It is 0.10.
On Mon, Mar 26, 2012 at 9:02 PM, Andreas <[email protected]> wrote:
> **
> Hi.
> Which version of scikit-learn are you using?
> Andy
>
>
>
> On 03/26/2012 03:07 PM, xinfan meng wrote:
>
> Alex, I am afraid some codes have been broken...
>
> In [127]: X = [[0], [1]]
>
> In [129]: Y = [0, 1]
>
> In [130]: clf.fit(X, Y)
> Out[130]:
> SVC(C=1.0, cache_size=200, coef0=0.0, degree=3, gamma=0.0, kernel='linear',
> probability=False, scale_C=False, shrinking=True, tol=0.001)
>
> In [132]: clf.decision_function(X)
> Out[132]:
> array([[-0.5],
> [ 0.5]])
>
> In [133]: X2 = [[1], [0]]
>
> In [134]: Y2 = [1, 0]
>
> In [135]: clf.fit(X2, Y2)
> Out[135]:
> SVC(C=1.0, cache_size=200, coef0=0.0, degree=3, gamma=0.0, kernel='linear',
> probability=False, scale_C=False, shrinking=True, tol=0.001)
>
> In [137]: clf.decision_function(X2)
> Out[137]:
> array([[ 0.5],
> [-0.5]])
>
> On Mon, Mar 26, 2012 at 8:59 PM, Alexandre Gramfort <
> [email protected]> wrote:
>
>> hi,
>>
>> from what I remember we fixed the random label ordering problem
>> at least for the 2 classes case.
>>
>> can you check that things behave fine and the same way with Y = [0,
>> 1] and Y = [1, 0]?
>>
>> Alex
>>
>> On Mon, Mar 26, 2012 at 5:00 AM, xinfan meng <[email protected]> wrote:
>> > I use the following codes to obtain decision values for SVC classifier
>> clf.
>> >
>> >
>> -----------------------------------------------------------------------------------------------
>> >
>> > In [5]: >>> clf = svm.SVC()
>> >
>> > In [23]: >>> X = [[0], [1], [2]]
>> >
>> > In [24]: >>> Y = [0, 1, 2]
>> >
>> > In [25]: clf.fit(X, Y)
>> > Out[25]:
>> > SVC(C=1.0, cache_size=200, coef0=0.0, degree=3, gamma=1.0, kernel='rbf',
>> > probability=False, scale_C=False, shrinking=True, tol=0.001)
>> >
>> > In [26]: clf.predict([[0]])
>> > Out[26]: array([ 0.])
>> >
>> > In [27]: clf.predict(X)
>> > Out[27]: array([ 0., 1., 2.])
>> >
>> > In [28]: clf.decision_function(X)
>> > Out[28]:
>> > array([[-0.63212056, -0.98168436, -0.3495638 ],
>> > [ 0.63212056, -0. , -0.63212056],
>> > [ 0.3495638 , 0.98168436, 0.63212056]])
>> >
>> >
>> -----------------------------------------------------------------------------------------------
>> >
>> >
>> >
>> > The decision_function return confusing results. Why [-0.63212056,
>> > -0.98168436, -0.3495638 ] corresponds to label 0 ?
>> > The encoding of labels seems to be different from the natural orders of
>> (0,
>> > 1, 2 ...) .
>> > After reading the README file of LibSVM, I found the label encoding can
>> be
>> > obtained by calling svm_get_labels().
>> > Where can I find this function wrapper in sklearn? Without that, the
>> return
>> > results of decision_function() are difficult to interpret.
>> > Thanks!
>> >
>> > --
>> > Best Wishes
>> > --------------------------------------------
>> > Meng Xinfan（蒙新泛）
>> > Institute of Computational Linguistics
>> > Department of Computer Science & Technology
>> > School of Electronic Engineering & Computer Science
>> > Peking University
>> > Beijing, 100871
>> > China
>> >
>> >
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>
>
>
> --
> Best Wishes
> --------------------------------------------
> Meng Xinfan（蒙新泛）
> Institute of Computational Linguistics
> Department of Computer Science & Technology
> School of Electronic Engineering & Computer Science
> Peking University
> Beijing, 100871
> China
>
>
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--
Best Wishes
--------------------------------------------
Meng Xinfan（蒙新泛）
Institute of Computational Linguistics
Department of Computer Science & Technology
School of Electronic Engineering & Computer Science
Peking University
Beijing, 100871
China
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