Hi Michael,
The LinearModelAlgorithm indeed relies on a linear basis (if no one is
provided).
However you can define your own basis and provide it to the algorithm class :
"""import openturns as ot
ot.RandomGenerator.SetSeed(0)g = ot.SymbolicFunction(['x', 'y'],
['0.5+sin(x)-2*y'])
npoints = 50
x = ot.Uniform(-2,2).getSample(npoints)x.stack(ot.Normal(0,
3).getSample(npoints))x.setDescription(["x", "y"])y = g(x)
# Create basis B = ( [x,y]-->x, [x,y]-->y )input_description =
x.getDescription()basis = ot.Basis([ot.SymbolicFunction(input_description,
[description_i]) for description_i in input_description])
algo = ot.LinearModelAlgorithm(x, basis, y)algo.run()..."""
The trend coefficients's size is 2 corresponding to the basis size Sofiane
Le jeudi 28 mars 2019 à 20:09:52 UTC+1, BAUDIN Michael
<[email protected]> a écrit :
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Dear all !
The LinearLeastSquares class in OT 1.12 always inserts a constant in the model,
be it wanted by the user or not:
https://github.com/openturns/openturns/blob/d0802a1b17b60bd86afa234662a047bc4f04492f/lib/src/Base/MetaModel/LinearLeastSquares.cxx#L105
In the API, this term corresponds to the getConstant() method.
The same is true for LinearModelFactory.
In the demo script in PS, I use linear least squares to approximate the sine
function with the polynomial basis 1, x, x^2, x^3. The linear model involves 4
coefficients. An intercept is always added leading to 5 estimated coefficients,
that I do not want.
g = ot.SymbolicFunction(['x'], ['0.5+sin(x)'])
npoints = 50
x=ot.Uniform(-2,2).getSample(npoints).sort()
y = g(x)
# Create input
basis = ot.SymbolicFunction(['x'], ['1','x','x^2','x^3'])
inputData = basis(x)
With LinearLeastSquares, I get a constant:
myLeastSquares = ot.LinearLeastSquares(inputData, y)
myLeastSquares.run()
beta0 = myLeastSquares.getConstant()[0]
With LinearModelFactory, I get 5 coefficicents instead of 4:
LMF = ot.LinearModelFactory()
linearModel = LMF.build(inputData, y)
beta = linearModel.getRegression()
As far as I can see, the LinearModelAlgorithm in OT 1.13 has the same behaviour:
https://github.com/openturns/openturns/blob/ce1bc890a907faeecde495f5528ed42e401153c7/lib/src/Uncertainty/Algorithm/MetaModel/LinearModel/LinearModelAlgorithm.cxx#L65
I assume that the constant is always there, so that the method prevents from
having a bias in the estimate. But in cases where you want really to perform
linear least squares, then there is an issue.
As far as I can see, the LeastSquaresMethod is the right tool. Unfortunately,
this cannot be used from the Python API.
Am I correct ?
Best regards,
Michaël
PS
import openturns as ot
from openturns.viewer import View
g = ot.SymbolicFunction(['x'], ['0.5+sin(x)'])
npoints = 50
x=ot.Uniform(-2,2).getSample(npoints).sort()
y = g(x)
# Create input
basis = ot.SymbolicFunction(['x'], ['1','x','x^2','x^3'])
inputData = basis(x)
# Solve
myLeastSquares = ot.LinearLeastSquares(inputData, y)
myLeastSquares.run()
beta0 = myLeastSquares.getConstant()[0]
print("beta0=%s" % (beta0))
beta = myLeastSquares.getLinear()
print("beta=%s" % (beta))
# Check
responseSurface = myLeastSquares.getResponseSurface()
ypredicted = responseSurface(inputData)
#
graph = ot.Graph("Linear Model","x","y",True,"topleft")
curve = ot.Curve(x,ypredicted)
curve.setLegend("Linear Model")
graph.add(curve)
cloud = ot.Cloud(x,y)
cloud.setColor("red")
cloud.setLegend("Data")
graph.add(cloud)
View(graph)
#
ot.ResourceMap.SetAsString('R-executable-command','bla\\bla\\R.exe')
LMF = ot.LinearModelFactory()
linearModel = LMF.build(inputData, y)
beta = linearModel.getRegression()
print(beta)
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