Nparam changes to num_params

GPy/core/fitc.py

@@ -126,7 +126,7 @@ class FITC(SparseGP):
             self._dpsi1_dX += self.kern.dK_dX(_dpsi1.T,self.Z,self.X[i:i+1,:])
 
         # the partial derivative vector for the likelihood
-        if self.likelihood.Nparams == 0:
+        if self.likelihood.num_params == 0:
             # save computation here.
             self.partial_for_likelihood = None
         elif self.likelihood.is_heteroscedastic:

GPy/core/sparse_gp.py

@@ -156,7 +156,7 @@ class SparseGP(GPBase):
 
 
         # the partial derivative vector for the likelihood
-        if self.likelihood.Nparams == 0:
+        if self.likelihood.num_params == 0:
             # save computation here.
             self.partial_for_likelihood = None
         elif self.likelihood.is_heteroscedastic:

GPy/kern/parts/periodic_Matern32.py

@@ -113,7 +113,7 @@ class PeriodicMatern32(Kernpart):
 
     @silence_errors
     def dK_dtheta(self,dL_dK,X,X2,target):
-        """derivative of the covariance matrix with respect to the parameters (shape is Nxnum_inducingxNparam)"""
+        """derivative of the covariance matrix with respect to the parameters (shape is num_data x num_inducing x num_params)"""
         if X2 is None: X2 = X
         FX  = self._cos(self.basis_alpha[None,:],self.basis_omega[None,:],self.basis_phi[None,:])(X)
         FX2 = self._cos(self.basis_alpha[None,:],self.basis_omega[None,:],self.basis_phi[None,:])(X2)

GPy/kern/parts/periodic_Matern52.py

@@ -115,7 +115,7 @@ class PeriodicMatern52(Kernpart):
 
     @silence_errors
     def dK_dtheta(self,dL_dK,X,X2,target):
-        """derivative of the covariance matrix with respect to the parameters (shape is Nxnum_inducingxNparam)"""
+        """derivative of the covariance matrix with respect to the parameters (shape is num_data x num_inducing x num_params)"""
         if X2 is None: X2 = X
         FX  = self._cos(self.basis_alpha[None,:],self.basis_omega[None,:],self.basis_phi[None,:])(X)
         FX2 = self._cos(self.basis_alpha[None,:],self.basis_omega[None,:],self.basis_phi[None,:])(X2)

GPy/kern/parts/periodic_exponential.py

@@ -111,7 +111,7 @@ class PeriodicExponential(Kernpart):
 
     @silence_errors
     def dK_dtheta(self,dL_dK,X,X2,target):
-        """derivative of the covariance matrix with respect to the parameters (shape is Nxnum_inducingxNparam)"""
+        """derivative of the covariance matrix with respect to the parameters (shape is N x num_inducing x num_params)"""
         if X2 is None: X2 = X
         FX  = self._cos(self.basis_alpha[None,:],self.basis_omega[None,:],self.basis_phi[None,:])(X)
         FX2 = self._cos(self.basis_alpha[None,:],self.basis_omega[None,:],self.basis_phi[None,:])(X2)

GPy/likelihoods/ep.py

@@ -18,7 +18,7 @@ class EP(likelihood):
         self.data = data
         self.num_data, self.output_dim = self.data.shape
         self.is_heteroscedastic = True
-        self.Nparams = 0
+        self.num_params = 0
         self._transf_data = self.noise_model._preprocess_values(data)
 
         #Initial values - Likelihood approximation parameters:

GPy/likelihoods/ep_mixed_noise.py

@@ -31,7 +31,7 @@ class EP_Mixed_Noise(likelihood):
         self.data = np.vstack(data_list)
         self.N, self.output_dim = self.data.shape
         self.is_heteroscedastic = True
-        self.Nparams = 0#FIXME
+        self.num_params = 0#FIXME
         self._transf_data = np.vstack([noise_model._preprocess_values(data) for noise_model,data in zip(noise_model_list,data_list)])
         #TODO non-gaussian index
 

GPy/likelihoods/gaussian.py

@@ -15,7 +15,7 @@ class Gaussian(likelihood):
     """
     def __init__(self, data, variance=1., normalize=False):
         self.is_heteroscedastic = False
-        self.Nparams = 1
+        self.num_params = 1
         self.Z = 0. # a correction factor which accounts for the approximation made
         N, self.output_dim = data.shape
 

GPy/likelihoods/gaussian_mixed_noise.py

@@ -23,14 +23,14 @@ class Gaussian_Mixed_Noise(likelihood):
     :type normalize: False|True
     """
     def __init__(self, data_list, noise_params=None, normalize=True):
-        self.Nparams = len(data_list)
+        self.num_params = len(data_list)
         self.n_list = [data.size for data in data_list]
-        self.index = np.vstack([np.repeat(i,n)[:,None] for i,n in zip(range(self.Nparams),self.n_list)])
+        self.index = np.vstack([np.repeat(i,n)[:,None] for i,n in zip(range(self.num_params),self.n_list)])
 
         if noise_params is None:
-            noise_params = [1.] * self.Nparams
+            noise_params = [1.] * self.num_params
         else:
-            assert self.Nparams == len(noise_params), 'Number of noise parameters does not match the number of noise models.'
+            assert self.num_params == len(noise_params), 'Number of noise parameters does not match the number of noise models.'
 
         self.noise_model_list = [Gaussian(Y,variance=v,normalize = normalize) for Y,v in zip(data_list,noise_params)]
         self.n_params = [noise_model._get_params().size for noise_model in self.noise_model_list]

GPy/models/mrd.py

@@ -211,8 +211,8 @@ class MRD(Model):
 #         g.Z = Z.reshape(self.num_inducing, self.input_dim)
 #
 #     def _set_kern_params(self, g, p):
-#         g.kern._set_params(p[:g.kern.Nparam])
+#         g.kern._set_params(p[:g.kern.num_params])
-#         g.likelihood._set_params(p[g.kern.Nparam:])
+#         g.likelihood._set_params(p[g.kern.num_params:])
 
     def _set_params(self, x):
         start = 0; end = self.NQ