Thanks, Greg,
Yes, sciket learn will automatically promote to arrays of float with
check_array()
function. What I am currently doing is
fpa = numpy.zeros((len(fp),),numpy.double)
DataStructs.ConvertToNumpyArray(fp,fpa)
np.sum(np.reshape(fpa, (4, -1)), axis = 0)
Is this the same as FoldFingerprint()?
Best,Jing
On Fri, Aug 28, 2015 at 5:03 AM, Greg Landrum <greg.land...@gmail.com>
wrote:
> 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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