#9770 closed defect (fixed)
SphericalDistribution() is not random
Reported by: | schilly | Owned by: | amhou |
---|---|---|---|
Priority: | major | Milestone: | sage-5.0 |
Component: | statistics | Keywords: | |
Cc: | Merged in: | sage-5.0.beta9 | |
Authors: | Douglas McNeil | Reviewers: | Jason Grout, Jeroen Demeyer |
Report Upstream: | N/A | Work issues: | |
Branch: | Commit: | ||
Dependencies: | #9958 | Stopgaps: |
Description (last modified by )
In the following list l
, some elements repeat quite often:
sage: l = [ SphericalDistribution(dimension=2).get_random_element() for _ in range(1000)] sage: uniq = [] sage: for x in l: if x not in uniq: uniq.append(x) ....: sage: len(uniq) 34
The output is not random. For example, the first line is repeated ~30 times in the 1000 lines of output. It works fine if SphericalDistribution is only instantiated once!
Attachments (4)
Change History (23)
Changed 7 years ago by
comment:1 Changed 7 years ago by
comment:2 Changed 6 years ago by
- Status changed from new to needs_review
Oh, wow. This is because of the lines
self.seed = random.randint(1, 2^32)
2 xor 32 is 34.. and the ^
vs. **
bug strikes again.
comment:3 Changed 6 years ago by
- Reviewers set to Jason Grout
- Status changed from needs_review to positive_review
Wow, good catch. Affected file passes tests; code looks good.
Can you fill in the author name?
comment:4 Changed 6 years ago by
comment:5 Changed 6 years ago by
- Merged in set to sage-4.8.alpha5
- Resolution set to fixed
- Status changed from positive_review to closed
comment:6 Changed 6 years ago by
- Merged in sage-4.8.alpha5 deleted
- Resolution fixed deleted
- Status changed from closed to new
On hawk (OpenSolaris? 06.2009-32):
sage -t -long -force_lib devel/sage/sage/gsl/probability_distribution.pyx ********************************************************************** File "/export/home/buildbot/build/sage/hawk-1/hawk_full/build/sage-4.8.alpha5/devel/sage-main/sage/gsl/probability_distribution.pyx", line 339: sage: T = RealDistribution('rayleigh', sigma) Exception raised: Traceback (most recent call last): File "/export/home/buildbot/build/sage/hawk-1/hawk_full/build/sage-4.8.alpha5/local/bin/ncadoctest.py", line 1231, in run_one_test self.run_one_example(test, example, filename, compileflags) File "/export/home/buildbot/build/sage/hawk-1/hawk_full/build/sage-4.8.alpha5/local/bin/sagedoctest.py", line 38, in run_one_example OrigDocTestRunner.run_one_example(self, test, example, filename, compileflags) File "/export/home/buildbot/build/sage/hawk-1/hawk_full/build/sage-4.8.alpha5/local/bin/ncadoctest.py", line 1172, in run_one_example compileflags, 1) in test.globs File "<doctest __main__.example_12[16]>", line 1, in <module> T = RealDistribution('rayleigh', sigma)###line 339: sage: T = RealDistribution('rayleigh', sigma) File "probability_distribution.pyx", line 503, in sage.gsl.probability_distribution.RealDistribution.__init__ (sage/gsl/probability_distribution.c:2309) self.seed = random.randint(1, 2**32) OverflowError: long int too large to convert to int ********************************************************************** [...many more like this...] sage -t -long -force_lib devel/sage/sage/matrix/constructor.py ********************************************************************** File "/export/home/buildbot/build/sage/hawk-1/hawk_full/build/sage-4.8.alpha5/devel/sage-main/sage/matrix/constructor.py", line 2944: sage: print "ignore this"; B=random_matrix(FiniteField(7), 4, 4, algorithm='echelon_form', num_pivots=3); B # random Exception raised: Traceback (most recent call last): File "/export/home/buildbot/build/sage/hawk-1/hawk_full/build/sage-4.8.alpha5/local/bin/ncadoctest.py", line 1231, in run_one_test self.run_one_example(test, example, filename, compileflags) File "/export/home/buildbot/build/sage/hawk-1/hawk_full/build/sage-4.8.alpha5/local/bin/sagedoctest.py", line 38, in run_one_example OrigDocTestRunner.run_one_example(self, test, example, filename, compileflags) File "/export/home/buildbot/build/sage/hawk-1/hawk_full/build/sage-4.8.alpha5/local/bin/ncadoctest.py", line 1172, in run_one_example compileflags, 1) in test.globs File "<doctest __main__.example_19[10]>", line 1, in <module> print "ignore this"; B=random_matrix(FiniteField(Integer(7)), Integer(4), Integer(4), algorithm='echelon_form', num_pivots=Integer(3)); B # random###line 2944: sage: print "ignore this"; B=random_matrix(FiniteField(7), 4, 4, algorithm='echelon_form', num_pivots=3); B # random File "/export/home/buildbot/build/sage/hawk-1/hawk_full/build/sage-4.8.alpha5/local/lib/python/site-packages/sage/matrix/constructor.py", line 1250, in random_matrix return random_rref_matrix(parent, *args, **kwds) File "/export/home/buildbot/build/sage/hawk-1/hawk_full/build/sage-4.8.alpha5/local/lib/python/site-packages/sage/matrix/constructor.py", line 3020, in random_rref_matrix pivot_generator=pd.RealDistribution("beta",[1.6,4.3]) File "probability_distribution.pyx", line 503, in sage.gsl.probability_distribution.RealDistribution.__init__ (sage/gsl/probability_distribution.c:2309) OverflowError: long int too large to convert to int ********************************************************************** [...]
comment:7 Changed 6 years ago by
- Status changed from new to needs_info
Urf. Probably (?) we can simply replace 2**32
here with sys.maxint, but I can't be sure because I can't reproduce.
Emailed a guy who I know has access to a solaris box :-) but haven't heard back. If anyone with an account on hawk could report the results of a cut-and-paste of the following
preparser(False) import random, sys nn = [2**31, 2**32, sys.maxint] for n in nn: for d in -1, 0, 1: print n, d, n+d, repr(n+d), type(n+d) try: seed = random.randint(1, n+d) except Exception as err: print err try: random.seed(n+d) except Exception as err: print err
from within Sage, that would test whether I understand what's going on.
comment:8 Changed 6 years ago by
Output of your Sage script on hawk
:
2147483648 -1 2147483647 2147483647L <type 'long'> 2147483648 0 2147483648 2147483648L <type 'long'> 2147483648 1 2147483649 2147483649L <type 'long'> 4294967296 -1 4294967295 4294967295L <type 'long'> 4294967296 0 4294967296 4294967296L <type 'long'> 4294967296 1 4294967297 4294967297L <type 'long'> 2147483647 -1 2147483646 2147483646 <type 'int'> 2147483647 0 2147483647 2147483647 <type 'int'> 2147483647 1 2147483648 2147483648L <type 'long'>
comment:9 Changed 6 years ago by
* bump *
comment:10 Changed 6 years ago by
- Status changed from needs_info to needs_review
Version modified to (hopefully) avoid overflow errors without sacrificing entropy. @jdemeyer, you mind trying it on hawk?
comment:11 Changed 6 years ago by
Apply trac_9770_fix_distribution_seeds_v2.patch
(for the patchbot, which is trying to install both patches at once)
comment:12 Changed 6 years ago by
- Dependencies set to #9958
- Reviewers changed from Jason Grout to Jason Grout, PatchBot
- Status changed from needs_review to needs_work
- Work issues set to needs rebase
This seems to conflict (in a rather trivial way) with #9958, and hence doesn't apply to the latest Sage beta.
comment:13 Changed 6 years ago by
- Status changed from needs_work to needs_review
comment:14 Changed 6 years ago by
Apply trac_9770_fix_distribution_seeds_v4.patch
(for patchbot)
comment:15 Changed 6 years ago by
- Work issues needs rebase deleted
comment:16 Changed 6 years ago by
- Reviewers changed from Jason Grout, PatchBot to Jason Grout
Let's not anthropomorphize the PatchBot? :-)
comment:17 Changed 6 years ago by
- Reviewers changed from Jason Grout to Jason Grout, Jeroen Demeyer
- Status changed from needs_review to positive_review
Seems to work as it should...
comment:18 Changed 6 years ago by
- Merged in set to sage-5.0.beta9
- Resolution set to fixed
- Status changed from positive_review to closed
comment:19 Changed 2 years ago by
- Description modified (diff)
The bug is not unique to the spherical distribution; rather, it has to do with whether the distribution is instantiated prior to the use of the random element method. The worksheet above illustrates the same behavior with the Gaussian and uniform distributions.