File: t_LatentVariableModel_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 (29 lines) | stat: -rwxr-xr-x 873 bytes parent folder | download | duplicates (2)
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
#! /usr/bin/env python

import openturns as ot
import openturns.experimental as otexp
import openturns.testing as ott

ot.TESTPREAMBLE()

# Latent variable model for 4 categorical levels and
# a 3-dimensional latent space
k = otexp.LatentVariableModel(4, 3)
k.setLatentVariables([0.1, 0.2, 0.3, -0.1, -0.2, -0.3, 0.4])
k.setScale([1.5])
k.setAmplitude([2.0])

# We define a squared exponential kernel in the latent space as a reference
kRef = ot.SquaredExponential(3)
kRef.setScale(ot.Point(3, 1.5))
kRef.setAmplitude(ot.Point(1, 2.0))

ott.assert_almost_equal(
    k(1, 1)[0, 0], kRef(ot.Point([0.1, 0.0, 0.0]), ot.Point([0.1, 0.0, 0.0]))[0, 0]
)
ott.assert_almost_equal(
    k(1, 2)[0, 0], kRef(ot.Point([0.1, 0.0, 0.0]), ot.Point([0.2, 0.3, -0.1]))[0, 0]
)
ott.assert_almost_equal(
    k(0, 3)[0, 0], kRef(ot.Point([0.0, 0.0, 0.0]), ot.Point([-0.2, -0.3, 0.4]))[0, 0]
)