I'm glad to inform you about new OpenOpt Suite release 0.54:
* Some changes for PyPy compatibility
* FuncDesigner translator() can handle sparse derivatives from automatic
differentiation
* New interalg parameter rTol (relative tolerance, default 10^-8)
* Bugfix and improvement
hi all,
I'm glad to inform you about new OpenOpt Suite release 0.53:
Stochastic programming addon now is available for free
Some minor changes
--
Regards, D.
http://openopt.org/Dm
Hi all,
I'm glad to inform you about new OpenOpt Suite release 0.52 (2013-Dec-15):
Minor interalg speedup
oofun expression
MATLAB solvers fmincon and fsolve have been connected
Several MATLAB ODE solvers have been connected
New ODE solvers, parameters abstol and relt
FYI scipy ODE solvers vode, dopri5, dop853 also have been connected to OpenOpt,
possibly with automatic differentiation by FuncDesigner (dopri5 and dop853
don't use derivatives although).
--
Regards, D. http://openopt.org/Dmitrey
--- Исходное сообщение ---
От
e them in http://openopt.org/ODE (just replace solver name from
scipy_lsoda to ode23s or any other), http://openopt.org/NLP ,
http://openopt.org/SNLE
------
Regards, D. http://openopt.org/Dmitrey
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It requires MATLAB or MATLAB Component Runtime (
http://www.mathworks.com/products/compiler/mcr/ )
I'm not regular subscriber of the mail list thus you'd better ask openopt
forum.
--
Regards, D. http://openopt.org/Dmitrey
--- Исходное сообщение ---
От к
.
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Hi all,
New solver for systems of nonlinear equations ( SNLE ) has been connected to
free Python framework OpenOpt: fsolve from MATLAB Optimization Toolbox;
uploaded into PYPI in v. 0.5112.
As well as fmincon , currently it's available for Python 2 only.
Unlike scipy.optimize fsolve, it ca
Hi all,
current state of Python <-> MATLAB connection soft doesn't allow passing of
function handlers, however, a walkaround has been implemented via some tricks,
so now MATLAB function fmincon is available in Python-written OpenOpt and
FuncDesigner frameworks (with possibility of automatic dif
Hi all,
new OpenOpt suite v 0.51 has been released: Some improvements for FuncDesigner
automatic differentiation and QP FuncDesigner now can model sparse (MI)(QC)QP
Octave QP solver has been connected MATLAB solvers linprog ( LP ), quadprog (
QP ), lsqlin ( LLSP ), bintprog ( MILP ) New NLP
Python 3.3.1 (default, Apr 17 2013, 22:32:14)
[GCC 4.7.3] on linux
>>> import numpy
>>> numpy.__version__
'1.8.0.dev-d62f11d'
>>> numpy.array((1,2,3)) / 2
array([ 0.5, 1. , 1.5])
#ok, but since division of integer arrays has been converted to float, pow is
expected as well, but it's no
lots of MILP solvers can be used.
See http://openopt.org/KSP for details.
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Hi all,
>
>
FYI some MATLAB solvers now can be involved with OpenOpt or FuncDesigner
:
>
* LP linprog
* QP quadprog
* LLSP lsqlin
* MILP bintprog
>
>
Sparsity handling is supported.
>
>
You should have
* MATLAB (or MATLAB Component Runtime)
* mlabwrap
>
Unfortunately, it will
d improvements
* (Proprietary) FuncDesigner stochastic addon now is available as
standalone pyc-file, became available for Python3 as well
>
>
Regards, Dmitrey.
>
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hance hash
> collision to silently give you wrong results. That is the worst kind
> of bug.
Hi Dmitrey,
Robert and Sebastien have taken their time to carefully explain
to your why your design is flawed. Your response has been only
that you rely on this design flaw and it has not bi
On 04/10/2013 10:31 AM, Robert Kern wrote:
You think comparing tracked bug counts across different projects
means anything? That's adorable. I admire your diligence at
addressing the bugs that you do acknowledge. That was never in
question. But refusing to acknowledge a bug is not the same thin
> --- Исходное сообщение ---
От кого: "Robert Kern"
Дата: 9 апреля 2013, 14:29:43
On Tue, Apr 9, 2013 at 4:15 PM, Dmitrey < tm...@ukr.net > wrote:
>
>
> --- Исходное сообщение ---
> От кого: "Robert Kern" < robert.k...@gmail.com >
> Дата
--- Исходное сообщение ---
От кого: "Robert Kern"
Дата: 16 марта 2013, 22:15:07
On Sat, Mar 16, 2013 at 6:19 PM, Dmitrey < tm...@ukr.net > wrote:
>
>
> --- Исходное сообщение ---
> От кого: "Robert Kern" < robert.k...@gmail.com >
> Дата: 16 м
returning __eq__() will prevent proper sorting, which
also seems to be used in the code snippet that Dmitrey showed.
as I have already mentioned, I ensured via debugger that my __eq__,
__le__ etc are not involved from the buggy place of the code, only
__hash__ is
__ge__ are not called from the buggy place of code, only __hash__ is
called from there. Python could check key objects equivalence via id(),
although, but I don't see any possible bug source from using id().
D.
2013/3/16 Dmitrey
--- Исходное сообщение ---
От кого: "Matthieu Brucher&
internal state changed and that the hash
is not the same anymore.
my objects (oofuns) definitely have different __hash__() results - it's
just integers 1,2,3 etc assigned to the oofuns (stored in oofun._id
field) when they are created.
D.
Matthieu
2013/3/16 Dmitrey
--- Исходное с
--- Исходное сообщение ---
От кого: "Alan G Isaac"
Дата: 15 марта 2013, 22:54:21
On 3/15/2013 3:34 PM, Dmitrey wrote:
> the suspected bugs are not documented yet
I'm going to guess that the state of the F_i changes
when you use them as keys (i.e., when you call __le__
--- Исходное сообщение ---
> От кого: "Alan G Isaac"
Дата: 15 марта 2013, 20:38:38
On 3/15/2013 9:21 AM, Dmitrey wrote:
> Temporary walkaround for a serious bug in FuncDesigner automatic
> differentiation kernel due to a bug in some versions of Python or NumPy,
Are
sions of Python or NumPy,
may affect optimization problems, including (MI)LP, (MI)NLP, TSP etc
* Some other minor bugfixes and improvements
>
>
---
>
Regards, D.
> http://openopt.org/Dmitrey
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Hi all,
I'm glad to inform you about new OpenOpt release 0.43 (2012-Dec-15):
* interalg now can solve SNLE in 2nd mode (parameter dataHandling =
"raw", before - only "sorted")
* Many other improvements for interalg
* Some improvements for FuncDesigner kernel
* FuncDesigner ODE now has 3 argu
ational or research purposes only.
For more details visit our website http://openopt.org
-
Regards, D.
http://openopt.org/Dmitrey
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Hi all,
I'm glad to inform you about new OpenOpt Suite release 0.42
(2012-Sept-15). Main changes:
* Some improvements for solver interalg, including handling of
categorical variables
* Some parameters for solver gsubg
* Speedup objective function for de and pswarm on FuncDesigner models
*
SP (traveling salesman problem).
Hello Dmitrey,
Can this tool solve ATSP problems?
Thanks,
Niki
>
Hi,
yes - asymmetric (see examples with networkx DiGraph), including
multigraphs (networkx MultiDiGraph) as well.
>
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Hi all,
New free tool for TSP solving is available (for downloading as well) -
OpenOpt TSP class: TSP (traveling salesman problem).
It is written in Python, uses NetworkX graphs on input (another
BSD-licensed Python library, de-facto standard graph lib for Python
language programmers), can
cussion] routine for linear least norms problems with
specifiable accuracy
>
On Mon, 2012-07-16 at 20:35 +0300, Dmitrey wrote:
> I have wrote a routine to solve dense / sparse problems
> min {alpha1*||A1 x - b1||_1 + alpha2*||A2 x - b2||^2 + beta1 * ||x||_1
> + beta2 * ||x||^2}
>
hi all,
I have wrote a routine to solve dense / sparse problems
min {alpha1*||A1 x - b1||_1 + alpha2*||A2 x - b2||^2 + beta1 * ||x||_1 +
beta2 * ||x||^2}
with specifiable accuracy fTol > 0: abs(f-f*) <= fTol (this parameter is
handled by solvers gsubg and maybe amsg2p, latter requires known g
.
Future plans (probably very long-term although) include TSP and some
other graph problems.
-
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hi all,
you may be interested in stochastic programming and optimization with
free Python module FuncDesigner.
We have wrote Stochastic addon for FuncDesigner, but (at least for
several years) it will be commercional (currently it's free for some
small-scaled problems only and for noncommerc
I will use walkaround but I think you'd better fix the numpy bug:
from numpy import ndarray, float64, asanyarray, array
class asdf(ndarray):
__array_priority__ = 10
def __new__(self, vals1, vals2):
obj = asanyarray(vals1).view(self)
obj.vals2 = vals2
return obj
def __add__(self, other):
print('
OpenOpt release.
In our website you could vote for most required OpenOpt Suite development
direction(s).
Regards, D.
http://openopt.org/Dmitrey
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>
On your website you wrote:
>> From my (Dmitrey) point of view numpypy development is
>> very unfriendly for newcomers - PyPy developers say "provide
>> code, preferably in interpreter level instead of AppLevel,
>> provide whole test coverage for all possible
onality working faster
than in CPython.
File with this functions you can get here
Also you may be interested in some info at http://openopt.org/PyPy
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hi all,
free solver interalg for global nonlinear optimization with specifiable
accuracy now can handle categorical variables, disjunctive (and other
logical) constraints, thus making it available to solve GDP, possibly in
multiobjective form.
There are ~ 2 months till next OpenOpt release, but
Hi,
I'm glad to inform you about new release 0.38 (2012-March-15):
OpenOpt:
interalg can handle discrete variables (see MINLP for examples)
interalg can handle multiobjective problems (MOP)
interalg can handle problems with parameters fixedVars/freeVars
Many interalg improvements and some bug
memory leak was observed in numpy versions 1.5.1 and latest git trunc
from numpy import *
for i in range(10):
if i % 100 == 0:
print(i)
a = empty(1,object)
for j in range(1):
a[j] = array(1)
a = take(a, range(9000),out=a[:9000])
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hi,
I'm glad to inform you about new Python solver for multiobjective
optimization (MOP).
Some changes committed to solver interalg made it capable of handling
global nonlinear constrained multiobjective problem (MOP), see the page
for more details.
>
>
Using interalg you can be 100% sure
hi all,
I've done support of discrete variables for interalg - free (license:
BSD) solver with specifiable accuracy, you can take a look at an example
here
It is written in Python + NumPy, and I hope it's speed will be
essentially increased when PyPy (Python with dynamic compilation) support
fo
Hi all,
I'm glad to inform you about new release 0.37 (2011-Dec-15) of our free
software:
>
OpenOpt (numerical optimization):
>
IPOPT initialization time gap (time till first iteration) for
FuncDesigner models has been decreased
Some improvements and bugfixes for interalg, especially for "se
ontend, written in
Python + tkinter. Maybe other (alternative) engines will be available
in future.
>
>
See its webpage for details.
>
>
Regards, Dmitrey.
>
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hi all,
now free solver interalg from OpenOpt framework (based on interval
analysis) can solve ODE dy/dt = f(t) with guaranteed specifiable
accuracy.
See the ODE webpage for more details, there is an example of
comparison with scipy.integrate.odeint, where latter fails to solve a
Hi all,
new release of our free soft (OpenOpt, FuncDesigner, DerApproximator,
SpaceFuncs) v. 0.36 is out:
OpenOpt:
>
* Now solver interalg can handle all types of constraints and
integration problems
> * Some minor improvements and code cleanup
>
FuncDes
ith guaranteed precision
Hi Dmitrey,
2011/8/15 Dmitrey < tm...@ukr.net >:
> Hi all,
> I'm glad to inform you that general constraints handling for interalg
(free
> solver with guaranteed user-defined precision) now is available. De
Hi all,
I'm glad to inform you that general constraints handling for interalg
(free solver with guaranteed user-defined precision) now is available.
Despite it is very premature and requires lots of improvements, it is
already capable of outperforming commercial BARON (example:
http
bug in KUBUNTU 11.04, latest numpy git snapshot build with Python3
>>> import numpy
Traceback (most recent call last):
File "", line 1, in
File "/usr/local/lib/python3.2/dist-packages/numpy/__init__.py", line
137, in
from . import add_newdocs
File "/usr/local/lib/python3.2/d
Hi all,
some ideas implemented in the solver interalg (INTERval ALGorithm)
that already turn out to be more effective than its competitors in
numerical optimization (benchmark) appears to be extremely effective
in numerical integration with guaranteed precision.
Here are some exampl
Hi all,
I'm glad to inform you about new quarterly release 0.34 of the OOSuite
package software (OpenOpt, FuncDesigner, SpaceFuncs, DerApproximator)
.
Main changes:
* Python 3 compatibility
* Lots of improvements and speedup for interval calculations
* Now interalg can obtain
--- Исходное сообщение ---
От кого: "Yosef Meller"
Кому: scipy-u...@scipy.org
Дата: 25 мая 2011, 08:54:16
Тема: Re: [SciPy-User] [ANN] Guaranteed solution of nonlinear
equation(s)
On ??? ? 24 ??? 2011 13:22:47 Dmitrey wrote:
> Hi all,
>
Hi all,
I have made my free solver interalg (http://openopt.org/interalg) be
capable of solving nonlinear equations and systems of them. Unlike
scipy optimize fsolve it doesn't matter which functions are involved -
convex, nonconvex, multiextremum etc. Even some discontinuous funcs
>>> from numpy import *
>>> nanargmax([nan,nan])
nan # ok
>>> nanargmax([nan,nan],0)
nan # ok
>>> nanargmax([[1,nan],[1,nan]],0)
Traceback (most recent call last):
File "", line 1, in
File
"/usr/local/lib/python2.6/site-packages/numpy/lib/function_base.py",
line 1606
On Thu, Mar 24, 2011 at 6:19 AM, Ralf Gommers
<
ralf.gomm...@googlemail.com > wrote:
> 2011/3/24 Dmitrey < tm...@ukr.net >:
>> hi,
>> is there any way to get argmin and argmax of an array w/o nans?
>> Curre
2011/3/24 Dmitrey <
tm...@ukr.net >:
> hi,
> is there any way to get argmin and argmax of an array w/o nans?
> Currently I have
>>>> from numpy import *
>>>> argmax([10,nan,100])
> 1
>>>>
hi,
is there any way to get argmin and argmax of an array w/o nans?
Currently I have
>>> from numpy import *
>>> argmax([10,nan,100])
1
>>> argmin([10,nan,100])
1
But it's not the values I would like to get.
The walkaround I use: get all indeces of nans, replace them by
Isnt [K]Ubuntu updated each 6 month?
2011/3/24 Dmitrey <
tm...@ukr.net >:
> hi,
> when numpy in Linux apt will be updated? It's still 1.3.0 with many bugs
There will always be bugs, but numpy 1.3 is a stable release, unles
Hi
2011/3/24 Dmitrey
>>> from numpy import inf, array
>>> inf*0
nan
(ok)
>>> array(inf) * 0.0
StdErr: Warning: invalid value encountered in multiply
nan
My cycled calculations yields t
>>> from numpy import inf, array
>>> inf*0
nan
(ok)
>>> array(inf) * 0.0
StdErr: Warning: invalid value encountered in multiply
nan
My cycled calculations yields this thousands times slowing
computations and making text output completely non-readable.
>>> from numpy
hi,
when numpy in Linux apt will be updated? It's still 1.3.0 with many
bugs
I tried to install numpy from PYPI where 1.5.1 seesm to be present,
but somehow it involves 1.3.0 instead:
$ sudo easy_install numpy
install_dir /usr/local/lib/python2.6/dist-packages/
Searching for
>>> 2**64
18446744073709551616L
>>> 2**array(64)
-9223372036854775808
>>> 2**100
1267650600228229401496703205376L
>>> 2**array(100)
-9223372036854775808
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>>> from numpy import log2, __version__
>>>
log2(2**63)
Traceback (most recent call
last):
I have ndarray subclass, its instance x and use
r = x**2
I expected it will call for each array element
elem.__pow__(2)
but it calls
elem.__mul__(elem)
instead.
It essentially (tens or even more times) decreases my calculations
speed for lots of cases.
>>> numpy.__ver
>
* Some bugfixes
>
DerApproximator:
>
* Adjusted with some changes in FuncDesigner
>
For more details visit our site http://openopt.org.
>
Regards, Dmitrey.
>
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hi all,
currently I use
a = array(m,n)
...
a = delete(a, indices, 0) # delete some rows
Can I somehow perform the operation in-place, without creating
auxiliary array?
If I'll use
numpy.compress(condition, a, axis=0, out=a),
or
numpy.take(a, indices, axis=0, out=a)
, you can try it online via our Sage-server (sometimes hangs due
to high load, through)
http://sage.openopt.org/welcome
Regards,
Dmitrey.
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12:07:29
Тема: Re: [Numpy-discussion] Is numpy/scipy linux apt or PYPI
installation linked with ACML?
2011/1/23 Dmitrey < tm...@ukr.net >:
> Hi all,
> I have AMD processor and I would like to get to know what's the easiest
way
> to install nu
Hi all,
I have AMD processor and I would like to get to know what's the
easiest way to install numpy/scipy linked with ACML.
Is it possible to link linux apt or PYPI installation linked with
ACML?
Answer for the same question about MKL also would be useful, however,
AFAIK it has
Hi all,
I'm glad to inform you about new quarterly OpenOpt/FuncDesigner
release (0.32):
OpenOpt:
* New class: LCP (and related solver)
* New QP solver: qlcp
* New NLP solver: sqlcp
* New large-scale NSP (nonsmooth) solver gsubg. Currently it still
requires lots of improvem
Hi all,
I'm glad to inform you about new releases:
OpenOpt 0.31, FuncDesigner 0.21, DerApproximator 0.21
For details see
http://forum.openopt.org/viewtopic.php?id=299
or visit our homepage
http://openopt.org
Regards,
Dm
Hi all,
I expected to have some speedup via using ldexp or multiplying an
array by a power of 2 (doesn't it have to perform a simple shift of
mantissa?), but I don't see the one.
Have I done something wrong? See the code below.
from scipy import rand
from numpy import dot, ones, zeros, array, lde
On May 21, 11:29 am, David Cournapeau
wrote:
> dmitrey wrote:
> > I have updated numpy to latest '1.4.0.dev7008', but the bug still
> > remains.
> > I use KUBUNTU 9.04, compilers - gcc (using build-essential), gfortran.
> > D.
>
> Can you post the b
I have updated numpy to latest '1.4.0.dev7008', but the bug still
remains.
I use KUBUNTU 9.04, compilers - gcc (using build-essential), gfortran.
D.
On May 21, 11:21 am, David Cournapeau
wrote:
> dmitrey wrote:
> > hi all,
> > I have tried the example from numpy/add_ne
hi all,
has anyone already tried to compare using an ordinary numpy ufunc vs
that one from corepy, first of all I mean the project
http://socghop.appspot.com/student_project/show/google/gsoc2009/python/t124024628235
It would be interesting to know what is speedup for (eg) vec ** 0.5 or
(if it's po
hi all,
I have tried the example from numpy/add_newdocs.py
np.ldexp(5., 2)
but instead of the 20 declared there it yields
TypeError: function not supported for these types, and can't coerce
safely to supported types
I have tried arrays but it yields same error
>>> np.ldexp(np.array([5., 2.]), np.
On May 20, 10:34 pm, Robert Kern wrote:
> On Wed, May 20, 2009 at 14:24, dmitrey wrote:
> > hi all,
>
> > suppose I have A that is numpy ndarray of floats, with shape n x n.
>
> > I want to obtain dot(A, b), b is vector of length n and norm(b)=1, but
> > instead
hi all,
suppose I have A that is numpy ndarray of floats, with shape n x n.
I want to obtain dot(A, b), b is vector of length n and norm(b)=1, but
instead of exact multiplication I want to approximate b as a vector
[+/- 2^m0, ± 2^m1, ± 2^m2 ,,, ± 2^m_n], m_i are integers, and then
invoke left_shi
Hi all,
I've got the error during building numpy from latest svn snapshot -
any ideas?
D.
...
executing numpy/core/code_generators/generate_numpy_api.py
adding 'build/src.linux-x86_64-2.6/numpy/core/include/numpy/
__multiarray_api.h' to sources.
numpy.core - nothing done with h_files = ['build/s
Hi all,
does numpy/scipy, or maybe wrapper for a lapack routine have solver
for Ax=b, L_inf (Chebyshev norm, i.e. max |Ax-b| -> min)? If there are
several ones, which ones are most suitable for large-scale, maybe ill-
conditioned problems?
Thank you in advance, D.
P.S. Currently I 'm not interested
Hi all,
I have orthonormal set of vectors B = [b_0, b_1,..., b_k-1],
b_i from R^n (k may be less than n), and vector a from R^n
What is most efficient way in numpy to get r from R^n and c_0, ...,
c_k-1 from R:
a = c_0*b_0+...+c_k-1*b_k-1 + r
(r is rest)
Thank you in advance, D.
_
Hi all,
I have orthonormal set of vectors B = [b_0, b_1,..., b_k-1],
b_i from R^n (k may be less than n), and vector a from R^n
What is most efficient way in numpy to get r from R^n and c_0, ...,
c_k-1 from R:
a = c_0*b_0+...+c_k-1*b_k-1 + r
(r is rest)
Thank you in advance, D.
_
hi all,
I have array A, A.ndim = n, and 1-dimensional array B of length n.
How can I get element of A with coords B[0],...,B[n-1]?
i.e. A[B[0], B[1], ..., B[n-1])
A, B, n are not known till execution time, and can have unpredictable
lengths (still n is usually small, no more than 4-5).
I have tr
Did you mean this one
http://www.netlib.org/scalapack/pblas_qref.html
?
As for the ParallelProgramming wiki page, there are some words in
section "Use parallel primitives" about numpy.dot still I can't
understand from the section: if I get numpy from sources and compile it
(via "python setup.py
hi all,
I wonder why numpy.asscalar(1.5) yields error, why it can't just return
1.5? Is it intended to be ever changed?
>>> numpy.__version__
'1.3.0.dev5864'
D.
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Alan G Isaac wrote:
> On 10/1/2008 9:04 AM dmitrey apparently wrote:
>
>> why array(1).tolist() returns 1? I expected to get [1] instead.
>>
>
> I guess I would expect it not to work at all.
> Given that it does work, this seems the best result.
> What list sh
hi all,
will array(Python set) (and asarray, asfarray etc) ever be implemented
as cast method?
Now it just puts the set into 1st element:
>>> asarray(set([11, 12, 13, 14]))
array(set([11, 12, 13, 14]), dtype=object)
>>> array(set([11, 12, 13, 14]))
array(set([11, 12, 13, 14]), dtype=object)
let me also note that list(array((1))) returns
Traceback (innermost last):
File "", line 1, in
TypeError: iteration over a 0-d array
D.
dmitrey wrote:
> hi all,
> why array(1).tolist() returns 1? I expected to get [1] instead.
> D.
>
> __
hi all,
why array(1).tolist() returns 1? I expected to get [1] instead.
D.
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hi all,
does numpy have funcs like isanynan(array) or isallfinite(array)?
I very often use any(isnan(my_array)) or all(isfinite(my_array)), I
guess having a single case triggered on would be enough here to omit
further checks.
Regards, D.
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As for me I can't understand the general rule: when numpy funcs return
copy and when reference?
For example why x.fill() returns None (do inplace modification) while
x.ravel(), x.flatten() returns copy? Why the latters don't do inplace
modification, as should be expected?
D.
Alan G Isaac wrot
Keith Goodman wrote:
> On Fri, Aug 29, 2008 at 10:51 AM, Keith Goodman <[EMAIL PROTECTED]> wrote:
>
>> On Fri, Aug 29, 2008 at 10:42 AM, dmitrey <[EMAIL PROTECTED]> wrote:
>>
>>> Keith Goodman wrote:
>>>
>>>> Yeah, I do
Keith Goodman wrote:
> Yeah, I do stuff like that too. fill works in place so it returns None.
>
>
>>> x = np.array([1,2])
>>> x.fill(10)
>>> x
>>>
>array([10, 10])
>
>>> x = x.fill(10) # <-- Danger!
>>> print x
>>>
> None
>
Since result "None" is never used it would be
sorry, it isn't a bug, it's my fault, fill() returns None and do
in-place modification.
D.
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hi all,
isn't it a bug
(latest numpy from svn, as well as my older version)
from numpy import array
print array((1,2,3)).fill(10)
None
Regards, D.
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Also, it would be very well if asfarray() doesn't drop down float128 to
float64.
D.
Alan G Isaac wrote:
> I never got a response to this:
> http://projects.scipy.org/pipermail/scipy-dev/2008-February/008424.html>
> (Two different types claim to be numpy.int32.)
>
> Cheers,
> Alan
>
>
>
>
Travis E. Oliphant wrote:
> Hi everybody,
>
> In writing some generic code, I've encountered situations where it would
> reduce code complexity to allow NumPy scalars to be "indexed" in the
> same number of limited ways, that 0-d arrays support.
>
> For example, 0-d arrays can be indexed with
>
>
hi all,
I intend to connect some C code to Python for some my purposes.
What is the best software for the aim?
Is it numpy.ctypes or swig or something else?
IIRC ctypes are present in Python since v2.5, so it's ok to use just
ctypes, not numpy.ctypes, or some difference is present?
Another one qu
isn't MLPY a new name to PyML?
http://mloss.org/software/view/28/
if no, I guess you'd better add link to your software to
http://mloss.org/software/
("mloss" is "machine learning open source software")
Regards, D.
Davide Albanese wrote:
> *Machine Learning Py* (MLPY) is a *Python/NumPy* based pa
As for me, it yields lots of inconveniences (lots of my code should be
rewritten, since I didn't know it before):
from numpy import *
a = array((1.0, 2.0), float128)
b=asfarray(a)
type(a[0])
#
type(b[0])
#
__version__
'1.0.5.dev4767'
Shouldn't it be changed? (I.e. let's left 128).
As for me I u
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