В Mon, 10 Oct 2011 11:20:08 -0400
Olivier Delalleau sh...@keba.be пишет:
The following doesn't use numpy but seems to be about 20x faster:
A_rows = {}
for i, row in enumerate(A):
A_rows[tuple(row)] = i
for i, row in enumerate(B):
C[i] = A_rows.get(tuple(row),
Dear numpy experts,
I could not find a satisfying solution to the following problem, so I
thought I would ask:
In one part of a large program I have to deal a lot with small (2d or
3d) vectors and matrices, performing simple linear algebra operations
with them (dot products and matrix
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On 11/10/11 07:49, Martin Raspaud wrote:
Hi all,
[...]
I'm looking for the operation needed to get the two (stacked) vectors
array([[0, 1, 2],
[6, 8, 10]]))
or its transpose.
Hi again,
Here is a solution I just found:
np.einsum(ik,
Here's a version that uses less Python loops and thus is faster. What still
takes time is the array creation (np.array(...)), I'm not sure exactly why.
It may be possible to speed it up.
def points_numpy(radius):
rr = radius**2
M = np.identity(2, dtype=int)
x_y =
Olivier Delalleau sh...@keba.be writes:
Here's a version that uses less Python loops and thus is faster. What
still takes time is the array creation (np.array(...)), I'm not sure
exactly why. It may be possible to speed it up.
Thank you for your suggestion. It doesn't help me however,
11.10.2011 14:14, Christoph Groth kirjoitti:
[clip]
Thank you for your suggestion. It doesn't help me however, because the
algorithm I'm _really_ trying to speed up cannot be vectorized with
numpy in the way you vectorized my toy example.
Any other ideas?
Reformulate the problem so that it
I don't really understand the operation you have in mind that should lead to
your desired result, so here's a way to get it that discards most of mat's
content: (which does not seem needed to compute what you want):
(stack.T * mat[0, 0]).T
-=- Olivier
2011/10/11 Martin Raspaud
Any other ideas?
I'm not an expert at all, but I far as I understand if you cannot
vectorize your problem, numpy is not the best tool to use if the speed
matter a bit.
Of course it's not a realistic example, but a simple loop computing a
cosine is 3-4 time slower using numpy cos than python
Hi all,
How do I use genfromtxt to read a file with the following
lines
11 2.2592365264892578D+01
22 2.2592365264892578D+01
13 2.669845581055D+00
33 2.2592365264892578D+01
Pauli Virtanen p...@iki.fi writes:
Thank you for your suggestion. It doesn't help me however, because
the algorithm I'm _really_ trying to speed up cannot be vectorized
with numpy in the way you vectorized my toy example.
Any other ideas?
Reformulate the problem so that it can be
On Tue, Oct 11, 2011 at 11:57 AM, Christoph Groth c...@falma.de wrote:
Pauli Virtanen p...@iki.fi writes:
Thank you for your suggestion. It doesn't help me however, because
the algorithm I'm _really_ trying to speed up cannot be vectorized
with numpy in the way you vectorized my toy example.
11.10.2011 17:57, Christoph Groth kirjoitti:
[clip]
My question was about ways to achieve a speedup without modifying the
algorithm. I was hoping that there is some numpy-like library for
python which for small arrays achieves a performance at least on par
with the implementation using
2011/10/11 Skipper Seabold jsseab...@gmail.com
On Tue, Oct 11, 2011 at 11:57 AM, Christoph Groth c...@falma.de wrote:
Pauli Virtanen p...@iki.fi writes:
Thank you for your suggestion. It doesn't help me however, because
the algorithm I'm _really_ trying to speed up cannot be vectorized
Hi Nils,
On 11 Oct 2011, at 16:34, Nils Wagner wrote:
How do I use genfromtxt to read a file with the following
lines
11 2.2592365264892578D+01
22 2.2592365264892578D+01
13 2.669845581055D+00
My question was about ways to achieve a speedup without modifying the
algorithm. I was hoping that there is some numpy-like library for
python which for small arrays achieves a performance at least on par
with the implementation using tuples. This should be possible
technically.
I'm not
Skipper Seabold jsseab...@gmail.com writes:
So it's the dot function being called repeatedly on smallish arrays
that's the bottleneck? I've run into this as well. See this thread
[1].
(...)
Thanks for the links. tokyo is interesting, though I fear the
intermediate matrix size regime where
On Tue, Oct 11, 2011 at 12:41 PM, Christoph Groth c...@falma.de wrote:
Skipper Seabold jsseab...@gmail.com writes:
So it's the dot function being called repeatedly on smallish arrays
that's the bottleneck? I've run into this as well. See this thread
[1].
(...)
Thanks for the links. tokyo
On 10/11/2011 12:06 PM, Skipper Seabold wrote:
On Tue, Oct 11, 2011 at 12:41 PM, Christoph Grothc...@falma.de wrote:
Skipper Seaboldjsseab...@gmail.com writes:
So it's the dot function being called repeatedly on smallish arrays
that's the bottleneck? I've run into this as well. See this
Hi,
Can anyone think of a clever way to round an integer to the next
lowest integer represented in a particular floating point format?
For example:
In [247]: a = 2**25+3
This is out of range of the continuous integers representable by float32, hence:
In [248]: print a, int(np.float32(a))
Hi,
Have I missed a fast way of doing nice float to integer conversion?
By nice I mean, rounding to the nearest integer, converting NaN to 0,
inf, -inf to the max and min of the integer range? The astype method
and cast functions don't do what I need here:
In [40]: np.array([1.6, np.nan,
Hi,
While struggling with floating point precision, I ran into this:
In [52]: a = 2**54+3
In [53]: a
Out[53]: 18014398509481987L
In [54]: np.float128(a)
Out[54]: 18014398509481988.0
In [55]: np.float128(a)-1
Out[55]: 18014398509481987.0
The line above tells us that float128 can exactly
Hi,
I recently ran into this:
In [68]: arr = np.array(-128, np.int8)
In [69]: arr
Out[69]: array(-128, dtype=int8)
In [70]: np.abs(arr)
Out[70]: -128
Of course, I can see why this happens, but it is still surprising, and
it seems to me that it would be a confusing source of bugs, because of
Hi,
I realize it is probably too late to do anything about this, but:
In [72]: info = np.finfo(np.float32)
In [73]: info.minexp
Out[73]: -126
In [74]: info.maxexp
Out[74]: 128
minexp is correct, in that 2**(-126) is the minimum value for the
exponent part of float32. But maxexp is not
On 11 Oct 2011, at 20:06, Matthew Brett wrote:
Have I missed a fast way of doing nice float to integer conversion?
By nice I mean, rounding to the nearest integer, converting NaN to 0,
inf, -inf to the max and min of the integer range? The astype method
and cast functions don't do what I
On Tue, Oct 11, 2011 at 12:23 PM, Matthew Brett matthew.br...@gmail.comwrote:
Hi,
I recently ran into this:
In [68]: arr = np.array(-128, np.int8)
In [69]: arr
Out[69]: array(-128, dtype=int8)
In [70]: np.abs(arr)
Out[70]: -128
This has come up for discussion before, but no consensus
On Tue, Oct 11, 2011 at 3:06 PM, Derek Homeier
de...@astro.physik.uni-goettingen.de wrote:
On 11 Oct 2011, at 20:06, Matthew Brett wrote:
Have I missed a fast way of doing nice float to integer conversion?
By nice I mean, rounding to the nearest integer, converting NaN to 0,
inf, -inf to the
If you are using integers, why not use Python's Long?
Colin W.
On 11/10/2011 2:00 PM, Matthew Brett wrote:
Hi,
Can anyone think of a clever way to round an integer to the next
lowest integer represented in a particular floating point format?
For example:
In [247]: a = 2**25+3
This is out
Hi,
On Tue, Oct 11, 2011 at 3:06 PM, Derek Homeier
de...@astro.physik.uni-goettingen.de wrote:
On 11 Oct 2011, at 20:06, Matthew Brett wrote:
Have I missed a fast way of doing nice float to integer conversion?
By nice I mean, rounding to the nearest integer, converting NaN to 0,
inf, -inf
Hi,
On Tue, Oct 11, 2011 at 3:16 PM, Charles R Harris
charlesr.har...@gmail.com wrote:
On Tue, Oct 11, 2011 at 12:23 PM, Matthew Brett matthew.br...@gmail.com
wrote:
Hi,
I recently ran into this:
In [68]: arr = np.array(-128, np.int8)
In [69]: arr
Out[69]: array(-128, dtype=int8)
Hi,
On Tue, Oct 11, 2011 at 3:20 PM, Colin J. Williams
cjwilliam...@gmail.com wrote:
If you are using integers, why not use Python's Long?
You mean, why do I need to know the next lowest representable integer
in a float type?
It's because I have a floating point array that I'm converting to
Hi
On Tue, Oct 11, 2011 at 3:16 PM, Charles R Harris
charlesr.har...@gmail.com wrote:
On Tue, Oct 11, 2011 at 12:23 PM, Matthew Brett matthew.br...@gmail.com
wrote:
Hi,
I recently ran into this:
In [68]: arr = np.array(-128, np.int8)
In [69]: arr
Out[69]: array(-128, dtype=int8)
In
Hi,
On Tue, Oct 11, 2011 at 2:39 PM, Matthew Brett matthew.br...@gmail.com wrote:
Hi,
I realize it is probably too late to do anything about this, but:
In [72]: info = np.finfo(np.float32)
In [73]: info.minexp
Out[73]: -126
In [74]: info.maxexp
Out[74]: 128
minexp is correct, in that
On 11.10.2011, at 9:18PM, josef.p...@gmail.com wrote:
In [42]: c = np.zeros(4, np.int16)
In [43]: d = np.zeros(4, np.int32)
In [44]: np.around([1.6,np.nan,np.inf,-np.inf], out=c)
Out[44]: array([2, 0, 0, 0], dtype=int16)
In [45]: np.around([1.6,np.nan,np.inf,-np.inf], out=d)
Out[45]:
On Tue, Oct 11, 2011 at 2:51 PM, Matthew Brett matthew.br...@gmail.comwrote:
Hi
On Tue, Oct 11, 2011 at 3:16 PM, Charles R Harris
charlesr.har...@gmail.com wrote:
On Tue, Oct 11, 2011 at 12:23 PM, Matthew Brett matthew.br...@gmail.com
wrote:
Hi,
I recently ran into this:
On Tue, Oct 11, 2011 at 2:06 PM, Derek Homeier
de...@astro.physik.uni-goettingen.de wrote:
On 11 Oct 2011, at 20:06, Matthew Brett wrote:
Have I missed a fast way of doing nice float to integer conversion?
By nice I mean, rounding to the nearest integer, converting NaN to 0,
inf, -inf
On Tue, Oct 11, 2011 at 7:13 PM, Benjamin Root ben.r...@ou.edu wrote:
On Tue, Oct 11, 2011 at 2:51 PM, Matthew Brett matthew.br...@gmail.com
wrote:
Hi
On Tue, Oct 11, 2011 at 3:16 PM, Charles R Harris
charlesr.har...@gmail.com wrote:
On Tue, Oct 11, 2011 at 12:23 PM, Matthew Brett
On Tue, Oct 11, 2011 at 6:33 PM, josef.p...@gmail.com wrote:
On Tue, Oct 11, 2011 at 7:13 PM, Benjamin Root ben.r...@ou.edu wrote:
On Tue, Oct 11, 2011 at 2:51 PM, Matthew Brett matthew.br...@gmail.com
wrote:
Hi
On Tue, Oct 11, 2011 at 3:16 PM, Charles R Harris
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