Hi there..
There's the `decision_function(..)` method which you could use for this, im
sure:
|
| decision_function(self, X)
| Distance of the samples X to the separating hyperplane.
| Parameters
| ----------
| X : array-like, shape = [n_samples, n_features]
|
| Returns
| -------
| X : array-like, shape = [n_samples, n_class * (n_class-1) / 2]
| Returns the decision function of the sample for each class
| in the model.
This is in the svm documentation.
If you wish find hyperplane yourself and do this manually, have a look at
this example:
http://scikit-learn.org/stable/auto_examples/svm/plot_svm_margin.html
Hope this helps!
Regards,
Jaques
2012/8/1 abdalrahman eweiwi <[email protected]>
>
> Hi all,
>
> Is there a direct way to obtain the distance from the SVM seperating
> hyperplance to a test sample in sklearn? if no? Could you please direct me
> to the parameters I need in sklearn to obtain this distance?
>
> Thanks
>
> A.Eweiwi
>
>
>
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