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Timestamp:
08/10/12 20:42:30 (12 years ago)
Author:
gkronber
Message:

#1902 worked on GPR: added line chart, made parameters of mean and covariance functions readable, removed target variable scaling, moved noise hyperparameter for likelihood function to the end of the parameter list, added methods to calculate the predicted variance, removed limits for scale of covariance functions and introduced exception handling to catch non-spd or singular cov matrixes, implemented rational quadratic covariance function, added unit test case from GBML book (however it does not work as the book seemingly uses a noise-less likelihood function)

File:
1 edited

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  • trunk/sources/HeuristicLab.Algorithms.GradientDescent/3.3/LbfgsInitializer.cs

    r8401 r8473  
    8787      alglib.minlbfgs.minlbfgsstate state = new alglib.minlbfgs.minlbfgsstate();
    8888      if (ApproximateGradients.Value) {
    89         alglib.minlbfgs.minlbfgscreatef(n, Math.Min(n, 7), initialPoint, 1E-5, state);
     89        alglib.minlbfgs.minlbfgscreatef(n, Math.Min(n, 10), initialPoint, 1E-5, state);
    9090      } else {
    91         alglib.minlbfgs.minlbfgscreate(n, Math.Min(n, 7), initialPoint, state);
     91        alglib.minlbfgs.minlbfgscreate(n, Math.Min(n, 10), initialPoint, state);
    9292      }
    93       alglib.minlbfgs.minlbfgssetcond(state, 0, 0, 0, Iterations.Value);
     93      alglib.minlbfgs.minlbfgssetcond(state, 0.0, 0, 0, Iterations.Value);
    9494      alglib.minlbfgs.minlbfgssetxrep(state, true);
    9595
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