ensure_default_constraints is on by default

GPy/models/bayesian_gplvm.py

@@ -60,7 +60,7 @@ class BayesianGPLVM(SparseGP, GPLVM):
             self._savedABCD = []
 
         SparseGP.__init__(self, X, likelihood, kernel, Z=Z, X_variance=X_variance, **kwargs)
-        self._set_params(self._get_params())
+        self.ensure_default_constraints()
 
     @property
     def oldps(self):

GPy/models/fitc_classification.py

@@ -44,4 +44,4 @@ class FITCClassification(FITC):
             assert Z.shape[1]==X.shape[1]
 
         FITC.__init__(self, X, likelihood, kernel, Z=Z, normalize_X=normalize_X)
-        self._set_params(self._get_params())
+        self.ensure_default_constraints()

GPy/models/gp_classification.py

@@ -38,4 +38,4 @@ class GPClassification(GP):
                 raise Warning, 'likelihood.data and Y are different.'
 
         GP.__init__(self, X, likelihood, kernel, normalize_X=normalize_X)
-        self._set_params(self._get_params())
+        self.ensure_default_constraints()

GPy/models/gp_regression.py

@@ -32,4 +32,4 @@ class GPRegression(GP):
         likelihood = likelihoods.Gaussian(Y,normalize=normalize_Y)
 
         GP.__init__(self, X, likelihood, kernel, normalize_X=normalize_X)
-        self._set_params(self._get_params())
+        self.ensure_default_constraints()

GPy/models/gplvm.py

@@ -33,7 +33,7 @@ class GPLVM(GP):
             kernel = kern.rbf(input_dim, ARD=input_dim>1) + kern.bias(input_dim, np.exp(-2)) + kern.white(input_dim, np.exp(-2))
         likelihood = Gaussian(Y, normalize=normalize_Y)
         GP.__init__(self, X, likelihood, kernel, normalize_X=False)
-        self._set_params(self._get_params())
+        self.ensure_default_constraints()
 
     def initialise_latent(self, init, input_dim, Y):
         if init == 'PCA':

GPy/models/mrd.py

@@ -79,7 +79,7 @@ class MRD(Model):
         self.MQ = self.num_inducing * self.input_dim
 
         Model.__init__(self)
-        self._set_params(self._get_params())
+        self.ensure_default_constraints()
 
     @property
     def X(self):

GPy/models/sparse_gp_classification.py

@@ -44,4 +44,4 @@ class SparseGPClassification(SparseGP):
             assert Z.shape[1]==X.shape[1]
 
         SparseGP.__init__(self, X, likelihood, kernel, Z=Z, normalize_X=normalize_X)
-        self._set_params(self._get_params())
+        self.ensure_default_constraints()

GPy/models/sparse_gp_regression.py

@@ -42,4 +42,4 @@ class SparseGPRegression(SparseGP):
         likelihood = likelihoods.Gaussian(Y, normalize=normalize_Y)
 
         SparseGP.__init__(self, X, likelihood, kernel, Z=Z, normalize_X=normalize_X, X_variance=X_variance)
-        self._set_params(self._get_params())
+        self.ensure_default_constraints()

GPy/models/sparse_gplvm.py

@@ -26,6 +26,7 @@ class SparseGPLVM(SparseGPRegression, GPLVM):
     def __init__(self, Y, input_dim, kernel=None, init='PCA', num_inducing=10):
         X = self.initialise_latent(init, input_dim, Y)
         SparseGPRegression.__init__(self, X, Y, kernel=kernel, num_inducing=num_inducing)
+        self.ensure_default_constraints()
 
     def _get_param_names(self):
         return (sum([['X_%i_%i' % (n, q) for q in range(self.input_dim)] for n in range(self.num_data)], [])