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
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
|
#!/usr/bin/env python
import openturns as ot
ot.TESTPREAMBLE()
def hyperplane(coefs):
"""
Generate a linear NMF from its coefficients
"""
dim = len(coefs)
constant = [0.0]
center = [0.0] * len(coefs)
linear = ot.Matrix(1, dim)
for i in range(dim):
linear[0, i] = coefs[i]
function = ot.LinearFunction(center, constant, linear)
return function
# To avoid discrepancy on CI
ot.ResourceMap.SetAsScalar("Solver-DefaultResidualError", 1.0e-8)
for dim in [3, 5, 8][1:2]:
functions = []
linears = []
linear = [1.0] * dim
linears.append(linear)
function = hyperplane(linear)
function.setName("H1")
functions.append(function)
linear = list(linear)
linear[0] = 0.25
linears.append(linear)
function = hyperplane(linear)
function.setName("H2")
functions.append(function)
linear = list(linear)
linear = [0.0] * dim
linear[dim - 1] = 1.0
linears.append(linear)
function = hyperplane(linear)
function.setName("H3")
functions.append(function)
for ih in range(len(functions)):
function = functions[ih]
distribution = ot.Normal(dim)
randomVector = ot.RandomVector(distribution)
composite = ot.CompositeRandomVector(function, randomVector)
for pft in [1e-4, 1e-6, 1e-8][1:2]:
k = ot.Normal().computeQuantile(pft)[0] * ot.Point(linears[ih]).norm()
event = ot.ThresholdEvent(composite, ot.Less(), k)
print("--------------------")
print("model H" + str(ih) + " dim=%d" % dim, "pft=%.2e" % pft, "k=%g" % k)
for n in [100, 1000][1:]:
for gamma1 in [0.25, 0.5, 0.75][1:2]:
# algo = ot.MonteCarlo(event)
# algo.setMaximumOuterSampling(100*n)
# algo.setMaximumCoefficientOfVariation(-1.)
# algo.run()
# result = algo.getResult()
# print result
algo = ot.AdaptiveDirectionalStratification(event)
algo.setMaximumOuterSampling(n)
algo.setGamma([gamma1, 1.0 - gamma1])
calls0 = function.getEvaluationCallsNumber()
algo.run()
calls = function.getEvaluationCallsNumber() - calls0
result = algo.getResult()
pf = result.getProbabilityEstimate()
var = result.getVarianceEstimate()
cov = result.getCoefficientOfVariation()
print(result, end=" ")
print("callsNumber=%d" % calls + " gamma=" + str(algo.getGamma()))
|
