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small changes in tutorial

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Nicolas 2013-02-07 16:09:58 +00:00
parent 38d2a5f91d
commit 1d6885f6d9
1 changed files with 3 additions and 2 deletions
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5
doc/tuto_GP_regression.rst
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@ -45,6 +45,7 @@ By default, some observation noise is added to the modle. The functions ``print`
m.plot() m.plot()
gives the following output: :: gives the following output: ::
Marginal log-likelihood: -4.479e+00 Marginal log-likelihood: -4.479e+00
Name | Value | Constraints | Ties | Prior Name | Value | Constraints | Ties | Prior
----------------------------------------------------------------- -----------------------------------------------------------------
@ -78,7 +79,7 @@ Once the constrains have been imposed, the model can be optimized::
m.optimize() m.optimize()
If we want to perform some restarts to try to improve the result of the optimization, we can use the optimize_restart function:: If we want to perform some restarts to try to improve the result of the optimization, we can use the ``optimize_restart`` function::
m.optimize_restarts(Nrestarts = 10) m.optimize_restarts(Nrestarts = 10)
@ -128,7 +129,7 @@ Here is a 2 dimensional example::
m.plot() m.plot()
print(m) print(m)
The flag ``ARD=True`` in the definition of the Matern kernel specifies that we want one lengthscale parameter per dimension (ie the GP is not isotropic). The output of the last 2 lines is:: The flag ``ARD=True`` in the definition of the Matern kernel specifies that we want one lengthscale parameter per dimension (ie the GP is not isotropic). The output of the last two lines is::
Marginal log-likelihood: 6.682e+01 Marginal log-likelihood: 6.682e+01
Name | Value | Constraints | Ties | Prior Name | Value | Constraints | Ties | Prior