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Use new binomtest function #6

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Use new binomtest function #6

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2 changes: 1 addition & 1 deletion README.md
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Original file line number Diff line number Diff line change
Expand Up @@ -110,7 +110,7 @@ The best distinguisher results are essentially following Binomial distribution:
`Bi(number_of_blocks, probability_of_dist_eval_to_1)`.

To compute the p-value we run the Binomial test:
```scipy.stats.binom_test(observed_ones, n=ntrials, p=dist_probab, alternative='two-sided')```
```scipy.stats.binomtest(observed_ones, n=ntrials, p=dist_probab, alternative='two-sided').pvalue```

This method eliminates a need to have a `pval_db.json` database computed with the reference data for given parameters.
The benefit is the halving method gives directly a p-value, without a need to run reference computations.
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6 changes: 3 additions & 3 deletions booltest/booltest_main.py
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Expand Up @@ -289,7 +289,7 @@ def analyse_input(self, num_evals, hws_input=None):

for res in results:
fail = 'x' if self.zscore_thresh and abs(res.zscore) > self.zscore_thresh else ' '
indiv_pval = ' indiv-pval: %.5e,' % (stats.binom_test(res.obs_cnt, n=num_evals, p=res.expp, alternative='two-sided'),) if self.comp_indiv_pval else ''
indiv_pval = ' indiv-pval: %.5e,' % (stats.binomtest(res.obs_cnt, n=num_evals, p=res.expp, alternative='two-sided').pvalue,) if self.comp_indiv_pval else ''
self.tprint(' - zscore[idx%02d]: %+05.5f, observed: %08d, expected: %08d %s idx: %6d,%s poly: %s'
% (res.idx, res.zscore, res.obs_cnt, res.exp_cnt, fail, res.idx, indiv_pval, self.input_poly[res.idx]))

Expand Down Expand Up @@ -533,7 +533,7 @@ def analyse(self, num_evals, hws=None, hws_input=None, ref_hws=None):

for i in range(min(len(top_res), 30)):
comb = top_res[i]
indiv_pval = ' indiv-pval: %.5e,' % (stats.binom_test(comb.obs_cnt, n=num_evals, p=comb.expp, alternative='two-sided'),) if self.comp_indiv_pval else ''
indiv_pval = ' indiv-pval: %.5e,' % (stats.binomtest(comb.obs_cnt, n=num_evals, p=comb.expp, alternative='two-sided').pvalue,) if self.comp_indiv_pval else ''
self.tprint(' - best poly zscore %9.5f, expp: %.4e, exp: %7d, obs: %7d, diff: %10.7f %%,%s poly: %s'
% (comb.zscore, comb.expp, comb.exp_cnt, comb.obs_cnt,
100.0 * (comb.exp_cnt - comb.obs_cnt) / comb.exp_cnt, indiv_pval, sorted(comb.poly)))
Expand Down Expand Up @@ -1365,7 +1365,7 @@ def analyze_iobj(self, iobj, coffset=0, tvsize=None, jscres=None):
jsres['halvings'] = []
for ix, cr in enumerate(self.sort_res_by_input_poly(rshort)):
ntrials = (tvsize * 8) // self.blocklen
pval = stats.binom_test(cr.obs_cnt, n=ntrials, p=cr.expp, alternative='two-sided')
pval = stats.binomtest(cr.obs_cnt, n=ntrials, p=cr.expp, alternative='two-sided').pvalue

jsresc = collections.OrderedDict()
jsresc['nsamples'] = ntrials
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2 changes: 1 addition & 1 deletion setup.py
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Expand Up @@ -9,7 +9,7 @@
install_requires = [
'pycryptodome', # pycrypto alternative, working also on Win
'bitarray_ph4>=1.6.4',
'scipy',
'scipy>=1.7.0',
'ufx',
'repoze.lru',

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