Re: [Numpy-discussion] Distance Matrix speed
Hi, def d4(): d = zeros([4, 1000], dtype=float) for i in range(4): xy = A[i] - B d[i] = sqrt( sum(xy**2, axis=1) ) return d Maybe there's another alternative to d4? Thanks again, I think this is the fastest you can get. Maybe it would be nicer to use the .sum() method instead of sum function, but that is just my personal opinion. I am curious how this compares to the matlab version. :) Johannes ___ Numpy-discussion mailing list Numpy-discussion@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/numpy-discussion
[Numpy-discussion] Recarray attributes writeable
Hi everyone -
Recarrays have convenience attributes such that
fields may be accessed through . in additioin to
the field() method. These attributes are designed for
read only; one cannot alter the data through them.
Yet they are writeable:
tr=numpy.recarray(10, formats='i4,f8,f8', names='id,ra,dec')
tr.field('ra')[:] = 0.0
tr.ra
array([ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])
tr.ra = 3
tr.ra
3
tr.field('ra')
array([ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])
I feel this should raise an exception, just as with trying to write
to the size attribute. Any thoughts?
Erin
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[Numpy-discussion] Recarray attributes writable
Hi everyone -
Recarrays have convenience attributes such that
fields may be accessed through . in additioin to
the field() method. These attributes are designed for
read only; one cannot alter the data through them.
Yet they are writeable:
tr=numpy.recarray(10, formats='i4,f8,f8', names='id,ra,dec')
tr.field('ra')[:] = 0.0
tr.ra
array([ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])
tr.ra = 3
tr.ra
3
tr.field('ra')
array([ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])
I feel this should raise an exception, just as with trying to write
to the size attribute. Any thoughts?
Erin
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Re: [Numpy-discussion] Recarray attributes writeable
El dv 16 de 06 del 2006 a les 14:46 -0700, en/na Andrew Straw va
escriure:
Erin Sheldon wrote:
Anyway - Recarrays have convenience attributes such that
fields may be accessed through . in additioin to
the field() method. These attributes are designed for
read only; one cannot alter the data through them.
Yet they are writeable:
tr=numpy.recarray(10, formats='i4,f8,f8', names='id,ra,dec')
tr.field('ra')[:] = 0.0
tr.ra
array([ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])
tr.ra = 3
tr.ra
3
tr.field('ra')
array([ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])
I feel this should raise an exception, just as with trying to write
to the size attribute. Any thoughts?
I have not used recarrays much, so take this with the appropriate
measure of salt.
I'd prefer to drop the entire pseudo-attribute thing completely before
it gets entrenched. (Perhaps it's too late.)
However, I think that this has its utility, specially when accessing to
nested fields (see later). In addition, I'd suggest introducing a
special accessor called, say, 'fields' in order to access the fields
themselves and not the attributes. For example, if you want to access
the 'strides' attribute, you can do it in the usual way:
import numpy
tr=numpy.recarray(10, formats='i4,f8,f8', names='id,ra,strides')
tr.strides
(20,)
but, if you want to access *field* 'strides' you could do it by issuing:
tr.fields.strides
repr of field accessor object (shape, type...)
tr.fields.strides[:]
array([ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])
We have several advantages in adopting the previous approach:
1. You don't mix (nor pollute) the namespaces for attributes and fields.
2. You have a clear idea when you are accessing a variable or a field.
3. Accessing nested columns would still be very easy:
tr.field('nested1').field('nested2').field('nested3') vs
tr.fields.nested1.nested2.nested3
4. You can also define a proper __getitem__ for accessing fields:
tr.fields['nested1']['nested2']['nested3'].
In the same way, elements of 'nested2' field could be accessed by:
tr.fields['nested1']['nested2'][2:10:2].
5. Finally, you can even prevent setting or deleting columns by
disabling the __setattr__ and __delattr__.
PyTables has adopted a similar schema for accessing nested columns,
except for 4, where we decided not to accept both strings and slices for
the __getitem__() method (you know the mantra: there should preferably
be just one way of doing things, although maybe we've been a bit too
much strict in this case), and I think it works reasonably well. In any
case, the idea is to decouple the attributes and fields so that they
doesn't get mixed.
Implementing this shouldn't be complicated at all, but I'm afraid that I
can't do this right now :-(
--
0,0 Francesc Altet http://www.carabos.com/
V V Cárabos Coop. V. Enjoy Data
-
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Re: [Numpy-discussion] Recarray attributes writeable
On 6/17/06, Francesc Altet [EMAIL PROTECTED] wrote: However, I think that this has its utility, specially when accessing to nested fields (see later). In addition, I'd suggest introducing a special accessor called, say, 'fields' in order to access the fields themselves and not the attributes. For example, if you want to access the 'strides' attribute, you can do it in the usual way: import numpy tr=numpy.recarray(10, formats='i4,f8,f8', names='id,ra,strides') tr.strides (20,) but, if you want to access *field* 'strides' you could do it by issuing: tr.fields.strides repr of field accessor object (shape, type...) tr.fields.strides[:] array([ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]) [...] +1 I meant to write exactly the same thing, but was too lazy to do it :) Cheers, f ___ Numpy-discussion mailing list Numpy-discussion@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/numpy-discussion
Re: [Numpy-discussion] Distance Matrix speed
How about this? def d5(): return add.outer(sum(A*A, axis=1), sum(B*B, axis=1)) - \ 2.*dot(A, transpose(B)) ___ Numpy-discussion mailing list Numpy-discussion@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/numpy-discussion
Re: [Numpy-discussion] Distance Matrix speed
Alex Cannon wrote: How about this? def d5(): return add.outer(sum(A*A, axis=1), sum(B*B, axis=1)) - \ 2.*dot(A, transpose(B)) You might lose some precision with that approach, so the OP should compare results and timings to look at the tradeoffs. -- Robert Kern I have come to believe that the whole world is an enigma, a harmless enigma that is made terrible by our own mad attempt to interpret it as though it had an underlying truth. -- Umberto Eco ___ Numpy-discussion mailing list Numpy-discussion@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/numpy-discussion
Re: [Numpy-discussion] Recarray attributes writeable
This reply sent 9:36 AM, Jun 17 (because it may not show up
for a day or so from my gmail account, if it shows up at all)
On 6/17/06, Francesc Altet [EMAIL PROTECTED] wrote:
El dv 16 de 06 del 2006 a les 14:46 -0700, en/na Andrew Straw va
escriure:
Erin Sheldon wrote:
Anyway - Recarrays have convenience attributes such that
fields may be accessed through . in additioin to
the field() method. These attributes are designed for
read only; one cannot alter the data through them.
Yet they are writeable:
tr=numpy.recarray(10, formats='i4,f8,f8', names='id,ra,dec')
tr.field('ra')[:] = 0.0
tr.ra
array([ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])
tr.ra = 3
tr.ra
3
tr.field('ra')
array([ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])
I feel this should raise an exception, just as with trying to write
to the size attribute. Any thoughts?
I have not used recarrays much, so take this with the appropriate
measure of salt.
I'd prefer to drop the entire pseudo-attribute thing completely before
it gets entrenched. (Perhaps it's too late.)
I think that initially I would concur to drop them. I am new to numpy,
however, so they are not entrenched for me. Anyway, see below.
However, I think that this has its utility, specially when accessing to
nested fields (see later). In addition, I'd suggest introducing a
special accessor called, say, 'fields' in order to access the fields
themselves and not the attributes. For example, if you want to access
the 'strides' attribute, you can do it in the usual way:
import numpy
tr=numpy.recarray(10, formats='i4,f8,f8', names='id,ra,strides')
tr.strides
(20,)
but, if you want to access *field* 'strides' you could do it by issuing:
tr.fields.strides
repr of field accessor object (shape, type...)
tr.fields.strides[:]
array([ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])
We have several advantages in adopting the previous approach:
1. You don't mix (nor pollute) the namespaces for attributes and fields.
2. You have a clear idea when you are accessing a variable or a field.
3. Accessing nested columns would still be very easy:
tr.field('nested1').field('nested2').field('nested3') vs
tr.fields.nested1.nested2.nested3
4. You can also define a proper __getitem__ for accessing fields:
tr.fields['nested1']['nested2']['nested3'].
In the same way, elements of 'nested2' field could be accessed by:
tr.fields['nested1']['nested2'][2:10:2].
5. Finally, you can even prevent setting or deleting columns by
disabling the __setattr__ and __delattr__.
This is interesting, and I would add a 6th to this:
6. The .fields by itself could return the names of the fields, which are
currently not accessible in any simple way. I always think that these
should be methods (.fields(),.size(), etc) but if we are going down
the attribute route, this might be a simple fix.
PyTables has adopted a similar schema for accessing nested columns,
except for 4, where we decided not to accept both strings and slices for
the __getitem__() method (you know the mantra: there should preferably
be just one way of doing things, although maybe we've been a bit too
much strict in this case), and I think it works reasonably well. In any
case, the idea is to decouple the attributes and fields so that they
doesn't get mixed.
Strings or fieldnum access greatly improves the scriptability, but this
can always be done through the .field() access.
Erin
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Re: [Numpy-discussion] (no subject)
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Re: [Numpy-discussion] (no subject)
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