regarding F2PY Windows Content
>
> Carl, An
?
>
> On Tue, Oct 28, 2008 at 16:28, Andrew Hawryluk <[EMAIL PROTECTED]>
> wrote:
> > I agree that the gradient functions should be combined, especially
> considering how much redundant code would be added by keeping them
> separate. Here is one possible implementation, bu
> -Original Message-
> From: [EMAIL PROTECTED] [mailto:numpy-discussion-
> [EMAIL PROTECTED] On Behalf Of David Warde-Farley
> Sent: 28 Oct 2008 10:15 PM
> To: Discussion of Numerical Python
> Subject: Re: [Numpy-discussion] any interest in includinga second-
> ordergradient?
>
> On 28-Oct
rent options for the implementation. The
> namespace is fairly cluttered, and it may be that we want to implement
> gradient3 some time in the future as well. Maybe something like
>
> gradient(f, 1, 2, 3, order=2)
>
> would work -- then we can combine gradient and gradient2 (an
We wrote a simple variation on the gradient() function to calculate the
second derivatives. Would there be any interest in including a
gradient2() in numpy?
Andrew
def gradient2(f, *varargs):
"""Calculate the second-order gradient of an N-dimensional scalar
function.
Uses central diffe
