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parameter handling with default constraints

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Max Zwiessele 2014-02-11 14:44:15 +00:00
parent b944427733
commit 79aca59a37
12 changed files with 53 additions and 87 deletions
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6
GPy/examples/regression.py
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@ -473,9 +473,9 @@ def uncertain_inputs_sparse_regression(max_iters=200, optimize=True, plot=True):
Z = np.random.uniform(-3., 3., (7, 1))
k = GPy.kern.rbf(1)
import ipdb;ipdb.set_trace()
# create simple GP Model - no input uncertainty on this one
m = GPy.models.SparseGPRegression(X, Y, kernel=k, Z=Z)
m = GPy.models.SparseGPRegression(X, Y, kernel=GPy.kern.rbf(1), Z=Z)
if optimize:
m.optimize('scg', messages=1, max_iters=max_iters)
@ -486,7 +486,7 @@ def uncertain_inputs_sparse_regression(max_iters=200, optimize=True, plot=True):
print m
# the same Model with uncertainty
m = GPy.models.SparseGPRegression(X, Y, kernel=k, Z=Z, X_variance=S)
m = GPy.models.SparseGPRegression(X, Y, kernel=GPy.kern.rbf(1), Z=Z, X_variance=S)
if optimize:
m.optimize('scg', messages=1, max_iters=max_iters)
if plot: