On Wed, May 2, 2012 at 4:47 PM, Olivier Grisel <[email protected]>wrote:
>
> I think the main issue with building such a wrapper for crfsuite is
> that the internal memory layout of crfsuite cannot be directly mapped
> to numpy array nor scipy sparse matrices hence the wrapper needs to do
> a memory copy of the training set.
>
How do you represent features used by CRF efficiently with Numpy arrays and
Sparse matrices? The reason I'm asking is because most CRF implementations
use "feature templates" to automatically generate features on the fly. So,
I think we may have the same problem with CRFs that we have with HMMs : the
data representation is fundamentally different from the rest of the scikit.
Mathieu
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