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

GPy/_models/__init__.py

@@ -0,0 +1,19 @@
+# Copyright (c) 2012, GPy authors (see AUTHORS.txt).
+# Licensed under the BSD 3-clause license (see LICENSE.txt)
+
+# from gp_regression import GPRegression; _gp_regression = gp_regression ; del gp_regression 
+# from gp_classification import GPClassification; _gp_classification = gp_classification ; del gp_classification 
+# from sparse_gp_regression import SparseGPRegression; _sparse_gp_regression = sparse_gp_regression ; del sparse_gp_regression 
+# from svigp_regression import SVIGPRegression; _svigp_regression = svigp_regression ; del svigp_regression 
+# from sparse_gp_classification import SparseGPClassification; _sparse_gp_classification = sparse_gp_classification ; del sparse_gp_classification 
+# from fitc_classification import FITCClassification; _fitc_classification = fitc_classification ; del fitc_classification 
+# from gplvm import GPLVM; _gplvm = gplvm ; del gplvm 
+# from bcgplvm import BCGPLVM; _bcgplvm = bcgplvm; del bcgplvm
+# from sparse_gplvm import SparseGPLVM; _sparse_gplvm = sparse_gplvm ; del sparse_gplvm 
+# from warped_gp import WarpedGP; _warped_gp = warped_gp ; del warped_gp 
+# from bayesian_gplvm import BayesianGPLVM; _bayesian_gplvm = bayesian_gplvm ; del bayesian_gplvm 
+# from mrd import MRD; _mrd = mrd ; del mrd 
+# from gradient_checker import GradientChecker; _gradient_checker = gradient_checker ; del gradient_checker 
+# from gp_multioutput_regression import GPMultioutputRegression; _gp_multioutput_regression = gp_multioutput_regression ; del gp_multioutput_regression 
+# from sparse_gp_multioutput_regression import SparseGPMultioutputRegression; _sparse_gp_multioutput_regression = sparse_gp_multioutput_regression ; del sparse_gp_multioutput_regression 
+

GPy/_models/bayesian_gplvm.py

@@ -2,14 +2,14 @@
 # Licensed under the BSD 3-clause license (see LICENSE.txt)
 
 import numpy as np
-from ..core import SparseGP
+from ..core.sparse_gp import SparseGP
 from ..likelihoods import Gaussian
 from .. import kern
 import itertools
 from matplotlib.colors import colorConverter
 from GPy.inference.optimization import SCG
 from GPy.util import plot_latent, linalg
-from GPy.models.gplvm import GPLVM
+from .gplvm import GPLVM
 from GPy.util.plot_latent import most_significant_input_dimensions
 from matplotlib import pyplot
 

GPy/_models/gp_regression.py

@@ -2,7 +2,6 @@
 # Licensed under the BSD 3-clause license (see LICENSE.txt)
 
 
-import numpy as np
 from ..core import GP
 from .. import likelihoods
 from .. import kern

GPy/_models/gplvm.py

@@ -4,15 +4,11 @@
 
 import numpy as np
 import pylab as pb
-import sys, pdb
 from .. import kern
-from ..core import Model
+from ..core import priors
-from ..util.linalg import pdinv, PCA
-from ..core.priors import Gaussian as Gaussian_prior
 from ..core import GP
 from ..likelihoods import Gaussian
 from .. import util
-from GPy.util import plot_latent
 
 
 class GPLVM(GP):
@@ -34,12 +30,13 @@ class GPLVM(GP):
             kernel = kern.rbf(input_dim, ARD=input_dim > 1) + kern.bias(input_dim, np.exp(-2))
         likelihood = Gaussian(Y, normalize=normalize_Y, variance=np.exp(-2.))
         GP.__init__(self, X, likelihood, kernel, normalize_X=False)
-        self.set_prior('.*X', Gaussian_prior(0, 1))
+        self.set_prior('.*X', priors.Gaussian(0, 1))
         self.ensure_default_constraints()
 
     def initialise_latent(self, init, input_dim, Y):
         Xr = np.random.randn(Y.shape[0], input_dim)
         if init == 'PCA':
+            from ..util.linalg import PCA
             PC = PCA(Y, input_dim)[0]
             Xr[:PC.shape[0], :PC.shape[1]] = PC
         return Xr
@@ -62,15 +59,15 @@ class GPLVM(GP):
     def jacobian(self,X):
         target = np.zeros((X.shape[0],X.shape[1],self.output_dim))
         for i in range(self.output_dim):
-        	target[:,:,i] = self.kern.dK_dX(np.dot(self.Ki,self.likelihood.Y[:,i])[None, :],X,self.X)
+            target[:,:,i] = self.kern.dK_dX(np.dot(self.Ki,self.likelihood.Y[:,i])[None, :],X,self.X)
         return target
    
     def magnification(self,X):
         target=np.zeros(X.shape[0])
         J = np.zeros((X.shape[0],X.shape[1],self.output_dim))
-    	J=self.jacobian(X)
+        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(pb.det(np.dot(J[i,:,:],np.transpose(J[i,:,:]))))
         return target
 
     def plot(self):

GPy/_models/mrd.py

@@ -9,8 +9,8 @@ from GPy.util.linalg import PCA
 import numpy
 import itertools
 import pylab
-from GPy.kern.kern import kern
+from ..kern import kern
-from GPy.models.bayesian_gplvm import BayesianGPLVM
+from bayesian_gplvm import BayesianGPLVM
 
 class MRD(Model):
     """

GPy/_models/sparse_gplvm.py

@@ -5,8 +5,8 @@
 import numpy as np
 import pylab as pb
 import sys, pdb
-from GPy.models.sparse_gp_regression import SparseGPRegression
+from sparse_gp_regression import SparseGPRegression
-from GPy.models.gplvm import GPLVM
+from gplvm import GPLVM
 # from .. import kern
 # from ..core import model
 # from ..util.linalg import pdinv, PCA

GPy/core/mapping.py

@@ -36,7 +36,6 @@ class Mapping(Parameterized):
 
     def df_dtheta(self, dL_df, X):
         """The gradient of the outputs of the multi-layer perceptron with respect to each of the parameters.
-        
         :param dL_df: gradient of the objective with respect to the function.
         :type dL_df: ndarray (num_data x output_dim)
         :param X: input locations where the function is evaluated.
@@ -44,7 +43,6 @@ class Mapping(Parameterized):
         :returns: Matrix containing gradients with respect to parameters of each output for each input data.
         :rtype: ndarray (num_params length)
         """
-        
         raise NotImplementedError
 
     def plot(self, plot_limits=None, which_data='all', which_parts='all', resolution=None, levels=20, samples=0, fignum=None, ax=None, fixed_inputs=[], linecol=Tango.colorsHex['darkBlue']):
@@ -126,7 +124,11 @@ class Mapping(Parameterized):
 from GPy.core.model import Model
 
 class Mapping_check_model(Model):
-    """This is a dummy model class used as a base class for checking that the gradients of a given mapping are implemented correctly. It enables checkgradient() to be called independently on each mapping."""
+    """
+    This is a dummy model class used as a base class for checking that the
+    gradients of a given mapping are implemented correctly. It enables
+    checkgradient() to be called independently on each mapping.
+    """
     def __init__(self, mapping=None, dL_df=None, X=None):
         num_samples = 20
         if mapping==None:

GPy/examples/dimensionality_reduction.py

@@ -6,8 +6,8 @@ from matplotlib import pyplot as plt, cm
 
 import GPy
 from GPy.core.transformations import logexp
-from GPy.models.bayesian_gplvm import BayesianGPLVM
 from GPy.likelihoods.gaussian import Gaussian
+from GPy.models import BayesianGPLVM
 
 default_seed = np.random.seed(123344)
 

GPy/kern/constructors.py

@@ -5,6 +5,7 @@ import numpy as np
 from kern import kern
 import parts
 
+
 def rbf_inv(input_dim,variance=1., inv_lengthscale=None,ARD=False):
     """
     Construct an RBF kernel
@@ -149,33 +150,6 @@ def white(input_dim,variance=1.):
     part = parts.white.White(input_dim,variance)
     return kern(input_dim, [part])
 
-def eq_ode1(output_dim, W=None, rank=1,  kappa=None, length_scale=1., decay=None, delay=None):
-    """Covariance function for first order differential equation driven by an exponentiated quadratic covariance.
-
-    This outputs of this kernel have the form
-    .. math::
-       \frac{\text{d}y_j}{\text{d}t} = \sum_{i=1}^R w_{j,i} f_i(t-\delta_j) +\sqrt{\kappa_j}g_j(t) - d_jy_j(t)
-
-    where :math:`R` is the rank of the system, :math:`w_{j,i}` is the sensitivity of the :math:`j`th output to the :math:`i`th latent function, :math:`d_j` is the decay rate of the :math:`j`th output and :math:`f_i(t)` and :math:`g_i(t)` are independent latent Gaussian processes goverened by an exponentiated quadratic covariance.
-    
-    :param output_dim: number of outputs driven by latent function.
-    :type output_dim: int
-    :param W: sensitivities of each output to the latent driving function. 
-    :type W: ndarray (output_dim x rank).
-    :param rank: If rank is greater than 1 then there are assumed to be a total of rank latent forces independently driving the system, each with identical covariance.
-    :type rank: int
-    :param decay: decay rates for the first order system. 
-    :type decay: array of length output_dim.
-    :param delay: delay between latent force and output response.
-    :type delay: array of length output_dim.
-    :param kappa: diagonal term that allows each latent output to have an independent component to the response.
-    :type kappa: array of length output_dim.
-    
-    .. Note: see first order differential equation examples in GPy.examples.regression for some usage.
-    """
-    part = parts.eq_ode1.Eq_ode1(output_dim, W, rank, kappa, length_scale, decay, delay)
-    return kern(2, [part])
-
 
 def exponential(input_dim,variance=1., lengthscale=None, ARD=False):
     """
@@ -292,7 +266,7 @@ except ImportError:
 if sympy_available:
     from parts.sympykern import spkern
     from sympy.parsing.sympy_parser import parse_expr
-    from GPy.util.symbolic import sinc
+    from GPy.util import symbolic
 
     def rbf_sympy(input_dim, ARD=False, variance=1., lengthscale=1.):
         """
@@ -313,9 +287,19 @@ if sympy_available:
             f =  variance*sp.exp(-dist/(2*lengthscale**2))
         return kern(input_dim, [spkern(input_dim, f, name='rbf_sympy')])
 
-    def eq_sympy(input_dim, output_dim, ARD=False, variance=1., lengthscale=1.):
+    def eq_sympy(input_dim, output_dim, ARD=False):
         """
-        Exponentiated quadratic with multiple outputs.
+        Latent force model covariance, exponentiated quadratic with multiple outputs. Derived from a diffusion equation with the initial spatial condition layed down by a Gaussian process with lengthscale given by shared_lengthscale.
+
+        See IEEE Trans Pattern Anal Mach Intell. 2013 Nov;35(11):2693-705. doi: 10.1109/TPAMI.2013.86. Linear latent force models using Gaussian processes. Alvarez MA, Luengo D, Lawrence ND.
+
+        :param input_dim: Dimensionality of the kernel
+        :type input_dim: int
+        :param output_dim: number of outputs in the covariance function.
+        :type output_dim: int
+        :param ARD: whether or not to user ARD (default False).
+        :type ARD: bool
+
         """
         real_input_dim = input_dim
         if output_dim>1:
@@ -326,7 +310,7 @@ if sympy_available:
         if ARD:
             lengthscales = [sp.var('lengthscale%i_i lengthscale%i_j' % i, positive=True) for i in range(real_input_dim)]
             shared_lengthscales = [sp.var('shared_lengthscale%i' % i, positive=True) for i in range(real_input_dim)]
-            dist_string = ' + '.join(['(x_%i-z_%i)**2/(shared_lengthscale%i**2 + lengthscale%i_i*lengthscale%i_j)' % (i, i, i) for i in range(real_input_dim)])
+            dist_string = ' + '.join(['(x_%i-z_%i)**2/(shared_lengthscale%i**2 + lengthscale%i_i**2 + lengthscale%i_j**2)' % (i, i, i) for i in range(real_input_dim)])
             dist = parse_expr(dist_string)
             f =  variance*sp.exp(-dist/2.)
         else:
@@ -337,26 +321,25 @@ if sympy_available:
             f =  scale_i*scale_j*sp.exp(-dist/(2*(lengthscale_i**2 + lengthscale_j**2 + shared_lengthscale**2)))
         return kern(input_dim, [spkern(input_dim, f, output_dim=output_dim, name='eq_sympy')])
 
-    def sinc(input_dim, ARD=False, variance=1., lengthscale=1.):
+    def ode1_eq(output_dim=1):
         """
-        TODO: Not clear why this isn't working, suggests argument of sinc is not a number.
+        Latent force model covariance, first order differential
-        sinc covariance funciton
+        equation driven by exponentiated quadratic.
-        """
-        X = sp.symbols('x_:' + str(input_dim))
-        Z = sp.symbols('z_:' + str(input_dim))
-        variance = sp.var('variance',positive=True)
-        if ARD:
-            lengthscales = [sp.var('lengthscale_%i' % i, positive=True) for i in range(input_dim)]
-            dist_string = ' + '.join(['(x_%i-z_%i)**2/lengthscale_%i**2' % (i, i, i) for i in range(input_dim)])
-            dist = parse_expr(dist_string)
-            f =  variance*sinc(sp.pi*sp.sqrt(dist))
-        else:
-            lengthscale = sp.var('lengthscale',positive=True)
-            dist_string = ' + '.join(['(x_%i-z_%i)**2' % (i, i) for i in range(input_dim)])
-            dist = parse_expr(dist_string)
-            f =  variance*sinc(sp.pi*sp.sqrt(dist)/lengthscale)
 
-        return kern(input_dim, [spkern(input_dim, f, name='sinc')])
+        See N. D. Lawrence, G. Sanguinetti and M. Rattray. (2007)
+        'Modelling transcriptional regulation using Gaussian
+        processes' in B. Schoelkopf, J. C. Platt and T. Hofmann (eds)
+        Advances in Neural Information Processing Systems, MIT Press,
+        Cambridge, MA, pp 785--792.
+
+        :param output_dim: number of outputs in the covariance function.
+        :type output_dim: int
+        """
+        input_dim = 2
+        x_0, z_0, decay_i, decay_j, scale_i, scale_j, lengthscale = sp.symbols('x_0, z_0, decay_i, decay_j, scale_i, scale_j, lengthscale')
+        f = scale_i*scale_j*(symbolic.h(x_0, z_0, decay_i, decay_j, lengthscale) 
+     + symbolic.h(z_0, x_0, decay_j, decay_i, lengthscale))
+        return kern(input_dim, [spkern(input_dim, f, output_dim=output_dim, name='ode1_eq')])
 
     def sympykern(input_dim, k=None, output_dim=1, name=None, param=None):
         """
@@ -600,3 +583,20 @@ def ODE_1(input_dim=1, varianceU=1.,  varianceY=1., lengthscaleU=None,  lengthsc
     """
     part = parts.ODE_1.ODE_1(input_dim, varianceU, varianceY, lengthscaleU, lengthscaleY)
     return kern(input_dim, [part])
+
+def ODE_UY(input_dim=2, varianceU=1.,  varianceY=1., lengthscaleU=None,  lengthscaleY=None):
+    """
+    kernel resultiong from a first order ODE with OU driving GP
+    :param input_dim: the number of input dimension, has to be equal to one
+    :type input_dim: int
+    :param input_lengthU: the number of input U length
+    :param varianceU: variance of the driving GP
+    :type varianceU: float
+    :param varianceY: 'variance' of the transfer function
+    :type varianceY: float
+    :param lengthscaleY: 'lengthscale' of the transfer function
+    :type lengthscaleY: float
+    :rtype: kernel object
+    """
+    part = parts.ODE_UY.ODE_UY(input_dim, varianceU, varianceY, lengthscaleU, lengthscaleY)
+    return kern(input_dim, [part])

GPy/kern/kern.py

@@ -412,6 +412,9 @@ class kern(Parameterized):
         [p.dpsi0_dtheta(dL_dpsi0, Z[:, i_s], mu[:, i_s], S[:, i_s], target[ps]) for p, ps, i_s in zip(self.parts, self.param_slices, self.input_slices)]
         return self._transform_gradients(target)
 
+    def dpsi0_dZ(self, dL_dpsi0, Z, mu, S):
+        return np.zeros_like(Z)
+
     def dpsi0_dmuS(self, dL_dpsi0, Z, mu, S):
         target_mu, target_S = np.zeros_like(mu), np.zeros_like(S)
         [p.dpsi0_dmuS(dL_dpsi0, Z[:, i_s], mu[:, i_s], S[:, i_s], target_mu[:, i_s], target_S[:, i_s]) for p, i_s in zip(self.parts, self.input_slices)]
@@ -456,31 +459,40 @@ class kern(Parameterized):
         from parts.linear import Linear
         from parts.fixed import Fixed
 
-        for (p1, i1), (p2, i2) in itertools.combinations(itertools.izip(self.parts, self.param_slices), 2):
+        for (p1, i1), (p2, i2) in itertools.combinations(itertools.izip(self.parts, self.input_slices), 2):
             # white doesn;t combine with anything
             if isinstance(p1, White) or isinstance(p2, White):
                 pass
             # rbf X bias
             elif isinstance(p1, (Bias, Fixed)) and isinstance(p2, (RBF, RBFInv)):
-                target += 2 * p1.variance * (p2._psi1[:, :, None] + p2._psi1[:, None, :])
+                target += p1.variance * (p2._psi1[:, :, None] + p2._psi1[:, None, :])
             elif isinstance(p2, (Bias, Fixed)) and isinstance(p1, (RBF, RBFInv)):
-                tmp1 = p2.variance * (p1._psi1[:, :, None] + p1._psi1[:, None, :])
-                renorm = p1.variance*np.exp()
                 target += p2.variance * (p1._psi1[:, :, None] + p1._psi1[:, None, :])
             # linear X bias
-            elif isinstance(p1, (Bias, Fixed)) and isinstance(p2, Linear):
+            elif isinstance(p1, (Bias, Fixed)) and isinstance(p2, (Linear, RBF, RBFInv)):
                 tmp = np.zeros((mu.shape[0], Z.shape[0]))
                 p2.psi1(Z, mu, S, tmp)
                 target += p1.variance * (tmp[:, :, None] + tmp[:, None, :])
-            elif isinstance(p2, (Bias, Fixed)) and isinstance(p1, Linear):
+            elif isinstance(p2, (Bias, Fixed)) and isinstance(p1, (Linear, RBF, RBFInv)):
                 tmp = np.zeros((mu.shape[0], Z.shape[0]))
                 p1.psi1(Z, mu, S, tmp)
                 target += p2.variance * (tmp[:, :, None] + tmp[:, None, :])
             # rbf X any
-            elif isinstance(p1, (RBF, RBFInv)):
+            elif False:#isinstance(p1, (RBF, RBFInv)) or isinstance(p2, (RBF, RBFInv)):
-                pass
+                if isinstance(p2, (RBF, RBFInv)) and not isinstance(p1, (RBF, RBFInv)):
-            elif isinstance(p2, (RBF, RBFInv)):
+                    p1t = p1; p1 = p2; p2 = p1t; del p1t  
-                raise NotImplementedError # TODO
+                N, M = mu.shape[0], Z.shape[0]; NM=N*M
+                psi11 = np.zeros((N, M))
+                psi12 = np.zeros((NM, M))
+                p1.psi1(Z, mu, S, psi11)
+                Mu, Sigma = p1._crossterm_mu_S(Z, mu, S)
+                Mu, Sigma = Mu.reshape(NM,self.input_dim), Sigma.reshape(NM,self.input_dim)
+                
+                p2.psi1(Z, Mu, Sigma, psi12)
+                eK2 = psi12.reshape(N, M, M)
+                crossterms = eK2 * (psi11[:, :, None] + psi11[:, None, :])
+                target += crossterms
+                #import ipdb;ipdb.set_trace()
             else:
                 raise NotImplementedError, "psi2 cannot be computed for this kernel"
         return target        
@@ -489,40 +501,81 @@ class kern(Parameterized):
         target = np.zeros(self.num_params)
         [p.dpsi2_dtheta(dL_dpsi2, Z[:, i_s], mu[:, i_s], S[:, i_s], target[ps]) for p, i_s, ps in zip(self.parts, self.input_slices, self.param_slices)]
 
+        from parts.white import White
+        from parts.rbf import RBF
+        from parts.rbf_inv import RBFInv
+        from parts.bias import Bias
+        from parts.linear import Linear
+        from parts.fixed import Fixed
+
         # compute the "cross" terms
         # TODO: better looping, input_slices
         for i1, i2 in itertools.combinations(range(len(self.parts)), 2):
             p1, p2 = self.parts[i1], self.parts[i2]
-#             ipsl1, ipsl2 = self.input_slices[i1], self.input_slices[i2]
+            #ipsl1, ipsl2 = self.input_slices[i1], self.input_slices[i2]
             ps1, ps2 = self.param_slices[i1], self.param_slices[i2]            
-
+            if isinstance(p1, White) or isinstance(p2, White):
-            # white doesn;t combine with anything
-            if p1.name == 'white' or p2.name == 'white':
                 pass
             # rbf X bias
-            elif p1.name == 'bias' and p2.name == 'rbf':
+            elif isinstance(p1, (Bias, Fixed)) and isinstance(p2, (RBF, RBFInv)):
                 p2.dpsi1_dtheta(dL_dpsi2.sum(1) * p1.variance * 2., Z, mu, S, target[ps2])
                 p1.dpsi1_dtheta(dL_dpsi2.sum(1) * p2._psi1 * 2., Z, mu, S, target[ps1])
-            elif p2.name == 'bias' and p1.name == 'rbf':
+            elif isinstance(p2, (Bias, Fixed)) and isinstance(p1, (RBF, RBFInv)):
                 p1.dpsi1_dtheta(dL_dpsi2.sum(1) * p2.variance * 2., Z, mu, S, target[ps1])
                 p2.dpsi1_dtheta(dL_dpsi2.sum(1) * p1._psi1 * 2., Z, mu, S, target[ps2])
             # linear X bias
-            elif p1.name == 'bias' and p2.name == 'linear':
+            elif isinstance(p1, (Bias, Fixed)) and isinstance(p2, Linear):
                 p2.dpsi1_dtheta(dL_dpsi2.sum(1) * p1.variance * 2., Z, mu, S, target[ps2]) # [ps1])
                 psi1 = np.zeros((mu.shape[0], Z.shape[0]))
                 p2.psi1(Z, mu, S, psi1)
                 p1.dpsi1_dtheta(dL_dpsi2.sum(1) * psi1 * 2., Z, mu, S, target[ps1])
-            elif p2.name == 'bias' and p1.name == 'linear':
+            elif isinstance(p2, (Bias, Fixed)) and isinstance(p1, Linear):
                 p1.dpsi1_dtheta(dL_dpsi2.sum(1) * p2.variance * 2., Z, mu, S, target[ps1])
                 psi1 = np.zeros((mu.shape[0], Z.shape[0]))
                 p1.psi1(Z, mu, S, psi1)
                 p2.dpsi1_dtheta(dL_dpsi2.sum(1) * psi1 * 2., Z, mu, S, target[ps2])
             # rbf X any
+            elif False:#isinstance(p1, (RBF, RBFInv)) or isinstance(p2, (RBF, RBFInv)):
+                if isinstance(p2, (RBF, RBFInv)) and not isinstance(p1, (RBF, RBFInv)):
+                    # turn around to have rbf in front
+                    p1, p2 = self.parts[i2], self.parts[i1]
+                    ps1, ps2 = self.param_slices[i2], self.param_slices[i1]  
                 
-            elif p1.name == 'linear' and p2.name == 'rbf':
+                N, M = mu.shape[0], Z.shape[0]; NM=N*M
-                raise NotImplementedError # TODO
+
-            elif p2.name == 'linear' and p1.name == 'rbf':
+                psi11 = np.zeros((N, M))
-                raise NotImplementedError # TODO
+                p1.psi1(Z, mu, S, psi11)
+                
+                Mu, Sigma = p1._crossterm_mu_S(Z, mu, S)
+                Mu, Sigma = Mu.reshape(NM,self.input_dim), Sigma.reshape(NM,self.input_dim)
+                
+                tmp1 = np.zeros_like(target[ps1])
+                tmp2 = np.zeros_like(target[ps2])
+#                 for n in range(N):
+#                     for m in range(M):
+#                         for m_prime in range(M):
+#                             p1.dpsi1_dtheta((dL_dpsi2[n:n+1,m:m+1,m_prime:m_prime+1]*psi12_t.reshape(N,M,M)[n:n+1,m:m+1,m_prime:m_prime+1])[0], Z[m:m+1], mu[n:n+1], S[n:n+1], tmp2)#Z[m_prime:m_prime+1], mu[n:n+1], S[n:n+1], tmp2)
+#                             p1.dpsi1_dtheta((dL_dpsi2[n:n+1,m:m+1,m_prime:m_prime+1]*psi12_t.reshape(N,M,M)[n:n+1,m_prime:m_prime+1,m:m+1])[0], Z[m_prime:m_prime+1], mu[n:n+1], S[n:n+1], tmp2)
+#                             Mu, Sigma= Mu.reshape(N,M,self.input_dim), Sigma.reshape(N,M,self.input_dim)
+#                             p2.dpsi1_dtheta((dL_dpsi2[n:n+1,m:m+1,m_prime:m_prime+1]*(psi11[n:n+1,m_prime:m_prime+1]))[0], Z[m:m+1], Mu[n:n+1,m], Sigma[n:n+1,m], target[ps2])
+#                             p2.dpsi1_dtheta((dL_dpsi2[n:n+1,m:m+1,m_prime:m_prime+1]*(psi11[n:n+1,m:m+1]))[0], Z[m_prime:m_prime+1], Mu[n:n+1, m_prime], Sigma[n:n+1, m_prime], target[ps2])#Z[m_prime:m_prime+1], Mu[n+m:(n+m)+1], Sigma[n+m:(n+m)+1], target[ps2])
+                
+                if isinstance(p1, RBF) and isinstance(p2, RBF):
+                    psi12 = np.zeros((N, M))
+                    p2.psi1(Z, mu, S, psi12)
+                    Mu2, Sigma2 = p2._crossterm_mu_S(Z, mu, S)
+                    Mu2, Sigma2 = Mu2.reshape(NM,self.input_dim), Sigma2.reshape(NM,self.input_dim)
+                    p1.dpsi1_dtheta((dL_dpsi2*(psi12[:,:,None] + psi12[:,None,:])).reshape(NM,M), Z, Mu2, Sigma2, tmp1)
+                    pass
+
+                if isinstance(p1, RBF) and isinstance(p2, Linear):
+                    #import ipdb;ipdb.set_trace()
+                    pass
+                
+                p2.dpsi1_dtheta((dL_dpsi2*(psi11[:,:,None] + psi11[:,None,:])).reshape(NM,M), Z, Mu, Sigma, tmp2)
+                
+                target[ps1] += tmp1
+                target[ps2] += tmp2                
             else:
                 raise NotImplementedError, "psi2 cannot be computed for this kernel"
 
@@ -532,61 +585,102 @@ class kern(Parameterized):
         target = np.zeros_like(Z)
         [p.dpsi2_dZ(dL_dpsi2, Z[:, i_s], mu[:, i_s], S[:, i_s], target[:, i_s]) for p, i_s in zip(self.parts, self.input_slices)]
 
+        from parts.white import White
+        from parts.rbf import RBF
+        from parts.rbf_inv import RBFInv
+        from parts.bias import Bias
+        from parts.linear import Linear
+        from parts.fixed import Fixed
+
         # compute the "cross" terms
-        # TODO: we need input_slices here.
+        # TODO: better looping, input_slices
         for p1, p2 in itertools.combinations(self.parts, 2):
-            # white doesn;t combine with anything
+            if isinstance(p1, White) or isinstance(p2, White):
-            if p1.name == 'white' or p2.name == 'white':
                 pass
             # rbf X bias
-            elif p1.name == 'bias' and p2.name == 'rbf':
+            elif isinstance(p1, (Bias, Fixed)) and isinstance(p2, (RBF, RBFInv)):
-                p2.dpsi1_dX(dL_dpsi2.sum(1).T * p1.variance, Z, mu, S, target)
+                p2.dpsi1_dZ(dL_dpsi2.sum(1) * p1.variance, Z, mu, S, target)
-            elif p2.name == 'bias' and p1.name == 'rbf':
+            elif isinstance(p2, (Bias, Fixed)) and isinstance(p1, (RBF, RBFInv)):
-                p1.dpsi1_dZ(dL_dpsi2.sum(1).T * p2.variance, Z, mu, S, target)
+                p1.dpsi1_dZ(dL_dpsi2.sum(1) * p2.variance, Z, mu, S, target)
             # linear X bias
-            elif p1.name == 'bias' and p2.name == 'linear':
+            elif isinstance(p1, (Bias, Fixed)) and isinstance(p2, Linear):
-                p2.dpsi1_dZ(dL_dpsi2.sum(1).T * p1.variance, Z, mu, S, target)
+                p2.dpsi1_dZ(dL_dpsi2.sum(1) * p1.variance, Z, mu, S, target)
-            elif p2.name == 'bias' and p1.name == 'linear':
+            elif isinstance(p2, (Bias, Fixed)) and isinstance(p1, Linear):
-                p1.dpsi1_dZ(dL_dpsi2.sum(1).T * p2.variance, Z, mu, S, target)
+                p1.dpsi1_dZ(dL_dpsi2.sum(1) * p2.variance, Z, mu, S, target)
-            # rbf X linear
+            # rbf X any
-            elif p1.name == 'linear' and p2.name == 'rbf':
+            elif False:#isinstance(p1, (RBF, RBFInv)) or isinstance(p2, (RBF, RBFInv)):
-                raise NotImplementedError # TODO
+                if isinstance(p2, (RBF, RBFInv)) and not isinstance(p1, (RBF, RBFInv)):
-            elif p2.name == 'linear' and p1.name == 'rbf':
+                    p1t = p1; p1 = p2; p2 = p1t; del p1t  
-                raise NotImplementedError # TODO
+                N, M = mu.shape[0], Z.shape[0]; NM=N*M
+                psi11 = np.zeros((N, M))
+                psi12 = np.zeros((NM, M))
+                #psi12_t = np.zeros((N,M))
+                
+                p1.psi1(Z, mu, S, psi11)
+                Mu, Sigma = p1._crossterm_mu_S(Z, mu, S)
+                Mu, Sigma = Mu.reshape(NM,self.input_dim), Sigma.reshape(NM,self.input_dim)
+                
+                p2.psi1(Z, Mu, Sigma, psi12)
+                tmp1 = np.zeros_like(target)
+                p1.dpsi1_dZ((dL_dpsi2*psi12.reshape(N,M,M)).sum(1), Z, mu, S, tmp1)
+                p1.dpsi1_dZ((dL_dpsi2*psi12.reshape(N,M,M)).sum(2), Z, mu, S, tmp1)
+                target += tmp1
+                
+                #p2.dpsi1_dtheta((dL_dpsi2*(psi11[:,:,None] + psi11[:,None,:])).reshape(NM,M), Z, Mu, Sigma, target)
+                p2.dpsi1_dZ((dL_dpsi2*(psi11[:,:,None] + psi11[:,None,:])).reshape(NM,M), Z, Mu, Sigma, target)
             else:
                 raise NotImplementedError, "psi2 cannot be computed for this kernel"
-
+        return target * 2
-        return target * 2.
 
     def dpsi2_dmuS(self, dL_dpsi2, Z, mu, S):
         target_mu, target_S = np.zeros((2, mu.shape[0], mu.shape[1]))
         [p.dpsi2_dmuS(dL_dpsi2, Z[:, i_s], mu[:, i_s], S[:, i_s], target_mu[:, i_s], target_S[:, i_s]) for p, i_s in zip(self.parts, self.input_slices)]
 
+        from parts.white import White
+        from parts.rbf import RBF
+        from parts.rbf_inv import RBFInv
+        from parts.bias import Bias
+        from parts.linear import Linear
+        from parts.fixed import Fixed
+
         # compute the "cross" terms
-        # TODO: we need input_slices here.
+        # TODO: better looping, input_slices
         for p1, p2 in itertools.combinations(self.parts, 2):
-            # white doesn;t combine with anything
+            if isinstance(p1, White) or isinstance(p2, White):
-            if p1.name == 'white' or p2.name == 'white':
                 pass
             # rbf X bias
-            elif p1.name == 'bias' and p2.name == 'rbf':
+            elif isinstance(p1, (Bias, Fixed)) and isinstance(p2, (RBF, RBFInv)):
-                p2.dpsi1_dmuS(dL_dpsi2.sum(1).T * p1.variance * 2., Z, mu, S, target_mu, target_S)
+                p2.dpsi1_dmuS(dL_dpsi2.sum(1) * p1.variance * 2., Z, mu, S, target_mu, target_S)
-            elif p2.name == 'bias' and p1.name == 'rbf':
+            elif isinstance(p2, (Bias, Fixed)) and isinstance(p1, (RBF, RBFInv)):
-                p1.dpsi1_dmuS(dL_dpsi2.sum(1).T * p2.variance * 2., Z, mu, S, target_mu, target_S)
+                p1.dpsi1_dmuS(dL_dpsi2.sum(1) * p2.variance * 2., Z, mu, S, target_mu, target_S)
             # linear X bias
-            elif p1.name == 'bias' and p2.name == 'linear':
+            elif isinstance(p1, (Bias, Fixed)) and isinstance(p2, Linear):
-                p2.dpsi1_dmuS(dL_dpsi2.sum(1).T * p1.variance * 2., Z, mu, S, target_mu, target_S)
+                p2.dpsi1_dmuS(dL_dpsi2.sum(1) * p1.variance * 2., Z, mu, S, target_mu, target_S)
-            elif p2.name == 'bias' and p1.name == 'linear':
+            elif isinstance(p2, (Bias, Fixed)) and isinstance(p1, Linear):
-                p1.dpsi1_dmuS(dL_dpsi2.sum(1).T * p2.variance * 2., Z, mu, S, target_mu, target_S)
+                p1.dpsi1_dmuS(dL_dpsi2.sum(1) * p2.variance * 2., Z, mu, S, target_mu, target_S)
-            # rbf X linear
+            # rbf X any
-            elif p1.name == 'linear' and p2.name == 'rbf':
+            elif False:#isinstance(p1, (RBF, RBFInv)) or isinstance(p2, (RBF, RBFInv)):
-                raise NotImplementedError # TODO
+                if isinstance(p2, (RBF, RBFInv)) and not isinstance(p1, (RBF, RBFInv)):
-            elif p2.name == 'linear' and p1.name == 'rbf':
+                    p1t = p1; p1 = p2; p2 = p1t; del p1t  
-                raise NotImplementedError # TODO
+                N, M = mu.shape[0], Z.shape[0]; NM=N*M
+                psi11 = np.zeros((N, M))
+                psi12 = np.zeros((NM, M))
+                #psi12_t = np.zeros((N,M))
+                
+                p1.psi1(Z, mu, S, psi11)
+                Mu, Sigma = p1._crossterm_mu_S(Z, mu, S)
+                Mu, Sigma = Mu.reshape(NM,self.input_dim), Sigma.reshape(NM,self.input_dim)
+                
+                p2.psi1(Z, Mu, Sigma, psi12)
+                p1.dpsi1_dmuS((dL_dpsi2*psi12.reshape(N,M,M)).sum(1), Z, mu, S, target_mu, target_S)
+                p1.dpsi1_dmuS((dL_dpsi2*psi12.reshape(N,M,M)).sum(2), Z, mu, S, target_mu, target_S)
+                
+                #p2.dpsi1_dtheta((dL_dpsi2*(psi11[:,:,None] + psi11[:,None,:])).reshape(NM,M), Z, Mu, Sigma, target)
+                p2.dpsi1_dmuS((dL_dpsi2*(psi11[:,:,None])).sum(1)*2, Z, Mu.reshape(N,M,self.input_dim).sum(1), Sigma.reshape(N,M,self.input_dim).sum(1), target_mu, target_S)
             else:
                 raise NotImplementedError, "psi2 cannot be computed for this kernel"
-
         return target_mu, target_S
+    
     def plot(self, x=None, plot_limits=None, which_parts='all', resolution=None, *args, **kwargs):
         if which_parts == 'all':
             which_parts = [True] * self.num_parts
@@ -653,7 +747,7 @@ class Kern_check_model(Model):
         if kernel==None:
             kernel = GPy.kern.rbf(1)
         if X==None:
-            X = np.random.randn(num_samples, kernel.input_dim)
+            X = np.random.normal(size=(num_samples, kernel.input_dim))
         if dL_dK==None:
             if X2==None:
                 dL_dK = np.ones((X.shape[0], X.shape[0]))
@@ -750,7 +844,7 @@ class Kern_check_dKdiag_dX(Kern_check_model):
     def _set_params(self, x):
         self.X=x.reshape(self.X.shape)
 
-def kern_test(kern, X=None, X2=None, output_ind=None, verbose=False):
+def kern_test(kern, X=None, X2=None, output_ind=None, verbose=False, X_positive=False):
     """This function runs on kernels to check the correctness of their implementation. It checks that the covariance function is positive definite for a randomly generated data set.
 
     :param kern: the kernel to be tested.
@@ -764,12 +858,16 @@ def kern_test(kern, X=None, X2=None, output_ind=None, verbose=False):
     pass_checks = True
     if X==None:
         X = np.random.randn(10, kern.input_dim)
+        if X_positive:
+            X = abs(X)
         if output_ind is not None:
-            X[:, output_ind] = np.random.randint(kern.output_dim, X.shape[0])
+            X[:, output_ind] = np.random.randint(kern.parts[0].output_dim, X.shape[0])
     if X2==None:
         X2 = np.random.randn(20, kern.input_dim)
+        if X_positive:
+            X2 = abs(X2)
         if output_ind is not None:
-            X2[:, output_ind] = np.random.randint(kern.output_dim, X2.shape[0])
+            X2[:, output_ind] = np.random.randint(kern.parts[0].output_dim, X2.shape[0])
 
     if verbose:
         print("Checking covariance function is positive definite.")

GPy/kern/parts/ODE_UY.py

@@ -0,0 +1,253 @@
+# Copyright (c) 2012, GPy authors (see AUTHORS.txt).
+# Licensed under the BSD 3-clause license (see LICENSE.txt)
+
+
+from kernpart import Kernpart
+import numpy as np
+
+def index_to_slices(index):
+    """
+    take a numpy array of integers (index) and return a  nested list of slices such that the slices describe the start, stop points for each integer in the index. 
+
+    e.g.
+    >>> index = np.asarray([0,0,0,1,1,1,2,2,2])
+    returns
+    >>> [[slice(0,3,None)],[slice(3,6,None)],[slice(6,9,None)]]
+
+    or, a more complicated example
+    >>> index = np.asarray([0,0,1,1,0,2,2,2,1,1])
+    returns
+    >>> [[slice(0,2,None),slice(4,5,None)],[slice(2,4,None),slice(8,10,None)],[slice(5,8,None)]]
+    """
+
+    #contruct the return structure
+    ind = np.asarray(index,dtype=np.int64)
+    ret = [[] for i in range(ind.max()+1)]
+
+    #find the switchpoints
+    ind_ = np.hstack((ind,ind[0]+ind[-1]+1))
+    switchpoints = np.nonzero(ind_ - np.roll(ind_,+1))[0]
+
+    [ret[ind_i].append(slice(*indexes_i)) for ind_i,indexes_i in zip(ind[switchpoints[:-1]],zip(switchpoints,switchpoints[1:]))]
+    return ret
+
+class ODE_UY(Kernpart):
+    """
+    kernel resultiong from a first order ODE with OU driving GP
+
+    :param input_dim: the number of input dimension, has to be equal to one
+    :type input_dim: int
+    :param input_lengthU: the number of input U length
+    :type input_dim: int   
+    :param varianceU: variance of the driving GP
+    :type varianceU: float
+    :param lengthscaleU: lengthscale of the driving GP  (sqrt(3)/lengthscaleU)
+    :type lengthscaleU: float
+    :param varianceY: 'variance' of the transfer function
+    :type varianceY: float
+    :param lengthscaleY: 'lengthscale' of the transfer function (1/lengthscaleY)
+    :type lengthscaleY: float
+    :rtype: kernel object
+
+    """
+
+
+
+
+    def __init__(self, input_dim=2,varianceU=1., varianceY=1., lengthscaleU=None, lengthscaleY=None):
+        assert input_dim==2, "Only defined for input_dim = 1"
+        self.input_dim = input_dim
+        self.num_params = 4
+        self.name = 'ODE_UY'
+
+
+        if lengthscaleU is not None:
+            lengthscaleU = np.asarray(lengthscaleU)
+            assert lengthscaleU.size == 1, "lengthscaleU should be one dimensional"
+        else:
+            lengthscaleU = np.ones(1)
+        if lengthscaleY is not None:
+            lengthscaleY = np.asarray(lengthscaleY)
+            assert lengthscaleY.size == 1, "lengthscaleY should be one dimensional"
+        else:
+            lengthscaleY = np.ones(1)
+            #lengthscaleY = 0.5
+        self._set_params(np.hstack((varianceU, varianceY, lengthscaleU,lengthscaleY)))
+
+    def _get_params(self):
+        """return the value of the parameters."""
+        return np.hstack((self.varianceU,self.varianceY, self.lengthscaleU,self.lengthscaleY))
+
+    def _set_params(self, x):
+        """set the value of the parameters."""
+        assert x.size == self.num_params
+
+        self.varianceU = x[0]
+        self.varianceY = x[1]
+        self.lengthscaleU = x[2]
+        self.lengthscaleY = x[3]
+
+
+    def _get_param_names(self):
+        """return parameter names."""
+        return ['varianceU','varianceY', 'lengthscaleU', 'lengthscaleY']
+
+
+    def K(self, X, X2, target):
+        """Compute the covariance matrix between X and X2."""
+
+        X,slices = X[:,:-1],index_to_slices(X[:,-1])
+        if X2 is None:
+            X2,slices2 = X,slices
+        else:
+            X2,slices2 = X2[:,:-1],index_to_slices(X2[:,-1])
+
+
+        #rdist = X[:,0][:,None] - X2[:,0][:,None].T
+        rdist = X - X2.T
+        ly=1/self.lengthscaleY
+        lu=np.sqrt(3)/self.lengthscaleU
+        #iu=self.input_lengthU  #dimention of U
+        
+        Vu=self.varianceU
+        Vy=self.varianceY
+
+        kuu = lambda dist:Vu * (1 + lu* np.abs(dist)) * np.exp(-lu * np.abs(dist))
+
+        k1 = lambda dist:np.exp(-ly*np.abs(dist))*(2*lu+ly)/(lu+ly)**2
+        k2 = lambda dist:(np.exp(-lu*dist)*(ly-2*lu+lu*ly*dist-lu**2*dist) + np.exp(-ly*dist)*(2*lu-ly) ) / (ly-lu)**2 
+        k3 = lambda dist:np.exp(-lu*dist) * ( (1+lu*dist)/(lu+ly) + (lu)/(lu+ly)**2 )
+        kyy = lambda dist:Vu*Vy*(k1(dist) + k2(dist) + k3(dist))
+
+        kyu3 = lambda dist:np.exp(-lu*dist)/(lu+ly)*(1+lu*(dist+1/(lu+ly)))
+        kyup = lambda dist:Vu*Vy*(k1(dist)+k2(dist))    #t>0 kyu
+        kyun = lambda dist:Vu*Vy*(kyu3(dist))       #t<0 kyu
+
+        kuyp = lambda dist:Vu*Vy*(kyu3(dist))       #t>0 kuy
+        kuyn = lambda dist:Vu*Vy*(k1(dist)+k2(dist))      #t<0 kuy
+        
+        for i, s1 in enumerate(slices):
+            for j, s2 in enumerate(slices2):
+                for ss1 in s1:
+                    for ss2 in s2:
+                        if i==0 and j==0:
+                            target[ss1,ss2] = kuu(np.abs(rdist[ss1,ss2]))
+                        elif i==0 and j==1:
+                            target[ss1,ss2] = np.where(  rdist[ss1,ss2]>0 , kuyp(np.abs(rdist[ss1,ss2])), kuyn(np.abs(rdist[s1[0],s2[0]]) )   )
+                        elif i==1 and j==1:
+                            target[ss1,ss2] = kyy(np.abs(rdist[ss1,ss2]))
+                        else:
+                            target[ss1,ss2] = np.where(  rdist[ss1,ss2]>0 , kyup(np.abs(rdist[ss1,ss2])), kyun(np.abs(rdist[s1[0],s2[0]]) )   )
+
+
+        #KUU = kuu(np.abs(rdist[:iu,:iu]))
+
+        #KYY = kyy(np.abs(rdist[iu:,iu:]))
+
+        #KYU = np.where(rdist[iu:,:iu]>0,kyup(np.abs(rdist[iu:,:iu])),kyun(np.abs(rdist[iu:,:iu]) ))
+
+        #KUY = np.where(rdist[:iu,iu:]>0,kuyp(np.abs(rdist[:iu,iu:])),kuyn(np.abs(rdist[:iu,iu:]) ))
+
+        #ker=np.vstack((np.hstack([KUU,KUY]),np.hstack([KYU,KYY])))
+
+        #np.add(ker, target, target)
+
+    def Kdiag(self, X, target):
+        """Compute the diagonal of the covariance matrix associated to X."""
+        ly=1/self.lengthscaleY
+        lu=np.sqrt(3)/self.lengthscaleU
+        #ly=self.lengthscaleY
+        #lu=self.lengthscaleU
+        
+        k1 = (2*lu+ly)/(lu+ly)**2
+        k2 = (ly-2*lu + 2*lu-ly ) / (ly-lu)**2 
+        k3 = 1/(lu+ly) + (lu)/(lu+ly)**2 
+
+        slices = index_to_slices(X[:,-1])
+
+        for i, ss1 in enumerate(slices):
+            for s1 in ss1:
+                if i==0:
+                    target[s1]+= self.varianceU 
+                elif i==1:
+                    target[s1]+= self.varianceU*self.varianceY*(k1+k2+k3)
+                else:
+                    raise ValueError, "invalid input/output index"
+        
+        #target[slices[0][0]]+= self.varianceU   #matern32 diag
+        #target[slices[1][0]]+= self.varianceU*self.varianceY*(k1+k2+k3)  #  diag
+
+
+
+
+
+
+    def dK_dtheta(self, dL_dK, X, X2, target):
+        """derivative of the covariance matrix with respect to the parameters."""
+        if X2 is None: X2 = X
+        dist = np.abs(X - X2.T)
+
+        ly=1/self.lengthscaleY
+        lu=np.sqrt(3)/self.lengthscaleU
+        #ly=self.lengthscaleY
+        #lu=self.lengthscaleU
+
+        dk1theta1 = lambda dist: np.exp(-ly*dist)*2*(-lu)/(lu+ly)**3
+        #c=np.sqrt(3)
+        #t1=c/lu
+        #t2=1/ly
+        #dk1theta1=np.exp(-dist*ly)*t2*( (2*c*t2+2*t1)/(c*t2+t1)**2 -2*(2*c*t2*t1+t1**2)/(c*t2+t1)**3   )
+
+        dk2theta1 = lambda dist: 1*( 
+            np.exp(-lu*dist)*dist*(-ly+2*lu-lu*ly*dist+dist*lu**2)*(ly-lu)**(-2) + np.exp(-lu*dist)*(-2+ly*dist-2*dist*lu)*(ly-lu)**(-2) 
+            +np.exp(-dist*lu)*(ly-2*lu+ly*lu*dist-dist*lu**2)*2*(ly-lu)**(-3) 
+            +np.exp(-dist*ly)*2*(ly-lu)**(-2)
+            +np.exp(-dist*ly)*2*(2*lu-ly)*(ly-lu)**(-3)
+            )
+      
+        dk3theta1 = lambda dist: np.exp(-dist*lu)*(lu+ly)**(-2)*((2*lu+ly+dist*lu**2+lu*ly*dist)*(-dist-2/(lu+ly))+2+2*lu*dist+ly*dist)
+
+        dktheta1 = lambda dist: self.varianceU*self.varianceY*(dk1theta1+dk2theta1+dk3theta1)
+
+
+
+
+        dk1theta2 = lambda dist: np.exp(-ly*dist) * ((lu+ly)**(-2)) * (  (-dist)*(2*lu+ly)  +  1  +  (-2)*(2*lu+ly)/(lu+ly)  )
+
+        dk2theta2 =lambda dist:  1*(
+            np.exp(-dist*lu)*(ly-lu)**(-2) * ( 1+lu*dist+(-2)*(ly-2*lu+lu*ly*dist-dist*lu**2)*(ly-lu)**(-1) )
+            +np.exp(-dist*ly)*(ly-lu)**(-2) * ( (-dist)*(2*lu-ly) -1+(2*lu-ly)*(-2)*(ly-lu)**(-1) )
+            )
+
+        dk3theta2 = lambda dist: np.exp(-dist*lu) * (-3*lu-ly-dist*lu**2-lu*ly*dist)/(lu+ly)**3
+
+        dktheta2 = lambda dist: self.varianceU*self.varianceY*(dk1theta2 + dk2theta2 +dk3theta2)
+
+
+
+        k1 = lambda dist: np.exp(-ly*dist)*(2*lu+ly)/(lu+ly)**2
+        k2 = lambda dist: (np.exp(-lu*dist)*(ly-2*lu+lu*ly*dist-lu**2*dist) + np.exp(-ly*dist)*(2*lu-ly) ) / (ly-lu)**2 
+        k3 = lambda dist: np.exp(-lu*dist) * ( (1+lu*dist)/(lu+ly) + (lu)/(lu+ly)**2 )
+        dkdvar = k1+k2+k3
+
+        target[0] += np.sum(self.varianceY*dkdvar * dL_dK)
+        target[1] += np.sum(self.varianceU*dkdvar * dL_dK)
+        target[2] += np.sum(dktheta1*(-np.sqrt(3)*self.lengthscaleU**(-2)) * dL_dK)
+        target[3] += np.sum(dktheta2*(-self.lengthscaleY**(-2)) * dL_dK)
+
+
+    # def dKdiag_dtheta(self, dL_dKdiag, X, target):
+    #     """derivative of the diagonal of the covariance matrix with respect to the parameters."""
+    #     # NB: derivative of diagonal elements wrt lengthscale is 0
+    #     target[0] += np.sum(dL_dKdiag)
+
+    # def dK_dX(self, dL_dK, X, X2, target):
+    #     """derivative of the covariance matrix with respect to X."""
+    #     if X2 is None: X2 = X
+    #     dist = np.sqrt(np.sum(np.square((X[:, None, :] - X2[None, :, :]) / self.lengthscale), -1))[:, :, None]
+    #     ddist_dX = (X[:, None, :] - X2[None, :, :]) / self.lengthscale ** 2 / np.where(dist != 0., dist, np.inf)
+    #     dK_dX = -np.transpose(self.variance * np.exp(-dist) * ddist_dX, (1, 0, 2))
+    #     target += np.sum(dK_dX * dL_dK.T[:, :, None], 0)
+
+    # def dKdiag_dX(self, dL_dKdiag, X, target):
+    #     pass

GPy/kern/parts/__init__.py

@@ -14,6 +14,7 @@ import Matern32
 import Matern52
 import mlp
 import ODE_1
+import ODE_UY
 import periodic_exponential
 import periodic_Matern32
 import periodic_Matern52
@@ -26,4 +27,5 @@ import rbf
 import rbf_inv
 import spline
 import symmetric
+import sympy_helpers
 import white

GPy/kern/parts/rbf.py

@@ -186,7 +186,7 @@ class RBF(Kernpart):
         self._psi_computations(Z, mu, S)
         target[0] += np.sum(dL_dpsi1 * self._psi1 / self.variance)
         d_length = self._psi1[:,:,None] * ((self._psi1_dist_sq - 1.)/(self.lengthscale*self._psi1_denom) +1./self.lengthscale)
-        dpsi1_dlength = d_length * dL_dpsi1[:, :, None]
+        dpsi1_dlength = d_length * np.atleast_3d(dL_dpsi1)
         if not self.ARD:
             target[1] += dpsi1_dlength.sum()
         else:
@@ -208,12 +208,19 @@ class RBF(Kernpart):
         self._psi_computations(Z, mu, S)
         target += self._psi2
 
+    def _crossterm_mu_S(self, Z, mu, S):
+        # compute the crossterm expectation for K as the other kernel:
+        Sigma = 1./self.lengthscale2[None,None,:] + 1./S[:,None,:] # is independent across M, 
+        Sigma_tilde = (self.lengthscale2[None, :] + S)
+        M = (S*mu/Sigma_tilde)[:, None, :] + (self.lengthscale2[None,:]*Z)[None, :, :]/Sigma_tilde[:, None, :]
+        # make sure return is [N x M x Q]
+        return M, Sigma.repeat(Z.shape[0],1) 
+
     def dpsi2_dtheta(self, dL_dpsi2, Z, mu, S, target):
         """Shape N,num_inducing,num_inducing,Ntheta"""
         self._psi_computations(Z, mu, S)
         d_var = 2.*self._psi2 / self.variance
         d_length = 2.*self._psi2[:, :, :, None] * (self._psi2_Zdist_sq * self._psi2_denom + self._psi2_mudist_sq + S[:, None, None, :] / self.lengthscale2) / (self.lengthscale * self._psi2_denom)
-
         target[0] += np.sum(dL_dpsi2 * d_var)
         dpsi2_dlength = d_length * dL_dpsi2[:, :, :, None]
         if not self.ARD:
@@ -296,8 +303,8 @@ class RBF(Kernpart):
         psi2 = np.empty((N, num_inducing, num_inducing))
 
         psi2_Zdist_sq = self._psi2_Zdist_sq
-        _psi2_denom = self._psi2_denom.squeeze().reshape(N, self.input_dim)
+        _psi2_denom = self._psi2_denom.squeeze().reshape(-1, input_dim)
-        half_log_psi2_denom = 0.5 * np.log(self._psi2_denom).squeeze().reshape(N, self.input_dim)
+        half_log_psi2_denom = 0.5 * np.log(self._psi2_denom).squeeze().reshape(-1, input_dim)
         variance_sq = float(np.square(self.variance))
         if self.ARD:
             lengthscale2 = self.lengthscale2

GPy/kern/parts/sympy_helpers.cpp

@@ -1,7 +1,9 @@
+#include "Python.h"
 #include <math.h>
 #include <float.h>
 #include <stdlib.h>
-
+#include <iostream>
+#include <stdexcept>
 double DiracDelta(double x){
   // TODO: this doesn't seem to be a dirac delta ... should return infinity. Neil
     if((x<0.000001) & (x>-0.000001))//go on, laugh at my c++ skills
@@ -14,6 +16,7 @@ double DiracDelta(double x,int foo){
 };
 
 double sinc(double x){
+  // compute the sinc function
   if (x==0)
     return 1.0;
   else 
@@ -21,28 +24,39 @@ double sinc(double x){
 }
 
 double sinc_grad(double x){
+  // compute the gradient of the sinc function.
   if (x==0)
     return 0.0;
   else 
     return (x*cos(x) - sin(x))/(x*x);
 }
-
 double erfcx(double x){
+  // Based on code by Soren Hauberg 2010 for Octave.
+  // compute the scaled complex error function.
+  //return erfc(x)*exp(x*x);
   double xneg=-sqrt(log(DBL_MAX/2));
   double xmax = 1/(sqrt(M_PI)*DBL_MIN);
   xmax = DBL_MAX<xmax ? DBL_MAX : xmax;
   // Find values where erfcx can be evaluated
-  double t = 3.97886080735226 / (abs(x) + 3.97886080735226);
+  double t = 3.97886080735226 / (fabs(x) + 3.97886080735226);
   double u = t-0.5;
   double y = (((((((((u * 0.00127109764952614092 + 1.19314022838340944e-4) * u 
-	      - 0.003963850973605135)   * u - 8.70779635317295828e-4) * u 
+		     - 0.003963850973605135)   * u - 8.70779635317295828e-4) * u 
-	    + 0.00773672528313526668) * u + 0.00383335126264887303) * u 
+		   + 0.00773672528313526668) * u + 0.00383335126264887303) * u 
-	  - 0.0127223813782122755)  * u - 0.0133823644533460069)  * u 
+		 - 0.0127223813782122755)  * u - 0.0133823644533460069)  * u 
-	+ 0.0161315329733252248)  * u + 0.0390976845588484035)  * u + 0.00249367200053503304;
+	       + 0.0161315329733252248)  * u + 0.0390976845588484035)  * u + 0.00249367200053503304;
+  y = ((((((((((((y * u - 0.0838864557023001992) * u -		       
+		 0.119463959964325415) * u + 0.0166207924969367356) * u + 
+	       0.357524274449531043) * u + 0.805276408752910567)  * u + 
+	     1.18902982909273333)  * u + 1.37040217682338167)   * u +	
+	   1.31314653831023098)  * u + 1.07925515155856677)   * u +	
+	 0.774368199119538609) * u + 0.490165080585318424)  * u +	
+       0.275374741597376782) * t;
+
   if (x<xneg)
     return -INFINITY;
   else if (x<0)
-    return 2*exp(x*x)-y;
+    return 2.0*exp(x*x)-y;
   else if (x>xmax)
     return 0.0;
   else 
@@ -50,12 +64,115 @@ double erfcx(double x){
 }
 
 double ln_diff_erf(double x0, double x1){
-  if (x0==x1)
+  // stably compute the log of difference between two erfs.
-    return INFINITY;
+  if (x1>x0){
-  else if(x0<0 && x1>0 || x0>0 && x1<0)
+    PyErr_SetString(PyExc_RuntimeError,"second argument must be smaller than or equal to first in ln_diff_erf");
+    throw 1;
+  }
+  if (x0==x1){
+    PyErr_WarnEx(PyExc_RuntimeWarning,"divide by zero encountered in log", 1);
+    return -INFINITY;
+  }
+  else if(x0<0 && x1>0 || x0>0 && x1<0) //x0 and x1 have opposite signs
     return log(erf(x0)-erf(x1));
-  else if(x1>0)
+  else if(x0>0) //x0 positive, x1 non-negative
-    return log(erfcx(x1)-erfcx(x0)*exp(x1*x1)- x0*x0)-x1*x1;
+    return log(erfcx(x1)-erfcx(x0)*exp(x1*x1- x0*x0))-x1*x1; 
-  else 
+  else //x0 and x1 non-positive
     return log(erfcx(-x0)-erfcx(-x1)*exp(x0*x0 - x1*x1))-x0*x0;
 }
+
+double h(double t, double tprime, double d_i, double d_j, double l){
+  // Compute the h function for the sim covariance.
+  double half_l_di = 0.5*l*d_i;
+  double arg_1 = half_l_di + tprime/l;
+  double arg_2 = half_l_di - (t-tprime)/l;
+  double ln_part_1 = ln_diff_erf(arg_1, arg_2);
+  arg_2 = half_l_di - t/l;
+  double sign_val = 1.0;
+  if(t/l==0)
+    sign_val = 0.0;
+  else if (t/l < 0)
+    sign_val = -1.0;
+  arg_2 = half_l_di - t/l;
+  double ln_part_2 = ln_diff_erf(half_l_di, arg_2);
+  // if either ln_part_1 or ln_part_2 are -inf, don't bother computing rest of that term.
+  double part_1 = 0.0;
+  if(isfinite(ln_part_1))
+    part_1 = sign_val*exp(half_l_di*half_l_di - d_i*(t-tprime) + ln_part_1 - log(d_i + d_j));
+  double part_2 = 0.0;
+  if(isfinite(ln_part_2))
+    part_2 = sign_val*exp(half_l_di*half_l_di - d_i*t - d_j*tprime + ln_part_2 - log(d_i + d_j));
+  return part_1 - part_2;
+}
+
+
+double dh_dd_i(double t, double tprime, double d_i, double d_j, double l){
+  double diff_t = (t-tprime);
+  double l2 = l*l;
+  double hv = h(t, tprime, d_i, d_j, l);
+  double half_l_di = 0.5*l*d_i;
+  double arg_1 = half_l_di + tprime/l;
+  double arg_2 = half_l_di - (t-tprime)/l;
+  double ln_part_1 = ln_diff_erf(arg_1, arg_2);
+  arg_1 = half_l_di;
+  arg_2 = half_l_di - t/l;
+  double sign_val = 1.0;
+  if(t/l==0)
+    sign_val = 0.0;
+  else if (t/l < 0)
+    sign_val = -1.0;
+  double ln_part_2 = ln_diff_erf(half_l_di, half_l_di - t/l);
+  double base = (0.5*d_i*l2*(d_i+d_j)-1)*hv;
+  if(isfinite(ln_part_1))
+    base -= diff_t*sign_val*exp(half_l_di*half_l_di
+				-d_i*diff_t
+				+ln_part_1);
+  if(isfinite(ln_part_2))
+    base += t*sign_val*exp(half_l_di*half_l_di
+			   -d_i*t-d_j*tprime
+			   +ln_part_2);
+  base += l/sqrt(M_PI)*(-exp(-diff_t*diff_t/l2)
+			+exp(-tprime*tprime/l2-d_i*t)
+			+exp(-t*t/l2-d_j*tprime)
+			-exp(-(d_i*t + d_j*tprime)));
+  return base/(d_i+d_j);
+
+}
+
+double dh_dd_j(double t, double tprime, double d_i, double d_j, double l){
+  double half_l_di = 0.5*l*d_i;
+  double hv = h(t, tprime, d_i, d_j, l);
+  double sign_val = 1.0;
+  if(t/l==0)
+    sign_val = 0.0;
+  else if (t/l < 0)
+    sign_val = -1.0;
+  double ln_part_2 = ln_diff_erf(half_l_di, half_l_di - t/l);
+  double base = -hv;
+  if(isfinite(ln_part_2))
+    base += tprime*sign_val*exp(half_l_di*half_l_di-(d_i*t+d_j*tprime)+ln_part_2);
+  return base/(d_i+d_j);
+}
+
+double dh_dl(double t, double tprime, double d_i, double d_j, double l){
+  // compute gradient of h function with respect to lengthscale for sim covariance
+  // TODO a lot of energy wasted recomputing things here, need to do this in a shared way somehow ... perhaps needs rewrite of sympykern.
+  double half_l_di = 0.5*l*d_i;
+  double arg_1 = half_l_di + tprime/l;
+  double arg_2 = half_l_di - (t-tprime)/l;
+  double ln_part_1 = ln_diff_erf(arg_1, arg_2);
+  arg_2 = half_l_di - t/l;
+  double ln_part_2 = ln_diff_erf(half_l_di, arg_2);
+  double diff_t = t - tprime;
+  double l2 = l*l;
+  double hv = h(t, tprime, d_i, d_j, l);
+  return 0.5*d_i*d_i*l*hv + 2/(sqrt(M_PI)*(d_i+d_j))*((-diff_t/l2-d_i/2)*exp(-diff_t*diff_t/l2)+(-tprime/l2+d_i/2)*exp(-tprime*tprime/l2-d_i*t)-(-t/l2-d_i/2)*exp(-t*t/l2-d_j*tprime)-d_i/2*exp(-(d_i*t+d_j*tprime)));
+}
+
+double dh_dt(double t, double tprime, double d_i, double d_j, double l){
+  return 0.0;
+}
+
+double dh_dtprime(double t, double tprime, double d_i, double d_j, double l){
+  return 0.0;
+}

GPy/kern/parts/sympy_helpers.h

@@ -7,3 +7,10 @@ double sinc_grad(double x);
 
 double erfcx(double x);
 double ln_diff_erf(double x0, double x1);
+
+double h(double t, double tprime, double d_i, double d_j, double l);
+double dh_dl(double t, double tprime, double d_i, double d_j, double l);
+double dh_dd_i(double t, double tprime, double d_i, double d_j, double l);
+double dh_dd_j(double t, double tprime, double d_i, double d_j, double l);
+double dh_dt(double t, double tprime, double d_i, double d_j, double l);
+double dh_dtprime(double t, double tprime, double d_i, double d_j, double l);

GPy/kern/parts/sympy_helpers.py

@@ -0,0 +1,71 @@
+# Code for testing functions written in sympy_helpers.cpp
+from scipy import weave
+import tempfile
+import os
+import numpy as np
+current_dir = os.path.dirname(os.path.abspath(os.path.dirname(__file__)))
+extra_compile_args = []
+
+weave_kwargs = {
+    'support_code': "",
+    'include_dirs':[tempfile.gettempdir(), current_dir],
+    'headers':['"parts/sympy_helpers.h"'],
+    'sources':[os.path.join(current_dir,"parts/sympy_helpers.cpp")],
+    'extra_compile_args':extra_compile_args,
+    'extra_link_args':['-lgomp'],
+    'verbose':True}
+
+def erfcx(x):
+    code = """
+        // Code for computing scaled complementary erf
+        int i;
+        int dim;
+        int elements = Ntarget[0];
+        for (dim=1; dim<Dtarget; dim++)
+          elements *= Ntarget[dim];
+        for (i=0;i<elements;i++) 
+            target[i] = erfcx(x[i]);
+        """
+    x = np.asarray(x)
+    arg_names = ['target','x']
+    target = np.zeros_like(x)
+    weave.inline(code=code, arg_names=arg_names,**weave_kwargs)
+    return target
+
+def ln_diff_erf(x, y):
+    code = """
+        // Code for computing scaled complementary erf
+        int i;
+        int dim;
+        int elements = Ntarget[0];
+        for (dim=1; dim<Dtarget; dim++)
+          elements *= Ntarget[dim];
+        for (i=0;i<elements;i++) 
+          target[i] = ln_diff_erf(x[i], y[i]);
+        """
+    x = np.asarray(x)
+    y = np.asarray(y)
+    assert(x.shape==y.shape)
+    target = np.zeros_like(x)
+    arg_names = ['target','x', 'y']
+    weave.inline(code=code, arg_names=arg_names,**weave_kwargs)
+    return target
+
+def h(t, tprime, d_i, d_j, l):
+    code = """
+        // Code for computing the 1st order ODE h helper function.
+        int i;
+        int dim;
+        int elements = Ntarget[0];
+        for (dim=1; dim<Dtarget; dim++)
+          elements *= Ntarget[dim];
+        for (i=0;i<elements;i++) 
+          target[i] = h(t[i], tprime[i], d_i, d_j, l);
+        """
+    t = np.asarray(t)
+    tprime = np.asarray(tprime)
+    assert(tprime.shape==t.shape)
+    target = np.zeros_like(t)
+    arg_names = ['target','t', 'tprime', 'd_i', 'd_j', 'l']
+    weave.inline(code=code, arg_names=arg_names,**weave_kwargs)
+    return target

GPy/kern/parts/sympykern.py

@@ -11,6 +11,7 @@ import tempfile
 import pdb
 import ast
 from kernpart import Kernpart
+from ...util.config import config
 
 class spkern(Kernpart):
     """
@@ -110,8 +111,9 @@ class spkern(Kernpart):
             'headers':['"sympy_helpers.h"'],
             'sources':[os.path.join(current_dir,"parts/sympy_helpers.cpp")],
             'extra_compile_args':extra_compile_args,
-            'extra_link_args':['-lgomp'],
+            'extra_link_args':[],
             'verbose':True}
+        if config.getboolean('parallel', 'openmp'): self.weave_kwargs.append('-lgomp')
 
     def __add__(self,other):
         return spkern(self._sp_k+other._sp_k)
@@ -177,7 +179,14 @@ class spkern(Kernpart):
         # Code to compute argument string when only diagonal is required.
         diag_arg_string = re.sub('int jj','//int jj',X_arg_string)
         diag_arg_string = re.sub('j','i',diag_arg_string)
-        diag_precompute_string = precompute_list[0]
+        if precompute_string == '':
+            # if it's not multioutput, the precompute strings are set to zero
+            diag_precompute_string = ''
+            diag_precompute_replace = ''
+        else:
+            # for multioutput we need to extract the index of the output form the input.
+            diag_precompute_string = precompute_list[0]
+            diag_precompute_replace = precompute_list[1]
         
 
         # Here's the code to do the looping for K
@@ -215,13 +224,13 @@ class spkern(Kernpart):
             TARGET2(i, i) += k(%s);
             for (j=0;j<i;j++){
               %s //int jj=(int)X2(j, 1);
-              double kval = k(%s); //double kval = k(X2(i, 0), X2(j, 0), shared_lengthscale, LENGTHSCALE1(ii), SCALE1(ii), LENGTHSCALE1(jj), SCALE1(jj));
+              double kval = k(%s); //double kval = k(X2(i, 0), shared_lengthscale, LENGTHSCALE1(ii), SCALE1(ii));
               TARGET2(i, j) += kval;
               TARGET2(j, i) += kval;
             }
         }
         /*%s*/
-        """%(diag_precompute_string, diag_arg_string, re.sub('Z2', 'X2', precompute_list[1]), X_arg_string,str(self._sp_k)) #adding a string representation forces recompile when needed
+        """%(diag_precompute_string, diag_arg_string, re.sub('Z2', 'X2', diag_precompute_replace), X_arg_string,str(self._sp_k)) #adding a string representation forces recompile when needed
 
         # Code to do the looping for Kdiag
         self._Kdiag_code =\
@@ -336,9 +345,9 @@ class spkern(Kernpart):
 
         # Code to use when only X is provided. 
         self._dK_dtheta_code_X = self._dK_dtheta_code.replace('Z[', 'X[')
-        self._dK_dX_code_X = self._dK_dX_code.replace('Z[', 'X[').replace('+= partial[', '+= 2*partial[')
+        self._dK_dX_code_X = self._dK_dX_code.replace('Z[', 'X[').replace('+= PARTIAL2(', '+= 2*PARTIAL2(') 
-        self._dK_dtheta_code_X = self._dK_dtheta_code.replace('Z2(', 'X2(')
+        self._dK_dtheta_code_X = self._dK_dtheta_code_X.replace('Z2(', 'X2(')
-        self._dK_dX_code_X = self._dK_dX_code.replace('Z2(', 'X2(')
+        self._dK_dX_code_X = self._dK_dX_code_X.replace('Z2(', 'X2(')
 
 
         #TODO: insert multiple functions here via string manipulation
@@ -393,7 +402,7 @@ class spkern(Kernpart):
             self._weave_inline(self._dK_dX_code, X, target, Z, partial)
 
     def dKdiag_dX(self,partial,X,target):
-        self._weave.inline(self._dKdiag_dX_code, X, target, Z, partial)
+        self._weave_inline(self._dKdiag_dX_code, X, target, Z=None, partial=partial)
 
     def compute_psi_stats(self):
         #define some normal distributions

GPy/likelihoods/laplace.py

@@ -278,7 +278,10 @@ class Laplace(likelihood):
         #W is diagonal so its sqrt is just the sqrt of the diagonal elements
         W_12 = np.sqrt(W)
         B = np.eye(self.N) + W_12*K*W_12.T
-        L = jitchol(B)
+        try:
+            L = jitchol(B)
+        except:
+            import ipdb; ipdb.set_trace()
 
         W12BiW12 = W_12*dpotrs(L, np.asfortranarray(W_12*a), lower=1)[0]
         ln_B_det = 2*np.sum(np.log(np.diag(L)))

GPy/likelihoods/noise_models/bernoulli_noise.py

@@ -22,6 +22,8 @@ class Bernoulli(NoiseDistribution):
     """
     def __init__(self,gp_link=None,analytical_mean=False,analytical_variance=False):
         super(Bernoulli, self).__init__(gp_link,analytical_mean,analytical_variance)
+        if isinstance(gp_link , (gp_transformations.Heaviside, gp_transformations.Probit)):
+            self.log_concave = True
 
     def _preprocess_values(self,Y):
         """

GPy/likelihoods/noise_models/gaussian_noise.py

@@ -24,6 +24,8 @@ class Gaussian(NoiseDistribution):
         self.N = N
         self._set_params(np.asarray(variance))
         super(Gaussian, self).__init__(gp_link,analytical_mean,analytical_variance)
+        if isinstance(gp_link , gp_transformations.Identity):
+            self.log_concave = True
 
     def _get_params(self):
         return np.array([self.variance])

GPy/likelihoods/noise_models/noise_distributions.py

@@ -33,7 +33,7 @@ class NoiseDistribution(object):
         else:
             self.predictive_variance = self._predictive_variance_numerical
 
-        self.log_concave = True
+        self.log_concave = False
 
     def _get_params(self):
         return np.zeros(0)

GPy/models.py

@@ -0,0 +1,22 @@
+'''
+Created on 14 Nov 2013
+
+@author: maxz
+'''
+
+from _models.bayesian_gplvm import BayesianGPLVM
+from _models.gp_regression import GPRegression
+from _models.gp_classification import GPClassification#; _gp_classification = gp_classification ; del gp_classification 
+from _models.sparse_gp_regression import SparseGPRegression#; _sparse_gp_regression = sparse_gp_regression ; del sparse_gp_regression 
+from _models.svigp_regression import SVIGPRegression#; _svigp_regression = svigp_regression ; del svigp_regression 
+from _models.sparse_gp_classification import SparseGPClassification#; _sparse_gp_classification = sparse_gp_classification ; del sparse_gp_classification 
+from _models.fitc_classification import FITCClassification#; _fitc_classification = fitc_classification ; del fitc_classification 
+from _models.gplvm import GPLVM#; _gplvm = gplvm ; del gplvm 
+from _models.bcgplvm import BCGPLVM#; _bcgplvm = bcgplvm; del bcgplvm
+from _models.sparse_gplvm import SparseGPLVM#; _sparse_gplvm = sparse_gplvm ; del sparse_gplvm 
+from _models.warped_gp import WarpedGP#; _warped_gp = warped_gp ; del warped_gp 
+from _models.bayesian_gplvm import BayesianGPLVM#; _bayesian_gplvm = bayesian_gplvm ; del bayesian_gplvm 
+from _models.mrd import MRD#; _mrd = mrd; del mrd 
+from _models.gradient_checker import GradientChecker#; _gradient_checker = gradient_checker ; del gradient_checker 
+from _models.gp_multioutput_regression import GPMultioutputRegression#; _gp_multioutput_regression = gp_multioutput_regression ; del gp_multioutput_regression 
+from _models.sparse_gp_multioutput_regression import SparseGPMultioutputRegression#; _sparse_gp_multioutput_regression = sparse_gp_multioutput_regression ; del sparse_gp_multioutput_regression 

GPy/models/__init__.py

@@ -1,18 +0,0 @@
-# Copyright (c) 2012, GPy authors (see AUTHORS.txt).
-# Licensed under the BSD 3-clause license (see LICENSE.txt)
-
-from gp_regression import GPRegression
-from gp_classification import GPClassification
-from sparse_gp_regression import SparseGPRegression
-from svigp_regression import SVIGPRegression
-from sparse_gp_classification import SparseGPClassification
-from fitc_classification import FITCClassification
-from gplvm import GPLVM
-from bcgplvm import BCGPLVM
-from sparse_gplvm import SparseGPLVM
-from warped_gp import WarpedGP
-from bayesian_gplvm import BayesianGPLVM
-from mrd import MRD
-from gradient_checker import GradientChecker
-from gp_multioutput_regression import GPMultioutputRegression
-from sparse_gp_multioutput_regression import SparseGPMultioutputRegression

GPy/testing/bgplvm_tests.py

@@ -4,7 +4,7 @@
 import unittest
 import numpy as np
 import GPy
-from GPy.models.bayesian_gplvm import BayesianGPLVM
+from ..models import BayesianGPLVM
 
 class BGPLVMTests(unittest.TestCase):
     def test_bias_kern(self):

GPy/testing/kernel_tests.py

@@ -34,12 +34,14 @@ class KernelTests(unittest.TestCase):
             self.assertTrue(GPy.kern.kern_test(kern, verbose=verbose))
 
     def test_eq_sympykernel(self):
-        kern = GPy.kern.eq_sympy(5, 3, output_ind=4)
+        if SYMPY_AVAILABLE:
-        self.assertTrue(GPy.kern.kern_test(kern, verbose=verbose))
+            kern = GPy.kern.eq_sympy(5, 3)
+            self.assertTrue(GPy.kern.kern_test(kern, output_ind=3, verbose=verbose))
 
-    def test_sinckernel(self):
+    def test_ode1_eqkernel(self):
-        kern = GPy.kern.sinc(5)
+        if SYMPY_AVAILABLE:
-        self.assertTrue(GPy.kern.kern_test(kern, verbose=verbose))
+            kern = GPy.kern.ode1_eq(3)
+            self.assertTrue(GPy.kern.kern_test(kern, output_ind=1, verbose=verbose, X_positive=True))
 
     def test_rbf_invkernel(self):
         kern = GPy.kern.rbf_inv(5)

GPy/testing/likelihoods_tests.py

@@ -186,33 +186,33 @@ class TestNoiseModels(object):
                             "laplace": True,
                             "ep": True
                             },
-                        "Gaussian_log": {
+                        #"Gaussian_log": {
-                            "model": GPy.likelihoods.gaussian(gp_link=gp_transformations.Log(), variance=self.var, D=self.D, N=self.N),
+                            #"model": GPy.likelihoods.gaussian(gp_link=gp_transformations.Log(), variance=self.var, D=self.D, N=self.N),
-                            "grad_params": {
+                            #"grad_params": {
-                                "names": ["noise_model_variance"],
+                                #"names": ["noise_model_variance"],
-                                "vals": [self.var],
+                                #"vals": [self.var],
-                                "constraints": [constrain_positive]
+                                #"constraints": [constrain_positive]
-                                },
+                                #},
-                            "laplace": True
+                            #"laplace": True
-                            },
+                            #},
-                        "Gaussian_probit": {
+                        #"Gaussian_probit": {
-                            "model": GPy.likelihoods.gaussian(gp_link=gp_transformations.Probit(), variance=self.var, D=self.D, N=self.N),
+                            #"model": GPy.likelihoods.gaussian(gp_link=gp_transformations.Probit(), variance=self.var, D=self.D, N=self.N),
-                            "grad_params": {
+                            #"grad_params": {
-                                "names": ["noise_model_variance"],
+                                #"names": ["noise_model_variance"],
-                                "vals": [self.var],
+                                #"vals": [self.var],
-                                "constraints": [constrain_positive]
+                                #"constraints": [constrain_positive]
-                                },
+                                #},
-                            "laplace": True
+                            #"laplace": True
-                            },
+                            #},
-                        "Gaussian_log_ex": {
+                        #"Gaussian_log_ex": {
-                            "model": GPy.likelihoods.gaussian(gp_link=gp_transformations.Log_ex_1(), variance=self.var, D=self.D, N=self.N),
+                            #"model": GPy.likelihoods.gaussian(gp_link=gp_transformations.Log_ex_1(), variance=self.var, D=self.D, N=self.N),
-                            "grad_params": {
+                            #"grad_params": {
-                                "names": ["noise_model_variance"],
+                                #"names": ["noise_model_variance"],
-                                "vals": [self.var],
+                                #"vals": [self.var],
-                                "constraints": [constrain_positive]
+                                #"constraints": [constrain_positive]
-                                },
+                                #},
-                            "laplace": True
+                            #"laplace": True
-                            },
+                            #},
                         "Bernoulli_default": {
                             "model": GPy.likelihoods.bernoulli(),
                             "link_f_constraints": [partial(constrain_bounded, lower=0, upper=1)],
@@ -253,6 +253,7 @@ class TestNoiseModels(object):
                 param_vals = []
                 param_names = []
                 constrain_positive = []
+                param_constraints = [] # ??? TODO: Saul to Fix.
             if "link_f_constraints" in attributes:
                 link_f_constraints = attributes["link_f_constraints"]
             else:
@@ -490,8 +491,14 @@ class TestNoiseModels(object):
             constraints[param_num](name, m)
 
         m.randomize()
-        m.checkgrad(verbose=1, step=step)
+        m.optimize(max_iters=8)
         print m
+        m.checkgrad(verbose=1, step=step)
+        if not m.checkgrad(step=step):
+            m.checkgrad(verbose=1, step=step)
+            #import ipdb; ipdb.set_trace()
+            #NOTE this test appears to be stochastic for some likelihoods (student t?)
+            # appears to all be working in test mode right now...
         assert m.checkgrad(step=step)
 
     ###########

GPy/testing/psi_stat_expectation_tests.py

@@ -27,9 +27,9 @@ def ard(p):
 @testing.deepTest(__test__())
 class Test(unittest.TestCase):
     input_dim = 9
-    num_inducing = 4
+    num_inducing = 13
-    N = 30
+    N = 300
-    Nsamples = 9e6
+    Nsamples = 1e6
 
     def setUp(self):
         i_s_dim_list = [2,4,3]
@@ -50,17 +50,20 @@ class Test(unittest.TestCase):
 #                        GPy.kern.linear(self.input_dim, ARD=True) +
 #                        GPy.kern.bias(self.input_dim) +
 #                        GPy.kern.white(self.input_dim)),
+                    (#GPy.kern.rbf(self.input_dim, np.random.rand(), np.random.rand(self.input_dim), ARD=True)
+                     GPy.kern.linear(self.input_dim, np.random.rand(self.input_dim), ARD=True)
+                     +GPy.kern.rbf(self.input_dim, np.random.rand(), np.random.rand(self.input_dim), ARD=True)
+#                      +GPy.kern.bias(self.input_dim)
+#                      +GPy.kern.white(self.input_dim)),
+                    ),
 #                     (GPy.kern.rbf(self.input_dim, np.random.rand(), np.random.rand(self.input_dim), ARD=True) +
-#                     GPy.kern.rbf(self.input_dim, np.random.rand(), np.random.rand(self.input_dim), ARD=True) +
+#                      GPy.kern.bias(self.input_dim, np.random.rand())),
-#                     GPy.kern.linear(self.input_dim, np.random.rand(self.input_dim), ARD=True) +
+#         (GPy.kern.rbf(self.input_dim, np.random.rand(), np.random.rand(self.input_dim), ARD=True)
-#                     GPy.kern.bias(self.input_dim) +
+#          +GPy.kern.rbf(self.input_dim, np.random.rand(), np.random.rand(self.input_dim), ARD=True)
-#                     GPy.kern.white(self.input_dim)),
+#          #+GPy.kern.bias(self.input_dim, np.random.rand())
-        (GPy.kern.linear(self.input_dim, np.random.rand(self.input_dim), ARD=True) +
+#          #+GPy.kern.white(self.input_dim, np.random.rand())),
-                    GPy.kern.bias(self.input_dim, np.random.rand()) +
+#         ),
-                    GPy.kern.white(self.input_dim, np.random.rand())),
+#                     GPy.kern.white(self.input_dim, np.random.rand())),
-                (GPy.kern.rbf(self.input_dim, np.random.rand(), np.random.rand(self.input_dim), ARD=True) +
-                    GPy.kern.bias(self.input_dim, np.random.rand()) +
-                    GPy.kern.white(self.input_dim, np.random.rand())),
 #                     GPy.kern.rbf(self.input_dim), GPy.kern.rbf(self.input_dim, ARD=True),
 #                       GPy.kern.linear(self.input_dim, ARD=False), GPy.kern.linear(self.input_dim, ARD=True),
 #                       GPy.kern.linear(self.input_dim) + GPy.kern.bias(self.input_dim),
@@ -117,25 +120,25 @@ class Test(unittest.TestCase):
             diffs = []
             for i, q_x_sample_stripe in enumerate(np.array_split(self.q_x_samples, self.Nsamples / Nsamples)):
                 K = kern.K(q_x_sample_stripe, self.Z)
-                K = (K[:, :, None] * K[:, None, :]).mean(0)
+                K = (K[:, :, None] * K[:, None, :])
-                K_ += K
+                K_ += K.sum(0) / self.Nsamples
-                diffs.append(((psi2 - (K_ / (i + 1)))**2).mean())
+                diffs.append(((psi2 - (K_*self.Nsamples/((i+1)*Nsamples)))**2).mean())
-            K_ /= self.Nsamples / Nsamples
+            #K_ /= self.Nsamples / Nsamples
             msg = "psi2: {}".format("+".join([p.name + ard(p) for p in kern.parts]))
             try:
                 import pylab
                 pylab.figure(msg)
-                pylab.plot(diffs, marker='x', mew=1.3)
+                pylab.plot(diffs, marker='x', mew=.2)
 #                 print msg, np.allclose(psi2.squeeze(), K_, rtol=1e-1, atol=.1)
                 self.assertTrue(np.allclose(psi2.squeeze(), K_),
                                             #rtol=1e-1, atol=.1),
                                 msg=msg + ": not matching")
 #                 sys.stdout.write(".")
             except:
-#                 import ipdb;ipdb.set_trace()
 #                 kern.psi2(self.Z, self.q_x_mean, self.q_x_variance)
 #                 sys.stdout.write("E")
                 print msg + ": not matching"
+                import ipdb;ipdb.set_trace()
                 pass
 
 if __name__ == "__main__":

GPy/testing/psi_stat_gradient_tests.py

@@ -40,10 +40,9 @@ class PsiStatModel(Model):
         return self.kern.__getattribute__(self.which)(self.Z, self.X, self.X_variance).sum()
     def _log_likelihood_gradients(self):
         psimu, psiS = self.kern.__getattribute__("d" + self.which + "_dmuS")(numpy.ones_like(self.psi_), self.Z, self.X, self.X_variance)
-        try:
+        #psimu, psiS = numpy.ones(self.N * self.input_dim), numpy.ones(self.N * self.input_dim)
-            psiZ = self.kern.__getattribute__("d" + self.which + "_dZ")(numpy.ones_like(self.psi_), self.Z, self.X, self.X_variance)
+        psiZ = self.kern.__getattribute__("d" + self.which + "_dZ")(numpy.ones_like(self.psi_), self.Z, self.X, self.X_variance)
-        except AttributeError:
+        #psiZ = numpy.ones(self.num_inducing * self.input_dim)
-            psiZ = numpy.zeros(self.num_inducing * self.input_dim)
         thetagrad = self.kern.__getattribute__("d" + self.which + "_dtheta")(numpy.ones_like(self.psi_), self.Z, self.X, self.X_variance).flatten()
         return numpy.hstack((psimu.flatten(), psiS.flatten(), psiZ.flatten(), thetagrad))
 
@@ -64,40 +63,54 @@ class DPsiStatTest(unittest.TestCase):
 
     def testPsi0(self):
         for k in self.kernels:
-            m = PsiStatModel('psi0', X=self.X, X_variance=self.X_var, Z=self.Z,
+            m = PsiStatModel('psi0', X=self.X, X_variance=self.X_var, Z=self.Z,\
                              num_inducing=self.num_inducing, kernel=k)
+            m.ensure_default_constraints()
+            m.randomize()
             assert m.checkgrad(), "{} x psi0".format("+".join(map(lambda x: x.name, k.parts)))
         
-#     def testPsi1(self):
+    def testPsi1(self):
-#         for k in self.kernels:
+        for k in self.kernels:
-#             m = PsiStatModel('psi1', X=self.X, X_variance=self.X_var, Z=self.Z,
+            m = PsiStatModel('psi1', X=self.X, X_variance=self.X_var, Z=self.Z,
-#                      num_inducing=self.num_inducing, kernel=k)
+                     num_inducing=self.num_inducing, kernel=k)
-#             assert m.checkgrad(), "{} x psi1".format("+".join(map(lambda x: x.name, k.parts)))
+            m.ensure_default_constraints()
+            m.randomize()
+            assert m.checkgrad(), "{} x psi1".format("+".join(map(lambda x: x.name, k.parts)))
 
     def testPsi2_lin(self):
         k = self.kernels[0]
         m = PsiStatModel('psi2', X=self.X, X_variance=self.X_var, Z=self.Z,
-                     num_inducing=self.num_inducing, kernel=k)
+                 num_inducing=self.num_inducing, kernel=k)
+        m.ensure_default_constraints()
+        m.randomize()
         assert m.checkgrad(), "{} x psi2".format("+".join(map(lambda x: x.name, k.parts)))
     def testPsi2_lin_bia(self):
         k = self.kernels[3]
         m = PsiStatModel('psi2', X=self.X, X_variance=self.X_var, Z=self.Z,
                      num_inducing=self.num_inducing, kernel=k)
+        m.ensure_default_constraints()
+        m.randomize()
         assert m.checkgrad(), "{} x psi2".format("+".join(map(lambda x: x.name, k.parts)))
     def testPsi2_rbf(self):
         k = self.kernels[1]
         m = PsiStatModel('psi2', X=self.X, X_variance=self.X_var, Z=self.Z,
                      num_inducing=self.num_inducing, kernel=k)
+        m.ensure_default_constraints()
+        m.randomize()
         assert m.checkgrad(), "{} x psi2".format("+".join(map(lambda x: x.name, k.parts)))
     def testPsi2_rbf_bia(self):
         k = self.kernels[-1]
         m = PsiStatModel('psi2', X=self.X, X_variance=self.X_var, Z=self.Z,
                      num_inducing=self.num_inducing, kernel=k)
+        m.ensure_default_constraints()
+        m.randomize()
         assert m.checkgrad(), "{} x psi2".format("+".join(map(lambda x: x.name, k.parts)))
     def testPsi2_bia(self):
         k = self.kernels[2]
         m = PsiStatModel('psi2', X=self.X, X_variance=self.X_var, Z=self.Z,
                      num_inducing=self.num_inducing, kernel=k)
+        m.ensure_default_constraints()
+        m.randomize()
         assert m.checkgrad(), "{} x psi2".format("+".join(map(lambda x: x.name, k.parts)))
 
 
@@ -116,9 +129,9 @@ if __name__ == "__main__":
 #         m.randomize()
 # #         self.assertTrue(m.checkgrad())
         numpy.random.seed(0)
-        input_dim = 5
+        input_dim = 3
-        N = 50
+        N = 3
-        num_inducing = 10
+        num_inducing = 2
         D = 15
         X = numpy.random.randn(N, input_dim)
         X_var = .5 * numpy.ones_like(X) + .1 * numpy.clip(numpy.random.randn(*X.shape), 0, 1)
@@ -135,18 +148,35 @@ if __name__ == "__main__":
 #                      num_inducing=num_inducing, kernel=k)
 #             assert m.checkgrad(), "{} x psi1".format("+".join(map(lambda x: x.name, k.parts)))
 #
-#         m0 = PsiStatModel('psi0', X=X, X_variance=X_var, Z=Z,
+        m0 = PsiStatModel('psi0', X=X, X_variance=X_var, Z=Z,
-#                          num_inducing=num_inducing, kernel=GPy.kern.linear(input_dim))
+                         num_inducing=num_inducing, kernel=GPy.kern.rbf(input_dim)+GPy.kern.bias(input_dim))
 #         m1 = PsiStatModel('psi1', X=X, X_variance=X_var, Z=Z,
 #                          num_inducing=num_inducing, kernel=kernel)
 #         m1 = PsiStatModel('psi1', X=X, X_variance=X_var, Z=Z,
 #                          num_inducing=num_inducing, kernel=kernel)
 #         m2 = PsiStatModel('psi2', X=X, X_variance=X_var, Z=Z,
 #                          num_inducing=num_inducing, kernel=GPy.kern.rbf(input_dim))
-        m3 = PsiStatModel('psi2', X=X, X_variance=X_var, Z=Z,
+#         m3 = PsiStatModel('psi2', X=X, X_variance=X_var, Z=Z,
-                         num_inducing=num_inducing, kernel=GPy.kern.linear(input_dim, ARD=True, variances=numpy.random.rand(input_dim)))
+#                          num_inducing=num_inducing, kernel=GPy.kern.linear(input_dim, ARD=True, variances=numpy.random.rand(input_dim)))
         # + GPy.kern.bias(input_dim))
-#         m4 = PsiStatModel('psi2', X=X, X_variance=X_var, Z=Z,
+#         m = PsiStatModel('psi2', X=X, X_variance=X_var, Z=Z,
-#                          num_inducing=num_inducing, kernel=GPy.kern.rbf(input_dim) + GPy.kern.bias(input_dim))
+#                          num_inducing=num_inducing, 
+#                          kernel=(
+#             GPy.kern.rbf(input_dim, ARD=1) 
+#             +GPy.kern.linear(input_dim, ARD=1) 
+#             +GPy.kern.bias(input_dim))
+#                          )
+#         m.ensure_default_constraints()
+        m2 = PsiStatModel('psi2', X=X, X_variance=X_var, Z=Z,
+                         num_inducing=num_inducing, kernel=(
+            GPy.kern.rbf(input_dim, numpy.random.rand(), numpy.random.rand(input_dim), ARD=1) 
+            #+GPy.kern.linear(input_dim, numpy.random.rand(input_dim), ARD=1) 
+            #+GPy.kern.rbf(input_dim, numpy.random.rand(), numpy.random.rand(input_dim), ARD=1) 
+            #+GPy.kern.rbf(input_dim, numpy.random.rand(), numpy.random.rand(), ARD=0) 
+            +GPy.kern.bias(input_dim)
+            +GPy.kern.white(input_dim)
+            )
+            )
+        m2.ensure_default_constraints()
     else:
         unittest.main()

GPy/testing/sparse_gplvm_tests.py

@@ -4,7 +4,7 @@
 import unittest
 import numpy as np
 import GPy
-from GPy.models.sparse_gplvm import SparseGPLVM
+from ..models import SparseGPLVM
 
 class sparse_GPLVMTests(unittest.TestCase):
     def test_bias_kern(self):

GPy/testing/unit_tests.py

@@ -163,14 +163,18 @@ class GradientTests(unittest.TestCase):
         rbflin = GPy.kern.rbf(2) + GPy.kern.linear(2)
         self.check_model(rbflin, model_type='SparseGPRegression', dimension=2)
 
+    #@unittest.expectedFailure
     def test_SparseGPRegression_rbf_linear_white_kern_2D_uncertain_inputs(self):
         ''' Testing the sparse GP regression with rbf, linear kernel on 2d data with uncertain inputs'''
         rbflin = GPy.kern.rbf(2) + GPy.kern.linear(2)
+        raise unittest.SkipTest("This is not implemented yet!")
         self.check_model(rbflin, model_type='SparseGPRegression', dimension=2, uncertain_inputs=1)
 
+    #@unittest.expectedFailure
     def test_SparseGPRegression_rbf_linear_white_kern_1D_uncertain_inputs(self):
         ''' Testing the sparse GP regression with rbf, linear kernel on 1d data with uncertain inputs'''
         rbflin = GPy.kern.rbf(1) + GPy.kern.linear(1)
+        raise unittest.SkipTest("This is not implemented yet!")
         self.check_model(rbflin, model_type='SparseGPRegression', dimension=1, uncertain_inputs=1)
 
     def test_GPLVM_rbf_bias_white_kern_2D(self):

GPy/util/__init__.py

@@ -14,5 +14,6 @@ import visualize
 import decorators
 import classification
 import latent_space_visualizations
+import symbolic
 
 import netpbmfile

GPy/util/config.py

@@ -14,6 +14,9 @@ print default_file, os.path.isfile(default_file)
 # 1. check if the user has a ~/.gpy_config.cfg
 if os.path.isfile(user_file):
     config.read(user_file)
-else:
+elif os.path.isfile(default_file):
     # 2. if not, use the default one
     config.read(default_file)
+else:
+    #3. panic
+    raise ValueError, "no configuration file found"

GPy/util/data_resources.json

@@ -0,0 +1,319 @@
+{
+   "rogers_girolami_data":{
+      "files":[
+         [
+            "firstcoursemldata.tar.gz"
+         ]
+      ],
+      "license":null,
+      "citation":"A First Course in Machine Learning. Simon Rogers and Mark Girolami: Chapman & Hall/CRC, ISBN-13: 978-1439824146",
+      "details":"Data from the textbook 'A First Course in Machine Learning'. Available from http://www.dcs.gla.ac.uk/~srogers/firstcourseml/.",
+      "urls":[
+         "https://www.dropbox.com/sh/7p6tu1t29idgliq/_XqlH_3nt9/"
+      ],
+      "suffices":[
+         [
+            "?dl=1"
+         ]
+      ],
+      "size":21949154
+   },
+   "ankur_pose_data":{
+      "files":[
+         [
+            "ankurDataPoseSilhouette.mat"
+         ]
+      ],
+      "citation":"3D Human Pose from Silhouettes by Relevance Vector Regression (In CVPR'04). A. Agarwal and B. Triggs.",
+      "license":null,
+      "urls":[
+         "http://staffwww.dcs.shef.ac.uk/people/N.Lawrence/dataset_mirror/ankur_pose_data/"
+      ],
+      "details":"Artificially generated data of silhouettes given poses. Note that the data does not display a left/right ambiguity because across the entire data set one of the arms sticks out more the the other, disambiguating the pose as to which way the individual is facing."
+   },
+   "osu_accad":{
+      "files":[
+         [
+            "swagger1TXT.ZIP",
+            "handspring1TXT.ZIP",
+            "quickwalkTXT.ZIP",
+            "run1TXT.ZIP",
+            "sprintTXT.ZIP",
+            "dogwalkTXT.ZIP",
+            "camper_04TXT.ZIP",
+            "dance_KB3_TXT.ZIP",
+            "per20_TXT.ZIP",
+            "perTWO07_TXT.ZIP",
+            "perTWO13_TXT.ZIP",
+            "perTWO14_TXT.ZIP",
+            "perTWO15_TXT.ZIP",
+            "perTWO16_TXT.ZIP"
+         ],
+         [
+            "connections.txt"
+         ]
+      ],
+      "license":"Data is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License (http://creativecommons.org/licenses/by-nc-sa/3.0/).",
+      "citation":"The Open Motion Data Project by The Ohio State University Advanced Computing Center for the Arts and Design, http://accad.osu.edu/research/mocap/mocap_data.htm.",
+      "details":"Motion capture data of different motions from the Open Motion Data Project at Ohio State University.",
+      "urls":[
+         "http://accad.osu.edu/research/mocap/data/",
+         "http://staffwww.dcs.shef.ac.uk/people/N.Lawrence/dataset_mirror/stick/"
+      ],
+      "size":15922790
+   },
+   "isomap_face_data":{
+      "files":[
+         [
+            "face_data.mat"
+         ]
+      ],
+      "license":null,
+      "citation":"A Global Geometric Framework for Nonlinear Dimensionality Reduction, J. B. Tenenbaum, V. de Silva and J. C. Langford, Science 290 (5500): 2319-2323, 22 December 2000",
+      "details":"Face data made available by Tenenbaum, de Silva and Langford to demonstrate isomap, available from http://isomap.stanford.edu/datasets.html.",
+      "urls":[
+         "http://staffwww.dcs.shef.ac.uk/people/N.Lawrence/dataset_mirror/isomap_face_data/"
+      ],
+      "size":24229368
+   },
+   "boston_housing":{
+      "files":[
+         [
+            "Index",
+            "housing.data",
+            "housing.names"
+         ]
+      ],
+      "license":null,
+      "citation":"Harrison, D. and Rubinfeld, D.L. 'Hedonic prices and the demand for clean air', J. Environ. Economics & Management, vol.5, 81-102, 1978.",
+      "details":"The Boston Housing data relates house values in Boston to a range of input variables.",
+      "urls":[
+         "http://archive.ics.uci.edu/ml/machine-learning-databases/housing/"
+      ],
+      "size":51276
+   },
+   "cmu_mocap_full":{
+      "files":[
+         [
+            "allasfamc.zip"
+         ]
+      ],
+      "license":"From http://mocap.cs.cmu.edu. This data is free for use in research projects. You may include this data in commercially-sold products, but you may not resell this data directly, even in converted form. If you publish results obtained using this data, we would appreciate it if you would send the citation to your published paper to jkh+mocap@cs.cmu.edu, and also would add this text to your acknowledgments section: The data used in this project was obtained from mocap.cs.cmu.edu. The database was created with funding from NSF EIA-0196217.",
+      "citation":"Please include this in your acknowledgements: The data used in this project was obtained from mocap.cs.cmu.edu.\nThe database was created with funding from NSF EIA-0196217.",
+      "details":"CMU Motion Capture data base. Captured by a Vicon motion capture system consisting of 12 infrared MX-40 cameras, each of which is capable of recording at 120 Hz with images of 4 megapixel resolution. Motions are captured in a working volume of approximately 3m x 8m. The capture subject wears 41 markers and a stylish black garment.",
+      "urls":[
+         "http://mocap.cs.cmu.edu"
+      ],
+      "size":null
+   },
+   "brendan_faces":{
+      "files":[
+         [
+            "frey_rawface.mat"
+         ]
+      ],
+      "license":null,
+      "citation":"Frey, B. J., Colmenarez, A and Huang, T. S. Mixtures of Local Linear Subspaces for Face Recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 1998, 32-37, June 1998. Computer Society Press, Los Alamitos, CA.",
+      "details":"A video of Brendan Frey's face popularized as a benchmark for visualization by the Locally Linear Embedding.",
+      "urls":[
+         "http://www.cs.nyu.edu/~roweis/data/"
+      ],
+      "size":1100584
+   },
+   "olympic_marathon_men":{
+      "files":[
+         [
+            "olympicMarathonTimes.csv"
+         ]
+      ],
+      "license":null,
+      "citation":null,
+      "details":"Olympic mens' marathon gold medal winning times from 1896 to 2012. Time given in pace (minutes per kilometer). Data is originally downloaded and collated from Wikipedia, we are not responsible for errors in the data",
+      "urls":[
+         "http://staffwww.dcs.shef.ac.uk/people/N.Lawrence/dataset_mirror/olympic_marathon_men/"
+      ],
+      "size":584
+   },
+   "pumadyn-32nm":{
+      "files":[
+         [
+            "pumadyn-32nm.tar.gz"
+         ]
+      ],
+      "license":"Data is made available by the Delve system at the University of Toronto",
+      "citation":"Created by Zoubin Ghahramani using the Matlab Robotics Toolbox of Peter Corke. Corke, P. I. (1996). A Robotics Toolbox for MATLAB. IEEE Robotics and Automation Magazine, 3 (1): 24-32.",
+      "details":"Pumadyn non linear 32 input data set with moderate noise. See http://www.cs.utoronto.ca/~delve/data/pumadyn/desc.html for details.",
+      "urls":[
+         "ftp://ftp.cs.toronto.edu/pub/neuron/delve/data/tarfiles/pumadyn-family/"
+      ],
+      "size":5861646
+   },
+   "ripley_prnn_data":{
+      "files":[
+         [
+            "Cushings.dat",
+            "README",
+            "crabs.dat",
+            "fglass.dat",
+            "fglass.grp",
+            "pima.te",
+            "pima.tr",
+            "pima.tr2",
+            "synth.te",
+            "synth.tr",
+            "viruses.dat",
+            "virus3.dat"
+         ]
+      ],
+      "license":null,
+      "citation":"Pattern Recognition and Neural Networks by B.D. Ripley (1996) Cambridge University Press ISBN 0 521 46986 7",
+      "details":"Data sets from Brian Ripley's Pattern Recognition and Neural Networks",
+      "urls":[
+         "http://www.stats.ox.ac.uk/pub/PRNN/"
+      ],
+      "size":93565
+   },
+   "three_phase_oil_flow":{
+      "files":[
+         [
+            "DataTrnLbls.txt",
+            "DataTrn.txt",
+            "DataTst.txt",
+            "DataTstLbls.txt",
+            "DataVdn.txt",
+            "DataVdnLbls.txt"
+         ]
+      ],
+      "license":null,
+      "citation":"Bishop, C. M. and G. D. James (1993). Analysis of multiphase flows using dual-energy gamma densitometry and neural networks. Nuclear Instruments and Methods in Physics Research A327, 580-593",
+      "details":"The three phase oil data used initially for demonstrating the Generative Topographic mapping.",
+      "urls":[
+         "http://staffwww.dcs.shef.ac.uk/people/N.Lawrence/dataset_mirror/three_phase_oil_flow/"
+      ],
+      "size":712796
+   },
+   "robot_wireless":{
+      "files":[
+         [
+            "uw-floor.txt"
+         ]
+      ],
+      "license":null,
+      "citation":"WiFi-SLAM using Gaussian Process Latent Variable Models by Brian Ferris, Dieter Fox and Neil Lawrence in IJCAI'07 Proceedings pages 2480-2485. Data used in A Unifying Probabilistic Perspective for Spectral Dimensionality Reduction: Insights and New Models by Neil D. Lawrence, JMLR 13 pg 1609--1638, 2012.",
+      "details":"Data created by Brian Ferris and Dieter Fox. Consists of WiFi access point strengths taken during a circuit of the Paul Allen building at the University of Washington.",
+      "urls":[
+         "http://staffwww.dcs.shef.ac.uk/people/N.Lawrence/dataset_mirror/robot_wireless/"
+      ],
+      "size":284390
+   },
+   "xw_pen":{
+      "files":[
+         [
+            "xw_pen_15.csv"
+         ]
+      ],
+      "license":null,
+      "citation":"Michael E. Tipping and Neil D. Lawrence. Variational inference for Student-t models: Robust Bayesian interpolation and generalised component analysis. Neurocomputing, 69:123--141, 2005",
+      "details":"Accelerometer pen data used for robust regression by Tipping and Lawrence.",
+      "urls":[
+         "http://staffwww.dcs.shef.ac.uk/people/N.Lawrence/dataset_mirror/xw_pen/"
+      ],
+      "size":3410
+   },
+   "swiss_roll":{
+      "files":[
+         [
+            "swiss_roll_data.mat"
+         ]
+      ],
+      "license":null,
+      "citation":"A Global Geometric Framework for Nonlinear Dimensionality Reduction, J. B. Tenenbaum, V. de Silva and J. C. Langford, Science 290 (5500): 2319-2323, 22 December 2000",
+      "details":"Swiss roll data made available by Tenenbaum, de Silva and Langford to demonstrate isomap, available from http://isomap.stanford.edu/datasets.html.",
+      "urls":[
+         "http://isomap.stanford.edu/"
+      ],
+      "size":800256
+   },
+   "osu_run1":{
+      "files":[
+         [
+            "run1TXT.ZIP"
+         ],
+         [
+            "connections.txt"
+         ]
+      ],
+      "license":"Data is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License (http://creativecommons.org/licenses/by-nc-sa/3.0/).",
+      "citation":"The Open Motion Data Project by The Ohio State University Advanced Computing Center for the Arts and Design, http://accad.osu.edu/research/mocap/mocap_data.htm.",
+      "details":"Motion capture data of a stick man running from the Open Motion Data Project at Ohio State University.",
+      "urls":[
+         "http://accad.osu.edu/research/mocap/data/",
+         "http://staffwww.dcs.shef.ac.uk/people/N.Lawrence/dataset_mirror/stick/"
+      ],
+      "size":338103
+   },
+   "creep_rupture":{
+      "files":[
+         [
+            "creeprupt.tar"
+         ]
+      ],
+      "license":null,
+      "citation":"Materials Algorithms Project Data Library: MAP_DATA_CREEP_RUPTURE. F. Brun and T. Yoshida.",
+      "details":"Provides 2066 creep rupture test results of steels (mainly of two kinds of steels: 2.25Cr and 9-12 wt% Cr ferritic steels). See http://www.msm.cam.ac.uk/map/data/materials/creeprupt-b.html.",
+      "urls":[
+         "http://www.msm.cam.ac.uk/map/data/tar/"
+      ],
+      "size":602797
+   },
+   "olivetti_faces":{
+      "files":[
+         [
+            "att_faces.zip"
+         ],
+         [
+            "olivettifaces.mat"
+         ]
+      ],
+      "license":null,
+      "citation":"Ferdinando Samaria and Andy Harter, Parameterisation of a Stochastic Model for Human Face Identification. Proceedings of 2nd IEEE Workshop on Applications of Computer Vision, Sarasota FL, December 1994",
+      "details":"Olivetti Research Labs Face data base, acquired between December 1992 and December 1994 in the Olivetti Research Lab, Cambridge (which later became AT&T Laboratories, Cambridge). When using these images please give credit to AT&T Laboratories, Cambridge. ",
+      "urls":[
+         "http://staffwww.dcs.shef.ac.uk/people/N.Lawrence/dataset_mirror/olivetti_faces/",
+         "http://www.cs.nyu.edu/~roweis/data/"
+      ],
+      "size":8561331
+   },
+   "della_gatta":{
+      "files":[
+         [
+            "DellaGattadata.mat"
+         ]
+      ],
+      "license":null,
+      "citation":"Direct targets of the TRP63 transcription factor revealed by a combination of gene expression profiling and reverse engineering. Giusy Della Gatta, Mukesh Bansal, Alberto Ambesi-Impiombato, Dario Antonini, Caterina Missero, and Diego di Bernardo, Genome Research 2008",
+      "details":"The full gene expression data set from della Gatta et al (http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2413161/) processed by RMA.",
+      "urls":[
+         "http://staffwww.dcs.shef.ac.uk/people/N.Lawrence/dataset_mirror/della_gatta/"
+      ],
+      "size":3729650
+   },
+   "epomeo_gpx":{
+      "files":[
+         [
+            "endomondo_1.gpx",
+            "endomondo_2.gpx",
+            "garmin_watch_via_endomondo.gpx",
+            "viewranger_phone.gpx",
+            "viewranger_tablet.gpx"
+         ]
+      ],
+      "license":null,
+      "citation":"",
+      "details":"Five different GPS traces of the same run up Mount Epomeo in Ischia. The traces are from different sources. endomondo_1 and endomondo_2 are traces from the mobile phone app Endomondo, with a split in the middle. garmin_watch_via_endomondo is the trace from a Garmin watch, with a segment missing about 4 kilometers in. viewranger_phone and viewranger_tablet are traces from a phone and a tablet through the viewranger app. The viewranger_phone data comes from the same mobile phone as the Endomondo data (i.e. there are 3 GPS devices, but one device recorded two traces).",
+      "urls":[
+         "http://staffwww.dcs.shef.ac.uk/people/N.Lawrence/dataset_mirror/epomeo_gpx/"
+      ],
+      "size":2031872
+   }
+}

GPy/util/datasets.py

@@ -7,7 +7,7 @@ import urllib as url
 import zipfile
 import tarfile
 import datetime
-
+import json
 ipython_available=True
 try:
     import IPython
@@ -29,129 +29,11 @@ data_path = os.path.join(os.path.dirname(__file__), 'datasets')
 default_seed = 10000
 overide_manual_authorize=False
 neil_url = 'http://staffwww.dcs.shef.ac.uk/people/N.Lawrence/dataset_mirror/'
-sam_url = 'http://www.cs.nyu.edu/~roweis/data/'
-cmu_url = 'http://mocap.cs.cmu.edu/subjects/'
 
-# Note: there may be a better way of storing data resources, for the
+# Read data resources from json file.
-# moment we are storing them in a dictionary.
+path = os.path.join(os.path.dirname(__file__), 'data_resources.json')
-data_resources = {'ankur_pose_data' : {'urls' : [neil_url + 'ankur_pose_data/'],
+json_data=open(path).read()
-                                       'files' : [['ankurDataPoseSilhouette.mat']],
+data_resources = json.loads(json_data)
-                                       'license' : None,
-                                       'citation' : """3D Human Pose from Silhouettes by Relevance Vector Regression (In CVPR'04). A. Agarwal and B. Triggs.""",
-                                       'details' : """Artificially generated data of silhouettes given poses. Note that the data does not display a left/right ambiguity because across the entire data set one of the arms sticks out more the the other, disambiguating the pose as to which way the individual is facing."""},
-
-                  'boston_housing' : {'urls' : ['http://archive.ics.uci.edu/ml/machine-learning-databases/housing/'],
-                                      'files' : [['Index', 'housing.data', 'housing.names']],
-                                      'citation' : """Harrison, D. and Rubinfeld, D.L. 'Hedonic prices and the demand for clean air', J. Environ. Economics & Management, vol.5, 81-102, 1978.""",
-                                      'details' : """The Boston Housing data relates house values in Boston to a range of input variables.""",
-                                      'license' : None,
-                                      'size' : 51276
-                                      },
-                  'brendan_faces' : {'urls' : [sam_url],
-                                     'files': [['frey_rawface.mat']],
-                                     'citation' : 'Frey, B. J., Colmenarez, A and Huang, T. S. Mixtures of Local Linear Subspaces for Face Recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 1998, 32-37, June 1998. Computer Society Press, Los Alamitos, CA.',
-                                     'details' : """A video of Brendan Frey's face popularized as a benchmark for visualization by the Locally Linear Embedding.""",
-                                     'license': None,
-                                     'size' : 1100584},
-                  'cmu_mocap_full' : {'urls' : ['http://mocap.cs.cmu.edu'],
-                                 'files' : [['allasfamc.zip']],
-                                 'citation' : """Please include this in your acknowledgements: The data used in this project was obtained from mocap.cs.cmu.edu.
-The database was created with funding from NSF EIA-0196217.""",
-                                 'details' : """CMU Motion Capture data base. Captured by a Vicon motion capture system consisting of 12 infrared MX-40 cameras, each of which is capable of recording at 120 Hz with images of 4 megapixel resolution. Motions are captured in a working volume of approximately 3m x 8m. The capture subject wears 41 markers and a stylish black garment.""",
-                                 'license' : """From http://mocap.cs.cmu.edu. This data is free for use in research projects. You may include this data in commercially-sold products, but you may not resell this data directly, even in converted form. If you publish results obtained using this data, we would appreciate it if you would send the citation to your published paper to jkh+mocap@cs.cmu.edu, and also would add this text to your acknowledgments section: The data used in this project was obtained from mocap.cs.cmu.edu. The database was created with funding from NSF EIA-0196217.""",
-                                 'size' : None},
-                  'creep_rupture' : {'urls' : ['http://www.msm.cam.ac.uk/map/data/tar/'],
-                                     'files' : [['creeprupt.tar']],
-                                     'citation' : 'Materials Algorithms Project Data Library: MAP_DATA_CREEP_RUPTURE. F. Brun and T. Yoshida.',
-                                     'details' : """Provides 2066 creep rupture test results of steels (mainly of two kinds of steels: 2.25Cr and 9-12 wt% Cr ferritic steels). See http://www.msm.cam.ac.uk/map/data/materials/creeprupt-b.html.""",
-                                     'license' : None,
-                                     'size' : 602797},
-                  'della_gatta' : {'urls' : [neil_url + 'della_gatta/'],
-                                   'files': [['DellaGattadata.mat']],
-                                   'citation' : 'Direct targets of the TRP63 transcription factor revealed by a combination of gene expression profiling and reverse engineering. Giusy Della Gatta, Mukesh Bansal, Alberto Ambesi-Impiombato, Dario Antonini, Caterina Missero, and Diego di Bernardo, Genome Research 2008',
-                                   'details': "The full gene expression data set from della Gatta et al (http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2413161/) processed by RMA.",
-                                   'license':None,
-                                   'size':3729650},
-                  'epomeo_gpx' : {'urls' : [neil_url + 'epomeo_gpx/'],
-                                   'files': [['endomondo_1.gpx', 'endomondo_2.gpx', 'garmin_watch_via_endomondo.gpx','viewranger_phone.gpx','viewranger_tablet.gpx']],
-                                   'citation' : '',
-                                   'details': "Five different GPS traces of the same run up Mount Epomeo in Ischia. The traces are from different sources. endomondo_1 and endomondo_2 are traces from the mobile phone app Endomondo, with a split in the middle. garmin_watch_via_endomondo is the trace from a Garmin watch, with a segment missing about 4 kilometers in. viewranger_phone and viewranger_tablet are traces from a phone and a tablet through the viewranger app. The viewranger_phone data comes from the same mobile phone as the Endomondo data (i.e. there are 3 GPS devices, but one device recorded two traces).",
-                                   'license':None,
-                                   'size': 2031872},
-                  'three_phase_oil_flow': {'urls' : [neil_url + 'three_phase_oil_flow/'],
-                                           'files' : [['DataTrnLbls.txt', 'DataTrn.txt', 'DataTst.txt', 'DataTstLbls.txt', 'DataVdn.txt', 'DataVdnLbls.txt']],
-                                           'citation' : 'Bishop, C. M. and G. D. James (1993). Analysis of multiphase flows using dual-energy gamma densitometry and neural networks. Nuclear Instruments and Methods in Physics Research A327, 580-593',
-                                           'details' : """The three phase oil data used initially for demonstrating the Generative Topographic mapping.""",
-                                           'license' : None,
-                                           'size' : 712796},
-                  'rogers_girolami_data' : {'urls' : ['https://www.dropbox.com/sh/7p6tu1t29idgliq/_XqlH_3nt9/'],
-                                            'files' : [['firstcoursemldata.tar.gz']],
-                                            'suffices' : [['?dl=1']],
-                                            'citation' : 'A First Course in Machine Learning. Simon Rogers and Mark Girolami: Chapman & Hall/CRC, ISBN-13: 978-1439824146',
-                                            'details' : """Data from the textbook 'A First Course in Machine Learning'. Available from http://www.dcs.gla.ac.uk/~srogers/firstcourseml/.""",
-                                            'license' : None,
-                                            'size' : 21949154},
-                  'olivetti_faces' : {'urls' : [neil_url + 'olivetti_faces/', sam_url],
-                                      'files' : [['att_faces.zip'], ['olivettifaces.mat']],
-                                            'citation' : 'Ferdinando Samaria and Andy Harter, Parameterisation of a Stochastic Model for Human Face Identification. Proceedings of 2nd IEEE Workshop on Applications of Computer Vision, Sarasota FL, December 1994',
-                                            'details' : """Olivetti Research Labs Face data base, acquired between December 1992 and December 1994 in the Olivetti Research Lab, Cambridge (which later became AT&T Laboratories, Cambridge). When using these images please give credit to AT&T Laboratories, Cambridge. """,
-                                            'license': None,
-                                            'size' : 8561331},
-                  'olympic_marathon_men' : {'urls' : [neil_url + 'olympic_marathon_men/'],
-                                            'files' : [['olympicMarathonTimes.csv']],
-                                            'citation' : None,
-                                            'details' : """Olympic mens' marathon gold medal winning times from 1896 to 2012. Time given in pace (minutes per kilometer). Data is originally downloaded and collated from Wikipedia, we are not responsible for errors in the data""",
-                                            'license': None,
-                                            'size' : 584},
-                  'osu_run1' : {'urls': ['http://accad.osu.edu/research/mocap/data/', neil_url + 'stick/'],
-                                'files': [['run1TXT.ZIP'],['connections.txt']],
-                                'details' : "Motion capture data of a stick man running from the Open Motion Data Project at Ohio State University.",
-                                'citation' : 'The Open Motion Data Project by The Ohio State University Advanced Computing Center for the Arts and Design, http://accad.osu.edu/research/mocap/mocap_data.htm.',
-                                'license' : 'Data is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License (http://creativecommons.org/licenses/by-nc-sa/3.0/).',
-                                'size': 338103},
-                  'osu_accad' : {'urls': ['http://accad.osu.edu/research/mocap/data/', neil_url + 'stick/'],
-                                'files': [['swagger1TXT.ZIP','handspring1TXT.ZIP','quickwalkTXT.ZIP','run1TXT.ZIP','sprintTXT.ZIP','dogwalkTXT.ZIP','camper_04TXT.ZIP','dance_KB3_TXT.ZIP','per20_TXT.ZIP','perTWO07_TXT.ZIP','perTWO13_TXT.ZIP','perTWO14_TXT.ZIP','perTWO15_TXT.ZIP','perTWO16_TXT.ZIP'],['connections.txt']],
-                                'details' : "Motion capture data of different motions from the Open Motion Data Project at Ohio State University.",
-                                'citation' : 'The Open Motion Data Project by The Ohio State University Advanced Computing Center for the Arts and Design, http://accad.osu.edu/research/mocap/mocap_data.htm.',
-                                'license' : 'Data is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License (http://creativecommons.org/licenses/by-nc-sa/3.0/).',
-                                'size': 15922790},
-                  'pumadyn-32nm' : {'urls' : ['ftp://ftp.cs.toronto.edu/pub/neuron/delve/data/tarfiles/pumadyn-family/'],
-                                    'files' : [['pumadyn-32nm.tar.gz']],
-                                    'details' : """Pumadyn non linear 32 input data set with moderate noise. See http://www.cs.utoronto.ca/~delve/data/pumadyn/desc.html for details.""",
-                                    'citation' : """Created by Zoubin Ghahramani using the Matlab Robotics Toolbox of Peter Corke. Corke, P. I. (1996). A Robotics Toolbox for MATLAB. IEEE Robotics and Automation Magazine, 3 (1): 24-32.""",
-                                    'license' : """Data is made available by the Delve system at the University of Toronto""",
-                                    'size' : 5861646},
-                  'robot_wireless' : {'urls' : [neil_url + 'robot_wireless/'],
-                                      'files' : [['uw-floor.txt']],
-                                      'citation' : """WiFi-SLAM using Gaussian Process Latent Variable Models by Brian Ferris, Dieter Fox and Neil Lawrence in IJCAI'07 Proceedings pages 2480-2485. Data used in A Unifying Probabilistic Perspective for Spectral Dimensionality Reduction: Insights and New Models by Neil D. Lawrence, JMLR 13 pg 1609--1638, 2012.""",
-                                      'details' : """Data created by Brian Ferris and Dieter Fox. Consists of WiFi access point strengths taken during a circuit of the Paul Allen building at the University of Washington.""",
-                                      'license' : None,
-                                      'size' : 284390},
-                  'swiss_roll' : {'urls' : ['http://isomap.stanford.edu/'],
-                                  'files' : [['swiss_roll_data.mat']],
-                                  'details' : """Swiss roll data made available by Tenenbaum, de Silva and Langford to demonstrate isomap, available from http://isomap.stanford.edu/datasets.html.""",
-                                  'citation' : 'A Global Geometric Framework for Nonlinear Dimensionality Reduction, J. B. Tenenbaum, V. de Silva and J. C. Langford, Science 290 (5500): 2319-2323, 22 December 2000',
-                                  'license' : None,
-                                  'size' : 800256},
-                  'ripley_prnn_data' : {'urls' : ['http://www.stats.ox.ac.uk/pub/PRNN/'],
-                                        'files' : [['Cushings.dat', 'README', 'crabs.dat', 'fglass.dat', 'fglass.grp', 'pima.te', 'pima.tr', 'pima.tr2', 'synth.te', 'synth.tr', 'viruses.dat', 'virus3.dat']],
-                                        'details' : """Data sets from Brian Ripley's Pattern Recognition and Neural Networks""",
-                                        'citation': """Pattern Recognition and Neural Networks by B.D. Ripley (1996) Cambridge University Press ISBN 0 521 46986 7""",
-                                        'license' : None,
-                                        'size' : 93565},
-                  'isomap_face_data' : {'urls' : [neil_url + 'isomap_face_data/'],
-                                        'files' : [['face_data.mat']],
-                                        'details' : """Face data made available by Tenenbaum, de Silva and Langford to demonstrate isomap, available from http://isomap.stanford.edu/datasets.html.""",
-                                        'citation' : 'A Global Geometric Framework for Nonlinear Dimensionality Reduction, J. B. Tenenbaum, V. de Silva and J. C. Langford, Science 290 (5500): 2319-2323, 22 December 2000',
-                                        'license' : None,
-                                        'size' : 24229368},
-                  'xw_pen' : {'urls' : [neil_url + 'xw_pen/'],
-                                        'files' : [['xw_pen_15.csv']],
-                                        'details' : """Accelerometer pen data used for robust regression by Tipping and Lawrence.""",
-                                        'citation' : 'Michael E. Tipping and Neil D. Lawrence. Variational inference for Student-t models: Robust Bayesian interpolation and generalised component analysis. Neurocomputing, 69:123--141, 2005',
-                                        'license' : None,
-                                        'size' : 3410}
-                  }
 
 
 def prompt_user(prompt):
@@ -623,7 +505,7 @@ def xw_pen(data_set='xw_pen'):
     return data_details_return({'Y': Y, 'X': X, 'info': "Tilt data from a personalized digital assistant pen. Plot in original paper showed regression between time steps 175 and 275."}, data_set)
 
 
-def download_rogers_girolami_data():
+def download_rogers_girolami_data(data_set='rogers_girolami_data'):
     if not data_available('rogers_girolami_data'):
         download_data(data_set)
         path = os.path.join(data_path, data_set)
@@ -689,7 +571,7 @@ def olympic_marathon_men(data_set='olympic_marathon_men'):
     Y = olympics[:, 1:2]
     return data_details_return({'X': X, 'Y': Y}, data_set)
 
-def olympics():
+def olympic_sprints(data_set='rogers_girolami_data'):
     """All olympics sprint winning times for multiple output prediction."""
     X = np.zeros((0, 2))
     Y = np.zeros((0, 1))
@@ -707,7 +589,18 @@ def olympics():
     data['X'] = X
     data['Y'] = Y
     data['info'] = "Olympics sprint event winning for men and women to 2008. Data is from Rogers and Girolami's First Course in Machine Learning."
-    return data
+    return data_details_return({
+        'X': X,
+        'Y': Y,
+        'info': "Olympics sprint event winning for men and women to 2008. Data is from Rogers and Girolami's First Course in Machine Learning.",
+        'output_info': {
+          0:'100m Men', 
+          1:'100m Women', 
+          2:'200m Men', 
+          3:'200m Women', 
+          4:'400m Men', 
+          5:'400m Women'}
+        }, data_set)
 
 # def movielens_small(partNo=1,seed=default_seed):
 #     np.random.seed(seed=seed)
@@ -898,3 +791,5 @@ def cmu_mocap(subject, train_motions, test_motions=[], sample_every=4, data_set=
     if sample_every != 1:
         info += ' Data is sub-sampled to every ' + str(sample_every) + ' frames.'
     return data_details_return({'Y': Y, 'lbls' : lbls, 'Ytest': Ytest, 'lblstest' : lblstest, 'info': info, 'skel': skel}, data_set)
+
+

GPy/util/datasets/data_resources_create.py

@@ -0,0 +1,127 @@
+import json
+
+neil_url = 'http://staffwww.dcs.shef.ac.uk/people/N.Lawrence/dataset_mirror/'
+sam_url = 'http://www.cs.nyu.edu/~roweis/data/'
+cmu_url = 'http://mocap.cs.cmu.edu/subjects/'
+
+data_resources = {'ankur_pose_data' : {'urls' : [neil_url + 'ankur_pose_data/'],
+                                       'files' : [['ankurDataPoseSilhouette.mat']],
+                                       'license' : None,
+                                       'citation' : """3D Human Pose from Silhouettes by Relevance Vector Regression (In CVPR'04). A. Agarwal and B. Triggs.""",
+                                       'details' : """Artificially generated data of silhouettes given poses. Note that the data does not display a left/right ambiguity because across the entire data set one of the arms sticks out more the the other, disambiguating the pose as to which way the individual is facing."""},
+
+                  'boston_housing' : {'urls' : ['http://archive.ics.uci.edu/ml/machine-learning-databases/housing/'],
+                                      'files' : [['Index', 'housing.data', 'housing.names']],
+                                      'citation' : """Harrison, D. and Rubinfeld, D.L. 'Hedonic prices and the demand for clean air', J. Environ. Economics & Management, vol.5, 81-102, 1978.""",
+                                      'details' : """The Boston Housing data relates house values in Boston to a range of input variables.""",
+                                      'license' : None,
+                                      'size' : 51276
+                                      },
+                  'brendan_faces' : {'urls' : [sam_url],
+                                     'files': [['frey_rawface.mat']],
+                                     'citation' : 'Frey, B. J., Colmenarez, A and Huang, T. S. Mixtures of Local Linear Subspaces for Face Recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 1998, 32-37, June 1998. Computer Society Press, Los Alamitos, CA.',
+                                     'details' : """A video of Brendan Frey's face popularized as a benchmark for visualization by the Locally Linear Embedding.""",
+                                     'license': None,
+                                     'size' : 1100584},
+                  'cmu_mocap_full' : {'urls' : ['http://mocap.cs.cmu.edu'],
+                                 'files' : [['allasfamc.zip']],
+                                 'citation' : """Please include this in your acknowledgements: The data used in this project was obtained from mocap.cs.cmu.edu.
+The database was created with funding from NSF EIA-0196217.""",
+                                 'details' : """CMU Motion Capture data base. Captured by a Vicon motion capture system consisting of 12 infrared MX-40 cameras, each of which is capable of recording at 120 Hz with images of 4 megapixel resolution. Motions are captured in a working volume of approximately 3m x 8m. The capture subject wears 41 markers and a stylish black garment.""",
+                                 'license' : """From http://mocap.cs.cmu.edu. This data is free for use in research projects. You may include this data in commercially-sold products, but you may not resell this data directly, even in converted form. If you publish results obtained using this data, we would appreciate it if you would send the citation to your published paper to jkh+mocap@cs.cmu.edu, and also would add this text to your acknowledgments section: The data used in this project was obtained from mocap.cs.cmu.edu. The database was created with funding from NSF EIA-0196217.""",
+                                 'size' : None},
+                  'creep_rupture' : {'urls' : ['http://www.msm.cam.ac.uk/map/data/tar/'],
+                                     'files' : [['creeprupt.tar']],
+                                     'citation' : 'Materials Algorithms Project Data Library: MAP_DATA_CREEP_RUPTURE. F. Brun and T. Yoshida.',
+                                     'details' : """Provides 2066 creep rupture test results of steels (mainly of two kinds of steels: 2.25Cr and 9-12 wt% Cr ferritic steels). See http://www.msm.cam.ac.uk/map/data/materials/creeprupt-b.html.""",
+                                     'license' : None,
+                                     'size' : 602797},
+                  'della_gatta' : {'urls' : [neil_url + 'della_gatta/'],
+                                   'files': [['DellaGattadata.mat']],
+                                   'citation' : 'Direct targets of the TRP63 transcription factor revealed by a combination of gene expression profiling and reverse engineering. Giusy Della Gatta, Mukesh Bansal, Alberto Ambesi-Impiombato, Dario Antonini, Caterina Missero, and Diego di Bernardo, Genome Research 2008',
+                                   'details': "The full gene expression data set from della Gatta et al (http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2413161/) processed by RMA.",
+                                   'license':None,
+                                   'size':3729650},
+                  'epomeo_gpx' : {'urls' : [neil_url + 'epomeo_gpx/'],
+                                   'files': [['endomondo_1.gpx', 'endomondo_2.gpx', 'garmin_watch_via_endomondo.gpx','viewranger_phone.gpx','viewranger_tablet.gpx']],
+                                   'citation' : '',
+                                   'details': "Five different GPS traces of the same run up Mount Epomeo in Ischia. The traces are from different sources. endomondo_1 and endomondo_2 are traces from the mobile phone app Endomondo, with a split in the middle. garmin_watch_via_endomondo is the trace from a Garmin watch, with a segment missing about 4 kilometers in. viewranger_phone and viewranger_tablet are traces from a phone and a tablet through the viewranger app. The viewranger_phone data comes from the same mobile phone as the Endomondo data (i.e. there are 3 GPS devices, but one device recorded two traces).",
+                                   'license':None,
+                                   'size': 2031872},
+                  'three_phase_oil_flow': {'urls' : [neil_url + 'three_phase_oil_flow/'],
+                                           'files' : [['DataTrnLbls.txt', 'DataTrn.txt', 'DataTst.txt', 'DataTstLbls.txt', 'DataVdn.txt', 'DataVdnLbls.txt']],
+                                           'citation' : 'Bishop, C. M. and G. D. James (1993). Analysis of multiphase flows using dual-energy gamma densitometry and neural networks. Nuclear Instruments and Methods in Physics Research A327, 580-593',
+                                           'details' : """The three phase oil data used initially for demonstrating the Generative Topographic mapping.""",
+                                           'license' : None,
+                                           'size' : 712796},
+                  'rogers_girolami_data' : {'urls' : ['https://www.dropbox.com/sh/7p6tu1t29idgliq/_XqlH_3nt9/'],
+                                            'files' : [['firstcoursemldata.tar.gz']],
+                                            'suffices' : [['?dl=1']],
+                                            'citation' : 'A First Course in Machine Learning. Simon Rogers and Mark Girolami: Chapman & Hall/CRC, ISBN-13: 978-1439824146',
+                                            'details' : """Data from the textbook 'A First Course in Machine Learning'. Available from http://www.dcs.gla.ac.uk/~srogers/firstcourseml/.""",
+                                            'license' : None,
+                                            'size' : 21949154},
+                  'olivetti_faces' : {'urls' : [neil_url + 'olivetti_faces/', sam_url],
+                                      'files' : [['att_faces.zip'], ['olivettifaces.mat']],
+                                            'citation' : 'Ferdinando Samaria and Andy Harter, Parameterisation of a Stochastic Model for Human Face Identification. Proceedings of 2nd IEEE Workshop on Applications of Computer Vision, Sarasota FL, December 1994',
+                                            'details' : """Olivetti Research Labs Face data base, acquired between December 1992 and December 1994 in the Olivetti Research Lab, Cambridge (which later became AT&T Laboratories, Cambridge). When using these images please give credit to AT&T Laboratories, Cambridge. """,
+                                            'license': None,
+                                            'size' : 8561331},
+                  'olympic_marathon_men' : {'urls' : [neil_url + 'olympic_marathon_men/'],
+                                            'files' : [['olympicMarathonTimes.csv']],
+                                            'citation' : None,
+                                            'details' : """Olympic mens' marathon gold medal winning times from 1896 to 2012. Time given in pace (minutes per kilometer). Data is originally downloaded and collated from Wikipedia, we are not responsible for errors in the data""",
+                                            'license': None,
+                                            'size' : 584},
+                  'osu_run1' : {'urls': ['http://accad.osu.edu/research/mocap/data/', neil_url + 'stick/'],
+                                'files': [['run1TXT.ZIP'],['connections.txt']],
+                                'details' : "Motion capture data of a stick man running from the Open Motion Data Project at Ohio State University.",
+                                'citation' : 'The Open Motion Data Project by The Ohio State University Advanced Computing Center for the Arts and Design, http://accad.osu.edu/research/mocap/mocap_data.htm.',
+                                'license' : 'Data is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License (http://creativecommons.org/licenses/by-nc-sa/3.0/).',
+                                'size': 338103},
+                  'osu_accad' : {'urls': ['http://accad.osu.edu/research/mocap/data/', neil_url + 'stick/'],
+                                'files': [['swagger1TXT.ZIP','handspring1TXT.ZIP','quickwalkTXT.ZIP','run1TXT.ZIP','sprintTXT.ZIP','dogwalkTXT.ZIP','camper_04TXT.ZIP','dance_KB3_TXT.ZIP','per20_TXT.ZIP','perTWO07_TXT.ZIP','perTWO13_TXT.ZIP','perTWO14_TXT.ZIP','perTWO15_TXT.ZIP','perTWO16_TXT.ZIP'],['connections.txt']],
+                                'details' : "Motion capture data of different motions from the Open Motion Data Project at Ohio State University.",
+                                'citation' : 'The Open Motion Data Project by The Ohio State University Advanced Computing Center for the Arts and Design, http://accad.osu.edu/research/mocap/mocap_data.htm.',
+                                'license' : 'Data is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License (http://creativecommons.org/licenses/by-nc-sa/3.0/).',
+                                'size': 15922790},
+                  'pumadyn-32nm' : {'urls' : ['ftp://ftp.cs.toronto.edu/pub/neuron/delve/data/tarfiles/pumadyn-family/'],
+                                    'files' : [['pumadyn-32nm.tar.gz']],
+                                    'details' : """Pumadyn non linear 32 input data set with moderate noise. See http://www.cs.utoronto.ca/~delve/data/pumadyn/desc.html for details.""",
+                                    'citation' : """Created by Zoubin Ghahramani using the Matlab Robotics Toolbox of Peter Corke. Corke, P. I. (1996). A Robotics Toolbox for MATLAB. IEEE Robotics and Automation Magazine, 3 (1): 24-32.""",
+                                    'license' : """Data is made available by the Delve system at the University of Toronto""",
+                                    'size' : 5861646},
+                  'robot_wireless' : {'urls' : [neil_url + 'robot_wireless/'],
+                                      'files' : [['uw-floor.txt']],
+                                      'citation' : """WiFi-SLAM using Gaussian Process Latent Variable Models by Brian Ferris, Dieter Fox and Neil Lawrence in IJCAI'07 Proceedings pages 2480-2485. Data used in A Unifying Probabilistic Perspective for Spectral Dimensionality Reduction: Insights and New Models by Neil D. Lawrence, JMLR 13 pg 1609--1638, 2012.""",
+                                      'details' : """Data created by Brian Ferris and Dieter Fox. Consists of WiFi access point strengths taken during a circuit of the Paul Allen building at the University of Washington.""",
+                                      'license' : None,
+                                      'size' : 284390},
+                  'swiss_roll' : {'urls' : ['http://isomap.stanford.edu/'],
+                                  'files' : [['swiss_roll_data.mat']],
+                                  'details' : """Swiss roll data made available by Tenenbaum, de Silva and Langford to demonstrate isomap, available from http://isomap.stanford.edu/datasets.html.""",
+                                  'citation' : 'A Global Geometric Framework for Nonlinear Dimensionality Reduction, J. B. Tenenbaum, V. de Silva and J. C. Langford, Science 290 (5500): 2319-2323, 22 December 2000',
+                                  'license' : None,
+                                  'size' : 800256},
+                  'ripley_prnn_data' : {'urls' : ['http://www.stats.ox.ac.uk/pub/PRNN/'],
+                                        'files' : [['Cushings.dat', 'README', 'crabs.dat', 'fglass.dat', 'fglass.grp', 'pima.te', 'pima.tr', 'pima.tr2', 'synth.te', 'synth.tr', 'viruses.dat', 'virus3.dat']],
+                                        'details' : """Data sets from Brian Ripley's Pattern Recognition and Neural Networks""",
+                                        'citation': """Pattern Recognition and Neural Networks by B.D. Ripley (1996) Cambridge University Press ISBN 0 521 46986 7""",
+                                        'license' : None,
+                                        'size' : 93565},
+                  'isomap_face_data' : {'urls' : [neil_url + 'isomap_face_data/'],
+                                        'files' : [['face_data.mat']],
+                                        'details' : """Face data made available by Tenenbaum, de Silva and Langford to demonstrate isomap, available from http://isomap.stanford.edu/datasets.html.""",
+                                        'citation' : 'A Global Geometric Framework for Nonlinear Dimensionality Reduction, J. B. Tenenbaum, V. de Silva and J. C. Langford, Science 290 (5500): 2319-2323, 22 December 2000',
+                                        'license' : None,
+                                        'size' : 24229368},
+                  'xw_pen' : {'urls' : [neil_url + 'xw_pen/'],
+                                        'files' : [['xw_pen_15.csv']],
+                                        'details' : """Accelerometer pen data used for robust regression by Tipping and Lawrence.""",
+                                        'citation' : 'Michael E. Tipping and Neil D. Lawrence. Variational inference for Student-t models: Robust Bayesian interpolation and generalised component analysis. Neurocomputing, 69:123--141, 2005',
+                                        'license' : None,
+                                        'size' : 3410}
+                  }
+
+with open('data_resources.json', 'w') as file:
+    json.dump(data_resources, file)

GPy/util/linalg.py

@@ -21,9 +21,9 @@ else:
 try:
     _blaslib = ctypes.cdll.LoadLibrary(np.core._dotblas.__file__) # @UndefinedVariable
     _blas_available = True
-    assert hasattr('dsyrk_',_blaslib)
+    assert hasattr(_blaslib, 'dsyrk_')
-    assert hasattr('dsyr_',_blaslib)
+    assert hasattr(_blaslib, 'dsyr_')
-except:
+except AssertionError:
     _blas_available = False
 
 def dtrtrs(A, B, lower=0, trans=0, unitdiag=0):

GPy/util/symbolic.py

@@ -1,4 +1,4 @@
-from sympy import Function, S, oo, I, cos, sin, asin, log, erf,pi,exp
+from sympy import Function, S, oo, I, cos, sin, asin, log, erf, pi, exp, sqrt, sign
 
 
 class ln_diff_erf(Function):
@@ -10,7 +10,7 @@ class ln_diff_erf(Function):
             return -2*exp(-x1**2)/(sqrt(pi)*(erf(x0)-erf(x1)))
         elif argindex == 1:
             x0, x1 = self.args
-            return 2*exp(-x0**2)/(sqrt(pi)*(erf(x0)-erf(x1)))
+            return 2.*exp(-x0**2)/(sqrt(pi)*(erf(x0)-erf(x1)))
         else:
             raise ArgumentIndexError(self, argindex)
         
@@ -19,15 +19,84 @@ class ln_diff_erf(Function):
         if x0.is_Number and x1.is_Number:            
             return log(erf(x0)-erf(x1))
 
-class sim_h(Function):
+class dh_dd_i(Function):
+    nargs = 5
+    @classmethod
+    def eval(cls, t, tprime, d_i, d_j, l):
+        if (t.is_Number
+            and tprime.is_Number
+            and d_i.is_Number
+            and d_j.is_Number
+            and l.is_Number):
+
+            diff_t = (t-tprime)
+            l2 = l*l
+            h = h(t, tprime, d_i, d_j, l)
+            half_l_di = 0.5*l*d_i
+            arg_1 = half_l_di + tprime/l
+            arg_2 = half_l_di - (t-tprime)/l
+            ln_part_1 = ln_diff_erf(arg_1, arg_2)
+            arg_1 = half_l_di 
+            arg_2 = half_l_di - t/l
+            sign_val = sign(t/l)
+            ln_part_2 = ln_diff_erf(half_l_di, half_l_di - t/l)
+
+            base = ((0.5*d_i*l2*(d_i+d_j)-1)*h 
+                    + (-diff_t*sign_val*exp(half_l_di*half_l_di
+                                          -d_i*diff_t
+                                          +ln_part_1)
+                       +t*sign_val*exp(half_l_di*half_l_di
+                                          -d_i*t-d_j*tprime
+                                          +ln_part_2))
+                    + l/sqrt(pi)*(-exp(-diff_t*diff_t/l2)
+                                     +exp(-tprime*tprime/l2-d_i*t)
+                                     +exp(-t*t/l2-d_j*tprime)
+                                     -exp(-(d_i*t + d_j*tprime))))
+            return base/(d_i+d_j)
+
+class dh_dd_j(Function):
+    nargs = 5
+    @classmethod
+    def eval(cls, t, tprime, d_i, d_j, l):
+        if (t.is_Number
+            and tprime.is_Number
+            and d_i.is_Number
+            and d_j.is_Number
+            and l.is_Number):
+            diff_t = (t-tprime)
+            l2 = l*l
+            half_l_di = 0.5*l*d_i
+            h = h(t, tprime, d_i, d_j, l)
+            arg_1 = half_l_di + tprime/l
+            arg_2 = half_l_di - (t-tprime)/l
+            ln_part_1 = ln_diff_erf(arg_1, arg_2)
+            arg_1 = half_l_di 
+            arg_2 = half_l_di - t/l
+            sign_val = sign(t/l)
+            ln_part_2 = ln_diff_erf(half_l_di, half_l_di - t/l)
+            sign_val = sign(t/l)
+            base = tprime*sign_val*exp(half_l_di*half_l_di-(d_i*t+d_j*tprime)+ln_part_2)-h
+            return base/(d_i+d_j)
+    
+class dh_dl(Function):
+    nargs = 5
+    @classmethod
+    def eval(cls, t, tprime, d_i, d_j, l):
+        if (t.is_Number
+            and tprime.is_Number
+            and d_i.is_Number
+            and d_j.is_Number
+            and l.is_Number):
+
+            diff_t = (t-tprime)
+            l2 = l*l
+            h = h(t, tprime, d_i, d_j, l)
+            return 0.5*d_i*d_i*l*h + 2./(sqrt(pi)*(d_i+d_j))*((-diff_t/l2-d_i/2.)*exp(-diff_t*diff_t/l2)+(-tprime/l2+d_i/2.)*exp(-tprime*tprime/l2-d_i*t)-(-t/l2-d_i/2.)*exp(-t*t/l2-d_j*tprime)-d_i/2.*exp(-(d_i*t+d_j*tprime)))
+
+class dh_dt(Function):
     nargs = 5
-
-    def fdiff(self, argindex=1):
-        pass
-    
     @classmethod
     def eval(cls, t, tprime, d_i, d_j, l):
-        # putting in the is_Number stuff forces it to look for a fdiff method for derivative.
         if (t.is_Number
             and tprime.is_Number
             and d_i.is_Number
@@ -40,13 +109,119 @@ class sim_h(Function):
                 or l is S.NaN):
                 return S.NaN
             else:
-                return (exp((d_j/2*l)**2)/(d_i+d_j)
+                half_l_di = 0.5*l*d_i
-                        *(exp(-d_j*(tprime - t))
+                arg_1 = half_l_di + tprime/l
-                          *(erf((tprime-t)/l - d_j/2*l)
+                arg_2 = half_l_di - (t-tprime)/l
-                            + erf(t/l + d_j/2*l))
+                ln_part_1 = ln_diff_erf(arg_1, arg_2)
-                          - exp(-(d_j*tprime + d_i))
+                arg_1 = half_l_di 
-                          *(erf(tprime/l - d_j/2*l)
+                arg_2 = half_l_di - t/l
-                            + erf(d_j/2*l))))
+                sign_val = sign(t/l)
+                ln_part_2 = ln_diff_erf(half_l_di, half_l_di - t/l)
+
+                
+                return (sign_val*exp(half_l_di*half_l_di
+                                        - d_i*(t-tprime)
+                                        + ln_part_1
+                                        - log(d_i + d_j))
+                        - sign_val*exp(half_l_di*half_l_di
+                                          - d_i*t - d_j*tprime
+                                          + ln_part_2
+                                          - log(d_i + d_j))).diff(t)
+
+class dh_dtprime(Function):
+    nargs = 5
+    @classmethod
+    def eval(cls, t, tprime, d_i, d_j, l):
+        if (t.is_Number
+            and tprime.is_Number
+            and d_i.is_Number
+            and d_j.is_Number
+            and l.is_Number):
+            if (t is S.NaN
+                or tprime is S.NaN
+                or d_i is S.NaN
+                or d_j is S.NaN
+                or l is S.NaN):
+                return S.NaN
+            else:
+                half_l_di = 0.5*l*d_i
+                arg_1 = half_l_di + tprime/l
+                arg_2 = half_l_di - (t-tprime)/l
+                ln_part_1 = ln_diff_erf(arg_1, arg_2)
+                arg_1 = half_l_di 
+                arg_2 = half_l_di - t/l
+                sign_val = sign(t/l)
+                ln_part_2 = ln_diff_erf(half_l_di, half_l_di - t/l)
+
+                
+                return (sign_val*exp(half_l_di*half_l_di
+                                        - d_i*(t-tprime)
+                                        + ln_part_1
+                                        - log(d_i + d_j))
+                        - sign_val*exp(half_l_di*half_l_di
+                                          - d_i*t - d_j*tprime
+                                          + ln_part_2
+                                          - log(d_i + d_j))).diff(tprime)
+
+
+class h(Function):
+    nargs = 5
+    def fdiff(self, argindex=5):
+        t, tprime, d_i, d_j, l = self.args
+        if argindex == 1:
+            return dh_dt(t, tprime, d_i, d_j, l)
+        elif argindex == 2:
+            return dh_dtprime(t, tprime, d_i, d_j, l)
+        elif argindex == 3:
+            return dh_dd_i(t, tprime, d_i, d_j, l)
+        elif argindex == 4:
+            return dh_dd_j(t, tprime, d_i, d_j, l)
+        elif argindex == 5:
+            return dh_dl(t, tprime, d_i, d_j, l)
+                                                                
+    
+    @classmethod
+    def eval(cls, t, tprime, d_i, d_j, l):
+        # putting in the is_Number stuff forces it to look for a fdiff method for derivative. If it's left out, then when asking for self.diff, it just does the diff on the eval symbolic terms directly. We want to avoid that because we are looking to ensure everything is numerically stable. Maybe it's because of the if statement that this happens? 
+        if (t.is_Number
+            and tprime.is_Number
+            and d_i.is_Number
+            and d_j.is_Number
+            and l.is_Number):
+            if (t is S.NaN
+                or tprime is S.NaN
+                or d_i is S.NaN
+                or d_j is S.NaN
+                or l is S.NaN):
+                return S.NaN
+            else:
+                half_l_di = 0.5*l*d_i
+                arg_1 = half_l_di + tprime/l
+                arg_2 = half_l_di - (t-tprime)/l
+                ln_part_1 = ln_diff_erf(arg_1, arg_2)
+                arg_1 = half_l_di 
+                arg_2 = half_l_di - t/l
+                sign_val = sign(t/l)
+                ln_part_2 = ln_diff_erf(half_l_di, half_l_di - t/l)
+
+                
+                return (sign_val*exp(half_l_di*half_l_di
+                                        - d_i*(t-tprime)
+                                        + ln_part_1
+                                        - log(d_i + d_j))
+                        - sign_val*exp(half_l_di*half_l_di
+                                          - d_i*t - d_j*tprime
+                                          + ln_part_2
+                                          - log(d_i + d_j)))
+            
+                                  
+                # return (exp((d_j/2.*l)**2)/(d_i+d_j)
+                #         *(exp(-d_j*(tprime - t))
+                #           *(erf((tprime-t)/l - d_j/2.*l)
+                #             + erf(t/l + d_j/2.*l))
+                #           - exp(-(d_j*tprime + d_i))
+                #           *(erf(tprime/l - d_j/2.*l)
+                #             + erf(d_j/2.*l))))
 
 class erfc(Function):
     nargs = 1
@@ -62,52 +237,3 @@ class erfcx(Function):
     def eval(cls, arg):
         return erfc(arg)*exp(arg*arg)
 
-class sinc_grad(Function):
-    nargs = 1
-    
-    def fdiff(self, argindex=1):
-        if argindex==1:
-            # Strictly speaking this should be computed separately, as it won't work when x=0. See http://calculus.subwiki.org/wiki/Sinc_function
-            return ((2-x*x)*sin(self.args[0]) - 2*x*cos(x))/(x*x*x)
-        else:
-            raise ArgumentIndexError(self, argindex)
-
-    
-    @classmethod
-    def eval(cls, x):
-        if x.is_Number:
-            if x is S.NaN:
-                return S.NaN
-            elif x is S.Zero:
-                return S.Zero
-            else:
-                return (x*cos(x) - sin(x))/(x*x)
-            
-class sinc(Function):
-    
-    nargs = 1
-    
-    def fdiff(self, argindex=1):
-        if argindex==1:
-            return sinc_grad(self.args[0])
-        else:
-            raise ArgumentIndexError(self, argindex)
-
-    
-    @classmethod
-    def eval(cls, arg):
-        if arg.is_Number:
-            if arg is S.NaN:
-                return S.NaN
-            elif arg is S.Zero:
-                return S.One
-            else:
-                return sin(arg)/arg
-
-        if arg.func is asin:
-            x = arg.args[0]
-            return x / arg
-
-    def _eval_is_real(self):
-        return self.args[0].is_real
-

GPy/util/warping_functions.py

@@ -222,7 +222,7 @@ class TanhWarpingFunction_d(WarpingFunction):
         """
 
 
-        mpsi = psi.coSpy()
+        mpsi = psi.copy()
         d = psi[-1]
         mpsi = mpsi[:self.num_parameters-1].reshape(self.n_terms, 3)