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
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/Dmitrey
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
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 кого
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 кого: Eric
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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--
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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
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
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 not:
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 hardly
) FuncDesigner stochastic addon now is available as
standalone pyc-file, became available for Python3 as well
Regards, Dmitrey.
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--- Исходное сообщение ---
От кого: Robert Kern robert.k...@gmail.com
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: 16 марта 2013, 22:15:07
On Sat, Mar 16
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 bitten you yet.
It had bitten me some times till I
--- Исходное сообщение ---
От кого: Robert Kern robert.k...@gmail.com
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 марта 2013, 19:54:51
On Sat, Mar 16
--- Исходное сообщение ---
От кого: Alan G Isaac alan.is...@gmail.com
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__
, it is that the 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 tm...@ukr.net
, __gt__,
__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 tm...@ukr.net
--- Исходное сообщение ---
От кого
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 involved from there.
--
Robert Kern
of Python or NumPy,
may affect optimization problems, including (MI)LP, (MI)NLP, TSP etc
* Some other minor bugfixes and improvements
---
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http://openopt.org/Dmitrey
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--- Исходное сообщение ---
От кого: Alan G Isaac alan.is...@gmail.com
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
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
(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
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
: Re: [Numpy-discussion] 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
.
Future plans (probably very long-term although) include TSP and some
other graph problems.
-
Regards, Dmitrey.
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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
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):
.
In our website you could vote for most required OpenOpt Suite development
direction(s).
Regards, D.
http://openopt.org/Dmitrey
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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
Regards, 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 corner cases,
provide hg diff for code
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,
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
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])
___
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
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
, written in
Python + tkinter. Maybe other (alternative) engines will be available
in future.
See its webpage for details.
Regards, Dmitrey.
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http
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
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
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:
solver
with 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. Despite
it
is very
bug in KUBUNTU 11.04, latest numpy git snapshot build with Python3
import numpy
Traceback (most recent call last):
File stdin, line 1, in module
File /usr/local/lib/python3.2/dist-packages/numpy/__init__.py, line
137, in module
from . import add_newdocs
File
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
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
--- Исходное сообщение ---
От кого: Yosef Meller yosef...@post.tau.ac.il
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 stdin, line 1, in module
File
/usr/local/lib/python2.6/site-packages/numpy/lib/function_base.py,
line 1606, in
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
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 import
Hi
2011/3/24 Dmitrey tm...@ukr.net
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
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, unless
there is a bug
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 -inf,
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
argmin([10,nan,100])
1
But it's not the values I would like
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?
Currently I have
from numpy import *
argmax
from numpy import log2, __version__
log2(2**63)
Traceback (most recent call
last):
2**64
18446744073709551616L
2**array(64)
-9223372036854775808
2**100
1267650600228229401496703205376L
2**array(100)
-9223372036854775808
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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.
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)
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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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
: 23 января 2011, 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 numpy/scipy
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
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,
Dmitrey
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.]),
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
On May 21, 11:29 am, David Cournapeau da...@ar.media.kyoto-u.ac.jp
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 build output (after having
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,
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
On May 20, 10:34 pm, Robert Kern robert.k...@gmail.com wrote:
On Wed, May 20, 2009 at 14:24, dmitrey dmitrey.kros...@scipy.org 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
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 =
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
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'
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hi all,
why array(1).tolist() returns 1? I expected to get [1] instead.
D.
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let me also note that list(array((1))) returns
Traceback (innermost last):
File stdin, line 1, in module
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,
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)
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 shape matches the shape of a 0-d array
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.
___
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
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
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sorry, it isn't a bug, it's my fault, fill() returns None and do
in-place modification.
D.
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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 better to return reference
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:
URL: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
*
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
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 'numpy.float128'
type(b[0])
#type 'numpy.float64'
__version__
'1.0.5.dev4767'
Shouldn't it be
hi all,
I need a good estimation of noise value for simple calculations.
I.e. when I calculate something like sin(15)+cos(80) I get a solution
with precision, for example, 1e-11.
I guess the precision depends on system arch, isn't it?
So what's the best way to estimate the value?
I guess here
and assing a
default value to the one.
So, the question is: what default value should be here? I was thinking
of either 0 or something like K*numpy.machine_precesion, where K is
something like 1...10...100.
Regards, D.
Timothy Hochberg wrote:
On Sun, Feb 10, 2008 at 4:23 AM, dmitrey [EMAIL
from numpy import array
a = array((1.0, 2.0))
b = c = 15
b = b*a#ok
c *= a#ok
d = array(15)
e = array(15)
d = d*a#this works ok
e *= a#this intended to be same as prev line, but yields error:
Traceback (innermost last):
File stdin, line 1, in module
ValueError: invalid return array shape
I don't know, maybe it's already fixed in more recent versions?
from numpy import *
a=mat('1 2')
b = asfarray(a).flatten()
print b[0]
[[ 1. 2.]]
# ^^ I expected getting 1.0 here
numpy.version.version
'1.0.3'
___
Numpy-discussion mailing
Hi all,
I don't know much about what are these scons are, if it's something
essential (as it seems to be from amount of mailing list traffic) why
can't it be just merged to numpy, w/o making any additional branches?
Regards, D.
David Cournapeau wrote:
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