Merge remote-tracking branch 'upstream/devel' into devel

GPy/__version__.py

@@ -1 +1 @@
-__version__ = "0.9.4"
+__version__ = "0.9.5"

GPy/inference/latent_function_inference/var_dtc.py

@@ -190,8 +190,8 @@ class VarDTC(LatentFunctionInference):
             tmp, _ = dtrtrs(Lm, psi1V, lower=1, trans=0)
             tmp, _ = dpotrs(LB, tmp, lower=1)
             woodbury_vector, _ = dtrtrs(Lm, tmp, lower=1, trans=1)
-        Bi, _ = dpotri(LB, lower=1)
-        symmetrify(Bi)
+        #Bi, _ = dpotri(LB, lower=1)
+        #symmetrify(Bi)
         Bi = -dpotri(LB, lower=1)[0]
         diag.add(Bi, 1)
 

GPy/kern/__init__.py

@@ -28,4 +28,4 @@ from .src.trunclinear import TruncLinear,TruncLinear_inf
 from .src.splitKern import SplitKern,DEtime
 from .src.splitKern import DEtime as DiffGenomeKern
 from .src.spline import Spline
-from .src.basis_funcs import LinearSlopeBasisFuncKernel, BasisFuncKernel, ChangePointBasisFuncKernel, DomainKernel
+from .src.basis_funcs import LogisticBasisFuncKernel, LinearSlopeBasisFuncKernel, BasisFuncKernel, ChangePointBasisFuncKernel, DomainKernel

GPy/kern/src/ODE_UYC.py

@@ -18,7 +18,7 @@ class ODE_UYC(Kern):
         self.lengthscale_U = Param('lengthscale_U', lengthscale_U, Logexp())
         self.ubias = Param('ubias', ubias, Logexp())
 
-        self.add_parameters(self.variance_Y, self.variance_U, self.lengthscale_Y, self.lengthscale_U, self.ubias)
+        self.link_parameters(self.variance_Y, self.variance_U, self.lengthscale_Y, self.lengthscale_U, self.ubias)
 
     def K(self, X, X2=None):
         # model :   a * dy/dt + b * y = U

GPy/kern/src/ODE_st.py

@@ -38,7 +38,7 @@ class ODE_st(Kern):
         self.b = Param('b', b, Logexp())
         self.c = Param('c', c, Logexp())
 
-        self.add_parameters(self.a, self.b, self.c, self.variance_Yt, self.variance_Yx, self.lengthscale_Yt,self.lengthscale_Yx)
+        self.link_parameters(self.a, self.b, self.c, self.variance_Yt, self.variance_Yx, self.lengthscale_Yt,self.lengthscale_Yx)
 
 
     def K(self, X, X2=None):        

GPy/kern/src/ODE_t.py

@@ -17,7 +17,7 @@ class ODE_t(Kern):
                 self.a= Param('a', a, Logexp())
                 self.c = Param('c', c, Logexp())
                 self.ubias = Param('ubias', ubias, Logexp())
-                self.add_parameters(self.a, self.c, self.variance_Yt, self.lengthscale_Yt,self.ubias)
+                self.link_parameters(self.a, self.c, self.variance_Yt, self.lengthscale_Yt,self.ubias)
 
         def K(self, X, X2=None):
                 """Compute the covariance matrix between X and X2."""        

GPy/plotting/gpy_plot/latent_plots.py

@@ -50,6 +50,17 @@ def _wait_for_updates(view, updates):
             # No updateable view:
             pass
 
+def _new_canvas(self, projection, kwargs, which_indices):
+    input_1, input_2, input_3 = sig_dims = self.get_most_significant_input_dimensions(which_indices)
+
+    if input_3 is None:
+        zlabel = None
+    else:
+        zlabel = 'latent dimension %i' % input_3
+    canvas, kwargs = pl().new_canvas(projection=projection, xlabel='latent dimension %i' % input_1,
+        ylabel='latent dimension %i' % input_2,
+        zlabel=zlabel, **kwargs)
+    return canvas, projection, kwargs, sig_dims
 
 def _plot_latent_scatter(canvas, X, visible_dims, labels, marker, num_samples, projection='2d', **kwargs):
     from .. import Tango
@@ -85,12 +96,8 @@ def plot_latent_scatter(self, labels=None,
     :param str marker: markers to use - cycle if more labels then markers are given
     :param kwargs: the kwargs for the scatter plots
     """
-    input_1, input_2, input_3 = sig_dims = self.get_most_significant_input_dimensions(which_indices)
+    canvas, projection, kwargs, sig_dims = _new_canvas(self, projection, kwargs, which_indices)
 
-    canvas, kwargs = pl().new_canvas(projection=projection,
-                              xlabel='latent dimension %i' % input_1,
-                              ylabel='latent dimension %i' % input_2,
-                              zlabel='latent dimension %i' % input_3, **kwargs)
     X, _, _ = get_x_y_var(self)
     if labels is None:
         labels = np.ones(self.num_data)
@@ -101,8 +108,6 @@ def plot_latent_scatter(self, labels=None,
     return pl().add_to_canvas(canvas, dict(scatter=scatters), legend=legend)
 
 
-
-
 def plot_latent_inducing(self,
                         which_indices=None,
                         legend=False,
@@ -122,17 +127,8 @@ def plot_latent_inducing(self,
     :param str marker: markers to use - cycle if more labels then markers are given
     :param kwargs: the kwargs for the scatter plots
     """
-    input_1, input_2, input_3 = sig_dims = self.get_most_significant_input_dimensions(which_indices)
-    if input_3 is None: zlabel=None
-    else: zlabel = 'latent dimension %i' % input_3
+    canvas, projection, kwargs, sig_dims = _new_canvas(self, projection, kwargs, which_indices)
 
-
-    if 'color' not in kwargs:
-        kwargs['color'] = 'white'
-    canvas, kwargs = pl().new_canvas(projection=projection,
-                              xlabel='latent dimension %i' % input_1,
-                              ylabel='latent dimension %i' % input_2,
-                              zlabel=zlabel, **kwargs)
     Z = self.Z.values
     labels = np.array(['inducing'] * Z.shape[0])
     scatters = _plot_latent_scatter(canvas, Z, sig_dims, labels, marker, num_samples, projection=projection, **kwargs)
@@ -231,7 +227,7 @@ def plot_latent(self, labels=None, which_indices=None,
                 plot_limits=None,
                 updates=False,
                 kern=None, marker='<>^vsd',
-                num_samples=1000,
+                num_samples=1000, projection='2d',
                 scatter_kwargs=None, **imshow_kwargs):
     """
     Plot the latent space of the GP on the inputs. This is the
@@ -251,6 +247,8 @@ def plot_latent(self, labels=None, which_indices=None,
     :param imshow_kwargs: the kwargs for the imshow (magnification factor)
     :param scatter_kwargs: the kwargs for the scatter plots
     """
+    if projection != '2d':
+        raise ValueError('Cannot plot latent in other then 2 dimensions, consider plot_scatter')
     input_1, input_2 = which_indices = self.get_most_significant_input_dimensions(which_indices)[:2]
     X = get_x_y_var(self)[0]
     _, _, Xgrid, _, _, xmin, xmax, resolution = helper_for_plot_data(self, X, plot_limits, which_indices, None, resolution)

GPy/plotting/matplot_dep/plot_definitions.py

@@ -48,7 +48,7 @@ class MatplotlibPlots(AbstractPlottingLibrary):
         fig.cols = cols
         return fig
 
-    def new_canvas(self, figure=None, col=1, row=1, projection='2d', xlabel=None, ylabel=None, zlabel=None, title=None, xlim=None, ylim=None, zlim=None, **kwargs):
+    def new_canvas(self, figure=None, row=1, col=1, projection='2d', xlabel=None, ylabel=None, zlabel=None, title=None, xlim=None, ylim=None, zlim=None, **kwargs):
         if projection == '3d':
             from mpl_toolkits.mplot3d import Axes3D
         elif projection == '2d':

GPy/plotting/plotly_dep/defaults.py

@@ -72,5 +72,5 @@ ard = dict(linewidth=1.2, barmode='stack')
 latent = dict(colorscale='Greys', reversescale=True, zsmooth='best')
 gradient = dict(colorscale='RdBu', opacity=.7)
 magnification = dict(colorscale='Greys', zsmooth='best', reversescale=True)
-latent_scatter = dict(marker_kwargs=dict(size='15', opacity=.7))
+latent_scatter = dict(marker_kwargs=dict(size='5', opacity=.7))
 # annotation = dict(fontdict=dict(family='sans-serif', weight='light', fontsize=9), zorder=.3, alpha=.7)

GPy/plotting/plotly_dep/plot_definitions.py

@@ -130,11 +130,12 @@ class PlotlyPlots(AbstractPlottingLibrary):
         except:
             #not matplotlib marker
             pass
+        marker_kwargs = marker_kwargs or {}
         marker_kwargs.setdefault('symbol', marker)
         if Z is not None:
             return Scatter3d(x=X, y=Y, z=Z, mode='markers',
                              showlegend=label is not None,
-                             marker=Marker(color=color, colorscale=cmap, **marker_kwargs or {}), 
+                             marker=Marker(color=color, colorscale=cmap, **marker_kwargs),
                              name=label, **kwargs)
         return Scatter(x=X, y=Y, mode='markers', showlegend=label is not None,
                        marker=Marker(color=color, colorscale=cmap, **marker_kwargs or {}),

GPy/testing/plotting_tests.py

@@ -27,13 +27,21 @@
 # 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.
 #===============================================================================
+
+
+#===============================================================================
+# SKIPPING PLOTTING BECAUSE IT BEHAVES DIFFERENTLY ON DIFFERENT
+# SYSTEMS, AND WILL MISBEHAVE
+from nose import SkipTest
+raise SkipTest("Skipping Matplotlib testing")
+#===============================================================================
+
 import matplotlib
 from unittest.case import TestCase
 matplotlib.use('agg')
 
 import numpy as np
 import GPy, os
-from nose import SkipTest
 
 from GPy.util.config import config
 from GPy.plotting import change_plotting_library, plotting_library

setup.cfg

@@ -1,5 +1,5 @@
 [bumpversion]
-current_version = 0.9.4
+current_version = 0.9.5
 tag = True
 commit = True