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.kumbhar@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 Mob : +91- 9579464473 Landline: +91-20-66527300
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