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#! /usr/bin/env python
from __future__ import print_function
from openturns import *
from math import *
TESTPREAMBLE()
try:
# TEST NUMBER ZERO : DEFAULT & COPY CONSTRUCTORs AND STRING CONVERTER
print(
"test number zero : default & copy constructors and string converter")
vectR = NumericalPoint(1, 12.0)
testSample = NumericalSample(1, 1)
begin = -1.0
end = 36.92
testInterval = Interval([begin], [end])
testInterval.setName("testInterval")
valColl = NumericalScalarCollection()
valColl.add(1.0)
# Constructor from NumericalPoint
# Y = Somme (ai * Xi), i=0, n
# linearModel : (ai), i=0, n
LM = LinearModel(vectR, testInterval, valColl)
emptyLM = LinearModel(vectR)
# String converter #
print("LM = ", repr(LM))
print("emptyLM = ", repr(emptyLM))
# * TEST NUMBER ONE : GET ELEMENTS #
print("test number one : get elements")
# coefficients ai
print("LM.getRegression = ", repr(LM.getRegression()))
# confidence intervals of the ai coefficients
print("LM.getConfidenceIntervals = ", repr(LM.getConfidenceIntervals()))
# p values of the (n+1) coefficients ai:
print("LM.getPValues = ", repr(LM.getPValues()))
# * TEST NUMBER TWO : GET PREDICT/RESIDUAL #
print("test number two : get predicted/residual")
lmfact = LinearModelFactory()
size = 20
oneSample = NumericalSample(size, 1)
twoSample = NumericalSample(size, 1)
for i in range(size):
oneSample[i] = NumericalPoint(
1, 7.0 * sin(-3.5 + (6.5 * i) / (size - 1.0)) + 2.0)
twoSample[i] = NumericalPoint(
1, -2.0 * oneSample[i, 0] + 3.0 + 0.05 * sin(oneSample[i, 0]))
test = lmfact.build(oneSample, twoSample)
# evaluate the predictions on sample : predict = Somme(aiXi)
print("LM.getPredicted = ", repr(test.getPredicted(oneSample)))
# get the residuals epsiloni
print("LM.getResidual = ", repr(test.getResidual(oneSample, twoSample)))
except:
import sys
print("t_LinearModel_std.py", sys.exc_info()[0], sys.exc_info()[1])
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