Commit d0fc4754 by Tiago Peixoto

### test_inference_mcmc: Simplify statistics

parent 9566e946
Pipeline #206 failed with stage
in 4651 minutes and 40 seconds
 ... ... @@ -116,7 +116,7 @@ for directed in [True, False]: for i, c in enumerate(cs): if c != "gibbs": mcmc_args=dict(beta=1, c=c, niter=20 if c != numpy.inf else 200) mcmc_args=dict(beta=1, c=c, niter=20) else: mcmc_args=dict(beta=1, niter=20) if i == 0: ... ... @@ -129,8 +129,8 @@ for directed in [True, False]: hists[c] = mcmc_equilibrate(state, mcmc_args=mcmc_args, gibbs=c=="gibbs", wait=10000, nbreaks=40, wait=4000, nbreaks=5, verbose=(1, "c = %s " % str(c)) if verbose else False, history=True) ... ... @@ -143,14 +143,15 @@ for directed in [True, False]: pass Ss1 = array(list(zip(*hists[c1]))[0]) Ss2 = array(list(zip(*hists[c2]))[0]) # add very small normal noise, to solve discreetness issue # add very small normal noise, to solve discreteness issue Ss1 += numpy.random.normal(0, 1e-6, len(Ss1)) Ss2 += numpy.random.normal(0, 1e-6, len(Ss2)) D, p = scipy.stats.ks_2samp(Ss1, Ss2) D_c = 1.63 * sqrt((len(Ss1) + len(Ss2)) / (len(Ss1) * len(Ss2))) if verbose: print("directed:", directed, "c1:", c1, "c2:", c2, "D", D, "p-value:", p) if p < .001: "D:", D, "D_c:", D_c, "p-value:", p) if p < .01: print(("Warning, distributions for directed=%s (c1, c2) = " + "(%s, %s) are not the same, with a p-value: %g (D=%g)") % (str(directed), str(c1), str(c2), p, D)) ... ...
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