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making examples working

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This commit is contained in:
Nicolas 2013-06-04 18:35:36 +01:00
parent 5a2722bec6
commit 392d36964c
2 changed files with 17 additions and 17 deletions
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30
GPy/examples/regression.py
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@ -85,10 +85,10 @@ def coregionalisation_toy2(max_nb_eval_optim=100):
k1 = GPy.kern.rbf(1) + GPy.kern.bias(1) k1 = GPy.kern.rbf(1) + GPy.kern.bias(1)
k2 = GPy.kern.coregionalise(2,1) k2 = GPy.kern.coregionalise(2,1)
k = k1.prod_orthogonal(k2) k = k1.prod(k2,tensor=True)
m = GPy.models.GP_regression(X,Y,kernel=k) m = GPy.models.GP_regression(X,Y,kernel=k)
m.constrain_fixed('rbf_var',1.) m.constrain_fixed('.*rbf_var',1.)
m.constrain_positive('kappa') #m.constrain_positive('.*kappa')
m.ensure_default_constraints() m.ensure_default_constraints()
m.optimize('sim',messages=1,max_f_eval=max_nb_eval_optim) m.optimize('sim',messages=1,max_f_eval=max_nb_eval_optim)
@ -117,10 +117,10 @@ def coregionalisation_toy(max_nb_eval_optim=100):
k1 = GPy.kern.rbf(1) k1 = GPy.kern.rbf(1)
k2 = GPy.kern.coregionalise(2,2) k2 = GPy.kern.coregionalise(2,2)
k = k1.prod_orthogonal(k2) k = k1.prod(k2,tensor=True)
m = GPy.models.GP_regression(X,Y,kernel=k) m = GPy.models.GP_regression(X,Y,kernel=k)
m.constrain_fixed('rbf_var',1.) m.constrain_fixed('.*rbf_var',1.)
m.constrain_positive('kappa') #m.constrain_positive('kappa')
m.ensure_default_constraints() m.ensure_default_constraints()
m.optimize(max_f_eval=max_nb_eval_optim) m.optimize(max_f_eval=max_nb_eval_optim)
@ -153,12 +153,12 @@ def coregionalisation_sparse(max_nb_eval_optim=100):
k1 = GPy.kern.rbf(1) k1 = GPy.kern.rbf(1)
k2 = GPy.kern.coregionalise(2,2) k2 = GPy.kern.coregionalise(2,2)
k = k1.prod_orthogonal(k2) + GPy.kern.white(2,0.001) k = k1.prod(k2,tensor=True) + GPy.kern.white(2,0.001)
m = GPy.models.sparse_GP_regression(X,Y,kernel=k,Z=Z) m = GPy.models.sparse_GP_regression(X,Y,kernel=k,Z=Z)
m.scale_factor = 10000. m.scale_factor = 10000.
m.constrain_fixed('rbf_var',1.) m.constrain_fixed('.*rbf_var',1.)
m.constrain_positive('kappa') #m.constrain_positive('kappa')
m.constrain_fixed('iip') m.constrain_fixed('iip')
m.ensure_default_constraints() m.ensure_default_constraints()
m.optimize_restarts(5, robust=True, messages=1, max_f_eval=max_nb_eval_optim) m.optimize_restarts(5, robust=True, messages=1, max_f_eval=max_nb_eval_optim)
@ -293,11 +293,11 @@ def sparse_GP_regression_2D(N = 400, M = 50, max_nb_eval_optim=100):
kernel = rbf + noise kernel = rbf + noise
# create simple GP model # create simple GP model
m = GPy.models.sparse_GP_regression(X,Y,kernel, M = M, max_nb_eval_optim=100) m = GPy.models.sparse_GP_regression(X,Y,kernel, M = M)
# contrain all parameters to be positive (but not inducing inputs) # contrain all parameters to be positive (but not inducing inputs)
m.constrain_positive('(variance|lengthscale|precision)') m.ensure_default_constraints()
m.set('len',2.) m.set('.*len',2.)
m.checkgrad() m.checkgrad()
@ -314,16 +314,16 @@ def uncertain_inputs_sparse_regression(max_nb_eval_optim=100):
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))
Y = np.sin(X)+np.random.randn(20,1)*0.05 Y = np.sin(X)+np.random.randn(20,1)*0.05
likelihood = GPy.likelihoods.Gaussian(Y) #likelihood = GPy.likelihoods.Gaussian(Y)
Z = np.random.uniform(-3.,3.,(7,1)) Z = np.random.uniform(-3.,3.,(7,1))
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
m = GPy.models.sparse_GP(X, likelihood, kernel=k, Z=Z, X_uncertainty=S) m = GPy.models.sparse_GP_regression(X, Y, kernel=k, Z=Z, X_variance=S)
# contrain all parameters to be positive # contrain all parameters to be positive
m.constrain_positive('(variance|prec)') m.ensure_default_constraints()
# optimize and plot # optimize and plot
m.optimize('tnc', messages=1, max_f_eval=max_nb_eval_optim) m.optimize('tnc', messages=1, max_f_eval=max_nb_eval_optim)

4
GPy/models/sparse_GP_regression.py
View file

@ -28,7 +28,7 @@ class sparse_GP_regression(sparse_GP):
""" """
def __init__(self, X, Y, kernel=None, normalize_X=False, normalize_Y=False, Z=None, M=10): def __init__(self, X, Y, kernel=None, normalize_X=False, normalize_Y=False, Z=None, M=10, X_variance=None):
#kern defaults to rbf (plus white for stability) #kern defaults to rbf (plus white for stability)
if kernel is None: if kernel is None:
kernel = kern.rbf(X.shape[1]) + kern.white(X.shape[1],1e-3) kernel = kern.rbf(X.shape[1]) + kern.white(X.shape[1],1e-3)
@ -43,5 +43,5 @@ class sparse_GP_regression(sparse_GP):
#likelihood defaults to Gaussian #likelihood defaults to Gaussian
likelihood = likelihoods.Gaussian(Y,normalize=normalize_Y) likelihood = likelihoods.Gaussian(Y,normalize=normalize_Y)
sparse_GP.__init__(self, X, likelihood, kernel, Z=Z, normalize_X=normalize_X) sparse_GP.__init__(self, X, likelihood, kernel, Z=Z, normalize_X=normalize_X, X_variance=X_variance)
self._set_params(self._get_params()) self._set_params(self._get_params())