>>> There should be no other parameters to tune here.
>>>
>>> Btw, you can try kernel_approximation.AdditiveChi2Sampler (if your data is
>>> positive).
> Note that this is not meant to be used as an exact kernel in a kernel
> SVM but as a explicit preprocessing transformer for a linear kernel.
> T
2012/9/20 abdalrahman eweiwi :
>
>
> On Thu, Sep 20, 2012 at 1:58 PM, Andreas Mueller
> wrote:
>>
>>
>>
>> return np.min(np.vstack((v[None,:],v2[None,:])),axis=0).sum()
>>
>> I'm not sure what the None does here, I would write
>> np.minimum(v, v2).sum()
>> which is the correct intersection kernel
On Thu, Sep 20, 2012 at 1:58 PM, Andreas Mueller
wrote:
>
> *
> *
> *return np.min(np.vstack((v[None,:],v2[None,:])),axis=0).sum(*)
>
> I'm not sure what the None does here, I would write
> np.minimum(v, v2).sum()
> which is the correct intersection kernel I think.
>
I know, sometimes i make thi
On Thu, Sep 20, 2012 at 2:08 PM, Andreas Mueller
wrote:
> On 09/20/2012 01:01 PM, Gael Varoquaux wrote:
> > On Thu, Sep 20, 2012 at 12:58:56PM +0100, Andreas Mueller wrote:
> >> return np.min(np.vstack((v[None,:],v2[None,:])),axis=0).sum()
> >> I'm not sure what the None does here, I wou
On 09/20/2012 01:01 PM, Gael Varoquaux wrote:
> On Thu, Sep 20, 2012 at 12:58:56PM +0100, Andreas Mueller wrote:
>> return np.min(np.vstack((v[None,:],v2[None,:])),axis=0).sum()
>> I'm not sure what the None does here, I would write
> None create a new axis. For readability, one should al
On 09/20/2012 01:01 PM, Gael Varoquaux wrote:
> On Thu, Sep 20, 2012 at 12:58:56PM +0100, Andreas Mueller wrote:
>> return np.min(np.vstack((v[None,:],v2[None,:])),axis=0).sum()
>> I'm not sure what the None does here, I would write
> None create a new axis. For readability, one should al
On Thu, Sep 20, 2012 at 12:58:56PM +0100, Andreas Mueller wrote:
> return np.min(np.vstack((v[None,:],v2[None,:])),axis=0).sum()
>I'm not sure what the None does here, I would write
None create a new axis. For readability, one should always write
v[np.newaxis, :], which is equivalent.
*
*
*return np.min(np.vstack((v[None,:],v2[None,:])),axis=0).sum(*)
I'm not sure what the None does here, I would write
np.minimum(v, v2).sum()
which is the correct intersection kernel I think.
What are the features you use? The features have to be positive for this
to work.
There should be
You can also debug by printing the kernel value of pairs of samples
that you know are qualitatively related and pairs that you know are
qualitatively unrelated.
If you kernel is positive (it should be), pairs of related samples
should always have a higher value than pairs of unrelated samples.
--
On Thu, Sep 20, 2012 at 10:54 AM, Olivier Grisel
wrote:
> 2012/9/20 abdalrahman eweiwi :
> > Hi,
> >
> > Just noticed that I coincidentally sent half of my email!! Anyway, here
> is
> > my question again:
> >
> > A couple of weeks ago I test the intersection kernel in my experiments,
> for
> > th
2012/9/20 abdalrahman eweiwi :
> Hi,
>
> Just noticed that I coincidentally sent half of my email!! Anyway, here is
> my question again:
>
> A couple of weeks ago I test the intersection kernel in my experiments, for
> that I wrote the following simple procedures:
>
> import numpy as np
> import c
Hi,
Just noticed that I coincidentally sent half of my email!! Anyway, here is
my question again:
A couple of weeks ago I test the intersection kernel in my experiments, for
that I wrote the following simple procedures:
import numpy as np
import cv2
def intersection_dist(v1,v2):
return
On Wed, Sep 19, 2012 at 02:53:10PM +0200, abdalrahman eweiwi wrote:
>Hi,
>A couple of weeks ago I test the intersection kernel in my experiments,
>for that I wrote the following simple procedures:
>def intersection_dist(v1,v2):
I am sorry, I don't believe that we got the full conte
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