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Fixed non_gaussian demo

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Alan Saul 2014-03-03 17:45:24 +00:00
parent 2feb849bf7
commit db57005826
1 changed files with 12 additions and 12 deletions
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24
GPy/examples/non_gaussian.py
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@ -36,21 +36,21 @@ def student_t_approx(optimize=True, plot=True):
edited_real_sd = initial_var_guess
# Kernel object
kernel1 = GPy.kern.rbf(X.shape[1]) + GPy.kern.white(X.shape[1])
kernel2 = GPy.kern.rbf(X.shape[1]) + GPy.kern.white(X.shape[1])
kernel3 = GPy.kern.rbf(X.shape[1]) + GPy.kern.white(X.shape[1])
kernel4 = GPy.kern.rbf(X.shape[1]) + GPy.kern.white(X.shape[1])
kernel1 = GPy.kern.RBF(X.shape[1]) + GPy.kern.White(X.shape[1])
kernel2 = GPy.kern.RBF(X.shape[1]) + GPy.kern.White(X.shape[1])
kernel3 = GPy.kern.RBF(X.shape[1]) + GPy.kern.White(X.shape[1])
kernel4 = GPy.kern.RBF(X.shape[1]) + GPy.kern.White(X.shape[1])
#Gaussian GP model on clean data
#m1 = GPy.models.GPRegression(X, Y.copy(), kernel=kernel1)
## optimize
#m1['white'].constrain_fixed(1e-5)
#m1.randomize()
m1 = GPy.models.GPRegression(X, Y.copy(), kernel=kernel1)
# optimize
m1['white'].constrain_fixed(1e-5)
m1.randomize()
##Gaussian GP model on corrupt data
#m2 = GPy.models.GPRegression(X, Yc.copy(), kernel=kernel2)
#m1['white'].constrain_fixed(1e-5)
#m2.randomize()
#Gaussian GP model on corrupt data
m2 = GPy.models.GPRegression(X, Yc.copy(), kernel=kernel2)
m2['white'].constrain_fixed(1e-5)
m2.randomize()
#Student t GP model on clean data
t_distribution = GPy.likelihoods.StudentT(deg_free=deg_free, sigma2=edited_real_sd)