iam-git / WellMet (public) (License: MIT) (since 2021-08-31) (hash sha1)
WellMet is pure Python framework for spatial structural reliability analysis. Or, more specifically, for "failure probability estimation and detection of failure surfaces by adaptive sequential decomposition of the design domain".
Clone URLs: https://rocketgit.com/user/iam-git/WellMet ssh://rocketgit@ssh.rocketgit.com/user/iam-git/WellMet git://git.rocketgit.com/user/iam-git/WellMet
release trunk
List of commits:
Subject Hash Author Date (UTC)
welford: add method for sparse data c9dd961c58243ac890b9a518cfff5901dd6a5789 I am 2021-06-28 13:47:09
welford: yet another Welford's algoritm implementation ec31907d6727c408306f4b25c09b7f87632b0ecf I am 2021-06-27 22:27:06
IS_stat.TrueIS: pridal jsem do komentářu vzpomínky 1c5b4596a4da1206b02ed91954cbe8147ec1e208 I am 2021-06-27 15:23:42
IS_stat: add get_IS_estimation() function d048b4cd4e90d8440e7ff2a3ad86bc0f3c160d4f I am 2021-06-26 19:55:14
spring: new brand hustý modul pro vyrovnání odhadů 3fc5eb00de1eb65cff2db71640c340602687f52f I am 2021-06-25 00:52:06
convex_hull.Ghull.rvs: hloupá chyba 7494eef4e2621ac9651037cd4a99e9f570704dee I am 2021-06-21 01:37:49
convex_hull.Ghull.rvs: ensure at least one node outside 1c6d05935c83d11637ae566528cf65eb384215b5 I am 2021-06-19 19:04:40
convex_hull.Ghull: add nonGaussian hulls support, fix MC integration 3e10c2cefff5f3e204c1d4118b195e37023a5a57 I am 2021-06-19 17:44:09
mart: add qhull polygon c3da6bdd1b6412f178c828c1eb1a6368a02eb74c I am 2021-06-18 23:27:48
dicebox.Goal: implement csv export odhadů abdb28f509e664c039a6e5ca4bf1036d00957349 I am 2021-06-15 15:06:15
reader: add export_estimation function. Jednoduchý jako busy ef8fcada4b01be8db29836aa09459d8a7ef382b6 I am 2021-06-15 03:08:53
convex_hull: add meaningless method 96a86b7eabceb15e6c937932c2002409c2df0178 I am 2021-06-14 16:35:39
dicebox: new brand box named Goal is ready 725c2561b62769af6034c690005d1d22e6af41c7 I am 2021-06-05 13:40:15
convex_hull: last node fix in fire() function ca938c11398dfb2e16441b989902dab1558336b3 I am 2021-06-05 13:25:55
convex_hull: small fix for QHull under single simplex integration b0b236cd14ae614ecd1310b8c7cfcafe8d1b0851 I am 2021-05-30 05:57:45
convex_hull: ensure Ghull to always have some nodes for expansion 7110d521d9fece58639a359c83773df904b1177f I am 2021-05-29 07:03:49
dicebox: throw away plot dependency, simplify increment function 1cf0c481b8df1937ff7eda6e0373d5d6a72387db I am 2021-05-28 16:42:19
simplex: add JustCubatureTriangulation class separated from Shull ea291f9deee9f3b5b8615ae1f14a49aecd327461 I am 2021-05-28 16:39:58
mart: add convex_hull routines 90105552e7aecf3df8e84a1f5c4bdc1b04be249b I am 2021-04-25 20:11:34
convex_hull.Ghull: try to outthink OS's memory management 116444dc08cc0261e149de02f21f14f74dc8816b I am 2021-04-25 08:02:25
Commit c9dd961c58243ac890b9a518cfff5901dd6a5789 - welford: add method for sparse data
Author: I am
Author date (UTC): 2021-06-28 13:47
Committer name: I am
Committer date (UTC): 2021-06-28 13:47
Parent(s): ec31907d6727c408306f4b25c09b7f87632b0ecf
Signer:
Signing key:
Signing status: N
Tree: 577d62e3924a318d6d145f59d334e41bb03cc78b
File Lines added Lines deleted
welford.py 24 1
File welford.py changed (mode: 100644) (index 6c167c5..d829bff)
... ... class Welford:
35 35 n = len(data) n = len(data)
36 36 T = np.sum(data) T = np.sum(data)
37 37 #č takhle je to numericky nejstabilnější #č takhle je to numericky nejstabilnější
38 S = np.sum(np.square(data - T/len(data)))
38 S = np.sum(np.square(data - T/n))
39
40 if self.n == 0:
41 self.n = n
42 self.T = T
43 self.S = S
44 else:
45 m = self.n #č to, co bylo
46 self.S += S + m /n /(m+n) * (n/m * self.T - T)**2
47 self.n += n
48 self.T += T
49
50 #č Pro řídká data, speciálita pro IS
51 #č uděláme explicitnou funkci s povinným sample size
52 def add_sparse(self, data, n):
53 """Method processes sparse data, assumes
54 only non-zero values of entire sample (of size n) need to be given"""
55 sample_size = len(data)
56 assert n >= sample_size
57
58 T = np.sum(data)
59 mean = T/n
60 #č takhle je to numericky nejstabilnější
61 S = np.sum(np.square(data - mean)) + (n - sample_size) * mean**2
39 62
40 63 if self.n == 0: if self.n == 0:
41 64 self.n = n self.n = n
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