Merge branch 'tjhgit-devel' into devel

GPy/kern/__init__.py

@@ -17,7 +17,7 @@ from ._src.eq_ode2 import EQ_ODE2
 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.eq_ode2 import EQ_ODE2
 from ._src.basis_funcs import LinearSlopeBasisFuncKernel, BasisFuncKernel, ChangePointBasisFuncKernel, DomainKernel
 

GPy/kern/_src/spline.py

@@ -0,0 +1,52 @@
+# Copyright (c) 2015, Thomas Hornung
+# Licensed under the BSD 3-clause license (see LICENSE.txt)
+
+import numpy as np
+from kern import Kern
+from ...core.parameterization import Param
+from ...core.parameterization.transformations import Logexp
+class Spline(Kern):
+    """
+    Linear spline kernel. You need to specify 2 parameters: the variance and c.
+    The variance is defined in powers of 10. Thus specifying -2 means 10^-2.
+    The parameter c allows to define the stiffness of the spline fit. A very stiff
+    spline equals linear regression.
+    See https://www.youtube.com/watch?v=50Vgw11qn0o starting at minute 1:17:28
+    Lit: Wahba, 1990
+    """
+
+    def __init__(self, input_dim, variance=1., c=1., active_dims=None, name='spline'):
+        super(Spline, self).__init__(input_dim, active_dims, name)
+        self.variance = Param('variance', variance, Logexp())
+        self.c = Param('c', c)
+        self.link_parameters(self.variance,self.c)
+
+
+    def K(self, X, X2=None):
+        if X2 is None: X2=X
+        term1 = (X+8.)*(X2.T+8.)/16.
+        term2 = abs((X-X2.T)/16.)**3
+        term3 = ((X+8.)/16.)**3 + ((X2.T+8.)/16.)**3
+        return (self.variance**2 * (1. + (1.+self.c) * term1 + self.c/3. * (term2 - term3)))
+
+    def Kdiag(self, X):
+        term1 = np.square(X+8.,X+8.)/16.
+        term3 = 2. * ((X+8.)/16.)**3
+        return (self.variance**2 * (1. + (1.+self.c) * term1 - self.c/3. * term3))[:,0]
+
+    def update_gradients_full(self, dL_dK, X, X2=None):
+        if X2 is None: X2=X
+        term1 = (X+8.)*(X2.T+8.)/16.
+        term2 = abs((X-X2.T)/16.)**3
+        term3 = ((X+8.)/16.)**3 + ((X2.T+8.)/16.)**3
+        self.variance.gradient = np.sum(dL_dK * (2*self.variance * (1. + (1.+self.c) * term1 + self.c/3. * ( term2 - term3))))
+        self.c.gradient = np.sum(dL_dK * (self.variance**2* (term1 + 1./3.*(term2 - term3))))
+
+    def update_gradients_diag(self, dL_dKdiag, X):
+        raise NotImplementedError
+
+    def gradients_X(self, dL_dK, X, X2=None):
+        raise NotImplementedError
+
+    def gradients_X_diag(self, dL_dKdiag, X):
+        raise NotImplementedError

GPy/plotting/matplot_dep/base_plots.py

@@ -3,7 +3,7 @@
 
 
 try:
-    import Tango
+    #import Tango
     import pylab as pb
 except:
     pass
@@ -17,11 +17,11 @@ def ax_default(fignum, ax):
         fig = ax.figure
     return fig, ax
 
-def meanplot(x, mu, color=Tango.colorsHex['darkBlue'], ax=None, fignum=None, linewidth=2,**kw):
+def meanplot(x, mu, color='#3300FF', ax=None, fignum=None, linewidth=2,**kw):
     _, axes = ax_default(fignum, ax)
     return axes.plot(x,mu,color=color,linewidth=linewidth,**kw)
 
-def gpplot(x, mu, lower, upper, edgecol=Tango.colorsHex['darkBlue'], fillcol=Tango.colorsHex['lightBlue'], ax=None, fignum=None, **kwargs):
+def gpplot(x, mu, lower, upper, edgecol='#3300FF', fillcol='#33CCFF', ax=None, fignum=None, **kwargs):
     _, axes = ax_default(fignum, ax)
 
     mu = mu.flatten()

GPy/plotting/matplot_dep/models_plots.py

@@ -2,7 +2,7 @@
 # Licensed under the BSD 3-clause license (see LICENSE.txt)
 
 try:
-    import Tango
+#    import Tango
     import pylab as pb
 except:
     pass
@@ -17,8 +17,12 @@ def plot_fit(model, plot_limits=None, which_data_rows='all',
         which_data_ycols='all', fixed_inputs=[],
         levels=20, samples=0, fignum=None, ax=None, resolution=None,
         plot_raw=False,
+<<<<<<< HEAD
         linecol=Tango.colorsHex['darkBlue'],fillcol=Tango.colorsHex['lightBlue'], Y_metadata=None, data_symbol='kx',
         apply_link=False, samples_f=0, plot_uncertain_inputs=True, predict_kw=None):
+=======
+        linecol='#3300FF',fillcol='#00FFFF', Y_metadata=None, data_symbol='kx'):
+>>>>>>> e115778d743c8979af2a10143e33861b88fb883a
     """
     Plot the posterior of the GP.
       - In one dimension, the function is plotted with a shaded region identifying two standard deviations.
@@ -126,7 +130,7 @@ def plot_fit(model, plot_limits=None, which_data_rows='all',
             print Ysim.shape
             print Xnew.shape
             for yi in Ysim.T:
-                plots['posterior_samples'] = ax.plot(Xnew, yi[:,None], Tango.colorsHex['darkBlue'], linewidth=0.25)
+                plots['posterior_samples'] = ax.plot(Xnew, yi[:,None], '#3300FF', linewidth=0.25)
                 #ax.plot(Xnew, yi[:,None], marker='x', linestyle='--',color=Tango.colorsHex['darkBlue']) #TODO apply this line for discrete outputs.
 
         if samples_f: #NOTE not tested with fixed_inputs