Merge branch 'devel' of github.com:SheffieldML/GPy into devel

GPy/core/__init__.py

@@ -2,7 +2,7 @@
 # Licensed under the BSD 3-clause license (see LICENSE.txt)
 
 from model import *
-from parameterised import *
+from parameterized import *
 import priors
 from gp import GP
 from sparse_gp import SparseGP

GPy/core/domains.py

@@ -2,6 +2,22 @@
 Created on 4 Jun 2013
 
 @author: maxz
+
+(Hyper-)Parameter domains defined for :py:mod:`~GPy.core.priors` and :py:mod:`~GPy.kern`.
+These domains specify the legitimate realm of the parameters to live in.
+
+:const:`~GPy.core.domains.REAL` :
+    real domain, all values in the real numbers are allowed
+
+:const:`~GPy.core.domains.POSITIVE`:
+    positive domain, only positive real values are allowed
+    
+:const:`~GPy.core.domains.NEGATIVE`:
+    same as :const:`~GPy.core.domains.POSITIVE`, but only negative values are allowed
+    
+:const:`~GPy.core.domains.BOUNDED`:
+    only values within the bounded range are allowed,
+    the bounds are specified withing the object with the bounded range
 '''
 
 REAL = 'real'

GPy/core/gp.py

@@ -6,7 +6,7 @@ import numpy as np
 import pylab as pb
 from .. import kern
 from ..util.linalg import pdinv, mdot, tdot, dpotrs, dtrtrs
-#from ..util.plot import gpplot,  Tango
+# from ..util.plot import gpplot,  Tango
 from ..likelihoods import EP
 from gp_base import GPBase
 
@@ -31,6 +31,13 @@ class GP(GPBase):
         GPBase.__init__(self, X, likelihood, kernel, normalize_X=normalize_X)
         self._set_params(self._get_params())
 
+    def getstate(self):
+        return GPBase.getstate(self)
+
+    def setstate(self, state):
+        GPBase.setstate(self, state)
+        self._set_params(self._get_params())
+
     def _set_params(self, p):
         self.kern._set_params_transformed(p[:self.kern.num_params_transformed()])
         self.likelihood._set_params(p[self.kern.num_params_transformed():])
@@ -42,12 +49,12 @@ class GP(GPBase):
 
         # the gradient of the likelihood wrt the covariance matrix
         if self.likelihood.YYT is None:
-            #alpha = np.dot(self.Ki, self.likelihood.Y)
-            alpha,_ = dpotrs(self.L, self.likelihood.Y,lower=1)
+            # alpha = np.dot(self.Ki, self.likelihood.Y)
+            alpha, _ = dpotrs(self.L, self.likelihood.Y, lower=1)
 
             self.dL_dK = 0.5 * (tdot(alpha) - self.output_dim * self.Ki)
         else:
-            #tmp = mdot(self.Ki, self.likelihood.YYT, self.Ki)
+            # tmp = mdot(self.Ki, self.likelihood.YYT, self.Ki)
             tmp, _ = dpotrs(self.L, np.asfortranarray(self.likelihood.YYT), lower=1)
             tmp, _ = dpotrs(self.L, np.asfortranarray(tmp.T), lower=1)
             self.dL_dK = 0.5 * (tmp - self.output_dim * self.Ki)
@@ -68,7 +75,7 @@ class GP(GPBase):
         """
         self.likelihood.restart()
         self.likelihood.fit_full(self.kern.K(self.X))
-        self._set_params(self._get_params())  # update the GP
+        self._set_params(self._get_params()) # update the GP
 
     def _model_fit_term(self):
         """
@@ -77,7 +84,7 @@ class GP(GPBase):
         if self.likelihood.YYT is None:
             tmp, _ = 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)))
+            # 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))
 
@@ -100,13 +107,13 @@ class GP(GPBase):
         """
         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):
+    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)
+        Kx = self.kern.K(_Xnew, self.X, which_parts=which_parts).T
+        # KiKx = np.dot(self.Ki, Kx)
         KiKx, _ = dpotrs(self.L, np.asfortranarray(Kx), lower=1)
         mu = np.dot(KiKx.T, self.likelihood.Y)
         if full_cov:

GPy/core/gp_base.py

@@ -29,10 +29,35 @@ class GPBase(Model):
             self._Xscale = np.ones((1, self.input_dim))
 
         super(GPBase, self).__init__()
-        #Model.__init__(self)
+        # Model.__init__(self)
         # All leaf nodes should call self._set_params(self._get_params()) at
         # the end
 
+    def getstate(self):
+        """
+        Get the current state of the class,
+        here just all the indices, rest can get recomputed
+        """
+        return Model.getstate(self) + [self.X,
+                self.num_data,
+                self.input_dim,
+                self.kern,
+                self.likelihood,
+                self.output_dim,
+                self._Xoffset,
+                self._Xscale]
+
+    def setstate(self, state):
+        self._Xscale = state.pop()
+        self._Xoffset = state.pop()
+        self.output_dim = state.pop()
+        self.likelihood = state.pop()
+        self.kern = state.pop()
+        self.input_dim = state.pop()
+        self.num_data = state.pop()
+        self.X = state.pop()
+        Model.setstate(self, state)
+
     def plot_f(self, samples=0, plot_limits=None, which_data='all', which_parts='all', resolution=None, full_cov=False, fignum=None, ax=None):
         """
         Plot the GP's view of the world, where the data is normalized and the

GPy/core/model.py

@@ -6,32 +6,50 @@ from .. import likelihoods
 from ..inference import optimization
 from ..util.linalg import jitchol
 from GPy.util.misc import opt_wrapper
-from parameterised import Parameterised
+from parameterized import Parameterized
 import multiprocessing as mp
 import numpy as np
 from GPy.core.domains import POSITIVE, REAL
 from numpy.linalg.linalg import LinAlgError
 # import numdifftools as ndt
 
-class Model(Parameterised):
+class Model(Parameterized):
     _fail_count = 0 # Count of failed optimization steps (see objective)
     _allowed_failures = 10 # number of allowed failures
     def __init__(self):
-        Parameterised.__init__(self)
+        Parameterized.__init__(self)
         self.priors = None
         self.optimization_runs = []
         self.sampling_runs = []
         self.preferred_optimizer = 'scg'
         # self._set_params(self._get_params()) has been taken out as it should only be called on leaf nodes
-    def _get_params(self):
-        raise NotImplementedError, "this needs to be implemented to use the Model class"
-    def _set_params(self, x):
-        raise NotImplementedError, "this needs to be implemented to use the Model class"
     def log_likelihood(self):
         raise NotImplementedError, "this needs to be implemented to use the Model class"
     def _log_likelihood_gradients(self):
         raise NotImplementedError, "this needs to be implemented to use the Model class"
 
+    def getstate(self):
+        """
+        Get the current state of the class.
+        
+        Inherited from Parameterized, so add those parameters to the state
+        """
+        return Parameterized.getstate(self) + \
+            [self.priors, self.optimization_runs,
+             self.sampling_runs, self.preferred_optimizer]
+
+    def setstate(self, state):
+        """
+        set state from previous call to getstate
+        
+        call Parameterized with the rest of the state
+        """
+        self.preferred_optimizer = state.pop()
+        self.sampling_runs = state.pop()
+        self.optimization_runs = state.pop()
+        self.priors = state.pop()
+        Parameterized.setstate(self, state)
+
     def set_prior(self, regexp, what):
         """
         Sets priors on the Model parameters.
@@ -254,12 +272,13 @@ class Model(Parameterised):
         """
         try:
             self._set_params_transformed(x)
+            obj_grads = -self._transform_gradients(self._log_likelihood_gradients() + self._log_prior_gradients())
             self._fail_count = 0
         except (LinAlgError, ZeroDivisionError, ValueError) as e:
             if self._fail_count >= self._allowed_failures:
                 raise e
             self._fail_count += 1
-        obj_grads = -self._transform_gradients(self._log_likelihood_gradients() + self._log_prior_gradients())
+            obj_grads = np.clip(-self._transform_gradients(self._log_likelihood_gradients() + self._log_prior_gradients()), -1e100, 1e100)
         return obj_grads
 
     def objective_and_gradients(self, x):
@@ -267,12 +286,13 @@ class Model(Parameterised):
             self._set_params_transformed(x)
             obj_f = -self.log_likelihood() - self.log_prior()
             self._fail_count = 0
+            obj_grads = -self._transform_gradients(self._log_likelihood_gradients() + self._log_prior_gradients())
         except (LinAlgError, ZeroDivisionError, ValueError) as e:
             if self._fail_count >= self._allowed_failures:
                 raise e
             self._fail_count += 1
             obj_f = np.inf
-        obj_grads = -self._transform_gradients(self._log_likelihood_gradients() + self._log_prior_gradients())
+            obj_grads = np.clip(-self._transform_gradients(self._log_likelihood_gradients() + self._log_prior_gradients()), -1e100, 1e100)
         return obj_f, obj_grads
 
     def optimize(self, optimizer=None, start=None, **kwargs):
@@ -293,7 +313,9 @@ class Model(Parameterised):
 
         optimizer = optimization.get_optimizer(optimizer)
         opt = optimizer(start, model=self, **kwargs)
+
         opt.run(f_fp=self.objective_and_gradients, f=self.objective_function, fp=self.objective_function_gradients)
+
         self.optimization_runs.append(opt)
 
         self._set_params_transformed(opt.x_opt)
@@ -336,7 +358,7 @@ class Model(Parameterised):
         return 0.5 * self._get_params().size * np.log(2 * np.pi) + self.log_likelihood() - hld
 
     def __str__(self):
-        s = Parameterised.__str__(self).split('\n')
+        s = Parameterized.__str__(self).split('\n')
         # add priors to the string
         if self.priors is not None:
             strs = [str(p) if p is not None else '' for p in self.priors]

GPy/core/parameterized.py

@@ -9,7 +9,7 @@ import cPickle
 import warnings
 import transformations
 
-class Parameterised(object):
+class Parameterized(object):
     def __init__(self):
         """
         This is the base class for model and kernel. Mostly just handles tieing and constraining of parameters
@@ -20,55 +20,60 @@ class Parameterised(object):
         self.constrained_indices = []
         self.constraints = []
 
-    def pickle(self, filename, protocol= -1):
-        f = file(filename, 'w')
-        cPickle.dump(self, f, protocol)
-        f.close()
+    def _get_params(self):
+        raise NotImplementedError, "this needs to be implemented to use the Model class"
+    def _set_params(self, x):
+        raise NotImplementedError, "this needs to be implemented to use the Model class"
+
+    def pickle(self, filename, protocol=None):
+        if protocol is None:
+            if self._has_get_set_state():
+                protocol = 0
+            else:
+                protocol = -1
+        with open(filename, 'w') as f:
+            cPickle.dump(self, f, protocol)
 
     def copy(self):
         """Returns a (deep) copy of the current model """
         return copy.deepcopy(self)
 
-    @property
-    def params(self):
+    def __getstate__(self):
+        if self._has_get_set_state():
+            return self.getstate()
+        return self.__dict__
+
+    def __setstate__(self, state):
+        if self._has_get_set_state():
+            self.setstate(state) # set state
+            self._set_params(self._get_params()) # restore all values
+            return
+        self.__dict__ = state
+
+    def _has_get_set_state(self):
+        return 'getstate' in vars(self.__class__) and 'setstate' in vars(self.__class__)
+
+    def getstate(self):
         """
-        Returns a **copy** of parameters in non transformed space
-
-        :see_also: :py:func:`GPy.core.Parameterised.params_transformed`
+        Get the current state of the class,
+        here just all the indices, rest can get recomputed
+        
+        For inheriting from Parameterized:
+        Allways append the state of the inherited object 
+        and call down to the inherited object in setstate!! 
         """
-        return self._get_params()
+        return [self.tied_indices,
+                self.fixed_indices,
+                self.fixed_values,
+                self.constrained_indices,
+                self.constraints]
 
-    @params.setter
-    def params(self, params):
-        self._set_params(params)
-
-    @property
-    def params_transformed(self):
-        """
-        Returns a **copy** of parameters in transformed space
-
-        :see_also: :py:func:`GPy.core.Parameterised.params`
-        """
-        return self._get_params_transformed()
-
-    @params_transformed.setter
-    def params_transformed(self, params):
-        self._set_params_transformed(params)
-
-    _get_set_deprecation = """get and set methods wont be available at next minor release
-        in the next releases you will get and set with following syntax:
-        Assume m is a model class:
-        print m['var']          # > prints all parameters matching 'var'
-        m['var'] = 2.           # > sets all parameters matching 'var' to 2.
-        m['var'] = <array-like> # > sets parameters matching 'var' to <array-like>
-        """
-    def get(self, regexp):
-        warnings.warn(self._get_set_deprecation, FutureWarning, stacklevel=2)
-        return self[regexp]
-
-    def set(self, regexp, val):
-        warnings.warn(self._get_set_deprecation, FutureWarning, stacklevel=2)
-        self[regexp] = val
+    def setstate(self, state):
+        self.constraints = state.pop()
+        self.constrained_indices = state.pop()
+        self.fixed_values = state.pop()
+        self.fixed_indices = state.pop()
+        self.tied_indices = state.pop()
 
     def __getitem__(self, regexp, return_names=False):
         """
@@ -95,13 +100,16 @@ class Parameterised(object):
         if len(matches):
             val = np.array(val)
             assert (val.size == 1) or val.size == len(matches), "Shape mismatch: {}:({},)".format(val.size, len(matches))
-            x = self.params
+            x = self._get_params()
             x[matches] = val
-            self.params = x
+            self._set_params(x)
         else:
             raise AttributeError, "no parameter matches %s" % name
 
     def tie_params(self, regexp):
+        """
+        Tie (all!) parameters matching the regular expression `regexp`. 
+        """
         matches = self.grep_param_names(regexp)
         assert matches.size > 0, "need at least something to tie together"
         if len(self.tied_indices):
@@ -181,7 +189,7 @@ class Parameterised(object):
 
     def constrain_negative(self, regexp):
         """ Set negative constraints. """
-        self.constrain(regexp, transformations.negative_exponent())
+        self.constrain(regexp, transformations.negative_logexp())
 
     def constrain_positive(self, regexp):
         """ Set positive constraints. """

GPy/core/sparse_gp.py

@@ -33,10 +33,11 @@ class SparseGP(GPBase):
 
         self.Z = Z
         self.num_inducing = Z.shape[0]
-        self.likelihood = likelihood
+#         self.likelihood = likelihood
 
         if X_variance is None:
             self.has_uncertain_inputs = False
+            self.X_variance = None
         else:
             assert X_variance.shape == X.shape
             self.has_uncertain_inputs = True
@@ -49,6 +50,23 @@ class SparseGP(GPBase):
         if self.has_uncertain_inputs:
             self.X_variance /= np.square(self._Xscale)
 
+    def getstate(self):
+        """
+        Get the current state of the class,
+        here just all the indices, rest can get recomputed
+        """
+        return GPBase.getstate(self) + [self.Z,
+                self.num_inducing,
+                self.has_uncertain_inputs,
+                self.X_variance]
+
+    def setstate(self, state):
+        self.X_variance = state.pop()
+        self.has_uncertain_inputs = state.pop()
+        self.num_inducing = state.pop()
+        self.Z = state.pop()
+        GPBase.setstate(self, state)
+
     def _compute_kernel_matrices(self):
         # kernel computations, using BGPLVM notation
         self.Kmm = self.kern.K(self.Z)
@@ -79,7 +97,7 @@ class SparseGP(GPBase):
             tmp = tmp.T
         else:
             if self.likelihood.is_heteroscedastic:
-                tmp = self.psi1 * (np.sqrt(self.likelihood.precision.flatten().reshape(self.num_data,1)))
+                tmp = self.psi1 * (np.sqrt(self.likelihood.precision.flatten().reshape(self.num_data, 1)))
             else:
                 tmp = self.psi1 * (np.sqrt(self.likelihood.precision))
         tmp, _ = dtrtrs(self.Lm, np.asfortranarray(tmp.T), lower=1)
@@ -166,7 +184,7 @@ class SparseGP(GPBase):
         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])],[])\
+        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):
@@ -223,7 +241,7 @@ class SparseGP(GPBase):
     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, _ = dpotri(self.LB, lower=0)  # WTH? this lower switch should be 1, but that doesn't work!
+        Bi, _ = 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.num_inducing) - Bi)
 
@@ -304,7 +322,7 @@ class SparseGP(GPBase):
         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._Xscale + self._Xoffset  # NOTE self.X are the normalized values now
+                Xu = self.X * self._Xscale + self._Xoffset # 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)

GPy/core/svigp.py

@@ -91,6 +91,14 @@ class SVIGP(GPBase):
         self._param_steplength_trace = []
         self._vb_steplength_trace = []
 
+    def getstate(self):
+        return GPBase.getstate(self)
+
+
+    def setstate(self, state):
+        return GPBase.setstate(self, state)
+
+
     def _compute_kernel_matrices(self):
         # kernel computations, using BGPLVM notation
         self.Kmm = self.kern.K(self.Z)

GPy/core/transformations.py

@@ -36,6 +36,20 @@ class logexp(transformation):
     def __str__(self):
         return '(+ve)'
 
+class negative_logexp(transformation):
+    domain = NEGATIVE
+    def f(self, x):
+        return -np.log(1. + np.exp(x))
+    def finv(self, f):
+        return np.log(np.exp(-f) - 1.)
+    def gradfactor(self, f):
+        ef = np.exp(-f)
+        return -(ef - 1.) / ef
+    def initialize(self, f):
+        return -np.abs(f)
+    def __str__(self):
+        return '(-ve)'
+
 class logexp_clipped(transformation):
     max_bound = 1e100
     min_bound = 1e-10

GPy/examples/dimensionality_reduction.py

@@ -254,7 +254,7 @@ 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 = BayesianGPLVM(Y, Q, init="PCA", num_inducing=num_inducing, kernel=k, _debug=True)
+    m = BayesianGPLVM(Y, Q, init="PCA", num_inducing=num_inducing, kernel=k)
 
     # m.constrain('variance|noise', logexp_clipped())
     m['noise'] = Y.var() / 100.
@@ -279,11 +279,12 @@ 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))
+    k = kern.linear(Q, ARD=True) + kern.bias(Q, np.exp(-2)) + kern.white(Q, np.exp(-2))
     m = mrd.MRD(Ylist, input_dim=Q, num_inducing=num_inducing, kernels=k, initx="", initz='permute', **kw)
+    m.ensure_default_constraints()
 
     for i, Y in enumerate(Ylist):
-        m['{}_noise'.format(i + 1)] = Y.var() / 100.
+        m['{}_noise'.format(i)] = Y.var() / 100.
 
 
     # DEBUG

GPy/inference/scg.py

@@ -26,11 +26,14 @@ 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 print_out(len_maxiters, fnow, current_grad, beta, iteration):
+    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 exponents(fnow, current_grad):
+    exps = [np.abs(fnow), current_grad]
+    return np.sign(exps) * np.log10(exps).astype(int)
 
 def SCG(f, gradf, x, optargs=(), maxiters=500, max_f_eval=500, display=True, xtol=None, ftol=None, gtol=None):
     """
@@ -52,6 +55,7 @@ def SCG(f, gradf, x, optargs=(), maxiters=500, max_f_eval=500, display=True, xto
         ftol = 1e-6
     if gtol is None:
         gtol = 1e-5
+
     sigma0 = 1.0e-8
     fold = f(x, *optargs) # Initial function value.
     function_eval = 1
@@ -74,6 +78,8 @@ def SCG(f, gradf, x, optargs=(), maxiters=500, max_f_eval=500, display=True, xto
     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)
+        exps = exponents(fnow, current_grad)
+        p_iter = iteration
 
     # Main optimization loop.
     while iteration < maxiters:
@@ -104,7 +110,7 @@ def SCG(f, gradf, x, optargs=(), maxiters=500, max_f_eval=500, display=True, xto
         function_eval += 1
 
         if function_eval >= max_f_eval:
-            status = "Maximum number of function evaluations exceeded"
+            status = "maximum number of function evaluations exceeded"
             break
 #             return x, flog, function_eval, status
 
@@ -122,15 +128,29 @@ def SCG(f, gradf, x, optargs=(), maxiters=500, max_f_eval=500, display=True, xto
         flog.append(fnow) # Current function value
 
         iteration += 1
-        print_out(len_maxiters, display, fnow, current_grad, beta, iteration)
+        if display:
+            print_out(len_maxiters, fnow, current_grad, beta, iteration)
+            n_exps = exponents(fnow, current_grad)
+            if iteration - p_iter >= 6:
+                a = iteration >= p_iter * 2.78
+                b = np.any(n_exps < exps)
+                if a or b:
+                    print ''
+                if a:
+                    p_iter = iteration
+                if b:
+                    exps = n_exps
 
         if success:
             # Test for termination
-            if (np.max(np.abs(alpha * d)) < xtol) or (np.abs(fnew - fold) < ftol):
-                status = 'converged'
+
+            if (np.abs(fnew - fold) < ftol):
+                status = 'converged - relative reduction in objective'
                 break
 #                 return x, flog, function_eval, status
-
+            elif (np.max(np.abs(alpha * d)) < xtol):
+                status = 'converged - relative stepsize'
+                break
             else:
                 # Update variables for new position
                 gradnew = gradf(x, *optargs)
@@ -139,7 +159,7 @@ def SCG(f, gradf, x, optargs=(), maxiters=500, max_f_eval=500, display=True, xto
                 fold = fnew
                 # If the gradient is zero then we are done.
                 if current_grad <= gtol:
-                    status = 'converged'
+                    status = 'converged - relative reduction in gradient'
                     break
                     # return x, flog, function_eval, status
 
@@ -164,6 +184,8 @@ def SCG(f, gradf, x, optargs=(), maxiters=500, max_f_eval=500, display=True, xto
         status = "maxiter exceeded"
 
     if display:
-        print_out(len_maxiters, display, fnow, current_grad, beta, iteration)
         print ""
+        print_out(len_maxiters, fnow, current_grad, beta, iteration)
+        print ""
+        print status
     return x, flog, function_eval, status

GPy/kern/kern.py

@@ -3,12 +3,12 @@
 
 import numpy as np
 import pylab as pb
-from ..core.parameterised import Parameterised
+from ..core.parameterized import Parameterized
 from parts.kernpart import Kernpart
 import itertools
 from parts.prod import Prod as prod
 
-class kern(Parameterised):
+class kern(Parameterized):
     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.
@@ -41,26 +41,51 @@ class kern(Parameterised):
 
         self.compute_param_slices()
 
-        Parameterised.__init__(self)
+        Parameterized.__init__(self)
+
+    def getstate(self):
+        """
+        Get the current state of the class,
+        here just all the indices, rest can get recomputed
+        """
+        return Parameterized.getstate(self) + [self.parts,
+                self.Nparts,
+                self.num_params,
+                self.input_dim,
+                self.input_slices,
+                self.param_slices
+                ]
+
+    def setstate(self, state):
+        self.param_slices = state.pop()
+        self.input_slices = state.pop()
+        self.input_dim = state.pop()
+        self.num_params = state.pop()
+        self.Nparts = state.pop()
+        self.parts = state.pop()
+        Parameterized.setstate(self, state)
 
 
-    def plot_ARD(self, fignum=None, ax=None):
+    def plot_ARD(self, fignum=None, ax=None, title=None):
         """If an ARD kernel is present, it bar-plots the ARD parameters"""
         if ax is None:
             fig = pb.figure(fignum)
             ax = fig.add_subplot(111)
         for p in self.parts:
             if hasattr(p, 'ARD') and p.ARD:
-                ax.set_title('ARD parameters, %s kernel' % p.name)
-
+                if title is None:
+                    ax.set_title('ARD parameters, %s kernel' % p.name)
+                else:
+                    ax.set_title(title)
                 if p.name == 'linear':
                     ard_params = p.variances
                 else:
                     ard_params = 1. / p.lengthscale
 
-                ax.bar(np.arange(len(ard_params)) - 0.4, ard_params)
-                ax.set_xticks(np.arange(len(ard_params)))
-                ax.set_xticklabels([r"${}$".format(i) for i in range(len(ard_params))])
+                x = np.arange(len(ard_params))
+                ax.bar(x - 0.4, ard_params)
+                ax.set_xticks(x)
+                ax.set_xticklabels([r"${}$".format(i) for i in x])
         return ax
 
     def _transform_gradients(self, g):

GPy/models/bayesian_gplvm.py

@@ -24,8 +24,7 @@ class BayesianGPLVM(SparseGP, GPLVM):
 
     """
     def __init__(self, likelihood_or_Y, input_dim, X=None, X_variance=None, init='PCA', num_inducing=10,
-                 Z=None, kernel=None, oldpsave=10, _debug=False,
-                 **kwargs):
+                 Z=None, kernel=None, **kwargs):
         if type(likelihood_or_Y) is np.ndarray:
             likelihood = Gaussian(likelihood_or_Y)
         else:
@@ -45,32 +44,19 @@ class BayesianGPLVM(SparseGP, GPLVM):
         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 = []
-
         SparseGP.__init__(self, X, likelihood, kernel, Z=Z, X_variance=X_variance, **kwargs)
         self.ensure_default_constraints()
 
-    @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 getstate(self):
+        """
+        Get the current state of the class,
+        here just all the indices, rest can get recomputed
+        """
+        return SparseGP.getstate(self) + [self.init]
+
+    def setstate(self, state):
+        self.init = state.pop()
+        SparseGP.setstate(self, state)
 
     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.num_data)], [])
@@ -90,24 +76,11 @@ class BayesianGPLVM(SparseGP, GPLVM):
         x = np.hstack((self.X.flatten(), self.X_variance.flatten(), SparseGP._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.num_data, 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()
-            SparseGP._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)
+        N, input_dim = self.num_data, 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()
+        SparseGP._set_params(self, x[(2 * N * input_dim):])
 
     def dKL_dmuS(self):
         dKL_dS = (1. - (1. / (self.X_variance))) * 0.5
@@ -131,56 +104,19 @@ class BayesianGPLVM(SparseGP, GPLVM):
     def log_likelihood(self):
         ll = SparseGP.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.num_data * self.input_dim * 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.input_dim * (np.sum(self.likelihood.precision.flatten() * self.psi0) - np.trace(self.A) * sf2)
-                    B = -0.5 * self.input_dim * (np.sum(self.likelihood.precision.flatten() * self.psi0) - np.trace(self.A))
-                else:
-                    A = -0.5 * self.num_data * self.input_dim * (np.log(2.*np.pi) + np.log(self.likelihood._variance)) - 0.5 * self.likelihood.precision * self.likelihood.trYYT
-#                     B = -0.5 * self.input_dim * (np.sum(self.likelihood.precision * self.psi0) - np.trace(self.A) * sf2)
-                    B = -0.5 * self.input_dim * (np.sum(self.likelihood.precision * self.psi0) - np.trace(self.A))
-                C = -self.input_dim * (np.sum(np.log(np.diag(self.LB)))) # + 0.5 * self.num_inducing * 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 = SparseGP._log_likelihood_gradients(self)
-        return self._clipped(np.hstack((self.dbound_dmuS.flatten(), self.dbound_dZtheta)))
+        return np.hstack((self.dbound_dmuS.flatten(), self.dbound_dZtheta))
 
     def plot_latent(self, *args, **kwargs):
-        return plot_latent.plot_latent_indices(self, *args, **kwargs)
+        return plot_latent.plot_latent(self, *args, **kwargs)
 
     def do_test_latents(self, Y):
         """
@@ -256,275 +192,6 @@ class BayesianGPLVM(SparseGP, GPLVM):
         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.num_inducing, 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.num_data, self.input_dim)
-        start, end = end, end + self.X_variance.size
-        X_v = x[start:end].reshape(self.num_data, self.input_dim)
-        start, end = end, end + (self.num_inducing * self.input_dim)
-        Z = x[start:end].reshape(self.num_inducing, 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"${\mathbf{A,B,C,input_dim}}$", 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='input_dim')
-        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

GPy/models/gp_regression.py

@@ -25,11 +25,20 @@ class GPRegression(GP):
 
     """
 
-    def __init__(self,X,Y,kernel=None,normalize_X=False,normalize_Y=False):
+    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)
+        likelihood = likelihoods.Gaussian(Y, normalize=normalize_Y)
 
         GP.__init__(self, X, likelihood, kernel, normalize_X=normalize_X)
         self.ensure_default_constraints()
+
+    def getstate(self):
+        return GP.getstate(self)
+
+
+    def setstate(self, state):
+        return GP.setstate(self, state)
+
+    pass

GPy/models/gplvm.py

@@ -1,4 +1,4 @@
-### Copyright (c) 2012, GPy authors (see AUTHORS.txt).
+# ## Copyright (c) 2012, GPy authors (see AUTHORS.txt).
 # Licensed under the BSD 3-clause license (see LICENSE.txt)
 
 
@@ -26,11 +26,11 @@ class GPLVM(GP):
     :type init: 'PCA'|'random'
 
     """
-    def __init__(self, Y, input_dim, init='PCA', X = None, kernel=None, normalize_Y=False):
+    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))
+            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.ensure_default_constraints()
@@ -42,26 +42,26 @@ class GPLVM(GP):
             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.num_data)],[]) + GP._get_param_names(self)
+        return sum([['X_%i_%i' % (n, q) for q in range(self.input_dim)] for n in range(self.num_data)], []) + 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.num_data*self.input_dim].reshape(self.num_data,self.input_dim).copy()
+    def _set_params(self, x):
+        self.X = x[:self.num_data * self.input_dim].reshape(self.num_data, 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)
+        dL_dX = 2.*self.kern.dK_dX(self.dL_dK, self.X)
 
-        return np.hstack((dL_dX.flatten(),GP._log_likelihood_gradients(self)))
+        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]
+        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)
+        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)

GPy/models/mrd.py

@@ -48,7 +48,7 @@ class MRD(Model):
                  kernels=None, initx='PCA',
                  initz='permute', _debug=False, **kw):
         if names is None:
-            self.names = ["{}".format(i + 1) for i in range(len(likelihood_or_Y_list))]
+            self.names = ["{}".format(i) for i in range(len(likelihood_or_Y_list))]
 
         # sort out the kernels
         if kernels is None:
@@ -61,12 +61,14 @@ class MRD(Model):
         assert not ('kernel' in kw), "pass kernels through `kernels` argument"
 
         self.input_dim = input_dim
-        self.num_inducing = num_inducing
         self._debug = _debug
+        self.num_inducing = num_inducing
 
         self._init = True
         X = self._init_X(initx, likelihood_or_Y_list)
         Z = self._init_Z(initz, X)
+        self.num_inducing = Z.shape[0] # ensure M==N if M>N
+
         self.bgplvms = [BayesianGPLVM(l, input_dim=input_dim, kernel=k, X=X, Z=Z, num_inducing=self.num_inducing, **kw) for l, k in zip(likelihood_or_Y_list, kernels)]
         del self._init
 
@@ -75,12 +77,36 @@ class MRD(Model):
         self.nparams = nparams.cumsum()
 
         self.num_data = self.gref.num_data
+
         self.NQ = self.num_data * self.input_dim
         self.MQ = self.num_inducing * self.input_dim
 
         Model.__init__(self)
         self.ensure_default_constraints()
 
+    def getstate(self):
+        return Model.getstate(self) + [self.names,
+                self.bgplvms,
+                self.gref,
+                self.nparams,
+                self.input_dim,
+                self.num_inducing,
+                self.num_data,
+                self.NQ,
+                self.MQ]
+
+    def setstate(self, state):
+        self.MQ = state.pop()
+        self.NQ = state.pop()
+        self.num_data = state.pop()
+        self.num_inducing = state.pop()
+        self.input_dim = state.pop()
+        self.nparams = state.pop()
+        self.gref = state.pop()
+        self.bgplvms = state.pop()
+        self.names = state.pop()
+        Model.setstate(self, state)
+
     @property
     def X(self):
         return self.gref.X
@@ -255,12 +281,23 @@ class MRD(Model):
         self.Z = Z
         return Z
 
-    def _handle_plotting(self, fignum, axes, plotf):
+    def _handle_plotting(self, fignum, axes, plotf, sharex=False, sharey=False):
         if axes is None:
-            fig = pylab.figure(num=fignum, figsize=(4 * len(self.bgplvms), 3))
+            fig = pylab.figure(num=fignum)
+        sharex_ax = None
+        sharey_ax = None
         for i, g in enumerate(self.bgplvms):
+            try:
+                if sharex:
+                    sharex_ax = ax # @UndefinedVariable
+                    sharex = False # dont set twice
+                if sharey:
+                    sharey_ax = ax # @UndefinedVariable
+                    sharey = False # dont set twice
+            except:
+                pass
             if axes is None:
-                ax = fig.add_subplot(1, len(self.bgplvms), i + 1)
+                ax = fig.add_subplot(1, len(self.bgplvms), i + 1, sharex=sharex_ax, sharey=sharey_ax)
             elif isinstance(axes, (tuple, list)):
                 ax = axes[i]
             else:
@@ -280,16 +317,27 @@ class MRD(Model):
         fig = self._handle_plotting(fignum, ax, lambda i, g, ax: ax.imshow(g.X))
         return fig
 
-    def plot_predict(self, fignum=None, ax=None, **kwargs):
-        fig = self._handle_plotting(fignum, ax, lambda i, g, ax: ax.imshow(g. predict(g.X)[0], **kwargs))
+    def plot_predict(self, fignum=None, ax=None, sharex=False, sharey=False, **kwargs):
+        fig = self._handle_plotting(fignum,
+                                    ax,
+                                    lambda i, g, ax: ax.imshow(g. predict(g.X)[0], **kwargs),
+                                    sharex=sharex, sharey=sharey)
         return fig
 
-    def plot_scales(self, fignum=None, ax=None, *args, **kwargs):
-        fig = self._handle_plotting(fignum, ax, lambda i, g, ax: g.kern.plot_ARD(ax=ax, *args, **kwargs))
+    def plot_scales(self, fignum=None, ax=None, titles=None, sharex=False, sharey=True, *args, **kwargs):
+        """
+        :param:`titles` :
+            titles for axes of datasets
+        """
+        if titles is None:
+            titles = [r'${}$'.format(name) for name in self.names]
+        def plotf(i, g, ax):
+            g.kern.plot_ARD(ax=ax, title=titles[i], *args, **kwargs)
+        fig = self._handle_plotting(fignum, ax, plotf, sharex=sharex, sharey=sharey)
         return fig
 
     def plot_latent(self, fignum=None, ax=None, *args, **kwargs):
-        fig = self._handle_plotting(fignum, ax, lambda i, g, ax: g.plot_latent(ax=ax, *args, **kwargs))
+        fig = self.gref.plot_latent(fignum=fignum, ax=ax, *args, **kwargs) # self._handle_plotting(fignum, ax, lambda i, g, ax: g.plot_latent(ax=ax, *args, **kwargs))
         return fig
 
     def _debug_plot(self):
@@ -305,13 +353,4 @@ class MRD(Model):
         pylab.draw()
         fig.tight_layout()
 
-    def _debug_optimize(self, opt='scg', maxiters=5000, itersteps=10):
-        iters = 0
-        optstep = lambda: self.optimize(opt, messages=1, max_f_eval=itersteps)
-        self._debug_plot()
-        raw_input("enter to start debug")
-        while iters < maxiters:
-            optstep()
-            self._debug_plot()
-            iters += itersteps
 

GPy/models/sparse_gp_classification.py

@@ -28,7 +28,7 @@ class SparseGPClassification(SparseGP):
 
     def __init__(self, X, Y=None, likelihood=None, kernel=None, normalize_X=False, normalize_Y=False, Z=None, num_inducing=10):
         if kernel is None:
-            kernel = kern.rbf(X.shape[1]) + kern.white(X.shape[1],1e-3)
+            kernel = kern.rbf(X.shape[1]) + kern.white(X.shape[1], 1e-3)
 
         if likelihood is None:
             distribution = likelihoods.likelihood_functions.Binomial()
@@ -41,7 +41,16 @@ class SparseGPClassification(SparseGP):
             i = np.random.permutation(X.shape[0])[:num_inducing]
             Z = X[i].copy()
         else:
-            assert Z.shape[1]==X.shape[1]
+            assert Z.shape[1] == X.shape[1]
 
         SparseGP.__init__(self, X, likelihood, kernel, Z=Z, normalize_X=normalize_X)
         self.ensure_default_constraints()
+
+    def getstate(self):
+        return SparseGP.getstate(self)
+
+
+    def setstate(self, state):
+        return SparseGP.setstate(self, state)
+
+    pass

GPy/models/sparse_gp_regression.py

@@ -43,3 +43,13 @@ class SparseGPRegression(SparseGP):
 
         SparseGP.__init__(self, X, likelihood, kernel, Z=Z, normalize_X=normalize_X, X_variance=X_variance)
         self.ensure_default_constraints()
+        pass
+
+    def getstate(self):
+        return SparseGP.getstate(self)
+
+
+    def setstate(self, state):
+        return SparseGP.setstate(self, state)
+
+    pass

GPy/models/sparse_gplvm.py

@@ -28,6 +28,14 @@ class SparseGPLVM(SparseGPRegression, GPLVM):
         SparseGPRegression.__init__(self, X, Y, kernel=kernel, num_inducing=num_inducing)
         self.ensure_default_constraints()
 
+    def getstate(self):
+        return SparseGPRegression.getstate(self)
+
+
+    def setstate(self, state):
+        return SparseGPRegression.setstate(self, state)
+
+
     def _get_param_names(self):
         return (sum([['X_%i_%i' % (n, q) for q in range(self.input_dim)] for n in range(self.num_data)], [])
                 + SparseGPRegression._get_param_names(self))

GPy/models/svigp_regression.py

@@ -42,3 +42,11 @@ class SVIGPRegression(SVIGP):
 
         SVIGP.__init__(self, X, likelihood, kernel, Z, q_u=q_u, batchsize=batchsize)
         self.load_batch()
+
+    def getstate(self):
+        return GPBase.getstate(self)
+
+
+    def setstate(self, state):
+        return GPBase.setstate(self, state)
+

GPy/models/warped_gp.py

@@ -28,6 +28,14 @@ class WarpedGP(GP):
         GP.__init__(self, X, likelihood, kernel, normalize_X=normalize_X)
         self._set_params(self._get_params())
 
+    def getstate(self):
+        return GP.getstate(self)
+
+
+    def setstate(self, state):
+        return GP.setstate(self, state)
+
+
     def _scale_data(self, Y):
         self._Ymax = Y.max()
         self._Ymin = Y.min()

GPy/testing/cgd_tests.py

@@ -7,7 +7,6 @@ import unittest
 import numpy
 from GPy.inference.conjugate_gradient_descent import CGD, RUNNING
 import pylab
-import time
 from scipy.optimize.optimize import rosen, rosen_der
 from GPy.inference.gradient_descent_update_rules import PolakRibiere
 

GPy/testing/examples_tests.py

@@ -19,14 +19,14 @@ class ExamplesTests(unittest.TestCase):
         self.assertTrue(isinstance(Model, GPy.models))
 
 """
-def model_instance_generator(Model):
+def model_instance_generator(model):
     def check_model_returned(self):
-        self._model_instance(Model)
+        self._model_instance(model)
     return check_model_returned
 
-def checkgrads_generator(Model):
+def checkgrads_generator(model):
     def model_checkgrads(self):
-        self._checkgrad(Model)
+        self._checkgrad(model)
     return model_checkgrads
 """
 
@@ -37,7 +37,7 @@ def model_checkgrads(model):
 
 def model_instance(model):
     #assert isinstance(model, GPy.core.model)
-    return isinstance(model, GPy.core.Model)
+    return isinstance(model, GPy.core.model)
 
 @nottest
 def test_models():

GPy/util/plot_latent.py

@@ -2,23 +2,24 @@ import pylab as pb
 import numpy as np
 from .. import util
 
-def plot_latent(model, labels=None, which_indices=None, resolution=50, ax=None, marker='o', s=40):
+def plot_latent(model, labels=None, which_indices=None, resolution=50, ax=None, marker='o', s=40, fignum=None, plot_inducing=False, legend=True):
     """
     :param labels: a np.array of size model.num_data containing labels for the points (can be number, strings, etc)
     :param resolution: the resolution of the grid on which to evaluate the predictive variance
     """
     if ax is None:
-        ax = pb.gca()
+        fig = pb.figure(num=fignum)
+        ax = fig.add_subplot(111)
     util.plot.Tango.reset()
 
     if labels is None:
         labels = np.ones(model.num_data)
     if which_indices is None:
-        if model.input_dim==1:
+        if model.input_dim == 1:
             input_1 = 0
             input_2 = None
-        if model.input_dim==2:
-            input_1, input_2 = 0,1
+        if model.input_dim == 2:
+            input_1, input_2 = 0, 1
         else:
             try:
                 input_1, input_2 = np.argsort(model.input_sensitivity())[:2]
@@ -27,14 +28,14 @@ def plot_latent(model, labels=None, which_indices=None, resolution=50, ax=None,
     else:
         input_1, input_2 = which_indices
 
-    #first, plot the output variance as a function of the latent space
-    Xtest, xx,yy,xmin,xmax = util.plot.x_frame2D(model.X[:,[input_1, input_2]],resolution=resolution)
+    # first, plot the output variance as a function of the latent space
+    Xtest, xx, yy, xmin, xmax = util.plot.x_frame2D(model.X[:, [input_1, input_2]], resolution=resolution)
     Xtest_full = np.zeros((Xtest.shape[0], model.X.shape[1]))
     Xtest_full[:, :2] = Xtest
     mu, var, low, up = model.predict(Xtest_full)
     var = var[:, :1]
     ax.imshow(var.reshape(resolution, resolution).T,
-              extent=[xmin[0], xmax[0], xmin[1], xmax[1]], cmap=pb.cm.binary,interpolation='bilinear',origin='lower')
+              extent=[xmin[0], xmax[0], xmin[1], xmax[1]], cmap=pb.cm.binary, interpolation='bilinear', origin='lower')
 
     # make sure labels are in order of input:
     ulabels = []
@@ -46,46 +47,35 @@ def plot_latent(model, labels=None, which_indices=None, resolution=50, ax=None,
         if type(ul) is np.string_:
             this_label = ul
         elif type(ul) is np.int64:
-            this_label = 'class %i'%ul
+            this_label = 'class %i' % ul
         else:
-            this_label = 'class %i'%i
+            this_label = 'class %i' % i
         if len(marker) == len(ulabels):
             m = marker[i]
         else:
             m = marker
 
-        index = np.nonzero(labels==ul)[0]
-        if model.input_dim==1:
-            x = model.X[index,input_1]
+        index = np.nonzero(labels == ul)[0]
+        if model.input_dim == 1:
+            x = model.X[index, input_1]
             y = np.zeros(index.size)
         else:
-            x = model.X[index,input_1]
-            y = model.X[index,input_2]
+            x = model.X[index, input_1]
+            y = model.X[index, input_2]
         ax.scatter(x, y, marker=m, s=s, color=util.plot.Tango.nextMedium(), label=this_label)
 
-    ax.set_xlabel('latent dimension %i'%input_1)
-    ax.set_ylabel('latent dimension %i'%input_2)
+    ax.set_xlabel('latent dimension %i' % input_1)
+    ax.set_ylabel('latent dimension %i' % input_2)
 
-    if not np.all(labels==1.):
-        ax.legend(loc=0,numpoints=1)
+    if not np.all(labels == 1.) and legend:
+        ax.legend(loc=0, numpoints=1)
 
-    ax.set_xlim(xmin[0],xmax[0])
-    ax.set_ylim(xmin[1],xmax[1])
+    ax.set_xlim(xmin[0], xmax[0])
+    ax.set_ylim(xmin[1], xmax[1])
     ax.grid(b=False) # remove the grid if present, it doesn't look good
     ax.set_aspect('auto') # set a nice aspect ratio
-    return ax
-
-
-def plot_latent_indices(Model, which_indices=None, *args, **kwargs):
-
-    if which_indices is None:
-        try:
-            input_1, input_2 = np.argsort(Model.input_sensitivity())[:2]
-        except:
-            raise ValueError, "cannot Automatically determine which dimensions to plot, please pass 'which_indices'"
-    else:
-        input_1, input_2 = which_indices
-    ax = plot_latent(Model, which_indices=[input_1, input_2], *args, **kwargs)
-    # TODO: Here test if there are inducing points...
-    ax.plot(Model.Z[:, input_1], Model.Z[:, input_2], '^w')
+
+    if plot_inducing:
+        ax.plot(model.Z[:, input_1], model.Z[:, input_2], '^w')
+
     return ax

GPy/util/visualize.py

@@ -103,7 +103,7 @@ class lvm(matplotlib_show):
             self.cid = latent_axes[0].figure.canvas.mpl_connect('axes_enter_event', self.on_enter)
 
         self.data_visualize = data_visualize
-        self.Model = model
+        self.model = model
         self.latent_axes = latent_axes
         self.sense_axes = sense_axes
         self.called = False
@@ -120,7 +120,7 @@ class lvm(matplotlib_show):
     def modify(self, vals):
         """When latent values are modified update the latent representation and ulso update the output visualization."""
         self.vals = vals.copy()
-        y = self.Model.predict(self.vals)[0]
+        y = self.model.predict(self.vals)[0]
         self.data_visualize.modify(y)
         self.latent_handle.set_data(self.vals[self.latent_index[0]], self.vals[self.latent_index[1]])
         self.axes.figure.canvas.draw()
@@ -148,15 +148,15 @@ class lvm(matplotlib_show):
         # A click in the bar chart axis for selection a dimension.
         if self.sense_axes != None:
             self.sense_axes.cla()
-            self.sense_axes.bar(np.arange(self.Model.input_dim),1./self.Model.input_sensitivity(),color='b')
+            self.sense_axes.bar(np.arange(self.model.input_dim),1./self.model.input_sensitivity(),color='b')
 
             if self.latent_index[1] == self.latent_index[0]:
-                self.sense_axes.bar(np.array(self.latent_index[0]),1./self.Model.input_sensitivity()[self.latent_index[0]],color='y')
-                self.sense_axes.bar(np.array(self.latent_index[1]),1./self.Model.input_sensitivity()[self.latent_index[1]],color='y')
+                self.sense_axes.bar(np.array(self.latent_index[0]),1./self.model.input_sensitivity()[self.latent_index[0]],color='y')
+                self.sense_axes.bar(np.array(self.latent_index[1]),1./self.model.input_sensitivity()[self.latent_index[1]],color='y')
 
             else:
-                self.sense_axes.bar(np.array(self.latent_index[0]),1./self.Model.input_sensitivity()[self.latent_index[0]],color='g')
-                self.sense_axes.bar(np.array(self.latent_index[1]),1./self.Model.input_sensitivity()[self.latent_index[1]],color='r')
+                self.sense_axes.bar(np.array(self.latent_index[0]),1./self.model.input_sensitivity()[self.latent_index[0]],color='g')
+                self.sense_axes.bar(np.array(self.latent_index[1]),1./self.model.input_sensitivity()[self.latent_index[1]],color='r')
 
             self.sense_axes.figure.canvas.draw()
 
@@ -193,7 +193,7 @@ class lvm_dimselect(lvm):
     GPy.examples.dimensionality_reduction.BGPVLM_oil()
 
     """
-    def __init__(self, vals, Model, data_visualize, latent_axes=None, sense_axes=None, latent_index=[0, 1], labels=None):
+    def __init__(self, vals, model, data_visualize, latent_axes=None, sense_axes=None, latent_index=[0, 1], labels=None):
         if latent_axes==None and sense_axes==None:
             self.fig,(latent_axes,self.sense_axes) = plt.subplots(1,2)
         elif sense_axes==None:
@@ -202,7 +202,7 @@ class lvm_dimselect(lvm):
         else:
             self.sense_axes = sense_axes
         self.labels = labels
-        lvm.__init__(self,vals,Model,data_visualize,latent_axes,sense_axes,latent_index)
+        lvm.__init__(self,vals,model,data_visualize,latent_axes,sense_axes,latent_index)
         self.show_sensitivities()
         print "use left and right mouse butons to select dimensions"
 
@@ -210,7 +210,7 @@ class lvm_dimselect(lvm):
     def on_click(self, event):
 
         if event.inaxes==self.sense_axes:
-            new_index = max(0,min(int(np.round(event.xdata-0.5)),self.Model.input_dim-1))
+            new_index = max(0,min(int(np.round(event.xdata-0.5)),self.model.input_dim-1))
             if event.button == 1:
                 # Make it red if and y-axis (red=port=left) if it is a left button click
                 self.latent_index[1] = new_index                
@@ -221,7 +221,7 @@ class lvm_dimselect(lvm):
             self.show_sensitivities()
 
             self.latent_axes.cla()
-            self.Model.plot_latent(which_indices=self.latent_index,
+            self.model.plot_latent(which_indices=self.latent_index,
                                    ax=self.latent_axes, labels=self.labels)
             self.latent_handle = self.latent_axes.plot([0],[0],'rx',mew=2)[0]
             self.modify(self.latent_values)
@@ -235,7 +235,7 @@ class lvm_dimselect(lvm):
 
     def on_leave(self,event):
         latent_values = self.latent_values.copy()
-        y = self.Model.predict(latent_values[None,:])[0]
+        y = self.model.predict(latent_values[None,:])[0]
         self.data_visualize.modify(y)