[updates] merged update structure

GPy/core/gp.py

@@ -51,7 +51,7 @@ class GP(Model):
         assert Y.ndim == 2
         logger.info("initializing Y")
 
-        if normalizer is None:
+        if normalizer is True:
             self.normalizer = MeanNorm()
         elif normalizer is False:
             self.normalizer = None

GPy/core/model.py

@@ -213,6 +213,7 @@ class Model(Parameterized):
     def optimize(self, optimizer=None, start=None, **kwargs):
         """
         Optimize the model using self.log_likelihood and self.log_likelihood_gradient, as well as self.priors.
+        
         kwargs are passed to the optimizer. They can be:
 
         :param max_f_eval: maximum number of function evaluations
@@ -222,7 +223,15 @@ class Model(Parameterized):
         :param optimizer: which optimizer to use (defaults to self.preferred optimizer)
         :type optimizer: string
 
-        TODO: valid args
+        Valid optimizers are:
+          - 'scg': scaled conjugate gradient method, recommended for stability.
+                   See also GPy.inference.optimization.scg
+          - 'fmin_tnc': truncated Newton method (see scipy.optimize.fmin_tnc)
+          - 'simplex': the Nelder-Mead simplex method (see scipy.optimize.fmin),
+          - 'lbfgsb': the l-bfgs-b method (see scipy.optimize.fmin_l_bfgs_b),
+          - 'sgd': stochastic gradient decsent (see scipy.optimize.sgd). For experts only!
+
+ 
         """
         if self.is_fixed:
             raise RuntimeError, "Cannot optimize, when everything is fixed"

GPy/core/sparse_gp_mpi.py

@@ -3,6 +3,7 @@
 
 import numpy as np
 from sparse_gp import SparseGP
+from numpy.linalg.linalg import LinAlgError
 from ..inference.latent_function_inference.var_dtc_parallel import update_gradients, VarDTC_minibatch
 
 import logging
@@ -42,10 +43,10 @@ class SparseGP_MPI(SparseGP):
                 assert isinstance(inference_method, VarDTC_minibatch), 'inference_method has to support MPI!'
                         
         super(SparseGP_MPI, self).__init__(X, Y, Z, kernel, likelihood, inference_method=inference_method, name=name, Y_metadata=Y_metadata, normalizer=normalizer)
-        self.updates = False
-        self.add_parameter(self.X, index=0)
+        self.update_model(False)
+        self.link_parameter(self.X, index=0)
         if variational_prior is not None:
-            self.add_parameter(variational_prior)
+            self.link_parameter(variational_prior)
 #         self.X.fix()
 
         self.mpi_comm = mpi_comm
@@ -58,7 +59,8 @@ class SparseGP_MPI(SparseGP):
             self.Y_local = self.Y[N_start:N_end]
             print 'MPI RANK '+str(self.mpi_comm.rank)+' with the data range '+str(self.N_range)
             mpi_comm.Bcast(self.param_array, root=0)
-        self.updates = True
+        self.update_model(True)
+
 
     def __getstate__(self):
         dc = super(SparseGP_MPI, self).__getstate__()
@@ -83,10 +85,6 @@ class SparseGP_MPI(SparseGP):
             if self._IN_OPTIMIZATION_ and self.mpi_comm.rank==0:
                 self.mpi_comm.Bcast(np.int32(1),root=0)
             self.mpi_comm.Bcast(p, root=0)        
-            
-        from ..util.debug import checkFinite
-        checkFinite(p, 'optimizer_array')
-        
         SparseGP.optimizer_array.fset(self,p)
         
     def optimize(self, optimizer=None, start=None, **kwargs):
@@ -102,7 +100,13 @@ class SparseGP_MPI(SparseGP):
             while True:
                 self.mpi_comm.Bcast(flag,root=0)
                 if flag==1:
-                    self.optimizer_array = x
+                    try:
+                        self.optimizer_array = x
+                        self._fail_count = 0
+                    except (LinAlgError, ZeroDivisionError, ValueError):
+                        if self._fail_count >= self._allowed_failures:
+                            raise
+                        self._fail_count += 1
                 elif flag==-1:
                     break
                 else:

GPy/examples/classification.py

@@ -5,9 +5,13 @@
 """
 Gaussian Processes classification
 """
-import pylab as pb
 import GPy
 
+try:
+    import pylab as pb
+except:
+    pass
+
 default_seed = 10000
 
 def oil(num_inducing=50, max_iters=100, kernel=None, optimize=True, plot=True):

GPy/examples/coreg_example.py

@@ -1,5 +1,8 @@
 import numpy as np
-import pylab as pb
+try:
+    import pylab as pb
+except:
+    pass
 import GPy
 pb.ion()
 pb.close('all')

GPy/examples/non_gaussian.py

@@ -1,7 +1,10 @@
 import GPy
 import numpy as np
-import matplotlib.pyplot as plt
 from GPy.util import datasets
+try:
+    import matplotlib.pyplot as plt
+except:
+    pass
 
 def student_t_approx(optimize=True, plot=True):
     """

GPy/examples/regression.py

@@ -4,7 +4,10 @@
 """
 Gaussian Processes regression examples
 """
-import pylab as pb
+try:
+    import pylab as pb
+except:
+    pass
 import numpy as np
 import GPy
 

GPy/examples/stochastic.py

@@ -1,7 +1,10 @@
 # Copyright (c) 2012, GPy authors (see AUTHORS.txt).
 # Licensed under the BSD 3-clause license (see LICENSE.txt)
 
-import pylab as pb
+try:
+    import pylab as pb
+except:
+    pass
 import numpy as np
 import GPy
 

GPy/examples/tutorials.py

@@ -6,8 +6,11 @@
 Code of Tutorials
 """
 
-import pylab as pb
-pb.ion()
+try:
+    import pylab as pb
+    pb.ion()
+except:
+    pass
 import numpy as np
 import GPy
 

GPy/inference/latent_function_inference/dtc.py

@@ -124,6 +124,7 @@ class vDTC(object):
         v, _ = dtrtrs(L, tmp, lower=1, trans=1)
         tmp, _ = dtrtrs(LA, Li, lower=1, trans=0)
         P = tdot(tmp.T)
+        stop
 
         #compute log marginal
         log_marginal = -0.5*num_data*output_dim*np.log(2*np.pi) + \

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
+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])
@@ -166,31 +167,18 @@ class VarDTC_minibatch(LatentFunctionInference):
         # Compute Common Components
         #======================================================================
         
-        from ...util.debug import checkFullRank
-        
         Kmm = kern.K(Z).copy()
         diag.add(Kmm, self.const_jitter)
-        r1 = checkFullRank(Kmm,name='Kmm')
-        Lm = jitchol(Kmm)
+        KmmInv,Lm,LmInv,_ = pdinv(Kmm)
         
-        LmInvPsi2LmInvT = backsub_both_sides(Lm,psi2_full,transpose='right')
+        LmInvPsi2LmInvT = LmInv.dot(psi2_full).dot(LmInv.T)
         Lambda = np.eye(Kmm.shape[0])+LmInvPsi2LmInvT
-        r2 = checkFullRank(Lambda,name='Lambda')
-        if (not r1) or (not r2):
-            raise
-        LL = jitchol(Lambda)
-        LL = np.dot(Lm,LL)
-        b,_ = dtrtrs(LL, psi1Y_full.T)
+        LInv,LL,LLInv,logdet_L = pdinv(Lambda)
+        b = LLInv.dot(LmInv.dot(psi1Y_full.T))
         bbt = np.square(b).sum()
-        v,_ = dtrtrs(LL.T,b,lower=False)
-        vvt = np.einsum('md,od->mo',v,v)
+        v = LmInv.T.dot(LLInv.T.dot(b))
         
-        Psi2LLInvT = dtrtrs(LL,psi2_full)[0].T
-        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
+        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.
         
         # Cache intermediate results
         self.midRes['dL_dpsi2R'] = dL_dpsi2R
@@ -203,20 +191,20 @@ 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 =  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
         
@@ -341,13 +329,7 @@ def update_gradients(model, mpi_comm=None):
         Y = model.Y_local
         X = model.X[model.N_range[0]:model.N_range[1]]
 
-    try:
-        model._log_marginal_likelihood, dL_dKmm, model.posterior = model.inference_method.inference_likelihood(model.kern, X, model.Z, model.likelihood, Y)
-    except Exception:
-        if model.mpi_comm is None or model.mpi_comm.rank==0:
-            import time
-            model.pickle('model_'+str(int(time.time()))+'.pickle')
-        raise
+    model._log_marginal_likelihood, dL_dKmm, model.posterior = model.inference_method.inference_likelihood(model.kern, X, model.Z, model.likelihood, Y)
     
     het_noise = model.likelihood.variance.size > 1
     

GPy/kern/_src/hierarchical.py

@@ -10,11 +10,11 @@ class Hierarchical(Kernpart):
     A kernel part which can reopresent a hierarchy of indepencnce: a generalisation of independent_outputs
 
     """
-    def __init__(self,parts):
+    def __init__(self,parts,name='hierarchy'):
         self.levels = len(parts)
         self.input_dim = parts[0].input_dim + 1
         self.num_params = np.sum([k.num_params for k in parts])
-        self.name = 'hierarchy'
+        self.name = name
         self.parts = parts
 
         self.param_starts = np.hstack((0,np.cumsum([k.num_params for k in self.parts[:-1]])))

GPy/kern/_src/rbf.py

@@ -20,8 +20,6 @@ class RBF(Stationary):
     _support_GPU = True
     def __init__(self, input_dim, variance=1., lengthscale=None, ARD=False, active_dims=None, name='rbf', useGPU=False):
         super(RBF, self).__init__(input_dim, variance, lengthscale, ARD, active_dims, name, useGPU=useGPU)
-        self.weave_options = {}
-        self.group_spike_prob = False
         self.psicomp = PSICOMP_RBF()
         if self.useGPU:
             self.psicomp = PSICOMP_RBF_GPU()

GPy/kern/_src/stationary.py

@@ -171,7 +171,8 @@ class Stationary(Kern):
 
         #the lower memory way with a loop
         ret = np.empty(X.shape, dtype=np.float64)
-        [np.sum(tmp*(X[:,q][:,None]-X2[:,q][None,:]), axis=1, out=ret[:,q]) for q in xrange(self.input_dim)]
+        for q in xrange(self.input_dim):
+            np.sum(tmp*(X[:,q][:,None]-X2[:,q][None,:]), axis=1, out=ret[:,q])
         ret /= self.lengthscale**2
 
         return ret
@@ -309,6 +310,19 @@ class Matern52(Stationary):
 
 
 class ExpQuad(Stationary):
+    """
+    The Exponentiated quadratic covariance function. 
+
+    .. math::
+
+       k(r) = \sigma^2 (1 + \sqrt{5} r + \\frac53 r^2) \exp(- \sqrt{5} r)
+
+    notes::
+     - Yes, this is exactly the same as the RBF covariance function, but the
+       RBF implementation also has some features for doing variational kernels
+       (the psi-statistics).
+
+    """
     def __init__(self, input_dim, variance=1., lengthscale=None, ARD=False, active_dims=None, name='ExpQuad'):
         super(ExpQuad, self).__init__(input_dim, variance, lengthscale, ARD, active_dims, name)
 

GPy/kern/_src/trunclinear.py

@@ -3,14 +3,10 @@
 
 
 import numpy as np
-from scipy import weave
 from kern import Kern
-from ...util.linalg import tdot
-from ...util.misc import param_to_array
 from ...core.parameterization import Param
 from ...core.parameterization.transformations import Logexp
 from ...util.caching import Cache_this
-from ...core.parameterization import variational
 from ...util.config import *
 
 class TruncLinear(Kern):

GPy/models/bcgplvm.py

@@ -3,8 +3,6 @@
 
 
 import numpy as np
-import pylab as pb
-import sys, pdb
 from ..core import GP
 from ..models import GPLVM
 from ..mappings import *

GPy/models/gplvm.py

@@ -3,7 +3,6 @@
 
 
 import numpy as np
-import pylab as pb
 from .. import kern
 from ..core import GP, Param
 from ..likelihoods import Gaussian
@@ -55,7 +54,7 @@ class GPLVM(GP):
         #J = np.zeros((X.shape[0],X.shape[1],self.output_dim))
         J = self.jacobian(X)
         for i in range(X.shape[0]):
-            target[i]=np.sqrt(pb.det(np.dot(J[i,:,:],np.transpose(J[i,:,:]))))
+            target[i]=np.sqrt(np.linalg.det(np.dot(J[i,:,:],np.transpose(J[i,:,:]))))
         return target
 
     def plot(self):
@@ -63,6 +62,7 @@ class GPLVM(GP):
         pb.scatter(self.likelihood.Y[:, 0], self.likelihood.Y[:, 1], 40, self.X[:, 0].copy(), linewidth=0, cmap=pb.cm.jet)  # @UndefinedVariable
         Xnew = np.linspace(self.X.min(), self.X.max(), 200)[:, None]
         mu, _ = self.predict(Xnew)
+        import pylab as pb
         pb.plot(mu[:, 0], mu[:, 1], 'k', linewidth=1.5)
 
     def plot_latent(self, labels=None, which_indices=None,

GPy/models/sparse_gplvm.py

@@ -3,13 +3,8 @@
 
 
 import numpy as np
-import pylab as pb
-import sys, pdb
+import sys
 from GPy.models.sparse_gp_regression import SparseGPRegression
-from GPy.models.gplvm import GPLVM
-# from .. import kern
-# from ..core import model
-# from ..util.linalg import pdinv, PCA
 
 class SparseGPLVM(SparseGPRegression):
     """

GPy/plotting/__init__.py

@@ -1,4 +1,7 @@
 # Copyright (c) 2014, GPy authors (see AUTHORS.txt).
 # Licensed under the BSD 3-clause license (see LICENSE.txt)
 
-import matplot_dep
+try:
+    import matplot_dep
+except (ImportError, NameError):
+    print 'Fail to load GPy.plotting.matplot_dep.'

GPy/plotting/matplot_dep/base_plots.py

@@ -2,8 +2,11 @@
 # Licensed under the BSD 3-clause license (see LICENSE.txt)
 
 
-import Tango
-import pylab as pb
+try:
+    import Tango
+    import pylab as pb
+except:
+    pass
 import numpy as np
 
 def ax_default(fignum, ax):

GPy/plotting/matplot_dep/dim_reduction_plots.py

@@ -1,12 +1,16 @@
-import pylab as pb
+
 import numpy as np
 from latent_space_visualizations.controllers.imshow_controller import ImshowController,ImAnnotateController
 from ...util.misc import param_to_array
 from ...core.parameterization.variational import VariationalPosterior
 from .base_plots import x_frame2D
 import itertools
-import Tango
-from matplotlib.cm import get_cmap
+try:
+    import Tango
+    from matplotlib.cm import get_cmap
+    import pylab as pb
+except:
+    pass
 
 def most_significant_input_dimensions(model, which_indices):
     """

GPy/plotting/matplot_dep/inference_plots.py

@@ -1,8 +1,10 @@
 # Copyright (c) 2012, GPy authors (see AUTHORS.txt).
 # Licensed under the BSD 3-clause license (see LICENSE.txt)
 
-import pylab as pb
-import sys
+try:
+    import pylab as pb
+except:
+    pass
 #import numpy as np
 #import Tango
 #from base_plots import gpplot, x_frame1D, x_frame2D

GPy/plotting/matplot_dep/kernel_plots.py

@@ -100,9 +100,7 @@ def plot_ARD(kernel, fignum=None, ax=None, title='', legend=False, filtering=Non
     return ax
 
 
-def plot(kernel, x=None, plot_limits=None, which_parts='all', resolution=None, *args, **kwargs):
-    if which_parts == 'all':
-        which_parts = [True] * kernel.size
+def plot(kernel, x=None, plot_limits=None, resolution=None, *args, **kwargs):
     if kernel.input_dim == 1:
         if x is None:
             x = np.zeros((1, 1))
@@ -133,7 +131,7 @@ def plot(kernel, x=None, plot_limits=None, which_parts='all', resolution=None, *
             assert x.size == 2, "The size of the fixed variable x is not 2"
             x = x.reshape((1, 2))
 
-        if plot_limits == None:
+        if plot_limits is None:
             xmin, xmax = (x - 5).flatten(), (x + 5).flatten()
         elif len(plot_limits) == 2:
             xmin, xmax = plot_limits
@@ -142,12 +140,10 @@ def plot(kernel, x=None, plot_limits=None, which_parts='all', resolution=None, *
 
         resolution = resolution or 51
         xx, yy = np.mgrid[xmin[0]:xmax[0]:1j * resolution, xmin[1]:xmax[1]:1j * resolution]
-        xg = np.linspace(xmin[0], xmax[0], resolution)
-        yg = np.linspace(xmin[1], xmax[1], resolution)
         Xnew = np.vstack((xx.flatten(), yy.flatten())).T
-        Kx = kernel.K(Xnew, x, which_parts)
+        Kx = kernel.K(Xnew, x)
         Kx = Kx.reshape(resolution, resolution).T
-        pb.contour(xg, yg, Kx, vmin=Kx.min(), vmax=Kx.max(), cmap=pb.cm.jet, *args, **kwargs) # @UndefinedVariable
+        pb.contour(xx, xx, Kx, vmin=Kx.min(), vmax=Kx.max(), cmap=pb.cm.jet, *args, **kwargs) # @UndefinedVariable
         pb.xlim(xmin[0], xmax[0])
         pb.ylim(xmin[1], xmax[1])
         pb.xlabel("x1")

GPy/plotting/matplot_dep/mapping_plots.py

@@ -1,9 +1,12 @@
 # Copyright (c) 2012, GPy authors (see AUTHORS.txt).
 # Licensed under the BSD 3-clause license (see LICENSE.txt)
 
-import pylab as pb
 import numpy as np
-import Tango
+try:
+    import Tango
+    import pylab as pb
+except:
+    pass
 from base_plots import x_frame1D, x_frame2D
 
 

GPy/plotting/matplot_dep/maps.py

@@ -1,13 +1,14 @@
 import numpy as np
-import pylab as pb
-import matplotlib.patches as patches
-from matplotlib.patches import Polygon
-from matplotlib.collections import PatchCollection
-#from matplotlib import cm
+try:
+    import pylab as pb
+    from matplotlib.patches import Polygon
+    from matplotlib.collections import PatchCollection
+    #from matplotlib import cm
+    pb.ion()
+except:
+    pass
 import re
 
-pb.ion()
-
 def plot(shape_records,facecolor='w',edgecolor='k',linewidths=.5, ax=None,xlims=None,ylims=None):
     """
     Plot the geometry of a shapefile

GPy/plotting/matplot_dep/models_plots.py

@@ -1,9 +1,12 @@
 # Copyright (c) 2012, GPy authors (see AUTHORS.txt).
 # Licensed under the BSD 3-clause license (see LICENSE.txt)
 
-import pylab as pb
+try:
+    import Tango
+    import pylab as pb
+except:
+    pass
 import numpy as np
-import Tango
 from base_plots import gpplot, x_frame1D, x_frame2D
 from ...util.misc import param_to_array
 from ...models.gp_coregionalized_regression import GPCoregionalizedRegression

GPy/plotting/matplot_dep/priors_plots.py

@@ -3,7 +3,10 @@
 
 
 import numpy as np
-import pylab as pb
+try:
+    import pylab as pb
+except:
+    pass
 
 
 def univariate_plot(prior):

GPy/plotting/matplot_dep/ssgplvm.py

@@ -6,7 +6,6 @@ import pylab
 
 from ...models import SSGPLVM
 from img_plots import plot_2D_images
-from ...util.misc import param_to_array
 
 class SSGPLVM_plot(object):
     def __init__(self,model, imgsize):

GPy/testing/kernel_tests.py

@@ -215,7 +215,10 @@ def check_kernel_gradient_functions(kern, X=None, X2=None, output_ind=None, verb
     if verbose:
         print("Checking gradients of Kdiag(X) wrt X.")
     try:
-        result = Kern_check_dKdiag_dX(kern, X=X).checkgrad(verbose=verbose)
+        testmodel = Kern_check_dKdiag_dX(kern, X=X)
+        if fixed_X_dims is not None:
+            testmodel.X[:,fixed_X_dims].fix()
+        result = testmodel.checkgrad(verbose=verbose)
     except NotImplementedError:
         result=True
         if verbose:
@@ -346,6 +349,7 @@ class KernelTestsNonContinuous(unittest.TestCase):
         kern = GPy.kern.IndependentOutputs(k, -1, name='ind_split')
         self.assertTrue(check_kernel_gradient_functions(kern, X=self.X, X2=self.X2, verbose=verbose, fixed_X_dims=-1))
 
+
     def test_ODE_UY(self):
         kern = GPy.kern.ODE_UY(2, active_dims=[0, self.D])
         X = self.X[self.X[:,-1]!=2]

GPy/testing/parameterized_tests.py

@@ -143,8 +143,9 @@ class ParameterizedTest(unittest.TestCase):
 
     def test_randomize(self):
         ps = self.test1.param.view(np.ndarray).copy()
+        self.test1.param[2:5].fix()
         self.test1.param.randomize()
-        self.assertFalse(np.all(ps==self.test1.param))
+        self.assertFalse(np.all(ps==self.test1.param),str(ps)+str(self.test1.param))
 
     def test_fixing_randomize_parameter_handling(self):
         self.rbf.fix(warning=True)
@@ -164,10 +165,8 @@ class ParameterizedTest(unittest.TestCase):
     def test_fixing_optimize(self):
         self.testmodel.kern.lengthscale.fix()
         val = float(self.testmodel.kern.lengthscale)
-        val2 = float(self.testmodel.kern.variance)
         self.testmodel.randomize()
         self.assertEqual(val, self.testmodel.kern.lengthscale)
-        self.assertNotEqual(val2, self.testmodel.kern.variance)
 
     def test_add_parameter_in_hierarchy(self):
         from GPy.core import Param

GPy/util/datasets.py

@@ -2,7 +2,6 @@ import csv
 import os
 import copy
 import numpy as np
-import pylab as pb
 import GPy
 import scipy.io
 import cPickle as pickle
@@ -346,6 +345,7 @@ def football_data(season='1314', data_set='football_data'):
     data_resources[data_set_season]['files'] = [files]
     if not data_available(data_set_season):
         download_data(data_set_season)
+    import pylab as pb
     for file in reversed(files):
         filename = os.path.join(data_path, data_set_season, file)
         # rewrite files removing blank rows.

GPy/util/pca.py

@@ -5,8 +5,11 @@ Created on 10 Sep 2012
 @copyright: Max Zwiessele 2012
 '''
 import numpy
-import pylab
-import matplotlib
+try:
+    import pylab
+    import matplotlib
+except:
+    pass
 from numpy.linalg.linalg import LinAlgError
 
 class pca(object):
@@ -88,13 +91,15 @@ class pca(object):
 
     def plot_2d(self, X, labels=None, s=20, marker='o',
                 dimensions=(0, 1), ax=None, colors=None,
-                fignum=None, cmap=matplotlib.cm.jet, # @UndefinedVariable
+                fignum=None, cmap=None, # @UndefinedVariable
                 ** kwargs):
         """
         Plot dimensions `dimensions` with given labels against each other in 
         PC space. Labels can be any sequence of labels of dimensions X.shape[0].
         Labels can be drawn with a subsequent call to legend()
         """
+        if cmap is None:
+            cmap = matplotlib.cm.jet
         if ax is None:
             fig = pylab.figure(fignum)
             ax = fig.add_subplot(111)

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 +1,31 @@
+==============
+ 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
+
+MacOSX
+===================================
+

setup.py

@@ -24,9 +24,9 @@ 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'],
       extras_require = {
-        'docs':['Sphinx', 'ipython'],
+        'docs':['matplotlib>=1.1','Sphinx','ipython'],
       },
       classifiers=[
       "License :: OSI Approved :: BSD License"],