Hi,
I opened issue #8140 for that on github
(https://github.com/sympy/sympy/issues/8140). I was analyzing the issue
further and wrote a corresponding comment. I'd suggest that we continue the
discussion on GH...
Regards - Clemens
Am Sonntag, 28. September 2014 21:26:32 UTC+2 schrieb Aaron M
In Python 3 the error is TypeError: unorderable types: complex() < int().
Aaron Meurer
On Sun, Sep 28, 2014 at 2:21 PM, clemens novak wrote:
> Hi,
>
> I investigated the topic a bit further. One problem seems to be that
> sympy.plotting is not able to deal with piecewise functions. The code belo
Hi,
I investigated the topic a bit further. One problem seems to be that
sympy.plotting is not able to deal with piecewise functions. The code below
throws an error (see message below) also when I use the latest git version:
from sympy import *
from sympy.plotting import *
from sympy.stats impo
This works for me, even in 0.7.5. I get a plot that looks like a step function.
Aaron Meurer
On Thu, Sep 25, 2014 at 6:04 AM, Matthew Rocklin wrote:
> Yeah, wow, that expression and plot does look horrible.
>
> Looks like a bug to me. Generally integration on piecewise functions
> probably isn'
Yeah, wow, that expression and plot does look horrible.
Looks like a bug to me. Generally integration on piecewise functions
probably isn't as pretty as integration on normal distributions.
On Wed, Sep 24, 2014 at 8:29 AM, clemens novak wrote:
> Hello,
>
> i'm using sympy 0.7.5. and ran into t
Hello,
i'm using sympy 0.7.5. and ran into the following issue when using the
stats module.
from sympy.stats import Uniform, density
from sympy.abc import x, y, z
from sympy import *
from sympy.plotting import plot
# I define two rv's with uniform distribution
X = Uniform('X', -1., 1.)
Y = Unif
It fails like that in master. AttributeError is pretty much always a
bug (unless you yourself accessed an attribute that is not there, or
passed in the wrong type to something).
Aaron Meurer
On Wed, Jan 29, 2014 at 12:56 PM, Matthew Rocklin wrote:
> This doesn't fail in 0.7.3 (Access to master i
This doesn't fail in 0.7.3 (Access to master isn't convenient at the
moment). Even when it doesn't err it still doesn't produce anything
meaningful.
On Wed, Jan 29, 2014 at 10:06 AM, F. B. wrote:
>
> In [5]: X = ContinuousRV(x, x, (0, 2))
>
> In [6]: Y = tan(X)
>
> In [7]: density(Y)
> Attribu
In [5]: X = ContinuousRV(x, x, (0, 2))
In [6]: Y = tan(X)
In [7]: density(Y)
AttributeError: 'tuple' object has no attribute 'is_Piecewise'
Is this a bug or a missing feature?
--
You received this message because you are subscribed to the Google Groups
"sympy" group.
To unsubscribe from this
On Tuesday, January 21, 2014 6:37:09 PM UTC+1, Matthew wrote:
>
> == doesn't mean SymPy equality, it means Python equality. Try Eq(X, 3)
> instead.
>
Oh, you're right.
--
You received this message because you are subscribed to the Google Groups
"sympy" group.
To unsubscribe from this group
== doesn't mean SymPy equality, it means Python equality. Try Eq(X, 3)
instead.
On Tue, Jan 21, 2014 at 9:07 AM, F. B. wrote:
>
> Concerning bugs and stats, is the following another bug?
>
> In [4]: X = Binomial('X', 4, S.Half)
>
> In [7]: density(X).dict
> Out[7]: {0: 1/16, 1: 1/4, 2: 3/8, 3:
Concerning bugs and stats, is the following another bug?
In [4]: X = Binomial('X', 4, S.Half)
In [7]: density(X).dict
Out[7]: {0: 1/16, 1: 1/4, 2: 3/8, 3: 1/4, 4: 1/16}
In [8]: P(X > 2)
Out[8]: 5/16
In [9]: P(X == 2)
AttributeError: 'NoneType' object has no attribute 'probability'
I would exp
Oh I didn't even notice that the summation is finite. So of course it
converges. I think the algorithm computes finite sums by computing an
infinite sum as an intermediary somehow (like summation(..., (k, 0,
oo)) - summation(..., (k, n + 1, oo)); assuming my memory serves me
correctly), but obvious
On Friday, January 17, 2014 11:27:49 PM UTC+1, Aaron Meurer wrote:
>
>
> We'll have to see if the conditions for this integral can be improved.
> Any idea what the full convergence conditions should be?
>
>
That distribution is given by the polynomial expansion of *1 = ( p + (1-p)
)^n*, the var
On Fri, Jan 17, 2014 at 11:24 AM, F. B. wrote:
>
>
> On Friday, January 17, 2014 1:51:22 AM UTC+1, Aaron Meurer wrote:
>>
>> it seems it is not true if p > 0.5.
>>
>
> No, this distribution is symmetric under { p ---> 1 - p, k ---> n - k }
> substitution, it has surely to converge for 0 <= p <= 1
> Is there any list of papers/algorithms about summations?
There is kind of a Todo list. I collected some material
about symbolic summation. Do you want to do some coding
in that direction?
--
You received this message because you are subscribed to the Google Groups
"sympy" group.
To unsubscrib
On Friday, January 17, 2014 1:51:22 AM UTC+1, Aaron Meurer wrote:
>
> it seems it is not true if p > 0.5.
>
>
No, this distribution is symmetric under *{ p ---> 1 - p, k ---> n - k
}*substitution, it has surely to converge for
*0 <= p <= 1*
> The summation module is just fine for this proble
On Thu, Jan 16, 2014 at 4:13 PM, F. B. wrote:
> On Wed, Jan 15, 2014 at 1:17 PM, Matthew Rocklin wrote:
>>
>> Discrete is the newest and least mature and could presumably use work.
>
>
> I tried to define a binomial distribution manually and do some calculations
> using summation:
>
> In [1]: n,
Yes, the summation module could certainly use more work. I think that this
has been on the GSoC project list for a while.
Fortunately this work can happen separately from work in stats. There is a
pretty clear separation between the two modules.
On Thu, Jan 16, 2014 at 2:13 PM, F. B. wrote:
On Wed, Jan 15, 2014 at 1:17 PM, Matthew Rocklin
> wrote:
> Discrete is the newest and least mature and could presumably use work.
>
I tried to define a binomial distribution manually and do some calculations
using summation:
In [1]: n, k = var('n k', positive=True, integer=True)
In [2]: p =
> we should think about providing built-in expected value formulas for
common types like Binomial (which has a very simple formula that should
return almost immediately even for large values).
This is also a good idea. I want to say that this might almost exist
already, at least for continuous va
Background - SymPy.stats has three major components for three different
kind of distributions
Finite Random Variables - like dice, coins, and binomials -- uses Python
iterators
Continuous Random Variables - like normals, exponentials, and chi squares
-- Uses SymPy Integrals
Discrete Random Vari
I recently downloaded SymPy and was looking to contribute to the stats
module. I noticed a few things.
1) The Geometric and Poisson random variable types exist in the
drv_types.py file, and I'm able to use them in code, but they aren't
mentioned in the documentation (
http://docs.sympy.org/late
23 matches
Mail list logo
