> google/paranoid_crypto has a number of Randomness Tests:
> google/paranoid_crypto has a number of Randomness Tests in Python IIR > From grep '^#' > https://github.com/google/paranoid_crypto/blob/main/docs/randomness_tests.md > : > > ```md > # Randomness tests > ## Goal of the tests > ## Non-goals > ## Usage > ## Tests > ### NIST SP 800-22 > #### Frequency (Monobits) Test > #### Frequency Test within a Block > #### Runs Test > #### Test for the Longest Run of Ones in a Block > #### Binary Matrix Rank Test > #### Discrete Fourier Transform (Spectral) Test > #### Non-Overlapping Template Matching Test > #### Overlapping Template Matching Test. > #### Maurer’s “Universal Statistical” Test > #### Linear Complexity Test > #### Serial Test. > #### Approximate Entropy Test > #### Cumulative Sums (Cusum) Test. > #### Random Excursions Test. > #### Random Excursions Variant Test > ### Additional tests > #### FindBias > #### LargeBinaryMatrixRank > #### LinearComplexityScatter > ## Interface > ### Repeating tests > ## Testing > ### Pseudorandom number generators for testing > #### urandom > #### mt19937 > #### gmp_n > #### mwc_n > #### java > #### lcgnist > #### xorshift128+ > #### xorshift* > #### xorwow > #### pcg64, philox, sfc64 > #### jsf32, jsf64 > ## Design decisions > ``` > > $ grep '^\s*class' > https://github.com/google/paranoid_crypto/blob/main/paranoid_crypto/lib/randomness_tests/rng.py > : > > ```python > class Rng: > class Urandom(Rng): > class Shake128(Rng): > class Mt19937(Rng): > class GmpRand(Rng): > class XorShift128plus(Rng): > class XorShiftStar(Rng): > class Xorwow(Rng): > class JavaRandom(Rng): > class LcgNist(Rng): > class Mwc(Rng): > class NumpyRng(Rng): > class Lehmer(Rng): > class Pcg64(NumpyRng): > class Philox(NumpyRng): > class Sfc64(NumpyRng): > class SubsetSum(Rng): > ``` > > From > https://github.com/google/paranoid_crypto/blob/16e5f47fcc11f51d3fb58b50adddd075f4373bbc/paranoid_crypto/lib/randomness_tests/random_test_suite.py#L42-L80 > : > > ```python > NIST_TESTS = [ > (nist_suite.Frequency, []), > (nist_suite.BlockFrequency, []), > (nist_suite.Runs, []), > (nist_suite.LongestRuns, []), > (nist_suite.BinaryMatrixRank, []), > (nist_suite.Spectral, []), > (nist_suite.NonOverlappingTemplateMatching, []), > (nist_suite.OverlappingTemplateMatching, []), > (nist_suite.Universal, []), > (nist_suite.LinearComplexity, [512]), > (nist_suite.LinearComplexity, [1024]), > (nist_suite.LinearComplexity, [2048]), > (nist_suite.LinearComplexity, [4096]), > (nist_suite.Serial, []), > (nist_suite.ApproximateEntropy, []), > (nist_suite.RandomWalk, []), > ] > > > EXTENDED_NIST_TESTS = [ > (extended_nist_suite.LargeBinaryMatrixRank, []), > # Computing the linear complexity has quadratic complexity. > # A consequence of this is that LinearComplexityScatter only > # uses a fraction of the input. A parameter [n, m] means > # that n m-bit sequences are tested, where the i-th sequence > # consists of the bits i, i + n, ..., i + (m-1) * m. > (extended_nist_suite.LinearComplexityScatter, [32, 100000]), > (extended_nist_suite.LinearComplexityScatter, [64, 50000]), > (extended_nist_suite.LinearComplexityScatter, [128, 40000]), > ] > > > LATTICE_TESTS = [ > (lattice_suite.FindBias, [256]), > (lattice_suite.FindBias, [384]), > (lattice_suite.FindBias, [512]), > (lattice_suite.FindBias, [1024]), > ] > > > TESTS = NIST_TESTS + EXTENDED_NIST_TESTS + LATTICE_TESTS > ``` > > > - [ ] ENH: paranoid_crypto: add a __main__ so that python -m > paranoid_crypto.randomness_tests calls eg: > - [x] > https://github.com/google/paranoid_crypto/blob/main/examples/randomness.py > - [ ] REF: examples/randomness.py -> lib/randomness_tests/main.py > - [ ] ENH: paranoid_crypto.randomness_tests: add a __main__ so that > `python -m paranoid_crypto.randomness_tests -h` works > - [ ] ENH: setup.py: console_scripts entrypoint for > examples/randomness_tests/main.py > > - [ ] DOC: > https://en.wikipedia.org/wiki/Randomness_test#Notable_software_implementations: > link to google/paranoid_crypto > > > > On Tue, Nov 15, 2022 at 7:25 AM Chris Angelico <ros...@gmail.com> wrote: > >> On Tue, 15 Nov 2022 at 22:41, Chris Angelico <ros...@gmail.com> wrote: >> > >> > (I'm assuming that you sent this personally by mistake, and am >> > redirecting back to the list. My apologies if you specifically didn't >> > want this to be public.) >> > >> > On Tue, 15 Nov 2022 at 22:34, James Johnson <jj126...@gmail.com> wrote: >> > > >> > > It’s been a couple of years ago, but as I recall the duplicates >> seemed to be of two or three responses, not randomly distributed. >> > > >> > > I looked at my code, and I DID salt the hash at every update. >> > > >> > > At this point, my curiosity is engaged to know if this s/w solution >> is as good as others. I don’t have the training to test how often 9 follows >> 5, for example, but I am competitive enough to ask how it holds up against >> MTprng. I think it’s possibly very good, for s/w, and I’m emboldened to ask >> you to modify the code (it requires you data enter the numbers back to the >> machine, allowing time to pass;) to accumulate the results for 2 or 3 >> million, and see how it holds up. I don’t think the numbers track the bell >> curve on distribution . I speculate it’s more square. I suppose this is >> desirable in a PRNG? >> > > >> > >> > I'll get you to do the first step of the modification. Turn your code >> > into a module that has a randbelow() function which will return a >> > random integer from 0 up to the provided argument. (This is equivalent >> > to the standard library's random.randrange() function when given just >> > one argument.) If you like, provide several of them, as randbelow1, >> > randbelow2, etc. >> > >> > Post that code, and then I'll post a test harness that can do some >> > analysis for you. >> >> Here's a simple test harness. There are other tests you could use, but >> this one is pretty straight-forward. >> >> https://github.com/Rosuav/shed/blob/master/howrandom.py >> >> For each test, it counts up how many times each possible sequence >> shows up, then displays the most and least common, rating them >> according to how close they came to a theoretical perfect >> distribution. A good random number generator should produce results >> that are close to 100% for all these tests, but the definition of >> "close" depends on the pool size used (larger means closer, but also >> means more CPU time) and the level of analysis done. In my testing, >> all of the coin-flip data showed values +/- 1%, and the others never >> got beyond 10%. >> >> This is the same kind of analysis that was used in the page that I >> linked to earlier, so you can play with it interactively there if >> you're curious. It also has better explanations than I would give. >> >> Again, there are plenty of other types of tests you could use, but >> this is a pretty easy one. True randomness should show no biases in >> any of these results, though there will always be some variance. >> >> ChrisA >> _______________________________________________ >> Python-ideas mailing list -- python-ideas@python.org >> To unsubscribe send an email to python-ideas-le...@python.org >> https://mail.python.org/mailman3/lists/python-ideas.python.org/ >> Message archived at >> https://mail.python.org/archives/list/python-ideas@python.org/message/5OWDW5XUE5JYAW3QKMNZYKXH3NNBPNNW/ >> Code of Conduct: http://python.org/psf/codeofconduct/ >> >
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