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made input_ubncertainty plotting work, modified example a little

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James Hensman 2013-06-05 08:59:47 +01:00
parent eb5f2ff5f0
commit 1c51eae954
2 changed files with 20 additions and 7 deletions
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3
GPy/core/sparse_GP.py
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@ -286,6 +286,9 @@ class sparse_GP(GPBase):
fig = pb.figure(num=fignum) fig = pb.figure(num=fignum)
ax = fig.add_subplot(111) ax = fig.add_subplot(111)
if which_data is 'all':
which_data = slice(None)
GPBase.plot(self, samples=0, plot_limits=None, which_data='all', which_parts='all', resolution=None, levels=20, ax=ax) GPBase.plot(self, samples=0, plot_limits=None, which_data='all', which_parts='all', resolution=None, levels=20, ax=ax)
if self.X.shape[1] == 1: if self.X.shape[1] == 1:
if self.has_uncertain_inputs: if self.has_uncertain_inputs:

24
GPy/examples/regression.py
View file

@ -310,6 +310,8 @@ def sparse_GP_regression_2D(N = 400, M = 50, max_nb_eval_optim=100):
def uncertain_inputs_sparse_regression(max_nb_eval_optim=100): def uncertain_inputs_sparse_regression(max_nb_eval_optim=100):
"""Run a 1D example of a sparse GP regression with uncertain inputs.""" """Run a 1D example of a sparse GP regression with uncertain inputs."""
fig, axes = pb.subplots(1,2,figsize=(12,5))
# sample inputs and outputs # sample inputs and outputs
S = np.ones((20,1)) S = np.ones((20,1))
X = np.random.uniform(-3.,3.,(20,1)) X = np.random.uniform(-3.,3.,(20,1))
@ -319,14 +321,22 @@ def uncertain_inputs_sparse_regression(max_nb_eval_optim=100):
k = GPy.kern.rbf(1) + GPy.kern.white(1) k = GPy.kern.rbf(1) + GPy.kern.white(1)
# create simple GP model # create simple GP model - no input uncertainty on this one
m = GPy.models.sparse_GP_regression(X, Y, kernel=k, Z=Z, X_variance=S) m = GPy.models.sparse_GP_regression(X, Y, kernel=k, Z=Z)
# contrain all parameters to be positive
m.ensure_default_constraints() m.ensure_default_constraints()
m.optimize('scg', messages=1, max_f_eval=max_nb_eval_optim)
m.plot(ax=axes[0])
axes[0].set_title('no input uncertainty')
# optimize and plot
m.optimize('tnc', messages=1, max_f_eval=max_nb_eval_optim) #the same model with uncertainty
m.plot() m = GPy.models.sparse_GP_regression(X, Y, kernel=k, Z=Z, X_variance=S)
m.ensure_default_constraints()
m.optimize('scg', messages=1, max_f_eval=max_nb_eval_optim)
m.plot(ax=axes[1])
axes[1].set_title('with input uncertainty')
print(m) print(m)
fig.canvas.draw()
return m return m