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

GPy/inference/latent_function_inference/var_dtc_parallel.py

@@ -2,7 +2,7 @@
 # Licensed under the BSD 3-clause license (see LICENSE.txt)
 
 from posterior import Posterior
-from ...util.linalg import jitchol, backsub_both_sides, tdot, dtrtrs, dtrtri
+from ...util.linalg import jitchol, backsub_both_sides, tdot, dtrtrs, dtrtri,pdinv
 from ...util import diag
 from ...core.parameterization.variational import VariationalPosterior
 import numpy as np
@@ -144,6 +144,7 @@ class VarDTC_minibatch(LatentFunctionInference):
         """
         
         num_data, output_dim = Y.shape 
+        input_dim = Z.shape[0]
         if self.mpi_comm != None:
             num_data_all = np.array(num_data,dtype=np.int32)
             self.mpi_comm.Allreduce([np.int32(num_data), MPI.INT], [num_data_all, MPI.INT])
@@ -171,28 +172,19 @@ class VarDTC_minibatch(LatentFunctionInference):
         Kmm = kern.K(Z).copy()
         diag.add(Kmm, self.const_jitter)
         r1 = checkFullRank(Kmm,name='Kmm')
-        Lm = jitchol(Kmm)
+        KmmInv,Lm,LmInv,_ = pdinv(Kmm)
-        LmInv = dtrtri(Lm)
         
-        #LmInvPsi2LmInvT = LmInv.dot(psi2_full).dot(LmInv.T)
+        LmInvPsi2LmInvT = LmInv.dot(psi2_full).dot(LmInv.T)
-        LmInvPsi2LmInvT = backsub_both_sides(Lm,psi2_full,transpose='right')
         Lambda = np.eye(Kmm.shape[0])+LmInvPsi2LmInvT
         r2 = checkFullRank(Lambda,name='Lambda')
-        if (not r1) or (not r2):
+#         if (not r1) or (not r2):
-            raise
+#             raise
-        LL = jitchol(Lambda)
+        LInv,LL,LLInv,logdet_L = pdinv(Lambda)
-        LL = np.dot(Lm,LL)
+        b = LLInv.dot(LmInv.dot(psi1Y_full.T))
-        b,_ = dtrtrs(LL, psi1Y_full.T)
         bbt = np.square(b).sum()
-        v,_ = dtrtrs(LL.T,b,lower=False)
+        v = LmInv.T.dot(LLInv.T.dot(b))
-        vvt = np.einsum('md,od->mo',v,v)
         
-        Psi2LLInvT = dtrtrs(LL,psi2_full)[0].T
+        dL_dpsi2R = LmInv.T.dot(-LLInv.T.dot(tdot(b)+output_dim*np.eye(input_dim)).dot(LLInv)+output_dim*np.eye(input_dim)).dot(LmInv)/2.
-        LmInvPsi2LLInvT= dtrtrs(Lm,Psi2LLInvT)[0]
-        KmmInvPsi2LLInvT = dtrtrs(Lm,LmInvPsi2LLInvT,trans=True)[0]
-        KmmInvPsi2P = dtrtrs(LL,KmmInvPsi2LLInvT.T, trans=True)[0].T
-        
-        dL_dpsi2R = (output_dim*KmmInvPsi2P - vvt)/2. # dL_dpsi2 with R inside psi2
         
         # Cache intermediate results
         self.midRes['dL_dpsi2R'] = dL_dpsi2R
@@ -205,20 +197,21 @@ class VarDTC_minibatch(LatentFunctionInference):
             logL_R = -np.log(beta).sum()
         else:
             logL_R = -num_data*np.log(beta)
-        logL = -(output_dim*(num_data*log_2_pi+logL_R+psi0_full-np.trace(LmInvPsi2LmInvT))+YRY_full-bbt)/2.-output_dim*(-np.log(np.diag(Lm)).sum()+np.log(np.diag(LL)).sum())
+        logL = -(output_dim*(num_data*log_2_pi+logL_R+psi0_full-np.trace(LmInvPsi2LmInvT))+YRY_full-bbt)/2.-output_dim*logdet_L/2.
 
         #======================================================================
         # Compute dL_dKmm
         #======================================================================
         
-        dL_dKmm =  -(output_dim*np.einsum('md,od->mo',KmmInvPsi2LLInvT,KmmInvPsi2LLInvT) + vvt)/2.
+#         dL_dKmm =  -(output_dim*np.einsum('md,od->mo',KmmInvPsi2LLInvT,KmmInvPsi2LLInvT) + vvt)/2.
+        dL_dKmm =  dL_dpsi2R - output_dim*KmmInv.dot(psi2_full).dot(KmmInv)/2.
 
         #======================================================================
         # Compute the Posterior distribution of inducing points p(u|Y)
         #======================================================================
         
         if not self.Y_speedup or het_noise:
-            post = Posterior(woodbury_inv=KmmInvPsi2P, woodbury_vector=v, K=Kmm, mean=None, cov=None, K_chol=Lm)
+            post = Posterior(woodbury_inv=LmInv.T.dot(np.eye(input_dim)-LInv).dot(LmInv), woodbury_vector=v, K=Kmm, mean=None, cov=None, K_chol=Lm)
         else:
             post = None
         

doc/GPy.testing.rst

@@ -84,6 +84,14 @@ GPy.testing.prior_tests module
     :undoc-members:
     :show-inheritance:
 
+GPy.testing.tie_tests module
+----------------------------
+
+.. automodule:: GPy.testing.tie_tests
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
 
 Module contents
 ---------------

doc/index.rst

@@ -19,6 +19,9 @@ You may also be interested by some examples in the GPy/examples folder.
 Contents:
 
 .. toctree::
+   :maxdepth: 2
+   
+   installation
    GPy
 
 

doc/installation.rst

@@ -0,0 +1,34 @@
+==============
+ Installation
+==============
+
+
+Linux
+============
+
+
+Windows
+======================
+One easy way to get a Python distribution with the required packages is to use the Anaconda environment from Continuum Analytics.
+
+* Download and install the free version of Anaconda according to your operating system  from `their website <https://store.continuum.io>`_.
+* Open a (new) terminal window:
+
+  * Navigate to Applications/Accessories/cmd, or
+  * open *anaconda Command Prompt* from windows *start*
+
+You should now be able to launch a Python interpreter by typing *ipython* in the terminal. In the ipython prompt, you can check your installation by importing the libraries we will need later:
+::
+    $ import numpy
+    $ import pylab
+
+To install the latest version of GPy, *git* is required. A *git* client on Windows can be found `here <http://git-scm.com/download/win>`_. It is recommened to install with the option "*Use Git from the Windows Command Prompt*". Then, GPy can be installed with the following command
+::
+    pip install git+https://github.com/SheffieldML/GPy.git@devel
+
+Note that some of the functionalities in GPy require a *C/C++* compiler. One option would be to install a MSVC compiler, e.g., an Express Edition can be found `here <http://www.microsoft.com/express/download>`_.
+
+
+MacOSX
+===================================
+

setup.py

@@ -24,7 +24,7 @@ setup(name = 'GPy',
       package_data = {'GPy': ['defaults.cfg', 'installation.cfg', 'util/data_resources.json', 'util/football_teams.json']},
       py_modules = ['GPy.__init__'],
       long_description=read('README.md'),
-      install_requires=['numpy>=1.6', 'scipy>=0.9','matplotlib>=1.1', 'nose'],
+      install_requires=['numpy>=1.6', 'scipy>=0.9','matplotlib>=1.1'],
       extras_require = {
         'docs':['Sphinx', 'ipython'],
       },