have a look at :

http://scikit-learn.org/stable/auto_examples/svm/plot_separating_hyperplane.html

Alex

On Wed, Feb 20, 2013 at 7:48 PM, Andreas Mueller
<[email protected]> wrote:
> Hi Guillermo.
> What do you mean with "get the hyperplane" for other kernels?
> The reason that "coef_" only works for the linear kernel is that it
> has no correspondence for non-linear kernels.
>
> And yes, you need to multiply dual_coef_ with X to get coef_ for the
> linear kernel.
>
> Hth,
> Andy
>
> On 02/20/2013 07:12 PM, [email protected] wrote:
>> Hi,
>>
>> I'm using sklearn for training a linear Support Vector Machine, but I cant
>> match dual_coef_ with coef_. Here goes the code:
>>
>> X = np.array([[0, 0], [1, 1]])
>> y = np.array([0, 1])
>> clf = svm.SVC(kernel = "linear")
>> clf.fit(X, y)
>>
>> this gives me a value for clf.dual_coef_ = [[ 1., -1.]] and clf.coef_ = [[
>> 1.,  1.]]. How do I get from the dual_coef_ to the normal vector coef_
>> (w)? I understand from
>> http://scikit-learn.org/stable/modules/svm.html#svc
>> that the dual_coef_ are y_i * alpha_i. In that sense I would only need to
>> multiply by X and sum in each coordinate in order to get w, but this gives
>> me [1, -1].
>>
>> This is just a test. I'm planning to use different kernels, and I
>> understand coef_ only works for linear kernels.
>>
>> Thanks in advance,
>> Guillermo.
>>
>>
>>
>>
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