On 11/03/2011 06:19 PM, Peter Prettenhofer wrote:
> Can you try if it segfaults on X[5000:6000] and if so, it would be
> great if you could send me the ndarray - then I can reproduce the
> error and see what's wrong.
>
As I said, the features consist of three distinct parts and if
I use any of those, it works. Since 5000:6000 is a subset
of the second split (each is 13824/3), I guess it will work.
I can still try, though.

> 2011/11/3 Andreas Müller <[email protected]>:
>> On 11/03/2011 06:10 PM, Peter Prettenhofer wrote:
>>> Hi Andreas,
>>>
>>> can you run the following line and send me the results (assume your
>>> data is stored in `X`)::
>>>
>>>     print X.flags
>>>
>> train_data.flags
>>  C_CONTIGUOUS : True
>>  F_CONTIGUOUS : False
>>  OWNDATA : True
>>  WRITEABLE : True
>>  ALIGNED : True
>>  UPDATEIFCOPY : False
>>
>> Seems reasonable to me.
>> Thanks,
>> Andy
>>
>>> 2011/11/3 Andreas Müller <[email protected]>:
>>>> Hi folks.
>>>> Today I ran across a segfault doing sgd multi class classification.
>>>> I tracked the error down to sgd_fast but don't know how to proceed.
>>>> My data has shape (50000, 13824) and is dense, the parameters
>>>> of the classifier are:
>>>> SGDClassifier(loss="hinge", penalty="l2")
>>>>
>>>> I ran it through scaler and the features seem to be in a reasonable
>>>> range and there are no infs or nans.
>>>> If I train only on the first 5000 of the 13824 features, I get
>>>> reasonable results, for 6000 it segfaults. The features split
>>>> into three parts of equal lenght and training on any of
>>>> these parts works fine.
>>>>
>>>> My RAM is big and empty during the crash.
>>>>
>>>> Does anyone have any idea how to find the problem?
>>>>
>>>> Cheers,
>>>> Andy
>>>>
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>
>


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