diff --git a/GPy/core/GP.py b/GPy/core/GP.py
deleted file mode 100644
index 04ea7af1..00000000
--- a/GPy/core/GP.py
+++ /dev/null
@@ -1,150 +0,0 @@
-# Copyright (c) 2012, GPy authors (see AUTHORS.txt).
-# Licensed under the BSD 3-clause license (see LICENSE.txt)
-
-
-import numpy as np
-from scipy import linalg
-import pylab as pb
-from .. import kern
-from ..util.linalg import pdinv, mdot, tdot
-#from ..util.plot import gpplot,  Tango
-from ..likelihoods import EP
-from gp_base import GPBase
-
-class GP(GPBase):
-    """
-    Gaussian Process model for regression and EP
-
-    :param X: input observations
-    :param kernel: a GPy kernel, defaults to rbf+white
-    :parm likelihood: a GPy likelihood
-    :param normalize_X:  whether to normalize the input data before computing (predictions will be in original scales)
-    :type normalize_X: False|True
-    :rtype: model object
-    :param epsilon_ep: convergence criterion for the Expectation Propagation algorithm, defaults to 0.1
-    :param powerep: power-EP parameters [$\eta$,$\delta$], defaults to [1.,1.]
-    :type powerep: list
-
-    .. Note:: Multiple independent outputs are allowed using columns of Y
-
-    """
-    def __init__(self, X, likelihood, kernel, normalize_X=False):
-        GPBase.__init__(self, X, likelihood, kernel, normalize_X=normalize_X)
-        self._set_params(self._get_params())
-
-    def _set_params(self, p):
-        self.kern._set_params_transformed(p[:self.kern.Nparam_transformed()])
-        self.likelihood._set_params(p[self.kern.Nparam_transformed():])
-
-        self.K = self.kern.K(self.X)
-        self.K += self.likelihood.covariance_matrix
-
-        self.Ki, self.L, self.Li, self.K_logdet = pdinv(self.K)
-
-        # the gradient of the likelihood wrt the covariance matrix
-        if self.likelihood.YYT is None:
-            #alpha = np.dot(self.Ki, self.likelihood.Y)
-            alpha,_ = linalg.lapack.flapack.dpotrs(self.L, self.likelihood.Y,lower=1)
-
-            self.dL_dK = 0.5 * (tdot(alpha) - self.D * self.Ki)
-        else:
-            #tmp = mdot(self.Ki, self.likelihood.YYT, self.Ki)
-            tmp, _ = linalg.lapack.flapack.dpotrs(self.L, np.asfortranarray(self.likelihood.YYT), lower=1)
-            tmp, _ = linalg.lapack.flapack.dpotrs(self.L, np.asfortranarray(tmp.T), lower=1)
-            self.dL_dK = 0.5 * (tmp - self.D * self.Ki)
-
-    def _get_params(self):
-        return np.hstack((self.kern._get_params_transformed(), self.likelihood._get_params()))
-
-
-    def _get_param_names(self):
-        return self.kern._get_param_names_transformed() + self.likelihood._get_param_names()
-
-    def update_likelihood_approximation(self):
-        """
-        Approximates a non-gaussian likelihood using Expectation Propagation
-
-        For a Gaussian likelihood, no iteration is required:
-        this function does nothing
-        """
-        self.likelihood.fit_full(self.kern.K(self.X))
-        self._set_params(self._get_params())  # update the GP
-
-    def _model_fit_term(self):
-        """
-        Computes the model fit using YYT if it's available
-        """
-        if self.likelihood.YYT is None:
-            tmp, _ = linalg.lapack.flapack.dtrtrs(self.L, np.asfortranarray(self.likelihood.Y), lower=1)
-            return -0.5 * np.sum(np.square(tmp))
-            #return -0.5 * np.sum(np.square(np.dot(self.Li, self.likelihood.Y)))
-        else:
-            return -0.5 * np.sum(np.multiply(self.Ki, self.likelihood.YYT))
-
-    def log_likelihood(self):
-        """
-        The log marginal likelihood of the GP.
-
-        For an EP model,  can be written as the log likelihood of a regression
-        model for a new variable Y* = v_tilde/tau_tilde, with a covariance
-        matrix K* = K + diag(1./tau_tilde) plus a normalization term.
-        """
-        return -0.5 * self.D * self.K_logdet + self._model_fit_term() + self.likelihood.Z
-
-
-    def _log_likelihood_gradients(self):
-        """
-        The gradient of all parameters.
-
-        Note, we use the chain rule: dL_dtheta = dL_dK * d_K_dtheta
-        """
-        return np.hstack((self.kern.dK_dtheta(dL_dK=self.dL_dK, X=self.X), self.likelihood._gradients(partial=np.diag(self.dL_dK))))
-
-    def _raw_predict(self, _Xnew, which_parts='all', full_cov=False,stop=False):
-        """
-        Internal helper function for making predictions, does not account
-        for normalization or likelihood
-        """
-        Kx = self.kern.K(_Xnew,self.X,which_parts=which_parts).T
-        #KiKx = np.dot(self.Ki, Kx)
-        KiKx, _ = linalg.lapack.flapack.dpotrs(self.L, np.asfortranarray(Kx), lower=1)
-        mu = np.dot(KiKx.T, self.likelihood.Y)
-        if full_cov:
-            Kxx = self.kern.K(_Xnew, which_parts=which_parts)
-            var = Kxx - np.dot(KiKx.T, Kx)
-        else:
-            Kxx = self.kern.Kdiag(_Xnew, which_parts=which_parts)
-            var = Kxx - np.sum(np.multiply(KiKx, Kx), 0)
-            var = var[:, None]
-        if stop:
-            debug_this
-        return mu, var
-
-    def predict(self, Xnew, which_parts='all', full_cov=False):
-        """
-        Predict the function(s) at the new point(s) Xnew.
-        Arguments
-        ---------
-        :param Xnew: The points at which to make a prediction
-        :type Xnew: np.ndarray, Nnew x self.input_dim
-        :param which_parts:  specifies which outputs kernel(s) to use in prediction
-        :type which_parts: ('all', list of bools)
-        :param full_cov: whether to return the folll covariance matrix, or just the diagonal
-        :type full_cov: bool
-        :rtype: posterior mean,  a Numpy array, Nnew x self.D
-        :rtype: posterior variance, a Numpy array, Nnew x 1 if full_cov=False, Nnew x Nnew otherwise
-        :rtype: lower and upper boundaries of the 95% confidence intervals, Numpy arrays,  Nnew x self.D
-
-
-           If full_cov and self.D > 1, the return shape of var is Nnew x Nnew x self.D. If self.D == 1, the return shape is Nnew x Nnew.
-           This is to allow for different normalizations of the output dimensions.
-
-        """
-        # normalize X values
-        Xnew = (Xnew.copy() - self._Xmean) / self._Xstd
-        mu, var = self._raw_predict(Xnew, full_cov=full_cov, which_parts=which_parts)
-
-        # now push through likelihood
-        mean, var, _025pm, _975pm = self.likelihood.predictive_values(mu, var, full_cov)
-
-        return mean, var, _025pm, _975pm
diff --git a/GPy/core/__init__.py b/GPy/core/__init__.py
index e49541b0..286eb0cd 100644
--- a/GPy/core/__init__.py
+++ b/GPy/core/__init__.py
@@ -1,8 +1,8 @@
 # Copyright (c) 2012, GPy authors (see AUTHORS.txt).
 # Licensed under the BSD 3-clause license (see LICENSE.txt)
 
-from GP import GP
-from sparse_GP import sparse_GP
 from model import *
 from parameterised import *
 import priors
+from GPy.core.gp import GP
+from GPy.core.sparse_gp import SparseGP
diff --git a/GPy/core/gp_base.py b/GPy/core/gp_base.py
index aa71b550..11b4726b 100644
--- a/GPy/core/gp_base.py
+++ b/GPy/core/gp_base.py
@@ -18,15 +18,15 @@ class GPBase(model.model):
         self.kern = kernel
         self.likelihood = likelihood
         assert self.X.shape[0] == self.likelihood.data.shape[0]
-        self.N, self.D = self.likelihood.data.shape
+        self.N, self.output_dim = self.likelihood.data.shape
 
         if normalize_X:
             self._Xmean = X.mean(0)[None, :]
             self._Xstd = X.std(0)[None, :]
             self.X = (X.copy() - self._Xmean) / self._Xstd
         else:
-            self._Xmean = np.zeros((1,self.input_dim))
-            self._Xstd = np.ones((1,self.input_dim))
+            self._Xmean = np.zeros((1, self.input_dim))
+            self._Xstd = np.ones((1, self.input_dim))
 
         model.model.__init__(self)
 
@@ -70,7 +70,7 @@ class GPBase(model.model):
             else:
                 m, v = self._raw_predict(Xnew, which_parts=which_parts, full_cov=True)
                 Ysim = np.random.multivariate_normal(m.flatten(), v, samples)
-                gpplot(Xnew, m, m - 2 * np.sqrt(np.diag(v)[:, None]), m + 2 * np.sqrt(np.diag(v))[:, None,], axes=ax)
+                gpplot(Xnew, m, m - 2 * np.sqrt(np.diag(v)[:, None]), m + 2 * np.sqrt(np.diag(v))[:, None, ], axes=ax)
                 for i in range(samples):
                     ax.plot(Xnew, Ysim[i, :], Tango.colorsHex['darkBlue'], linewidth=0.25)
             ax.plot(self.X[which_data], self.likelihood.Y[which_data], 'kx', mew=1.5)
@@ -108,19 +108,19 @@ class GPBase(model.model):
 
         if self.X.shape[1] == 1:
 
-            Xu = self.X * self._Xstd + self._Xmean  # NOTE self.X are the normalized values now
+            Xu = self.X * self._Xstd + self._Xmean # NOTE self.X are the normalized values now
 
             Xnew, xmin, xmax = x_frame1D(Xu, plot_limits=plot_limits)
             m, var, lower, upper = self.predict(Xnew, which_parts=which_parts)
             for d in range(m.shape[1]):
-                gpplot(Xnew, m[:,d], lower[:,d], upper[:,d],axes=ax)
-                ax.plot(Xu[which_data], self.likelihood.data[which_data,d], 'kx', mew=1.5)
+                gpplot(Xnew, m[:, d], lower[:, d], upper[:, d], axes=ax)
+                ax.plot(Xu[which_data], self.likelihood.data[which_data, d], 'kx', mew=1.5)
             ymin, ymax = min(np.append(self.likelihood.data, lower)), max(np.append(self.likelihood.data, upper))
             ymin, ymax = ymin - 0.1 * (ymax - ymin), ymax + 0.1 * (ymax - ymin)
             ax.set_xlim(xmin, xmax)
             ax.set_ylim(ymin, ymax)
 
-        elif self.X.shape[1] == 2:  # FIXME
+        elif self.X.shape[1] == 2: # FIXME
             resolution = resolution or 50
             Xnew, xx, yy, xmin, xmax = x_frame2D(self.X, plot_limits, resolution)
             x, y = np.linspace(xmin[0], xmax[0], resolution), np.linspace(xmin[1], xmax[1], resolution)
diff --git a/GPy/core/priors.py b/GPy/core/priors.py
index 7b6379de..43090ae3 100644
--- a/GPy/core/priors.py
+++ b/GPy/core/priors.py
@@ -99,9 +99,9 @@ class MultivariateGaussian:
         assert len(self.var.shape) == 2
         assert self.var.shape[0] == self.var.shape[1]
         assert self.var.shape[0] == self.mu.size
-        self.D = self.mu.size
+        self.input_dim = self.mu.size
         self.inv, self.hld = pdinv(self.var)
-        self.constant = -0.5 * self.D * np.log(2 * np.pi) - self.hld
+        self.constant = -0.5 * self.input_dim * np.log(2 * np.pi) - self.hld
 
     def summary(self):
         raise NotImplementedError
@@ -121,7 +121,7 @@ class MultivariateGaussian:
         return np.random.multivariate_normal(self.mu, self.var, n)
 
     def plot(self):
-        if self.D == 2:
+        if self.input_dim == 2:
             rvs = self.rvs(200)
             pb.plot(rvs[:, 0], rvs[:, 1], 'kx', mew=1.5)
             xmin, xmax = pb.xlim()
diff --git a/GPy/core/sparse_GP.py b/GPy/core/sparse_GP.py
deleted file mode 100644
index c4fe6763..00000000
--- a/GPy/core/sparse_GP.py
+++ /dev/null
@@ -1,304 +0,0 @@
-# Copyright (c) 2012, GPy authors (see AUTHORS.txt).
-# Licensed under the BSD 3-clause license (see LICENSE.txt)
-
-import numpy as np
-import pylab as pb
-from ..util.linalg import mdot, jitchol, tdot, symmetrify, backsub_both_sides, chol_inv
-from scipy import linalg
-from ..likelihoods import Gaussian
-from gp_base import GPBase
-
-class sparse_GP(GPBase):
-    """
-    Variational sparse GP model
-
-    :param X: inputs
-    :type X: np.ndarray (N x input_dim)
-    :param likelihood: a likelihood instance, containing the observed data
-    :type likelihood: GPy.likelihood.(Gaussian | EP | Laplace)
-    :param kernel : the kernel (covariance function). See link kernels
-    :type kernel: a GPy.kern.kern instance
-    :param X_variance: The uncertainty in the measurements of X (Gaussian variance)
-    :type X_variance: np.ndarray (N x input_dim) | None
-    :param Z: inducing inputs (optional, see note)
-    :type Z: np.ndarray (M x input_dim) | None
-    :param M : Number of inducing points (optional, default 10. Ignored if Z is not None)
-    :type M: int
-    :param normalize_(X|Y) : whether to normalize the data before computing (predictions will be in original scales)
-    :type normalize_(X|Y): bool
-    """
-
-    def __init__(self, X, likelihood, kernel, Z, X_variance=None, normalize_X=False):
-        GPBase.__init__(self, X, likelihood, kernel, normalize_X=normalize_X)
-
-        self.Z = Z
-        self.M = Z.shape[0]
-        self.likelihood = likelihood
-
-        if X_variance is None:
-            self.has_uncertain_inputs = False
-        else:
-            assert X_variance.shape == X.shape
-            self.has_uncertain_inputs = True
-            self.X_variance = X_variance
-
-        if normalize_X:
-            self.Z = (self.Z.copy() - self._Xmean) / self._Xstd
-
-        # normalize X uncertainty also
-        if self.has_uncertain_inputs:
-            self.X_variance /= np.square(self._Xstd)
-
-    def _compute_kernel_matrices(self):
-        # kernel computations, using BGPLVM notation
-        self.Kmm = self.kern.K(self.Z)
-        if self.has_uncertain_inputs:
-            self.psi0 = self.kern.psi0(self.Z, self.X, self.X_variance)
-            self.psi1 = self.kern.psi1(self.Z, self.X, self.X_variance).T
-            self.psi2 = self.kern.psi2(self.Z, self.X, self.X_variance)
-        else:
-            self.psi0 = self.kern.Kdiag(self.X)
-            self.psi1 = self.kern.K(self.Z, self.X)
-            self.psi2 = None
-
-    def _computations(self):
-
-        # factor Kmm
-        self.Lm = jitchol(self.Kmm)
-
-        # The rather complex computations of self.A
-        if self.has_uncertain_inputs:
-            if self.likelihood.is_heteroscedastic:
-                psi2_beta = (self.psi2 * (self.likelihood.precision.flatten().reshape(self.N, 1, 1))).sum(0)
-            else:
-                psi2_beta = self.psi2.sum(0) * self.likelihood.precision
-            evals, evecs = linalg.eigh(psi2_beta)
-            clipped_evals = np.clip(evals, 0., 1e6) # TODO: make clipping configurable
-            tmp = evecs * np.sqrt(clipped_evals)
-        else:
-            if self.likelihood.is_heteroscedastic:
-                tmp = self.psi1 * (np.sqrt(self.likelihood.precision.flatten().reshape(1, self.N)))
-            else:
-                tmp = self.psi1 * (np.sqrt(self.likelihood.precision))
-        tmp, _ = linalg.lapack.flapack.dtrtrs(self.Lm, np.asfortranarray(tmp), lower=1)
-        self.A = tdot(tmp)
-
-
-        # factor B
-        self.B = np.eye(self.M) + self.A
-        self.LB = jitchol(self.B)
-
-        # TODO: make a switch for either first compute psi1V, or VV.T
-        self.psi1V = np.dot(self.psi1, self.likelihood.V)
-
-        # back substutue C into psi1V
-        tmp, info1 = linalg.lapack.flapack.dtrtrs(self.Lm, np.asfortranarray(self.psi1V), lower=1, trans=0)
-        self._LBi_Lmi_psi1V, _ = linalg.lapack.flapack.dtrtrs(self.LB, np.asfortranarray(tmp), lower=1, trans=0)
-        tmp, info2 = linalg.lapack.flapack.dpotrs(self.LB, tmp, lower=1)
-        self.Cpsi1V, info3 = linalg.lapack.flapack.dtrtrs(self.Lm, tmp, lower=1, trans=1)
-
-        # Compute dL_dKmm
-        tmp = tdot(self._LBi_Lmi_psi1V)
-        self.DBi_plus_BiPBi = backsub_both_sides(self.LB, self.D * np.eye(self.M) + tmp)
-        tmp = -0.5 * self.DBi_plus_BiPBi
-        tmp += -0.5 * self.B * self.D
-        tmp += self.D * np.eye(self.M)
-        self.dL_dKmm = backsub_both_sides(self.Lm, tmp)
-
-        # Compute dL_dpsi # FIXME: this is untested for the heterscedastic + uncertain inputs case
-        self.dL_dpsi0 = -0.5 * self.D * (self.likelihood.precision * np.ones([self.N, 1])).flatten()
-        self.dL_dpsi1 = np.dot(self.Cpsi1V, self.likelihood.V.T)
-        dL_dpsi2_beta = 0.5 * backsub_both_sides(self.Lm, self.D * np.eye(self.M) - self.DBi_plus_BiPBi)
-
-        if self.likelihood.is_heteroscedastic:
-            if self.has_uncertain_inputs:
-                self.dL_dpsi2 = self.likelihood.precision.flatten()[:, None, None] * dL_dpsi2_beta[None, :, :]
-            else:
-                self.dL_dpsi1 += 2.*np.dot(dL_dpsi2_beta, self.psi1 * self.likelihood.precision.reshape(1, self.N))
-                self.dL_dpsi2 = None
-        else:
-            dL_dpsi2 = self.likelihood.precision * dL_dpsi2_beta
-            if self.has_uncertain_inputs:
-                # repeat for each of the N psi_2 matrices
-                self.dL_dpsi2 = np.repeat(dL_dpsi2[None, :, :], self.N, axis=0)
-            else:
-                # subsume back into psi1 (==Kmn)
-                self.dL_dpsi1 += 2.*np.dot(dL_dpsi2, self.psi1)
-                self.dL_dpsi2 = None
-
-
-        # the partial derivative vector for the likelihood
-        if self.likelihood.Nparams == 0:
-            # save computation here.
-            self.partial_for_likelihood = None
-        elif self.likelihood.is_heteroscedastic:
-            raise NotImplementedError, "heteroscedatic derivates not implemented"
-        else:
-            # likelihood is not heterscedatic
-            self.partial_for_likelihood = -0.5 * self.N * self.D * self.likelihood.precision + 0.5 * self.likelihood.trYYT * self.likelihood.precision ** 2
-            self.partial_for_likelihood += 0.5 * self.D * (self.psi0.sum() * self.likelihood.precision ** 2 - np.trace(self.A) * self.likelihood.precision)
-            self.partial_for_likelihood += self.likelihood.precision * (0.5 * np.sum(self.A * self.DBi_plus_BiPBi) - np.sum(np.square(self._LBi_Lmi_psi1V)))
-
-    def log_likelihood(self):
-        """ Compute the (lower bound on the) log marginal likelihood """
-        if self.likelihood.is_heteroscedastic:
-            A = -0.5 * self.N * self.D * np.log(2.*np.pi) + 0.5 * np.sum(np.log(self.likelihood.precision)) - 0.5 * np.sum(self.likelihood.V * self.likelihood.Y)
-            B = -0.5 * self.D * (np.sum(self.likelihood.precision.flatten() * self.psi0) - np.trace(self.A))
-        else:
-            A = -0.5 * self.N * self.D * (np.log(2.*np.pi) - np.log(self.likelihood.precision)) - 0.5 * self.likelihood.precision * self.likelihood.trYYT
-            B = -0.5 * self.D * (np.sum(self.likelihood.precision * self.psi0) - np.trace(self.A))
-        C = -self.D * (np.sum(np.log(np.diag(self.LB)))) # + 0.5 * self.M * np.log(sf2))
-        D = 0.5 * np.sum(np.square(self._LBi_Lmi_psi1V))
-        return A + B + C + D + self.likelihood.Z
-
-    def _set_params(self, p):
-        self.Z = p[:self.M * self.input_dim].reshape(self.M, self.input_dim)
-        self.kern._set_params(p[self.Z.size:self.Z.size + self.kern.Nparam])
-        self.likelihood._set_params(p[self.Z.size + self.kern.Nparam:])
-        self._compute_kernel_matrices()
-        self._computations()
-
-    def _get_params(self):
-        return np.hstack([self.Z.flatten(), self.kern._get_params_transformed(), self.likelihood._get_params()])
-
-    def _get_param_names(self):
-        return sum([['iip_%i_%i' % (i, j) for j in range(self.Z.shape[1])] for i in range(self.Z.shape[0])],[])\
-            + self.kern._get_param_names_transformed() + self.likelihood._get_param_names()
-
-    def update_likelihood_approximation(self):
-        """
-        Approximates a non-gaussian likelihood using Expectation Propagation
-
-        For a Gaussian likelihood, no iteration is required:
-        this function does nothing
-        """
-        if not isinstance(self.likelihood, Gaussian): # Updates not needed for Gaussian likelihood
-            self.likelihood.restart() # TODO check consistency with pseudo_EP
-            if self.has_uncertain_inputs:
-                Lmi = chol_inv(self.Lm)
-                Kmmi = tdot(Lmi.T)
-                diag_tr_psi2Kmmi = np.array([np.trace(psi2_Kmmi) for psi2_Kmmi in np.dot(self.psi2, Kmmi)])
-
-                self.likelihood.fit_FITC(self.Kmm, self.psi1, diag_tr_psi2Kmmi) # This uses the fit_FITC code, but does not perfomr a FITC-EP.#TODO solve potential confusion
-                # raise NotImplementedError, "EP approximation not implemented for uncertain inputs"
-            else:
-                self.likelihood.fit_DTC(self.Kmm, self.psi1)
-                # self.likelihood.fit_FITC(self.Kmm,self.psi1,self.psi0)
-                self._set_params(self._get_params()) # update the GP
-
-    def _log_likelihood_gradients(self):
-        return np.hstack((self.dL_dZ().flatten(), self.dL_dtheta(), self.likelihood._gradients(partial=self.partial_for_likelihood)))
-
-    def dL_dtheta(self):
-        """
-        Compute and return the derivative of the log marginal likelihood wrt the parameters of the kernel
-        """
-        dL_dtheta = self.kern.dK_dtheta(self.dL_dKmm, self.Z)
-        if self.has_uncertain_inputs:
-            dL_dtheta += self.kern.dpsi0_dtheta(self.dL_dpsi0, self.Z, self.X, self.X_variance)
-            dL_dtheta += self.kern.dpsi1_dtheta(self.dL_dpsi1.T, self.Z, self.X, self.X_variance)
-            dL_dtheta += self.kern.dpsi2_dtheta(self.dL_dpsi2, self.Z, self.X, self.X_variance)
-        else:
-            dL_dtheta += self.kern.dK_dtheta(self.dL_dpsi1, self.Z, self.X)
-            dL_dtheta += self.kern.dKdiag_dtheta(self.dL_dpsi0, self.X)
-
-        return dL_dtheta
-
-    def dL_dZ(self):
-        """
-        The derivative of the bound wrt the inducing inputs Z
-        """
-        dL_dZ = 2.*self.kern.dK_dX(self.dL_dKmm, self.Z) # factor of two becase of vertical and horizontal 'stripes' in dKmm_dZ
-        if self.has_uncertain_inputs:
-            dL_dZ += self.kern.dpsi1_dZ(self.dL_dpsi1, self.Z, self.X, self.X_variance)
-            dL_dZ += self.kern.dpsi2_dZ(self.dL_dpsi2, self.Z, self.X, self.X_variance)
-        else:
-            dL_dZ += self.kern.dK_dX(self.dL_dpsi1, self.Z, self.X)
-        return dL_dZ
-
-    def _raw_predict(self, Xnew, X_variance_new=None, which_parts='all', full_cov=False):
-        """Internal helper function for making predictions, does not account for normalization"""
-
-        Bi, _ = linalg.lapack.flapack.dpotri(self.LB, lower=0)  # WTH? this lower switch should be 1, but that doesn't work!
-        symmetrify(Bi)
-        Kmmi_LmiBLmi = backsub_both_sides(self.Lm, np.eye(self.M) - Bi)
-
-        if X_variance_new is None:
-            Kx = self.kern.K(self.Z, Xnew, which_parts=which_parts)
-            mu = np.dot(Kx.T, self.Cpsi1V)
-            if full_cov:
-                Kxx = self.kern.K(Xnew, which_parts=which_parts)
-                var = Kxx - mdot(Kx.T, Kmmi_LmiBLmi, Kx) # NOTE this won't work for plotting
-            else:
-                Kxx = self.kern.Kdiag(Xnew, which_parts=which_parts)
-                var = Kxx - np.sum(Kx * np.dot(Kmmi_LmiBLmi, Kx), 0)
-        else:
-            # assert which_parts=='all', "swithching out parts of variational kernels is not implemented"
-            Kx = self.kern.psi1(self.Z, Xnew, X_variance_new) # , which_parts=which_parts) TODO: which_parts
-            mu = np.dot(Kx, self.Cpsi1V)
-            if full_cov:
-                raise NotImplementedError, "TODO"
-            else:
-                Kxx = self.kern.psi0(self.Z, Xnew, X_variance_new)
-                psi2 = self.kern.psi2(self.Z, Xnew, X_variance_new)
-                var = Kxx - np.sum(np.sum(psi2 * Kmmi_LmiBLmi[None, :, :], 1), 1)
-
-        return mu, var[:, None]
-
-    def predict(self, Xnew, X_variance_new=None, which_parts='all', full_cov=False):
-        """
-        Predict the function(s) at the new point(s) Xnew.
-
-        Arguments
-        ---------
-        :param Xnew: The points at which to make a prediction
-        :type Xnew: np.ndarray, Nnew x self.input_dim
-        :param X_variance_new: The uncertainty in the prediction points
-        :type X_variance_new: np.ndarray, Nnew x self.input_dim
-        :param which_parts:  specifies which outputs kernel(s) to use in prediction
-        :type which_parts: ('all', list of bools)
-        :param full_cov: whether to return the folll covariance matrix, or just the diagonal
-        :type full_cov: bool
-        :rtype: posterior mean,  a Numpy array, Nnew x self.D
-        :rtype: posterior variance, a Numpy array, Nnew x 1 if full_cov=False, Nnew x Nnew otherwise
-        :rtype: lower and upper boundaries of the 95% confidence intervals, Numpy arrays,  Nnew x self.D
-
-
-           If full_cov and self.D > 1, the return shape of var is Nnew x Nnew x self.D. If self.D == 1, the return shape is Nnew x Nnew.
-           This is to allow for different normalizations of the output dimensions.
-
-        """
-        # normalize X values
-        Xnew = (Xnew.copy() - self._Xmean) / self._Xstd
-        if X_variance_new is not None:
-            X_variance_new = X_variance_new / self._Xstd ** 2
-
-        # here's the actual prediction by the GP model
-        mu, var = self._raw_predict(Xnew, X_variance_new, full_cov=full_cov, which_parts=which_parts)
-
-        # now push through likelihood
-        mean, var, _025pm, _975pm = self.likelihood.predictive_values(mu, var, full_cov)
-
-        return mean, var, _025pm, _975pm
-
-    def plot(self, samples=0, plot_limits=None, which_data='all', which_parts='all', resolution=None, levels=20, fignum=None, ax=None):
-        if ax is None:
-            fig = pb.figure(num=fignum)
-            ax = fig.add_subplot(111)
-
-        if which_data is 'all':
-            which_data = slice(None)
-
-        GPBase.plot(self, samples=0, plot_limits=None, which_data='all', which_parts='all', resolution=None, levels=20, ax=ax)
-        if self.X.shape[1] == 1:
-            if self.has_uncertain_inputs:
-                Xu = self.X * self._Xstd + self._Xmean  # NOTE self.X are the normalized values now
-                ax.errorbar(Xu[which_data, 0], self.likelihood.data[which_data, 0],
-                            xerr=2 * np.sqrt(self.X_variance[which_data, 0]),
-                            ecolor='k', fmt=None, elinewidth=.5, alpha=.5)
-            Zu = self.Z * self._Xstd + self._Xmean
-            ax.plot(Zu, np.zeros_like(Zu) + ax.get_ylim()[0], 'r|', mew=1.5, markersize=12)
-
-        elif self.X.shape[1] == 2:
-            Zu = self.Z * self._Xstd + self._Xmean
-            ax.plot(Zu[:, 0], Zu[:, 1], 'wo')
diff --git a/GPy/examples/dimensionality_reduction.py b/GPy/examples/dimensionality_reduction.py
index 5e3eb964..df643935 100644
--- a/GPy/examples/dimensionality_reduction.py
+++ b/GPy/examples/dimensionality_reduction.py
@@ -5,9 +5,9 @@ import numpy as np
 from matplotlib import pyplot as plt
 
 import GPy
-from GPy.models.Bayesian_GPLVM import Bayesian_GPLVM
 from GPy.util.datasets import swiss_roll_generated
 from GPy.core.transformations import logexp
+from GPy.models.bayesian_gplvm import BayesianGPLVM
 
 default_seed = np.random.seed(123344)
 
@@ -20,14 +20,14 @@ def BGPLVM(seed=default_seed):
     X = np.random.rand(N, Q)
     k = GPy.kern.rbf(Q) + GPy.kern.white(Q, 0.00001)
     K = k.K(X)
-    Y = np.random.multivariate_normal(np.zeros(N), K, D).T
+    Y = np.random.multivariate_normal(np.zeros(N), K, Q).T
 
     k = GPy.kern.rbf(Q, ARD=True) + GPy.kern.linear(Q, ARD=True) + GPy.kern.rbf(Q, ARD=True) + GPy.kern.white(Q)
     # k = GPy.kern.rbf(Q) + GPy.kern.rbf(Q) + GPy.kern.white(Q)
     # k = GPy.kern.rbf(Q) + GPy.kern.bias(Q) + GPy.kern.white(Q, 0.00001)
     # k = GPy.kern.rbf(Q, ARD = False)  + GPy.kern.white(Q, 0.00001)
 
-    m = GPy.models.Bayesian_GPLVM(Y, Q, kernel=k, M=M)
+    m = GPy.models.BayesianGPLVM(Y, Q, kernel=k, M=M)
     m.constrain_positive('(rbf|bias|noise|white|S)')
     # m.constrain_fixed('S', 1)
 
@@ -105,7 +105,7 @@ def swiss_roll(optimize=True, N=1000, M=15, Q=4, sigma=.2, plot=False):
 
     kernel = GPy.kern.rbf(Q, ARD=True) + GPy.kern.bias(Q, np.exp(-2)) + GPy.kern.white(Q, np.exp(-2))
 
-    m = Bayesian_GPLVM(Y, Q, X=X, X_variance=S, M=M, Z=Z, kernel=kernel)
+    m = BayesianGPLVM(Y, Q, X=X, X_variance=S, M=M, Z=Z, kernel=kernel)
     m.data_colors = c
     m.data_t = t
 
@@ -129,7 +129,7 @@ def BGPLVM_oil(optimize=True, N=100, Q=5, M=25, max_f_eval=4e3, plot=False, **k)
     Yn = Y - Y.mean(0)
     Yn /= Yn.std(0)
 
-    m = GPy.models.Bayesian_GPLVM(Yn, Q, kernel=kernel, M=M, **k)
+    m = GPy.models.BayesianGPLVM(Yn, Q, kernel=kernel, M=M, **k)
     m.data_labels = data['Y'][:N].argmax(axis=1)
 
     # m.constrain('variance|leng', logexp_clipped())
@@ -234,7 +234,7 @@ def bgplvm_simulation_matlab_compare():
     from GPy import kern
     reload(mrd); reload(kern)
     k = kern.linear(Q, ARD=True) + kern.bias(Q, np.exp(-2)) + kern.white(Q, np.exp(-2))
-    m = Bayesian_GPLVM(Y, Q, init="PCA", M=M, kernel=k,
+    m = BayesianGPLVM(Y, Q, init="PCA", M=M, kernel=k,
 #                        X=mu,
 #                        X_variance=S,
                        _debug=False)
@@ -259,7 +259,7 @@ def bgplvm_simulation(optimize='scg',
     Y = Ylist[0]
 
     k = kern.linear(Q, ARD=True) + kern.bias(Q, np.exp(-2)) + kern.white(Q, np.exp(-2)) # + kern.bias(Q)
-    m = Bayesian_GPLVM(Y, Q, init="PCA", M=M, kernel=k, _debug=True)
+    m = BayesianGPLVM(Y, Q, init="PCA", M=M, kernel=k, _debug=True)
     # m.constrain('variance|noise', logexp_clipped())
     m.ensure_default_constraints()
     m['noise'] = Y.var() / 100.
@@ -285,7 +285,7 @@ def mrd_simulation(optimize=True, plot=True, plot_sim=True, **kw):
     reload(mrd); reload(kern)
 
     k = kern.linear(Q, [.05] * Q, ARD=True) + kern.bias(Q, np.exp(-2)) + kern.white(Q, np.exp(-2))
-    m = mrd.MRD(Ylist, Q=Q, M=M, kernels=k, initx="", initz='permute', **kw)
+    m = mrd.MRD(Ylist, input_dim=Q, M=M, kernels=k, initx="", initz='permute', **kw)
 
     for i, Y in enumerate(Ylist):
         m['{}_noise'.format(i + 1)] = Y.var() / 100.
@@ -313,7 +313,7 @@ def brendan_faces():
     Yn /= Yn.std()
 
     m = GPy.models.GPLVM(Yn, Q)
-    # m = GPy.models.Bayesian_GPLVM(Yn, Q, M=100)
+    # m = GPy.models.BayesianGPLVM(Yn, Q, M=100)
 
     # optimize
     m.constrain('rbf|noise|white', GPy.core.transformations.logexp_clipped())
@@ -380,7 +380,7 @@ def cmu_mocap(subject='35', motion=['01'], in_place=True):
 #     M = 30
 #
 #     kernel = GPy.kern.rbf(Q, ARD=True) + GPy.kern.bias(Q) + GPy.kern.white(Q)
-#     m = GPy.models.Bayesian_GPLVM(X, Q, kernel=kernel, M=M)
+#     m = GPy.models.BayesianGPLVM(X, Q, kernel=kernel, M=M)
 #     # m.scale_factor = 100.0
 #     m.constrain_positive('(white|noise|bias|X_variance|rbf_variance|rbf_length)')
 #     from sklearn import cluster
diff --git a/GPy/examples/regression.py b/GPy/examples/regression.py
index fd2e85d4..19ee1552 100644
--- a/GPy/examples/regression.py
+++ b/GPy/examples/regression.py
@@ -15,7 +15,7 @@ def toy_rbf_1d(max_nb_eval_optim=100):
     data = GPy.util.datasets.toy_rbf_1d()
 
     # create simple GP model
-    m = GPy.models.GP_regression(data['X'],data['Y'])
+    m = GPy.models.GPRegression(data['X'],data['Y'])
 
     # optimize
     m.ensure_default_constraints()
@@ -30,7 +30,7 @@ def rogers_girolami_olympics(max_nb_eval_optim=100):
     data = GPy.util.datasets.rogers_girolami_olympics()
 
     # create simple GP model
-    m = GPy.models.GP_regression(data['X'],data['Y'])
+    m = GPy.models.GPRegression(data['X'],data['Y'])
 
     #set the lengthscale to be something sensible (defaults to 1)
     m['rbf_lengthscale'] = 10
@@ -49,7 +49,7 @@ def toy_rbf_1d_50(max_nb_eval_optim=100):
     data = GPy.util.datasets.toy_rbf_1d_50()
 
     # create simple GP model
-    m = GPy.models.GP_regression(data['X'],data['Y'])
+    m = GPy.models.GPRegression(data['X'],data['Y'])
 
     # optimize
     m.ensure_default_constraints()
@@ -65,7 +65,7 @@ def silhouette(max_nb_eval_optim=100):
     data = GPy.util.datasets.silhouette()
 
     # create simple GP model
-    m = GPy.models.GP_regression(data['X'],data['Y'])
+    m = GPy.models.GPRegression(data['X'],data['Y'])
 
     # optimize
     m.ensure_default_constraints()
@@ -87,9 +87,9 @@ def coregionalisation_toy2(max_nb_eval_optim=100):
     Y = np.vstack((Y1,Y2))
 
     k1 = GPy.kern.rbf(1) + GPy.kern.bias(1)
-    k2 = GPy.kern.coregionalise(2,1)
+    k2 = GPy.kern.Coregionalise(2,1)
     k = k1.prod(k2,tensor=True)
-    m = GPy.models.GP_regression(X,Y,kernel=k)
+    m = GPy.models.GPRegression(X,Y,kernel=k)
     m.constrain_fixed('.*rbf_var',1.)
     #m.constrain_positive('.*kappa')
     m.ensure_default_constraints()
@@ -119,9 +119,9 @@ def coregionalisation_toy(max_nb_eval_optim=100):
     Y = np.vstack((Y1,Y2))
 
     k1 = GPy.kern.rbf(1)
-    k2 = GPy.kern.coregionalise(2,2)
+    k2 = GPy.kern.Coregionalise(2,2)
     k = k1.prod(k2,tensor=True)
-    m = GPy.models.GP_regression(X,Y,kernel=k)
+    m = GPy.models.GPRegression(X,Y,kernel=k)
     m.constrain_fixed('.*rbf_var',1.)
     #m.constrain_positive('kappa')
     m.ensure_default_constraints()
@@ -155,10 +155,10 @@ def coregionalisation_sparse(max_nb_eval_optim=100):
     Z = np.hstack((np.random.rand(M,1)*8,np.random.randint(0,2,M)[:,None]))
 
     k1 = GPy.kern.rbf(1)
-    k2 = GPy.kern.coregionalise(2,2)
+    k2 = GPy.kern.Coregionalise(2,2)
     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.SparseGPRegression(X,Y,kernel=k,Z=Z)
     m.scale_factor = 10000.
     m.constrain_fixed('.*rbf_var',1.)
     #m.constrain_positive('kappa')
@@ -213,7 +213,7 @@ def multiple_optima(gene_number=937,resolution=80, model_restarts=10, seed=10000
     for i in range(0, model_restarts):
         kern = GPy.kern.rbf(1, variance=np.random.exponential(1.), lengthscale=np.random.exponential(50.)) + GPy.kern.white(1,variance=np.random.exponential(1.))
 
-        m = GPy.models.GP_regression(data['X'],data['Y'], kernel=kern)
+        m = GPy.models.GPRegression(data['X'],data['Y'], kernel=kern)
         optim_point_x[0] = m.get('rbf_lengthscale')
         optim_point_y[0] = np.log10(m.get('rbf_variance')) - np.log10(m.get('white_variance'));
 
@@ -260,7 +260,7 @@ def _contour_data(data, length_scales, log_SNRs, signal_kernel_call=GPy.kern.rbf
 
             kernel = signal_kernel_call(1, variance=signal_var, lengthscale=length_scale) + GPy.kern.white(1, variance=noise_var)
 
-            model = GPy.models.GP_regression(data['X'], data['Y'], kernel=kernel)
+            model = GPy.models.GPRegression(data['X'], data['Y'], kernel=kernel)
             model.constrain_positive('')
             length_scale_lls.append(model.log_likelihood())
         lls.append(length_scale_lls)
@@ -276,7 +276,7 @@ def sparse_GP_regression_1D(N = 400, M = 5, max_nb_eval_optim=100):
     noise = GPy.kern.white(1)
     kernel = rbf + noise
     # create simple GP model
-    m = GPy.models.sparse_GP_regression(X, Y, kernel, M=M)
+    m = GPy.models.SparseGPRegression(X, Y, kernel, M=M)
 
     m.ensure_default_constraints()
 
@@ -296,7 +296,7 @@ def sparse_GP_regression_2D(N = 400, M = 50, max_nb_eval_optim=100):
     kernel = rbf + noise
 
     # create simple GP model
-    m = GPy.models.sparse_GP_regression(X,Y,kernel, M = M)
+    m = GPy.models.SparseGPRegression(X,Y,kernel, M = M)
 
     # contrain all parameters to be positive (but not inducing inputs)
     m.ensure_default_constraints()
@@ -325,7 +325,7 @@ def uncertain_inputs_sparse_regression(max_nb_eval_optim=100):
     k = GPy.kern.rbf(1) + GPy.kern.white(1)
 
     # create simple GP model - no input uncertainty on this one
-    m = GPy.models.sparse_GP_regression(X, Y, kernel=k, Z=Z)
+    m = GPy.models.SparseGPRegression(X, Y, kernel=k, Z=Z)
     m.ensure_default_constraints()
     m.optimize('scg', messages=1, max_f_eval=max_nb_eval_optim)
     m.plot(ax=axes[0])
@@ -333,7 +333,7 @@ def uncertain_inputs_sparse_regression(max_nb_eval_optim=100):
 
 
     #the same model with uncertainty
-    m = GPy.models.sparse_GP_regression(X, Y, kernel=k, Z=Z, X_variance=S)
+    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=max_nb_eval_optim)
     m.plot(ax=axes[1])
diff --git a/GPy/inference/SCG.py b/GPy/inference/SCG.py
deleted file mode 100644
index 5753be7f..00000000
--- a/GPy/inference/SCG.py
+++ /dev/null
@@ -1,169 +0,0 @@
-# Copyright I. Nabney, N.Lawrence and James Hensman (1996 - 2012)
-
-# Scaled Conjuagte Gradients, originally in Matlab as part of the Netlab toolbox by I. Nabney, converted to python N. Lawrence and given a pythonic interface by James Hensman
-
-#      THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT
-#      HOLDERS AND CONTRIBUTORS "AS IS" AND ANY
-#      EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT
-#      NOT LIMITED TO, THE IMPLIED WARRANTIES OF
-#      MERCHANTABILITY AND FITNESS FOR A PARTICULAR
-#      PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
-#      REGENTS OR CONTRIBUTORS BE LIABLE FOR ANY
-#      DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
-#      EXEMPLARY, OR CONSEQUENTIAL DAMAGES
-#      (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT
-#      OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
-#      DATA, OR PROFITS; OR BUSINESS INTERRUPTION)
-#      HOWEVER CAUSED AND ON ANY THEORY OF
-#      LIABILITY, WHETHER IN CONTRACT, STRICT
-#      LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR
-#      OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
-#      OF THIS SOFTWARE, EVEN IF ADVISED OF THE
-#      POSSIBILITY OF SUCH DAMAGE.
-
-
-import numpy as np
-import sys
-
-
-def print_out(len_maxiters, display, fnow, current_grad, beta, iteration):
-    if display:
-        print '\r',
-        print '{0:>0{mi}g}  {1:> 12e}  {2:> 12e}  {3:> 12e}'.format(iteration, float(fnow), float(beta), float(current_grad), mi=len_maxiters), # print 'Iteration:', iteration, ' Objective:', fnow, '  Scale:', beta, '\r',
-        sys.stdout.flush()
-
-def SCG(f, gradf, x, optargs=(), maxiters=500, max_f_eval=500, display=True, xtol=None, ftol=None, gtol=None):
-    """
-    Optimisation through Scaled Conjugate Gradients (SCG)
-
-    f: the objective function
-    gradf : the gradient function (should return a 1D np.ndarray)
-    x : the initial condition
-
-    Returns
-    x the optimal value for x
-    flog : a list of all the objective values
-    function_eval number of fn evaluations
-    status: string describing convergence status
-    """
-    if xtol is None:
-        xtol = 1e-6
-    if ftol is None:
-        ftol = 1e-6
-    if gtol is None:
-        gtol = 1e-5
-    sigma0 = 1.0e-8
-    fold = f(x, *optargs) # Initial function value.
-    function_eval = 1
-    fnow = fold
-    gradnew = gradf(x, *optargs) # Initial gradient.
-    current_grad = np.dot(gradnew, gradnew)
-    gradold = gradnew.copy()
-    d = -gradnew # Initial search direction.
-    success = True # Force calculation of directional derivs.
-    nsuccess = 0 # nsuccess counts number of successes.
-    beta = 1.0 # Initial scale parameter.
-    betamin = 1.0e-60 # Lower bound on scale.
-    betamax = 1.0e100 # Upper bound on scale.
-    status = "Not converged"
-
-    flog = [fold]
-
-    iteration = 0
-
-    len_maxiters = len(str(maxiters))
-    if display:
-        print ' {0:{mi}s}   {1:11s}    {2:11s}    {3:11s}'.format("I", "F", "Scale", "|g|", mi=len_maxiters)
-
-    # Main optimization loop.
-    while iteration < maxiters:
-
-        # Calculate first and second directional derivatives.
-        if success:
-            mu = np.dot(d, gradnew)
-            if mu >= 0:
-                d = -gradnew
-                mu = np.dot(d, gradnew)
-            kappa = np.dot(d, d)
-            sigma = sigma0 / np.sqrt(kappa)
-            xplus = x + sigma * d
-            gplus = gradf(xplus, *optargs)
-            theta = np.dot(d, (gplus - gradnew)) / sigma
-
-        # Increase effective curvature and evaluate step size alpha.
-        delta = theta + beta * kappa
-        if delta <= 0:
-            delta = beta * kappa
-            beta = beta - theta / kappa
-
-        alpha = -mu / delta
-
-        # Calculate the comparison ratio.
-        xnew = x + alpha * d
-        fnew = f(xnew, *optargs)
-        function_eval += 1
-
-        if function_eval >= max_f_eval:
-            status = "Maximum number of function evaluations exceeded"
-            break
-#             return x, flog, function_eval, status
-
-        Delta = 2.*(fnew - fold) / (alpha * mu)
-        if Delta >= 0.:
-            success = True
-            nsuccess += 1
-            x = xnew
-            fnow = fnew
-        else:
-            success = False
-            fnow = fold
-
-        # Store relevant variables
-        flog.append(fnow) # Current function value
-
-        iteration += 1
-        print_out(len_maxiters, display, fnow, current_grad, beta, iteration)
-
-        if success:
-            # Test for termination
-            if (np.max(np.abs(alpha * d)) < xtol) or (np.abs(fnew - fold) < ftol):
-                status = 'converged'
-                break
-#                 return x, flog, function_eval, status
-
-            else:
-                # Update variables for new position
-                gradnew = gradf(x, *optargs)
-                current_grad = np.dot(gradnew, gradnew)
-                gradold = gradnew
-                fold = fnew
-                # If the gradient is zero then we are done.
-                if current_grad <= gtol:
-                    status = 'converged'
-                    break
-                    # return x, flog, function_eval, status
-
-        # Adjust beta according to comparison ratio.
-        if Delta < 0.25:
-            beta = min(4.0 * beta, betamax)
-        if Delta > 0.75:
-            beta = max(0.5 * beta, betamin)
-
-        # Update search direction using Polak-Ribiere formula, or re-start
-        # in direction of negative gradient after nparams steps.
-        if nsuccess == x.size:
-            d = -gradnew
-#             beta = 1.  # TODO: betareset!!
-            nsuccess = 0
-        elif success:
-            Gamma = np.dot(gradold - gradnew, gradnew) / (mu)
-            d = Gamma * d - gradnew
-    else:
-        # If we get here, then we haven't terminated in the given number of
-        # iterations.
-        status = "maxiter exceeded"
-
-    if display:
-        print_out(len_maxiters, display, fnow, current_grad, beta, iteration)
-        print ""
-    return x, flog, function_eval, status
diff --git a/GPy/inference/SGD.py b/GPy/inference/SGD.py
deleted file mode 100644
index c2a77e40..00000000
--- a/GPy/inference/SGD.py
+++ /dev/null
@@ -1,356 +0,0 @@
-import numpy as np
-import scipy as sp
-import scipy.sparse
-from optimization import Optimizer
-from scipy import linalg, optimize
-import pylab as plt
-import copy, sys, pickle
-
-class opt_SGD(Optimizer):
-    """
-    Optimize using stochastic gradient descent.
-
-    *** Parameters ***
-    model: reference to the model object
-    iterations: number of iterations
-    learning_rate: learning rate
-    momentum: momentum
-
-    """
-
-    def __init__(self, start, iterations = 10, learning_rate = 1e-4, momentum = 0.9, model = None, messages = False, batch_size = 1, self_paced = False, center = True, iteration_file = None, learning_rate_adaptation=None, actual_iter=None, schedule=None, **kwargs):
-        self.opt_name = "Stochastic Gradient Descent"
-
-        self.model = model
-        self.iterations = iterations
-        self.momentum = momentum
-        self.learning_rate = learning_rate
-        self.x_opt = None
-        self.f_opt = None
-        self.messages = messages
-        self.batch_size = batch_size
-        self.self_paced = self_paced
-        self.center = center
-        self.param_traces = [('noise',[])]
-        self.iteration_file = iteration_file
-        self.learning_rate_adaptation = learning_rate_adaptation
-        self.actual_iter = actual_iter
-        if self.learning_rate_adaptation != None:
-            if self.learning_rate_adaptation == 'annealing':
-                self.learning_rate_0 = self.learning_rate
-            else:
-                self.learning_rate_0 = self.learning_rate.mean()
-
-        self.schedule = schedule
-        # if len([p for p in self.model.kern.parts if p.name == 'bias']) == 1:
-        #     self.param_traces.append(('bias',[]))
-        # if len([p for p in self.model.kern.parts if p.name == 'linear']) == 1:
-        #     self.param_traces.append(('linear',[]))
-        # if len([p for p in self.model.kern.parts if p.name == 'rbf']) == 1:
-        #     self.param_traces.append(('rbf_var',[]))
-
-        self.param_traces = dict(self.param_traces)
-        self.fopt_trace = []
-
-        num_params = len(self.model._get_params())
-        if isinstance(self.learning_rate, float):
-            self.learning_rate = np.ones((num_params,)) * self.learning_rate
-
-        assert (len(self.learning_rate) == num_params), "there must be one learning rate per parameter"
-
-    def __str__(self):
-        status = "\nOptimizer: \t\t\t %s\n" % self.opt_name
-        status += "f(x_opt): \t\t\t %.4f\n" % self.f_opt
-        status += "Number of iterations: \t\t %d\n" % self.iterations
-        status += "Learning rate: \t\t\t max %.3f, min %.3f\n" % (self.learning_rate.max(), self.learning_rate.min())
-        status += "Momentum: \t\t\t %.3f\n" % self.momentum
-        status += "Batch size: \t\t\t %d\n" % self.batch_size
-        status += "Time elapsed: \t\t\t %s\n" % self.time
-        return status
-
-    def plot_traces(self):
-        plt.figure()
-        plt.subplot(211)
-        plt.title('Parameters')
-        for k in self.param_traces.keys():
-            plt.plot(self.param_traces[k], label=k)
-        plt.legend(loc=0)
-        plt.subplot(212)
-        plt.title('Objective function')
-        plt.plot(self.fopt_trace)
-
-
-    def non_null_samples(self, data):
-        return (np.isnan(data).sum(axis=1) == 0)
-
-    def check_for_missing(self, data):
-        if sp.sparse.issparse(self.model.likelihood.Y):
-            return True
-        else:
-            return np.isnan(data).sum() > 0
-
-    def subset_parameter_vector(self, x, samples, param_shapes):
-        subset = np.array([], dtype = int)
-        x = np.arange(0, len(x))
-        i = 0
-
-        for s in param_shapes:
-            N, input_dim = s
-            X = x[i:i+N*input_dim].reshape(N, input_dim)
-            X = X[samples]
-            subset = np.append(subset, X.flatten())
-            i += N*input_dim
-
-        subset = np.append(subset, x[i:])
-
-        return subset
-
-    def shift_constraints(self, j):
-
-        constrained_indices = copy.deepcopy(self.model.constrained_indices)
-
-        for c, constraint in enumerate(constrained_indices):
-            mask = (np.ones_like(constrained_indices[c]) == 1)
-            for i in range(len(constrained_indices[c])):
-                pos = np.where(j == constrained_indices[c][i])[0]
-                if len(pos) == 1:
-                    self.model.constrained_indices[c][i] = pos
-                else:
-                    mask[i] = False
-
-            self.model.constrained_indices[c] = self.model.constrained_indices[c][mask]
-        return constrained_indices
-        # back them up
-        # bounded_i = copy.deepcopy(self.model.constrained_bounded_indices)
-        # bounded_l = copy.deepcopy(self.model.constrained_bounded_lowers)
-        # bounded_u = copy.deepcopy(self.model.constrained_bounded_uppers)
-
-        # for b in range(len(bounded_i)): # for each group of constraints
-        #     for bc in range(len(bounded_i[b])):
-        #         pos = np.where(j == bounded_i[b][bc])[0]
-        #         if len(pos) == 1:
-        #             pos2 = np.where(self.model.constrained_bounded_indices[b] == bounded_i[b][bc])[0][0]
-        #             self.model.constrained_bounded_indices[b][pos2] = pos[0]
-        #         else:
-        #             if len(self.model.constrained_bounded_indices[b]) == 1:
-        #                 # if it's the last index to be removed
-        #                 # the logic here is just a mess. If we remove the last one, then all the
-        #                 # b-indices change and we have to iterate through everything to find our
-        #                 # current index. Can't deal with this right now.
-        #                 raise NotImplementedError
-
-        #             else: # just remove it from the indices
-        #                 mask = self.model.constrained_bounded_indices[b] != bc
-        #                 self.model.constrained_bounded_indices[b] = self.model.constrained_bounded_indices[b][mask]
-
-
-        # # here we shif the positive constraints. We cycle through each positive
-        # # constraint
-        # positive = self.model.constrained_positive_indices.copy()
-        # mask = (np.ones_like(positive) == 1)
-        # for p in range(len(positive)):
-        #     # we now check whether the constrained index appears in the j vector
-        #     # (the vector of the "active" indices)
-        #     pos = np.where(j == self.model.constrained_positive_indices[p])[0]
-        #     if len(pos) == 1:
-        #         self.model.constrained_positive_indices[p] = pos
-        #     else:
-        #         mask[p] = False
-        # self.model.constrained_positive_indices = self.model.constrained_positive_indices[mask]
-
-        # return (bounded_i, bounded_l, bounded_u), positive
-
-    def restore_constraints(self, c):#b, p):
-        # self.model.constrained_bounded_indices = b[0]
-        # self.model.constrained_bounded_lowers = b[1]
-        # self.model.constrained_bounded_uppers = b[2]
-        # self.model.constrained_positive_indices = p
-        self.model.constrained_indices = c
-
-    def get_param_shapes(self, N = None, input_dim = None):
-        model_name = self.model.__class__.__name__
-        if model_name == 'GPLVM':
-            return [(N, input_dim)]
-        if model_name == 'Bayesian_GPLVM':
-            return [(N, input_dim), (N, input_dim)]
-        else:
-            raise NotImplementedError
-
-    def step_with_missing_data(self, f_fp, X, step, shapes):
-        N, input_dim = X.shape
-
-        if not sp.sparse.issparse(self.model.likelihood.Y):
-            Y = self.model.likelihood.Y
-            samples = self.non_null_samples(self.model.likelihood.Y)
-            self.model.N = samples.sum()
-            Y = Y[samples]
-        else:
-            samples = self.model.likelihood.Y.nonzero()[0]
-            self.model.N = len(samples)
-            Y = np.asarray(self.model.likelihood.Y[samples].todense(), dtype = np.float64)
-
-        if self.model.N == 0 or Y.std() == 0.0:
-            return 0, step, self.model.N
-
-        self.model.likelihood._offset = Y.mean()
-        self.model.likelihood._scale = Y.std()
-        self.model.likelihood.set_data(Y)
-        # self.model.likelihood.V = self.model.likelihood.Y*self.model.likelihood.precision
-
-        sigma = self.model.likelihood._variance
-        self.model.likelihood._variance = None # invalidate cache
-        self.model.likelihood._set_params(sigma)
-
-
-        j = self.subset_parameter_vector(self.x_opt, samples, shapes)
-        self.model.X = X[samples]
-
-        model_name = self.model.__class__.__name__
-
-        if model_name == 'Bayesian_GPLVM':
-            self.model.likelihood.YYT = np.dot(self.model.likelihood.Y, self.model.likelihood.Y.T)
-            self.model.likelihood.trYYT = np.trace(self.model.likelihood.YYT)
-
-        ci = self.shift_constraints(j)
-        f, fp = f_fp(self.x_opt[j])
-
-        step[j] = self.momentum * step[j] + self.learning_rate[j] * fp
-        self.x_opt[j] -= step[j]
-        self.restore_constraints(ci)
-
-        self.model.grads[j] = fp
-        # restore likelihood _offset and _scale, otherwise when we call set_data(y) on
-        # the next feature, it will get normalized with the mean and std of this one.
-        self.model.likelihood._offset = 0
-        self.model.likelihood._scale = 1
-
-        return f, step, self.model.N
-
-    def adapt_learning_rate(self, t, D):
-        if self.learning_rate_adaptation == 'adagrad':
-            if t > 0:
-                g_k = self.model.grads
-                self.s_k += np.square(g_k)
-                t0 = 100.0
-                self.learning_rate = 0.1/(t0 + np.sqrt(self.s_k))
-
-                import pdb; pdb.set_trace()
-            else:
-                self.learning_rate = np.zeros_like(self.learning_rate)
-                self.s_k = np.zeros_like(self.x_opt)
-
-        elif self.learning_rate_adaptation == 'annealing':
-            #self.learning_rate = self.learning_rate_0/(1+float(t+1)/10)
-            self.learning_rate = np.ones_like(self.learning_rate) * self.schedule[t]
-
-
-        elif self.learning_rate_adaptation == 'semi_pesky':
-            if self.model.__class__.__name__ == 'Bayesian_GPLVM':
-                g_t = self.model.grads
-                if t == 0:
-                    self.hbar_t = 0.0
-                    self.tau_t = 100.0
-                    self.gbar_t = 0.0
-
-                self.gbar_t = (1-1/self.tau_t)*self.gbar_t + 1/self.tau_t * g_t
-                self.hbar_t = (1-1/self.tau_t)*self.hbar_t + 1/self.tau_t * np.dot(g_t.T, g_t)
-                self.learning_rate = np.ones_like(self.learning_rate)*(np.dot(self.gbar_t.T, self.gbar_t) / self.hbar_t)
-                tau_t = self.tau_t*(1-self.learning_rate) + 1
-
-
-    def opt(self, f_fp=None, f=None, fp=None):
-        self.x_opt = self.model._get_params_transformed()
-        self.grads = []
-
-        X, Y = self.model.X.copy(), self.model.likelihood.Y.copy()
-
-        self.model.likelihood.YYT = 0
-        self.model.likelihood.trYYT = 0
-        self.model.likelihood._offset = 0.0
-        self.model.likelihood._scale = 1.0
-
-        N, input_dim = self.model.X.shape
-        D = self.model.likelihood.Y.shape[1]
-        num_params = self.model._get_params()
-        self.trace = []
-        missing_data = self.check_for_missing(self.model.likelihood.Y)
-
-        step = np.zeros_like(num_params)
-        for it in range(self.iterations):
-            if self.actual_iter != None:
-                it = self.actual_iter
-
-            self.model.grads = np.zeros_like(self.x_opt) # TODO this is ugly
-
-            if it == 0 or self.self_paced is False:
-                features = np.random.permutation(Y.shape[1])
-            else:
-                features = np.argsort(NLL)
-
-            b = len(features)/self.batch_size
-            features = [features[i::b] for i in range(b)]
-            NLL = []
-            import pylab as plt
-            for count, j in enumerate(features):
-                self.model.D = len(j)
-                self.model.likelihood.D = len(j)
-                self.model.likelihood.set_data(Y[:, j])
-                # self.model.likelihood.V = self.model.likelihood.Y*self.model.likelihood.precision
-
-                sigma = self.model.likelihood._variance
-                self.model.likelihood._variance = None # invalidate cache
-                self.model.likelihood._set_params(sigma)
-
-                if missing_data:
-                    shapes = self.get_param_shapes(N, input_dim)
-                    f, step, Nj = self.step_with_missing_data(f_fp, X, step, shapes)
-                else:
-                    self.model.likelihood.YYT = np.dot(self.model.likelihood.Y, self.model.likelihood.Y.T)
-                    self.model.likelihood.trYYT = np.trace(self.model.likelihood.YYT)
-                    Nj = N
-                    f, fp = f_fp(self.x_opt)
-                    self.model.grads = fp.copy()
-                    step = self.momentum * step + self.learning_rate * fp
-                    self.x_opt -= step
-
-                if self.messages == 2:
-                    noise = self.model.likelihood._variance
-                    status = "evaluating {feature: 5d}/{tot: 5d} \t f: {f: 2.3f} \t non-missing: {nm: 4d}\t noise: {noise: 2.4f}\r".format(feature = count, tot = len(features), f = f, nm = Nj, noise = noise)
-                    sys.stdout.write(status)
-                    sys.stdout.flush()
-                    self.param_traces['noise'].append(noise)
-
-                self.adapt_learning_rate(it+count, D)
-                NLL.append(f)
-                self.fopt_trace.append(NLL[-1])
-                # fig = plt.figure('traces')
-                # plt.clf()
-                # plt.plot(self.param_traces['noise'])
-
-                # for k in self.param_traces.keys():
-                #     self.param_traces[k].append(self.model.get(k)[0])
-            self.grads.append(self.model.grads.tolist())
-            # should really be a sum(), but earlier samples in the iteration will have a very crappy ll
-            self.f_opt = np.mean(NLL)
-            self.model.N = N
-            self.model.X = X
-            self.model.D = D
-            self.model.likelihood.N = N
-            self.model.likelihood.D = D
-            self.model.likelihood.Y = Y
-            sigma = self.model.likelihood._variance
-            self.model.likelihood._variance = None # invalidate cache
-            self.model.likelihood._set_params(sigma)
-
-            self.trace.append(self.f_opt)
-            if self.iteration_file is not None:
-                f = open(self.iteration_file + "iteration%d.pickle" % it, 'w')
-                data = [self.x_opt, self.fopt_trace, self.param_traces]
-                pickle.dump(data, f)
-                f.close()
-
-            if self.messages != 0:
-                sys.stdout.write('\r' + ' '*len(status)*2 + '  \r')
-                status = "SGD Iteration: {0: 3d}/{1: 3d}  f: {2: 2.3f}   max eta: {3: 1.5f}\n".format(it+1, self.iterations, self.f_opt, self.learning_rate.max())
-                sys.stdout.write(status)
-                sys.stdout.flush()
diff --git a/GPy/inference/optimization.py b/GPy/inference/optimization.py
index e208392b..433d5f41 100644
--- a/GPy/inference/optimization.py
+++ b/GPy/inference/optimization.py
@@ -1,18 +1,16 @@
 # Copyright (c) 2012, GPy authors (see AUTHORS.txt).
 # Licensed under the BSD 3-clause license (see LICENSE.txt)
 
-import pdb
 import pylab as pb
 import datetime as dt
 from scipy import optimize
-import numpy as np
 
 try:
     import rasmussens_minimize as rasm
     rasm_available = True
 except ImportError:
     rasm_available = False
-from SCG import SCG
+from scg import SCG
 
 class Optimizer():
     """
@@ -51,9 +49,9 @@ class Optimizer():
         start = dt.datetime.now()
         self.opt(**kwargs)
         end = dt.datetime.now()
-        self.time = str(end-start)
+        self.time = str(end - start)
 
-    def opt(self, f_fp = None, f = None, fp = None):
+    def opt(self, f_fp=None, f=None, fp=None):
         raise NotImplementedError, "this needs to be implemented to use the optimizer class"
 
     def plot(self):
@@ -78,7 +76,7 @@ class opt_tnc(Optimizer):
         Optimizer.__init__(self, *args, **kwargs)
         self.opt_name = "TNC (Scipy implementation)"
 
-    def opt(self, f_fp = None, f = None, fp = None):
+    def opt(self, f_fp=None, f=None, fp=None):
         """
         Run the TNC optimizer
 
@@ -96,8 +94,8 @@ class opt_tnc(Optimizer):
         if self.gtol is not None:
             opt_dict['pgtol'] = self.gtol
 
-        opt_result = optimize.fmin_tnc(f_fp, self.x_init, messages = self.messages,
-                       maxfun = self.max_f_eval, **opt_dict)
+        opt_result = optimize.fmin_tnc(f_fp, self.x_init, messages=self.messages,
+                       maxfun=self.max_f_eval, **opt_dict)
         self.x_opt = opt_result[0]
         self.f_opt = f_fp(self.x_opt)[0]
         self.funct_eval = opt_result[1]
@@ -108,7 +106,7 @@ class opt_lbfgsb(Optimizer):
         Optimizer.__init__(self, *args, **kwargs)
         self.opt_name = "L-BFGS-B (Scipy implementation)"
 
-    def opt(self, f_fp = None, f = None, fp = None):
+    def opt(self, f_fp=None, f=None, fp=None):
         """
         Run the optimizer
 
@@ -130,8 +128,8 @@ class opt_lbfgsb(Optimizer):
         if self.gtol is not None:
             opt_dict['pgtol'] = self.gtol
 
-        opt_result = optimize.fmin_l_bfgs_b(f_fp, self.x_init, iprint = iprint,
-                                            maxfun = self.max_f_eval, **opt_dict)
+        opt_result = optimize.fmin_l_bfgs_b(f_fp, self.x_init, iprint=iprint,
+                                            maxfun=self.max_f_eval, **opt_dict)
         self.x_opt = opt_result[0]
         self.f_opt = f_fp(self.x_opt)[0]
         self.funct_eval = opt_result[2]['funcalls']
@@ -142,12 +140,12 @@ class opt_simplex(Optimizer):
         Optimizer.__init__(self, *args, **kwargs)
         self.opt_name = "Nelder-Mead simplex routine (via Scipy)"
 
-    def opt(self, f_fp = None, f = None, fp = None):
+    def opt(self, f_fp=None, f=None, fp=None):
         """
         The simplex optimizer does not require gradients.
         """
 
-        statuses = ['Converged', 'Maximum number of function evaluations made','Maximum number of iterations reached']
+        statuses = ['Converged', 'Maximum number of function evaluations made', 'Maximum number of iterations reached']
 
         opt_dict = {}
         if self.xtol is not None:
@@ -157,8 +155,8 @@ class opt_simplex(Optimizer):
         if self.gtol is not None:
             print "WARNING: simplex doesn't have an gtol arg, so I'm going to ignore it"
 
-        opt_result = optimize.fmin(f, self.x_init, (), disp = self.messages,
-                   maxfun = self.max_f_eval, full_output=True, **opt_dict)
+        opt_result = optimize.fmin(f, self.x_init, (), disp=self.messages,
+                   maxfun=self.max_f_eval, full_output=True, **opt_dict)
 
         self.x_opt = opt_result[0]
         self.f_opt = opt_result[1]
@@ -172,7 +170,7 @@ class opt_rasm(Optimizer):
         Optimizer.__init__(self, *args, **kwargs)
         self.opt_name = "Rasmussen's Conjugate Gradient"
 
-    def opt(self, f_fp = None, f = None, fp = None):
+    def opt(self, f_fp=None, f=None, fp=None):
         """
         Run Rasmussen's Conjugate Gradient optimizer
         """
@@ -189,8 +187,8 @@ class opt_rasm(Optimizer):
         if self.gtol is not None:
             print "WARNING: minimize doesn't have an gtol arg, so I'm going to ignore it"
 
-        opt_result = rasm.minimize(self.x_init, f_fp, (), messages = self.messages,
-                                   maxnumfuneval = self.max_f_eval)
+        opt_result = rasm.minimize(self.x_init, f_fp, (), messages=self.messages,
+                                   maxnumfuneval=self.max_f_eval)
         self.x_opt = opt_result[0]
         self.f_opt = opt_result[1][-1]
         self.funct_eval = opt_result[2]
@@ -203,7 +201,7 @@ class opt_SCG(Optimizer):
         Optimizer.__init__(self, *args, **kwargs)
         self.opt_name = "Scaled Conjugate Gradients"
 
-    def opt(self, f_fp = None, f = None, fp = None):
+    def opt(self, f_fp=None, f=None, fp=None):
         assert not f is None
         assert not fp is None
         opt_result = SCG(f, fp, self.x_init, display=self.messages,
@@ -218,7 +216,7 @@ class opt_SCG(Optimizer):
         self.status = opt_result[3]
 
 def get_optimizer(f_min):
-    from SGD import opt_SGD
+    from sgd import opt_SGD
 
     optimizers = {'fmin_tnc': opt_tnc,
           'simplex': opt_simplex,
diff --git a/GPy/kern/Brownian.py b/GPy/kern/Brownian.py
index c5b19653..fe60ec59 100644
--- a/GPy/kern/Brownian.py
+++ b/GPy/kern/Brownian.py
@@ -13,14 +13,14 @@ class Brownian(kernpart):
     """
     Brownian Motion kernel.
 
-    :param D: the number of input dimensions
-    :type D: int
+    :param input_dim: the number of input dimensions
+    :type input_dim: int
     :param variance:
     :type variance: float
     """
-    def __init__(self,D,variance=1.):
-        self.D = D
-        assert self.D==1, "Brownian motion in 1D only"
+    def __init__(self,input_dim,variance=1.):
+        self.input_dim = input_dim
+        assert self.input_dim==1, "Brownian motion in 1D only"
         self.Nparam = 1.
         self.name = 'Brownian'
         self._set_params(np.array([variance]).flatten())
diff --git a/GPy/kern/__init__.py b/GPy/kern/__init__.py
index 81dce75f..a71d38f4 100644
--- a/GPy/kern/__init__.py
+++ b/GPy/kern/__init__.py
@@ -2,7 +2,7 @@
 # Licensed under the BSD 3-clause license (see LICENSE.txt)
 
 
-from constructors import rbf, Matern32, Matern52, exponential, linear, white, bias, finite_dimensional, spline, Brownian, periodic_exponential, periodic_Matern32, periodic_Matern52, prod, symmetric, coregionalise, rational_quadratic, fixed, rbfcos, independent_outputs
+from constructors import rbf, Matern32, Matern52, exponential, linear, white, bias, finite_dimensional, spline, Brownian, periodic_exponential, periodic_Matern32, periodic_Matern52, prod, symmetric, Coregionalise, rational_quadratic, fixed, rbfcos, independent_outputs
 try:
     from constructors import rbf_sympy, sympykern # these depend on sympy
 except:
diff --git a/GPy/kern/bias.py b/GPy/kern/bias.py
index 09f0afa9..9defbf95 100644
--- a/GPy/kern/bias.py
+++ b/GPy/kern/bias.py
@@ -7,14 +7,14 @@ import numpy as np
 import hashlib
 
 class bias(kernpart):
-    def __init__(self,D,variance=1.):
+    def __init__(self,input_dim,variance=1.):
         """
-        :param D: the number of input dimensions
-        :type D: int
+        :param input_dim: the number of input dimensions
+        :type input_dim: int
         :param variance: the variance of the kernel
         :type variance: float
         """
-        self.D = D
+        self.input_dim = input_dim
         self.Nparam = 1
         self.name = 'bias'
         self._set_params(np.array([variance]).flatten())
diff --git a/GPy/kern/constructors.py b/GPy/kern/constructors.py
index f2e4b57b..a6ed1145 100644
--- a/GPy/kern/constructors.py
+++ b/GPy/kern/constructors.py
@@ -21,7 +21,7 @@ from periodic_Matern32 import periodic_Matern32 as periodic_Matern32part
 from periodic_Matern52 import periodic_Matern52 as periodic_Matern52part
 from prod import prod as prodpart
 from symmetric import symmetric as symmetric_part
-from coregionalise import coregionalise as coregionalise_part
+from coregionalise import Coregionalise as coregionalise_part
 from rational_quadratic import rational_quadratic as rational_quadraticpart
 from rbfcos import rbfcos as rbfcospart
 from independent_outputs import independent_outputs as independent_output_part
@@ -33,8 +33,8 @@ def rbf(D,variance=1., lengthscale=None,ARD=False):
     """
     Construct an RBF kernel
 
-    :param D: dimensionality of the kernel, obligatory
-    :type D: int
+    :param input_dim: dimensionality of the kernel, obligatory
+    :type input_dim: int
     :param variance: the variance of the kernel
     :type variance: float
     :param lengthscale: the lengthscale of the kernel
@@ -51,7 +51,7 @@ def linear(D,variances=None,ARD=False):
 
      Arguments
      ---------
-     D (int), obligatory
+    input_dimD (int), obligatory
      variances (np.ndarray)
      ARD (boolean)
     """
@@ -64,7 +64,7 @@ def white(D,variance=1.):
 
      Arguments
      ---------
-     D (int), obligatory
+    input_dimD (int), obligatory
      variance (float)
     """
     part = whitepart(D,variance)
@@ -74,8 +74,8 @@ def exponential(D,variance=1., lengthscale=None, ARD=False):
     """
     Construct an exponential kernel
 
-    :param D: dimensionality of the kernel, obligatory
-    :type D: int
+    :param input_dim: dimensionality of the kernel, obligatory
+    :type input_dim: int
     :param variance: the variance of the kernel
     :type variance: float
     :param lengthscale: the lengthscale of the kernel
@@ -90,8 +90,8 @@ def Matern32(D,variance=1., lengthscale=None, ARD=False):
     """
      Construct a Matern 3/2 kernel.
 
-    :param D: dimensionality of the kernel, obligatory
-    :type D: int
+    :param input_dim: dimensionality of the kernel, obligatory
+    :type input_dim: int
     :param variance: the variance of the kernel
     :type variance: float
     :param lengthscale: the lengthscale of the kernel
@@ -106,8 +106,8 @@ def Matern52(D,variance=1., lengthscale=None, ARD=False):
     """
      Construct a Matern 5/2 kernel.
 
-    :param D: dimensionality of the kernel, obligatory
-    :type D: int
+    :param input_dim: dimensionality of the kernel, obligatory
+    :type input_dim: int
     :param variance: the variance of the kernel
     :type variance: float
     :param lengthscale: the lengthscale of the kernel
@@ -124,7 +124,7 @@ def bias(D,variance=1.):
 
      Arguments
      ---------
-     D (int), obligatory
+     input_dim (int), obligatory
      variance (float)
     """
     part = biaspart(D,variance)
@@ -133,7 +133,7 @@ def bias(D,variance=1.):
 def finite_dimensional(D,F,G,variances=1.,weights=None):
     """
     Construct a finite dimensional kernel.
-    D: int - the number of input dimensions
+    input_dim: int - the number of input dimensions
     F: np.array of functions with shape (n,) - the n basis functions
     G: np.array with shape (n,n) - the Gram matrix associated to F
     variances : np.ndarray with shape (n,)
@@ -145,8 +145,8 @@ def spline(D,variance=1.):
     """
     Construct a spline kernel.
 
-    :param D: Dimensionality of the kernel
-    :type D: int
+    :param input_dim: Dimensionality of the kernel
+    :type input_dim: int
     :param variance: the variance of the kernel
     :type variance: float
     """
@@ -157,8 +157,8 @@ def Brownian(D,variance=1.):
     """
     Construct a Brownian motion kernel.
 
-    :param D: Dimensionality of the kernel
-    :type D: int
+    :param input_dim: Dimensionality of the kernel
+    :type input_dim: int
     :param variance: the variance of the kernel
     :type variance: float
     """
@@ -204,8 +204,8 @@ def periodic_exponential(D=1,variance=1., lengthscale=None, period=2*np.pi,n_fre
     """
     Construct an periodic exponential kernel
 
-    :param D: dimensionality, only defined for D=1
-    :type D: int
+    :param input_dim: dimensionality, only defined for input_dim=1
+    :type input_dim: int
     :param variance: the variance of the kernel
     :type variance: float
     :param lengthscale: the lengthscale of the kernel
@@ -222,8 +222,8 @@ def periodic_Matern32(D,variance=1., lengthscale=None, period=2*np.pi,n_freq=10,
     """
      Construct a periodic Matern 3/2 kernel.
 
-     :param D: dimensionality, only defined for D=1
-     :type D: int
+     :param input_dim: dimensionality, only defined for input_dim=1
+     :type input_dim: int
      :param variance: the variance of the kernel
      :type variance: float
      :param lengthscale: the lengthscale of the kernel
@@ -240,8 +240,8 @@ def periodic_Matern52(D,variance=1., lengthscale=None, period=2*np.pi,n_freq=10,
     """
      Construct a periodic Matern 5/2 kernel.
 
-     :param D: dimensionality, only defined for D=1
-     :type D: int
+     :param input_dim: dimensionality, only defined for input_dim=1
+     :type input_dim: int
      :param variance: the variance of the kernel
      :type variance: float
      :param lengthscale: the lengthscale of the kernel
@@ -256,14 +256,14 @@ def periodic_Matern52(D,variance=1., lengthscale=None, period=2*np.pi,n_freq=10,
 
 def prod(k1,k2,tensor=False):
     """
-     Construct a product kernel over D from two kernels over D
+     Construct a product kernel over input_dim from two kernels over input_dim
 
     :param k1, k2: the kernels to multiply
     :type k1, k2: kernpart
     :rtype: kernel object
     """
     part = prodpart(k1,k2,tensor)
-    return kern(part.D, [part])
+    return kern(part.input_dim, [part])
 
 def symmetric(k):
     """
@@ -273,7 +273,7 @@ def symmetric(k):
     k_.parts = [symmetric_part(p) for p in k.parts]
     return k_
 
-def coregionalise(Nout,R=1, W=None, kappa=None):
+def Coregionalise(Nout,R=1, W=None, kappa=None):
     p = coregionalise_part(Nout,R,W,kappa)
     return kern(1,[p])
 
@@ -282,8 +282,8 @@ def rational_quadratic(D,variance=1., lengthscale=1., power=1.):
     """
      Construct rational quadratic kernel.
 
-    :param D: the number of input dimensions
-    :type D: int (D=1 is the only value currently supported)
+    :param input_dim: the number of input dimensions
+    :type input_dim: int (input_dim=1 is the only value currently supported)
     :param variance: the variance :math:`\sigma^2`
     :type variance: float
     :param lengthscale: the lengthscale :math:`\ell`
@@ -300,7 +300,7 @@ def fixed(D, K, variance=1.):
 
      Arguments
      ---------
-     D (int), obligatory
+     input_dim (int), obligatory
      K (np.array), obligatory
      variance (float)
     """
@@ -321,6 +321,6 @@ def independent_outputs(k):
     for sl in k.input_slices:
         assert (sl.start is None) and (sl.stop is None), "cannot adjust input slices! (TODO)"
     parts = [independent_output_part(p) for p in k.parts]
-    return kern(k.D+1,parts)
+    return kern(k.input_dim+1,parts)
 
 
diff --git a/GPy/kern/coregionalise.py b/GPy/kern/coregionalise.py
index a4d22c2d..7fe922fd 100644
--- a/GPy/kern/coregionalise.py
+++ b/GPy/kern/coregionalise.py
@@ -7,12 +7,12 @@ from GPy.util.linalg import mdot, pdinv
 import pdb
 from scipy import weave
 
-class coregionalise(kernpart):
+class Coregionalise(kernpart):
     """
     Kernel for Intrinsic Corregionalization Models
     """
     def __init__(self,Nout,R=1, W=None, kappa=None):
-        self.D = 1
+        self.input_dim = 1
         self.name = 'coregion'
         self.Nout = Nout
         self.R = R
diff --git a/GPy/kern/kern.py b/GPy/kern/kern.py
index 0d3b4ccf..05af1997 100644
--- a/GPy/kern/kern.py
+++ b/GPy/kern/kern.py
@@ -11,14 +11,14 @@ from prod import prod
 from ..util.linalg import symmetrify
 
 class kern(parameterised):
-    def __init__(self, D, parts=[], input_slices=None):
+    def __init__(self, input_dim, parts=[], input_slices=None):
         """
         This is the main kernel class for GPy. It handles multiple (additive) kernel functions, and keeps track of variaous things like which parameters live where.
 
         The technical code for kernels is divided into _parts_ (see e.g. rbf.py). This obnject contains a list of parts, which are computed additively. For multiplication, special _prod_ parts are used.
 
-        :param D: The dimensioality of the kernel's input space
-        :type D: int
+        :param input_dim: The dimensioality of the kernel's input space
+        :type input_dim: int
         :param parts: the 'parts' (PD functions) of the kernel
         :type parts: list of kernpart objects
         :param input_slices: the slices on the inputs which apply to each kernel
@@ -29,7 +29,7 @@ class kern(parameterised):
         self.Nparts = len(parts)
         self.Nparam = sum([p.Nparam for p in self.parts])
 
-        self.D = D
+        self.input_dim = input_dim
 
         # deal with input_slices
         if input_slices is None:
@@ -96,10 +96,10 @@ class kern(parameterised):
         :type other: GPy.kern
         """
         if tensor:
-            D = self.D + other.D
-            self_input_slices = [slice(*sl.indices(self.D)) for sl in self.input_slices]
-            other_input_indices = [sl.indices(other.D) for sl in other.input_slices]
-            other_input_slices = [slice(i[0] + self.D, i[1] + self.D, i[2]) for i in other_input_indices]
+            D = self.input_dim + other.input_dim
+            self_input_slices = [slice(*sl.indices(self.input_dim)) for sl in self.input_slices]
+            other_input_indices = [sl.indices(other.input_dim) for sl in other.input_slices]
+            other_input_slices = [slice(i[0] + self.input_dim, i[1] + self.input_dim, i[2]) for i in other_input_indices]
 
             newkern = kern(D, self.parts + other.parts, self_input_slices + other_input_slices)
 
@@ -111,8 +111,8 @@ class kern(parameterised):
             newkern.constraints = self.constraints + other.constraints
             newkern.tied_indices = self.tied_indices + [self.Nparam + x for x in other.tied_indices]
         else:
-            assert self.D == other.D
-            newkern = kern(self.D, self.parts + other.parts, self.input_slices + other.input_slices)
+            assert self.input_dim == other.input_dim
+            newkern = kern(self.input_dim, self.parts + other.parts, self.input_slices + other.input_slices)
             # transfer constraints:
             newkern.constrained_indices = self.constrained_indices + [i + self.Nparam  for i in other.constrained_indices]
             newkern.constraints = self.constraints + other.constraints
@@ -138,16 +138,16 @@ class kern(parameterised):
 
         slices = []
         for sl1, sl2 in itertools.product(K1.input_slices, K2.input_slices):
-            s1, s2 = [False] * K1.D, [False] * K2.D
+            s1, s2 = [False] * K1.input_dim, [False] * K2.input_dim
             s1[sl1], s2[sl2] = [True], [True]
             slices += [s1 + s2]
 
         newkernparts = [prod(k1, k2, tensor) for k1, k2 in itertools.product(K1.parts, K2.parts)]
 
         if tensor:
-            newkern = kern(K1.D + K2.D, newkernparts, slices)
+            newkern = kern(K1.input_dim + K2.input_dim, newkernparts, slices)
         else:
-            newkern = kern(K1.D, newkernparts, slices)
+            newkern = kern(K1.input_dim, newkernparts, slices)
 
         newkern._follow_constrains(K1, K2)
         return newkern
@@ -211,7 +211,7 @@ class kern(parameterised):
     def K(self, X, X2=None, which_parts='all'):
         if which_parts == 'all':
             which_parts = [True] * self.Nparts
-        assert X.shape[1] == self.D
+        assert X.shape[1] == self.input_dim
         if X2 is None:
             target = np.zeros((X.shape[0], X.shape[0]))
             [p.K(X[:, i_s], None, target=target) for p, i_s, part_i_used in zip(self.parts, self.input_slices, which_parts) if part_i_used]
@@ -225,11 +225,11 @@ class kern(parameterised):
         :param dL_dK: An array of dL_dK derivaties, dL_dK
         :type dL_dK: Np.ndarray (N x M)
         :param X: Observed data inputs
-        :type X: np.ndarray (N x D)
+        :type X: np.ndarray (N x input_dim)
         :param X2: Observed dara inputs (optional, defaults to X)
-        :type X2: np.ndarray (M x D)
+        :type X2: np.ndarray (M x input_dim)
         """
-        assert X.shape[1] == self.D
+        assert X.shape[1] == self.input_dim
         target = np.zeros(self.Nparam)
         if X2 is None:
             [p.dK_dtheta(dL_dK, X[:, i_s], None, target[ps]) for p, i_s, ps, in zip(self.parts, self.input_slices, self.param_slices)]
@@ -251,20 +251,20 @@ class kern(parameterised):
     def Kdiag(self, X, which_parts='all'):
         if which_parts == 'all':
             which_parts = [True] * self.Nparts
-        assert X.shape[1] == self.D
+        assert X.shape[1] == self.input_dim
         target = np.zeros(X.shape[0])
         [p.Kdiag(X[:, i_s], target=target) for p, i_s, part_on in zip(self.parts, self.input_slices, which_parts) if part_on]
         return target
 
     def dKdiag_dtheta(self, dL_dKdiag, X):
-        assert X.shape[1] == self.D
+        assert X.shape[1] == self.input_dim
         assert dL_dKdiag.size == X.shape[0]
         target = np.zeros(self.Nparam)
         [p.dKdiag_dtheta(dL_dKdiag, X[:, i_s], target[ps]) for p, i_s, ps in zip(self.parts, self.input_slices, self.param_slices)]
         return self._transform_gradients(target)
 
     def dKdiag_dX(self, dL_dKdiag, X):
-        assert X.shape[1] == self.D
+        assert X.shape[1] == self.input_dim
         target = np.zeros_like(X)
         [p.dKdiag_dX(dL_dKdiag, X[:, i_s], target[:, i_s]) for p, i_s in zip(self.parts, self.input_slices)]
         return target
@@ -386,7 +386,7 @@ class kern(parameterised):
     def plot(self, x=None, plot_limits=None, which_parts='all', resolution=None, *args, **kwargs):
         if which_parts == 'all':
             which_parts = [True] * self.Nparts
-        if self.D == 1:
+        if self.input_dim == 1:
             if x is None:
                 x = np.zeros((1, 1))
             else:
@@ -408,7 +408,7 @@ class kern(parameterised):
             pb.xlabel("x")
             pb.ylabel("k(x,%0.1f)" % x)
 
-        elif self.D == 2:
+        elif self.input_dim == 2:
             if x is None:
                 x = np.zeros((1, 2))
             else:
diff --git a/GPy/kern/kernpart.py b/GPy/kern/kernpart.py
index 7de150e9..a4dfdd92 100644
--- a/GPy/kern/kernpart.py
+++ b/GPy/kern/kernpart.py
@@ -3,16 +3,16 @@
 
 
 class kernpart(object):
-    def __init__(self,D):
+    def __init__(self,input_dim):
         """
         The base class for a kernpart: a positive definite function which forms part of a kernel
 
-        :param D: the number of input dimensions to the function
-        :type D: int
+        :param input_dim: the number of input dimensions to the function
+        :type input_dim: int
 
         Do not instantiate.
         """
-        self.D = D
+        self.input_dim = input_dim
         self.Nparam = 1
         self.name = 'unnamed'
 
diff --git a/GPy/kern/linear.py b/GPy/kern/linear.py
index 8744eda0..425b81bb 100644
--- a/GPy/kern/linear.py
+++ b/GPy/kern/linear.py
@@ -13,10 +13,10 @@ class linear(kernpart):
 
     .. math::
 
-       k(x,y) = \sum_{i=1}^D \sigma^2_i x_iy_i
+       k(x,y) = \sum_{i=1}^input_dim \sigma^2_i x_iy_i
 
-    :param D: the number of input dimensions
-    :type D: int
+    :param input_dim: the number of input dimensions
+    :type input_dim: int
     :param variances: the vector of variances :math:`\sigma^2_i`
     :type variances: array or list of the appropriate size (or float if there is only one variance parameter)
     :param ARD: Auto Relevance Determination. If equal to "False", the kernel has only one variance parameter \sigma^2, otherwise there is one variance parameter per dimension.
@@ -24,8 +24,8 @@ class linear(kernpart):
     :rtype: kernel object
     """
 
-    def __init__(self, D, variances=None, ARD=False):
-        self.D = D
+    def __init__(self, input_dim, variances=None, ARD=False):
+        self.input_dim = input_dim
         self.ARD = ARD
         if ARD == False:
             self.Nparam = 1
@@ -37,13 +37,13 @@ class linear(kernpart):
                 variances = np.ones(1)
             self._Xcache, self._X2cache = np.empty(shape=(2,))
         else:
-            self.Nparam = self.D
+            self.Nparam = self.input_dim
             self.name = 'linear'
             if variances is not None:
                 variances = np.asarray(variances)
-                assert variances.size == self.D, "bad number of lengthscales"
+                assert variances.size == self.input_dim, "bad number of lengthscales"
             else:
-                variances = np.ones(self.D)
+                variances = np.ones(self.input_dim)
         self._set_params(variances.flatten())
 
         # initialize cache
@@ -82,7 +82,7 @@ class linear(kernpart):
     def dK_dtheta(self, dL_dK, X, X2, target):
         if self.ARD:
             if X2 is None:
-                [np.add(target[i:i + 1], np.sum(dL_dK * tdot(X[:, i:i + 1])), target[i:i + 1]) for i in range(self.D)]
+                [np.add(target[i:i + 1], np.sum(dL_dK * tdot(X[:, i:i + 1])), target[i:i + 1]) for i in range(self.input_dim)]
             else:
                 product = X[:, None, :] * X2[None, :, :]
                 target += (dL_dK[:, :, None] * product).sum(0).sum(0)
@@ -153,7 +153,7 @@ class linear(kernpart):
 #                     psi2_real[n, m, m_prime] = np.dot(tmp, (
 #                             self._Z[m_prime:m_prime + 1] * self.variances).T)
 #         mu2_S = (self._mu[:, None, :] * self._mu[:, :, None])
-#         mu2_S[:, np.arange(self.D), np.arange(self.D)] += self._S
+#         mu2_S[:, np.arange(self.input_dim), np.arange(self.input_dim)] += self._S
 #         psi2 = (self.ZA[None, :, None, :] * mu2_S[:, None]).sum(-1)
 #         psi2 = (psi2[:, :, None] * self.ZA[None, None]).sum(-1)
 #         psi2_tensor = np.tensordot(self.ZZ[None, :, :, :] * np.square(self.variances), self.mu2_S[:, None, None, :], ((3), (3))).squeeze().T
diff --git a/GPy/kern/prod.py b/GPy/kern/prod.py
index f61906bb..4d1fe17f 100644
--- a/GPy/kern/prod.py
+++ b/GPy/kern/prod.py
@@ -22,14 +22,14 @@ class prod(kernpart):
         self.k1 = k1
         self.k2 = k2
         if tensor:
-            self.D = k1.D + k2.D
-            self.slice1 = slice(0,self.k1.D)
-            self.slice2 = slice(self.k1.D,self.k1.D+self.k2.D)
+            self.input_dim = k1.input_dim + k2.input_dim
+            self.slice1 = slice(0,self.k1.input_dim)
+            self.slice2 = slice(self.k1.input_dim,self.k1.input_dim+self.k2.input_dim)
         else:
-            assert k1.D == k2.D, "Error: The input spaces of the kernels to sum don't have the same dimension."
-            self.D = k1.D
-            self.slice1 = slice(0,self.D)
-            self.slice2 = slice(0,self.D)
+            assert k1.input_dim == k2.input_dim, "Error: The input spaces of the kernels to sum don't have the same dimension."
+            self.input_dim = k1.input_dim
+            self.slice1 = slice(0,self.input_dim)
+            self.slice2 = slice(0,self.input_dim)
 
         self._X, self._X2, self._params = np.empty(shape=(3,1))
         self._set_params(np.hstack((k1._get_params(),k2._get_params())))
diff --git a/GPy/kern/rbf.py b/GPy/kern/rbf.py
index a89b7d45..39cf878e 100644
--- a/GPy/kern/rbf.py
+++ b/GPy/kern/rbf.py
@@ -18,8 +18,8 @@ class rbf(kernpart):
 
     where \ell_i is the lengthscale, \sigma^2 the variance and d the dimensionality of the input.
 
-    :param D: the number of input dimensions
-    :type D: int
+    :param input_dim: the number of input dimensions
+    :type input_dim: int
     :param variance: the variance of the kernel
     :type variance: float
     :param lengthscale: the vector of lengthscale of the kernel
@@ -31,8 +31,8 @@ class rbf(kernpart):
     .. Note: this object implements both the ARD and 'spherical' version of the function
     """
 
-    def __init__(self,D,variance=1.,lengthscale=None,ARD=False):
-        self.D = D
+    def __init__(self,input_dim,variance=1.,lengthscale=None,ARD=False):
+        self.input_dim = input_dim
         self.name = 'rbf'
         self.ARD = ARD
         if not ARD:
@@ -43,12 +43,12 @@ class rbf(kernpart):
             else:
                 lengthscale = np.ones(1)
         else:
-            self.Nparam = self.D + 1
+            self.Nparam = self.input_dim + 1
             if lengthscale is not None:
                 lengthscale = np.asarray(lengthscale)
-                assert lengthscale.size == self.D, "bad number of lengthscales"
+                assert lengthscale.size == self.input_dim, "bad number of lengthscales"
             else:
-                lengthscale = np.ones(self.D)
+                lengthscale = np.ones(self.input_dim)
 
         self._set_params(np.hstack((variance,lengthscale.flatten())))
 
@@ -100,7 +100,7 @@ class rbf(kernpart):
                 code = """
                 int q,i,j;
                 double tmp;
-                for(q=0; q<D; q++){
+                for(q=0; q<input_dim; q++){
                   tmp = 0;
                   for(i=0; i<N; i++){
                     for(j=0; j<i; j++){
@@ -110,12 +110,12 @@ class rbf(kernpart):
                   target(q+1) += var_len3(q)*tmp;
                 }
                 """
-                N,M,D = X.shape[0], X.shape[0], self.D
+                N, M, input_dim = X.shape[0], X.shape[0], self.input_dim
             else:
                 code = """
                 int q,i,j;
                 double tmp;
-                for(q=0; q<D; q++){
+                for(q=0; q<input_dim; q++){
                   tmp = 0;
                   for(i=0; i<N; i++){
                     for(j=0; j<M; j++){
@@ -125,9 +125,9 @@ class rbf(kernpart):
                   target(q+1) += var_len3(q)*tmp;
                 }
                 """
-                N,M,D = X.shape[0], X2.shape[0], self.D
-            #[np.add(target[1+q:2+q],var_len3[q]*np.sum(dvardLdK*np.square(X[:,q][:,None]-X2[:,q][None,:])),target[1+q:2+q]) for q in range(self.D)]
-            weave.inline(code, arg_names=['N','M','D','X','X2','target','dvardLdK','var_len3'],
+                N, M, input_dim = X.shape[0], X2.shape[0], self.input_dim
+            #[np.add(target[1+q:2+q],var_len3[q]*np.sum(dvardLdK*np.square(X[:,q][:,None]-X2[:,q][None,:])),target[1+q:2+q]) for q in range(self.input_dim)]
+            weave.inline(code, arg_names=['N','M','input_dim','X','X2','target','dvardLdK','var_len3'],
                  type_converters=weave.converters.blitz,**self.weave_options)
         else:
             target[1] += (self.variance/self.lengthscale)*np.sum(self._K_dvar*self._K_dist2*dL_dK)
@@ -278,8 +278,8 @@ class rbf(kernpart):
         psi2 = np.empty((N,M,M))
 
         psi2_Zdist_sq = self._psi2_Zdist_sq
-        _psi2_denom = self._psi2_denom.squeeze().reshape(N,self.D)
-        half_log_psi2_denom = 0.5*np.log(self._psi2_denom).squeeze().reshape(N,self.D)
+        _psi2_denom = self._psi2_denom.squeeze().reshape(N,self.input_dim)
+        half_log_psi2_denom = 0.5*np.log(self._psi2_denom).squeeze().reshape(N,self.input_dim)
         variance_sq = float(np.square(self.variance))
         if self.ARD:
             lengthscale2 = self.lengthscale2
diff --git a/GPy/kern/white.py b/GPy/kern/white.py
index d5701cd9..92e25bde 100644
--- a/GPy/kern/white.py
+++ b/GPy/kern/white.py
@@ -8,13 +8,13 @@ class white(kernpart):
     """
     White noise kernel.
 
-    :param D: the number of input dimensions
-    :type D: int
+    :param input_dim: the number of input dimensions
+    :type input_dim: int
     :param variance:
     :type variance: float
     """
-    def __init__(self,D,variance=1.):
-        self.D = D
+    def __init__(self,input_dim,variance=1.):
+        self.input_dim = input_dim
         self.Nparam = 1
         self.name = 'white'
         self._set_params(np.array([variance]).flatten())
diff --git a/GPy/likelihoods/EP.py b/GPy/likelihoods/EP.py
deleted file mode 100644
index caa10987..00000000
--- a/GPy/likelihoods/EP.py
+++ /dev/null
@@ -1,336 +0,0 @@
-import numpy as np
-from scipy import stats, linalg
-from ..util.linalg import pdinv,mdot,jitchol,chol_inv,DSYR,tdot
-from likelihood import likelihood
-
-class EP(likelihood):
-    def __init__(self,data,likelihood_function,epsilon=1e-3,power_ep=[1.,1.]):
-        """
-        Expectation Propagation
-
-        Arguments
-        ---------
-        epsilon : Convergence criterion, maximum squared difference allowed between mean updates to stop iterations (float)
-        likelihood_function : a likelihood function (see likelihood_functions.py)
-        """
-        self.likelihood_function = likelihood_function
-        self.epsilon = epsilon
-        self.eta, self.delta = power_ep
-        self.data = data
-        self.N, self.D = self.data.shape
-        self.is_heteroscedastic = True
-        self.Nparams = 0
-        self._transf_data = self.likelihood_function._preprocess_values(data)
-
-        #Initial values - Likelihood approximation parameters:
-        #p(y|f) = t(f|tau_tilde,v_tilde)
-        self.tau_tilde = np.zeros(self.N)
-        self.v_tilde = np.zeros(self.N)
-
-        #initial values for the GP variables
-        self.Y = np.zeros((self.N,1))
-        self.covariance_matrix = np.eye(self.N)
-        self.precision = np.ones(self.N)[:,None]
-        self.Z = 0
-        self.YYT = None
-        self.V = self.precision * self.Y
-
-    def restart(self):
-        self.tau_tilde = np.zeros(self.N)
-        self.v_tilde = np.zeros(self.N)
-        self.Y = np.zeros((self.N,1))
-        self.covariance_matrix = np.eye(self.N)
-        self.precision = np.ones(self.N)[:,None]
-        self.Z = 0
-        self.YYT = None
-        self.V = self.precision * self.Y
-
-    def predictive_values(self,mu,var,full_cov):
-        if full_cov:
-            raise NotImplementedError, "Cannot make correlated predictions with an EP likelihood"
-        return self.likelihood_function.predictive_values(mu,var)
-
-    def _get_params(self):
-        return np.zeros(0)
-    def _get_param_names(self):
-        return []
-    def _set_params(self,p):
-        pass # TODO: the EP likelihood might want to take some parameters...
-    def _gradients(self,partial):
-        return np.zeros(0) # TODO: the EP likelihood might want to take some parameters...
-
-    def _compute_GP_variables(self):
-        #Variables to be called from GP
-        mu_tilde = self.v_tilde/self.tau_tilde #When calling EP, this variable is used instead of Y in the GP model
-        sigma_sum = 1./self.tau_ + 1./self.tau_tilde
-        mu_diff_2 = (self.v_/self.tau_ - mu_tilde)**2
-        self.Z = np.sum(np.log(self.Z_hat)) + 0.5*np.sum(np.log(sigma_sum)) + 0.5*np.sum(mu_diff_2/sigma_sum) #Normalization constant, aka Z_ep
-
-        self.Y =  mu_tilde[:,None]
-        self.YYT = np.dot(self.Y,self.Y.T)
-        self.covariance_matrix = np.diag(1./self.tau_tilde)
-        self.precision = self.tau_tilde[:,None]
-        self.V = self.precision * self.Y
-
-    def fit_full(self,K):
-        """
-        The expectation-propagation algorithm.
-        For nomenclature see Rasmussen & Williams 2006.
-        """
-        #Initial values - Posterior distribution parameters: q(f|X,Y) = N(f|mu,Sigma)
-        mu = np.zeros(self.N)
-        Sigma = K.copy()
-
-        """
-        Initial values - Cavity distribution parameters:
-        q_(f|mu_,sigma2_) = Product{q_i(f|mu_i,sigma2_i)}
-        sigma_ = 1./tau_
-        mu_ = v_/tau_
-        """
-        self.tau_ = np.empty(self.N,dtype=float)
-        self.v_ = np.empty(self.N,dtype=float)
-
-        #Initial values - Marginal moments
-        z = np.empty(self.N,dtype=float)
-        self.Z_hat = np.empty(self.N,dtype=float)
-        phi = np.empty(self.N,dtype=float)
-        mu_hat = np.empty(self.N,dtype=float)
-        sigma2_hat = np.empty(self.N,dtype=float)
-
-        #Approximation
-        epsilon_np1 = self.epsilon + 1.
-        epsilon_np2 = self.epsilon + 1.
-       	self.iterations = 0
-        self.np1 = [self.tau_tilde.copy()]
-        self.np2 = [self.v_tilde.copy()]
-        while epsilon_np1 > self.epsilon or epsilon_np2 > self.epsilon:
-            update_order = np.random.permutation(self.N)
-            for i in update_order:
-                #Cavity distribution parameters
-                self.tau_[i] = 1./Sigma[i,i] - self.eta*self.tau_tilde[i]
-                self.v_[i] = mu[i]/Sigma[i,i] - self.eta*self.v_tilde[i]
-                #Marginal moments
-                self.Z_hat[i], mu_hat[i], sigma2_hat[i] = self.likelihood_function.moments_match(self._transf_data[i],self.tau_[i],self.v_[i])
-                #Site parameters update
-                Delta_tau = self.delta/self.eta*(1./sigma2_hat[i] - 1./Sigma[i,i])
-                Delta_v = self.delta/self.eta*(mu_hat[i]/sigma2_hat[i] - mu[i]/Sigma[i,i])
-                self.tau_tilde[i] += Delta_tau
-                self.v_tilde[i] += Delta_v
-                #Posterior distribution parameters update
-                DSYR(Sigma,Sigma[:,i].copy(), -float(Delta_tau/(1.+ Delta_tau*Sigma[i,i])))
-                mu = np.dot(Sigma,self.v_tilde)
-                self.iterations += 1
-            #Sigma recomptutation with Cholesky decompositon
-            Sroot_tilde_K = np.sqrt(self.tau_tilde)[:,None]*K
-            B = np.eye(self.N) + np.sqrt(self.tau_tilde)[None,:]*Sroot_tilde_K
-            L = jitchol(B)
-            V,info = linalg.lapack.flapack.dtrtrs(L,Sroot_tilde_K,lower=1)
-            Sigma = K - np.dot(V.T,V)
-            mu = np.dot(Sigma,self.v_tilde)
-            epsilon_np1 = sum((self.tau_tilde-self.np1[-1])**2)/self.N
-            epsilon_np2 = sum((self.v_tilde-self.np2[-1])**2)/self.N
-            self.np1.append(self.tau_tilde.copy())
-            self.np2.append(self.v_tilde.copy())
-
-        return self._compute_GP_variables()
-
-    def fit_DTC(self, Kmm, Kmn):
-        """
-        The expectation-propagation algorithm with sparse pseudo-input.
-        For nomenclature see ... 2013.
-        """
-        M = Kmm.shape[0]
-
-        #TODO: this doesn't work with uncertain inputs!
-
-        """
-        Prior approximation parameters:
-        q(f|X) = int_{df}{N(f|KfuKuu_invu,diag(Kff-Qff)*N(u|0,Kuu)} = N(f|0,Sigma0)
-        Sigma0 = Qnn = Knm*Kmmi*Kmn
-        """
-        KmnKnm = np.dot(Kmn,Kmn.T)
-        Lm = jitchol(Kmm)
-        Lmi = chol_inv(Lm)
-        Kmmi = np.dot(Lmi.T,Lmi)
-        KmmiKmn = np.dot(Kmmi,Kmn)
-        Qnn_diag = np.sum(Kmn*KmmiKmn,-2)
-        LLT0 = Kmm.copy()
-
-        #Kmmi, Lm, Lmi, Kmm_logdet = pdinv(Kmm)
-        #KmnKnm = np.dot(Kmn, Kmn.T)
-        #KmmiKmn = np.dot(Kmmi,Kmn)
-        #Qnn_diag = np.sum(Kmn*KmmiKmn,-2)
-        #LLT0 = Kmm.copy()
-
-        """
-        Posterior approximation: q(f|y) = N(f| mu, Sigma)
-        Sigma = Diag + P*R.T*R*P.T + K
-        mu = w + P*Gamma
-        """
-        mu = np.zeros(self.N)
-        LLT = Kmm.copy()
-        Sigma_diag = Qnn_diag.copy()
-
-        """
-        Initial values - Cavity distribution parameters:
-        q_(g|mu_,sigma2_) = Product{q_i(g|mu_i,sigma2_i)}
-        sigma_ = 1./tau_
-        mu_ = v_/tau_
-        """
-        self.tau_ = np.empty(self.N,dtype=float)
-        self.v_ = np.empty(self.N,dtype=float)
-
-        #Initial values - Marginal moments
-        z = np.empty(self.N,dtype=float)
-        self.Z_hat = np.empty(self.N,dtype=float)
-        phi = np.empty(self.N,dtype=float)
-        mu_hat = np.empty(self.N,dtype=float)
-        sigma2_hat = np.empty(self.N,dtype=float)
-
-        #Approximation
-        epsilon_np1 = 1
-        epsilon_np2 = 1
-       	self.iterations = 0
-        np1 = [self.tau_tilde.copy()]
-        np2 = [self.v_tilde.copy()]
-        while epsilon_np1 > self.epsilon or epsilon_np2 > self.epsilon:
-            update_order = np.random.permutation(self.N)
-            for i in update_order:
-                #Cavity distribution parameters
-                self.tau_[i] = 1./Sigma_diag[i] - self.eta*self.tau_tilde[i]
-                self.v_[i] = mu[i]/Sigma_diag[i] - self.eta*self.v_tilde[i]
-                #Marginal moments
-                self.Z_hat[i], mu_hat[i], sigma2_hat[i] = self.likelihood_function.moments_match(self._transf_data[i],self.tau_[i],self.v_[i])
-                #Site parameters update
-                Delta_tau = self.delta/self.eta*(1./sigma2_hat[i] - 1./Sigma_diag[i])
-                Delta_v = self.delta/self.eta*(mu_hat[i]/sigma2_hat[i] - mu[i]/Sigma_diag[i])
-                self.tau_tilde[i] += Delta_tau
-                self.v_tilde[i] += Delta_v
-                #Posterior distribution parameters update
-                DSYR(LLT,Kmn[:,i].copy(),Delta_tau) #LLT = LLT + np.outer(Kmn[:,i],Kmn[:,i])*Delta_tau
-                L = jitchol(LLT)
-                #cholUpdate(L,Kmn[:,i]*np.sqrt(Delta_tau))
-                V,info = linalg.lapack.flapack.dtrtrs(L,Kmn,lower=1)
-                Sigma_diag = np.sum(V*V,-2)
-                si = np.sum(V.T*V[:,i],-1)
-                mu += (Delta_v-Delta_tau*mu[i])*si
-                self.iterations += 1
-            #Sigma recomputation with Cholesky decompositon
-            LLT = LLT0 + np.dot(Kmn*self.tau_tilde[None,:],Kmn.T)
-            L = jitchol(LLT)
-            V,info = linalg.lapack.flapack.dtrtrs(L,Kmn,lower=1)
-            V2,info = linalg.lapack.flapack.dtrtrs(L.T,V,lower=0)
-            Sigma_diag = np.sum(V*V,-2)
-            Knmv_tilde = np.dot(Kmn,self.v_tilde)
-            mu = np.dot(V2.T,Knmv_tilde)
-            epsilon_np1 = sum((self.tau_tilde-np1[-1])**2)/self.N
-            epsilon_np2 = sum((self.v_tilde-np2[-1])**2)/self.N
-            np1.append(self.tau_tilde.copy())
-            np2.append(self.v_tilde.copy())
-
-        self._compute_GP_variables()
-
-    def fit_FITC(self, Kmm, Kmn, Knn_diag):
-        """
-        The expectation-propagation algorithm with sparse pseudo-input.
-        For nomenclature see Naish-Guzman and Holden, 2008.
-        """
-        M = Kmm.shape[0]
-
-        """
-        Prior approximation parameters:
-        q(f|X) = int_{df}{N(f|KfuKuu_invu,diag(Kff-Qff)*N(u|0,Kuu)} = N(f|0,Sigma0)
-        Sigma0 = diag(Knn-Qnn) + Qnn, Qnn = Knm*Kmmi*Kmn
-        """
-        Lm = jitchol(Kmm)
-        Lmi = chol_inv(Lm)
-        Kmmi = np.dot(Lmi.T,Lmi)
-        P0 = Kmn.T
-        KmnKnm = np.dot(P0.T, P0)
-        KmmiKmn = np.dot(Kmmi,P0.T)
-        Qnn_diag = np.sum(P0.T*KmmiKmn,-2)
-        Diag0 = Knn_diag - Qnn_diag
-        R0 = jitchol(Kmmi).T
-
-        """
-        Posterior approximation: q(f|y) = N(f| mu, Sigma)
-        Sigma = Diag + P*R.T*R*P.T + K
-        mu = w + P*Gamma
-        """
-        self.w = np.zeros(self.N)
-        self.Gamma = np.zeros(M)
-        mu = np.zeros(self.N)
-        P = P0.copy()
-        R = R0.copy()
-        Diag = Diag0.copy()
-        Sigma_diag = Knn_diag
-        RPT0 = np.dot(R0,P0.T)
-
-        """
-        Initial values - Cavity distribution parameters:
-        q_(g|mu_,sigma2_) = Product{q_i(g|mu_i,sigma2_i)}
-        sigma_ = 1./tau_
-        mu_ = v_/tau_
-        """
-        self.tau_ = np.empty(self.N,dtype=float)
-        self.v_ = np.empty(self.N,dtype=float)
-
-        #Initial values - Marginal moments
-        z = np.empty(self.N,dtype=float)
-        self.Z_hat = np.empty(self.N,dtype=float)
-        phi = np.empty(self.N,dtype=float)
-        mu_hat = np.empty(self.N,dtype=float)
-        sigma2_hat = np.empty(self.N,dtype=float)
-
-        #Approximation
-        epsilon_np1 = 1
-        epsilon_np2 = 1
-       	self.iterations = 0
-        self.np1 = [self.tau_tilde.copy()]
-        self.np2 = [self.v_tilde.copy()]
-        while epsilon_np1 > self.epsilon or epsilon_np2 > self.epsilon:
-            update_order = np.random.permutation(self.N)
-            for i in update_order:
-                #Cavity distribution parameters
-                self.tau_[i] = 1./Sigma_diag[i] - self.eta*self.tau_tilde[i]
-                self.v_[i] = mu[i]/Sigma_diag[i] - self.eta*self.v_tilde[i]
-                #Marginal moments
-                self.Z_hat[i], mu_hat[i], sigma2_hat[i] = self.likelihood_function.moments_match(self._transf_data[i],self.tau_[i],self.v_[i])
-                #Site parameters update
-                Delta_tau = self.delta/self.eta*(1./sigma2_hat[i] - 1./Sigma_diag[i])
-                Delta_v = self.delta/self.eta*(mu_hat[i]/sigma2_hat[i] - mu[i]/Sigma_diag[i])
-                self.tau_tilde[i] += Delta_tau
-                self.v_tilde[i] += Delta_v
-                #Posterior distribution parameters update
-                dtd1 = Delta_tau*Diag[i] + 1.
-                dii = Diag[i]
-                Diag[i] = dii - (Delta_tau * dii**2.)/dtd1
-                pi_ = P[i,:].reshape(1,M)
-                P[i,:] = pi_ - (Delta_tau*dii)/dtd1 * pi_
-                Rp_i = np.dot(R,pi_.T)
-                RTR = np.dot(R.T,np.dot(np.eye(M) - Delta_tau/(1.+Delta_tau*Sigma_diag[i]) * np.dot(Rp_i,Rp_i.T),R))
-                R = jitchol(RTR).T
-                self.w[i] += (Delta_v - Delta_tau*self.w[i])*dii/dtd1
-                self.Gamma += (Delta_v - Delta_tau*mu[i])*np.dot(RTR,P[i,:].T)
-                RPT = np.dot(R,P.T)
-                Sigma_diag = Diag + np.sum(RPT.T*RPT.T,-1)
-                mu = self.w + np.dot(P,self.Gamma)
-                self.iterations += 1
-            #Sigma recomptutation with Cholesky decompositon
-            Iplus_Dprod_i = 1./(1.+ Diag0 * self.tau_tilde)
-            Diag = Diag0 * Iplus_Dprod_i
-            P = Iplus_Dprod_i[:,None] * P0
-            safe_diag = np.where(Diag0 < self.tau_tilde, self.tau_tilde/(1.+Diag0*self.tau_tilde), (1. - Iplus_Dprod_i)/Diag0)
-            L = jitchol(np.eye(M) + np.dot(RPT0,safe_diag[:,None]*RPT0.T))
-            R,info = linalg.lapack.flapack.dtrtrs(L,R0,lower=1)
-            RPT = np.dot(R,P.T)
-            Sigma_diag = Diag + np.sum(RPT.T*RPT.T,-1)
-            self.w = Diag * self.v_tilde
-            self.Gamma = np.dot(R.T, np.dot(RPT,self.v_tilde))
-            mu = self.w + np.dot(P,self.Gamma)
-            epsilon_np1 = sum((self.tau_tilde-self.np1[-1])**2)/self.N
-            epsilon_np2 = sum((self.v_tilde-self.np2[-1])**2)/self.N
-            self.np1.append(self.tau_tilde.copy())
-            self.np2.append(self.v_tilde.copy())
-
-        return self._compute_GP_variables()
diff --git a/GPy/likelihoods/Gaussian.py b/GPy/likelihoods/Gaussian.py
deleted file mode 100644
index 6c3c4edf..00000000
--- a/GPy/likelihoods/Gaussian.py
+++ /dev/null
@@ -1,93 +0,0 @@
-import numpy as np
-from likelihood import likelihood
-
-class Gaussian(likelihood):
-    """
-    Likelihood class for doing Expectation propagation
-
-    :param Y: observed output (Nx1 numpy.darray)
-    ..Note:: Y values allowed depend on the likelihood_function used
-    :param variance : 
-    :param normalize:  whether to normalize the data before computing (predictions will be in original scales)
-    :type normalize: False|True
-    """
-    def __init__(self, data, variance=1., normalize=False):
-        self.is_heteroscedastic = False
-        self.Nparams = 1
-        self.Z = 0. # a correction factor which accounts for the approximation made
-        N, self.D = data.shape
-
-        # normalization
-        if normalize:
-            self._offset = data.mean(0)[None, :]
-            self._scale = data.std(0)[None, :]
-            # Don't scale outputs which have zero variance to zero.
-            self._scale[np.nonzero(self._scale == 0.)] = 1.0e-3
-        else:
-            self._offset = np.zeros((1, self.D))
-            self._scale = np.ones((1, self.D))
-
-        self.set_data(data)
-
-        self._variance = np.asarray(variance) + 1.
-        self._set_params(np.asarray(variance))
-
-    def set_data(self, data):
-        self.data = data
-        self.N, D = data.shape
-        assert D == self.D
-        self.Y = (self.data - self._offset) / self._scale
-        if D > self.N:
-            self.YYT = np.dot(self.Y, self.Y.T)
-            self.trYYT = np.trace(self.YYT)
-        else:
-            self.YYT = None
-            self.trYYT = np.sum(np.square(self.Y))
-
-    def _get_params(self):
-        return np.asarray(self._variance)
-
-    def _get_param_names(self):
-        return ["noise_variance"]
-
-    def _set_params(self, x):
-        x = np.float64(x)
-        if self._variance != x:
-            if x == 0.:
-                self.precision = None
-                self.V = None
-            else:
-                self.precision = 1. / x
-                self.V = (self.precision) * self.Y
-            self.covariance_matrix = np.eye(self.N) * x
-            self._variance = x
-
-    def predictive_values(self, mu, var, full_cov):
-        """
-        Un-normalize the prediction and add the likelihood variance, then return the 5%, 95% interval
-        """
-        mean = mu * self._scale + self._offset
-        if full_cov:
-            if self.D > 1:
-                raise NotImplementedError, "TODO"
-                # Note. for D>1, we need to re-normalise all the outputs independently.
-                # This will mess up computations of diag(true_var), below.
-                # note that the upper, lower quantiles should be the same shape as mean
-            # Augment the output variance with the likelihood variance and rescale.
-            true_var = (var + np.eye(var.shape[0]) * self._variance) * self._scale ** 2
-            _5pc = mean - 2.*np.sqrt(np.diag(true_var))
-            _95pc = mean + 2.*np.sqrt(np.diag(true_var))
-        else:
-            true_var = (var + self._variance) * self._scale ** 2
-            _5pc = mean - 2.*np.sqrt(true_var)
-            _95pc = mean + 2.*np.sqrt(true_var)
-        return mean, true_var, _5pc, _95pc
-
-    def fit_full(self):
-        """
-        No approximations needed
-        """
-        pass
-
-    def _gradients(self, partial):
-        return np.sum(partial)
diff --git a/GPy/likelihoods/__init__.py b/GPy/likelihoods/__init__.py
index 83413255..99e88b6d 100644
--- a/GPy/likelihoods/__init__.py
+++ b/GPy/likelihoods/__init__.py
@@ -1,4 +1,4 @@
-from EP import EP
-from Gaussian import Gaussian
+from ep import EP
+from gaussian import Gaussian
 # TODO: from Laplace import Laplace
 import likelihood_functions as functions
diff --git a/GPy/likelihoods/likelihood_functions.py b/GPy/likelihoods/likelihood_functions.py
index 00100d17..c801b9a9 100644
--- a/GPy/likelihoods/likelihood_functions.py
+++ b/GPy/likelihoods/likelihood_functions.py
@@ -10,12 +10,12 @@ from ..util.plot import gpplot
 from ..util.univariate_Gaussian import std_norm_pdf,std_norm_cdf
 import link_functions
 
-class likelihood_function(object):
+class LikelihoodFunction(object):
     """
     Likelihood class for doing Expectation propagation
 
     :param Y: observed output (Nx1 numpy.darray)
-    ..Note:: Y values allowed depend on the likelihood_function used
+    ..Note:: Y values allowed depend on the LikelihoodFunction used
     """
     def __init__(self,link):
         if link == self._analytical:
@@ -69,7 +69,7 @@ class likelihood_function(object):
         sigma2_hat = m2 - mu_hat**2 # Second central moment
         return float(Z_hat), float(mu_hat), float(sigma2_hat)
 
-class binomial(likelihood_function):
+class Binomial(LikelihoodFunction):
     """
     Probit likelihood
     Y is expected to take values in {-1,1}
@@ -82,7 +82,7 @@ class binomial(likelihood_function):
         self._analytical = link_functions.probit
         if not link:
             link = self._analytical
-        super(binomial, self).__init__(link)
+        super(Binomial, self).__init__(link)
 
     def _distribution(self,gp,obs):
         pass
@@ -134,7 +134,7 @@ class binomial(likelihood_function):
         p_975 = stats.norm.cdf(norm_975/np.sqrt(1+var))
         return mean[:,None], np.nan*var, p_025[:,None], p_975[:,None] # TODO: var
 
-class Poisson(likelihood_function):
+class Poisson(LikelihoodFunction):
     """
     Poisson likelihood
     Y is expected to take values in {0,1,2,...}
diff --git a/GPy/models/Bayesian_GPLVM.py b/GPy/models/Bayesian_GPLVM.py
deleted file mode 100644
index e69c4840..00000000
--- a/GPy/models/Bayesian_GPLVM.py
+++ /dev/null
@@ -1,588 +0,0 @@
-# Copyright (c) 2012, GPy authors (see AUTHORS.txt).
-# Licensed under the BSD 3-clause license (see LICENSE.txt)
-
-import numpy as np
-import pylab as pb
-import sys, pdb
-from GPLVM import GPLVM
-from ..core import sparse_GP
-from GPy.util.linalg import pdinv
-from ..likelihoods import Gaussian
-from .. import kern
-from numpy.linalg.linalg import LinAlgError
-import itertools
-from matplotlib.colors import colorConverter
-from matplotlib.figure import SubplotParams
-from GPy.inference.optimization import SCG
-from GPy.util import plot_latent
-
-class Bayesian_GPLVM(sparse_GP, GPLVM):
-    """
-    Bayesian Gaussian Process Latent Variable Model
-
-    :param Y: observed data (np.ndarray) or GPy.likelihood
-    :type Y: np.ndarray| GPy.likelihood instance
-    :param input_dim: latent dimensionality
-    :type input_dim: int
-    :param init: initialisation method for the latent space
-    :type init: 'PCA'|'random'
-
-    """
-    def __init__(self, likelihood_or_Y, input_dim, X=None, X_variance=None, init='PCA', M=10,
-                 Z=None, kernel=None, oldpsave=10, _debug=False,
-                 **kwargs):
-        if type(likelihood_or_Y) is np.ndarray:
-            likelihood = Gaussian(likelihood_or_Y)
-        else:
-            likelihood = likelihood_or_Y
-
-        if X == None:
-            X = self.initialise_latent(init, input_dim, likelihood.Y)
-        self.init = init
-
-        if X_variance is None:
-            X_variance = np.clip((np.ones_like(X) * 0.5) + .01 * np.random.randn(*X.shape), 0.001, 1)
-
-        if Z is None:
-            Z = np.random.permutation(X.copy())[:M]
-        assert Z.shape[1] == X.shape[1]
-
-        if kernel is None:
-            kernel = kern.rbf(input_dim) + kern.white(input_dim)
-
-        self.oldpsave = oldpsave
-        self._oldps = []
-        self._debug = _debug
-
-        if self._debug:
-            self.f_call = 0
-            self._count = itertools.count()
-            self._savedklll = []
-            self._savedparams = []
-            self._savedgradients = []
-            self._savederrors = []
-            self._savedpsiKmm = []
-            self._savedABCD = []
-
-        sparse_GP.__init__(self, X, likelihood, kernel, Z=Z, X_variance=X_variance, **kwargs)
-        self._set_params(self._get_params())
-
-    @property
-    def oldps(self):
-        return self._oldps
-    @oldps.setter
-    def oldps(self, p):
-        if len(self._oldps) == (self.oldpsave + 1):
-            self._oldps.pop()
-        # if len(self._oldps) == 0 or not np.any([np.any(np.abs(p - op) > 1e-5) for op in self._oldps]):
-        self._oldps.insert(0, p.copy())
-
-    def _get_param_names(self):
-        X_names = sum([['X_%i_%i' % (n, q) for q in range(self.input_dim)] for n in range(self.N)], [])
-        S_names = sum([['X_variance_%i_%i' % (n, q) for q in range(self.input_dim)] for n in range(self.N)], [])
-        return (X_names + S_names + sparse_GP._get_param_names(self))
-
-    def _get_params(self):
-        """
-        Horizontally stacks the parameters in order to present them to the optimizer.
-        The resulting 1-D array has this structure:
-
-        ===============================================================
-        |       mu       |        S        |    Z    | theta |  beta  |
-        ===============================================================
-
-        """
-        x = np.hstack((self.X.flatten(), self.X_variance.flatten(), sparse_GP._get_params(self)))
-        return x
-
-    def _clipped(self, x):
-        return x # np.clip(x, -1e300, 1e300)
-
-    def _set_params(self, x, save_old=True, save_count=0):
-#         try:
-            x = self._clipped(x)
-            N, input_dim = self.N, self.input_dim
-            self.X = x[:self.X.size].reshape(N, input_dim).copy()
-            self.X_variance = x[(N * input_dim):(2 * N * input_dim)].reshape(N, input_dim).copy()
-            sparse_GP._set_params(self, x[(2 * N * input_dim):])
-#             self.oldps = x
-#         except (LinAlgError, FloatingPointError, ZeroDivisionError):
-#             print "\rWARNING: Caught LinAlgError, continueing without setting            "
-#             if self._debug:
-#                 self._savederrors.append(self.f_call)
-#             if save_count > 10:
-#                 raise
-#             self._set_params(self.oldps[-1], save_old=False, save_count=save_count + 1)
-
-    def dKL_dmuS(self):
-        dKL_dS = (1. - (1. / (self.X_variance))) * 0.5
-        dKL_dmu = self.X
-        return dKL_dmu, dKL_dS
-
-    def dL_dmuS(self):
-        dL_dmu_psi0, dL_dS_psi0 = self.kern.dpsi0_dmuS(self.dL_dpsi0, self.Z, self.X, self.X_variance)
-        dL_dmu_psi1, dL_dS_psi1 = self.kern.dpsi1_dmuS(self.dL_dpsi1, self.Z, self.X, self.X_variance)
-        dL_dmu_psi2, dL_dS_psi2 = self.kern.dpsi2_dmuS(self.dL_dpsi2, self.Z, self.X, self.X_variance)
-        dL_dmu = dL_dmu_psi0 + dL_dmu_psi1 + dL_dmu_psi2
-        dL_dS = dL_dS_psi0 + dL_dS_psi1 + dL_dS_psi2
-
-        return dL_dmu, dL_dS
-
-    def KL_divergence(self):
-        var_mean = np.square(self.X).sum()
-        var_S = np.sum(self.X_variance - np.log(self.X_variance))
-        return 0.5 * (var_mean + var_S) - 0.5 * self.input_dim * self.N
-
-    def log_likelihood(self):
-        ll = sparse_GP.log_likelihood(self)
-        kl = self.KL_divergence()
-
-#         if ll < -2E4:
-#             ll = -2E4 + np.random.randn()
-#         if kl > 5E4:
-#             kl = 5E4 + np.random.randn()
-
-        if self._debug:
-            self.f_call = self._count.next()
-            if self.f_call % 1 == 0:
-                self._savedklll.append([self.f_call, ll, kl])
-                self._savedparams.append([self.f_call, self._get_params()])
-                self._savedgradients.append([self.f_call, self._log_likelihood_gradients()])
-                self._savedpsiKmm.append([self.f_call, [self.Kmm, self.dL_dKmm]])
-#                 sf2 = self.scale_factor ** 2
-                if self.likelihood.is_heteroscedastic:
-                    A = -0.5 * self.N * self.D * np.log(2.*np.pi) + 0.5 * np.sum(np.log(self.likelihood.precision)) - 0.5 * np.sum(self.V * self.likelihood.Y)
-#                     B = -0.5 * self.D * (np.sum(self.likelihood.precision.flatten() * self.psi0) - np.trace(self.A) * sf2)
-                    B = -0.5 * self.D * (np.sum(self.likelihood.precision.flatten() * self.psi0) - np.trace(self.A))
-                else:
-                    A = -0.5 * self.N * self.D * (np.log(2.*np.pi) + np.log(self.likelihood._variance)) - 0.5 * self.likelihood.precision * self.likelihood.trYYT
-#                     B = -0.5 * self.D * (np.sum(self.likelihood.precision * self.psi0) - np.trace(self.A) * sf2)
-                    B = -0.5 * self.D * (np.sum(self.likelihood.precision * self.psi0) - np.trace(self.A))
-                C = -self.D * (np.sum(np.log(np.diag(self.LB)))) # + 0.5 * self.M * np.log(sf2))
-                D = 0.5 * np.sum(np.square(self._LBi_Lmi_psi1V))
-                self._savedABCD.append([self.f_call, A, B, C, D])
-
-        # print "\nkl:", kl, "ll:", ll
-        return ll - kl
-
-    def _log_likelihood_gradients(self):
-        dKL_dmu, dKL_dS = self.dKL_dmuS()
-        dL_dmu, dL_dS = self.dL_dmuS()
-        # TODO: find way to make faster
-
-        d_dmu = (dL_dmu - dKL_dmu).flatten()
-        d_dS = (dL_dS - dKL_dS).flatten()
-        # TEST KL: ====================
-        # d_dmu = (dKL_dmu).flatten()
-        # d_dS = (dKL_dS).flatten()
-        # ========================
-        # TEST L: ====================
-#         d_dmu = (dL_dmu).flatten()
-#         d_dS = (dL_dS).flatten()
-        # ========================
-        self.dbound_dmuS = np.hstack((d_dmu, d_dS))
-        self.dbound_dZtheta = sparse_GP._log_likelihood_gradients(self)
-        return self._clipped(np.hstack((self.dbound_dmuS.flatten(), self.dbound_dZtheta)))
-
-    def plot_latent(self, *args, **kwargs):
-        return plot_latent.plot_latent_indices(self, *args, **kwargs)
-
-    def do_test_latents(self, Y):
-        """
-        Compute the latent representation for a set of new points Y
-
-        Notes:
-        This will only work with a univariate Gaussian likelihood (for now)
-        """
-        assert not self.likelihood.is_heteroscedastic
-        N_test = Y.shape[0]
-        input_dim = self.Z.shape[1]
-        means = np.zeros((N_test, input_dim))
-        covars = np.zeros((N_test, input_dim))
-
-        dpsi0 = -0.5 * self.D * self.likelihood.precision
-        dpsi2 = self.dL_dpsi2[0][None, :, :] # TODO: this may change if we ignore het. likelihoods
-        V = self.likelihood.precision * Y
-        dpsi1 = np.dot(self.Cpsi1V, V.T)
-
-        start = np.zeros(self.input_dim * 2)
-
-        for n, dpsi1_n in enumerate(dpsi1.T[:, :, None]):
-            args = (self.kern, self.Z, dpsi0, dpsi1_n, dpsi2)
-            xopt, fopt, neval, status = SCG(f=latent_cost, gradf=latent_grad, x=start, optargs=args, display=False)
-
-            mu, log_S = xopt.reshape(2, 1, -1)
-            means[n] = mu[0].copy()
-            covars[n] = np.exp(log_S[0]).copy()
-
-        return means, covars
-
-
-    def plot_X_1d(self, fignum=None, ax=None, colors=None):
-        """
-        Plot latent space X in 1D:
-
-            -if fig is given, create input_dim subplots in fig and plot in these
-            -if ax is given plot input_dim 1D latent space plots of X into each `axis`
-            -if neither fig nor ax is given create a figure with fignum and plot in there
-
-        colors:
-            colors of different latent space dimensions input_dim
-        """
-        import pylab
-        if ax is None:
-            fig = pylab.figure(num=fignum, figsize=(8, min(12, (2 * self.X.shape[1]))))
-        if colors is None:
-            colors = pylab.gca()._get_lines.color_cycle
-            pylab.clf()
-        else:
-            colors = iter(colors)
-        plots = []
-        x = np.arange(self.X.shape[0])
-        for i in range(self.X.shape[1]):
-            if ax is None:
-                a = fig.add_subplot(self.X.shape[1], 1, i + 1)
-            elif isinstance(ax, (tuple, list)):
-                a = ax[i]
-            else:
-                raise ValueError("Need one ax per latent dimnesion input_dim")
-            a.plot(self.X, c='k', alpha=.3)
-            plots.extend(a.plot(x, self.X.T[i], c=colors.next(), label=r"$\mathbf{{X_{{{}}}}}$".format(i)))
-            a.fill_between(x,
-                            self.X.T[i] - 2 * np.sqrt(self.X_variance.T[i]),
-                            self.X.T[i] + 2 * np.sqrt(self.X_variance.T[i]),
-                            facecolor=plots[-1].get_color(),
-                            alpha=.3)
-            a.legend(borderaxespad=0.)
-            a.set_xlim(x.min(), x.max())
-            if i < self.X.shape[1] - 1:
-                a.set_xticklabels('')
-        pylab.draw()
-        fig.tight_layout(h_pad=.01) # , rect=(0, 0, 1, .95))
-        return fig
-
-    def __getstate__(self):
-        return (self.likelihood, self.input_dim, self.X, self.X_variance,
-                self.init, self.M, self.Z, self.kern,
-                self.oldpsave, self._debug)
-
-    def __setstate__(self, state):
-        self.__init__(*state)
-
-    def _debug_filter_params(self, x):
-        start, end = 0, self.X.size,
-        X = x[start:end].reshape(self.N, self.input_dim)
-        start, end = end, end + self.X_variance.size
-        X_v = x[start:end].reshape(self.N, self.input_dim)
-        start, end = end, end + (self.M * self.input_dim)
-        Z = x[start:end].reshape(self.M, self.input_dim)
-        start, end = end, end + self.input_dim
-        theta = x[start:]
-        return X, X_v, Z, theta
-
-
-    def _debug_get_axis(self, figs):
-        if figs[-1].axes:
-            ax1 = figs[-1].axes[0]
-            ax1.cla()
-        else:
-            ax1 = figs[-1].add_subplot(111)
-        return ax1
-
-    def _debug_plot(self):
-        assert self._debug, "must enable _debug, to debug-plot"
-        import pylab
-#         from mpl_toolkits.mplot3d import Axes3D
-        figs = [pylab.figure('BGPLVM DEBUG', figsize=(12, 4))]
-#         fig.clf()
-
-        # log like
-#         splotshape = (6, 4)
-#         ax1 = pylab.subplot2grid(splotshape, (0, 0), 1, 4)
-        ax1 = self._debug_get_axis(figs)
-        ax1.text(.5, .5, "Optimization", alpha=.3, transform=ax1.transAxes,
-                 ha='center', va='center')
-        kllls = np.array(self._savedklll)
-        LL, = ax1.plot(kllls[:, 0], kllls[:, 1] - kllls[:, 2], '-', label=r'$\log p(\mathbf{Y})$', mew=1.5)
-        KL, = ax1.plot(kllls[:, 0], kllls[:, 2], '-', label=r'$\mathcal{KL}(p||q)$', mew=1.5)
-        L, = ax1.plot(kllls[:, 0], kllls[:, 1], '-', label=r'$L$', mew=1.5) # \mathds{E}_{q(\mathbf{X})}[p(\mathbf{Y|X})\frac{p(\mathbf{X})}{q(\mathbf{X})}]
-
-        param_dict = dict(self._savedparams)
-        gradient_dict = dict(self._savedgradients)
-#         kmm_dict = dict(self._savedpsiKmm)
-        iters = np.array(param_dict.keys())
-        ABCD_dict = np.array(self._savedABCD)
-        self.showing = 0
-
-#         ax2 = pylab.subplot2grid(splotshape, (1, 0), 2, 4)
-        figs.append(pylab.figure("BGPLVM DEBUG X", figsize=(12, 4)))
-        ax2 = self._debug_get_axis(figs)
-        ax2.text(.5, .5, r"$\mathbf{X}$", alpha=.5, transform=ax2.transAxes,
-                 ha='center', va='center')
-        figs[-1].canvas.draw()
-        figs[-1].tight_layout(rect=(0, 0, 1, .86))
-#         ax3 = pylab.subplot2grid(splotshape, (3, 0), 2, 4, sharex=ax2)
-        figs.append(pylab.figure("BGPLVM DEBUG S", figsize=(12, 4)))
-        ax3 = self._debug_get_axis(figs)
-        ax3.text(.5, .5, r"$\mathbf{S}$", alpha=.5, transform=ax3.transAxes,
-                 ha='center', va='center')
-        figs[-1].canvas.draw()
-        figs[-1].tight_layout(rect=(0, 0, 1, .86))
-#         ax4 = pylab.subplot2grid(splotshape, (5, 0), 2, 2)
-        figs.append(pylab.figure("BGPLVM DEBUG Z", figsize=(6, 4)))
-        ax4 = self._debug_get_axis(figs)
-        ax4.text(.5, .5, r"$\mathbf{Z}$", alpha=.5, transform=ax4.transAxes,
-                 ha='center', va='center')
-        figs[-1].canvas.draw()
-        figs[-1].tight_layout(rect=(0, 0, 1, .86))
-#         ax5 = pylab.subplot2grid(splotshape, (5, 2), 2, 2)
-        figs.append(pylab.figure("BGPLVM DEBUG theta", figsize=(6, 4)))
-        ax5 = self._debug_get_axis(figs)
-        ax5.text(.5, .5, r"${\theta}$", alpha=.5, transform=ax5.transAxes,
-                 ha='center', va='center')
-        figs[-1].canvas.draw()
-        figs[-1].tight_layout(rect=(.15, 0, 1, .86))
-#         figs.append(pylab.figure("BGPLVM DEBUG Kmm", figsize=(12, 6)))
-#         fig = figs[-1]
-#         ax6 = fig.add_subplot(121)
-#         ax6.text(.5, .5, r"${\mathbf{K}_{mm}}$", color='magenta', alpha=.5, transform=ax6.transAxes,
-#                  ha='center', va='center')
-#         ax7 = fig.add_subplot(122)
-#         ax7.text(.5, .5, r"${\frac{dL}{dK_{mm}}}$", color='magenta', alpha=.5, transform=ax7.transAxes,
-#                  ha='center', va='center')
-        figs.append(pylab.figure("BGPLVM DEBUG Kmm", figsize=(12, 6)))
-        fig = figs[-1]
-        ax8 = fig.add_subplot(121)
-        ax8.text(.5, .5, r"${\mathbf{A,B,C,D}}$", color='k', alpha=.5, transform=ax8.transAxes,
-                 ha='center', va='center')
-        ax8.plot(ABCD_dict[:, 0], ABCD_dict[:, 1], label='A')
-        ax8.plot(ABCD_dict[:, 0], ABCD_dict[:, 2], label='B')
-        ax8.plot(ABCD_dict[:, 0], ABCD_dict[:, 3], label='C')
-        ax8.plot(ABCD_dict[:, 0], ABCD_dict[:, 4], label='D')
-        ax8.legend()
-        figs[-1].canvas.draw()
-        figs[-1].tight_layout(rect=(.15, 0, 1, .86))
-
-        X, S, Z, theta = self._debug_filter_params(param_dict[self.showing])
-        Xg, Sg, Zg, thetag = self._debug_filter_params(gradient_dict[self.showing])
-#         Xg, Sg, Zg, thetag = -Xg, -Sg, -Zg, -thetag
-
-        quiver_units = 'xy'
-        quiver_scale = 1
-        quiver_scale_units = 'xy'
-        Xlatentplts = ax2.plot(X, ls="-", marker="x")
-        colors = colorConverter.to_rgba_array([p.get_color() for p in Xlatentplts], .4)
-        Ulatent = np.zeros_like(X)
-        xlatent = np.tile(np.arange(0, X.shape[0])[:, None], X.shape[1])
-        Xlatentgrads = ax2.quiver(xlatent, X, Ulatent, Xg, color=colors,
-                                  units=quiver_units, scale_units=quiver_scale_units,
-                                  scale=quiver_scale)
-
-        Slatentplts = ax3.plot(S, ls="-", marker="x")
-        Slatentgrads = ax3.quiver(xlatent, S, Ulatent, Sg, color=colors,
-                                  units=quiver_units, scale_units=quiver_scale_units,
-                                  scale=quiver_scale)
-        ax3.set_ylim(0, 1.)
-
-        xZ = np.tile(np.arange(0, Z.shape[0])[:, None], Z.shape[1])
-        UZ = np.zeros_like(Z)
-        Zplts = ax4.plot(Z, ls="-", marker="x")
-        Zgrads = ax4.quiver(xZ, Z, UZ, Zg, color=colors,
-                                  units=quiver_units, scale_units=quiver_scale_units,
-                                  scale=quiver_scale)
-
-        xtheta = np.arange(len(theta))
-        Utheta = np.zeros_like(theta)
-        thetaplts = ax5.bar(xtheta - .4, theta, color=colors)
-        thetagrads = ax5.quiver(xtheta, theta, Utheta, thetag, color=colors,
-                                  units=quiver_units, scale_units=quiver_scale_units,
-                                  scale=quiver_scale,
-                                  edgecolors=('k',), linewidths=[1])
-        pylab.setp(thetaplts, zorder=0)
-        pylab.setp(thetagrads, zorder=10)
-        ax5.set_xticks(np.arange(len(theta)))
-        ax5.set_xticklabels(self._get_param_names()[-len(theta):], rotation=17)
-
-#         imkmm = ax6.imshow(kmm_dict[self.showing][0])
-#         from mpl_toolkits.axes_grid1 import make_axes_locatable
-#         divider = make_axes_locatable(ax6)
-#         caxkmm = divider.append_axes("right", "5%", pad="1%")
-#         cbarkmm = pylab.colorbar(imkmm, cax=caxkmm)
-#
-#         imkmmdl = ax7.imshow(kmm_dict[self.showing][1])
-#         divider = make_axes_locatable(ax7)
-#         caxkmmdl = divider.append_axes("right", "5%", pad="1%")
-#         cbarkmmdl = pylab.colorbar(imkmmdl, cax=caxkmmdl)
-
-#         input_dimleg = ax1.legend(Xlatentplts, [r"$input_dim_{}$".format(i + 1) for i in range(self.input_dim)],
-#                    loc=3, ncol=self.input_dim, bbox_to_anchor=(0, 1.15, 1, 1.15),
-#                    borderaxespad=0, mode="expand")
-        ax2.legend(Xlatentplts, [r"$input_dim_{}$".format(i + 1) for i in range(self.input_dim)],
-                   loc=3, ncol=self.input_dim, bbox_to_anchor=(0, 1.1, 1, 1.1),
-                   borderaxespad=0, mode="expand")
-        ax3.legend(Xlatentplts, [r"$input_dim_{}$".format(i + 1) for i in range(self.input_dim)],
-                   loc=3, ncol=self.input_dim, bbox_to_anchor=(0, 1.1, 1, 1.1),
-                   borderaxespad=0, mode="expand")
-        ax4.legend(Xlatentplts, [r"$input_dim_{}$".format(i + 1) for i in range(self.input_dim)],
-                   loc=3, ncol=self.input_dim, bbox_to_anchor=(0, 1.1, 1, 1.1),
-                   borderaxespad=0, mode="expand")
-        ax5.legend(Xlatentplts, [r"$input_dim_{}$".format(i + 1) for i in range(self.input_dim)],
-                   loc=3, ncol=self.input_dim, bbox_to_anchor=(0, 1.1, 1, 1.1),
-                   borderaxespad=0, mode="expand")
-        Lleg = ax1.legend()
-        Lleg.draggable()
-#         ax1.add_artist(input_dimleg)
-
-        indicatorKL, = ax1.plot(kllls[self.showing, 0], kllls[self.showing, 2], 'o', c=KL.get_color())
-        indicatorLL, = ax1.plot(kllls[self.showing, 0], kllls[self.showing, 1] - kllls[self.showing, 2], 'o', c=LL.get_color())
-        indicatorL, = ax1.plot(kllls[self.showing, 0], kllls[self.showing, 1], 'o', c=L.get_color())
-#         for err in self._savederrors:
-#             if err < kllls.shape[0]:
-#                 ax1.scatter(kllls[err, 0], kllls[err, 2], s=50, marker=(5, 2), c=KL.get_color())
-#                 ax1.scatter(kllls[err, 0], kllls[err, 1] - kllls[err, 2], s=50, marker=(5, 2), c=LL.get_color())
-#                 ax1.scatter(kllls[err, 0], kllls[err, 1], s=50, marker=(5, 2), c=L.get_color())
-
-#         try:
-#             for f in figs:
-#                 f.canvas.draw()
-#                 f.tight_layout(box=(0, .15, 1, .9))
-# #             pylab.draw()
-# #             pylab.tight_layout(box=(0, .1, 1, .9))
-#         except:
-#             pass
-
-        # parameter changes
-        # ax2 = pylab.subplot2grid((4, 1), (1, 0), 3, 1, projection='3d')
-        button_options = [0, 0] # [0]: clicked -- [1]: dragged
-
-        def update_plots(event):
-            if button_options[0] and not button_options[1]:
-#               event.button, event.x, event.y, event.xdata, event.ydata)
-                tmp = np.abs(iters - event.xdata)
-                closest_hit = iters[tmp == tmp.min()][0]
-
-                if closest_hit != self.showing:
-                    self.showing = closest_hit
-                    # print closest_hit, iters, event.xdata
-
-                    indicatorLL.set_data(self.showing, kllls[self.showing, 1] - kllls[self.showing, 2])
-                    indicatorKL.set_data(self.showing, kllls[self.showing, 2])
-                    indicatorL.set_data(self.showing, kllls[self.showing, 1])
-
-                    X, S, Z, theta = self._debug_filter_params(param_dict[self.showing])
-                    Xg, Sg, Zg, thetag = self._debug_filter_params(gradient_dict[self.showing])
-#                     Xg, Sg, Zg, thetag = -Xg, -Sg, -Zg, -thetag
-
-                    for i, Xlatent in enumerate(Xlatentplts):
-                        Xlatent.set_ydata(X[:, i])
-                    Xlatentgrads.set_offsets(np.array([xlatent.ravel(), X.ravel()]).T)
-                    Xlatentgrads.set_UVC(Ulatent, Xg)
-
-                    for i, Slatent in enumerate(Slatentplts):
-                        Slatent.set_ydata(S[:, i])
-                    Slatentgrads.set_offsets(np.array([xlatent.ravel(), S.ravel()]).T)
-                    Slatentgrads.set_UVC(Ulatent, Sg)
-
-                    for i, Zlatent in enumerate(Zplts):
-                        Zlatent.set_ydata(Z[:, i])
-                    Zgrads.set_offsets(np.array([xZ.ravel(), Z.ravel()]).T)
-                    Zgrads.set_UVC(UZ, Zg)
-
-                    for p, t in zip(thetaplts, theta):
-                        p.set_height(t)
-                    thetagrads.set_offsets(np.array([xtheta.ravel(), theta.ravel()]).T)
-                    thetagrads.set_UVC(Utheta, thetag)
-
-#                     imkmm.set_data(kmm_dict[self.showing][0])
-#                     imkmm.autoscale()
-#                     cbarkmm.update_normal(imkmm)
-#
-#                     imkmmdl.set_data(kmm_dict[self.showing][1])
-#                     imkmmdl.autoscale()
-#                     cbarkmmdl.update_normal(imkmmdl)
-
-                    ax2.relim()
-                    # ax3.relim()
-                    ax4.relim()
-                    ax5.relim()
-                    ax2.autoscale()
-                    # ax3.autoscale()
-                    ax4.autoscale()
-                    ax5.autoscale()
-
-                    [fig.canvas.draw() for fig in figs]
-            button_options[0] = 0
-            button_options[1] = 0
-
-        def onclick(event):
-            if event.inaxes is ax1 and event.button == 1:
-                button_options[0] = 1
-        def motion(event):
-            if button_options[0]:
-                button_options[1] = 1
-
-        cidr = figs[0].canvas.mpl_connect('button_release_event', update_plots)
-        cidp = figs[0].canvas.mpl_connect('button_press_event', onclick)
-        cidd = figs[0].canvas.mpl_connect('motion_notify_event', motion)
-
-        return ax1, ax2, ax3, ax4, ax5 # , ax6, ax7
-
-
-
-
-def latent_cost_and_grad(mu_S, kern, Z, dL_dpsi0, dL_dpsi1, dL_dpsi2):
-    """
-    objective function for fitting the latent variables for test points
-    (negative log-likelihood: should be minimised!)
-    """
-    mu, log_S = mu_S.reshape(2, 1, -1)
-    S = np.exp(log_S)
-
-    psi0 = kern.psi0(Z, mu, S)
-    psi1 = kern.psi1(Z, mu, S)
-    psi2 = kern.psi2(Z, mu, S)
-
-    lik = dL_dpsi0 * psi0 + np.dot(dL_dpsi1.flatten(), psi1.flatten()) + np.dot(dL_dpsi2.flatten(), psi2.flatten()) - 0.5 * np.sum(np.square(mu) + S) + 0.5 * np.sum(log_S)
-
-    mu0, S0 = kern.dpsi0_dmuS(dL_dpsi0, Z, mu, S)
-    mu1, S1 = kern.dpsi1_dmuS(dL_dpsi1, Z, mu, S)
-    mu2, S2 = kern.dpsi2_dmuS(dL_dpsi2, Z, mu, S)
-
-    dmu = mu0 + mu1 + mu2 - mu
-    # dS = S0 + S1 + S2 -0.5 + .5/S
-    dlnS = S * (S0 + S1 + S2 - 0.5) + .5
-    return -lik, -np.hstack((dmu.flatten(), dlnS.flatten()))
-
-def latent_cost(mu_S, kern, Z, dL_dpsi0, dL_dpsi1, dL_dpsi2):
-    """
-    objective function for fitting the latent variables (negative log-likelihood: should be minimised!)
-    This is the same as latent_cost_and_grad but only for the objective
-    """
-    mu, log_S = mu_S.reshape(2, 1, -1)
-    S = np.exp(log_S)
-
-    psi0 = kern.psi0(Z, mu, S)
-    psi1 = kern.psi1(Z, mu, S)
-    psi2 = kern.psi2(Z, mu, S)
-
-    lik = dL_dpsi0 * psi0 + np.dot(dL_dpsi1.flatten(), psi1.flatten()) + np.dot(dL_dpsi2.flatten(), psi2.flatten()) - 0.5 * np.sum(np.square(mu) + S) + 0.5 * np.sum(log_S)
-    return -float(lik)
-
-def latent_grad(mu_S, kern, Z, dL_dpsi0, dL_dpsi1, dL_dpsi2):
-    """
-    This is the same as latent_cost_and_grad but only for the grad
-    """
-    mu, log_S = mu_S.reshape(2, 1, -1)
-    S = np.exp(log_S)
-
-    mu0, S0 = kern.dpsi0_dmuS(dL_dpsi0, Z, mu, S)
-    mu1, S1 = kern.dpsi1_dmuS(dL_dpsi1, Z, mu, S)
-    mu2, S2 = kern.dpsi2_dmuS(dL_dpsi2, Z, mu, S)
-
-    dmu = mu0 + mu1 + mu2 - mu
-    # dS = S0 + S1 + S2 -0.5 + .5/S
-    dlnS = S * (S0 + S1 + S2 - 0.5) + .5
-
-    return -np.hstack((dmu.flatten(), dlnS.flatten()))
-
-
diff --git a/GPy/models/FITC.py b/GPy/models/FITC.py
deleted file mode 100644
index e8078780..00000000
--- a/GPy/models/FITC.py
+++ /dev/null
@@ -1,252 +0,0 @@
-# Copyright (c) 2012, GPy authors (see AUTHORS.txt).
-# Licensed under the BSD 3-clause license (see LICENSE.txt)
-
-import numpy as np
-import pylab as pb
-from ..util.linalg import mdot, jitchol, chol_inv, tdot, symmetrify,pdinv
-from ..util.plot import gpplot
-from .. import kern
-from scipy import stats, linalg
-from ..core import sparse_GP
-
-def backsub_both_sides(L,X):
-    """ Return L^-T * X * L^-1, assumuing X is symmetrical and L is lower cholesky"""
-    tmp,_ = linalg.lapack.flapack.dtrtrs(L,np.asfortranarray(X),lower=1,trans=1)
-    return linalg.lapack.flapack.dtrtrs(L,np.asfortranarray(tmp.T),lower=1,trans=1)[0].T
-
-class FITC(sparse_GP):
-
-    def __init__(self, X, likelihood, kernel, Z, X_variance=None, normalize_X=False):
-        super(FITC, self).__init__(X, likelihood, kernel, normalize_X=normalize_X)
-
-    def update_likelihood_approximation(self):
-        """
-        Approximates a non-gaussian likelihood using Expectation Propagation
-
-        For a Gaussian (or direct: TODO) likelihood, no iteration is required:
-        this function does nothing
-
-        Diag(Knn - Qnn) is added to the noise term to use the tools already implemented in sparse_GP.
-        The true precison is now 'true_precision' not 'precision'.
-        """
-        if self.has_uncertain_inputs:
-            raise NotImplementedError, "FITC approximation not implemented for uncertain inputs"
-        else:
-            self.likelihood.fit_FITC(self.Kmm,self.psi1,self.psi0)
-            self._set_params(self._get_params()) # update the GP
-
-    def _computations(self):
-
-        #factor Kmm
-        self.Lm = jitchol(self.Kmm)
-        self.Lmi,info = linalg.lapack.flapack.dtrtrs(self.Lm,np.eye(self.M),lower=1)
-        Lmipsi1 = np.dot(self.Lmi,self.psi1)
-        self.Qnn = np.dot(Lmipsi1.T,Lmipsi1).copy()
-        self.Diag0 = self.psi0 - np.diag(self.Qnn)
-        self.beta_star = self.likelihood.precision/(1. + self.likelihood.precision*self.Diag0[:,None]) #Includes Diag0 in the precision
-        self.V_star = self.beta_star * self.likelihood.Y
-
-        # The rather complex computations of self.A
-        if self.has_uncertain_inputs:
-                raise NotImplementedError
-        else:
-            if self.likelihood.is_heteroscedastic:
-                assert self.likelihood.D == 1
-            tmp = self.psi1 * (np.sqrt(self.beta_star.flatten().reshape(1, self.N)))
-            tmp, _ = linalg.lapack.flapack.dtrtrs(self.Lm, np.asfortranarray(tmp), lower=1)
-            self.A = tdot(tmp)
-
-        # factor B
-        self.B = np.eye(self.M) + self.A
-        self.LB = jitchol(self.B)
-        self.LBi = chol_inv(self.LB)
-        self.psi1V = np.dot(self.psi1, self.V_star)
-
-        Lmi_psi1V, info = linalg.lapack.flapack.dtrtrs(self.Lm, np.asfortranarray(self.psi1V), lower=1, trans=0)
-        self._LBi_Lmi_psi1V, _ = linalg.lapack.flapack.dtrtrs(self.LB, np.asfortranarray(Lmi_psi1V), lower=1, trans=0)
-
-        Kmmipsi1 = np.dot(self.Lmi.T,Lmipsi1)
-        b_psi1_Ki = self.beta_star * Kmmipsi1.T
-        Ki_pbp_Ki = np.dot(Kmmipsi1,b_psi1_Ki)
-        Kmmi = np.dot(self.Lmi.T,self.Lmi)
-        LBiLmi = np.dot(self.LBi,self.Lmi)
-        LBL_inv = np.dot(LBiLmi.T,LBiLmi)
-        VVT = np.outer(self.V_star,self.V_star)
-        VV_p_Ki = np.dot(VVT,Kmmipsi1.T)
-        Ki_pVVp_Ki = np.dot(Kmmipsi1,VV_p_Ki)
-        psi1beta = self.psi1*self.beta_star.T
-        H = self.Kmm + mdot(self.psi1,psi1beta.T)
-        LH = jitchol(H)
-        LHi = chol_inv(LH)
-        Hi = np.dot(LHi.T,LHi)
-
-        betapsi1TLmiLBi = np.dot(psi1beta.T,LBiLmi.T)
-        alpha = np.array([np.dot(a.T,a) for a in betapsi1TLmiLBi])[:,None]
-        gamma_1 = mdot(VVT,self.psi1.T,Hi)
-        pHip = mdot(self.psi1.T,Hi,self.psi1)
-        gamma_2 = mdot(self.beta_star*pHip,self.V_star)
-        gamma_3 = self.V_star * gamma_2
-
-        self._dL_dpsi0 = -0.5 * self.beta_star#dA_dpsi0: logdet(self.beta_star)
-        self._dL_dpsi0 += .5 * self.V_star**2 #dA_psi0: yT*beta_star*y
-        self._dL_dpsi0 += .5 *alpha #dC_dpsi0
-        self._dL_dpsi0 += 0.5*mdot(self.beta_star*pHip,self.V_star)**2 - self.V_star * mdot(self.V_star.T,pHip*self.beta_star).T #dD_dpsi0
-
-        self._dL_dpsi1 = b_psi1_Ki.copy() #dA_dpsi1: logdet(self.beta_star)
-        self._dL_dpsi1 += -np.dot(psi1beta.T,LBL_inv) #dC_dpsi1
-        self._dL_dpsi1 += gamma_1 - mdot(psi1beta.T,Hi,self.psi1,gamma_1) #dD_dpsi1
-
-        self._dL_dKmm = -0.5 * np.dot(Kmmipsi1,b_psi1_Ki) #dA_dKmm: logdet(self.beta_star)
-        self._dL_dKmm += .5*(LBL_inv - Kmmi) + mdot(LBL_inv,psi1beta,Kmmipsi1.T) #dC_dKmm
-        self._dL_dKmm += -.5 * mdot(Hi,self.psi1,gamma_1) #dD_dKmm
-
-        self._dpsi1_dtheta = 0
-        self._dpsi1_dX = 0
-        self._dKmm_dtheta = 0
-        self._dKmm_dX = 0
-
-        self._dpsi1_dX_jkj = 0
-        self._dpsi1_dtheta_jkj = 0
-
-        for i,V_n,alpha_n,gamma_n,gamma_k in zip(range(self.N),self.V_star,alpha,gamma_2,gamma_3):
-            K_pp_K = np.dot(Kmmipsi1[:,i:(i+1)],Kmmipsi1[:,i:(i+1)].T)
-
-            #Diag_dpsi1 = Diag_dA_dpsi1: yT*beta_star*y + Diag_dC_dpsi1 +Diag_dD_dpsi1
-            _dpsi1 = (-V_n**2 - alpha_n + 2.*gamma_k - gamma_n**2) * Kmmipsi1.T[i:(i+1),:]
-
-            #Diag_dKmm = Diag_dA_dKmm: yT*beta_star*y +Diag_dC_dKmm +Diag_dD_dKmm
-            _dKmm = .5*(V_n**2 + alpha_n + gamma_n**2 - 2.*gamma_k) * K_pp_K #Diag_dD_dKmm
-
-            self._dpsi1_dtheta += self.kern.dK_dtheta(_dpsi1,self.X[i:i+1,:],self.Z)
-            self._dKmm_dtheta += self.kern.dK_dtheta(_dKmm,self.Z)
-
-            self._dKmm_dX += 2.*self.kern.dK_dX(_dKmm ,self.Z)
-            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:
-            # save computation here.
-            self.partial_for_likelihood = None
-        elif self.likelihood.is_heteroscedastic:
-            raise NotImplementedError, "heteroscedatic derivates not implemented"
-        else:
-            # likelihood is not heterscedatic
-            dbstar_dnoise = self.likelihood.precision * (self.beta_star**2 * self.Diag0[:,None] - self.beta_star)
-            Lmi_psi1 = mdot(self.Lmi,self.psi1)
-            LBiLmipsi1 = np.dot(self.LBi,Lmi_psi1)
-            aux_0 = np.dot(self._LBi_Lmi_psi1V.T,LBiLmipsi1)
-            aux_1 = self.likelihood.Y.T * np.dot(self._LBi_Lmi_psi1V.T,LBiLmipsi1)
-            aux_2 = np.dot(LBiLmipsi1.T,self._LBi_Lmi_psi1V)
-
-            dA_dnoise = 0.5 * self.D * (dbstar_dnoise/self.beta_star).sum() - 0.5 * self.D * np.sum(self.likelihood.Y**2 * dbstar_dnoise)
-            dC_dnoise = -0.5 * np.sum(mdot(self.LBi.T,self.LBi,Lmi_psi1) *  Lmi_psi1 * dbstar_dnoise.T)
-            dC_dnoise = -0.5 * np.sum(mdot(self.LBi.T,self.LBi,Lmi_psi1) *  Lmi_psi1 * dbstar_dnoise.T)
-
-            dD_dnoise_1 =  mdot(self.V_star*LBiLmipsi1.T,LBiLmipsi1*dbstar_dnoise.T*self.likelihood.Y.T)
-            alpha = mdot(LBiLmipsi1,self.V_star)
-            alpha_ = mdot(LBiLmipsi1.T,alpha)
-            dD_dnoise_2 = -0.5 * self.D * np.sum(alpha_**2 * dbstar_dnoise )
-
-            dD_dnoise_1 = mdot(self.V_star.T,self.psi1.T,self.Lmi.T,self.LBi.T,self.LBi,self.Lmi,self.psi1,dbstar_dnoise*self.likelihood.Y)
-            dD_dnoise_2 = 0.5*mdot(self.V_star.T,self.psi1.T,Hi,self.psi1,dbstar_dnoise*self.psi1.T,Hi,self.psi1,self.V_star)
-            dD_dnoise = dD_dnoise_1 + dD_dnoise_2
-
-            self.partial_for_likelihood = dA_dnoise + dC_dnoise + dD_dnoise
-
-    def log_likelihood(self):
-        """ Compute the (lower bound on the) log marginal likelihood """
-        A = -0.5 * self.N * self.D * np.log(2.*np.pi) + 0.5 * np.sum(np.log(self.beta_star)) - 0.5 * np.sum(self.V_star * self.likelihood.Y)
-        C = -self.D * (np.sum(np.log(np.diag(self.LB))))
-        D = 0.5 * np.sum(np.square(self._LBi_Lmi_psi1V))
-        return A + C + D
-
-    def _log_likelihood_gradients(self):
-        pass
-        return np.hstack((self.dL_dZ().flatten(), self.dL_dtheta(), self.likelihood._gradients(partial=self.partial_for_likelihood)))
-
-    def dL_dtheta(self):
-        if self.has_uncertain_inputs:
-            raise NotImplementedError, "FITC approximation not implemented for uncertain inputs"
-        else:
-            dL_dtheta = self.kern.dKdiag_dtheta(self._dL_dpsi0,self.X)
-            dL_dtheta += self.kern.dK_dtheta(self._dL_dpsi1,self.X,self.Z)
-            dL_dtheta += self.kern.dK_dtheta(self._dL_dKmm,X=self.Z)
-            dL_dtheta += self._dKmm_dtheta
-            dL_dtheta += self._dpsi1_dtheta
-        return dL_dtheta
-
-    def dL_dZ(self):
-        if self.has_uncertain_inputs:
-            raise NotImplementedError, "FITC approximation not implemented for uncertain inputs"
-        else:
-            dL_dZ = self.kern.dK_dX(self._dL_dpsi1.T,self.Z,self.X)
-            dL_dZ += 2. * self.kern.dK_dX(self._dL_dKmm,X=self.Z)
-            dL_dZ += self._dpsi1_dX
-            dL_dZ += self._dKmm_dX
-        return dL_dZ
-
-    def _raw_predict(self, Xnew, which_parts, full_cov=False):
-
-        if self.likelihood.is_heteroscedastic:
-            Iplus_Dprod_i = 1./(1.+ self.Diag0 * self.likelihood.precision.flatten())
-            self.Diag = self.Diag0 * Iplus_Dprod_i
-            self.P = Iplus_Dprod_i[:,None] * self.psi1.T
-            self.RPT0 = np.dot(self.Lmi,self.psi1)
-            self.L = np.linalg.cholesky(np.eye(self.M) + np.dot(self.RPT0,((1. - Iplus_Dprod_i)/self.Diag0)[:,None]*self.RPT0.T))
-            self.R,info = linalg.flapack.dtrtrs(self.L,self.Lmi,lower=1)
-            self.RPT = np.dot(self.R,self.P.T)
-            self.Sigma = np.diag(self.Diag) + np.dot(self.RPT.T,self.RPT)
-            self.w = self.Diag * self.likelihood.v_tilde
-            self.Gamma = np.dot(self.R.T, np.dot(self.RPT,self.likelihood.v_tilde))
-            self.mu = self.w + np.dot(self.P,self.Gamma)
-
-            """
-            Make a prediction for the generalized FITC model
-
-            Arguments
-            ---------
-            X : Input prediction data - Nx1 numpy array (floats)
-            """
-            # q(u|f) = N(u| R0i*mu_u*f, R0i*C*R0i.T)
-
-            # Ci = I + (RPT0)Di(RPT0).T
-            # C = I - [RPT0] * (D+[RPT0].T*[RPT0])^-1*[RPT0].T
-            #   = I - [RPT0] * (D + self.Qnn)^-1 * [RPT0].T
-            #   = I - [RPT0] * (U*U.T)^-1 * [RPT0].T
-            #   = I - V.T * V
-            U = np.linalg.cholesky(np.diag(self.Diag0) + self.Qnn)
-            V,info = linalg.flapack.dtrtrs(U,self.RPT0.T,lower=1)
-            C = np.eye(self.M) - np.dot(V.T,V)
-            mu_u = np.dot(C,self.RPT0)*(1./self.Diag0[None,:])
-            #self.C = C
-            #self.RPT0 = np.dot(self.R0,self.Knm.T) P0.T
-            #self.mu_u = mu_u
-            #self.U = U
-            # q(u|y) = N(u| R0i*mu_H,R0i*Sigma_H*R0i.T)
-            mu_H = np.dot(mu_u,self.mu)
-            self.mu_H = mu_H
-            Sigma_H = C + np.dot(mu_u,np.dot(self.Sigma,mu_u.T))
-            # q(f_star|y) = N(f_star|mu_star,sigma2_star)
-            Kx = self.kern.K(self.Z, Xnew, which_parts=which_parts)
-            KR0T = np.dot(Kx.T,self.Lmi.T)
-            mu_star = np.dot(KR0T,mu_H)
-            if full_cov:
-                Kxx = self.kern.K(Xnew,which_parts=which_parts)
-                var = Kxx + np.dot(KR0T,np.dot(Sigma_H - np.eye(self.M),KR0T.T))
-            else:
-                Kxx = self.kern.Kdiag(Xnew,which_parts=which_parts)
-                var = (Kxx + np.sum(KR0T.T*np.dot(Sigma_H - np.eye(self.M),KR0T.T),0))[:,None]
-            return mu_star[:,None],var
-        else:
-            raise NotImplementedError, "homoscedastic fitc not implemented"
-            """
-            Kx = self.kern.K(self.Z, Xnew)
-            mu = mdot(Kx.T, self.C/self.scale_factor, self.psi1V)
-            if full_cov:
-                Kxx = self.kern.K(Xnew)
-                var = Kxx - mdot(Kx.T, (self.Kmmi - self.C/self.scale_factor**2), Kx) #NOTE this won't work for plotting
-            else:
-                Kxx = self.kern.Kdiag(Xnew)
-                var = Kxx - np.sum(Kx*np.dot(self.Kmmi - self.C/self.scale_factor**2, Kx),0)
-            return mu,var[:,None]
-            """
diff --git a/GPy/models/GPLVM.py b/GPy/models/GPLVM.py
deleted file mode 100644
index 5589304a..00000000
--- a/GPy/models/GPLVM.py
+++ /dev/null
@@ -1,67 +0,0 @@
-### Copyright (c) 2012, GPy authors (see AUTHORS.txt).
-# Licensed under the BSD 3-clause license (see LICENSE.txt)
-
-
-import numpy as np
-import pylab as pb
-import sys, pdb
-from .. import kern
-from ..core import model
-from ..util.linalg import pdinv, PCA
-from ..core import GP
-from ..likelihoods import Gaussian
-from .. import util
-from GPy.util import plot_latent
-
-
-class GPLVM(GP):
-    """
-    Gaussian Process Latent Variable Model
-
-    :param Y: observed data
-    :type Y: np.ndarray
-    :param input_dim: latent dimensionality
-    :type input_dim: int
-    :param init: initialisation method for the latent space
-    :type init: 'PCA'|'random'
-
-    """
-    def __init__(self, Y, input_dim, init='PCA', X = None, kernel=None, normalize_Y=False):
-        if X is None:
-            X = self.initialise_latent(init, input_dim, Y)
-        if kernel is None:
-            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())
-
-    def initialise_latent(self, init, input_dim, Y):
-        if init == 'PCA':
-            return PCA(Y, input_dim)[0]
-        else:
-            return np.random.randn(Y.shape[0], input_dim)
-
-    def _get_param_names(self):
-        return sum([['X_%i_%i'%(n,q) for q in range(self.input_dim)] for n in range(self.N)],[]) + GP._get_param_names(self)
-
-    def _get_params(self):
-        return np.hstack((self.X.flatten(), GP._get_params(self)))
-
-    def _set_params(self,x):
-        self.X = x[:self.N*self.input_dim].reshape(self.N,self.input_dim).copy()
-        GP._set_params(self, x[self.X.size:])
-
-    def _log_likelihood_gradients(self):
-        dL_dX = 2.*self.kern.dK_dX(self.dL_dK,self.X)
-
-        return np.hstack((dL_dX.flatten(),GP._log_likelihood_gradients(self)))
-
-    def plot(self):
-        assert self.likelihood.Y.shape[1]==2
-        pb.scatter(self.likelihood.Y[:,0],self.likelihood.Y[:,1],40,self.X[:,0].copy(),linewidth=0,cmap=pb.cm.jet)
-        Xnew = np.linspace(self.X.min(),self.X.max(),200)[:,None]
-        mu, var, upper, lower = self.predict(Xnew)
-        pb.plot(mu[:,0], mu[:,1],'k',linewidth=1.5)
-
-    def plot_latent(self, *args, **kwargs):
-        return util.plot_latent.plot_latent(self, *args, **kwargs)
diff --git a/GPy/models/GP_classification.py b/GPy/models/GP_classification.py
deleted file mode 100644
index 2b47aa08..00000000
--- a/GPy/models/GP_classification.py
+++ /dev/null
@@ -1,41 +0,0 @@
-# Copyright (c) 2013, Ricardo Andrade
-# Licensed under the BSD 3-clause license (see LICENSE.txt)
-
-
-import numpy as np
-from ..core import GP
-from .. import likelihoods
-from .. import kern
-
-class GP_classification(GP):
-    """
-    Gaussian Process classification
-
-    This is a thin wrapper around the models.GP class, with a set of sensible defalts
-
-    :param X: input observations
-    :param Y: observed values
-    :param likelihood: a GPy likelihood, defaults to binomial with probit link_function
-    :param kernel: a GPy kernel, defaults to rbf
-    :param normalize_X:  whether to normalize the input data before computing (predictions will be in original scales)
-    :type normalize_X: False|True
-    :param normalize_Y:  whether to normalize the input data before computing (predictions will be in original scales)
-    :type normalize_Y: False|True
-
-    .. Note:: Multiple independent outputs are allowed using columns of Y
-
-    """
-
-    def __init__(self,X,Y=None,likelihood=None,kernel=None,normalize_X=False,normalize_Y=False):
-        if kernel is None:
-            kernel = kern.rbf(X.shape[1])
-
-        if likelihood is None:
-            distribution = likelihoods.likelihood_functions.binomial()
-            likelihood = likelihoods.EP(Y, distribution)
-        elif Y is not None:
-            if not all(Y.flatten() == likelihood.data.flatten()):
-                raise Warning, 'likelihood.data and Y are different.'
-
-        GP.__init__(self, X, likelihood, kernel, normalize_X=normalize_X)
-        self._set_params(self._get_params())
diff --git a/GPy/models/GP_regression.py b/GPy/models/GP_regression.py
deleted file mode 100644
index d19ebc5b..00000000
--- a/GPy/models/GP_regression.py
+++ /dev/null
@@ -1,35 +0,0 @@
-# Copyright (c) 2012, James Hensman
-# Licensed under the BSD 3-clause license (see LICENSE.txt)
-
-
-import numpy as np
-from ..core import GP
-from .. import likelihoods
-from .. import kern
-
-class GP_regression(GP):
-    """
-    Gaussian Process model for regression
-
-    This is a thin wrapper around the models.GP class, with a set of sensible defalts
-
-    :param X: input observations
-    :param Y: observed values
-    :param kernel: a GPy kernel, defaults to rbf
-    :param normalize_X:  whether to normalize the input data before computing (predictions will be in original scales)
-    :type normalize_X: False|True
-    :param normalize_Y:  whether to normalize the input data before computing (predictions will be in original scales)
-    :type normalize_Y: False|True
-
-    .. Note:: Multiple independent outputs are allowed using columns of Y
-
-    """
-
-    def __init__(self,X,Y,kernel=None,normalize_X=False,normalize_Y=False):
-        if kernel is None:
-            kernel = kern.rbf(X.shape[1])
-
-        likelihood = likelihoods.Gaussian(Y,normalize=normalize_Y)
-
-        GP.__init__(self, X, likelihood, kernel, normalize_X=normalize_X)
-        self._set_params(self._get_params())
diff --git a/GPy/models/__init__.py b/GPy/models/__init__.py
index d762f3e4..63e9e296 100644
--- a/GPy/models/__init__.py
+++ b/GPy/models/__init__.py
@@ -2,14 +2,12 @@
 # Licensed under the BSD 3-clause license (see LICENSE.txt)
 
 
-from GP_regression import GP_regression
-from GP_classification import GP_classification
-from sparse_GP_regression import sparse_GP_regression
-from sparse_GP_classification import sparse_GP_classification
-from GPLVM import GPLVM
-from warped_GP import warpedGP
-from sparse_GPLVM import sparse_GPLVM
-from Bayesian_GPLVM import Bayesian_GPLVM
+from gp_regression import GPRegression
+from sparse_gp_regression import SparseGPRegression
+from gplvm import GPLVM
+from warped_gp import WarpedGP
+from bayesian_gplvm import BayesianGPLVM
 from mrd import MRD
-from generalized_FITC import generalized_FITC
-from FITC import FITC
+from generalized_fitc import GeneralizedFITC
+from fitc import FITC
+
diff --git a/GPy/models/generalized_FITC.py b/GPy/models/generalized_FITC.py
deleted file mode 100644
index 6e44baf6..00000000
--- a/GPy/models/generalized_FITC.py
+++ /dev/null
@@ -1,221 +0,0 @@
-# Copyright (c) 2012, GPy authors (see AUTHORS.txt).
-# Licensed under the BSD 3-clause license (see LICENSE.txt)
-
-import numpy as np
-import pylab as pb
-from ..util.linalg import mdot, jitchol, chol_inv, pdinv, trace_dot
-from ..util.plot import gpplot
-from .. import kern
-from scipy import stats, linalg
-from ..core import sparse_GP
-
-def backsub_both_sides(L,X):
-    """ Return L^-T * X * L^-1, assumuing X is symmetrical and L is lower cholesky"""
-    tmp,_ = linalg.lapack.flapack.dtrtrs(L,np.asfortranarray(X),lower=1,trans=1)
-    return linalg.lapack.flapack.dtrtrs(L,np.asfortranarray(tmp.T),lower=1,trans=1)[0].T
-
-
-class generalized_FITC(sparse_GP):
-    """
-    Naish-Guzman, A. and Holden, S. (2008) implemantation of EP with FITC.
-
-    :param X: inputs
-    :type X: np.ndarray (N x input_dim)
-    :param likelihood: a likelihood instance, containing the observed data
-    :type likelihood: GPy.likelihood.(Gaussian | EP)
-    :param kernel : the kernel/covariance function. See link kernels
-    :type kernel: a GPy kernel
-    :param X_variance: The variance in the measurements of X (Gaussian variance)
-    :type X_variance: np.ndarray (N x input_dim) | None
-    :param Z: inducing inputs (optional, see note)
-    :type Z: np.ndarray (M x input_dim) | None
-    :param M : Number of inducing points (optional, default 10. Ignored if Z is not None)
-    :type M: int
-    :param normalize_(X|Y) : whether to normalize the data before computing (predictions will be in original scales)
-    :type normalize_(X|Y): bool
-    """
-
-    def __init__(self, X, likelihood, kernel, Z, X_variance=None, normalize_X=False):
-        self.Z = Z
-        self.M = self.Z.shape[0]
-        self.true_precision = likelihood.precision
-
-        super(generalized_FITC, self).__init__(X, likelihood, kernel=kernel, Z=self.Z, X_variance=X_variance, normalize_X=normalize_X)
-        self._set_params(self._get_params())
-
-    def _set_params(self, p):
-        self.Z = p[:self.M*self.input_dim].reshape(self.M, self.input_dim)
-        self.kern._set_params(p[self.Z.size:self.Z.size+self.kern.Nparam])
-        self.likelihood._set_params(p[self.Z.size+self.kern.Nparam:])
-        self._compute_kernel_matrices()
-        self._computations()
-        self._FITC_computations()
-
-    def update_likelihood_approximation(self):
-        """
-        Approximates a non-gaussian likelihood using Expectation Propagation
-
-        For a Gaussian (or direct: TODO) likelihood, no iteration is required:
-        this function does nothing
-
-        Diag(Knn - Qnn) is added to the noise term to use the tools already implemented in sparse_GP.
-        The true precison is now 'true_precision' not 'precision'.
-        """
-        if self.has_uncertain_inputs:
-            raise NotImplementedError, "FITC approximation not implemented for uncertain inputs"
-        else:
-            self.likelihood.fit_FITC(self.Kmm,self.psi1,self.psi0)
-            self.true_precision = self.likelihood.precision # Save the true precision
-            self.likelihood.precision = self.true_precision/(1. + self.true_precision*self.Diag0[:,None]) # Add the diagonal element of the FITC approximation
-            self._set_params(self._get_params()) # update the GP
-
-    def _FITC_computations(self):
-        """
-        FITC approximation doesn't have the correction term in the log-likelihood bound,
-        but adds a diagonal term to the covariance matrix: diag(Knn - Qnn).
-        This function:
-            - computes the FITC diagonal term
-            - removes the extra terms computed in the sparse_GP approximation
-            - computes the likelihood gradients wrt the true precision.
-        """
-        #NOTE the true precison is now 'true_precision' not 'precision'
-        if self.likelihood.is_heteroscedastic:
-
-            # Compute generalized FITC's diagonal term of the covariance
-            self.Lmi,info = linalg.lapack.flapack.dtrtrs(self.Lm,np.eye(self.M),lower=1)
-            Lmipsi1 = np.dot(self.Lmi,self.psi1)
-            self.Qnn = np.dot(Lmipsi1.T,Lmipsi1)
-            #self.Kmmi, Lm, Lmi, Kmm_logdet = pdinv(self.Kmm)
-            #self.Qnn = mdot(self.psi1.T,self.Kmmi,self.psi1)
-            #a = kj
-            self.Diag0 = self.psi0 - np.diag(self.Qnn)
-            Iplus_Dprod_i = 1./(1.+ self.Diag0 * self.true_precision.flatten())
-            self.Diag = self.Diag0 * Iplus_Dprod_i
-
-            self.P = Iplus_Dprod_i[:,None] * self.psi1.T
-            self.RPT0 = np.dot(self.Lmi,self.psi1)
-            self.L = np.linalg.cholesky(np.eye(self.M) + np.dot(self.RPT0,((1. - Iplus_Dprod_i)/self.Diag0)[:,None]*self.RPT0.T))
-            self.R,info = linalg.flapack.dtrtrs(self.L,self.Lmi,lower=1)
-            self.RPT = np.dot(self.R,self.P.T)
-            self.Sigma = np.diag(self.Diag) + np.dot(self.RPT.T,self.RPT)
-            self.w = self.Diag * self.likelihood.v_tilde
-            self.Gamma = np.dot(self.R.T, np.dot(self.RPT,self.likelihood.v_tilde))
-            self.mu = self.w + np.dot(self.P,self.Gamma)
-
-            # Remove extra term from dL_dpsi1
-            self.dL_dpsi1 -= mdot(self.Lmi.T,Lmipsi1*self.likelihood.precision.flatten().reshape(1,self.N))
-            #self.Kmmi, Lm, Lmi, Kmm_logdet = pdinv(self.Kmm)
-            #self.dL_dpsi1 -= mdot(self.Kmmi,self.psi1*self.likelihood.precision.flatten().reshape(1,self.N)) #dB
-
-            #########333333
-            #self.Bi, self.LB, self.LBi, self.B_logdet = pdinv(self.B)
-            #########333333
-
-
-
-        else:
-            raise NotImplementedError, "homoscedastic fitc not implemented"
-            # Remove extra term from dL_dpsi1
-            #self.dL_dpsi1 += -mdot(self.Kmmi,self.psi1*self.likelihood.precision) #dB
-
-        sf = self.scale_factor
-        sf2 = sf**2
-
-        # Remove extra term from dL_dKmm
-        self.dL_dKmm += 0.5 * self.D * mdot(self.Lmi.T, self.A, self.Lmi)*sf2 # dB
-        self.dL_dpsi0 = None
-
-        #the partial derivative vector for the likelihood
-        if self.likelihood.Nparams == 0:
-            self.partial_for_likelihood = None
-        elif self.likelihood.is_heteroscedastic:
-            raise NotImplementedError, "heteroscedastic derivates not implemented"
-        else:
-            raise NotImplementedError, "homoscedastic derivatives not implemented"
-            #likelihood is not heterscedatic
-            #self.partial_for_likelihood =   - 0.5 * self.N*self.D*self.likelihood.precision + 0.5 * np.sum(np.square(self.likelihood.Y))*self.likelihood.precision**2
-            #self.partial_for_likelihood += 0.5 * self.D * trace_dot(self.Bi,self.A)*self.likelihood.precision
-            #self.partial_for_likelihood += self.likelihood.precision*(0.5*trace_dot(self.psi2_beta_scaled,self.E*sf2) - np.trace(self.Cpsi1VVpsi1))
-        #TODO partial derivative vector for the likelihood not implemented
-
-    def dL_dtheta(self):
-        """
-        Compute and return the derivative of the log marginal likelihood wrt the parameters of the kernel
-        """
-        dL_dtheta = self.kern.dK_dtheta(self.dL_dKmm,self.Z)
-        if self.has_uncertain_inputs:
-            raise NotImplementedError, "heteroscedatic derivates not implemented"
-        else:
-            #NOTE in sparse_GP this would include the gradient wrt psi0
-            dL_dtheta += self.kern.dK_dtheta(self.dL_dpsi1,self.Z,self.X)
-        return dL_dtheta
-
-
-    def log_likelihood(self):
-        """ Compute the (lower bound on the) log marginal likelihood """
-        sf2 = self.scale_factor**2
-        if self.likelihood.is_heteroscedastic:
-            A = -0.5*self.N*self.D*np.log(2.*np.pi) +0.5*np.sum(np.log(self.likelihood.precision)) -0.5*np.sum(self.V*self.likelihood.Y)
-        else:
-            A = -0.5*self.N*self.D*(np.log(2.*np.pi) + np.log(self.likelihood._variance)) -0.5*self.likelihood.precision*self.likelihood.trYYT
-        C = -self.D * (np.sum(np.log(np.diag(self.LB))) + 0.5*self.M*np.log(sf2))
-        #C = -0.5*self.D * (self.B_logdet + self.M*np.log(sf2))
-        D = 0.5*np.sum(np.square(self._LBi_Lmi_psi1V))
-        #self.Cpsi1VVpsi1 = np.dot(self.Cpsi1V,self.psi1V.T)
-        #D_ = 0.5*np.trace(self.Cpsi1VVpsi1)
-        return A+C+D
-
-    def _raw_predict(self, Xnew, which_parts, full_cov=False):
-        if self.likelihood.is_heteroscedastic:
-            """
-            Make a prediction for the generalized FITC model
-
-            Arguments
-            ---------
-            X : Input prediction data - Nx1 numpy array (floats)
-            """
-            # q(u|f) = N(u| R0i*mu_u*f, R0i*C*R0i.T)
-
-            # Ci = I + (RPT0)Di(RPT0).T
-            # C = I - [RPT0] * (D+[RPT0].T*[RPT0])^-1*[RPT0].T
-            #   = I - [RPT0] * (D + self.Qnn)^-1 * [RPT0].T
-            #   = I - [RPT0] * (U*U.T)^-1 * [RPT0].T
-            #   = I - V.T * V
-            U = np.linalg.cholesky(np.diag(self.Diag0) + self.Qnn)
-            V,info = linalg.flapack.dtrtrs(U,self.RPT0.T,lower=1)
-            C = np.eye(self.M) - np.dot(V.T,V)
-            mu_u = np.dot(C,self.RPT0)*(1./self.Diag0[None,:])
-            #self.C = C
-            #self.RPT0 = np.dot(self.R0,self.Knm.T) P0.T
-            #self.mu_u = mu_u
-            #self.U = U
-            # q(u|y) = N(u| R0i*mu_H,R0i*Sigma_H*R0i.T)
-            mu_H = np.dot(mu_u,self.mu)
-            self.mu_H = mu_H
-            Sigma_H = C + np.dot(mu_u,np.dot(self.Sigma,mu_u.T))
-            # q(f_star|y) = N(f_star|mu_star,sigma2_star)
-            Kx = self.kern.K(self.Z, Xnew, which_parts=which_parts)
-            KR0T = np.dot(Kx.T,self.Lmi.T)
-            mu_star = np.dot(KR0T,mu_H)
-            if full_cov:
-                Kxx = self.kern.K(Xnew,which_parts=which_parts)
-                var = Kxx + np.dot(KR0T,np.dot(Sigma_H - np.eye(self.M),KR0T.T))
-            else:
-                Kxx = self.kern.Kdiag(Xnew,which_parts=which_parts)
-                Kxx_ = self.kern.K(Xnew,which_parts=which_parts) # TODO: RA, is this line needed?
-                var_ = Kxx_ + np.dot(KR0T,np.dot(Sigma_H - np.eye(self.M),KR0T.T)) # TODO: RA, is this line needed?
-                var = (Kxx + np.sum(KR0T.T*np.dot(Sigma_H - np.eye(self.M),KR0T.T),0))[:,None]
-            return mu_star[:,None],var
-        else:
-            raise NotImplementedError, "homoscedastic fitc not implemented"
-            """
-            Kx = self.kern.K(self.Z, Xnew)
-            mu = mdot(Kx.T, self.C/self.scale_factor, self.psi1V)
-            if full_cov:
-                Kxx = self.kern.K(Xnew)
-                var = Kxx - mdot(Kx.T, (self.Kmmi - self.C/self.scale_factor**2), Kx) #NOTE this won't work for plotting
-            else:
-                Kxx = self.kern.Kdiag(Xnew)
-                var = Kxx - np.sum(Kx*np.dot(self.Kmmi - self.C/self.scale_factor**2, Kx),0)
-            return mu,var[:,None]
-            """
diff --git a/GPy/models/mrd.py b/GPy/models/mrd.py
index e91298aa..aa3a97a3 100644
--- a/GPy/models/mrd.py
+++ b/GPy/models/mrd.py
@@ -4,14 +4,13 @@ Created on 10 Apr 2013
 @author: Max Zwiessele
 '''
 from GPy.core import model
-from GPy.models.Bayesian_GPLVM import Bayesian_GPLVM
-from GPy.core import sparse_GP
+from GPy.core import SparseGP
 from GPy.util.linalg import PCA
-from scipy import linalg
 import numpy
 import itertools
 import pylab
 from GPy.kern.kern import kern
+from GPy.models.bayesian_gplvm import BayesianGPLVM
 
 class MRD(model):
     """
@@ -38,7 +37,7 @@ class MRD(model):
             *concat: PCA on concatenated outputs
             *single: PCA on each output
             *random: random
-    :param M:
+    :param num_inducing:
         number of inducing inputs to use
     :param Z:
         initial inducing inputs
@@ -62,22 +61,22 @@ class MRD(model):
         assert not ('kernel' in kw), "pass kernels through `kernels` argument"
 
         self.input_dim = input_dim
-        self.M = M
+        self.num_inducing = M
         self._debug = _debug
 
         self._init = True
         X = self._init_X(initx, likelihood_or_Y_list)
         Z = self._init_Z(initz, X)
-        self.bgplvms = [Bayesian_GPLVM(l, input_dim=input_dim, kernel=k, X=X, Z=Z, M=self.M, **kw) for l, k in zip(likelihood_or_Y_list, kernels)]
+        self.bgplvms = [BayesianGPLVM(l, input_dim=input_dim, kernel=k, X=X, Z=Z, M=self.num_inducing, **kw) for l, k in zip(likelihood_or_Y_list, kernels)]
         del self._init
 
         self.gref = self.bgplvms[0]
-        nparams = numpy.array([0] + [sparse_GP._get_params(g).size - g.Z.size for g in self.bgplvms])
+        nparams = numpy.array([0] + [SparseGP._get_params(g).size - g.Z.size for g in self.bgplvms])
         self.nparams = nparams.cumsum()
 
         self.N = self.gref.N
         self.NQ = self.N * self.input_dim
-        self.MQ = self.M * self.input_dim
+        self.MQ = self.num_inducing * self.input_dim
 
         model.__init__(self) # @UndefinedVariable
         self._set_params(self._get_params())
@@ -151,7 +150,7 @@ class MRD(model):
                                          itertools.izip(ns,
                                                         itertools.repeat(name)))
         return list(itertools.chain(n1var, *(map_names(\
-                sparse_GP._get_param_names(g)[self.MQ:], n) \
+                SparseGP._get_param_names(g)[self.MQ:], n) \
                 for g, n in zip(self.bgplvms, self.names))))
 
     def _get_params(self):
@@ -165,14 +164,14 @@ class MRD(model):
         X = self.gref.X.ravel()
         X_var = self.gref.X_variance.ravel()
         Z = self.gref.Z.ravel()
-        thetas = [sparse_GP._get_params(g)[g.Z.size:] for g in self.bgplvms]
+        thetas = [SparseGP._get_params(g)[g.Z.size:] for g in self.bgplvms]
         params = numpy.hstack([X, X_var, Z, numpy.hstack(thetas)])
         return params
 
 #     def _set_var_params(self, g, X, X_var, Z):
 #         g.X = X.reshape(self.N, self.input_dim)
 #         g.X_variance = X_var.reshape(self.N, self.input_dim)
-#         g.Z = Z.reshape(self.M, self.input_dim)
+#         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])
@@ -206,7 +205,7 @@ class MRD(model):
     def log_likelihood(self):
         ll = -self.gref.KL_divergence()
         for g in self.bgplvms:
-            ll += sparse_GP.log_likelihood(g)
+            ll += SparseGP.log_likelihood(g)
         return ll
 
     def _log_likelihood_gradients(self):
@@ -215,7 +214,7 @@ class MRD(model):
         dLdmu -= dKLmu
         dLdS -= dKLdS
         dLdmuS = numpy.hstack((dLdmu.flatten(), dLdS.flatten())).flatten()
-        dldzt1 = reduce(lambda a, b: a + b, (sparse_GP._log_likelihood_gradients(g)[:self.MQ] for g in self.bgplvms))
+        dldzt1 = reduce(lambda a, b: a + b, (SparseGP._log_likelihood_gradients(g)[:self.MQ] for g in self.bgplvms))
 
         return numpy.hstack((dLdmuS,
                              dldzt1,
@@ -250,9 +249,9 @@ class MRD(model):
         if X is None:
             X = self.X
         if init in "permute":
-            Z = numpy.random.permutation(X.copy())[:self.M]
+            Z = numpy.random.permutation(X.copy())[:self.num_inducing]
         elif init in "random":
-            Z = numpy.random.randn(self.M, self.input_dim) * X.var()
+            Z = numpy.random.randn(self.num_inducing, self.input_dim) * X.var()
         self.Z = Z
         return Z
 
diff --git a/GPy/models/sparse_GPLVM.py b/GPy/models/sparse_GPLVM.py
deleted file mode 100644
index ce71cd9b..00000000
--- a/GPy/models/sparse_GPLVM.py
+++ /dev/null
@@ -1,61 +0,0 @@
-# Copyright (c) 2012, GPy authors (see AUTHORS.txt).
-# Licensed under the BSD 3-clause license (see LICENSE.txt)
-
-
-import numpy as np
-import pylab as pb
-import sys, pdb
-# from .. import kern
-# from ..core import model
-# from ..util.linalg import pdinv, PCA
-from GPLVM import GPLVM
-from sparse_GP_regression import sparse_GP_regression
-
-class sparse_GPLVM(sparse_GP_regression, GPLVM):
-    """
-    Sparse Gaussian Process Latent Variable Model
-
-    :param Y: observed data
-    :type Y: np.ndarray
-    :param input_dim: latent dimensionality
-    :type input_dim: int
-    :param init: initialisation method for the latent space
-    :type init: 'PCA'|'random'
-
-    """
-    def __init__(self, Y, input_dim, kernel=None, init='PCA', M=10):
-        X = self.initialise_latent(init, input_dim, Y)
-        sparse_GP_regression.__init__(self, X, Y, kernel=kernel,M=M)
-
-    def _get_param_names(self):
-        return (sum([['X_%i_%i'%(n,q) for q in range(self.input_dim)] for n in range(self.N)],[])
-                + sparse_GP_regression._get_param_names(self))
-
-    def _get_params(self):
-        return np.hstack((self.X.flatten(), sparse_GP_regression._get_params(self)))
-
-    def _set_params(self,x):
-        self.X = x[:self.X.size].reshape(self.N,self.input_dim).copy()
-        sparse_GP_regression._set_params(self, x[self.X.size:])
-
-    def log_likelihood(self):
-        return sparse_GP_regression.log_likelihood(self)
-
-    def dL_dX(self):
-        dL_dX = self.kern.dKdiag_dX(self.dL_dpsi0,self.X)
-        dL_dX += self.kern.dK_dX(self.dL_dpsi1.T,self.X,self.Z)
-
-        return dL_dX
-
-    def _log_likelihood_gradients(self):
-        return np.hstack((self.dL_dX().flatten(), sparse_GP_regression._log_likelihood_gradients(self)))
-
-    def plot(self):
-        GPLVM.plot(self)
-        #passing Z without a small amout of jitter will induce the white kernel where we don;t want it!
-        mu, var, upper, lower = sparse_GP_regression.predict(self, self.Z+np.random.randn(*self.Z.shape)*0.0001)
-        pb.plot(mu[:, 0] , mu[:, 1], 'ko')
-
-    def plot_latent(self, *args, **kwargs):
-        input_1, input_2 = GPLVM.plot_latent(*args, **kwargs)
-        pb.plot(m.Z[:, input_1], m.Z[:, input_2], '^w')
diff --git a/GPy/models/sparse_GP_classification.py b/GPy/models/sparse_GP_classification.py
deleted file mode 100644
index a3412ea2..00000000
--- a/GPy/models/sparse_GP_classification.py
+++ /dev/null
@@ -1,50 +0,0 @@
-# Copyright (c) 2013, Ricardo Andrade
-# Licensed under the BSD 3-clause license (see LICENSE.txt)
-
-
-import numpy as np
-from ..core import sparse_GP
-from .. import likelihoods
-from .. import kern
-from ..likelihoods import likelihood
-from GP_regression import GP_regression
-
-class sparse_GP_classification(sparse_GP):
-    """
-    sparse Gaussian Process model for classification
-
-    This is a thin wrapper around the sparse_GP class, with a set of sensible defalts
-
-    :param X: input observations
-    :param Y: observed values
-    :param likelihood: a GPy likelihood, defaults to binomial with probit link_function
-    :param kernel: a GPy kernel, defaults to rbf+white
-    :param normalize_X:  whether to normalize the input data before computing (predictions will be in original scales)
-    :type normalize_X: False|True
-    :param normalize_Y:  whether to normalize the input data before computing (predictions will be in original scales)
-    :type normalize_Y: False|True
-    :rtype: model object
-
-    .. Note:: Multiple independent outputs are allowed using columns of Y
-
-    """
-
-    def __init__(self, X, Y=None, likelihood=None, kernel=None, normalize_X=False, normalize_Y=False, Z=None, M=10):
-        if kernel is None:
-            kernel = kern.rbf(X.shape[1]) + kern.white(X.shape[1],1e-3)
-
-        if likelihood is None:
-            distribution = likelihoods.likelihood_functions.binomial()
-            likelihood = likelihoods.EP(Y, distribution)
-        elif Y is not None:
-            if not all(Y.flatten() == likelihood.data.flatten()):
-                raise Warning, 'likelihood.data and Y are different.'
-
-        if Z is None:
-            i = np.random.permutation(X.shape[0])[:M]
-            Z = X[i].copy()
-        else:
-            assert Z.shape[1]==X.shape[1]
-
-        sparse_GP.__init__(self, X, likelihood, kernel, Z=Z, normalize_X=normalize_X)
-        self._set_params(self._get_params())
diff --git a/GPy/models/sparse_GP_regression.py b/GPy/models/sparse_GP_regression.py
deleted file mode 100644
index 78089ded..00000000
--- a/GPy/models/sparse_GP_regression.py
+++ /dev/null
@@ -1,47 +0,0 @@
-# Copyright (c) 2012, James Hensman
-# Licensed under the BSD 3-clause license (see LICENSE.txt)
-
-
-import numpy as np
-from ..core import sparse_GP
-from .. import likelihoods
-from .. import kern
-from ..likelihoods import likelihood
-from GP_regression import GP_regression
-
-class sparse_GP_regression(sparse_GP):
-    """
-    Gaussian Process model for regression
-
-    This is a thin wrapper around the sparse_GP class, with a set of sensible defalts
-
-    :param X: input observations
-    :param Y: observed values
-    :param kernel: a GPy kernel, defaults to rbf+white
-    :param normalize_X:  whether to normalize the input data before computing (predictions will be in original scales)
-    :type normalize_X: False|True
-    :param normalize_Y:  whether to normalize the input data before computing (predictions will be in original scales)
-    :type normalize_Y: False|True
-    :rtype: model object
-
-    .. Note:: Multiple independent outputs are allowed using columns of Y
-
-    """
-
-    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)
-        if kernel is None:
-            kernel = kern.rbf(X.shape[1]) + kern.white(X.shape[1],1e-3)
-
-        #Z defaults to a subset of the data
-        if Z is None:
-            i = np.random.permutation(X.shape[0])[:M]
-            Z = X[i].copy()
-        else:
-            assert Z.shape[1]==X.shape[1]
-
-        #likelihood defaults to Gaussian
-        likelihood = likelihoods.Gaussian(Y,normalize=normalize_Y)
-
-        sparse_GP.__init__(self, X, likelihood, kernel, Z=Z, normalize_X=normalize_X, X_variance=X_variance)
-        self._set_params(self._get_params())
diff --git a/GPy/models/warped_GP.py b/GPy/models/warped_GP.py
deleted file mode 100644
index 86a69226..00000000
--- a/GPy/models/warped_GP.py
+++ /dev/null
@@ -1,93 +0,0 @@
-# Copyright (c) 2012, GPy authors (see AUTHORS.txt).
-# Licensed under the BSD 3-clause license (see LICENSE.txt)
-
-
-import numpy as np
-from .. import kern
-from ..core import model
-from ..util.linalg import pdinv
-from ..util.plot import gpplot
-from ..util.warping_functions import *
-from GP_regression import GP_regression
-from ..core import GP
-from .. import likelihoods
-from .. import kern
-
-class warpedGP(GP):
-    def __init__(self, X, Y, kernel=None, warping_function = None, warping_terms = 3, normalize_X=False, normalize_Y=False):
-
-        if kernel is None:
-            kernel = kern.rbf(X.shape[1])
-
-        if warping_function == None:
-            self.warping_function = TanhWarpingFunction_d(warping_terms)
-            self.warping_params = (np.random.randn(self.warping_function.n_terms*3+1,) * 1)
-
-        Y = self._scale_data(Y)
-        self.has_uncertain_inputs = False
-        self.Y_untransformed = Y.copy()
-        self.predict_in_warped_space = False
-        likelihood = likelihoods.Gaussian(self.transform_data(), normalize=normalize_Y)
-
-        GP.__init__(self, X, likelihood, kernel, normalize_X=normalize_X)
-        self._set_params(self._get_params())
-
-    def _scale_data(self, Y):
-        self._Ymax = Y.max()
-        self._Ymin = Y.min()
-        return (Y-self._Ymin)/(self._Ymax-self._Ymin) - 0.5
-
-    def _unscale_data(self, Y):
-        return (Y + 0.5)*(self._Ymax - self._Ymin) + self._Ymin
-
-    def _set_params(self, x):
-        self.warping_params = x[:self.warping_function.num_parameters]
-        Y = self.transform_data()
-        self.likelihood.set_data(Y)
-        GP._set_params(self, x[self.warping_function.num_parameters:].copy())
-
-    def _get_params(self):
-        return np.hstack((self.warping_params.flatten().copy(), GP._get_params(self).copy()))
-
-    def _get_param_names(self):
-        warping_names = self.warping_function._get_param_names()
-        param_names = GP._get_param_names(self)
-        return warping_names + param_names
-
-    def transform_data(self):
-        Y = self.warping_function.f(self.Y_untransformed.copy(), self.warping_params).copy()
-        return Y
-
-    def log_likelihood(self):
-        ll = GP.log_likelihood(self)
-        jacobian = self.warping_function.fgrad_y(self.Y_untransformed, self.warping_params)
-        return ll + np.log(jacobian).sum()
-
-    def _log_likelihood_gradients(self):
-        ll_grads = GP._log_likelihood_gradients(self)
-        alpha = np.dot(self.Ki, self.likelihood.Y.flatten())
-        warping_grads = self.warping_function_gradients(alpha)
-
-        warping_grads = np.append(warping_grads[:,:-1].flatten(), warping_grads[0,-1])
-        return np.hstack((warping_grads.flatten(), ll_grads.flatten()))
-
-    def warping_function_gradients(self, Kiy):
-        grad_y = self.warping_function.fgrad_y(self.Y_untransformed, self.warping_params)
-        grad_y_psi, grad_psi = self.warping_function.fgrad_y_psi(self.Y_untransformed, self.warping_params,
-                                                                 return_covar_chain = True)
-        djac_dpsi = ((1.0/grad_y[:,:, None, None])*grad_y_psi).sum(axis=0).sum(axis=0)
-        dquad_dpsi = (Kiy[:,None,None,None] * grad_psi).sum(axis=0).sum(axis=0)
-
-        return -dquad_dpsi + djac_dpsi
-
-    def plot_warping(self):
-        self.warping_function.plot(self.warping_params, self.Y_untransformed.min(), self.Y_untransformed.max())
-
-    def _raw_predict(self, *args, **kwargs):
-        mu, var = GP._raw_predict(self, *args, **kwargs)
-
-        if self.predict_in_warped_space:
-            mu = self.warping_function.f_inv(mu, self.warping_params)
-            var = self.warping_function.f_inv(var, self.warping_params)
-            mu = self._unscale_data(mu)
-        return mu, var
diff --git a/GPy/testing/bgplvm_tests.py b/GPy/testing/bgplvm_tests.py
index ae72983a..3ca32660 100644
--- a/GPy/testing/bgplvm_tests.py
+++ b/GPy/testing/bgplvm_tests.py
@@ -4,6 +4,7 @@
 import unittest
 import numpy as np
 import GPy
+from GPy.models.bayesian_gplvm import BayesianGPLVM
 
 class BGPLVMTests(unittest.TestCase):
     def test_bias_kern(self):
@@ -11,10 +12,10 @@ class BGPLVMTests(unittest.TestCase):
         X = np.random.rand(N, input_dim)
         k = GPy.kern.rbf(input_dim) + GPy.kern.white(input_dim, 0.00001)
         K = k.K(X)
-        Y = np.random.multivariate_normal(np.zeros(N),K,D).T
+        Y = np.random.multivariate_normal(np.zeros(N),K,input_dim).T
         Y -= Y.mean(axis=0)
         k = GPy.kern.bias(input_dim) + GPy.kern.white(input_dim, 0.00001)
-        m = GPy.models.Bayesian_GPLVM(Y, input_dim, kernel = k,  M=M)
+        m = BayesianGPLVM(Y, input_dim, kernel=k, M=M)
         m.ensure_default_constraints()
         m.randomize()
         self.assertTrue(m.checkgrad())
@@ -24,10 +25,10 @@ class BGPLVMTests(unittest.TestCase):
         X = np.random.rand(N, input_dim)
         k = GPy.kern.rbf(input_dim) + GPy.kern.white(input_dim, 0.00001)
         K = k.K(X)
-        Y = np.random.multivariate_normal(np.zeros(N),K,D).T
+        Y = np.random.multivariate_normal(np.zeros(N),K,input_dim).T
         Y -= Y.mean(axis=0)
         k = GPy.kern.linear(input_dim) + GPy.kern.white(input_dim, 0.00001)
-        m = GPy.models.Bayesian_GPLVM(Y, input_dim, kernel = k,  M=M)
+        m = BayesianGPLVM(Y, input_dim, kernel=k, M=M)
         m.ensure_default_constraints()
         m.randomize()
         self.assertTrue(m.checkgrad())
@@ -37,10 +38,10 @@ class BGPLVMTests(unittest.TestCase):
         X = np.random.rand(N, input_dim)
         k = GPy.kern.rbf(input_dim) + GPy.kern.white(input_dim, 0.00001)
         K = k.K(X)
-        Y = np.random.multivariate_normal(np.zeros(N),K,D).T
+        Y = np.random.multivariate_normal(np.zeros(N),K,input_dim).T
         Y -= Y.mean(axis=0)
         k = GPy.kern.rbf(input_dim) + GPy.kern.white(input_dim, 0.00001)
-        m = GPy.models.Bayesian_GPLVM(Y, input_dim, kernel = k,  M=M)
+        m = BayesianGPLVM(Y, input_dim, kernel=k, M=M)
         m.ensure_default_constraints()
         m.randomize()
         self.assertTrue(m.checkgrad())
@@ -50,10 +51,10 @@ class BGPLVMTests(unittest.TestCase):
         X = np.random.rand(N, input_dim)
         k = GPy.kern.rbf(input_dim) +  GPy.kern.bias(input_dim) + GPy.kern.white(input_dim, 0.00001)
         K = k.K(X)
-        Y = np.random.multivariate_normal(np.zeros(N),K,D).T
+        Y = np.random.multivariate_normal(np.zeros(N),K,input_dim).T
         Y -= Y.mean(axis=0)
         k = GPy.kern.rbf(input_dim) + GPy.kern.bias(input_dim) + GPy.kern.white(input_dim, 0.00001)
-        m = GPy.models.Bayesian_GPLVM(Y, input_dim, kernel = k,  M=M)
+        m = BayesianGPLVM(Y, input_dim, kernel=k, M=M)
         m.ensure_default_constraints()
         m.randomize()
         self.assertTrue(m.checkgrad())
@@ -64,10 +65,10 @@ class BGPLVMTests(unittest.TestCase):
         X = np.random.rand(N, input_dim)
         k = GPy.kern.linear(input_dim) +  GPy.kern.bias(input_dim) + GPy.kern.white(input_dim, 0.00001)
         K = k.K(X)
-        Y = np.random.multivariate_normal(np.zeros(N),K,D).T
+        Y = np.random.multivariate_normal(np.zeros(N),K,input_dim).T
         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,  M=M)
+        m = BayesianGPLVM(Y, input_dim, kernel=k, M=M)
         m.ensure_default_constraints()
         m.randomize()
         self.assertTrue(m.checkgrad())
diff --git a/GPy/testing/examples_tests.py b/GPy/testing/examples_tests.py
index a06f1090..51131fc3 100644
--- a/GPy/testing/examples_tests.py
+++ b/GPy/testing/examples_tests.py
@@ -9,6 +9,7 @@ import pkgutil
 import os
 import random
 from nose.tools import nottest
+import sys
 
 class ExamplesTests(unittest.TestCase):
     def _checkgrad(self, model):
@@ -40,9 +41,9 @@ def model_instance(model):
 @nottest
 def test_models():
     examples_path = os.path.dirname(GPy.examples.__file__)
-    #Load modules
+    # Load modules
     for loader, module_name, is_pkg in pkgutil.iter_modules([examples_path]):
-        #Load examples
+        # Load examples
         module_examples = loader.find_module(module_name).load_module(module_name)
         print "MODULE", module_examples
         print "Before"
@@ -56,11 +57,11 @@ def test_models():
                 continue
 
             print "Testing example: ", example[0]
-            #Generate model
+            # Generate model
             model = example[1]()
             print model
 
-            #Create tests for instance check
+            # Create tests for instance check
             """
             test = model_instance_generator(model)
             test.__name__ = 'test_instance_%s' % example[0]
@@ -78,4 +79,5 @@ def test_models():
 
 if __name__ == "__main__":
     print "Running unit tests, please be (very) patient..."
-    unittest.main()
+    # unittest.main()
+    test_models()
diff --git a/GPy/testing/gplvm_tests.py b/GPy/testing/gplvm_tests.py
index b1721a3c..4194698f 100644
--- a/GPy/testing/gplvm_tests.py
+++ b/GPy/testing/gplvm_tests.py
@@ -11,7 +11,7 @@ class GPLVMTests(unittest.TestCase):
         X = np.random.rand(N, input_dim)
         k = GPy.kern.rbf(input_dim) + GPy.kern.white(input_dim, 0.00001)
         K = k.K(X)
-        Y = np.random.multivariate_normal(np.zeros(N),K,D).T
+        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()
@@ -23,7 +23,7 @@ class GPLVMTests(unittest.TestCase):
         X = np.random.rand(N, input_dim)
         k = GPy.kern.rbf(input_dim) + GPy.kern.white(input_dim, 0.00001)
         K = k.K(X)
-        Y = np.random.multivariate_normal(np.zeros(N),K,D).T
+        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()
@@ -35,7 +35,7 @@ class GPLVMTests(unittest.TestCase):
         X = np.random.rand(N, input_dim)
         k = GPy.kern.rbf(input_dim) + GPy.kern.white(input_dim, 0.00001)
         K = k.K(X)
-        Y = np.random.multivariate_normal(np.zeros(N),K,D).T
+        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()
diff --git a/GPy/testing/kernel_tests.py b/GPy/testing/kernel_tests.py
index b48bc813..2d97c82c 100644
--- a/GPy/testing/kernel_tests.py
+++ b/GPy/testing/kernel_tests.py
@@ -12,7 +12,7 @@ class KernelTests(unittest.TestCase):
         K.constrain_fixed('2')
         X = np.random.rand(5,5)
         Y = np.ones((5,1))
-        m = GPy.models.GP_regression(X,Y,K)
+        m = GPy.models.GPRegression(X,Y,K)
         self.assertTrue(m.checkgrad())
 
     def test_fixedkernel(self):
@@ -23,7 +23,7 @@ class KernelTests(unittest.TestCase):
         K = np.dot(X, X.T)
         kernel = GPy.kern.fixed(4, K)
         Y = np.ones((30,1))
-        m = GPy.models.GP_regression(X,Y,kernel=kernel)
+        m = GPy.models.GPRegression(X,Y,kernel=kernel)
         self.assertTrue(m.checkgrad())
 
     def test_coregionalisation(self):
@@ -36,9 +36,9 @@ class KernelTests(unittest.TestCase):
         Y = np.vstack((Y1,Y2))
 
         k1 = GPy.kern.rbf(1) + GPy.kern.bias(1)
-        k2 = GPy.kern.coregionalise(2,1)
+        k2 = GPy.kern.Coregionalise(2,1)
         k = k1.prod(k2,tensor=True)
-        m = GPy.models.GP_regression(X,Y,kernel=k)
+        m = GPy.models.GPRegression(X,Y,kernel=k)
         self.assertTrue(m.checkgrad())
 
 
diff --git a/GPy/testing/mrd_tests.py b/GPy/testing/mrd_tests.py
index 25adbca6..df4c2723 100644
--- a/GPy/testing/mrd_tests.py
+++ b/GPy/testing/mrd_tests.py
@@ -20,7 +20,7 @@ class MRDTests(unittest.TestCase):
         k = GPy.kern.linear(input_dim) + GPy.kern.bias(input_dim) + GPy.kern.white(input_dim)
         K = k.K(X)
 
-        Ylist = [np.random.multivariate_normal(np.zeros(N), K, D).T for _ in range(num_m)]
+        Ylist = [np.random.multivariate_normal(np.zeros(N), K, input_dim).T for _ in range(num_m)]
         likelihood_list = [GPy.likelihoods.Gaussian(Y) for Y in Ylist]
 
         m = GPy.models.MRD(likelihood_list, input_dim=input_dim, kernels=k, M=M)
diff --git a/GPy/testing/prior_tests.py b/GPy/testing/prior_tests.py
index d3269560..e0226751 100644
--- a/GPy/testing/prior_tests.py
+++ b/GPy/testing/prior_tests.py
@@ -13,7 +13,7 @@ class PriorTests(unittest.TestCase):
         y  = b*X + C + 1*np.sin(X)
         y += 0.05*np.random.randn(len(X))
         X, y = X[:, None], y[:, None]
-        m = GPy.models.GP_regression(X, y)
+        m = GPy.models.GPRegression(X, y)
         m.ensure_default_constraints()
         lognormal = GPy.priors.LogGaussian(1, 2)
         m.set_prior('rbf', lognormal)
@@ -27,7 +27,7 @@ class PriorTests(unittest.TestCase):
         y  = b*X + C + 1*np.sin(X)
         y += 0.05*np.random.randn(len(X))
         X, y = X[:, None], y[:, None]
-        m = GPy.models.GP_regression(X, y)
+        m = GPy.models.GPRegression(X, y)
         m.ensure_default_constraints()
         Gamma = GPy.priors.Gamma(1, 1)
         m.set_prior('rbf', Gamma)
@@ -41,7 +41,7 @@ class PriorTests(unittest.TestCase):
         y  = b*X + C + 1*np.sin(X)
         y += 0.05*np.random.randn(len(X))
         X, y = X[:, None], y[:, None]
-        m = GPy.models.GP_regression(X, y)
+        m = GPy.models.GPRegression(X, y)
         m.ensure_default_constraints()
         gaussian = GPy.priors.Gaussian(1, 1)
         success = False
diff --git a/GPy/testing/psi_stat_expactation_tests.py b/GPy/testing/psi_stat_expactation_tests.py
index 95f83fb5..da71754b 100644
--- a/GPy/testing/psi_stat_expactation_tests.py
+++ b/GPy/testing/psi_stat_expactation_tests.py
@@ -21,36 +21,36 @@ def ard(p):
 
 @testing.deepTest(__test__)
 class Test(unittest.TestCase):
-    D = 9
-    M = 4
+    input_dim = 9
+    num_inducing = 4
     N = 3
     Nsamples = 6e6
 
     def setUp(self):
         self.kerns = (
-#                       (GPy.kern.rbf(self.D, ARD=True) +
-#                        GPy.kern.linear(self.D, ARD=True) +
-#                        GPy.kern.bias(self.D) +
-#                        GPy.kern.white(self.D)),
-                      (GPy.kern.rbf(self.D, np.random.rand(), np.random.rand(self.D), ARD=True) +
-                       GPy.kern.rbf(self.D, np.random.rand(), np.random.rand(self.D), ARD=True) +
-                       GPy.kern.linear(self.D, np.random.rand(self.D), ARD=True) +
-                       GPy.kern.bias(self.D) +
-                       GPy.kern.white(self.D)),
-#                       GPy.kern.rbf(self.D), GPy.kern.rbf(self.D, ARD=True),
-#                       GPy.kern.linear(self.D, ARD=False), GPy.kern.linear(self.D, ARD=True),
-#                       GPy.kern.linear(self.D) + GPy.kern.bias(self.D),
-#                       GPy.kern.rbf(self.D) + GPy.kern.bias(self.D),
-#                       GPy.kern.linear(self.D) + GPy.kern.bias(self.D) + GPy.kern.white(self.D),
-#                       GPy.kern.rbf(self.D) + GPy.kern.bias(self.D) + GPy.kern.white(self.D),
-#                       GPy.kern.bias(self.D), GPy.kern.white(self.D),
+#                       (GPy.kern.rbf(self.input_dim, ARD=True) +
+#                        GPy.kern.linear(self.input_dim, ARD=True) +
+#                        GPy.kern.bias(self.input_dim) +
+#                        GPy.kern.white(self.input_dim)),
+                      (GPy.kern.rbf(self.input_dim, np.random.rand(), np.random.rand(self.input_dim), ARD=True) +
+                       GPy.kern.rbf(self.input_dim, np.random.rand(), np.random.rand(self.input_dim), ARD=True) +
+                       GPy.kern.linear(self.input_dim, np.random.rand(self.input_dim), ARD=True) +
+                       GPy.kern.bias(self.input_dim) +
+                       GPy.kern.white(self.input_dim)),
+#                       GPy.kern.rbf(self.input_dim), GPy.kern.rbf(self.input_dim, ARD=True),
+#                       GPy.kern.linear(self.input_dim, ARD=False), GPy.kern.linear(self.input_dim, ARD=True),
+#                       GPy.kern.linear(self.input_dim) + GPy.kern.bias(self.input_dim),
+#                       GPy.kern.rbf(self.input_dim) + GPy.kern.bias(self.input_dim),
+#                       GPy.kern.linear(self.input_dim) + GPy.kern.bias(self.input_dim) + GPy.kern.white(self.input_dim),
+#                       GPy.kern.rbf(self.input_dim) + GPy.kern.bias(self.input_dim) + GPy.kern.white(self.input_dim),
+#                       GPy.kern.bias(self.input_dim), GPy.kern.white(self.input_dim),
                       )
-        self.q_x_mean = np.random.randn(self.D)
-        self.q_x_variance = np.exp(np.random.randn(self.D))
-        self.q_x_samples = np.random.randn(self.Nsamples, self.D) * np.sqrt(self.q_x_variance) + self.q_x_mean
-        self.Z = np.random.randn(self.M, self.D)
-        self.q_x_mean.shape = (1, self.D)
-        self.q_x_variance.shape = (1, self.D)
+        self.q_x_mean = np.random.randn(self.input_dim)
+        self.q_x_variance = np.exp(np.random.randn(self.input_dim))
+        self.q_x_samples = np.random.randn(self.Nsamples, self.input_dim) * np.sqrt(self.q_x_variance) + self.q_x_mean
+        self.Z = np.random.randn(self.num_inducing, self.input_dim)
+        self.q_x_mean.shape = (1, self.input_dim)
+        self.q_x_variance.shape = (1, self.input_dim)
 
     def test_psi0(self):
         for kern in self.kerns:
@@ -63,7 +63,7 @@ class Test(unittest.TestCase):
         for kern in self.kerns:
             Nsamples = 100
             psi1 = kern.psi1(self.Z, self.q_x_mean, self.q_x_variance)
-            K_ = np.zeros((Nsamples, self.M))
+            K_ = np.zeros((Nsamples, self.num_inducing))
             diffs = []
             for i, q_x_sample_stripe in enumerate(np.array_split(self.q_x_samples, self.Nsamples / Nsamples)):
                 K = kern.K(q_x_sample_stripe, self.Z)
@@ -89,7 +89,7 @@ class Test(unittest.TestCase):
         for kern in self.kerns:
             Nsamples = 100
             psi2 = kern.psi2(self.Z, self.q_x_mean, self.q_x_variance)
-            K_ = np.zeros((self.M, self.M))
+            K_ = np.zeros((self.num_inducing, self.num_inducing))
             diffs = []
             for i, q_x_sample_stripe in enumerate(np.array_split(self.q_x_samples, self.Nsamples / Nsamples)):
                 K = kern.K(q_x_sample_stripe, self.Z)
diff --git a/GPy/testing/psi_stat_gradient_tests.py b/GPy/testing/psi_stat_gradient_tests.py
index 23f841b5..2257d16c 100644
--- a/GPy/testing/psi_stat_gradient_tests.py
+++ b/GPy/testing/psi_stat_gradient_tests.py
@@ -17,14 +17,14 @@ class PsiStatModel(model):
         self.X_variance = X_variance
         self.Z = Z
         self.N, self.input_dim = X.shape
-        self.M, input_dim = Z.shape
+        self.num_inducing, input_dim = Z.shape
         assert self.input_dim == input_dim, "shape missmatch: Z:{!s} X:{!s}".format(Z.shape, X.shape)
         self.kern = kernel
         super(PsiStatModel, self).__init__()
         self.psi_ = self.kern.__getattribute__(self.which)(self.Z, self.X, self.X_variance)
     def _get_param_names(self):
         Xnames = ["{}_{}_{}".format(what, i, j) for what, i, j in itertools.product(['X', 'X_variance'], range(self.N), range(self.input_dim))]
-        Znames = ["Z_{}_{}".format(i, j) for i, j in itertools.product(range(self.M), range(self.input_dim))]
+        Znames = ["Z_{}_{}".format(i, j) for i, j in itertools.product(range(self.num_inducing), range(self.input_dim))]
         return Xnames + Znames + self.kern._get_param_names()
     def _get_params(self):
         return numpy.hstack([self.X.flatten(), self.X_variance.flatten(), self.Z.flatten(), self.kern._get_params()])
@@ -34,7 +34,7 @@ class PsiStatModel(model):
         start, end = end, end + self.X_variance.size
         self.X_variance = x[start: end].reshape(self.N, self.input_dim)
         start, end = end, end + self.Z.size
-        self.Z = x[start: end].reshape(self.M, self.input_dim)
+        self.Z = x[start: end].reshape(self.num_inducing, self.input_dim)
         self.kern._set_params(x[end:])
     def log_likelihood(self):
         return self.kern.__getattribute__(self.which)(self.Z, self.X, self.X_variance).sum()
@@ -43,19 +43,19 @@ class PsiStatModel(model):
         try:
             psiZ = self.kern.__getattribute__("d" + self.which + "_dZ")(numpy.ones_like(self.psi_), self.Z, self.X, self.X_variance)
         except AttributeError:
-            psiZ = numpy.zeros(self.M * self.input_dim)
+            psiZ = numpy.zeros(self.num_inducing * self.input_dim)
         thetagrad = self.kern.__getattribute__("d" + self.which + "_dtheta")(numpy.ones_like(self.psi_), self.Z, self.X, self.X_variance).flatten()
         return numpy.hstack((psimu.flatten(), psiS.flatten(), psiZ.flatten(), thetagrad))
 
 class DPsiStatTest(unittest.TestCase):
     input_dim = 5
     N = 50
-    M = 10
-    D = 20
+    num_inducing = 10
+    input_dim = 20
     X = numpy.random.randn(N, input_dim)
     X_var = .5 * numpy.ones_like(X) + .4 * numpy.clip(numpy.random.randn(*X.shape), 0, 1)
-    Z = numpy.random.permutation(X)[:M]
-    Y = X.dot(numpy.random.randn(input_dim, D))
+    Z = numpy.random.permutation(X)[:num_inducing]
+    Y = X.dot(numpy.random.randn(input_dim, input_dim))
 #     kernels = [GPy.kern.linear(input_dim, ARD=True, variances=numpy.random.rand(input_dim)), GPy.kern.rbf(input_dim, ARD=True), GPy.kern.bias(input_dim)]
 
     kernels = [GPy.kern.linear(input_dim), GPy.kern.rbf(input_dim), GPy.kern.bias(input_dim),
@@ -65,7 +65,7 @@ class DPsiStatTest(unittest.TestCase):
     def testPsi0(self):
         for k in self.kernels:
             m = PsiStatModel('psi0', X=self.X, X_variance=self.X_var, Z=self.Z,
-                         M=self.M, kernel=k)
+                         M=self.num_inducing, kernel=k)
             try:
                 assert m.checkgrad(), "{} x psi0".format("+".join(map(lambda x: x.name, k.parts)))
             except:
@@ -80,27 +80,27 @@ class DPsiStatTest(unittest.TestCase):
     def testPsi2_lin(self):
         k = self.kernels[0]
         m = PsiStatModel('psi2', X=self.X, X_variance=self.X_var, Z=self.Z,
-                     M=self.M, kernel=k)
+                     M=self.num_inducing, kernel=k)
         assert m.checkgrad(), "{} x psi2".format("+".join(map(lambda x: x.name, k.parts)))
     def testPsi2_lin_bia(self):
         k = self.kernels[3]
         m = PsiStatModel('psi2', X=self.X, X_variance=self.X_var, Z=self.Z,
-                     M=self.M, kernel=k)
+                     M=self.num_inducing, kernel=k)
         assert m.checkgrad(), "{} x psi2".format("+".join(map(lambda x: x.name, k.parts)))
     def testPsi2_rbf(self):
         k = self.kernels[1]
         m = PsiStatModel('psi2', X=self.X, X_variance=self.X_var, Z=self.Z,
-                     M=self.M, kernel=k)
+                     M=self.num_inducing, kernel=k)
         assert m.checkgrad(), "{} x psi2".format("+".join(map(lambda x: x.name, k.parts)))
     def testPsi2_rbf_bia(self):
         k = self.kernels[-1]
         m = PsiStatModel('psi2', X=self.X, X_variance=self.X_var, Z=self.Z,
-                     M=self.M, kernel=k)
+                     M=self.num_inducing, kernel=k)
         assert m.checkgrad(), "{} x psi2".format("+".join(map(lambda x: x.name, k.parts)))
     def testPsi2_bia(self):
         k = self.kernels[2]
         m = PsiStatModel('psi2', X=self.X, X_variance=self.X_var, Z=self.Z,
-                     M=self.M, kernel=k)
+                     M=self.num_inducing, kernel=k)
         assert m.checkgrad(), "{} x psi2".format("+".join(map(lambda x: x.name, k.parts)))
 
 
@@ -108,14 +108,14 @@ if __name__ == "__main__":
     import sys
     interactive = 'i' in sys.argv
     if interactive:
-#         N, M, input_dim, D = 30, 5, 4, 30
+#         N, num_inducing, input_dim, input_dim = 30, 5, 4, 30
 #         X = numpy.random.rand(N, input_dim)
 #         k = GPy.kern.linear(input_dim) + GPy.kern.bias(input_dim) + GPy.kern.white(input_dim, 0.00001)
 #         K = k.K(X)
-#         Y = numpy.random.multivariate_normal(numpy.zeros(N), K, D).T
+#         Y = numpy.random.multivariate_normal(numpy.zeros(N), K, input_dim).T
 #         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, M=M)
+#         m = GPy.models.Bayesian_GPLVM(Y, input_dim, kernel=k, num_inducing=num_inducing)
 #         m.ensure_default_constraints()
 #         m.randomize()
 # #         self.assertTrue(m.checkgrad())
@@ -136,22 +136,22 @@ if __name__ == "__main__":
 
 #         for k in kernels:
 #             m = PsiStatModel('psi1', X=X, X_variance=X_var, Z=Z,
-#                      M=M, kernel=k)
+#                      num_inducing=num_inducing, kernel=k)
 #             assert m.checkgrad(), "{} x psi1".format("+".join(map(lambda x: x.name, k.parts)))
 #
 #         m0 = PsiStatModel('psi0', X=X, X_variance=X_var, Z=Z,
-#                          M=M, kernel=GPy.kern.linear(input_dim))
+#                          num_inducing=num_inducing, kernel=GPy.kern.linear(input_dim))
 #         m1 = PsiStatModel('psi1', X=X, X_variance=X_var, Z=Z,
-#                          M=M, kernel=kernel)
+#                          num_inducing=num_inducing, kernel=kernel)
 #         m1 = PsiStatModel('psi1', X=X, X_variance=X_var, Z=Z,
-#                          M=M, kernel=kernel)
+#                          num_inducing=num_inducing, kernel=kernel)
 #         m2 = PsiStatModel('psi2', X=X, X_variance=X_var, Z=Z,
-#                          M=M, kernel=GPy.kern.rbf(input_dim))
+#                          num_inducing=num_inducing, kernel=GPy.kern.rbf(input_dim))
         m3 = PsiStatModel('psi2', X=X, X_variance=X_var, Z=Z,
                          M=M, 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,
-#                          M=M, kernel=GPy.kern.rbf(input_dim) + GPy.kern.bias(input_dim))
+#                          num_inducing=num_inducing, kernel=GPy.kern.rbf(input_dim) + GPy.kern.bias(input_dim))
     else:
         unittest.main()
diff --git a/GPy/testing/sparse_gplvm_tests.py b/GPy/testing/sparse_gplvm_tests.py
index a790ce54..38f95730 100644
--- a/GPy/testing/sparse_gplvm_tests.py
+++ b/GPy/testing/sparse_gplvm_tests.py
@@ -4,6 +4,7 @@
 import unittest
 import numpy as np
 import GPy
+from GPy.models.sparse_gplvm import SparseGPLVM
 
 class sparse_GPLVMTests(unittest.TestCase):
     def test_bias_kern(self):
@@ -11,9 +12,9 @@ class sparse_GPLVMTests(unittest.TestCase):
         X = np.random.rand(N, input_dim)
         k = GPy.kern.rbf(input_dim) + GPy.kern.white(input_dim, 0.00001)
         K = k.K(X)
-        Y = np.random.multivariate_normal(np.zeros(N),K,D).T
+        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.sparse_GPLVM(Y, input_dim, kernel = k, M=M)
+        m = SparseGPLVM(Y, input_dim, kernel=k, M=M)
         m.ensure_default_constraints()
         m.randomize()
         self.assertTrue(m.checkgrad())
@@ -24,9 +25,9 @@ class sparse_GPLVMTests(unittest.TestCase):
         X = np.random.rand(N, input_dim)
         k = GPy.kern.rbf(input_dim) + GPy.kern.white(input_dim, 0.00001)
         K = k.K(X)
-        Y = np.random.multivariate_normal(np.zeros(N),K,D).T
+        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.sparse_GPLVM(Y, input_dim, kernel = k, M=M)
+        m = SparseGPLVM(Y, input_dim, kernel=k, M=M)
         m.ensure_default_constraints()
         m.randomize()
         self.assertTrue(m.checkgrad())
@@ -36,9 +37,9 @@ class sparse_GPLVMTests(unittest.TestCase):
         X = np.random.rand(N, input_dim)
         k = GPy.kern.rbf(input_dim) + GPy.kern.white(input_dim, 0.00001)
         K = k.K(X)
-        Y = np.random.multivariate_normal(np.zeros(N),K,D).T
+        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.sparse_GPLVM(Y, input_dim, kernel = k, M=M)
+        m = SparseGPLVM(Y, input_dim, kernel=k, M=M)
         m.ensure_default_constraints()
         m.randomize()
         self.assertTrue(m.checkgrad())
diff --git a/GPy/testing/unit_tests.py b/GPy/testing/unit_tests.py
index 8224c7a8..73b5c800 100644
--- a/GPy/testing/unit_tests.py
+++ b/GPy/testing/unit_tests.py
@@ -5,33 +5,33 @@
 import unittest
 import numpy as np
 import GPy
+from GPy.likelihoods.likelihood_functions import Binomial
 
 class GradientTests(unittest.TestCase):
     def setUp(self):
         ######################################
-        ## 1 dimensional example
+        # # 1 dimensional example
 
         # sample inputs and outputs
-        self.X1D = np.random.uniform(-3.,3.,(20,1))
-        self.Y1D = np.sin(self.X1D)+np.random.randn(20,1)*0.05
+        self.X1D = np.random.uniform(-3., 3., (20, 1))
+        self.Y1D = np.sin(self.X1D) + np.random.randn(20, 1) * 0.05
 
         ######################################
-        ## 2 dimensional example
+        # # 2 dimensional example
 
         # sample inputs and outputs
-        self.X2D = np.random.uniform(-3.,3.,(40,2))
-        self.Y2D = np.sin(self.X2D[:,0:1]) * np.sin(self.X2D[:,1:2])+np.random.randn(40,1)*0.05
+        self.X2D = np.random.uniform(-3., 3., (40, 2))
+        self.Y2D = np.sin(self.X2D[:, 0:1]) * np.sin(self.X2D[:, 1:2]) + np.random.randn(40, 1) * 0.05
 
-    def check_model_with_white(self, kern, model_type='GP_regression', dimension=1):
-        #Get the correct gradients
+    def check_model_with_white(self, kern, model_type='GPRegression', dimension=1):
+        # Get the correct gradients
         if dimension == 1:
             X = self.X1D
             Y = self.Y1D
         else:
             X = self.X2D
             Y = self.Y2D
-
-        #Get model type (GP_regression, GP_sparse_regression, etc)
+        # Get model type (GPRegression, SparseGPRegression, etc)
         model_fit = getattr(GPy.models, model_type)
 
         noise = GPy.kern.white(dimension)
@@ -42,114 +42,114 @@ class GradientTests(unittest.TestCase):
         # contrain all parameters to be positive
         self.assertTrue(m.checkgrad())
 
-    def test_gp_regression_rbf_1d(self):
+    def test_GPRegression_rbf_1d(self):
         ''' Testing the GP regression with rbf kernel with white kernel on 1d data '''
         rbf = GPy.kern.rbf(1)
-        self.check_model_with_white(rbf, model_type='GP_regression', dimension=1)
+        self.check_model_with_white(rbf, model_type='GPRegression', dimension=1)
 
-    def test_GP_regression_rbf_2D(self):
+    def test_GPRegression_rbf_2D(self):
         ''' Testing the GP regression with rbf and white kernel on 2d data '''
         rbf = GPy.kern.rbf(2)
-        self.check_model_with_white(rbf, model_type='GP_regression', dimension=2)
+        self.check_model_with_white(rbf, model_type='GPRegression', dimension=2)
 
-    def test_GP_regression_rbf_ARD_2D(self):
+    def test_GPRegression_rbf_ARD_2D(self):
         ''' Testing the GP regression with rbf and white kernel on 2d data '''
-        k = GPy.kern.rbf(2,ARD=True)
-        self.check_model_with_white(k, model_type='GP_regression', dimension=2)
+        k = GPy.kern.rbf(2, ARD=True)
+        self.check_model_with_white(k, model_type='GPRegression', dimension=2)
 
-    def test_GP_regression_matern52_1D(self):
+    def test_GPRegression_matern52_1D(self):
         ''' Testing the GP regression with matern52 kernel on 1d data '''
         matern52 = GPy.kern.Matern52(1)
-        self.check_model_with_white(matern52, model_type='GP_regression', dimension=1)
+        self.check_model_with_white(matern52, model_type='GPRegression', dimension=1)
 
-    def test_GP_regression_matern52_2D(self):
+    def test_GPRegression_matern52_2D(self):
         ''' Testing the GP regression with matern52 kernel on 2d data '''
         matern52 = GPy.kern.Matern52(2)
-        self.check_model_with_white(matern52, model_type='GP_regression', dimension=2)
+        self.check_model_with_white(matern52, model_type='GPRegression', dimension=2)
 
-    def test_GP_regression_matern52_ARD_2D(self):
+    def test_GPRegression_matern52_ARD_2D(self):
         ''' Testing the GP regression with matern52 kernel on 2d data '''
-        matern52 = GPy.kern.Matern52(2,ARD=True)
-        self.check_model_with_white(matern52, model_type='GP_regression', dimension=2)
+        matern52 = GPy.kern.Matern52(2, ARD=True)
+        self.check_model_with_white(matern52, model_type='GPRegression', dimension=2)
 
-    def test_GP_regression_matern32_1D(self):
+    def test_GPRegression_matern32_1D(self):
         ''' Testing the GP regression with matern32 kernel on 1d data '''
         matern32 = GPy.kern.Matern32(1)
-        self.check_model_with_white(matern32, model_type='GP_regression', dimension=1)
+        self.check_model_with_white(matern32, model_type='GPRegression', dimension=1)
 
-    def test_GP_regression_matern32_2D(self):
+    def test_GPRegression_matern32_2D(self):
         ''' Testing the GP regression with matern32 kernel on 2d data '''
         matern32 = GPy.kern.Matern32(2)
-        self.check_model_with_white(matern32, model_type='GP_regression', dimension=2)
+        self.check_model_with_white(matern32, model_type='GPRegression', dimension=2)
 
-    def test_GP_regression_matern32_ARD_2D(self):
+    def test_GPRegression_matern32_ARD_2D(self):
         ''' Testing the GP regression with matern32 kernel on 2d data '''
-        matern32 = GPy.kern.Matern32(2,ARD=True)
-        self.check_model_with_white(matern32, model_type='GP_regression', dimension=2)
+        matern32 = GPy.kern.Matern32(2, ARD=True)
+        self.check_model_with_white(matern32, model_type='GPRegression', dimension=2)
 
-    def test_GP_regression_exponential_1D(self):
+    def test_GPRegression_exponential_1D(self):
         ''' Testing the GP regression with exponential kernel on 1d data '''
         exponential = GPy.kern.exponential(1)
-        self.check_model_with_white(exponential, model_type='GP_regression', dimension=1)
+        self.check_model_with_white(exponential, model_type='GPRegression', dimension=1)
 
-    def test_GP_regression_exponential_2D(self):
+    def test_GPRegression_exponential_2D(self):
         ''' Testing the GP regression with exponential kernel on 2d data '''
         exponential = GPy.kern.exponential(2)
-        self.check_model_with_white(exponential, model_type='GP_regression', dimension=2)
+        self.check_model_with_white(exponential, model_type='GPRegression', dimension=2)
 
-    def test_GP_regression_exponential_ARD_2D(self):
+    def test_GPRegression_exponential_ARD_2D(self):
         ''' Testing the GP regression with exponential kernel on 2d data '''
-        exponential = GPy.kern.exponential(2,ARD=True)
-        self.check_model_with_white(exponential, model_type='GP_regression', dimension=2)
+        exponential = GPy.kern.exponential(2, ARD=True)
+        self.check_model_with_white(exponential, model_type='GPRegression', dimension=2)
 
-    def test_GP_regression_bias_kern_1D(self):
+    def test_GPRegression_bias_kern_1D(self):
         ''' Testing the GP regression with bias kernel on 1d data '''
         bias = GPy.kern.bias(1)
-        self.check_model_with_white(bias, model_type='GP_regression', dimension=1)
+        self.check_model_with_white(bias, model_type='GPRegression', dimension=1)
 
-    def test_GP_regression_bias_kern_2D(self):
+    def test_GPRegression_bias_kern_2D(self):
         ''' Testing the GP regression with bias kernel on 2d data '''
         bias = GPy.kern.bias(2)
-        self.check_model_with_white(bias, model_type='GP_regression', dimension=2)
+        self.check_model_with_white(bias, model_type='GPRegression', dimension=2)
 
-    def test_GP_regression_linear_kern_1D_ARD(self):
+    def test_GPRegression_linear_kern_1D_ARD(self):
         ''' Testing the GP regression with linear kernel on 1d data '''
-        linear = GPy.kern.linear(1,ARD=True)
-        self.check_model_with_white(linear, model_type='GP_regression', dimension=1)
+        linear = GPy.kern.linear(1, ARD=True)
+        self.check_model_with_white(linear, model_type='GPRegression', dimension=1)
 
-    def test_GP_regression_linear_kern_2D_ARD(self):
+    def test_GPRegression_linear_kern_2D_ARD(self):
         ''' Testing the GP regression with linear kernel on 2d data '''
-        linear = GPy.kern.linear(2,ARD=True)
-        self.check_model_with_white(linear, model_type='GP_regression', dimension=2)
+        linear = GPy.kern.linear(2, ARD=True)
+        self.check_model_with_white(linear, model_type='GPRegression', dimension=2)
 
-    def test_GP_regression_linear_kern_1D(self):
+    def test_GPRegression_linear_kern_1D(self):
         ''' Testing the GP regression with linear kernel on 1d data '''
         linear = GPy.kern.linear(1)
-        self.check_model_with_white(linear, model_type='GP_regression', dimension=1)
+        self.check_model_with_white(linear, model_type='GPRegression', dimension=1)
 
-    def test_GP_regression_linear_kern_2D(self):
+    def test_GPRegression_linear_kern_2D(self):
         ''' Testing the GP regression with linear kernel on 2d data '''
         linear = GPy.kern.linear(2)
-        self.check_model_with_white(linear, model_type='GP_regression', dimension=2)
+        self.check_model_with_white(linear, model_type='GPRegression', dimension=2)
 
-    def test_sparse_GP_regression_rbf_white_kern_1d(self):
+    def test_SparseGPRegression_rbf_white_kern_1d(self):
         ''' Testing the sparse GP regression with rbf kernel with white kernel on 1d data '''
         rbf = GPy.kern.rbf(1)
-        self.check_model_with_white(rbf, model_type='sparse_GP_regression', dimension=1)
+        self.check_model_with_white(rbf, model_type='SparseGPRegression', dimension=1)
 
-    def test_sparse_GP_regression_rbf_white_kern_2D(self):
+    def test_SparseGPRegression_rbf_white_kern_2D(self):
         ''' Testing the sparse GP regression with rbf and white kernel on 2d data '''
         rbf = GPy.kern.rbf(2)
-        self.check_model_with_white(rbf, model_type='sparse_GP_regression', dimension=2)
+        self.check_model_with_white(rbf, model_type='SparseGPRegression', dimension=2)
 
     def test_GPLVM_rbf_bias_white_kern_2D(self):
         """ Testing GPLVM with rbf + bias and white kernel """
         N, input_dim, D = 50, 1, 2
         X = np.random.rand(N, input_dim)
-        k = GPy.kern.rbf(input_dim, 0.5, 0.9*np.ones((1,))) + GPy.kern.bias(input_dim, 0.1) + GPy.kern.white(input_dim, 0.05)
+        k = GPy.kern.rbf(input_dim, 0.5, 0.9 * np.ones((1,))) + GPy.kern.bias(input_dim, 0.1) + GPy.kern.white(input_dim, 0.05)
         K = k.K(X)
-        Y = np.random.multivariate_normal(np.zeros(N),K,D).T
-        m = GPy.models.GPLVM(Y, input_dim, kernel = k)
+        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())
 
@@ -159,44 +159,44 @@ class GradientTests(unittest.TestCase):
         X = np.random.rand(N, input_dim)
         k = GPy.kern.linear(input_dim) + GPy.kern.bias(input_dim, 0.1) + GPy.kern.white(input_dim, 0.05)
         K = k.K(X)
-        Y = np.random.multivariate_normal(np.zeros(N),K,D).T
-        m = GPy.models.GPLVM(Y, input_dim, init = 'PCA', kernel = k)
+        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):
         N = 20
-        X = np.hstack([np.random.normal(5,2,N/2),np.random.normal(10,2,N/2)])[:,None]
-        Y = np.hstack([np.ones(N/2),np.zeros(N/2)])[:,None]
+        X = np.hstack([np.random.normal(5, 2, N / 2), np.random.normal(10, 2, N / 2)])[:, None]
+        Y = np.hstack([np.ones(N / 2), np.zeros(N / 2)])[:, None]
         kernel = GPy.kern.rbf(1)
-        distribution = GPy.likelihoods.likelihood_functions.binomial()
+        distribution = GPy.likelihoods.likelihood_functions.Binomial()
         likelihood = GPy.likelihoods.EP(Y, distribution)
         m = GPy.core.GP(X, likelihood, kernel)
         m.ensure_default_constraints()
         m.update_likelihood_approximation()
         self.assertTrue(m.checkgrad())
-        #self.assertTrue(m.EPEM)
+        # self.assertTrue(m.EPEM)
 
     def test_sparse_EP_DTC_probit(self):
         N = 20
-        X = np.hstack([np.random.normal(5,2,N/2),np.random.normal(10,2,N/2)])[:,None]
-        Y = np.hstack([np.ones(N/2),np.zeros(N/2)])[:,None]
-        Z = np.linspace(0,15,4)[:,None]
+        X = np.hstack([np.random.normal(5, 2, N / 2), np.random.normal(10, 2, N / 2)])[:, None]
+        Y = np.hstack([np.ones(N / 2), np.zeros(N / 2)])[:, None]
+        Z = np.linspace(0, 15, 4)[:, None]
         kernel = GPy.kern.rbf(1)
-        distribution = GPy.likelihoods.likelihood_functions.binomial()
+        distribution = GPy.likelihoods.likelihood_functions.Binomial()
         likelihood = GPy.likelihoods.EP(Y, distribution)
-        m = GPy.core.sparse_GP(X, likelihood, kernel,Z)
+        m = GPy.core.SparseGP(X, likelihood, kernel, Z)
         m.ensure_default_constraints()
         m.update_likelihood_approximation()
         self.assertTrue(m.checkgrad())
 
     def test_generalized_FITC(self):
         N = 20
-        X = np.hstack([np.random.rand(N/2)+1,np.random.rand(N/2)-1])[:,None]
+        X = np.hstack([np.random.rand(N / 2) + 1, np.random.rand(N / 2) - 1])[:, None]
         k = GPy.kern.rbf(1) + GPy.kern.white(1)
-        Y = np.hstack([np.ones(N/2),-np.ones(N/2)])[:,None]
-        likelihood = GPy.inference.likelihoods.binomial(Y)
-        m = GPy.models.generalized_FITC(X,likelihood,k,inducing=4)
+        Y = np.hstack([np.ones(N / 2), -np.ones(N / 2)])[:, None]
+        likelihood = Binomial(Y)
+        m = GPy.models.GeneralizedFITC(X, likelihood, k, inducing=4)
         m.constrain_positive('(var|len)')
         m.approximate_likelihood()
         self.assertTrue(m.checkgrad())
diff --git a/GPy/util/datasets.py b/GPy/util/datasets.py
index 36fa4f61..8f22a7d0 100644
--- a/GPy/util/datasets.py
+++ b/GPy/util/datasets.py
@@ -13,7 +13,7 @@ default_seed = 10000
 # Some general utilities.
 def sample_class(f):
     p = 1. / (1. + np.exp(-f))
-    c = np.random.binomial(1, p)
+    c = np.random.Binomial(1, p)
     c = np.where(c, 1, -1)
     return c
 
diff --git a/GPy/util/linalg.py b/GPy/util/linalg.py
index 6e7d26d8..c04fc460 100644
--- a/GPy/util/linalg.py
+++ b/GPy/util/linalg.py
@@ -1,87 +1,80 @@
 # Copyright (c) 2012, GPy authors (see AUTHORS.txt).
 # Licensed under the BSD 3-clause license (see LICENSE.txt)
 
-#tdot function courtesy of Ian Murray:
+# tdot function courtesy of Ian Murray:
 # Iain Murray, April 2013. iain contactable via iainmurray.net
 # http://homepages.inf.ed.ac.uk/imurray2/code/tdot/tdot.py
 
 import numpy as np
-from scipy import linalg, optimize, weave
-import pylab as pb
-import Tango
-import sys
-import re
-import pdb
-import cPickle
+from scipy import linalg, weave
 import types
 import ctypes
 from ctypes import byref, c_char, c_int, c_double # TODO
-#import scipy.lib.lapack
-import scipy as sp
+# import scipy.lib.lapack
 
 try:
-    _blaslib = ctypes.cdll.LoadLibrary(np.core._dotblas.__file__)
+    _blaslib = ctypes.cdll.LoadLibrary(np.core._dotblas.__file__) # @UndefinedVariable
     _blas_available = True
 except:
     _blas_available = False
 
-def trace_dot(a,b):
+def trace_dot(a, b):
     """
     efficiently compute the trace of the matrix product of a and b
     """
-    return np.sum(a*b)
+    return np.sum(a * b)
 
 def mdot(*args):
-   """Multiply all the arguments using matrix product rules.
-   The output is equivalent to multiplying the arguments one by one
-   from left to right using dot().
-   Precedence can be controlled by creating tuples of arguments,
-   for instance mdot(a,((b,c),d)) multiplies a (a*((b*c)*d)).
-   Note that this means the output of dot(a,b) and mdot(a,b) will differ if
-   a or b is a pure tuple of numbers.
-   """
-   if len(args)==1:
-       return args[0]
-   elif len(args)==2:
-       return _mdot_r(args[0],args[1])
-   else:
-       return _mdot_r(args[:-1],args[-1])
+    """Multiply all the arguments using matrix product rules.
+    The output is equivalent to multiplying the arguments one by one
+    from left to right using dot().
+    Precedence can be controlled by creating tuples of arguments,
+    for instance mdot(a,((b,c),d)) multiplies a (a*((b*c)*d)).
+    Note that this means the output of dot(a,b) and mdot(a,b) will differ if
+    a or b is a pure tuple of numbers.
+    """
+    if len(args) == 1:
+        return args[0]
+    elif len(args) == 2:
+        return _mdot_r(args[0], args[1])
+    else:
+        return _mdot_r(args[:-1], args[-1])
 
-def _mdot_r(a,b):
-   """Recursive helper for mdot"""
-   if type(a)==types.TupleType:
-       if len(a)>1:
-           a = mdot(*a)
-       else:
-           a = a[0]
-   if type(b)==types.TupleType:
-       if len(b)>1:
-           b = mdot(*b)
-       else:
-           b = b[0]
-   return np.dot(a,b)
+def _mdot_r(a, b):
+    """Recursive helper for mdot"""
+    if type(a) == types.TupleType:
+        if len(a) > 1:
+            a = mdot(*a)
+        else:
+            a = a[0]
+    if type(b) == types.TupleType:
+        if len(b) > 1:
+            b = mdot(*b)
+        else:
+            b = b[0]
+    return np.dot(a, b)
 
-def jitchol(A,maxtries=5):
+def jitchol(A, maxtries=5):
     A = np.asfortranarray(A)
-    L,info = linalg.lapack.flapack.dpotrf(A,lower=1)
-    if info ==0:
+    L, info = linalg.lapack.flapack.dpotrf(A, lower=1)
+    if info == 0:
         return L
     else:
         diagA = np.diag(A)
-        if np.any(diagA<0.):
+        if np.any(diagA < 0.):
             raise linalg.LinAlgError, "not pd: negative diagonal elements"
-        jitter= diagA.mean()*1e-6
-        for i in range(1,maxtries+1):
+        jitter = diagA.mean() * 1e-6
+        for i in range(1, maxtries + 1):
             print 'Warning: adding jitter of {:.10e}'.format(jitter)
             try:
-                return linalg.cholesky(A+np.eye(A.shape[0]).T*jitter, lower = True)
+                return linalg.cholesky(A + np.eye(A.shape[0]).T * jitter, lower=True)
             except:
                 jitter *= 10
-        raise linalg.LinAlgError,"not positive definite, even with jitter."
+        raise linalg.LinAlgError, "not positive definite, even with jitter."
 
 
 
-def jitchol_old(A,maxtries=5):
+def jitchol_old(A, maxtries=5):
     """
     :param A : An almost pd square matrix
 
@@ -93,20 +86,20 @@ def jitchol_old(A,maxtries=5):
       np.allclose(sp.linalg.cholesky(XXT, lower = True), np.triu(sp.linalg.cho_factor(XXT)[0]).T)
     """
     try:
-        return linalg.cholesky(A, lower = True)
+        return linalg.cholesky(A, lower=True)
     except linalg.LinAlgError:
         diagA = np.diag(A)
-        if np.any(diagA<0.):
+        if np.any(diagA < 0.):
             raise linalg.LinAlgError, "not pd: negative diagonal elements"
-        jitter= diagA.mean()*1e-6
-        for i in range(1,maxtries+1):
+        jitter = diagA.mean() * 1e-6
+        for i in range(1, maxtries + 1):
             print '\rWarning: adding jitter of {:.10e}                        '.format(jitter),
             try:
-                return linalg.cholesky(A+np.eye(A.shape[0]).T*jitter, lower = True)
+                return linalg.cholesky(A + np.eye(A.shape[0]).T * jitter, lower=True)
             except:
                 jitter *= 10
 
-        raise linalg.LinAlgError,"not positive definite, even with jitter."
+        raise linalg.LinAlgError, "not positive definite, even with jitter."
 
 def pdinv(A, *args):
     """
@@ -125,7 +118,7 @@ def pdinv(A, *args):
     logdet = 2.*np.sum(np.log(np.diag(L)))
     Li = chol_inv(L)
     Ai, _ = linalg.lapack.flapack.dpotri(L)
-    #Ai = np.tril(Ai) + np.tril(Ai,-1).T
+    # Ai = np.tril(Ai) + np.tril(Ai,-1).T
     symmetrify(Ai)
 
     return Ai, L, Li, logdet
@@ -140,7 +133,7 @@ def chol_inv(L):
 
     """
 
-    return linalg.lapack.flapack.dtrtri(L, lower = True)[0]
+    return linalg.lapack.flapack.dtrtri(L, lower=True)[0]
 
 
 def multiple_pdinv(A):
@@ -155,11 +148,11 @@ def multiple_pdinv(A):
     hld: 0.5* the log of the determinants of A
     """
     N = A.shape[-1]
-    chols = [jitchol(A[:,:,i]) for i in range(N)]
+    chols = [jitchol(A[:, :, i]) for i in range(N)]
     halflogdets = [np.sum(np.log(np.diag(L[0]))) for L in chols]
-    invs = [linalg.lapack.flapack.dpotri(L[0],True)[0] for L in chols]
-    invs = [np.triu(I)+np.triu(I,1).T for I in invs]
-    return np.dstack(invs),np.array(halflogdets)
+    invs = [linalg.lapack.flapack.dpotri(L[0], True)[0] for L in chols]
+    invs = [np.triu(I) + np.triu(I, 1).T for I in invs]
+    return np.dstack(invs), np.array(halflogdets)
 
 
 def PCA(Y, input_dim):
@@ -179,18 +172,18 @@ def PCA(Y, input_dim):
     if not np.allclose(Y.mean(axis=0), 0.0):
         print "Y is not zero mean, centering it locally (GPy.util.linalg.PCA)"
 
-        #Y -= Y.mean(axis=0)
+        # Y -= Y.mean(axis=0)
 
-    Z = linalg.svd(Y-Y.mean(axis=0), full_matrices = False)
-    [X, W] = [Z[0][:,0:input_dim], np.dot(np.diag(Z[1]), Z[2]).T[:,0:input_dim]]
+    Z = linalg.svd(Y - Y.mean(axis=0), full_matrices=False)
+    [X, W] = [Z[0][:, 0:input_dim], np.dot(np.diag(Z[1]), Z[2]).T[:, 0:input_dim]]
     v = X.std(axis=0)
     X /= v;
     W *= v;
     return X, W.T
 
 
-def tdot_numpy(mat,out=None):
-    return np.dot(mat,mat.T,out)
+def tdot_numpy(mat, out=None):
+    return np.dot(mat, mat.T, out)
 
 def tdot_blas(mat, out=None):
     """returns np.dot(mat, mat.T), but faster for large 2D arrays of doubles."""
@@ -198,16 +191,16 @@ def tdot_blas(mat, out=None):
         return np.dot(mat, mat.T)
     nn = mat.shape[0]
     if out is None:
-        out = np.zeros((nn,nn))
+        out = np.zeros((nn, nn))
     else:
         assert(out.dtype == 'float64')
-        assert(out.shape == (nn,nn))
+        assert(out.shape == (nn, nn))
         # FIXME: should allow non-contiguous out, and copy output into it:
         assert(8 in out.strides)
         # zeroing needed because of dumb way I copy across triangular answer
         out[:] = 0.0
 
-    ## Call to DSYRK from BLAS
+    # # Call to DSYRK from BLAS
     # If already in Fortran order (rare), and has the right sorts of strides I
     # could avoid the copy. I also thought swapping to cblas API would allow use
     # of C order. However, I tried that and had errors with large matrices:
@@ -226,17 +219,17 @@ def tdot_blas(mat, out=None):
     _blaslib.dsyrk_(byref(UPLO), byref(TRANS), byref(N), byref(K),
             byref(ALPHA), A, byref(LDA), byref(BETA), C, byref(LDC))
 
-    symmetrify(out,upper=True)
+    symmetrify(out, upper=True)
 
     return out
 
 def tdot(*args, **kwargs):
     if _blas_available:
-        return tdot_blas(*args,**kwargs)
+        return tdot_blas(*args, **kwargs)
     else:
-        return tdot_numpy(*args,**kwargs)
+        return tdot_numpy(*args, **kwargs)
 
-def DSYR_blas(A,x,alpha=1.):
+def DSYR_blas(A, x, alpha=1.):
     """
     Performs a symmetric rank-1 update operation:
     A <- A + alpha * np.dot(x,x.T)
@@ -256,9 +249,9 @@ def DSYR_blas(A,x,alpha=1.):
     INCX = c_int(1)
     _blaslib.dsyr_(byref(UPLO), byref(N), byref(ALPHA),
             x_, byref(INCX), A_, byref(LDA))
-    symmetrify(A,upper=True)
+    symmetrify(A, upper=True)
 
-def DSYR_numpy(A,x,alpha=1.):
+def DSYR_numpy(A, x, alpha=1.):
     """
     Performs a symmetric rank-1 update operation:
     A <- A + alpha * np.dot(x,x.T)
@@ -269,23 +262,23 @@ def DSYR_numpy(A,x,alpha=1.):
     :param x: Nx1 np.array
     :param alpha: scalar
     """
-    A += alpha*np.dot(x[:,None],x[None,:])
+    A += alpha * np.dot(x[:, None], x[None, :])
 
 
 def DSYR(*args, **kwargs):
     if _blas_available:
-        return DSYR_blas(*args,**kwargs)
+        return DSYR_blas(*args, **kwargs)
     else:
-        return DSYR_numpy(*args,**kwargs)
+        return DSYR_numpy(*args, **kwargs)
 
-def symmetrify(A,upper=False):
+def symmetrify(A, upper=False):
     """
     Take the square matrix A and make it symmetrical by copting elements from the lower half to the upper
 
     works IN PLACE.
     """
-    N,M = A.shape
-    assert N==M
+    N, M = A.shape
+    assert N == M
     c_contig_code = """
     int iN;
     for (int i=1; i<N; i++){
@@ -305,13 +298,13 @@ def symmetrify(A,upper=False):
     }
     """
     if A.flags['C_CONTIGUOUS'] and upper:
-        weave.inline(f_contig_code,['A','N'], extra_compile_args=['-O3'])
+        weave.inline(f_contig_code, ['A', 'N'], extra_compile_args=['-O3'])
     elif A.flags['C_CONTIGUOUS'] and not upper:
-        weave.inline(c_contig_code,['A','N'], extra_compile_args=['-O3'])
+        weave.inline(c_contig_code, ['A', 'N'], extra_compile_args=['-O3'])
     elif A.flags['F_CONTIGUOUS'] and upper:
-        weave.inline(c_contig_code,['A','N'], extra_compile_args=['-O3'])
+        weave.inline(c_contig_code, ['A', 'N'], extra_compile_args=['-O3'])
     elif A.flags['F_CONTIGUOUS'] and not upper:
-        weave.inline(f_contig_code,['A','N'], extra_compile_args=['-O3'])
+        weave.inline(f_contig_code, ['A', 'N'], extra_compile_args=['-O3'])
     else:
         if upper:
             tmp = np.tril(A.T)
@@ -319,15 +312,15 @@ def symmetrify(A,upper=False):
             tmp = np.tril(A)
         A[:] = 0.0
         A += tmp
-        A += np.tril(tmp,-1).T
+        A += np.tril(tmp, -1).T
 
 
 def symmetrify_murray(A):
     A += A.T
     nn = A.shape[0]
-    A[[range(nn),range(nn)]] /= 2.0
+    A[[range(nn), range(nn)]] /= 2.0
 
-def cholupdate(L,x):
+def cholupdate(L, x):
     """
     update the LOWER cholesky factor of a pd matrix IN PLACE
 
@@ -337,7 +330,7 @@ def cholupdate(L,x):
     support_code = """
     #include <math.h>
     """
-    code="""
+    code = """
     double r,c,s;
     int j,i;
     for(j=0; j<N; j++){
@@ -353,11 +346,11 @@ def cholupdate(L,x):
     """
     x = x.copy()
     N = x.size
-    weave.inline(code, support_code=support_code, arg_names=['N','L','x'], type_converters=weave.converters.blitz)
+    weave.inline(code, support_code=support_code, arg_names=['N', 'L', 'x'], type_converters=weave.converters.blitz)
 
-def backsub_both_sides(L, X,transpose='left'):
+def backsub_both_sides(L, X, transpose='left'):
     """ Return L^-T * X * L^-1, assumuing X is symmetrical and L is lower cholesky"""
-    if transpose=='left':
+    if transpose == 'left':
         tmp, _ = linalg.lapack.flapack.dtrtrs(L, np.asfortranarray(X), lower=1, trans=1)
         return linalg.lapack.flapack.dtrtrs(L, np.asfortranarray(tmp.T), lower=1, trans=1)[0].T
     else:
diff --git a/GPy/util/visualize.py b/GPy/util/visualize.py
index 47e632bc..a3c10b97 100644
--- a/GPy/util/visualize.py
+++ b/GPy/util/visualize.py
@@ -221,7 +221,7 @@ class image_show(data_show):
         if not self.palette == []: # Can just show the image (self.set_image() took care of setting the palette)
             self.handle = self.axes.imshow(self.vals, interpolation='nearest')
         else: # Use a boring gray map.
-            self.handle = self.axes.imshow(self.vals, cmap=plt.cm.gray, interpolation='nearest')
+            self.handle = self.axes.imshow(self.vals, cmap=plt.cm.gray, interpolation='nearest') # @UndefinedVariable
         plt.show()
 
     def modify(self, vals):