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 settings I get completely different results:
>
> ------------
> from sklearn.mixture import GMM
> from numpy import array, shape
> from numpy.random import randn
> from random import choice
>
> # centroids will be normally-distributed around zero:
> truelumps = randn(20, 5) * 10
>
> # data randomly sampled from the centroids:
> data = array([choice(truelumps) + randn(5) for _ in xrange(1000)])
>
> for scaler in [0.01, 1, 100]:
> scdata = data * scaler
> thegmm = GMM(n_components=10)
> thegmm.fit(scdata, n_iter=1000)
> ll = thegmm.score(scdata)
> print sum(ll)
> ------------
>
> Here's the output I get:
>
> GMM(cvtype='diag', n_components=10)
> 7094.87886779
> GMM(cvtype='diag', n_components=10)
> -14681.566456
> GMM(cvtype='diag', n_components=10)
> -37576.4496656
>
>
> In principle, I don't think the overall data scaling should matter, but
> maybe there's an implementation issue I'm overlooking?
>
> Thanks
> Dan
Hi Dan,
But even if the solution is the same, you expect the likelihood value to
change, i.e; it offseted by something like 0.5 * n_dim * n_samples *
log(scale). I'm not suprised by your result.
Bertrand
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