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
|
#! /usr/bin/env python
from __future__ import print_function
import openturns as ot
ot.RandomGenerator.SetSeed(0)
size = 200
# input sample
inputSample = ot.Uniform(-1.0, 1.0).getSample(size)
outputSample = ot.NumericalSample(inputSample)
# Evaluation of y = ax + b (a: scale, b: translate)
# scale
scale = [3.0]
outputSample *= scale
# translate sample
translate = [3.1]
outputSample += translate
# Finally inverse transform using an arbitrary lambda
lamb = [1.8]
boxCoxFunction = ot.InverseBoxCoxEvaluationImplementation(lamb)
# transform y using BoxCox function
outputSample = boxCoxFunction(outputSample)
# Add small noise
epsilon = ot.Normal(0, 1.0e-2).getSample(size)
outputSample += epsilon
# Now we build the factory
factory = ot.BoxCoxFactory()
# Creation of the BoxCoxTransform
result = ot.GeneralizedLinearModelResult()
basis = ot.LinearBasisFactory(1).build()
covarianceModel = ot.DiracCovarianceModel()
shift = [1.0e-1]
myBoxCox = factory.build(inputSample, outputSample, covarianceModel, basis, shift, result)
print("myBoxCox (GLM) =", myBoxCox)
print("GLM result =", result)
|
