The line is in the 0.10 release..
I standardized my X, with xmin, xmax = 0, 100
and that works fine - don't get that error in that case.
Jaques
2012/5/23 Peter Prettenhofer <[email protected]>
> Jacob,
>
> which file are you referring to? I cannot find the line you mentioned
> in the plot_sgd_ols file...
> It seems like an learning rate issue - I strongly recommend to
> standardizing your inputs first.
>
> best,
> Peter
>
> 2012/5/22 Jacob VanderPlas <[email protected]>:
> > Hi,
> > I've been playing with the SGD classifier (using the current master
> > branch), and found that when there is a large range in x, the classifier
> > fails. For example, when running the example script found here:
> >
> http://scikit-learn.org/0.10/auto_examples/linear_model/plot_sgd_ols.html
> > If I replace the line:
> > xmin, xmax = -5, 5
> > with:
> > xmin, xmax = 0, 100
> > then the regression fails (full traceback copied below). If the range
> > is set to (0, 50), the call does not produce an error, but leads to a
> > line which clearly does not fit the data (intercept on order -2E30).
> > Any ideas about what is going on here?
> > Jake
> >
> >
>
> "/usr/local/lib/python2.6/dist-packages/scikit_learn-0.12_git-py2.6-linux-i686.egg/sklearn/linear_model/stochastic_gradient.py",
> > line 737, in fit
> > coef_init, intercept_init)
> > File
> >
> "/usr/local/lib/python2.6/dist-packages/scikit_learn-0.12_git-py2.6-linux-i686.egg/sklearn/linear_model/stochastic_gradient.py",
> > line 672, in _partial_fit
> > self._fit_regressor(X, y, sample_weight, n_iter)
> > File
> >
> "/usr/local/lib/python2.6/dist-packages/scikit_learn-0.12_git-py2.6-linux-i686.egg/sklearn/linear_model/stochastic_gradient.py",
> > line 786, in _fit_regressor
> > intercept_decay)
> > File "sgd_fast.pyx", line 423, in
> > sklearn.linear_model.sgd_fast.plain_sgd
> > (sklearn/linear_model/sgd_fast.c:6713)
> > ValueError: floating-point under-/overflow occured.
> >
> >
> >
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>
>
> --
> Peter Prettenhofer
>
>
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