scikit-learn versions of libsvm has been extended to be able to work
with training data represented as dense arrays (directly addressing
the memory of a floating point numpy array) in addition to the native
sparse data reprenentation of the original libsvm. So presumably you
might have to convert y
I have not looked into it my-self but I don't see any particular
road-block that would prevent you to do that.
Be careful that you will have to also re-implement the feature
preprocessing (e.g. scaling) in your C++ code base to get the correct
predictions.
--
Olivier
---
Thanks for your reply. I get it now.
The all zeros case implies that the two sets are empty. Which is a 0/0
situation. Hence, it is taken to be 1.
--
sp
On Mon, May 9, 2016 at 10:11 PM, Maniteja Nandana <
maniteja.modesty...@gmail.com> wrote:
>
> On 9 May 2016 9:47 pm, "Shishir Pandey" wrote:
>
