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#! /usr/bin/env python
from __future__ import print_function
from openturns import *
TESTPREAMBLE()
RandomGenerator.SetSeed(0)
try:
size = 100
dim = 10
R = CorrelationMatrix(dim)
for i in range(dim):
for j in range(i):
R[i, j] = (i + j + 1.0) / (2.0 * dim)
mean = NumericalPoint(dim, 2.0)
sigma = NumericalPoint(dim, 3.0)
distribution = Normal(mean, sigma, R)
sample = distribution.getSample(size)
sampleX = NumericalSample(size, dim - 1)
sampleY = NumericalSample(size, 1)
for i in range(size):
sampleY[i] = NumericalPoint(1, sample[i, 0])
p = NumericalPoint(dim - 1)
for j in range(dim - 1):
p[j] = sample[i, j + 1]
sampleX[i] = p
sampleZ = NumericalSample(size, 1)
for i in range(size):
sampleZ[i] = NumericalPoint(1, sampleY[i, 0] * sampleY[i, 0])
print("LinearModelAdjustedRSquared=",
LinearModelTest.LinearModelAdjustedRSquared(sampleY, sampleZ))
print("LinearModelFisher=",
LinearModelTest.LinearModelFisher(sampleY, sampleZ))
print("LinearModelResidualMean=",
LinearModelTest.LinearModelResidualMean(sampleY, sampleZ))
print("LinearModelRSquared=",
LinearModelTest.LinearModelRSquared(sampleY, sampleZ))
except:
import sys
print("t_LinearModelTest_std.py", sys.exc_info()[0], sys.exc_info()[1])
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