ensure_default_constraints is on by default

GPy/examples/classification.py

@@ -25,7 +25,6 @@ def crescent_data(seed=default_seed): # FIXME
     Y[Y.flatten()==-1] = 0
 
     m = GPy.models.GPClassification(data['X'], Y)
-    m.ensure_default_constraints()
     #m.update_likelihood_approximation()
     #m.optimize()
     m.pseudo_EM()
@@ -75,7 +74,6 @@ def toy_linear_1d_classification(seed=default_seed):
 
     # Model definition
     m = GPy.models.GPClassification(data['X'], Y)
-    m.ensure_default_constraints()
 
     # Optimize
     #m.update_likelihood_approximation()
@@ -106,7 +104,6 @@ def sparse_toy_linear_1d_classification(num_inducing=10,seed=default_seed):
     m = GPy.models.SparseGPClassification(data['X'], Y,num_inducing=num_inducing)
     m['.*len']= 4.
 
-    m.ensure_default_constraints()
     # Optimize
     #m.update_likelihood_approximation()
     # Parameters optimization:
@@ -137,7 +134,6 @@ def sparse_crescent_data(num_inducing=10, seed=default_seed):
     Y[Y.flatten()==-1]=0
 
     m = GPy.models.SparseGPClassification(data['X'], Y,num_inducing=num_inducing)
-    m.ensure_default_constraints()
     m['.*len'] = 10.
     #m.update_likelihood_approximation()
     #m.optimize()
@@ -163,7 +159,6 @@ def FITC_crescent_data(num_inducing=10, seed=default_seed):
 
     m = GPy.models.FITCClassification(data['X'], Y,num_inducing=num_inducing)
     m.constrain_bounded('.*len',1.,1e3)
-    m.ensure_default_constraints()
     m['.*len'] = 3.
     #m.update_likelihood_approximation()
     #m.optimize()

GPy/examples/dimensionality_reduction.py

@@ -37,7 +37,6 @@ def BGPLVM(seed=default_seed):
     # m.optimize(messages = 1)
     # m.plot()
     # pb.title('After optimisation')
-    m.ensure_default_constraints()
     m.randomize()
     m.checkgrad(verbose=1)
 
@@ -53,7 +52,6 @@ def GPLVM_oil_100(optimize=True):
     m.data_labels = data['Y'].argmax(axis=1)
 
     # optimize
-    m.ensure_default_constraints()
     if optimize:
         m.optimize('scg', messages=1)
 
@@ -108,7 +106,6 @@ def swiss_roll(optimize=True, N=1000, num_inducing=15, Q=4, sigma=.2, plot=False
     m.data_colors = c
     m.data_t = t
 
-    m.ensure_default_constraints()
     m['rbf_lengthscale'] = 1. # X.var(0).max() / X.var(0)
     m['noise_variance'] = Y.var() / 100.
     m['bias_variance'] = 0.05
@@ -134,7 +131,6 @@ def BGPLVM_oil(optimize=True, N=200, Q=10, num_inducing=15, max_f_eval=50, plot=
     m['.*lengt'] = 1. # m.X.var(0).max() / m.X.var(0)
     m['noise'] = Yn.var() / 100.
 
-    m.ensure_default_constraints()
 
     # optimize
     if optimize:
@@ -159,7 +155,6 @@ def oil_100():
     m = GPy.models.GPLVM(data['X'], 2)
 
     # optimize
-    m.ensure_default_constraints()
     m.optimize(messages=1, max_iters=2)
 
     # plot
@@ -239,7 +234,6 @@ def bgplvm_simulation_matlab_compare():
 #                        X=mu,
 #                        X_variance=S,
                        _debug=False)
-    m.ensure_default_constraints()
     m.auto_scale_factor = True
     m['noise'] = Y.var() / 100.
     m['linear_variance'] = .01
@@ -262,7 +256,6 @@ def bgplvm_simulation(optimize='scg',
     k = kern.linear(Q, ARD=True) + kern.bias(Q, np.exp(-2)) + kern.white(Q, np.exp(-2)) # + kern.bias(Q)
     m = BayesianGPLVM(Y, Q, init="PCA", num_inducing=num_inducing, kernel=k, _debug=True)
     # m.constrain('variance|noise', logexp_clipped())
-    m.ensure_default_constraints()
     m['noise'] = Y.var() / 100.
     m['linear_variance'] = .01
 
@@ -291,7 +284,6 @@ def mrd_simulation(optimize=True, plot=True, plot_sim=True, **kw):
     for i, Y in enumerate(Ylist):
         m['{}_noise'.format(i + 1)] = Y.var() / 100.
 
-    m.ensure_default_constraints()
 
     # DEBUG
     # np.seterr("raise")
@@ -319,7 +311,6 @@ def brendan_faces():
     # optimize
     m.constrain('rbf|noise|white', GPy.core.transformations.logexp_clipped())
 
-    m.ensure_default_constraints()
     m.optimize('scg', messages=1, max_f_eval=10000)
 
     ax = m.plot_latent(which_indices=(0, 1))
@@ -336,7 +327,6 @@ def stick():
     m = GPy.models.GPLVM(data['Y'], 2)
 
     # optimize
-    m.ensure_default_constraints()
     m.optimize(messages=1, max_f_eval=10000)
     m._set_params(m._get_params())
 
@@ -359,7 +349,6 @@ def cmu_mocap(subject='35', motion=['01'], in_place=True):
     m = GPy.models.GPLVM(data['Y'], 2, normalize_Y=True)
 
     # optimize
-    m.ensure_default_constraints()
     m.optimize(messages=1, max_f_eval=10000)
 
     ax = m.plot_latent()
@@ -391,7 +380,6 @@ def cmu_mocap(subject='35', motion=['01'], in_place=True):
 #     m.set('iip', Z)
 #     m.set('bias', 1e-4)
 #     # optimize
-#     # m.ensure_default_constraints()
 #
 #     import pdb; pdb.set_trace()
 #     m.optimize('tnc', messages=1)

GPy/examples/regression.py

@@ -18,7 +18,6 @@ def toy_rbf_1d(optimizer='tnc', max_nb_eval_optim=100):
     m = GPy.models.GPRegression(data['X'],data['Y'])
 
     # optimize
-    m.ensure_default_constraints()
     m.optimize(optimizer, max_f_eval=max_nb_eval_optim)
     # plot
     m.plot()
@@ -36,7 +35,6 @@ def rogers_girolami_olympics(optim_iters=100):
     m['rbf_lengthscale'] = 10
 
     # optimize
-    m.ensure_default_constraints()
     m.optimize(max_f_eval=optim_iters)
 
     # plot
@@ -52,7 +50,6 @@ def toy_rbf_1d_50(optim_iters=100):
     m = GPy.models.GPRegression(data['X'],data['Y'])
 
     # optimize
-    m.ensure_default_constraints()
     m.optimize(max_f_eval=optim_iters)
 
     # plot
@@ -68,7 +65,6 @@ def silhouette(optim_iters=100):
     m = GPy.models.GPRegression(data['X'],data['Y'])
 
     # optimize
-    m.ensure_default_constraints()
     m.optimize(messages=True,max_f_eval=optim_iters)
 
     print(m)
@@ -92,7 +88,6 @@ def coregionalisation_toy2(optim_iters=100):
     m = GPy.models.GPRegression(X,Y,kernel=k)
     m.constrain_fixed('.*rbf_var',1.)
     #m.constrain_positive('.*kappa')
-    m.ensure_default_constraints()
     m.optimize('sim',messages=1,max_f_eval=optim_iters)
 
     pb.figure()
@@ -124,7 +119,6 @@ def coregionalisation_toy(optim_iters=100):
     m = GPy.models.GPRegression(X,Y,kernel=k)
     m.constrain_fixed('.*rbf_var',1.)
     #m.constrain_positive('kappa')
-    m.ensure_default_constraints()
     m.optimize(max_f_eval=optim_iters)
 
     pb.figure()
@@ -162,7 +156,6 @@ def coregionalisation_sparse(optim_iters=100):
     m.constrain_fixed('.*rbf_var',1.)
     m.constrain_fixed('iip')
     m.constrain_bounded('noise_variance',1e-3,1e-1)
-    m.ensure_default_constraints()
     m.optimize_restarts(5, robust=True, messages=1, max_f_eval=optim_iters)
 
     #plotting:
@@ -189,11 +182,9 @@ def multiple_optima(gene_number=937,resolution=80, model_restarts=10, seed=10000
     log_SNRs = np.linspace(-3., 4., resolution)
 
     data = GPy.util.datasets.della_gatta_TRP63_gene_expression(gene_number)
-    # Sub sample the data to ensure multiple optima
     #data['Y'] = data['Y'][0::2, :]
     #data['X'] = data['X'][0::2, :]
 
-    # Remove the mean (no bias kernel to ensure signal/noise is in RBF/white)
     data['Y'] = data['Y'] - np.mean(data['Y'])
 
     lls = GPy.examples.regression._contour_data(data, length_scales, log_SNRs, GPy.kern.rbf)
@@ -220,7 +211,6 @@ def multiple_optima(gene_number=937,resolution=80, model_restarts=10, seed=10000
         optim_point_y[0] = np.log10(m['rbf_variance']) - np.log10(m['noise_variance']);
 
         # optimize
-        m.ensure_default_constraints()
         m.optimize('scg', xtol=1e-6, ftol=1e-6, max_f_eval=optim_iters)
 
         optim_point_x[1] = m['rbf_lengthscale']
@@ -273,7 +263,6 @@ def sparse_GP_regression_1D(N = 400, num_inducing = 5, optim_iters=100):
     # create simple GP Model
     m = GPy.models.SparseGPRegression(X, Y, kernel, num_inducing=num_inducing)
 
-    m.ensure_default_constraints()
 
     m.checkgrad(verbose=1)
     m.optimize('tnc', messages = 1, max_f_eval=optim_iters)
@@ -294,7 +283,6 @@ def sparse_GP_regression_2D(N = 400, num_inducing = 50, optim_iters=100):
     m = GPy.models.SparseGPRegression(X,Y,kernel, num_inducing = num_inducing)
 
     # contrain all parameters to be positive (but not inducing inputs)
-    m.ensure_default_constraints()
     m.set('.*len',2.)
 
     m.checkgrad()
@@ -320,7 +308,6 @@ def uncertain_inputs_sparse_regression(optim_iters=100):
 
     # create simple GP Model - no input uncertainty on this one
     m = GPy.models.SparseGPRegression(X, Y, kernel=k, Z=Z)
-    m.ensure_default_constraints()
     m.optimize('scg', messages=1, max_f_eval=optim_iters)
     m.plot(ax=axes[0])
     axes[0].set_title('no input uncertainty')
@@ -328,7 +315,6 @@ def uncertain_inputs_sparse_regression(optim_iters=100):
 
     #the same Model with uncertainty
     m = GPy.models.SparseGPRegression(X, Y, kernel=k, Z=Z, X_variance=S)
-    m.ensure_default_constraints()
     m.optimize('scg', messages=1, max_f_eval=optim_iters)
     m.plot(ax=axes[1])
     axes[1].set_title('with input uncertainty')

GPy/examples/stochastic.py

@@ -15,7 +15,6 @@ def toy_1d():
     Y = np.sin(X) + np.cos(0.3*X) + np.random.randn(*X.shape)/np.sqrt(50.)
 
     m = GPy.models.SVIGPRegression(X,Y, batchsize=10, Z=Z)
-    m.ensure_default_constraints()
     m.constrain_bounded('noise_variance',1e-3,1e-1)
 
     m.param_steplength = 1e-4

GPy/examples/tutorials.py

@@ -24,7 +24,6 @@ def tuto_GP_regression():
     print m
     m.plot()
 
-    m.ensure_default_constraints() 
     m.constrain_positive('')
 
     m.unconstrain('')               # may be used to remove the previous constrains
@@ -135,7 +134,6 @@ def tuto_kernel_overview():
     pb.ylabel("+   ",rotation='horizontal',fontsize='30')
     m.plot(ax=axs, which_parts=[False,False,False,True])
 
-    m.ensure_default_constraints()
     return(m)
 
 
@@ -144,6 +142,5 @@ def model_interaction():
     Y = np.sin(X) + np.random.randn(*X.shape)*0.01 + 5.
     k = GPy.kern.rbf(1) + GPy.kern.bias(1)
     m = GPy.models.GPRegression(X, Y, kernel=k)
-    m.ensure_default_constraints()
     return m
 

GPy/kern/rbf.py

@@ -241,9 +241,9 @@ class rbf(Kernpart):
         # here are the "statistics" for psi1 and psi2
         if not np.array_equal(Z, self._Z):
             #Z has changed, compute Z specific stuff
-            self._psi2_Zhat = 0.5*(Z[:,None,:] +Z[None,:,:]) # num_inducing,num_inducing,input_dim
+            self._psi2_Zhat = 0.5*(Z[:,None,:] +Z[None,:,:]) # M,M,Q
-            self._psi2_Zdist = 0.5*(Z[:,None,:]-Z[None,:,:]) # num_inducing,num_inducing,input_dim
+            self._psi2_Zdist = 0.5*(Z[:,None,:]-Z[None,:,:]) # M,M,Q
-            self._psi2_Zdist_sq = np.square(self._psi2_Zdist/self.lengthscale) # num_inducing,num_inducing,input_dim
+            self._psi2_Zdist_sq = np.square(self._psi2_Zdist/self.lengthscale) # M,M,Q
             self._Z = Z
 
         if not (np.array_equal(Z, self._Z) and np.array_equal(mu, self._mu) and np.array_equal(S, self._S)):
@@ -257,12 +257,12 @@ class rbf(Kernpart):
             self._psi1 = self.variance*np.exp(self._psi1_exponent)
 
             #psi2
-            self._psi2_denom = 2.*S[:,None,None,:]/self.lengthscale2+1. # N,num_inducing,num_inducing,input_dim
+            self._psi2_denom = 2.*S[:,None,None,:]/self.lengthscale2+1. # N,M,M,Q
             self._psi2_mudist, self._psi2_mudist_sq, self._psi2_exponent, _ = self.weave_psi2(mu,self._psi2_Zhat)
-            #self._psi2_mudist = mu[:,None,None,:]-self._psi2_Zhat #N,num_inducing,num_inducing,input_dim
+            #self._psi2_mudist = mu[:,None,None,:]-self._psi2_Zhat #N,M,M,Q
             #self._psi2_mudist_sq = np.square(self._psi2_mudist)/(self.lengthscale2*self._psi2_denom)
-            #self._psi2_exponent = np.sum(-self._psi2_Zdist_sq -self._psi2_mudist_sq -0.5*np.log(self._psi2_denom),-1) #N,num_inducing,num_inducing
+            #self._psi2_exponent = np.sum(-self._psi2_Zdist_sq -self._psi2_mudist_sq -0.5*np.log(self._psi2_denom),-1) #N,M,M,Q
-            self._psi2 = np.square(self.variance)*np.exp(self._psi2_exponent) # N,num_inducing,num_inducing
+            self._psi2 = np.square(self.variance)*np.exp(self._psi2_exponent) # N,M,M,Q
 
             #store matrices for caching
             self._Z, self._mu, self._S = Z, mu,S

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)], [])

GPy/testing/bgplvm_tests.py

@@ -16,7 +16,6 @@ class BGPLVMTests(unittest.TestCase):
         Y -= Y.mean(axis=0)
         k = GPy.kern.bias(input_dim) + GPy.kern.white(input_dim, 0.00001)
         m = BayesianGPLVM(Y, input_dim, kernel=k, num_inducing=num_inducing)
-        m.ensure_default_constraints()
         m.randomize()
         self.assertTrue(m.checkgrad())
 
@@ -29,7 +28,6 @@ class BGPLVMTests(unittest.TestCase):
         Y -= Y.mean(axis=0)
         k = GPy.kern.linear(input_dim) + GPy.kern.white(input_dim, 0.00001)
         m = BayesianGPLVM(Y, input_dim, kernel=k, num_inducing=num_inducing)
-        m.ensure_default_constraints()
         m.randomize()
         self.assertTrue(m.checkgrad())
 
@@ -42,7 +40,6 @@ class BGPLVMTests(unittest.TestCase):
         Y -= Y.mean(axis=0)
         k = GPy.kern.rbf(input_dim) + GPy.kern.white(input_dim, 0.00001)
         m = BayesianGPLVM(Y, input_dim, kernel=k, num_inducing=num_inducing)
-        m.ensure_default_constraints()
         m.randomize()
         self.assertTrue(m.checkgrad())
 
@@ -55,7 +52,6 @@ class BGPLVMTests(unittest.TestCase):
         Y -= Y.mean(axis=0)
         k = GPy.kern.rbf(input_dim) + GPy.kern.bias(input_dim) + GPy.kern.white(input_dim, 0.00001)
         m = BayesianGPLVM(Y, input_dim, kernel=k, num_inducing=num_inducing)
-        m.ensure_default_constraints()
         m.randomize()
         self.assertTrue(m.checkgrad())
 
@@ -69,7 +65,6 @@ class BGPLVMTests(unittest.TestCase):
         Y -= Y.mean(axis=0)
         k = GPy.kern.linear(input_dim) + GPy.kern.bias(input_dim) + GPy.kern.white(input_dim, 0.00001)
         m = BayesianGPLVM(Y, input_dim, kernel=k, num_inducing=num_inducing)
-        m.ensure_default_constraints()
         m.randomize()
         self.assertTrue(m.checkgrad())
 

GPy/testing/gplvm_tests.py

@@ -14,7 +14,6 @@ class GPLVMTests(unittest.TestCase):
         Y = np.random.multivariate_normal(np.zeros(N),K,input_dim).T
         k = GPy.kern.bias(input_dim) + GPy.kern.white(input_dim, 0.00001)
         m = GPy.models.GPLVM(Y, input_dim, kernel = k)
-        m.ensure_default_constraints()
         m.randomize()
         self.assertTrue(m.checkgrad())
 
@@ -26,7 +25,6 @@ class GPLVMTests(unittest.TestCase):
         Y = np.random.multivariate_normal(np.zeros(N),K,input_dim).T
         k = GPy.kern.linear(input_dim) + GPy.kern.white(input_dim, 0.00001)
         m = GPy.models.GPLVM(Y, input_dim, kernel = k)
-        m.ensure_default_constraints()
         m.randomize()
         self.assertTrue(m.checkgrad())
 
@@ -38,7 +36,6 @@ class GPLVMTests(unittest.TestCase):
         Y = np.random.multivariate_normal(np.zeros(N),K,input_dim).T
         k = GPy.kern.rbf(input_dim) + GPy.kern.white(input_dim, 0.00001)
         m = GPy.models.GPLVM(Y, input_dim, kernel = k)
-        m.ensure_default_constraints()
         m.randomize()
         self.assertTrue(m.checkgrad())
 

GPy/testing/mrd_tests.py

@@ -24,7 +24,6 @@ class MRDTests(unittest.TestCase):
         likelihood_list = [GPy.likelihoods.Gaussian(Y) for Y in Ylist]
 
         m = GPy.models.MRD(likelihood_list, input_dim=input_dim, kernels=k, num_inducing=num_inducing)
-        m.ensure_default_constraints()
 
         self.assertTrue(m.checkgrad())
 

GPy/testing/prior_tests.py

@@ -14,7 +14,6 @@ class PriorTests(unittest.TestCase):
         y += 0.05*np.random.randn(len(X))
         X, y = X[:, None], y[:, None]
         m = GPy.models.GPRegression(X, y)
-        m.ensure_default_constraints()
         lognormal = GPy.priors.LogGaussian(1, 2)
         m.set_prior('rbf', lognormal)
         m.randomize()
@@ -28,7 +27,6 @@ class PriorTests(unittest.TestCase):
         y += 0.05*np.random.randn(len(X))
         X, y = X[:, None], y[:, None]
         m = GPy.models.GPRegression(X, y)
-        m.ensure_default_constraints()
         Gamma = GPy.priors.Gamma(1, 1)
         m.set_prior('rbf', Gamma)
         m.randomize()
@@ -42,7 +40,6 @@ class PriorTests(unittest.TestCase):
         y += 0.05*np.random.randn(len(X))
         X, y = X[:, None], y[:, None]
         m = GPy.models.GPRegression(X, y)
-        m.ensure_default_constraints()
         gaussian = GPy.priors.Gaussian(1, 1)
         success = False
 

GPy/testing/psi_stat_gradient_tests.py

@@ -113,7 +113,6 @@ if __name__ == "__main__":
 #         Y -= Y.mean(axis=0)
 #         k = GPy.kern.linear(input_dim) + GPy.kern.bias(input_dim) + GPy.kern.white(input_dim, 0.00001)
 #         m = GPy.models.Bayesian_GPLVM(Y, input_dim, kernel=k, num_inducing=num_inducing)
-#         m.ensure_default_constraints()
 #         m.randomize()
 # #         self.assertTrue(m.checkgrad())
         numpy.random.seed(0)
@@ -146,7 +145,6 @@ if __name__ == "__main__":
 #                          num_inducing=num_inducing, kernel=GPy.kern.rbf(input_dim))
         m3 = PsiStatModel('psi2', X=X, X_variance=X_var, Z=Z,
                          num_inducing=num_inducing, kernel=GPy.kern.linear(input_dim, ARD=True, variances=numpy.random.rand(input_dim)))
-        m3.ensure_default_constraints()
         # + GPy.kern.bias(input_dim))
 #         m4 = PsiStatModel('psi2', X=X, X_variance=X_var, Z=Z,
 #                          num_inducing=num_inducing, kernel=GPy.kern.rbf(input_dim) + GPy.kern.bias(input_dim))

GPy/testing/sparse_gplvm_tests.py

@@ -15,7 +15,6 @@ class sparse_GPLVMTests(unittest.TestCase):
         Y = np.random.multivariate_normal(np.zeros(N),K,input_dim).T
         k = GPy.kern.bias(input_dim) + GPy.kern.white(input_dim, 0.00001)
         m = SparseGPLVM(Y, input_dim, kernel=k, num_inducing=num_inducing)
-        m.ensure_default_constraints()
         m.randomize()
         self.assertTrue(m.checkgrad())
 
@@ -27,7 +26,6 @@ class sparse_GPLVMTests(unittest.TestCase):
         Y = np.random.multivariate_normal(np.zeros(N),K,input_dim).T
         k = GPy.kern.linear(input_dim) + GPy.kern.white(input_dim, 0.00001)
         m = SparseGPLVM(Y, input_dim, kernel=k, num_inducing=num_inducing)
-        m.ensure_default_constraints()
         m.randomize()
         self.assertTrue(m.checkgrad())
 
@@ -39,7 +37,6 @@ class sparse_GPLVMTests(unittest.TestCase):
         Y = np.random.multivariate_normal(np.zeros(N),K,input_dim).T
         k = GPy.kern.rbf(input_dim) + GPy.kern.white(input_dim, 0.00001)
         m = SparseGPLVM(Y, input_dim, kernel=k, num_inducing=num_inducing)
-        m.ensure_default_constraints()
         m.randomize()
         self.assertTrue(m.checkgrad())
 

GPy/testing/unit_tests.py

@@ -37,7 +37,6 @@ class GradientTests(unittest.TestCase):
         noise = GPy.kern.white(dimension)
         kern = kern + noise
         m = model_fit(X, Y, kernel=kern)
-        m.ensure_default_constraints()
         m.randomize()
         # contrain all parameters to be positive
         self.assertTrue(m.checkgrad())
@@ -150,7 +149,6 @@ class GradientTests(unittest.TestCase):
         K = k.K(X)
         Y = np.random.multivariate_normal(np.zeros(N), K, input_dim).T
         m = GPy.models.GPLVM(Y, input_dim, kernel=k)
-        m.ensure_default_constraints()
         self.assertTrue(m.checkgrad())
 
     def test_GPLVM_rbf_linear_white_kern_2D(self):
@@ -161,7 +159,6 @@ class GradientTests(unittest.TestCase):
         K = k.K(X)
         Y = np.random.multivariate_normal(np.zeros(N), K, input_dim).T
         m = GPy.models.GPLVM(Y, input_dim, init='PCA', kernel=k)
-        m.ensure_default_constraints()
         self.assertTrue(m.checkgrad())
 
     def test_GP_EP_probit(self):
@@ -195,7 +192,6 @@ class GradientTests(unittest.TestCase):
         k = GPy.kern.rbf(1) + GPy.kern.white(1)
         Y = np.hstack([np.ones(N/2),np.zeros(N/2)])[:,None]
         m = GPy.models.FITCClassification(X, Y=Y)
-        m.ensure_default_constraints()
         m.update_likelihood_approximation()
         self.assertTrue(m.checkgrad())