Mahalanobis is always tricky, the covariance is between the features, not
the samples. This works:
OPTICS(metric='mahalanobis',metric_params={'VI':
np.linalg.inv(np.cov(test_array.T))}).fit(test_array)
Not sure why it wouldn't work when you pass V, as it suggests as an
alternative.
On Fri, May 31, 2019 at 12:16 PM Omkar Kumbhar <omkar.kumb...@innoplexus.com>
wrote:
> Hello,
>
> I was having issues while fitting OPTICS using Mahalanobis metric. I tried
> many things and had a hard time fitting it to my data distribution.
> I have replicated the issue in the ipython notebook below. You could also
> take a look at the html version of the notebook to look at the issues. Can
> you guide me on how to resolve this bug?
>
> PFA,
> ipython notebook to replicate the issue
> html of ipynb to look at stack traces.
> Thanks & Regards,
>
> Omkar Kumbhar
> Associate Data Scientist
> Innoplexus Consulting Services Pvt. Ltd.
> www.innoplexus.com
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