If that doesn't help (and it may not since some Scikit-Learn functions
automatically promote their arguments to arrays of doubles), you can always
just generate a shorter fingerprint from the beginning (all the
fingerprinting functions take an optional argument for this) or fold the
existing fingerprints to a new size using the function
rdkit.DataStructs.FoldFingerprint().
Best,
-greg
On Thu, Aug 27, 2015 at 4:33 PM, Maciek Wójcikowski <mac...@wojcikowski.pl>
wrote:
> Hi Jing,
>
> Most fingerprints are binary, thus can be stored as np.bool_, which
> compared to double should be 64 times more memory efficient.
>
> Best,
> Maciej
>
> ----
> Pozdrawiam, | Best regards,
> Maciek Wójcikowski
> mac...@wojcikowski.pl
>
> 2015-08-27 16:15 GMT+02:00 Jing Lu <ajin...@gmail.com>:
>
>> Hi Greg,
>>
>> Thanks! It works! But, is that possible to fold the fingerprint to
>> smaller size? np.zeros((1000000,2048)) still takes a lot of memory...
>>
>>
>> Best,
>> Jing
>>
>> On Wed, Aug 26, 2015 at 11:02 PM, Greg Landrum <greg.land...@gmail.com>
>> wrote:
>>
>>>
>>> On Thu, Aug 27, 2015 at 3:00 AM, Jing Lu <ajin...@gmail.com> wrote:
>>>
>>>>
>>>> So, I wonder is there any way to convert fingerprint to a numpy vector?
>>>>
>>>
>>> Indeed there is:
>>>
>>> In [11]: from rdkit import Chem
>>>
>>> In [12]: from rdkit import DataStructs
>>>
>>> In [13]: import numpy
>>>
>>> In [14]: m =Chem.MolFromSmiles('C1CCC1')
>>>
>>> In [15]: fp = Chem.RDKFingerprint(m)
>>>
>>> In [16]: fpa = numpy.zeros((len(fp),),numpy.double)
>>>
>>> In [17]: DataStructs.ConvertToNumpyArray(fp,fpa)
>>>
>>>
>>> Best,
>>> -greg
>>>
>>>
>>
>>
>> ------------------------------------------------------------------------------
>>
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>>
>>
>
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