File: t_AdaptiveDirectionalStratification_std.py

package info (click to toggle)
openturns 1.24-4
  • links: PTS, VCS
  • area: main
  • in suites: forky, sid, trixie
  • size: 66,204 kB
  • sloc: cpp: 256,662; python: 63,381; ansic: 4,414; javascript: 406; sh: 180; xml: 164; yacc: 123; makefile: 98; lex: 55
file content (40 lines) | stat: -rwxr-xr-x 923 bytes parent folder | download | duplicates (3)
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
#!/usr/bin/env python

import openturns as ot

ot.TESTPREAMBLE()

# create a function
dim = 4
function = ot.SymbolicFunction(["E", "F", "L", "I"], ["F*L^3/(3.*E*I)"])

# create a distribution
distribution = ot.Normal([50.0, 1.0, 10.0, 5.0], [1.0] * dim, ot.IdentityMatrix(dim))
vect = ot.RandomVector(distribution)
composite = ot.CompositeRandomVector(function, vect)
event = ot.ThresholdEvent(composite, ot.Less(), -3.0)

# create an ADS algorithm
n = int(1e4)
algo = ot.AdaptiveDirectionalStratification(event)
algo.setMaximumOuterSampling(n)
algo.setGamma([0.6, 0.4])

algo.run()
result = algo.getResult()
print(result)

# ADS-2+
algo2 = algo
algo2.setPartialStratification(True)
algo2.run()
print("T=", algo2.getTStatistic())
result = algo2.getResult()
print(result)

# DPADS-2
algo3 = algo2
algo3.setQuadrantOrientation([1.0] * dim)  # enables DPADS-2, sets design
algo3.run()
result = algo3.getResult()
print(result)