I'm also having trouble with Nearest neighbor, although not sure if its
related. I'm doing a regression, and everytime I see that particular
warning, one or more of my predicted values ends up with a value of 'nan'.
I checked my inputs and none of them are nan.If I can create a small sample
that sh
I played around with this a bit: it appears to be related to a memory error.
https://gist.github.com/1666570
This fails after a few iterations. If the print statement is
uncommented, then it no longer fails.
The ball tree code uses a lot of raw memory views for speed... I'll have
a look through
On 01/23/2012 10:38 PM, Andreas wrote:
> Hi everybody.
> I created a gist to illustrate the behavior:
> https://gist.github.com/1665623
> This reproduces a warning that is quite weird.
>
> After trying it out for some time, I found
> the following fun fact:
> It behaves only like it does when the m
Hi everybody.
I created a gist to illustrate the behavior:
https://gist.github.com/1665623
This reproduces a warning that is quite weird.
After trying it out for some time, I found
the following fun fact:
It behaves only like it does when the manifold
module is imported.
So if you remove the manif
Hi.
I don't know much about the modules that are involved
here but this looks like a bug to me.
I can reproduce the behavior you observe and am
looking into it.
I think Jake will be able to tell you more about this.
Cheers,
Andy
On 01/23/2012 08:15 PM, Alejandro Weinstein wrote:
> Hi:
>
> When
Hi:
When I run manifold.LocallyLinearEmbedding (using sklearn 0.10), as in
the following code,
###
from sklearn import manifold, datasets
n_points = 1000
n_neighbors = 10
out_dim = 2
X, _ = datasets.samples_generator.ma
