# %%
# The algorithm added a point to the right bound of the domain.
# %%
for krigingStep in range(5):
xNew = getNewPoint(xMin, xMax, krigResult)
yNew = g(xNew)
X.add(xNew)
Y.add(yNew)
krigResult = createMyBasicKriging(X, Y)
graph = plotMyBasicKriging(krigResult, xMin, xMax, X, Y)
graph.setTitle("Kriging #%d " % (krigingStep + 1) + graph.getTitle())
View(graph)
# %%
# We observe that the second added point is the left bound of the domain. The remaining points were added strictly inside the domain where the accuracy was drastically improved.
#
# With only 10 points, the metamodel accuracy is already very good with a Q2 which is equal to 99.9%.
# %%
# Conclusion
# ----------
#
# The current example presents the naive implementation on the creation of a sequential design of experiments based on kriging. More pratical algorithms are presented in the following references.
#
# * Mona Abtini. Plans prédictifs à taille fixe et séquentiels pour le krigeage (2008). Thèse de doctorat de l'Université de Lyon.
# * Céline Scheidt. Analyse statistique d’expériences simulées : Modélisation adaptative de réponses non régulières par krigeage et plans d’expériences (2007). Thèse présentée pour obtenir le grade de Docteur de l’Université Louis Pasteur.
# * David Ginsbourger. Sequential Design of Computer Experiments. Wiley StatsRef: Statistics Reference Online, Wiley (2018 )
View.ShowAll()
graph = ot.HistogramFactory().build(sample, 10).drawPDF()
# graph.draw('curve3.png')
view = View(graph)
# view.save('curve3.png')
view.show()
# QQPlot tests
size = 100
normal = ot.Normal(1)
sample = normal.getSample(size)
sample2 = ot.Gamma(3.0, 4.0, 0.0).getSample(size)
graph = ot.VisualTest.DrawQQplot(sample, sample2, 100)
# graph.draw('curve4.png')
view = View(graph)
# view.save('curve4.png')
view.ShowAll(block=True)
# Clouds tests
dimension = 2
R = ot.CorrelationMatrix(dimension)
R[0, 1] = 0.8
distribution = ot.Normal(ot.Point(dimension, 3.0),
ot.Point(dimension, 2.0), R)
size = 100
sample1 = ot.Normal([3.0] * dimension, [2.0] * dimension,
R).getSample(size)
sample2 = ot.Normal([2.0] * dimension, [3.0] * dimension,
R).getSample(size // 2)
cloud1 = ot.Cloud(sample1, "blue", "fsquare", "Sample1 Cloud")
cloud2 = ot.Cloud(sample2, "red", "fsquare", "Sample2 Cloud")
graph = ot.Graph("two samples clouds", "x1", "x2", True, "topright")
