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

GPy/core/parameterization/array_core.py

@@ -12,6 +12,7 @@ class ListArray(np.ndarray):
     WARNING: This overrides the functionality of x==y!!!
     Use numpy.equal(x,y) for element-wise equality testing.
     """
+    
     def __new__(cls, input_array):
         obj = np.asanyarray(input_array).view(cls)
         return obj
@@ -27,24 +28,6 @@ class ParamList(list):
         return False
     
     pass
-class C(np.ndarray):
-    __array_priority__ = 1.
-    def __new__(cls, array):
-        obj = array.view(cls)
-        return obj
-    #def __array_finalize__(self, obj):
-    #    #print 'finalize'
-    #    return obj
-    def __array_prepare__(self, out_arr, context):
-        #print 'prepare'
-        while type(out_arr) is C:
-            out_arr = out_arr.base
-        return out_arr
-    def __array_wrap__(self, out_arr, context):
-        #print 'wrap', type(self), type(out_arr), context
-        while type(out_arr) is C:
-            out_arr = out_arr.base
-        return out_arr
 
 class ObservableArray(ListArray, Observable):
     """
@@ -63,25 +46,168 @@ class ObservableArray(ListArray, Observable):
         # see InfoArray.__array_finalize__ for comments
         if obj is None: return
         self._observers_ = getattr(obj, '_observers_', None)
+    
     def __setitem__(self, s, val, update=True):
         super(ObservableArray, self).__setitem__(s, val)
         if update:
             self._notify_observers()
-#         if self.ndim:
-#             if not np.all(np.equal(self[s], val)):
-#                 super(ObservableArray, self).__setitem__(s, val)
-#                 if update:
-#                     self._notify_observers()
-#         else:
-#             if not np.all(np.equal(self, val)):
-#                 super(ObservableArray, self).__setitem__(Ellipsis, val)
-#                 if update:
-#                     self._notify_observers()
     def __getslice__(self, start, stop):
         return self.__getitem__(slice(start, stop))
     def __setslice__(self, start, stop, val):
         return self.__setitem__(slice(start, stop), val)  
+
     def __copy__(self, *args):
         return ObservableArray(self.base.base.copy(*args))
     def copy(self, *args):
         return self.__copy__(*args)
+
+    def __ror__(self, *args, **kwargs):
+        r =  np.ndarray.__ror__(self, *args, **kwargs)
+        self._notify_observers()
+        return r
+        
+
+    def __ilshift__(self, *args, **kwargs):
+        r = np.ndarray.__ilshift__(self, *args, **kwargs)
+        self._notify_observers()
+        return r
+
+
+    def __irshift__(self, *args, **kwargs):
+        r = np.ndarray.__irshift__(self, *args, **kwargs)
+        self._notify_observers()
+        return r
+
+
+    def __rrshift__(self, *args, **kwargs):
+        r = np.ndarray.__rrshift__(self, *args, **kwargs)
+        self._notify_observers()
+        return r
+
+
+    def __ixor__(self, *args, **kwargs):
+        r = np.ndarray.__ixor__(self, *args, **kwargs)
+        self._notify_observers()
+        return r
+
+
+    def __rxor__(self, *args, **kwargs):
+        r = np.ndarray.__rxor__(self, *args, **kwargs)
+        self._notify_observers()
+        return r
+
+
+
+    def __rdivmod__(self, *args, **kwargs):
+        r = np.ndarray.__rdivmod__(self, *args, **kwargs)
+        self._notify_observers()
+        return r
+
+
+    def __radd__(self, *args, **kwargs):
+        r = np.ndarray.__radd__(self, *args, **kwargs)
+        self._notify_observers()
+        return r
+
+
+    def __rdiv__(self, *args, **kwargs):
+        r = np.ndarray.__rdiv__(self, *args, **kwargs)
+        self._notify_observers()
+        return r
+
+
+    def __rtruediv__(self, *args, **kwargs):
+        r = np.ndarray.__rtruediv__(self, *args, **kwargs)
+        self._notify_observers()
+        return r
+
+
+    def __ipow__(self, *args, **kwargs):
+        r = np.ndarray.__ipow__(self, *args, **kwargs)
+        self._notify_observers()
+        return r
+
+
+    def __rmul__(self, *args, **kwargs):
+        r = np.ndarray.__rmul__(self, *args, **kwargs)
+        self._notify_observers()
+        return r
+
+
+    def __rpow__(self, *args, **kwargs):
+        r = np.ndarray.__rpow__(self, *args, **kwargs)
+        self._notify_observers()
+        return r
+
+
+    def __rsub__(self, *args, **kwargs):
+        r = np.ndarray.__rsub__(self, *args, **kwargs)
+        self._notify_observers()
+        return r
+
+
+    def __ifloordiv__(self, *args, **kwargs):
+        r = np.ndarray.__ifloordiv__(self, *args, **kwargs)
+        self._notify_observers()
+        return r
+
+
+    def __isub__(self, *args, **kwargs):
+        r = np.ndarray.__isub__(self, *args, **kwargs)
+        self._notify_observers()
+        return r
+
+
+    def __ior__(self, *args, **kwargs):
+        r = np.ndarray.__ior__(self, *args, **kwargs)
+        self._notify_observers()
+        return r
+
+
+    def __itruediv__(self, *args, **kwargs):
+        r = np.ndarray.__itruediv__(self, *args, **kwargs)
+        self._notify_observers()
+        return r
+
+
+    def __idiv__(self, *args, **kwargs):
+        r = np.ndarray.__idiv__(self, *args, **kwargs)
+        self._notify_observers()
+        return r
+
+
+    def __rfloordiv__(self, *args, **kwargs):
+        r = np.ndarray.__rfloordiv__(self, *args, **kwargs)
+        self._notify_observers()
+        return r
+
+
+    def __iand__(self, *args, **kwargs):
+        r = np.ndarray.__iand__(self, *args, **kwargs)
+        self._notify_observers()
+        return r
+
+
+    def __imod__(self, *args, **kwargs):
+        r = np.ndarray.__imod__(self, *args, **kwargs)
+        self._notify_observers()
+        return r
+
+
+    def __iadd__(self, *args, **kwargs):
+        r = np.ndarray.__iadd__(self, *args, **kwargs)
+        self._notify_observers()
+        return r
+
+
+    def __imul__(self, *args, **kwargs):
+        r = np.ndarray.__imul__(self, *args, **kwargs)
+        self._notify_observers()
+        return r
+
+
+    def __rshift__(self, *args, **kwargs):
+        r = np.ndarray.__rshift__(self, *args, **kwargs)
+        self._notify_observers()
+        return r
+

GPy/core/parameterization/variational.py

@@ -15,9 +15,9 @@ class Normal(Parameterized):
     '''
     def __init__(self, means, variances, name='latent space'):
         Parameterized.__init__(self, name=name)
-        self.means = Param("mean", means)
+        self.mean = Param("mean", means)
-        self.variances = Param('variance', variances, Logexp())
+        self.variance = Param('variance', variances, Logexp())
-        self.add_parameters(self.means, self.variances)
+        self.add_parameters(self.mean, self.variance)
 
     def plot(self, *args):
         """

GPy/core/sparse_gp.py

@@ -55,10 +55,7 @@ class SparseGP(GP):
         self.add_parameter(self.Z, index=0)
 
     def parameters_changed(self):
-        Xvar = self.X_variance
+        self.posterior, self._log_marginal_likelihood, self.grad_dict = self.inference_method.inference(self.kern, self.X, self.X_variance, self.Z, self.likelihood, self.Y)
-        if self.X_variance is not None:
-            Xvar = param_to_array(self.X_variance)
-        self.posterior, self._log_marginal_likelihood, self.grad_dict = self.inference_method.inference(self.kern, param_to_array(self.X), Xvar, param_to_array(self.Z), self.likelihood, self.Y)
 
         #The derivative of the bound wrt the inducing inputs Z
         self.Z.gradient = self.kern.gradients_X(self.grad_dict['dL_dKmm'], self.Z)

GPy/examples/dimensionality_reduction.py

@@ -3,7 +3,7 @@
 import numpy as _np
 default_seed = _np.random.seed(123344)
 
-def bgplvm_test_model(seed=default_seed, optimize=False, verbose=1, plot=False):
+def bgplvm_test_model(seed=default_seed, optimize=False, verbose=1, plot=False, output_dim=1e4):
     """
     model for testing purposes. Samples from a GP with rbf kernel and learns
     the samples with a new kernel. Normally not for optimization, just model cheking
@@ -18,7 +18,7 @@ def bgplvm_test_model(seed=default_seed, optimize=False, verbose=1, plot=False):
         input_dim = 3
     else:
         input_dim = 1
-        output_dim = 25
+        output_dim = output_dim
 
     # generate GPLVM-like data
     X = _np.random.rand(num_inputs, input_dim)
@@ -27,7 +27,7 @@ def bgplvm_test_model(seed=default_seed, optimize=False, verbose=1, plot=False):
          #+ GPy.kern.white(input_dim, 0.01)
          )
     K = k.K(X)
-    Y = _np.random.multivariate_normal(_np.zeros(num_inputs), K, output_dim).T
+    Y = _np.random.multivariate_normal(_np.zeros(num_inputs), K, (output_dim,)).T
 
     # k = GPy.kern.rbf_inv(input_dim, .5, _np.ones(input_dim) * 2., ARD=True) + GPy.kern.bias(input_dim) + GPy.kern.white(input_dim)
     k = GPy.kern.linear(input_dim)# + GPy.kern.bias(input_dim) + GPy.kern.white(input_dim, 0.00001)
@@ -266,11 +266,10 @@ def bgplvm_simulation(optimize=True, verbose=1,
     Y = Ylist[0]
     k = kern.linear(Q, ARD=True)# + kern.white(Q, _np.exp(-2)) # + kern.bias(Q)
     m = BayesianGPLVM(Y, Q, init="PCA", num_inducing=num_inducing, kernel=k)
-    m.Gaussian_noise = Y.var() / 100.
     
     if optimize:
         print "Optimizing model:"
-        m.optimize('scg', messages=verbose, max_iters=max_iters,
+        m.optimize('bfgs', messages=verbose, max_iters=max_iters,
                    gtol=.05)
     if plot:
         m.q.plot("BGPLVM Latent Space 1D")

GPy/inference/latent_function_inference/varDTC.py

@@ -2,9 +2,8 @@
 # Licensed under the BSD 3-clause license (see LICENSE.txt)
 
 from posterior import Posterior
-from ...util.linalg import jitchol, backsub_both_sides, tdot, dtrtrs, dpotri, symmetrify
+from ...util.linalg import jitchol, backsub_both_sides, tdot, dtrtrs, dtrtri, dpotri, dpotrs, symmetrify
 import numpy as np
-from ...util.linalg import dtrtri
 from ...util.caching import Cacher
 from ...util.misc import param_to_array
 log_2_pi = np.log(2*np.pi)
@@ -85,7 +84,7 @@ class VarDTC(object):
                 tmp = tmp.T
             # no backsubstitution because of bound explosion on tr(A) if not...
             LmInv, _ = dtrtri(Lm, lower=1)
-            A = LmInv.T.dot(psi2_beta.dot(LmInv))
+            A = LmInv.dot(psi2_beta.dot(LmInv.T))
             #print A.sum()
         else:
             if het_noise:
@@ -97,6 +96,7 @@ class VarDTC(object):
 
         # factor B
         B = np.eye(num_inducing) + A
+        self.A = A
         LB = jitchol(B)
 
         # VVT_factor is a matrix such that tdot(VVT_factor) = VVT...this is for efficiency!

GPy/inference/optimization/scg.py

@@ -69,8 +69,8 @@ def SCG(f, gradf, x, optargs=(), maxiters=500, max_f_eval=np.inf, display=True,
     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.
+    betamin = 1.0e-15 # Lower bound on scale.
-    betamax = 1.0e50 # Upper bound on scale.
+    betamax = 1.0e15 # Upper bound on scale.
     status = "Not converged"
 
     flog = [fold]

GPy/models/bayesian_gplvm.py

@@ -71,17 +71,17 @@ class BayesianGPLVM(SparseGP, GPLVM):
 
     def parameters_changed(self):
         super(BayesianGPLVM, self).parameters_changed()
-        #self._log_marginal_likelihood -= self.KL_divergence()
+        self._log_marginal_likelihood -= self.KL_divergence()
 
         dL_dmu, dL_dS = self.dL_dmuS()
 
         # dL:
-        self.q.means.gradient  = dL_dmu
+        self.q.mean.gradient  = dL_dmu
-        self.q.variances.gradient  = dL_dS  
+        self.q.variance.gradient  = dL_dS  
 
         # dKL:
-        #self.q.means.gradient -= self.X
+        self.q.mean.gradient -= self.X
-        #self.q.variances.gradient -= (1. - (1. / (self.X_variance))) * 0.5
+        self.q.variance.gradient -= (1. - (1. / (self.X_variance))) * 0.5
     
     def plot_latent(self, plot_inducing=True, *args, **kwargs):
         """

GPy/models/sparse_gp_regression.py

@@ -32,7 +32,7 @@ class SparseGPRegression(SparseGP):
 
         # kern defaults to rbf (plus white for stability)
         if kernel is None:
-            kernel = kern.rbf(input_dim)  + kern.white(input_dim, variance=1e-3)
+            kernel = kern.rbf(input_dim)#  + kern.white(input_dim, variance=1e-3)
 
         # Z defaults to a subset of the data
         if Z is None:

GPy/plotting/matplot_dep/kernel_plots.py

@@ -7,6 +7,7 @@ import pylab as pb
 import Tango
 from matplotlib.textpath import TextPath
 from matplotlib.transforms import offset_copy
+from ...kern.parts.linear import Linear
 
 
 def plot_ARD(kernel, fignum=None, ax=None, title='', legend=False):

GPy/plotting/matplot_dep/variational_plots.py

@@ -14,14 +14,14 @@ def plot(parameterized, fignum=None, ax=None, colors=None):
 
     """
     if ax is None:
-        fig = pb.figure(num=fignum, figsize=(8, min(12, (2 * parameterized.means.shape[1]))))
+        fig = pb.figure(num=fignum, figsize=(8, min(12, (2 * parameterized.mean.shape[1]))))
     if colors is None:
         colors = pb.gca()._get_lines.color_cycle
         pb.clf()
     else:
         colors = iter(colors)
     plots = []
-    means, variances = param_to_array(parameterized.means, parameterized.variances)
+    means, variances = param_to_array(parameterized.mean, parameterized.variance)
     x = np.arange(means.shape[0])
     for i in range(means.shape[1]):
         if ax is None: