Joseph,
My understanding is that the n_components in DPGMM is an upper-bound. I've
gotten similarly bad results for n_components=2.
-Aron
From: <[email protected]>
Reply-To: <[email protected]>
Date: Thursday, October 18, 2012 1:32 PM
To: <[email protected]>
Subject: Re: [Scikit-learn-general] sklearn.mixture.DPGMM: Unexpected
results
> On Thu, Oct 18, 2012 at 1:57 PM, Aron Culotta <[email protected]> wrote:
>> The results I get from DPGMM are not what I expect. E.g.:
>>
>>>>> import sklearn.mixture
>>>>> sklearn.__version__
>> '0.12-git'
>>>>> data = [[1.1],[0.9],[1.0],[1.2],[1.0], [6.0],[6.1],[6.1]]
>>>>> m = sklearn.mixture.DPGMM(n_components=5, n_iter=1000, alpha=1)
>>>>> m.fit(data)
>> DPGMM(alpha=1, covariance_type='diag', init_params='wmc', min_covar=None,
>> n_components=5, n_iter=1000, params='wmc',
>> random_state=<mtrand.RandomState object at 0x108a3f168>, thresh=0.01,
>> verbose=False)
>>>>> m.converged_
>> True
>>>>> m.weights_
>> array([ 0.2, 0.2, 0.2, 0.2, 0.2])
>>>>> m.means_
>> array([[ 0.62019109],
>> [ 1.16867356],
>> [ 0.55713292],
>> [ 0.36860511],
>> [ 0.17886128]])
>>
>> I expected the result to be more similar to the vanilla GMM; that is, two
>> gaussians (around values 1 and 6), with non-uniform weights (like [ 0.625,
>> 0.375]). I expected the "unused" gaussians to have weights near zero.
>>
>> Am I using the model incorrectly?
>>
>> I've also tried changing alpha without any luck.
>>
>> I've also tried a different data in a smaller range with no luck: [[0.1],
>> [0.2], [0.15], [0.112], [0.13], [0.8], [0.85], [0.79]]
>
> Do I read this correctly that you are fitting a mixture with 5
> components to 8 data points?
>
> Josef
>
>
>>
>> Thanks,
>>
>> Aron
>>
>>
>>
>>
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