sampleX[i] = p
sampleZ = ot.NumericalSample(size, 1)
for i in range(size):
sampleZ[i] = ot.NumericalPoint(1, sampleY[i, 0] * sampleY[i, 0])
print("LinearModelAdjustedRSquared=",
ot.LinearModelTest.LinearModelAdjustedRSquared(sampleY, sampleZ))
print("LinearModelFisher=",
ot.LinearModelTest.LinearModelFisher(sampleY, sampleZ))
print("LinearModelResidualMean=",
ot.LinearModelTest.LinearModelResidualMean(sampleY, sampleZ))
print("LinearModelRSquared=",
ot.LinearModelTest.LinearModelRSquared(sampleY, sampleZ))
# Durbin Watson
ot.RandomGenerator.SetSeed(5415)
eps = ot.Normal(0, 20)
f = ot.NumericalMathFunction('x', '5+2*x+x^2-0.1*x^3')
N = 15
x = ot.NumericalSample([[0],[1.42857],[2.85714],[4.28571],[5.71429],[7.14286],
[8.57143],[10],[11.4286],[12.8571],[14.2857],[15.7143],
[17.1429],[18.5714],[20]])
y = f(x) + eps.getSample(N)
linmodel = ot.LinearModelFactory().build(x, y)
dwTest = ot.LinearModelTest.LinearModelDurbinWatson(x, y)
print('Durbin Watson = ', dwTest)
except:
import sys
print("t_LinearModelTest_std.py", sys.exc_info()[0], sys.exc_info()[1])
import openturns as ot
from openturns.viewer import View
ot.RandomGenerator.SetSeed(0)
dimension = 2
R = ot.CorrelationMatrix(dimension)
R[0, 1] = 0.8
distribution = ot.Normal([3.] * dimension, [2.] * dimension, R)
size = 100
sample = distribution.getSample(size)
firstSample = ot.Sample(size, 1)
secondSample = ot.Sample(size, 1)
for i in range(size):
firstSample[i] = ot.Point(1, sample[i, 0])
secondSample[i] = ot.Point(1, sample[i, 1])
lmtest = ot.LinearModelFactory().build(firstSample, secondSample)
drawLinearModelResidual = ot.VisualTest_DrawLinearModelResidual(
firstSample, secondSample, lmtest)
View(drawLinearModelResidual, figure_kwargs={'figsize': (5, 5)})
graph = ot.Normal().drawPDF()
fname = 'testDraw.png'
try:
graph.draw(fname)
os.remove(fname)
except:
raise
print('OK')
except:
print('no')
# check that rot package is installed
print('4: linear model (R.rot)'.ljust(width), end=' ')
try:
lm = ot.LinearModelFactory().build(
ot.Normal(2).getSample(10),
ot.Normal().getSample(10))
print('OK')
except:
print('no')
# check XML support
print('5: serialization (LibXML2)'.ljust(width), end=' ')
try:
storageManager = ot.XMLStorageManager('myFile.xml')
print('OK')
except:
print('no')
# check that analytical function are available
print('6: analytical function (muParser)'.ljust(width), end=' ')
import openturns as ot
from openturns.viewer import View
N = 1000
#create a sample X
dist = ot.Triangular(1.0, 5.0, 10.0)
# create a Y sample : Y = 0.5 + 3 * X + eps
eps = ot.Normal(0.0, 1.0)
sample = ot.ComposedDistribution([dist, eps]).getSample(N)
f = ot.SymbolicFunction(['x', 'eps'], ['0.5+3.0*x+eps'])
sampleY = f(sample)
sampleX = sample.getMarginal(0)
sampleX.setName('X')
# Fit this linear model
factory = ot.LinearModelFactory()
regressionModel = factory.build(sampleX, sampleY, 0.9)
# Test the linear model fitting
graph = ot.VisualTest.DrawLinearModel(sampleX, sampleY, regressionModel)
cloud = graph.getDrawable(0)
cloud.setPointStyle('times')
graph.setDrawable(cloud, 0)
graph.setTitle('')
View(graph)
import openturns as ot
from openturns.viewer import View
N = 1000
#create a sample X
dist = ot.Triangular(1.0, 5.0, 10.0)
# create a Y sample : Y = exp(X/2) + eps
eps = ot.Normal(0.0, 1.0)
sample = ot.ComposedDistribution([dist, eps]).getSample(N)
f = ot.SymbolicFunction(['x', 'eps'], ['exp(0.5*x)+eps'])
sampleY = f(sample)
sampleX = sample.getMarginal(0)
sampleX.setName('X')
# same as good test
regressionModel = ot.LinearModelFactory().build(sampleX, sampleY, 0.9)
graph = ot.VisualTest.DrawLinearModel(sampleX, sampleY, regressionModel)
cloud = graph.getDrawable(0)
cloud.setPointStyle('times')
graph.setDrawable(cloud, 0)
graph.setTitle('')
View(graph)
