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working mean function examples

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James Hensman 2015-03-26 08:48:56 +00:00
parent 254157ce04
commit cf0e29b207
4 changed files with 51 additions and 4 deletions
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45
GPy/examples/regression.py
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@ -505,3 +505,48 @@ def uncertain_inputs_sparse_regression(max_iters=200, optimize=True, plot=True):
print m
return m
def simple_mean_function(max_iters=100, optimize=True, plot=True):
"""
The simplest possible mean function. No parameters, just a simple Sinusoid.
"""
#create simple mean function
mf = GPy.core.Mapping(1,1)
mf.f = np.sin
mf.update_gradients = lambda a,b: None
X = np.linspace(0,10,50).reshape(-1,1)
Y = np.sin(X) + 0.5*np.cos(3*X) + 0.1*np.random.randn(*X.shape)
k =GPy.kern.RBF(1)
lik = GPy.likelihoods.Gaussian()
m = GPy.core.GP(X, Y, kernel=k, likelihood=lik, mean_function=mf)
if optimize:
m.optimize(max_iters=max_iters)
if plot:
m.plot(plot_limits=(-10,15))
return m
def parametric_mean_function(max_iters=100, optimize=True, plot=True):
"""
A linear mean function with parameters that we'll learn alongside the kernel
"""
#create simple mean function
mf = GPy.core.Mapping(1,1)
mf.f = np.sin
X = np.linspace(0,10,50).reshape(-1,1)
Y = np.sin(X) + 0.5*np.cos(3*X) + 0.1*np.random.randn(*X.shape) + 3*X
mf = GPy.mappings.Linear(1,1)
k =GPy.kern.RBF(1)
lik = GPy.likelihoods.Gaussian()
m = GPy.core.GP(X, Y, kernel=k, likelihood=lik, mean_function=mf)
if optimize:
m.optimize(max_iters=max_iters)
if plot:
m.plot()
return m