We're not so far apart.
I've used SAS or 25 years, and R/S-PLUS for 10.
I think you've said it better than I did, though: R requires more attention
(which is often needed).
I certainly didn't mean that R crashed - just an indictment of how much I
thought I was holding in my head.
Gerry
--
Gerry,
I have the similar background as yours, many years using SAS/R. Right
now I am trying to pick up python.
From your point, is there anything that can be done with python easily
but not with SAS/R?
thanks for your insight.
wensui
On 1/1/07, [EMAIL PROTECTED] [EMAIL PROTECTED] wrote:
sturlamolden wrote:
as well as looping over the data only once. This is one of the main
reasons why Fortran is better than C++ for scientific computing. I.e.
instead of
for (i=0; in; i++)
array1[i] = (array1[i] + array2[i]) * (array3[i] + array4[i]);
one actually gets something like
Klaas wrote:
C/C++ do not allocate extra arrays. What you posted _might_ bear a
small resemblance to what numpy might produce (if using vectorized
code, not explicit loop code). This is entirely unrelated to the
reasons why fortran can be faster than c.
Array libraries in C++ that use
On Fri, 29 Dec 2006 19:35:22 -0800, Beliavsky wrote:
Especially I like:
- more relaxed behavior of exceeded the upper limit of a (1-dimensional)
array
Could you explain what this means? In general, I don't want a
programming language to be relaxed about exceeding array bounds.
I'm not
MatLab: 14 msec
Python: 2 msec
For times this small, I wonder if timing comparisons are valid. I do
NOT think SciPy is in general an order of magnitude faster than Matlab
for the task typically performed with Matlab.
The algorithm is meant for real-time analysis,
where these kind of
I'm not sure about SciPy,
Yes SciPy allows it too !
but lists in standard Python allow this:
array = [1, 2, 3, 4]
array[2:5]
[3, 4]
That's generally a good thing.
You're not perhaps by origin an analog engineer ;-)
cheers,
Stef Mientki
--
A other great thing: With rpy you have R bindings for python.
So you have the power of R and the easy syntax and big standard lib of python!
:)
--
http://mail.python.org/mailman/listinfo/python-list
Mathias Panzenboeck wrote:
A other great thing: With rpy you have R bindings for python.
forgive my ignorance, what's R, rpy ?
Or is only relevant for Linux users ?
cheers
Stef
So you have the power of R and the easy syntax and big standard lib of
python! :)
--
: [EMAIL PROTECTED]
[mailto:[EMAIL PROTECTED] On
Behalf Of Stef Mientki
Sent: Saturday, December 30, 2006 9:24 AM
To: python-list@python.org
Subject: Re: Wow, Python much faster than MatLab
Mathias Panzenboeck wrote:
A other great thing: With rpy you have R bindings for python.
forgive
Doran, Harold wrote:
R is the open-source implementation of the S language developed at Bell
laboratories. It is a statistical programming language that is becoming
the de facto standard among statisticians.
Thanks for the information
I always thought that SPSS or SAS where thé standards.
Stef
Stef Mientki [EMAIL PROTECTED] writes:
Mathias Panzenboeck wrote:
A other great thing: With rpy you have R bindings for python.
forgive my ignorance, what's R, rpy ?
Or is only relevant for Linux users ?
[...]
R is a language / environment for statistical programming. RPy is a
Python
Stef Mientki [EMAIL PROTECTED] writes:
Doran, Harold wrote:
R is the open-source implementation of the S language developed at Bell
laboratories. It is a statistical programming language that is becoming
the de facto standard among statisticians.
Thanks for the information
I always
R is the free version of the S language. S-PLUS is a commercial version.
Both are targeted at statisticians per se. Their strengths are in
exploratory data analysis (in my opinion).
SAS has many statistical featues, and is phenomenally well-documented and
supported. One of its great strengths
I think of SAS and R as being like airliners and helicopters --
I like that comparison,...
.. Airplanes are inherent stable,
.. Helicopters are inherent not-stable ;-)
cheers,
Stef
--
http://mail.python.org/mailman/listinfo/python-list
On 12/31/06, [EMAIL PROTECTED] [EMAIL PROTECTED] wrote:
R is the free version of the S language. S-PLUS is a commercial version.
Both are targeted at statisticians per se. Their strengths are in
exploratory data analysis (in my opinion).
SAS has many statistical featues, and is phenomenally
Stef Mientki wrote:
I always thought that SPSS or SAS where thé standards.
Stef
As far as SPSS is a standard, it is in the field of religious use of
statistical procedures I don't understand (as I'm a math retard), but
hey p0.05 is always significant (and any other value is proof of the
Sturla,
I am working in the healthcare and seeing people loves to use excel /
spss as database or statistical tool without know what he/she is
doing. However, that is not the fault of excel/spss itself but of
people who is using it. Things, even include SAS/R, would look stupid,
when it has been
Stef Mientki wrote:
MatLab: 14 msec
Python: 2 msec
I have the same experience. NumPy is usually faster than Matlab. But it
very much depends on how the code is structured.
I wonder if it is possible to improve the performance of NumPy by
having its fundamental types in the language, instead
Wensui Liu wrote:
doing. However, that is not the fault of excel/spss itself but of
people who is using it.
Yes and no. I think SPSS makes it too tempting. Like children playing
with fire, they may not even know it's dangerous. You can do an GLM in
SPSS by just filling out a form - but how
sturlamolden wrote:
array3[:] = array1[:] + array2[:]
OT, but why are you slicing array1 and array2? All that does is create new array
objects pointing to the same data.
Now for my question: operator overloading is (as shown) not the
solution to efficient scientific computing. It creates
Stef Mientki wrote:
hi All,
instead of questions,
my first success story:
I converted my first MatLab algorithm into Python (using SciPy),
and it not only works perfectly,
but also runs much faster:
MatLab: 14 msec
Python: 2 msec
For times this small, I wonder if timing comparisons
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