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renaming: posterior_variationa -> variational_posterior

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James Hensman 2014-02-24 19:31:13 +00:00
parent 17f9764a55
commit da4686dd3c
9 changed files with 58 additions and 63 deletions
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21
GPy/examples/regression.py
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@ -16,7 +16,7 @@ def olympic_marathon_men(optimize=True, plot=True):
m = GPy.models.GPRegression(data['X'], data['Y'])
# set the lengthscale to be something sensible (defaults to 1)
m['rbf_lengthscale'] = 10
m.kern.lengthscale = 10.
if optimize:
m.optimize('bfgs', max_iters=200)
@ -41,11 +41,10 @@ def coregionalization_toy2(optimize=True, plot=True):
Y = np.vstack((Y1, Y2))
#build the kernel
k1 = GPy.kern.RBF(1) + GPy.kern.bias(1)
k2 = GPy.kern.coregionalize(2,1)
k1 = GPy.kern.RBF(1) + GPy.kern.Bias(1)
k2 = GPy.kern.Coregionalize(2,1)
k = k1**k2
m = GPy.models.GPRegression(X, Y, kernel=k)
m.constrain_fixed('.*rbf_var', 1.)
if optimize:
m.optimize('bfgs', max_iters=100)
@ -86,11 +85,13 @@ def coregionalization_sparse(optimize=True, plot=True):
"""
#fetch the data from the non sparse examples
m = coregionalization_toy2(optimize=False, plot=False)
X, Y = m.X, m.likelihood.Y
X, Y = m.X, m.Y
k = GPy.kern.RBF(1)**GPy.kern.Coregionalize(2)
#construct a model
m = GPy.models.SparseGPRegression(X,Y)
m.constrain_fixed('iip_\d+_1') # don't optimize the inducing input indexes
m = GPy.models.SparseGPRegression(X,Y, num_inducing=25, kernel=k)
m.Z[:,1].fix() # don't optimize the inducing input indexes
if optimize:
m.optimize('bfgs', max_iters=100, messages=1)
@ -128,7 +129,7 @@ def epomeo_gpx(max_iters=200, optimize=True, plot=True):
np.random.randint(0, 4, num_inducing)[:, None]))
k1 = GPy.kern.RBF(1)
k2 = GPy.kern.coregionalize(output_dim=5, rank=5)
k2 = GPy.kern.Coregionalize(output_dim=5, rank=5)
k = k1**k2
m = GPy.models.SparseGPRegression(t, Y, kernel=k, Z=Z, normalize_Y=True)
@ -322,7 +323,7 @@ def toy_ARD(max_iters=1000, kernel_type='linear', num_samples=300, D=4, optimize
kernel = GPy.kern.RBF_inv(X.shape[1], ARD=1)
else:
kernel = GPy.kern.RBF(X.shape[1], ARD=1)
kernel += GPy.kern.White(X.shape[1]) + GPy.kern.bias(X.shape[1])
kernel += GPy.kern.White(X.shape[1]) + GPy.kern.Bias(X.shape[1])
m = GPy.models.GPRegression(X, Y, kernel)
# len_prior = GPy.priors.inverse_gamma(1,18) # 1, 25
# m.set_prior('.*lengthscale',len_prior)
@ -361,7 +362,7 @@ def toy_ARD_sparse(max_iters=1000, kernel_type='linear', num_samples=300, D=4, o
kernel = GPy.kern.RBF_inv(X.shape[1], ARD=1)
else:
kernel = GPy.kern.RBF(X.shape[1], ARD=1)
#kernel += GPy.kern.bias(X.shape[1])
#kernel += GPy.kern.Bias(X.shape[1])
X_variance = np.ones(X.shape) * 0.5
m = GPy.models.SparseGPRegression(X, Y, kernel, X_variance=X_variance)
# len_prior = GPy.priors.inverse_gamma(1,18) # 1, 25