Hi Peter
On Wed, Nov 9, 2011 at 3:38 AM, Peter Prettenhofer
wrote:
> I recently got the latest numpy version (2.0.0.dev-7297785) from the
> git repo and realized that `np.dot` causes a segfault if its operands
> are scipy sparse matrices. Here's some code to reproduce the problem::
>
> import n
Hi Oliver, Robert,
Thanks to both of you for your speedy and enlightening responses. They
really cleared things up.
Now my simulations seem to work as intended.
All the best,
Josh
On 11/9/11 12:14 PM, Robert Kern wrote:
> On Wed, Nov 9, 2011 at 16:40, Joshua Anthony Reyes
> wrote:
>> Hi List
On Wed, Nov 9, 2011 at 16:40, Joshua Anthony Reyes
wrote:
> Hi List,
>
> I'm new to Numpy and I'm a little confused about the behavior of
> numpy.random.multivariate_normal(). I'm not sure I'm passing the
> variances correctly. My goal is to sample from a bivariate normal, but
> the kooky behavior
The "normal" function takes as input the standard deviation, while
"multivariate_normal" takes as input the covariance.
-=- Olivier
2011/11/9 Joshua Anthony Reyes
> Hi List,
>
> I'm new to Numpy and I'm a little confused about the behavior of
> numpy.random.multivariate_normal(). I'm not sure I
Hi List,
I'm new to Numpy and I'm a little confused about the behavior of
numpy.random.multivariate_normal(). I'm not sure I'm passing the
variances correctly. My goal is to sample from a bivariate normal, but
the kooky behavior shows up when I sample from a univariate distribution.
In short,
Hi everybody,
I recently got the latest numpy version (2.0.0.dev-7297785) from the
git repo and realized that `np.dot` causes a segfault if its operands
are scipy sparse matrices. Here's some code to reproduce the problem::
import numpy as np
from scipy import sparse as sp
A = np.random.
hi,
Pycluster reports an error when the distancematrix is a matrix 6000*6000:
ValueError: Row 0 in the distance matrix has incorrect size (6417 should be 0)
Is anyone encountered this problem before? Is that because I should only use a
left-lower matrix, and leave all else blank?
Best,