> Hi,
>
> Thanks for this - yes I think I see that now. (The values do indeed
> differ by n_dim * n_samples * log(scale), but no 0.5 here.)
>
> I guess in a way the issue is that we typically evaluate point
> likelihoods, rather than e.g. integrals within some bounds of certainty
> of the measurem
On 31/10/12 16:09, bthirion wrote:
> On 10/31/2012 04:50 PM, Dan Stowell wrote:
>> Hi all,
>>
>> I'm still getting odd results using mixture.GMM depending on data
>> scaling. In the following code example, I change the overall scaling but
>> I do NOT change the relative scaling of the dimensions. Y
Hi Dan,
I would have thought that it is the relative scaling that is important, not
the overall scaling. I.e. each feature of your data set should have zero
mean and unit variance.
Martin
On 31 October 2012 16:09, bthirion wrote:
> On 10/31/2012 04:50 PM, Dan Stowell wrote:
> > Hi all,
> >
>
On 10/31/2012 04:50 PM, Dan Stowell wrote:
> Hi all,
>
> I'm still getting odd results using mixture.GMM depending on data
> scaling. In the following code example, I change the overall scaling but
> I do NOT change the relative scaling of the dimensions. Yet under the
> three different scaling set
Hi all,
I'm still getting odd results using mixture.GMM depending on data
scaling. In the following code example, I change the overall scaling but
I do NOT change the relative scaling of the dimensions. Yet under the
three different scaling settings I get completely different results:
On 02/10/12 13:58, Alexandre Passos wrote:
> On Tue, Oct 2, 2012 at 7:48 AM, Dan Stowell
> wrote:
>>
>> Hi all,
>>
>> I'm using the GMM class as part of a larger system, and something is
>> misbehaving. Can someone confirm please: the results of using GMM.fit()
>> shouldn't have a strong dependen
On Tue, Oct 2, 2012 at 7:48 AM, Dan Stowell wrote:
>
> Hi all,
>
> I'm using the GMM class as part of a larger system, and something is
> misbehaving. Can someone confirm please: the results of using GMM.fit()
> shouldn't have a strong dependence on the data ranges, should they? For
> example, if
Hi all,
I'm using the GMM class as part of a larger system, and something is
misbehaving. Can someone confirm please: the results of using GMM.fit()
shouldn't have a strong dependence on the data ranges, should they? For
example, if one variable has a range 0-1000, while the other has a range
