Gpushift dev - #1
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boykovdn
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Feb 17, 2021
boykovdn
commented
- Basic clustering step
- sklearn-like interface
imagirom
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imagirom
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Thanks a lot for the PR Boyko! I've added some minor comments, it would be great if you could have a look before merging :)
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| def predict(self, X): | ||
| r""" | ||
| Predict cluster belonging based on which cluster center is the closest. |
| # spherical = self._get_distance_metric('spherical') | ||
| # | ||
| # composite = lambda x,y : euclidean(x,y)**2 + spherical(x,y) | ||
| # |
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If this block is no longer needed (as you have implemented it differently above), I would prefer to delete it. Same for the other commented blocks above.
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| self.cluster_centers_ = None | ||
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| self.meanshift_step = MeanShiftStep(bandwidth=bandwidth, kernel=kernel, use_keops=use_keops) |
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Was it your intention to not pass the distance metric here? I thought not only the final clustering but also every MS step should use the chosen metric.
| :param distance_metric: callable or None | ||
| If None, uses standard Euclidean distances. For special applications, | ||
| the passing of a custom distance function is allowed. |
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I think it would be good to add the required signature of distance metric if it is a callable, including the shapes of the input tensors, to the docstring.
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Reminder to also add an example using the new interface (I will do that) |
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