merge SSGPLVM into params branch

GPy/core/sparse_gp.py

@@ -52,10 +52,7 @@ class SparseGP(GP):
         self.parameters_changed()
 
     def has_uncertain_inputs(self):
-        if isinstance(self.X, VariationalPosterior):
-            return True
-        else:
-            return False
+        return isinstance(self.X, VariationalPosterior)
 
     def parameters_changed(self):
         self.posterior, self._log_marginal_likelihood, self.grad_dict = self.inference_method.inference(self.kern, self.X, self.Z, self.likelihood, self.Y)

GPy/examples/regression.py

@@ -16,7 +16,7 @@ def olympic_marathon_men(optimize=True, plot=True):
     m = GPy.models.GPRegression(data['X'], data['Y'])
 
     # set the lengthscale to be something sensible (defaults to 1)
-    m['rbf_lengthscale'] = 10
+    m.kern.lengthscale = 10.
 
     if optimize:
         m.optimize('bfgs', max_iters=200)
@@ -41,11 +41,10 @@ def coregionalization_toy2(optimize=True, plot=True):
     Y = np.vstack((Y1, Y2))
 
     #build the kernel
-    k1 = GPy.kern.RBF(1) + GPy.kern.bias(1)
-    k2 = GPy.kern.coregionalize(2,1)
+    k1 = GPy.kern.RBF(1) + GPy.kern.Bias(1)
+    k2 = GPy.kern.Coregionalize(2,1)
     k = k1**k2
     m = GPy.models.GPRegression(X, Y, kernel=k)
-    m.constrain_fixed('.*rbf_var', 1.)
 
     if optimize:
         m.optimize('bfgs', max_iters=100)
@@ -86,11 +85,13 @@ def coregionalization_sparse(optimize=True, plot=True):
     """
     #fetch the data from the non sparse examples
     m = coregionalization_toy2(optimize=False, plot=False)
-    X, Y = m.X, m.likelihood.Y
+    X, Y = m.X, m.Y
+
+    k = GPy.kern.RBF(1)**GPy.kern.Coregionalize(2)
 
     #construct a model
-    m = GPy.models.SparseGPRegression(X,Y)
-    m.constrain_fixed('iip_\d+_1') # don't optimize the inducing input indexes
+    m = GPy.models.SparseGPRegression(X,Y, num_inducing=25, kernel=k)
+    m.Z[:,1].fix() # don't optimize the inducing input indexes
 
     if optimize:
         m.optimize('bfgs', max_iters=100, messages=1)
@@ -128,7 +129,7 @@ def epomeo_gpx(max_iters=200, optimize=True, plot=True):
                    np.random.randint(0, 4, num_inducing)[:, None]))
 
     k1 = GPy.kern.RBF(1)
-    k2 = GPy.kern.coregionalize(output_dim=5, rank=5)
+    k2 = GPy.kern.Coregionalize(output_dim=5, rank=5)
     k = k1**k2
 
     m = GPy.models.SparseGPRegression(t, Y, kernel=k, Z=Z, normalize_Y=True)
@@ -322,7 +323,7 @@ def toy_ARD(max_iters=1000, kernel_type='linear', num_samples=300, D=4, optimize
         kernel = GPy.kern.RBF_inv(X.shape[1], ARD=1)
     else:
         kernel = GPy.kern.RBF(X.shape[1], ARD=1)
-    kernel += GPy.kern.White(X.shape[1]) + GPy.kern.bias(X.shape[1])
+    kernel += GPy.kern.White(X.shape[1]) + GPy.kern.Bias(X.shape[1])
     m = GPy.models.GPRegression(X, Y, kernel)
     # len_prior = GPy.priors.inverse_gamma(1,18) # 1, 25
     # m.set_prior('.*lengthscale',len_prior)
@@ -361,7 +362,7 @@ def toy_ARD_sparse(max_iters=1000, kernel_type='linear', num_samples=300, D=4, o
         kernel = GPy.kern.RBF_inv(X.shape[1], ARD=1)
     else:
         kernel = GPy.kern.RBF(X.shape[1], ARD=1)
-    #kernel += GPy.kern.bias(X.shape[1])
+    #kernel += GPy.kern.Bias(X.shape[1])
     X_variance = np.ones(X.shape) * 0.5
     m = GPy.models.SparseGPRegression(X, Y, kernel, X_variance=X_variance)
     # len_prior = GPy.priors.inverse_gamma(1,18) # 1, 25

GPy/inference/optimization/conjugate_gradient_descent.py

@@ -3,7 +3,7 @@ Created on 24 Apr 2013
 
 @author: maxz
 '''
-from GPy.inference.gradient_descent_update_rules import FletcherReeves, \
+from gradient_descent_update_rules import FletcherReeves, \
     PolakRibiere
 from Queue import Empty
 from multiprocessing import Value

GPy/kern/__init__.py

@@ -3,25 +3,10 @@ from _src.rbf import RBF
 from _src.linear import Linear
 from _src.static import Bias, White
 from _src.brownian import Brownian
+from _src.sympykern import Sympykern
 from _src.stationary import Exponential, Matern32, Matern52, ExpQuad, RatQuad, Cosine
 from _src.mlp import MLP
 from _src.periodic import PeriodicExponential, PeriodicMatern32, PeriodicMatern52
-from _src.independent_outputs import IndependentOutputs
+from _src.independent_outputs import IndependentOutputs, Hierarchical
+from _src.coregionalize import Coregionalize
 from _src.ssrbf import SSRBF
-#import coregionalize
-#import eq_ode1
-#import finite_dimensional
-#import fixed
-#import gibbs
-#import hetero
-#import hierarchical
-#import ODE_1
-#import periodic_exponential
-#import periodic_Matern32
-#import periodic_Matern52
-#import poly
-#import rbfcos
-#import rbf
-#import rbf_inv
-#import spline
-#import symmetric

GPy/kern/_src/add.py

@@ -45,9 +45,6 @@ class Add(Kern):
     def update_gradients_full(self, dL_dK, X):
         [p.update_gradients_full(dL_dK, X[:,i_s]) for p, i_s in zip(self._parameters_, self.input_slices)]
 
-    def update_gradients_sparse(self, dL_dKmm, dL_dKnm, dL_dKdiag, X, Z):
-        [p.update_gradients_sparse(dL_dKmm, dL_dKnm, dL_dKdiag, X[:,i_s], Z[:,i_s]) for p, i_s in zip(self._parameters_, self.input_slices)]
-
     def gradients_X(self, dL_dK, X, X2=None):
         """Compute the gradient of the objective function with respect to X.
 
@@ -70,13 +67,13 @@ class Add(Kern):
         return sum([p.Kdiag(X[:, i_s]) for p, i_s in zip(self._parameters_, self.input_slices)])
 
 
-    def psi0(self, Z, mu, S):
+    def psi0(self, Z, variational_posterior):
         return np.sum([p.psi0(Z[:, i_s], mu[:, i_s], S[:, i_s]) for p, i_s in zip(self._parameters_, self.input_slices)],0)
 
-    def psi1(self, Z, mu, S):
+    def psi1(self, Z, variational_posterior):
         return np.sum([p.psi1(Z[:, i_s], mu[:, i_s], S[:, i_s]) for p, i_s in zip(self._parameters_, self.input_slices)], 0)
 
-    def psi2(self, Z, mu, S):
+    def psi2(self, Z, variational_posterior):
         psi2 = np.sum([p.psi2(Z[:, i_s], mu[:, i_s], S[:, i_s]) for p, i_s in zip(self._parameters_, self.input_slices)], 0)
 
         # compute the "cross" terms
@@ -104,7 +101,7 @@ class Add(Kern):
                 raise NotImplementedError, "psi2 cannot be computed for this kernel"
         return psi2
 
-    def update_gradients_variational(self, dL_dKmm, dL_dpsi0, dL_dpsi1, dL_dpsi2, mu, S, Z):
+    def update_gradients_variational(self, dL_dKmm, dL_dpsi0, dL_dpsi1, dL_dpsi2, variational_posterior, Z):
         from white import White
         from rbf import RBF
         #from rbf_inv import RBFInv

GPy/kern/_src/constructors.py

@@ -1,568 +0,0 @@
-# Copyright (c) 2012, GPy authors (see AUTHORS.txt).
-# Licensed under the BSD 3-clause license (see LICENSE.txt)
-
-import numpy as np
-from kern import kern
-import parts
-
-def rbf_inv(input_dim,variance=1., inv_lengthscale=None,ARD=False,name='inverse rbf'):
-    """
-    Construct an RBF kernel
-
-    :param input_dim: dimensionality of the kernel, obligatory
-    :type input_dim: int
-    :param variance: the variance of the kernel
-    :type variance: float
-    :param lengthscale: the lengthscale of the kernel
-    :type lengthscale: float
-    :param ARD: Auto Relevance Determination (one lengthscale per dimension)
-    :type ARD: Boolean
-
-    """
-    part = parts.rbf_inv.RBFInv(input_dim,variance,inv_lengthscale,ARD,name=name)
-    return kern(input_dim, [part])
-
-def rbf(input_dim,variance=1., lengthscale=None,ARD=False, name='rbf'):
-    """
-    Construct an RBF kernel
-
-    :param input_dim: dimensionality of the kernel, obligatory
-    :type input_dim: int
-    :param variance: the variance of the kernel
-    :type variance: float
-    :param lengthscale: the lengthscale of the kernel
-    :type lengthscale: float
-    :param ARD: Auto Relevance Determination (one lengthscale per dimension)
-    :type ARD: Boolean
-
-    """
-    part = parts.rbf.RBF(input_dim,variance,lengthscale,ARD, name=name)
-    return kern(input_dim, [part])
-
-def linear(input_dim,variances=None,ARD=False,name='linear'):
-    """
-     Construct a linear kernel.
-
-    :param input_dim: dimensionality of the kernel, obligatory
-    :type input_dim: int
-    :param variances:
-    :type variances: np.ndarray
-    :param ARD: Auto Relevance Determination (one lengthscale per dimension)
-    :type ARD: Boolean
-
-    """
-    part = parts.linear.Linear(input_dim,variances,ARD,name=name)
-    return kern(input_dim, [part])
-
-def mlp(input_dim,variance=1., weight_variance=None,bias_variance=100.,ARD=False):
-    """
-    Construct an MLP kernel
-
-    :param input_dim: dimensionality of the kernel, obligatory
-    :type input_dim: int
-    :param variance: the variance of the kernel
-    :type variance: float
-    :param weight_scale: the lengthscale of the kernel
-    :type weight_scale: vector of weight variances for input weights in neural network (length 1 if kernel is isotropic)
-    :param bias_variance: the variance of the biases in the neural network.
-    :type bias_variance: float
-    :param ARD: Auto Relevance Determination (allows for ARD version of covariance)
-    :type ARD: Boolean
-
-    """
-    part = parts.mlp.MLP(input_dim,variance,weight_variance,bias_variance,ARD)
-    return kern(input_dim, [part])
-
-def gibbs(input_dim,variance=1., mapping=None):
-    """
-
-    Gibbs and MacKay non-stationary covariance function.
-
-    .. math::
-
-       r = \\sqrt{((x_i - x_j)'*(x_i - x_j))}
-
-       k(x_i, x_j) = \\sigma^2*Z*exp(-r^2/(l(x)*l(x) + l(x')*l(x')))
-
-       Z = \\sqrt{2*l(x)*l(x')/(l(x)*l(x) + l(x')*l(x')}
-
-    Where :math:`l(x)` is a function giving the length scale as a function of space.
-
-    This is the non stationary kernel proposed by Mark Gibbs in his 1997
-    thesis. It is similar to an RBF but has a length scale that varies
-    with input location. This leads to an additional term in front of
-    the kernel.
-
-    The parameters are :math:`\\sigma^2`, the process variance, and the parameters of l(x) which is a function that can be specified by the user, by default an multi-layer peceptron is used is used.
-
-    :param input_dim: the number of input dimensions
-    :type input_dim: int
-    :param variance: the variance :math:`\\sigma^2`
-    :type variance: float
-    :param mapping: the mapping that gives the lengthscale across the input space.
-    :type mapping: GPy.core.Mapping
-    :param ARD: Auto Relevance Determination. If equal to "False", the kernel is isotropic (ie. one weight variance parameter :math:`\\sigma^2_w`), otherwise there is one weight variance parameter per dimension.
-    :type ARD: Boolean
-    :rtype: Kernpart object
-
-    """
-    part = parts.gibbs.Gibbs(input_dim,variance,mapping)
-    return kern(input_dim, [part])
-
-def hetero(input_dim, mapping=None, transform=None):
-    """
-    """
-    part = parts.hetero.Hetero(input_dim,mapping,transform)
-    return kern(input_dim, [part])
-
-def poly(input_dim,variance=1., weight_variance=None,bias_variance=1.,degree=2, ARD=False):
-    """
-    Construct a polynomial kernel
-
-    :param input_dim: dimensionality of the kernel, obligatory
-    :type input_dim: int
-    :param variance: the variance of the kernel
-    :type variance: float
-    :param weight_scale: the lengthscale of the kernel
-    :type weight_scale: vector of weight variances for input weights.
-    :param bias_variance: the variance of the biases.
-    :type bias_variance: float
-    :param degree: the degree of the polynomial
-    :type degree: int
-    :param ARD: Auto Relevance Determination (allows for ARD version of covariance)
-    :type ARD: Boolean
-
-    """
-    part = parts.poly.POLY(input_dim,variance,weight_variance,bias_variance,degree,ARD)
-    return kern(input_dim, [part])
-
-def white(input_dim,variance=1.,name='white'):
-    """
-     Construct a white kernel.
-
-    :param input_dim: dimensionality of the kernel, obligatory
-    :type input_dim: int
-    :param variance: the variance of the kernel
-    :type variance: float
-
-    """
-    part = parts.white.White(input_dim,variance,name=name)
-    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):
-    """
-    Construct an exponential kernel
-
-    :param input_dim: dimensionality of the kernel, obligatory
-    :type input_dim: int
-    :param variance: the variance of the kernel
-    :type variance: float
-    :param lengthscale: the lengthscale of the kernel
-    :type lengthscale: float
-    :param ARD: Auto Relevance Determination (one lengthscale per dimension)
-    :type ARD: Boolean
-
-    """
-    part = parts.exponential.Exponential(input_dim,variance, lengthscale, ARD)
-    return kern(input_dim, [part])
-
-def Matern32(input_dim,variance=1., lengthscale=None, ARD=False):
-    """
-     Construct a Matern 3/2 kernel.
-
-    :param input_dim: dimensionality of the kernel, obligatory
-    :type input_dim: int
-    :param variance: the variance of the kernel
-    :type variance: float
-    :param lengthscale: the lengthscale of the kernel
-    :type lengthscale: float
-    :param ARD: Auto Relevance Determination (one lengthscale per dimension)
-    :type ARD: Boolean
-
-    """
-    part = parts.Matern32.Matern32(input_dim,variance, lengthscale, ARD)
-    return kern(input_dim, [part])
-
-def Matern52(input_dim, variance=1., lengthscale=None, ARD=False):
-    """
-     Construct a Matern 5/2 kernel.
-
-    :param input_dim: dimensionality of the kernel, obligatory
-    :type input_dim: int
-    :param variance: the variance of the kernel
-    :type variance: float
-    :param lengthscale: the lengthscale of the kernel
-    :type lengthscale: float
-    :param ARD: Auto Relevance Determination (one lengthscale per dimension)
-    :type ARD: Boolean
-
-    """
-    part = parts.Matern52.Matern52(input_dim, variance, lengthscale, ARD)
-    return kern(input_dim, [part])
-
-def bias(input_dim, variance=1., name='bias'):
-    """
-     Construct a bias kernel.
-
-    :param input_dim: dimensionality of the kernel, obligatory
-    :type input_dim: int
-    :param variance: the variance of the kernel
-    :type variance: float
-
-    """
-    part = parts.bias.Bias(input_dim, variance, name=name)
-    return kern(input_dim, [part])
-
-def finite_dimensional(input_dim, F, G, variances=1., weights=None):
-    """
-    Construct a finite dimensional kernel.
-
-    :param input_dim: the number of input dimensions
-    :type input_dim: int
-    :param F: np.array of functions with shape (n,) - the n basis functions
-    :type F: np.array
-    :param G: np.array with shape (n,n) - the Gram matrix associated to F
-    :type G: np.array
-    :param variances: np.ndarray with shape (n,)
-    :type: np.ndarray
-    """
-    part = parts.finite_dimensional.FiniteDimensional(input_dim, F, G, variances, weights)
-    return kern(input_dim, [part])
-
-def spline(input_dim, variance=1.):
-    """
-    Construct a spline kernel.
-
-    :param input_dim: Dimensionality of the kernel
-    :type input_dim: int
-    :param variance: the variance of the kernel
-    :type variance: float
-
-    """
-    part = parts.spline.Spline(input_dim, variance)
-    return kern(input_dim, [part])
-
-def Brownian(input_dim, variance=1.):
-    """
-    Construct a Brownian motion kernel.
-
-    :param input_dim: Dimensionality of the kernel
-    :type input_dim: int
-    :param variance: the variance of the kernel
-    :type variance: float
-
-    """
-    part = parts.Brownian.Brownian(input_dim, variance)
-    return kern(input_dim, [part])
-
-try:
-    import sympy as sp
-    sympy_available = True
-except ImportError:
-    sympy_available = False
-
-if sympy_available:
-    from parts.sympykern import spkern
-    from sympy.parsing.sympy_parser import parse_expr
-
-    def rbf_sympy(input_dim, ARD=False, variance=1., lengthscale=1.):
-        """
-        Radial Basis Function covariance.
-        """
-        X = sp.symbols('x_:' + str(input_dim))
-        Z = sp.symbols('z_:' + str(input_dim))
-        variance = sp.var('variance',positive=True)
-        if ARD:
-            lengthscales = sp.symbols('lengthscale_:' + str(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*sp.exp(-dist/2.)
-        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*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.):
-        """
-        Exponentiated quadratic with multiple outputs.
-        """
-        real_input_dim = input_dim
-        if output_dim>1:
-            real_input_dim -= 1
-        X = sp.symbols('x_:' + str(real_input_dim))
-        Z = sp.symbols('z_:' + str(real_input_dim))
-        scale = sp.var('scale_i scale_j',positive=True)
-        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 = parse_expr(dist_string)
-            f =  variance*sp.exp(-dist/2.)
-        else:
-            lengthscale = sp.var('lengthscale_i lengthscale_j',positive=True)
-            shared_lengthscale = sp.var('shared_lengthscale',positive=True)
-            dist_string = ' + '.join(['(x_%i-z_%i)**2' % (i, i) for i in range(real_input_dim)])
-            dist = parse_expr(dist_string)
-            f =  scale_i*scale_j*sp.exp(-dist/(2*(shared_lengthscale**2 + lengthscale_i*lengthscale_j)))
-        return kern(input_dim, [spkern(input_dim, f, output_dim=output_dim, name='eq_sympy')])
-
-    def sympykern(input_dim, k=None, output_dim=1, name=None, param=None):
-        """
-        A base kernel object, where all the hard work in done by sympy.
-
-        :param k: the covariance function
-        :type k: a positive definite sympy function of x1, z1, x2, z2...
-
-        To construct a new sympy kernel, you'll need to define:
-         - a kernel function using a sympy object. Ensure that the kernel is of the form k(x,z).
-         - that's it! we'll extract the variables from the function k.
-
-        Note:
-         - to handle multiple inputs, call them x1, z1, etc
-         - to handle multpile correlated outputs, you'll need to define each covariance function and 'cross' variance function. TODO
-        """
-        return kern(input_dim, [spkern(input_dim, k=k, output_dim=output_dim, name=name, param=param)])
-del sympy_available
-
-def periodic_exponential(input_dim=1, variance=1., lengthscale=None, period=2 * np.pi, n_freq=10, lower=0., upper=4 * np.pi):
-    """
-    Construct an periodic exponential kernel
-
-    :param input_dim: dimensionality, only defined for input_dim=1
-    :type input_dim: int
-    :param variance: the variance of the kernel
-    :type variance: float
-    :param lengthscale: the lengthscale of the kernel
-    :type lengthscale: float
-    :param period: the period
-    :type period: float
-    :param n_freq: the number of frequencies considered for the periodic subspace
-    :type n_freq: int
-
-    """
-    part = parts.periodic_exponential.PeriodicExponential(input_dim, variance, lengthscale, period, n_freq, lower, upper)
-    return kern(input_dim, [part])
-
-def periodic_Matern32(input_dim, variance=1., lengthscale=None, period=2 * np.pi, n_freq=10, lower=0., upper=4 * np.pi):
-    """
-     Construct a periodic Matern 3/2 kernel.
-
-     :param input_dim: dimensionality, only defined for input_dim=1
-     :type input_dim: int
-     :param variance: the variance of the kernel
-     :type variance: float
-     :param lengthscale: the lengthscale of the kernel
-     :type lengthscale: float
-     :param period: the period
-     :type period: float
-     :param n_freq: the number of frequencies considered for the periodic subspace
-     :type n_freq: int
-
-    """
-    part = parts.periodic_Matern32.PeriodicMatern32(input_dim, variance, lengthscale, period, n_freq, lower, upper)
-    return kern(input_dim, [part])
-
-def periodic_Matern52(input_dim, variance=1., lengthscale=None, period=2 * np.pi, n_freq=10, lower=0., upper=4 * np.pi):
-    """
-     Construct a periodic Matern 5/2 kernel.
-
-     :param input_dim: dimensionality, only defined for input_dim=1
-     :type input_dim: int
-     :param variance: the variance of the kernel
-     :type variance: float
-     :param lengthscale: the lengthscale of the kernel
-     :type lengthscale: float
-     :param period: the period
-     :type period: float
-     :param n_freq: the number of frequencies considered for the periodic subspace
-     :type n_freq: int
-
-    """
-    part = parts.periodic_Matern52.PeriodicMatern52(input_dim, variance, lengthscale, period, n_freq, lower, upper)
-    return kern(input_dim, [part])
-
-def prod(k1,k2,tensor=False):
-    """
-     Construct a product kernel over input_dim from two kernels over input_dim
-
-    :param k1, k2: the kernels to multiply
-    :type k1, k2: kernpart
-    :param tensor: The kernels are either multiply as functions defined on the same input space (default) or on the product of the input spaces
-    :type tensor: Boolean
-    :rtype: kernel object
-
-    """
-    part = parts.prod.Prod(k1, k2, tensor)
-    return kern(part.input_dim, [part])
-
-def symmetric(k):
-    """
-    Construct a symmetric kernel from an existing kernel
-    """
-    k_ = k.copy()
-    k_.parts = [symmetric.Symmetric(p) for p in k.parts]
-    return k_
-
-def coregionalize(output_dim,rank=1, W=None, kappa=None):
-    """
-    Coregionlization matrix B, of the form:
-
-    .. math::
-       \mathbf{B} = \mathbf{W}\mathbf{W}^\top + kappa \mathbf{I}
-
-    An intrinsic/linear coregionalization kernel of the form:
-
-    .. math::
-       k_2(x, y)=\mathbf{B} k(x, y)
-
-    it is obtainded as the tensor product between a kernel k(x,y) and B.
-
-    :param output_dim: the number of outputs to corregionalize
-    :type output_dim: int
-    :param rank: number of columns of the W matrix (this parameter is ignored if parameter W is not None)
-    :type rank: int
-    :param W: a low rank matrix that determines the correlations between the different outputs, together with kappa it forms the coregionalization matrix B
-    :type W: numpy array of dimensionality (num_outpus, rank)
-    :param kappa: a vector which allows the outputs to behave independently
-    :type kappa: numpy array of dimensionality  (output_dim,)
-    :rtype: kernel object
-
-    """
-    p = parts.coregionalize.Coregionalize(output_dim,rank,W,kappa)
-    return kern(1,[p])
-
-
-def rational_quadratic(input_dim, variance=1., lengthscale=1., power=1.):
-    """
-     Construct rational quadratic kernel.
-
-    :param input_dim: the number of input dimensions
-    :type input_dim: int (input_dim=1 is the only value currently supported)
-    :param variance: the variance :math:`\sigma^2`
-    :type variance: float
-    :param lengthscale: the lengthscale :math:`\ell`
-    :type lengthscale: float
-    :rtype: kern object
-
-    """
-    part = parts.rational_quadratic.RationalQuadratic(input_dim, variance, lengthscale, power)
-    return kern(input_dim, [part])
-
-def fixed(input_dim, K, variance=1.):
-    """
-     Construct a Fixed effect kernel.
-
-    :param input_dim: the number of input dimensions
-    :type input_dim: int (input_dim=1 is the only value currently supported)
-    :param K: the variance :math:`\sigma^2`
-    :type K: np.array
-    :param variance: kernel variance
-    :type variance: float
-    :rtype: kern object
-    """
-    part = parts.fixed.Fixed(input_dim, K, variance)
-    return kern(input_dim, [part])
-
-def rbfcos(input_dim, variance=1., frequencies=None, bandwidths=None, ARD=False):
-    """
-    construct a rbfcos kernel
-    """
-    part = parts.rbfcos.RBFCos(input_dim, variance, frequencies, bandwidths, ARD)
-    return kern(input_dim, [part])
-
-def independent_outputs(k):
-    """
-    Construct a kernel with independent outputs from an existing kernel
-    """
-    for sl in k.input_slices:
-        assert (sl.start is None) and (sl.stop is None), "cannot adjust input slices! (TODO)"
-    _parts = [parts.independent_outputs.IndependentOutputs(p) for p in k.parts]
-    return kern(k.input_dim+1,_parts)
-
-def hierarchical(k):
-    """
-    TODO This can't be right! Construct a kernel with independent outputs from an existing kernel
-    """
-    # for sl in k.input_slices:
-    #     assert (sl.start is None) and (sl.stop is None), "cannot adjust input slices! (TODO)"
-    _parts = [parts.hierarchical.Hierarchical(k.parts)]
-    return kern(k.input_dim+len(k.parts),_parts)
-
-def build_lcm(input_dim, output_dim, kernel_list = [], rank=1,W=None,kappa=None):
-    """
-    Builds a kernel of a linear coregionalization model
-
-    :input_dim: Input dimensionality
-    :output_dim: Number of outputs
-    :kernel_list: List of coregionalized kernels, each element in the list will be multiplied by a different corregionalization matrix
-    :type kernel_list: list of GPy kernels
-    :param rank: number tuples of the corregionalization parameters 'coregion_W'
-    :type rank: integer
-
-    ..note the kernels dimensionality is overwritten to fit input_dim
-
-    """
-
-    for k in kernel_list:
-        if k.input_dim <> input_dim:
-            k.input_dim = input_dim
-            warnings.warn("kernel's input dimension overwritten to fit input_dim parameter.")
-
-    k_coreg = coregionalize(output_dim,rank,W,kappa)
-    kernel = kernel_list[0]**k_coreg.copy()
-
-    for k in kernel_list[1:]:
-        k_coreg = coregionalize(output_dim,rank,W,kappa)
-        kernel += k**k_coreg.copy()
-
-    return kernel
-
-def ODE_1(input_dim=1, 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 varianceU: variance of the driving GP
-    :type varianceU: float
-    :param lengthscaleU: lengthscale of the driving GP
-    :type lengthscaleU: 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_1.ODE_1(input_dim, varianceU, varianceY, lengthscaleU, lengthscaleY)
-    return kern(input_dim, [part])

GPy/kern/_src/coregionalize.py

@@ -129,7 +129,7 @@ class Coregionalize(Kern):
 
     def update_gradients_diag(self, dL_dKdiag, X):
         index = np.asarray(X, dtype=np.int).flatten()
-        dL_dKdiag_small = np.array([dL_dKdiag[index==i] for i in xrange(output_dim)])
+        dL_dKdiag_small = np.array([dL_dKdiag[index==i].sum() for i in xrange(self.output_dim)])
         self.W.gradient = 2.*self.W*dL_dKdiag_small[:, None]
         self.kappa.gradient = dL_dKdiag_small
 

GPy/kern/_src/independent_outputs.py

@@ -102,7 +102,7 @@ class IndependentOutputs(Kern):
         [[collate_grads(dL_dKdiag[s], X[s,:]) for s in slices_i] for slices_i in slices]
         self.kern._set_gradient(target)
 
-def Hierarchical(kern_f, kern_g, name='hierarchy'):
+class Hierarchical(Kern):
     """
     A kernel which can reopresent a simple hierarchical model.
 
@@ -110,10 +110,51 @@ def Hierarchical(kern_f, kern_g, name='hierarchy'):
     series across irregularly sampled replicates and clusters"
     http://www.biomedcentral.com/1471-2105/14/252
 
-    The index of the functions is given by the last column in the input X
-    the rest of the columns of X are passed to the underlying kernel for computation (in blocks).
+    The index of the functions is given by additional columns in the input X.
 
     """
-    assert kern_f.input_dim == kern_g.input_dim
-    return kern_f + IndependentOutputs(kern_g)
+    def __init__(self, kerns, name='hierarchy'):
+        assert all([k.input_dim==kerns[0].input_dim for k in kerns])
+        super(Hierarchical, self).__init__(kerns[0].input_dim + len(kerns) - 1, name)
+        self.kerns = kerns
+        self.add_parameters(self.kerns)
+
+    def K(self,X ,X2=None):
+        X, slices = X[:,:-self.levels], [index_to_slices(X[:,i]) for i in range(self.kerns[0].input_dim, self.input_dim)]
+        K = self.kerns[0].K(X, X2)
+        if X2 is None:
+            [[[np.copyto(K[s,s], k.K(X[s], None)) for s in slices_i] for slices_i in slices_k] for k, slices_k in zip(self.kerns[1:], slices)]
+        else:
+            X2, slices2 = X2[:,:-1],index_to_slices(X2[:,-1])
+            [[[[np.copyto(K[s, s2], self.kern.K(X[s],X2[s2])) for s in slices_i] for s2 in slices_j] for slices_i,slices_j in zip(slices_k,slices_k2)] for k, slices_k, slices_k2 in zip(self.kerns[1:], slices, slices2)]
+        return target
+
+    def Kdiag(self,X):
+        X, slices = X[:,:-self.levels], [index_to_slices(X[:,i]) for i in range(self.kerns[0].input_dim, self.input_dim)]
+        K = self.kerns[0].K(X, X2)
+        [[[np.copyto(target[s], self.kern.Kdiag(X[s])) for s in slices_i] for slices_i in slices_k] for k, slices_k in zip(self.kerns[1:], slices)]
+        return target
+
+    def update_gradients_full(self,dL_dK,X,X2=None):
+        X,slices = X[:,:-1],index_to_slices(X[:,-1])
+        if X2 is None:
+            self.kerns[0].update_gradients_full(dL_dK, X, None)
+            for k, slices_k in zip(self.kerns[1:], slices):
+                target = np.zeros(k.size)
+                def collate_grads(dL, X, X2):
+                    k.update_gradients_full(dL,X,X2)
+                    k._collect_gradient(target)
+                [[k.update_gradients_full(dL_dK[s,s], X[s], None) for s in slices_i] for slices_i in slices_k]
+                k._set_gradient(target)
+        else:
+            X2, slices2 = X2[:,:-1], index_to_slices(X2[:,-1])
+            self.kerns[0].update_gradients_full(dL_dK, X, None)
+            for k, slices_k in zip(self.kerns[1:], slices):
+                target = np.zeros(k.size)
+                def collate_grads(dL, X, X2):
+                    k.update_gradients_full(dL,X,X2)
+                    k._collect_gradient(target)
+                [[[collate_grads(dL_dK[s,s2],X[s],X2[s2]) for s in slices_i] for s2 in slices_j] for slices_i,slices_j in zip(slices,slices2)]
+                k._set_gradient(target)
+
 

GPy/kern/_src/kern.py

@@ -26,17 +26,17 @@ class Kern(Parameterized):
         raise NotImplementedError
     def Kdiag(self, Xa):
         raise NotImplementedError
-    def psi0(self,Z,posterior_variational):
+    def psi0(self,Z,variational_posterior):
         raise NotImplementedError
-    def psi1(self,Z,posterior_variational):
+    def psi1(self,Z,variational_posterior):
         raise NotImplementedError
-    def psi2(self,Z,posterior_variational):
+    def psi2(self,Z,variational_posterior):
         raise NotImplementedError
     def gradients_X(self, dL_dK, X, X2):
         raise NotImplementedError
     def gradients_X_diag(self, dL_dK, X):
         raise NotImplementedError
-    def update_gradients_full(self, dL_dK, X):
+    def update_gradients_full(self, dL_dK, X, X2):
         """Set the gradients of all parameters when doing full (N) inference."""
         raise NotImplementedError
     def update_gradients_sparse(self, dL_dKmm, dL_dKnm, dL_dKdiag, X, Z):
@@ -49,16 +49,16 @@ class Kern(Parameterized):
         self._collect_gradient(target)
         self._set_gradient(target)
 
-    def update_gradients_variational(self, dL_dKmm, dL_dpsi0, dL_dpsi1, dL_dpsi2, Z, posterior_variational):
+    def update_gradients_variational(self, dL_dKmm, dL_dpsi0, dL_dpsi1, dL_dpsi2, Z, variational_posterior):
         """Set the gradients of all parameters when doing variational (M) inference with uncertain inputs."""
         raise NotImplementedError
     def gradients_Z_sparse(self, dL_dKmm, dL_dKnm, dL_dKdiag, X, Z):
         grad = self.gradients_X(dL_dKmm, Z)
         grad += self.gradients_X(dL_dKnm.T, Z, X)
         return grad
-    def gradients_Z_variational(self, dL_dKmm, dL_dpsi0, dL_dpsi1, dL_dpsi2, Z, posterior_variational):
+    def gradients_Z_variational(self, dL_dKmm, dL_dpsi0, dL_dpsi1, dL_dpsi2, Z, variational_posterior):
         raise NotImplementedError
-    def gradients_q_variational(self, dL_dKmm, dL_dpsi0, dL_dpsi1, dL_dpsi2, Z, posterior_variational):
+    def gradients_q_variational(self, dL_dKmm, dL_dpsi0, dL_dpsi1, dL_dpsi2, Z, variational_posterior):
         raise NotImplementedError
     
     def plot_ARD(self, *args, **kw):
@@ -124,209 +124,3 @@ class Kern(Parameterized):
         assert isinstance(other, Kern), "only kernels can be added to kernels..."
         from prod import Prod
         return Prod(self, other, tensor)
-
-
-from GPy.core.model import Model
-
-class Kern_check_model(Model):
-    """This is a dummy model class used as a base class for checking that the gradients of a given kernel are implemented correctly. It enables checkgradient() to be called independently on a kernel."""
-    def __init__(self, kernel=None, dL_dK=None, X=None, X2=None):
-        from GPy.kern import RBF
-        Model.__init__(self, 'kernel_test_model')
-        num_samples = 20
-        num_samples2 = 10
-        if kernel==None:
-            kernel = RBF(1)
-        if X==None:
-            X = np.random.randn(num_samples, kernel.input_dim)
-        if dL_dK==None:
-            if X2==None:
-                dL_dK = np.ones((X.shape[0], X.shape[0]))
-            else:
-                dL_dK = np.ones((X.shape[0], X2.shape[0]))
-        
-        self.kernel=kernel
-        self.add_parameter(kernel)
-        self.X = X
-        self.X2 = X2
-        self.dL_dK = dL_dK
-
-    def is_positive_definite(self):
-        v = np.linalg.eig(self.kernel.K(self.X))[0]
-        if any(v<-10*sys.float_info.epsilon):
-            return False
-        else:
-            return True
-
-    def log_likelihood(self):
-        return (self.dL_dK*self.kernel.K(self.X, self.X2)).sum()
-
-    def _log_likelihood_gradients(self):
-        raise NotImplementedError, "This needs to be implemented to use the kern_check_model class."
-
-class Kern_check_dK_dtheta(Kern_check_model):
-    """This class allows gradient checks for the gradient of a kernel with respect to parameters. """
-    def __init__(self, kernel=None, dL_dK=None, X=None, X2=None):
-        Kern_check_model.__init__(self,kernel=kernel,dL_dK=dL_dK, X=X, X2=X2)
-
-    def _log_likelihood_gradients(self):
-        return self.kernel._param_grad_helper(self.dL_dK, self.X, self.X2)
-
-
-
-
-
-class Kern_check_dKdiag_dtheta(Kern_check_model):
-    """This class allows gradient checks of the gradient of the diagonal of a kernel with respect to the parameters."""
-    def __init__(self, kernel=None, dL_dK=None, X=None):
-        Kern_check_model.__init__(self,kernel=kernel,dL_dK=dL_dK, X=X, X2=None)
-        if dL_dK==None:
-            self.dL_dK = np.ones((self.X.shape[0]))
-    def parameters_changed(self):
-        self.kernel.update_gradients_full(self.dL_dK, self.X)        
-
-    def log_likelihood(self):
-        return (self.dL_dK*self.kernel.Kdiag(self.X)).sum()
-
-    def _log_likelihood_gradients(self):
-        return self.kernel.dKdiag_dtheta(self.dL_dK, self.X)
-
-class Kern_check_dK_dX(Kern_check_model):
-    """This class allows gradient checks for the gradient of a kernel with respect to X. """
-    def __init__(self, kernel=None, dL_dK=None, X=None, X2=None):
-        Kern_check_model.__init__(self,kernel=kernel,dL_dK=dL_dK, X=X, X2=X2)
-        self.remove_parameter(kernel)
-        self.X = Param('X', self.X)
-        self.add_parameter(self.X)
-    def _log_likelihood_gradients(self):
-        return self.kernel.gradients_X(self.dL_dK, self.X, self.X2).flatten()
-
-class Kern_check_dKdiag_dX(Kern_check_dK_dX):
-    """This class allows gradient checks for the gradient of a kernel diagonal with respect to X. """
-    def __init__(self, kernel=None, dL_dK=None, X=None, X2=None):
-        Kern_check_dK_dX.__init__(self,kernel=kernel,dL_dK=dL_dK, X=X, X2=None)
-        if dL_dK==None:
-            self.dL_dK = np.ones((self.X.shape[0]))
-        
-    def log_likelihood(self):
-        return (self.dL_dK*self.kernel.Kdiag(self.X)).sum()
-
-    def _log_likelihood_gradients(self):
-        return self.kernel.dKdiag_dX(self.dL_dK, self.X).flatten()
-
-def kern_test(kern, X=None, X2=None, output_ind=None, verbose=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.
-    :type kern: GPy.kern.Kernpart
-    :param X: X input values to test the covariance function.
-    :type X: ndarray
-    :param X2: X2 input values to test the covariance function.
-    :type X2: ndarray
-
-    """
-    pass_checks = True
-    if X==None:
-        X = np.random.randn(10, kern.input_dim)
-        if output_ind is not None:
-            X[:, output_ind] = np.random.randint(kern.output_dim, X.shape[0])
-    if X2==None:
-        X2 = np.random.randn(20, kern.input_dim)
-        if output_ind is not None:
-            X2[:, output_ind] = np.random.randint(kern.output_dim, X2.shape[0])
-
-    if verbose:
-        print("Checking covariance function is positive definite.")
-    result = Kern_check_model(kern, X=X).is_positive_definite()
-    if result and verbose:
-        print("Check passed.")
-    if not result:
-        print("Positive definite check failed for " + kern.name + " covariance function.")
-        pass_checks = False
-        return False
-
-    if verbose:
-        print("Checking gradients of K(X, X) wrt theta.")
-    result = Kern_check_dK_dtheta(kern, X=X, X2=None).checkgrad(verbose=verbose)
-    if result and verbose:
-        print("Check passed.")
-    if not result:
-        print("Gradient of K(X, X) wrt theta failed for " + kern.name + " covariance function. Gradient values as follows:")
-        Kern_check_dK_dtheta(kern, X=X, X2=None).checkgrad(verbose=True)
-        pass_checks = False
-        return False
-
-    if verbose:
-        print("Checking gradients of K(X, X2) wrt theta.")
-    result = Kern_check_dK_dtheta(kern, X=X, X2=X2).checkgrad(verbose=verbose)
-    if result and verbose:
-        print("Check passed.")
-    if not result:
-        print("Gradient of K(X, X) wrt theta failed for " + kern.name + " covariance function. Gradient values as follows:")
-        Kern_check_dK_dtheta(kern, X=X, X2=X2).checkgrad(verbose=True)
-        pass_checks = False
-        return False
-
-    if verbose:
-        print("Checking gradients of Kdiag(X) wrt theta.")
-    result = Kern_check_dKdiag_dtheta(kern, X=X).checkgrad(verbose=verbose)
-    if result and verbose:
-        print("Check passed.")
-    if not result:
-        print("Gradient of Kdiag(X) wrt theta failed for " + kern.name + " covariance function. Gradient values as follows:")
-        Kern_check_dKdiag_dtheta(kern, X=X).checkgrad(verbose=True)
-        pass_checks = False
-        return False
-
-    if verbose:
-        print("Checking gradients of K(X, X) wrt X.")
-    try:
-        result = Kern_check_dK_dX(kern, X=X, X2=None).checkgrad(verbose=verbose)
-    except NotImplementedError:
-        result=True
-        if verbose:
-            print("gradients_X not implemented for " + kern.name)
-    if result and verbose:
-        print("Check passed.")
-    if not result:
-        print("Gradient of K(X, X) wrt X failed for " + kern.name + " covariance function. Gradient values as follows:")
-        Kern_check_dK_dX(kern, X=X, X2=None).checkgrad(verbose=True)
-        pass_checks = False
-        return False
-
-    if verbose:
-        print("Checking gradients of K(X, X2) wrt X.")
-    try:
-        result = Kern_check_dK_dX(kern, X=X, X2=X2).checkgrad(verbose=verbose)
-    except NotImplementedError:
-        result=True
-        if verbose:
-            print("gradients_X not implemented for " + kern.name)
-    if result and verbose:
-        print("Check passed.")
-    if not result:
-        print("Gradient of K(X, X) wrt X failed for " + kern.name + " covariance function. Gradient values as follows:")
-        Kern_check_dK_dX(kern, X=X, X2=X2).checkgrad(verbose=True)
-        pass_checks = False
-        return False
-
-    if verbose:
-        print("Checking gradients of Kdiag(X) wrt X.")
-    try:
-        result = Kern_check_dKdiag_dX(kern, X=X).checkgrad(verbose=verbose)
-    except NotImplementedError:
-        result=True
-        if verbose:
-            print("gradients_X not implemented for " + kern.name)
-    if result and verbose:
-        print("Check passed.")
-    if not result:
-        print("Gradient of Kdiag(X) wrt X failed for " + kern.name + " covariance function. Gradient values as follows:")
-        Kern_check_dKdiag_dX(kern, X=X).checkgrad(verbose=True)
-        pass_checks = False
-        return False
-
-    return pass_checks

GPy/kern/_src/linear.py

@@ -106,52 +106,52 @@ class Linear(Kern):
     #              variational              #
     #---------------------------------------#
 
-    def psi0(self, Z, posterior_variational):
-        return np.sum(self.variances * self._mu2S(posterior_variational), 1)
+    def psi0(self, Z, variational_posterior):
+        return np.sum(self.variances * self._mu2S(variational_posterior), 1)
 
-    def psi1(self, Z, posterior_variational):
-        return self.K(posterior_variational.mean, Z) #the variance, it does nothing
+    def psi1(self, Z, variational_posterior):
+        return self.K(variational_posterior.mean, Z) #the variance, it does nothing
 
-    def psi2(self, Z, posterior_variational):
+    def psi2(self, Z, variational_posterior):
         ZA = Z * self.variances
-        ZAinner = self._ZAinner(posterior_variational, Z)
+        ZAinner = self._ZAinner(variational_posterior, Z)
         return np.dot(ZAinner, ZA.T)
 
-    def update_gradients_variational(self, dL_dKmm, dL_dpsi0, dL_dpsi1, dL_dpsi2, posterior_variational, Z):
-        mu, S = posterior_variational.mean, posterior_variational.variance
+    def update_gradients_variational(self, dL_dKmm, dL_dpsi0, dL_dpsi1, dL_dpsi2, variational_posterior, Z):
+        mu, S = variational_posterior.mean, variational_posterior.variance
         # psi0:
-        tmp = dL_dpsi0[:, None] * self._mu2S(posterior_variational)
+        tmp = dL_dpsi0[:, None] * self._mu2S(variational_posterior)
         if self.ARD: grad = tmp.sum(0)
         else: grad = np.atleast_1d(tmp.sum())
         #psi1
         self.update_gradients_full(dL_dpsi1, mu, Z)
         grad += self.variances.gradient
         #psi2
-        tmp = dL_dpsi2[:, :, :, None] * (self._ZAinner(posterior_variational, Z)[:, :, None, :] * (2. * Z)[None, None, :, :])
+        tmp = dL_dpsi2[:, :, :, None] * (self._ZAinner(variational_posterior, Z)[:, :, None, :] * (2. * Z)[None, None, :, :])
         if self.ARD: grad += tmp.sum(0).sum(0).sum(0)
         else: grad += tmp.sum()
         #from Kmm
         self.update_gradients_full(dL_dKmm, Z, None)
         self.variances.gradient += grad
 
-    def gradients_Z_variational(self, dL_dKmm, dL_dpsi0, dL_dpsi1, dL_dpsi2, posterior_variational, Z):
+    def gradients_Z_variational(self, dL_dKmm, dL_dpsi0, dL_dpsi1, dL_dpsi2, variational_posterior, Z):
         # Kmm
         grad = self.gradients_X(dL_dKmm, Z, None)
         #psi1
-        grad += self.gradients_X(dL_dpsi1.T, Z, posterior_variational.mean)
+        grad += self.gradients_X(dL_dpsi1.T, Z, variational_posterior.mean)
         #psi2
-        self._weave_dpsi2_dZ(dL_dpsi2, Z, posterior_variational, grad)
+        self._weave_dpsi2_dZ(dL_dpsi2, Z, variational_posterior, grad)
         return grad
 
-    def gradients_q_variational(self, dL_dKmm, dL_dpsi0, dL_dpsi1, dL_dpsi2, posterior_variational, Z):
-        grad_mu, grad_S = np.zeros(posterior_variational.mean.shape), np.zeros(posterior_variational.mean.shape)
+    def gradients_q_variational(self, dL_dKmm, dL_dpsi0, dL_dpsi1, dL_dpsi2, variational_posterior, Z):
+        grad_mu, grad_S = np.zeros(variational_posterior.mean.shape), np.zeros(variational_posterior.mean.shape)
         # psi0
-        grad_mu += dL_dpsi0[:, None] * (2.0 * posterior_variational.mean * self.variances)
+        grad_mu += dL_dpsi0[:, None] * (2.0 * variational_posterior.mean * self.variances)
         grad_S += dL_dpsi0[:, None] * self.variances
         # psi1
         grad_mu += (dL_dpsi1[:, :, None] * (Z * self.variances)).sum(1)
         # psi2
-        self._weave_dpsi2_dmuS(dL_dpsi2, Z, posterior_variational, grad_mu, grad_S)
+        self._weave_dpsi2_dmuS(dL_dpsi2, Z, variational_posterior, grad_mu, grad_S)
 
         return grad_mu, grad_S
 

GPy/kern/_src/prod.py

@@ -42,10 +42,6 @@ class Prod(Kern):
         self.k1.update_gradients_full(dL_dK*self.k2(X[:,self.slice2]), X[:,self.slice1])
         self.k2.update_gradients_full(dL_dK*self.k1(X[:,self.slice1]), X[:,self.slice2])
 
-    def update_gradients_sparse(self, dL_dKmm, dL_dKnm, dL_dKdiag, X, Z):
-        self.k1.update_gradients_sparse(dL_dKmm * self.k2.K(Z[:,self.slice2]), dL_dKnm * self.k2(X[:,self.slice2], Z[:,self.slice2]), dL_dKdiag * self.k2.Kdiag(X[:,self.slice2]), X[:,self.slice1], Z[:,self.slice1] )
-        self.k2.update_gradients_sparse(dL_dKmm * self.k1.K(Z[:,self.slice1]), dL_dKnm * self.k1(X[:,self.slice1], Z[:,self.slice1]), dL_dKdiag * self.k1.Kdiag(X[:,self.slice1]), X[:,self.slice2], Z[:,self.slice2] )
-
     def gradients_X(self, dL_dK, X, X2=None):
         target = np.zeros(X.shape)
         if X2 is None:

GPy/kern/_src/rbf.py

@@ -40,27 +40,27 @@ class RBF(Stationary):
         self._Z, self._mu, self._S = np.empty(shape=(3, 1)) # cached versions of Z,mu,S
 
 
-    def psi0(self, Z, posterior_variational):
-        return self.Kdiag(posterior_variational.mean)
+    def psi0(self, Z, variational_posterior):
+        return self.Kdiag(variational_posterior.mean)
 
-    def psi1(self, Z, posterior_variational):
-        mu = posterior_variational.mean
-        S = posterior_variational.variance
+    def psi1(self, Z, variational_posterior):
+        mu = variational_posterior.mean
+        S = variational_posterior.variance
         self._psi_computations(Z, mu, S)
         return self._psi1
 
-    def psi2(self, Z, posterior_variational):
-        mu = posterior_variational.mean
-        S = posterior_variational.variance
+    def psi2(self, Z, variational_posterior):
+        mu = variational_posterior.mean
+        S = variational_posterior.variance
         self._psi_computations(Z, mu, S)
         return self._psi2
 
-    def update_gradients_variational(self, dL_dKmm, dL_dpsi0, dL_dpsi1, dL_dpsi2, Z, posterior_variational):
+    def update_gradients_variational(self, dL_dKmm, dL_dpsi0, dL_dpsi1, dL_dpsi2, Z, variational_posterior):
         #contributions from Kmm
         sself.update_gradients_full(dL_dKmm, Z)
 
-        mu = posterior_variational.mean
-        S = posterior_variational.variance
+        mu = variational_posterior.mean
+        S = variational_posterior.variance
         self._psi_computations(Z, mu, S)
         l2 = self.lengthscale **2
 
@@ -87,9 +87,9 @@ class RBF(Stationary):
         else:
             self.lengthscale.gradient += dpsi2_dlength.sum(0).sum(0).sum(0)
 
-    def gradients_Z_variational(self, dL_dKmm, dL_dpsi0, dL_dpsi1, dL_dpsi2, Z, posterior_variational):
-        mu = posterior_variational.mean
-        S = posterior_variational.variance
+    def gradients_Z_variational(self, dL_dKmm, dL_dpsi0, dL_dpsi1, dL_dpsi2, Z, variational_posterior):
+        mu = variational_posterior.mean
+        S = variational_posterior.variance
         self._psi_computations(Z, mu, S)
         l2 = self.lengthscale **2
 
@@ -108,9 +108,9 @@ class RBF(Stationary):
 
         return grad
 
-    def gradients_q_variational(self, dL_dKmm, dL_dpsi0, dL_dpsi1, dL_dpsi2, Z, posterior_variational):
-        mu = posterior_variational.mean
-        S = posterior_variational.variance
+    def gradients_q_variational(self, dL_dKmm, dL_dpsi0, dL_dpsi1, dL_dpsi2, Z, variational_posterior):
+        mu = variational_posterior.mean
+        S = variational_posterior.variance
         self._psi_computations(Z, mu, S)
         l2 = self.lengthscale **2
         #psi1

GPy/kern/_src/rbfcos.py

@@ -1,115 +0,0 @@
-# Copyright (c) 2012, James Hensman and Andrew Gordon Wilson
-# Licensed under the BSD 3-clause license (see LICENSE.txt)
-
-
-from kernpart import Kernpart
-import numpy as np
-from ...core.parameterization import Param
-
-class RBFCos(Kernpart):
-    def __init__(self,input_dim,variance=1.,frequencies=None,bandwidths=None,ARD=False):
-        self.input_dim = input_dim
-        self.name = 'rbfcos'
-        if self.input_dim>10:
-            print "Warning: the rbfcos kernel requires a lot of memory for high dimensional inputs"
-        self.ARD = ARD
-
-        #set the default frequencies and bandwidths, appropriate num_params
-        if ARD:
-            self.num_params = 2*self.input_dim + 1
-            if frequencies is not None:
-                frequencies = np.asarray(frequencies)
-                assert frequencies.size == self.input_dim, "bad number of frequencies"
-            else:
-                frequencies = np.ones(self.input_dim)
-            if bandwidths is not None:
-                bandwidths = np.asarray(bandwidths)
-                assert bandwidths.size == self.input_dim, "bad number of bandwidths"
-            else:
-                bandwidths = np.ones(self.input_dim)
-        else:
-            self.num_params = 3
-            if frequencies is not None:
-                frequencies = np.asarray(frequencies)
-                assert frequencies.size == 1, "Exactly one frequency needed for non-ARD kernel"
-            else:
-                frequencies = np.ones(1)
-
-            if bandwidths is not None:
-                bandwidths = np.asarray(bandwidths)
-                assert bandwidths.size == 1, "Exactly one bandwidth needed for non-ARD kernel"
-            else:
-                bandwidths = np.ones(1)
-
-        self.variance = Param('variance', variance)
-        self.frequencies = Param('frequencies', frequencies)
-        self.bandwidths = Param('bandwidths', bandwidths)
-
-        #initialise cache
-        self._X, self._X2 = np.empty(shape=(3,1))
-
-#     def _get_params(self):
-#         return np.hstack((self.variance,self.frequencies, self.bandwidths))
-
-#     def _set_params(self,x):
-#         assert x.size==(self.num_params)
-#         if self.ARD:
-#             self.variance = x[0]
-#             self.frequencies = x[1:1+self.input_dim]
-#             self.bandwidths = x[1+self.input_dim:]
-#         else:
-#             self.variance, self.frequencies, self.bandwidths = x
-
-#     def _get_param_names(self):
-#         if self.num_params == 3:
-#             return ['variance','frequency','bandwidth']
-#         else:
-#             return ['variance']+['frequency_%i'%i for i in range(self.input_dim)]+['bandwidth_%i'%i for i in range(self.input_dim)]
-
-    def K(self,X,X2,target):
-        self._K_computations(X,X2)
-        target += self.variance*self._dvar
-
-    def Kdiag(self,X,target):
-        np.add(target,self.variance,target)
-
-    def _param_grad_helper(self,dL_dK,X,X2,target):
-        self._K_computations(X,X2)
-        target[0] += np.sum(dL_dK*self._dvar)
-        if self.ARD:
-            for q in xrange(self.input_dim):
-                target[q+1] += -2.*np.pi*self.variance*np.sum(dL_dK*self._dvar*np.tan(2.*np.pi*self._dist[:,:,q]*self.frequencies[q])*self._dist[:,:,q])
-                target[q+1+self.input_dim] += -2.*np.pi**2*self.variance*np.sum(dL_dK*self._dvar*self._dist2[:,:,q])
-        else:
-            target[1] += -2.*np.pi*self.variance*np.sum(dL_dK*self._dvar*np.sum(np.tan(2.*np.pi*self._dist*self.frequencies)*self._dist,-1))
-            target[2] += -2.*np.pi**2*self.variance*np.sum(dL_dK*self._dvar*self._dist2.sum(-1))
-
-
-    def dKdiag_dtheta(self,dL_dKdiag,X,target):
-        target[0] += np.sum(dL_dKdiag)
-
-    def gradients_X(self,dL_dK,X,X2,target):
-        #TODO!!!
-        raise NotImplementedError
-
-    def dKdiag_dX(self,dL_dKdiag,X,target):
-        pass
-
-    def parameters_changed(self):
-        self._rbf_part = np.exp(-2.*np.pi**2*np.sum(self._dist2*self.bandwidths,-1))
-        self._cos_part = np.prod(np.cos(2.*np.pi*self._dist*self.frequencies),-1)
-        self._dvar = self._rbf_part*self._cos_part
-
-    def _K_computations(self,X,X2):
-        if not (np.all(X==self._X) and np.all(X2==self._X2)):
-            if X2 is None: X2 = X
-            self._X = X.copy()
-            self._X2 = X2.copy()
-
-            #do the distances: this will be high memory for large input_dim
-            #NB: we don't take the abs of the dist because cos is symmetric
-            self._dist = X[:,None,:] - X2[None,:,:]
-            self._dist2 = np.square(self._dist)
-
-            #ensure the next section is computed:
-            self._params = np.empty(self.num_params)

GPy/kern/_src/static.py

@@ -3,6 +3,7 @@
 
 
 from kern import Kern
+import numpy as np
 from ...core.parameterization import Param
 from ...core.parameterization.transformations import Logexp
 import numpy as np
@@ -18,32 +19,32 @@ class Static(Kern):
         ret[:] = self.variance
         return ret
 
-    def gradients_X(self, dL_dK, X, X2, target):
+    def gradients_X(self, dL_dK, X, X2=None):
         return np.zeros(X.shape)
 
-    def gradients_X_diag(self, dL_dKdiag, X, target):
+    def gradients_X_diag(self, dL_dKdiag, X):
         return np.zeros(X.shape)
 
-    def gradients_Z_variational(self, dL_dKmm, dL_dpsi0, dL_dpsi1, dL_dpsi2, mu, S, Z):
+    def gradients_Z_variational(self, dL_dKmm, dL_dpsi0, dL_dpsi1, dL_dpsi2, Z, variational_posterior):
         return np.zeros(Z.shape)
 
-    def gradients_muS_variational(self, dL_dKmm, dL_dpsi0, dL_dpsi1, dL_dpsi2, mu, S, Z):
-        return np.zeros(mu.shape), np.zeros(S.shape)
+    def gradients_muS_variational(self, dL_dKmm, dL_dpsi0, dL_dpsi1, dL_dpsi2, Z, variational_posterior):
+        return np.zeros(variational_posterior.shape), np.zeros(variational_posterior.shape)
 
-    def psi0(self, Z, mu, S):
-        return self.Kdiag(mu)
+    def psi0(self, Z, variational_posterior):
+        return self.Kdiag(variational_posterior.mean)
 
-    def psi1(self, Z, mu, S, target):
-        return self.K(mu, Z)
+    def psi1(self, Z, variational_posterior):
+        return self.K(variational_posterior.mean, Z)
 
-    def psi2(Z, mu, S):
-        K = self.K(mu, Z)
+    def psi2(self, Z, variational_posterior):
+        K = self.K(variational_posterior.mean, Z)
         return K[:,:,None]*K[:,None,:] # NB. more efficient implementations on inherriting classes
 
 
 class White(Static):
     def __init__(self, input_dim, variance=1., name='white'):
-        super(White, self).__init__(input_dim, name)
+        super(White, self).__init__(input_dim, variance, name)
 
     def K(self, X, X2=None):
         if X2 is None:
@@ -51,8 +52,8 @@ class White(Static):
         else:
             return np.zeros((X.shape[0], X2.shape[0]))
 
-    def psi2(self, Z, mu, S, target):
-        return np.zeros((mu.shape[0], Z.shape[0], Z.shape[0]), dtype=np.float64)
+    def psi2(self, Z, variational_posterior):
+        return np.zeros((variational_posterior.shape[0], Z.shape[0], Z.shape[0]), dtype=np.float64)
 
     def update_gradients_full(self, dL_dK, X):
         self.variance.gradient = np.trace(dL_dK)
@@ -60,13 +61,13 @@ class White(Static):
     def update_gradients_diag(self, dL_dKdiag, X):
         self.variance.gradient = dL_dKdiag.sum()
 
-    def update_gradients_variational(self, dL_dKmm, dL_dpsi0, dL_dpsi1, dL_dpsi2, mu, S, Z):
+    def update_gradients_variational(self, dL_dKmm, dL_dpsi0, dL_dpsi1, dL_dpsi2, Z, variational_posterior):
         self.variance.gradient = np.trace(dL_dKmm) + dL_dpsi0.sum()
 
 
 class Bias(Static):
     def __init__(self, input_dim, variance=1., name='bias'):
-        super(Bias, self).__init__(input_dim, name)
+        super(Bias, self).__init__(input_dim, variance, name)
 
     def K(self, X, X2=None):
         shape = (X.shape[0], X.shape[0] if X2 is None else X2.shape[0])
@@ -80,11 +81,11 @@ class Bias(Static):
     def update_gradients_diag(self, dL_dKdiag, X):
         self.variance.gradient = dL_dK.sum()
 
-    def psi2(self, Z, mu, S, target):
+    def psi2(self, Z, variational_posterior):
         ret = np.empty((mu.shape[0], Z.shape[0], Z.shape[0]), dtype=np.float64)
         ret[:] = self.variance**2
         return ret
 
-    def update_gradients_variational(self, dL_dKmm, dL_dpsi0, dL_dpsi1, dL_dpsi2, mu, S, Z):
+    def update_gradients_variational(self, dL_dKmm, dL_dpsi0, dL_dpsi1, dL_dpsi2, Z, variational_posterior):
         self.variance.gradient = dL_dKmm.sum() + dL_dpsi0.sum() + dL_dpsi1.sum() + 2.*self.variance*dL_dpsi2.sum()
 

GPy/kern/_src/stationary.py

@@ -5,6 +5,7 @@
 from kern import Kern
 from ...core.parameterization import Param
 from ...core.parameterization.transformations import Logexp
+from ...util.linalg import tdot
 from ... import util
 import numpy as np
 from scipy import integrate
@@ -33,10 +34,10 @@ class Stationary(Kern):
         self.add_parameters(self.variance, self.lengthscale)
 
     def K_of_r(self, r):
-        raise NotImplementedError, "implement the covaraiance functino and a fn of r to use this class"
+        raise NotImplementedError, "implement the covaraiance function as a fn of r to use this class"
 
     def dK_dr(self, r):
-        raise NotImplementedError, "implement the covaraiance functino and a fn of r to use this class"
+        raise NotImplementedError, "implement the covaraiance function as a fn of r to use this class"
 
     def K(self, X, X2=None):
         r = self._scaled_dist(X, X2)
@@ -47,8 +48,44 @@ class Stationary(Kern):
             X2 = X
         return X[:, None, :] - X2[None, :, :]
 
+    def _unscaled_dist(self, X, X2=None):
+        """
+        Compute the square distance between each row of X and X2, or between
+        each pair of rows of X if X2 is None.
+        """
+        if X2 is None:
+            Xsq = np.sum(np.square(X),1)
+            return np.sqrt(-2.*tdot(X) + (Xsq[:,None] + Xsq[None,:]))
+        else:
+            X1sq = np.sum(np.square(X),1)
+            X2sq = np.sum(np.square(X2),1)
+            return np.sqrt(-2.*np.dot(X, X2.T) + (X1sq[:,None] + X2sq[None,:]))
+
     def _scaled_dist(self, X, X2=None):
-        return np.sqrt(np.sum(np.square(self._dist(X, X2) / self.lengthscale), -1))
+        """
+        Efficiently compute the scaled distance, r.
+
+        r = \sum_{q=1}^Q (x_q - x'q)^2/l_q^2
+
+        Note that if thre is only one lengthscale, l comes outside the sum. In
+        this case we compute the unscaled distance first (in a separate
+        function for caching) and divide by lengthscale afterwards
+
+        """
+        if self.ARD:
+            if X2 is None:
+                Xl = X/self.lengthscale
+                Xsq = np.sum(np.square(Xl),1)
+                return np.sqrt(np.sqrt(-2.*tdot(Xl) +(Xsq[:,None] + Xsq[None,:])))
+            else:
+                X1l = X/self.lengthscale
+                X2l = X2/self.lengthscale
+                X1sq = np.sum(np.square(X1l),1)
+                X2sq = np.sum(np.square(X2l),1)
+                return np.sqrt(-2.*np.dot(X, X2.T) + (X1sq[:,None] + X2sq[None,:]))
+        else:
+            return self._unscaled_dist(X, X2)/self.lengthscale
+
 
     def Kdiag(self, X):
         ret = np.empty(X.shape[0])
@@ -65,16 +102,22 @@ class Stationary(Kern):
 
         rinv = self._inv_dist(X, X2)
         dL_dr = self.dK_dr(r) * dL_dK
-        x_xl3 = np.square(self._dist(X, X2)) / self.lengthscale**3
 
         if self.ARD:
+            x_xl3 = np.square(self._dist(X, X2)) / self.lengthscale**3
             self.lengthscale.gradient = -((dL_dr*rinv)[:,:,None]*x_xl3).sum(0).sum(0)
         else:
+            x_xl3 = np.square(self._dist(X, X2)) / self.lengthscale**3
             self.lengthscale.gradient = -((dL_dr*rinv)[:,:,None]*x_xl3).sum()
 
         self.variance.gradient = np.sum(K * dL_dK)/self.variance
 
     def _inv_dist(self, X, X2=None):
+        """
+        Compute the elementwise inverse of the distance matrix, expecpt on the
+        diagonal, where we return zero (the distance on the diagonal is zero).
+        This term appears in derviatives.
+        """
         dist = self._scaled_dist(X, X2)
         if X2 is None:
             nondiag = util.diag.offdiag_view(dist)
@@ -84,12 +127,25 @@ class Stationary(Kern):
             return 1./np.where(dist != 0., dist, np.inf)
 
     def gradients_X(self, dL_dK, X, X2=None):
+        """
+        Given the derivative of the objective wrt K (dL_dK), compute the derivative wrt X
+        """
         r = self._scaled_dist(X, X2)
-        dL_dr = self.dK_dr(r) * dL_dK
         invdist = self._inv_dist(X, X2)
-        ret = np.sum((invdist*dL_dr)[:,:,None]*self._dist(X, X2),1)/self.lengthscale**2
+        dL_dr = self.dK_dr(r) * dL_dK
+        #The high-memory numpy way: ret = np.sum((invdist*dL_dr)[:,:,None]*self._dist(X, X2),1)/self.lengthscale**2
+        #if X2 is None:
+            #ret *= 2.
+
+        #the lower memory way with a loop
+        tmp = invdist*dL_dr
         if X2 is None:
-            ret *= 2.
+            tmp *= 2.
+            X2 = X
+        ret = np.empty(X.shape, dtype=np.float64)
+        [np.copyto(ret[:,q], np.sum(tmp*(X[:,q][:,None]-X2[:,q][None,:]), 1)) for q in xrange(self.input_dim)]
+        ret /= self.lengthscale**2
+
         return ret
 
     def gradients_X_diag(self, dL_dKdiag, X):
@@ -162,6 +218,8 @@ class Matern52(Stationary):
 
        k(r) = \sigma^2 (1 + \sqrt{5} r + \\frac53 r^2) \exp(- \sqrt{5} r) \ \ \ \ \  \\text{ where  } r = \sqrt{\sum_{i=1}^input_dim \\frac{(x_i-y_i)^2}{\ell_i^2} }
        """
+    def __init__(self, input_dim, variance=1., lengthscale=None, ARD=False, name='Mat52'):
+        super(Matern52, self).__init__(input_dim, variance, lengthscale, ARD, name)
 
     def K_of_r(self, r):
         return self.variance*(1+np.sqrt(5.)*r+5./3*r**2)*np.exp(-np.sqrt(5.)*r)
@@ -239,17 +297,19 @@ class RatQuad(Stationary):
         self.add_parameters(self.power)
 
     def K_of_r(self, r):
-        return self.variance*(1. + r**2/2.)**(-self.power)
+        r2 = np.power(r, 2.)
+        return self.variance*np.power(1. + r2/2., -self.power)
 
     def dK_dr(self, r):
-        return -self.variance*self.power*r*(1. + r**2/2)**(-self.power - 1.)
+        r2 = np.power(r, 2.)
+        return -self.variance*self.power*r*np.power(1. + r2/2., - self.power - 1.)
 
     def update_gradients_full(self, dL_dK, X, X2=None):
         super(RatQuad, self).update_gradients_full(dL_dK, X, X2)
         r = self._scaled_dist(X, X2)
-        r2 = r**2
-        dpow = -2.**self.power*(r2 + 2.)**(-self.power)*np.log(0.5*(r2+2.))
-        self.power.gradient = np.sum(dL_dK*dpow)
-
+        r2 = np.power(r, 2.)
+        dK_dpow = -self.variance * np.power(2., self.power) * np.power(r2 + 2., -self.power) * np.log(0.5*(r2+2.))
+        grad = np.sum(dL_dK*dK_dpow)
+        self.power.gradient = grad
 
 

GPy/kern/_src/sympykern.py

@@ -2,35 +2,17 @@
 try: 
     import sympy as sp
     sympy_available=True
+    from sympy.utilities.lambdify import lambdify
 except ImportError:
     sympy_available=False
     exit()
 
-from sympy.core.cache import clear_cache
-from sympy.utilities.codegen import codegen
-
-try:
-    from scipy import weave
-    weave_available = True
-except ImportError:
-    weave_available = False
-
-import os
-current_dir = os.path.dirname(os.path.abspath(os.path.dirname(__file__)))
-import sys
 import numpy as np
-import re
-import tempfile
-import pdb
-import ast
-
-from kernpart import Kernpart
+from kern import Kern
 from ...core.parameterization import Param
 from ...core.parameterization.transformations import Logexp
-# TODO have this set up in a set up file!
-user_code_storage = tempfile.gettempdir()
 
-class spkern(Kernpart):
+class Sympykern(Kern):
     """
     A kernel object, where all the hard work in done by sympy.
 
@@ -51,10 +33,10 @@ class spkern(Kernpart):
             name='sympykern'
         if k is None:
             raise ValueError, "You must provide an argument for the covariance function."
-        super(spkern, self).__init__(input_dim, name)
+        super(Sympykern, self).__init__(input_dim, name)
 
         self._sp_k = k
-
+        
         # pull the variable names out of the symbolic covariance function.
         sp_vars = [e for e in k.atoms() if e.is_Symbol]
         self._sp_x= sorted([e for e in sp_vars if e.name[0:2]=='x_'],key=lambda x:int(x.name[2:]))
@@ -66,6 +48,10 @@ class spkern(Kernpart):
         assert len(self._sp_x)==len(self._sp_z)
         x_dim=len(self._sp_x)
 
+        self._sp_kdiag = k
+        for x, z in zip(self._sp_x, self._sp_z):
+            self._sp_kdiag = self._sp_kdiag.subs(z, x)
+            
         # If it is a multi-output covariance, add an input for indexing the outputs.
         self._real_input_dim = x_dim
         # Check input dim is number of xs + 1 if output_dim is >1
@@ -97,6 +83,8 @@ class spkern(Kernpart):
                 #setattr(self, theta, np.ones(self.output_dim))
             
             self.num_shared_params = len(self._sp_theta)
+            for theta_i, theta_j in zip(self._sp_theta_i, self._sp_theta_j):
+                self._sp_kdiag = self._sp_kdiag.subs(theta_j, theta_i)
             #self.num_params = self.num_shared_params+self.num_split_params*self.output_dim
             
         else:
@@ -112,43 +100,33 @@ class spkern(Kernpart):
             if param is not None:
                 if param.has_key(theta):
                     val = param[theta]
-            #setattr(self, theta.name, val)
             setattr(self, theta.name, Param(theta.name, val, None))
             self.add_parameters(getattr(self, theta.name))
         #deal with param            
         #self._set_params(self._get_params())
 
         # Differentiate with respect to parameters.
-        self._sp_dk_dtheta = [sp.diff(k,theta).simplify() for theta in self._sp_theta]
+        derivative_arguments = self._sp_x + self._sp_theta
         if self.output_dim > 1:
-            self._sp_dk_dtheta_i = [sp.diff(k,theta).simplify() for theta in self._sp_theta_i]
-
-        # differentiate with respect to input variables.
-        self._sp_dk_dx = [sp.diff(k,xi).simplify() for xi in self._sp_x]
-
+            derivative_arguments += self._sp_theta_i
+        
+        self.derivatives = {theta.name : sp.diff(self._sp_k,theta).simplify() for theta in derivative_arguments}
+        self.diag_derivatives = {theta.name : sp.diff(self._sp_kdiag,theta).simplify() for theta in derivative_arguments}
+        
+        
+        # This gives the parameters for the arg list.
+        self.arg_list = self._sp_x + self._sp_z + self._sp_theta
+        self.diag_arg_list = self._sp_x + self._sp_theta
+        if self.output_dim > 1:
+            self.arg_list += self._sp_theta_i + self._sp_theta_j
+            self.diag_arg_list += self._sp_theta_i
         # psi_stats aren't yet implemented.
         if False:
             self.compute_psi_stats()
 
-        self._code = {}
-
         # generate the code for the covariance functions
         self._gen_code()
 
-        if weave_available:
-            if False:
-                extra_compile_args = ['-ftree-vectorize', '-mssse3', '-ftree-vectorizer-verbose=5']
-            else:
-                extra_compile_args = []
-            
-                self.weave_kwargs = {
-                    'support_code': None, #self._function_code,
-                    'include_dirs':[user_code_storage, os.path.join(current_dir,'parts/')],
-                    'headers':['"sympy_helpers.h"', '"'+self.name+'.h"'],
-                    'sources':[os.path.join(current_dir,"parts/sympy_helpers.cpp"), os.path.join(user_code_storage, self.name+'.cpp')],
-                    'extra_compile_args':extra_compile_args,
-                    'extra_link_args':['-lgomp'],
-                    'verbose':True}
         self.parameters_changed() # initializes caches
 
 
@@ -157,407 +135,128 @@ class spkern(Kernpart):
 
     def _gen_code(self):
 
-        argument_sequence = self._sp_x+self._sp_z+self._sp_theta
-        code_list = [('k',self._sp_k)]
-        # gradients with respect to covariance input
-        code_list += [('dk_d%s'%x.name,dx) for x,dx in zip(self._sp_x,self._sp_dk_dx)]
-        # gradient with respect to parameters
-        code_list += [('dk_d%s'%theta.name,dtheta) for theta,dtheta in zip(self._sp_theta,self._sp_dk_dtheta)]
-        # gradient with respect to multiple output parameters
-        if self.output_dim > 1:
-            argument_sequence += self._sp_theta_i + self._sp_theta_j
-            code_list += [('dk_d%s'%theta.name,dtheta) for theta,dtheta in zip(self._sp_theta_i,self._sp_dk_dtheta_i)]
-        # generate c functions from sympy objects
-        if weave_available:
-            code_type = "C"
-        else:
-            code_type = "PYTHON"
-        # Need to add the sympy_helpers header in here.
-        (foo_c,self._function_code), (foo_h,self._function_header) = \
-                                     codegen(code_list,
-                                             code_type,
-                                             self.name,
-                                             argument_sequence=argument_sequence)
+        self._K_function = lambdify(self.arg_list, self._sp_k, 'numpy')
+        for key in self.derivatives.keys():
+            setattr(self, '_K_diff_' + key, lambdify(self.arg_list, self.derivatives[key], 'numpy'))
+
+        self._Kdiag_function = lambdify(self.diag_arg_list, self._sp_kdiag, 'numpy')
+        for key in self.derivatives.keys():
+            setattr(self, '_Kdiag_diff_' + key, lambdify(self.diag_arg_list, self.diag_derivatives[key], 'numpy'))
+
+    def K(self,X,X2=None):        
+        self._K_computations(X, X2)
+        return self._K_function(**self._arguments)
 
 
-        # Use weave to compute the underlying functions.
-        if weave_available:
-            # put the header file where we can find it
-            f = file(os.path.join(user_code_storage, self.name + '.h'),'w')
-            f.write(self._function_header)
-            f.close()
+    def Kdiag(self,X):
+        self._K_computations(X)
+        return self._Kdiag_function(**self._diag_arguments)
     
-
-        if weave_available:
-            # Substitute any known derivatives which sympy doesn't compute
-            self._function_code = re.sub('DiracDelta\(.+?,.+?\)','0.0',self._function_code)
-            # put the cpp file in user code storage (defaults to temp file location)
-            f = file(os.path.join(user_code_storage, self.name + '.cpp'),'w')
-        else:
-            # put the python file in user code storage
-            f = file(os.path.join(user_code_storage, self.name + '.py'),'w')
-        f.write(self._function_code)
-        f.close()
-
-        if weave_available:
-            # arg_list will store the arguments required for the C code.
-            input_arg_list = (["X2(i, %s)"%x.name[2:] for x in self._sp_x]
-                        + ["Z2(j, %s)"%z.name[2:] for z in self._sp_z])
-
-            # for multiple outputs reverse argument list is also required
-            if self.output_dim>1:
-                reverse_input_arg_list = list(input_arg_list)
-                reverse_input_arg_list.reverse()
-
-            # This gives the parameters for the arg list.
-            param_arg_list = [shared_params.name for shared_params in self._sp_theta]
-            arg_list = input_arg_list + param_arg_list
-
-            precompute_list=[]
-            if self.output_dim > 1:
-                reverse_arg_list= reverse_input_arg_list + list(param_arg_list)
-                # For multiple outputs, also need the split parameters.
-                split_param_arg_list = ["%s1(%s)"%(theta.name[:-2].upper(),index) for index in ['ii', 'jj'] for theta in self._sp_theta_i]
-                split_param_reverse_arg_list = ["%s1(%s)"%(theta.name[:-2].upper(),index) for index in ['jj', 'ii'] for theta in self._sp_theta_i]
-                arg_list += split_param_arg_list
-                reverse_arg_list += split_param_reverse_arg_list
-                # Extract the right output indices from the inputs.
-                c_define_output_indices = [' '*16 + "int %s=(int)%s(%s, %i);"%(index, var, index2, self.input_dim-1) for index, var, index2 in zip(['ii', 'jj'], ['X2', 'Z2'], ['i', 'j'])]
-                precompute_list += c_define_output_indices
-                reverse_arg_string = ", ".join(reverse_arg_list)
-            arg_string = ", ".join(arg_list)
-            precompute_string = "\n".join(precompute_list)
-
-            # Now we use the arguments in code that computes the separate parts.
-
-            # Any precomputations will be done here eventually.
-            self._precompute = \
-                """
-                // Precompute code would go here. It will be called when parameters are updated. 
-                """
-
-            # Here's the code to do the looping for K
-            self._code['K'] =\
-            """
-            // _K_code
-            // Code for computing the covariance function.
-            int i;
-            int j;
-            int n = target_array->dimensions[0];
-            int num_inducing = target_array->dimensions[1];
-            int input_dim = X_array->dimensions[1];
-            //#pragma omp parallel for private(j)
-            for (i=0;i<n;i++){
-                for (j=0;j<num_inducing;j++){
-                    %s
-                    //target[i*num_inducing+j] = 
-                    TARGET2(i, j) += k(%s);
-                }
-            }
-            %s
-            """%(precompute_string,arg_string,"/*"+str(self._sp_k)+"*/")
-           # adding a string representation of the function in the
-           # comment forces recompile when needed
-            self._code['K_X'] = self._code['K'].replace('Z2(', 'X2(')
-
-
-            # Code to compute diagonal of covariance.
-            diag_arg_string = re.sub('Z','X',arg_string)
-            diag_arg_string = re.sub('int jj','//int jj',diag_arg_string)
-            diag_arg_string = re.sub('j','i',diag_arg_string)
-            diag_precompute_string = re.sub('int jj','//int jj',precompute_string)
-            diag_precompute_string = re.sub('Z','X',diag_precompute_string)
-            diag_precompute_string = re.sub('j','i',diag_precompute_string)
-            # Code to do the looping for Kdiag
-            self._code['Kdiag'] =\
-            """
-            // _code['Kdiag']
-            // Code for computing diagonal of covariance function.
-            int i;
-            int n = target_array->dimensions[0];
-            int input_dim = X_array->dimensions[1];
-            //#pragma omp parallel for
-            for (i=0;i<n;i++){
-                    %s
-                    //target[i] =
-                    TARGET1(i)=k(%s);
-            }
-            %s
-            """%(diag_precompute_string,diag_arg_string,"/*"+str(self._sp_k)+"*/") #adding a string representation forces recompile when needed
-
-            # Code to compute gradients
-            if self.output_dim>1:
-                for i, theta in enumerate(self._sp_theta_i):
-                    grad_func_list = [' '*26 + 'TARGET1(ii) += PARTIAL2(i, j)*dk_d%s(%s);'%(theta.name, arg_string)]
-                    grad_func_list += [' '*26 + 'TARGET1(jj) += PARTIAL2(i, j)*dk_d%s(%s);'%(theta.name, reverse_arg_string)]
-                    grad_func_list = c_define_output_indices+grad_func_list
-
-                    grad_func_string = '\n'.join(grad_func_list) 
-                    self._code['dK_d' + theta.name] =\
-                      """
-                      int i;
-                      int j;
-                      int n = partial_array->dimensions[0];
-                      int num_inducing = partial_array->dimensions[1];
-                      int input_dim = X_array->dimensions[1];
-                      //#pragma omp parallel for private(j)
-                      for (i=0;i<n;i++){
-                        for (j=0;j<num_inducing;j++){
-%s
-                        }
-                      }
-                      %s
-                      """%(grad_func_string,"/*"+str(self._sp_k)+"*/") # adding a string representation forces recompile when needed
-                    self._code['dK_d' +theta.name + '_X'] = self._code['dK_d' + theta.name].replace('Z2(', 'X2(')
-                    # Code to compute gradients for Kdiag TODO: needs clean up
-                    diag_grad_func_string = re.sub('Z','X',grad_func_string,count=0)
-                    diag_grad_func_string = re.sub('int jj','//int jj',diag_grad_func_string)
-                    diag_grad_func_string = re.sub('j','i',diag_grad_func_string)
-                    diag_grad_func_string = re.sub('PARTIAL2\(i, i\)','PARTIAL(i)',diag_grad_func_string)
-                    self._code['dKdiag_d' + theta.name] =\
-                      """
-                      // _dKdiag_dtheta_code
-                      // Code for computing gradient of diagonal with respect to parameters.
-                      int i;
-                      int n = partial_array->dimensions[0];
-                      int input_dim = X_array->dimensions[1];
-                      for (i=0;i<n;i++){
-                        %s
-                      }
-                      %s
-                      """%(diag_grad_func_string,"/*"+str(self._sp_k)+"*/") #adding a string representation forces recompile when needed
-
-            for i, theta in enumerate(self._sp_theta):
-                grad_func_list = [' '*26 + 'TARGET1(%i) += PARTIAL2(i, j)*dk_d%s(%s);'%(i,theta.name,arg_string)]
-                grad_func_string = '\n'.join(grad_func_list) 
-
-                self._code['dK_d' + theta.name] =\
-                  """
-                  // _dK_dtheta_code
-                  // Code for computing gradient of covariance with respect to parameters.
-                  int i;
-                  int j;
-                  int n = partial_array->dimensions[0];
-                  int num_inducing = partial_array->dimensions[1];
-                  int input_dim = X_array->dimensions[1];
-                  //#pragma omp parallel for private(j)
-                  for (i=0;i<n;i++){
-                    for (j=0;j<num_inducing;j++){
-                      %s
-                    }
-                  }
-                  %s
-                  """%(grad_func_string,"/*"+str(self._sp_k)+"*/") # adding a string representation forces recompile when needed
-                self._code['dK_d' + theta.name +'_X'] = self._code['dK_d' + theta.name].replace('Z2(', 'X2(')
-                # Code to compute gradients for Kdiag TODO: needs clean up
-                diag_grad_func_string = re.sub('Z','X',grad_func_string,count=0)
-                diag_grad_func_string = re.sub('int jj','//int jj',diag_grad_func_string)
-                diag_grad_func_string = re.sub('j','i',diag_grad_func_string)
-                diag_grad_func_string = re.sub('PARTIAL2\(i, i\)','PARTIAL(i)',diag_grad_func_string)
-                self._code['dKdiag_d' + theta.name] =\
-                   """
-                   // _dKdiag_dtheta_code
-                   // Code for computing gradient of diagonal with respect to parameters.
-                   int i;
-                   int n = partial_array->dimensions[0];
-                   int input_dim = X_array->dimensions[1];
-                   for (i=0;i<n;i++){
-                     %s
-                   }
-                   %s
-                   """%(diag_grad_func_string,"/*"+str(self._sp_k)+"*/") #adding a string representation forces recompile when needed
-
-
-
-            # Code for gradients wrt X, TODO: may need to deal with special case where one input is actually an output.
-            gradX_func_list = []
-            if self.output_dim>1:
-                gradX_func_list += c_define_output_indices
-            gradX_func_list += ["TARGET2(i, %i) += partial[i*num_inducing+j]*dk_dx_%i(%s);"%(q,q,arg_string) for q in range(self._real_input_dim)]
-            gradX_func_string = "\n".join(gradX_func_list)
-
-            self._code['dK_dX'] = \
-            """
-            // _dK_dX_code
-            // Code for computing gradient of covariance with respect to inputs.
-            int i;
-            int j;
-            int n = partial_array->dimensions[0];
-            int num_inducing = partial_array->dimensions[1];
-            int input_dim = X_array->dimensions[1];
-            //#pragma omp parallel for private(j)
-            for (i=0;i<n; i++){
-              for (j=0; j<num_inducing; j++){
-                %s
-              }
-            }
-            %s
-            """%(gradX_func_string,"/*"+str(self._sp_k)+"*/") #adding a string representation forces recompile when needed
-            self._code['dK_dX_X'] = self._code['dK_dX'].replace('Z2(', 'X2(')
-
-
-            diag_gradX_func_string = re.sub('Z','X',gradX_func_string,count=0)
-            diag_gradX_func_string = re.sub('int jj','//int jj',diag_gradX_func_string)
-            diag_gradX_func_string = re.sub('j','i',diag_gradX_func_string)
-            diag_gradX_func_string = re.sub('PARTIAL2\(i\, i\)','2*PARTIAL(i)',diag_gradX_func_string)
-
-            # Code for gradients of Kdiag wrt X
-            self._code['dKdiag_dX'] = \
-            """
-            // _dKdiag_dX_code
-            // Code for computing gradient of diagonal with respect to inputs.
-            int n = partial_array->dimensions[0];
-            int input_dim = X_array->dimensions[1];
-            for (int i=0;i<n; i++){
-                %s
-            }
-            %s
-            """%(diag_gradX_func_string,"/*"+str(self._sp_k)+"*/") #adding a
-            # string representation forces recompile when needed Get rid
-            # of Zs in argument for diagonal. TODO: Why wasn't
-            # diag_func_string called here? Need to check that.
-
-            
-
-
-            #TODO: insert multiple functions here via string manipulation
-            #TODO: similar functions for psi_stats
-            #TODO: similar functions when cython available.
-            #TODO: similar functions when only python available.
-            
-    def _get_arg_names(self, target=None, Z=None, partial=None):
-        arg_names = ['X']
-        if target is not None:
-            arg_names += ['target']
-        for shared_params in self._sp_theta:
-            arg_names += [shared_params.name]
-        if Z is not None:
-            arg_names += ['Z']
-        if partial is not None:
-            arg_names += ['partial']
-        if self.output_dim>1:
-            arg_names += self._split_theta_names
-            arg_names += ['output_dim']
-        return arg_names
-
-    def _generate_inline(self, code, X, target=None, Z=None, partial=None):
-        output_dim = self.output_dim
-        # Need to extract parameters to local variables first
-        for shared_params in self._sp_theta:
-            locals()[shared_params.name] = getattr(self, shared_params.name)
-            
-        for split_params in self._split_theta_names:
-            locals()[split_params] = np.asarray(getattr(self, split_params))
-        arg_names = self._get_arg_names(target, Z, partial)        
-
-        if weave_available:
-            return weave.inline(code=code, arg_names=arg_names,**self.weave_kwargs)
-        else:
-            raise RuntimeError('Weave not available and other variants of sympy covariance not yet implemented')
-
-    def K(self,X,Z,target):        
-        if Z is None:
-            self._generate_inline(self._code['K_X'], X, target)
-        else:
-            self._generate_inline(self._code['K'], X, target, Z)
-
-
-    def Kdiag(self,X,target):
-        self._generate_inline(self._code['Kdiag'], X, target)
-
     def _param_grad_helper(self,partial,X,Z,target):
-        if Z is None:
-            self._generate_inline(self._code['dK_dtheta_X'], X, target, Z, partial)
-        else:
-            self._generate_inline(self._code['dK_dtheta'], X, target, Z, partial)
+        pass
 
-    def dKdiag_dtheta(self,partial,X,target):
-        self._generate_inline(self._code['dKdiag_dtheta'], X, target, Z=None, partial=partial).namelocals()[shared_params.name] = getattr(self, shared_params.name)
                
-    def gradients_X(self,partial,X,Z,target):
-        if Z is None:
-            self._generate_inline(self._code['dK_dX_X'], X, target, Z, partial)
-        else:
-            self._generate_inline(self._code['dK_dX'], X, target, Z, partial)
+    def gradients_X(self, dL_dK, X, X2=None):
+        #if self._X is None or X.base is not self._X.base or X2 is not None:
+        self._K_computations(X, X2)
+        gradients_X = np.zeros((X.shape[0], X.shape[1]))
+        for i, x in enumerate(self._sp_x):
+            gf = getattr(self, '_K_diff_' + x.name)
+            gradients_X[:, i] = (gf(**self._arguments)*dL_dK).sum(1)
+        if X2 is None:
+            gradients_X *= 2
+        return gradients_X
 
-    def dKdiag_dX(self,partial,X,target):
-        self._generate_inline(self._code['dKdiag_dX'], X, target, Z, partial)
-
-    def compute_psi_stats(self):
-        #define some normal distributions
-        mus = [sp.var('mu_%i'%i,real=True) for i in range(self.input_dim)]
-        Ss = [sp.var('S_%i'%i,positive=True) for i in range(self.input_dim)]
-        normals = [(2*sp.pi*Si)**(-0.5)*sp.exp(-0.5*(xi-mui)**2/Si) for xi, mui, Si in zip(self._sp_x, mus, Ss)]
-
-        #do some integration!
-        #self._sp_psi0 = ??
-        self._sp_psi1 = self._sp_k
-        for i in range(self.input_dim):
-            print 'perfoming integrals %i of %i'%(i+1,2*self.input_dim)
-            sys.stdout.flush()
-            self._sp_psi1 *= normals[i]
-            self._sp_psi1 = sp.integrate(self._sp_psi1,(self._sp_x[i],-sp.oo,sp.oo))
-            clear_cache()
-        self._sp_psi1 = self._sp_psi1.simplify()
-
-        #and here's psi2 (eek!)
-        zprime = [sp.Symbol('zp%i'%i) for i in range(self.input_dim)]
-        self._sp_psi2 = self._sp_k.copy()*self._sp_k.copy().subs(zip(self._sp_z,zprime))
-        for i in range(self.input_dim):
-            print 'perfoming integrals %i of %i'%(self.input_dim+i+1,2*self.input_dim)
-            sys.stdout.flush()
-            self._sp_psi2 *= normals[i]
-            self._sp_psi2 = sp.integrate(self._sp_psi2,(self._sp_x[i],-sp.oo,sp.oo))
-            clear_cache()
-        self._sp_psi2 = self._sp_psi2.simplify()
-
-    def parameters_changed(self):
-        # Reset the caches
-        self._cache, self._cache2 = np.empty(shape=(2, 1))
-        self._cache3, self._cache4, self._cache5 = np.empty(shape=(3, 1)) 
-
-    def update_gradients_full(self, dL_dK, X):
-        # Need to extract parameters to local variables first
-        self._K_computations(X, None)
-        for shared_params in self._sp_theta:
-            parameter = getattr(self, shared_params.name)
-            code = self._code['dK_d' + shared_params.name]
-            setattr(parameter, 'gradient', self._generate_inline(code, X, target=None, Z=None, partial=dL_dK))
-            
-        for split_params in self._split_theta_names:
-            parameter = getattr(self, split_params.name)
-            code = self._code['dK_d' + split_params.name]
-            setattr(parameter, 'gradient', self._generate_inline(code, X, target=None, Z=None, partial=dL_dK))
+    def gradients_X_diag(self, dL_dK, X):
+        self._K_computations(X)
+        dX = np.zeros_like(X)
+        for i, x in enumerate(self._sp_x):
+            gf = getattr(self, '_Kdiag_diff_' + x.name)
+            dX[:, i] = gf(**self._diag_arguments)*dL_dK
+        return dX
     
+    def update_gradients_full(self, dL_dK, X, X2=None):
+        # Need to extract parameters to local variables first
+        self._K_computations(X, X2)
+        for theta in self._sp_theta:
+            parameter = getattr(self, theta.name)
+            gf = getattr(self, '_K_diff_' + theta.name)
+            gradient = (gf(**self._arguments)*dL_dK).sum()
+            if X2 is not None:
+                gradient += (gf(**self._reverse_arguments)*dL_dK).sum()
+            setattr(parameter, 'gradient', gradient)
+        if self.output_dim>1:
+            for theta in self._sp_theta_i:
+                parameter = getattr(self, theta.name[:-2])
+                gf = getattr(self, '_K_diff_' + theta.name)
+                A = gf(**self._arguments)*dL_dK
+                gradient = np.asarray([A[np.where(self._output_ind==i)].T.sum()
+                                       for i in np.arange(self.output_dim)])
+                if X2 is None:
+                    gradient *= 2
+                else:
+                    A = gf(**self._reverse_arguments)*dL_dK.T
+                    gradient += np.asarray([A[np.where(self._output_ind2==i)].T.sum()
+                                 for i in np.arange(self.output_dim)])
+                setattr(parameter, 'gradient', gradient)
+                
 
-    # def update_gradients_sparse(self, dL_dKmm, dL_dKnm, dL_dKdiag, X, Z):
-    #     #contributions from Kdiag
-    #     self.variance.gradient = np.sum(dL_dKdiag)
-        
-    #     #from Knm
-    #     self._K_computations(X, Z)
-    #     self.variance.gradient += np.sum(dL_dKnm * self._K_dvar)
-    #     if self.ARD:
-    #         self.lengthscale.gradient = self._dL_dlengthscales_via_K(dL_dKnm, X, Z)
-            
-    #     else:
-    #         self.lengthscale.gradient = (self.variance / self.lengthscale) * np.sum(self._K_dvar * self._K_dist2 * dL_dKnm)
-            
-    #     #from Kmm
-    #     self._K_computations(Z, None)
-    #     self.variance.gradient += np.sum(dL_dKmm * self._K_dvar)
-    #     if self.ARD:
-    #         self.lengthscale.gradient += self._dL_dlengthscales_via_K(dL_dKmm, Z, None)
-    #     else:
-    #         self.lengthscale.gradient += (self.variance / self.lengthscale) * np.sum(self._K_dvar * self._K_dist2 * dL_dKmm)
-            
-            
-    #---------------------------------------#
-    #            Precomputations            #
-    #---------------------------------------#
+    def update_gradients_diag(self, dL_dKdiag, X):
+        self._K_computations(X)
+        for theta in self._sp_theta:
+            parameter = getattr(self, theta.name)
+            gf = getattr(self, '_Kdiag_diff_' + theta.name)
+            setattr(parameter, 'gradient', (gf(**self._diag_arguments)*dL_dKdiag).sum())
+        if self.output_dim>1:
+            for theta in self._sp_theta_i:
+                parameter = getattr(self, theta.name[:-2])
+                gf = getattr(self, '_Kdiag_diff_' + theta.name)
+                a = gf(**self._diag_arguments)*dL_dKdiag
+                setattr(parameter, 'gradient',
+                        np.asarray([a[np.where(self._output_ind==i)].sum()
+                         for i in np.arange(self.output_dim)]))
+    
+    def _K_computations(self, X, X2=None):
+        """Set up argument lists for the derivatives."""
+        # Could check if this needs doing or not, there could
+        # definitely be some computational savings by checking for
+        # parameter updates here.
+        self._arguments = {}
+        self._diag_arguments = {}
+        for i, x in enumerate(self._sp_x):
+            self._arguments[x.name] =  X[:, i][:, None]
+            self._diag_arguments[x.name] =  X[:, i][:, None]
+        if self.output_dim > 1:
+            self._output_ind = np.asarray(X[:, -1], dtype='int')
+            for i, theta in enumerate(self._sp_theta_i):
+                self._arguments[theta.name] = np.asarray(getattr(self, theta.name[:-2])[self._output_ind])[:, None]
+                self._diag_arguments[theta.name] = self._arguments[theta.name]
+        for theta in self._sp_theta:
+            self._arguments[theta.name] = np.asarray(getattr(self, theta.name))
+            self._diag_arguments[theta.name] = self._arguments[theta.name]
 
-    def _K_computations(self, X, Z):
-        if Z is None:
-            self._generate_inline(self._precompute, X)
+        if X2 is not None:
+            for i, z in enumerate(self._sp_z):
+                self._arguments[z.name] =  X2[:, i][None, :]
+            if self.output_dim > 1:
+                self._output_ind2 = np.asarray(X2[:, -1], dtype='int')
+                for i, theta in enumerate(self._sp_theta_j):
+                    self._arguments[theta.name] = np.asarray(getattr(self, theta.name[:-2])[self._output_ind2])[None, :]
         else:
-            self._generate_inline(self._precompute, X, Z=Z)
+            for z in self._sp_z:
+                self._arguments[z.name] =  self._arguments['x_'+z.name[2:]].T
+            if self.output_dim > 1:
+                self._output_ind2 = self._output_ind
+                for theta in self._sp_theta_j:
+                    self._arguments[theta.name] = self._arguments[theta.name[:-2] + '_i'].T
+        if X2 is not None:
+            # These arguments are needed in gradients when X2 is not equal to X.
+            self._reverse_arguments = self._arguments
+            for x, z in zip(self._sp_x, self._sp_z):
+                self._reverse_arguments[x.name] = self._arguments[z.name].T
+                self._reverse_arguments[z.name] = self._arguments[x.name].T
+            if self.output_dim > 1:
+                for theta_i, theta_j in zip(self._sp_theta_i, self._sp_theta_j):
+                    self._reverse_arguments[theta_i.name] = self._arguments[theta_j.name].T
+                    self._reverse_arguments[theta_j.name] = self._arguments[theta_i.name].T
+

GPy/models/sparse_gp_regression.py

@@ -7,6 +7,7 @@ from ..core import SparseGP
 from .. import likelihoods
 from .. import kern
 from ..inference.latent_function_inference import VarDTC
+from ..util.misc import param_to_array
 
 class SparseGPRegression(SparseGP):
     """
@@ -33,18 +34,18 @@ class SparseGPRegression(SparseGP):
 
         # kern defaults to rbf (plus white for stability)
         if kernel is None:
-            kernel = kern.rbf(input_dim)#  + kern.white(input_dim, variance=1e-3)
+            kernel = kern.RBF(input_dim)#  + kern.white(input_dim, variance=1e-3)
 
         # Z defaults to a subset of the data
         if Z is None:
             i = np.random.permutation(num_data)[:min(num_inducing, num_data)]
-            Z = X[i].copy()
+            Z = param_to_array(X)[i].copy()
         else:
             assert Z.shape[1] == input_dim
 
         likelihood = likelihoods.Gaussian()
 
-        SparseGP.__init__(self, X, Y, Z, kernel, likelihood, X_variance=X_variance, inference_method=VarDTC())
+        SparseGP.__init__(self, X, Y, Z, kernel, likelihood, inference_method=VarDTC())
 
     def _getstate(self):
         return SparseGP._getstate(self)

GPy/testing/bgplvm_tests.py

@@ -10,11 +10,11 @@ class BGPLVMTests(unittest.TestCase):
     def test_bias_kern(self):
         N, num_inducing, input_dim, D = 10, 3, 2, 4
         X = np.random.rand(N, input_dim)
-        k = GPy.kern.rbf(input_dim) + GPy.kern.white(input_dim, 0.00001)
+        k = GPy.kern.RBF(input_dim) + GPy.kern.White(input_dim, 0.00001)
         K = k.K(X)
         Y = np.random.multivariate_normal(np.zeros(N),K,input_dim).T
         Y -= Y.mean(axis=0)
-        k = GPy.kern.bias(input_dim) + GPy.kern.white(input_dim, 0.00001)
+        k = GPy.kern.bias(input_dim) + GPy.kern.White(input_dim, 0.00001)
         m = BayesianGPLVM(Y, input_dim, kernel=k, num_inducing=num_inducing)
         m.randomize()
         self.assertTrue(m.checkgrad())
@@ -22,11 +22,11 @@ class BGPLVMTests(unittest.TestCase):
     def test_linear_kern(self):
         N, num_inducing, input_dim, D = 10, 3, 2, 4
         X = np.random.rand(N, input_dim)
-        k = GPy.kern.rbf(input_dim) + GPy.kern.white(input_dim, 0.00001)
+        k = GPy.kern.RBF(input_dim) + GPy.kern.White(input_dim, 0.00001)
         K = k.K(X)
         Y = np.random.multivariate_normal(np.zeros(N),K,input_dim).T
         Y -= Y.mean(axis=0)
-        k = GPy.kern.linear(input_dim) + GPy.kern.white(input_dim, 0.00001)
+        k = GPy.kern.Linear(input_dim) + GPy.kern.White(input_dim, 0.00001)
         m = BayesianGPLVM(Y, input_dim, kernel=k, num_inducing=num_inducing)
         m.randomize()
         self.assertTrue(m.checkgrad())
@@ -34,11 +34,11 @@ class BGPLVMTests(unittest.TestCase):
     def test_rbf_kern(self):
         N, num_inducing, input_dim, D = 10, 3, 2, 4
         X = np.random.rand(N, input_dim)
-        k = GPy.kern.rbf(input_dim) + GPy.kern.white(input_dim, 0.00001)
+        k = GPy.kern.RBF(input_dim) + GPy.kern.White(input_dim, 0.00001)
         K = k.K(X)
         Y = np.random.multivariate_normal(np.zeros(N),K,input_dim).T
         Y -= Y.mean(axis=0)
-        k = GPy.kern.rbf(input_dim) + GPy.kern.white(input_dim, 0.00001)
+        k = GPy.kern.RBF(input_dim) + GPy.kern.White(input_dim, 0.00001)
         m = BayesianGPLVM(Y, input_dim, kernel=k, num_inducing=num_inducing)
         m.randomize()
         self.assertTrue(m.checkgrad())
@@ -46,11 +46,11 @@ class BGPLVMTests(unittest.TestCase):
     def test_rbf_bias_kern(self):
         N, num_inducing, input_dim, D = 10, 3, 2, 4
         X = np.random.rand(N, input_dim)
-        k = GPy.kern.rbf(input_dim) +  GPy.kern.bias(input_dim) + GPy.kern.white(input_dim, 0.00001)
+        k = GPy.kern.RBF(input_dim) +  GPy.kern.Bias(input_dim) + GPy.kern.White(input_dim, 0.00001)
         K = k.K(X)
         Y = np.random.multivariate_normal(np.zeros(N),K,input_dim).T
         Y -= Y.mean(axis=0)
-        k = GPy.kern.rbf(input_dim) + GPy.kern.bias(input_dim) + GPy.kern.white(input_dim, 0.00001)
+        k = GPy.kern.RBF(input_dim) + GPy.kern.Bias(input_dim) + GPy.kern.White(input_dim, 0.00001)
         m = BayesianGPLVM(Y, input_dim, kernel=k, num_inducing=num_inducing)
         m.randomize()
         self.assertTrue(m.checkgrad())
@@ -58,11 +58,11 @@ class BGPLVMTests(unittest.TestCase):
     def test_rbf_line_kern(self):
         N, num_inducing, input_dim, D = 10, 3, 2, 4
         X = np.random.rand(N, input_dim)
-        k = GPy.kern.rbf(input_dim) +  GPy.kern.linear(input_dim) + GPy.kern.white(input_dim, 0.00001)
+        k = GPy.kern.RBF(input_dim) +  GPy.kern.Linear(input_dim) + GPy.kern.White(input_dim, 0.00001)
         K = k.K(X)
         Y = np.random.multivariate_normal(np.zeros(N),K,input_dim).T
         Y -= Y.mean(axis=0)
-        k = GPy.kern.rbf(input_dim) + GPy.kern.bias(input_dim) + GPy.kern.white(input_dim, 0.00001)
+        k = GPy.kern.RBF(input_dim) + GPy.kern.Bias(input_dim) + GPy.kern.White(input_dim, 0.00001)
         m = BayesianGPLVM(Y, input_dim, kernel=k, num_inducing=num_inducing)
         m.randomize()
         self.assertTrue(m.checkgrad())
@@ -70,11 +70,11 @@ class BGPLVMTests(unittest.TestCase):
     def test_linear_bias_kern(self):
         N, num_inducing, input_dim, D = 30, 5, 4, 30
         X = np.random.rand(N, input_dim)
-        k = GPy.kern.linear(input_dim) +  GPy.kern.bias(input_dim) + GPy.kern.white(input_dim, 0.00001)
+        k = GPy.kern.Linear(input_dim) +  GPy.kern.Bias(input_dim) + GPy.kern.White(input_dim, 0.00001)
         K = k.K(X)
         Y = np.random.multivariate_normal(np.zeros(N),K,input_dim).T
         Y -= Y.mean(axis=0)
-        k = GPy.kern.linear(input_dim) + GPy.kern.bias(input_dim) + GPy.kern.white(input_dim, 0.00001)
+        k = GPy.kern.Linear(input_dim) + GPy.kern.Bias(input_dim) + GPy.kern.White(input_dim, 0.00001)
         m = BayesianGPLVM(Y, input_dim, kernel=k, num_inducing=num_inducing)
         m.randomize()
         self.assertTrue(m.checkgrad())

GPy/testing/gplvm_tests.py

@@ -9,10 +9,10 @@ class GPLVMTests(unittest.TestCase):
     def test_bias_kern(self):
         num_data, num_inducing, input_dim, output_dim = 10, 3, 2, 4
         X = np.random.rand(num_data, input_dim)
-        k = GPy.kern.rbf(input_dim) + GPy.kern.white(input_dim, 0.00001)
+        k = GPy.kern.RBF(input_dim) + GPy.kern.White(input_dim, 0.00001)
         K = k.K(X)
         Y = np.random.multivariate_normal(np.zeros(num_data),K,output_dim).T
-        k = GPy.kern.bias(input_dim) + GPy.kern.white(input_dim, 0.00001)
+        k = GPy.kern.Bias(input_dim) + GPy.kern.White(input_dim, 0.00001)
         m = GPy.models.GPLVM(Y, input_dim, kernel = k)
         m.randomize()
         self.assertTrue(m.checkgrad())
@@ -20,10 +20,10 @@ class GPLVMTests(unittest.TestCase):
     def test_linear_kern(self):
         num_data, num_inducing, input_dim, output_dim = 10, 3, 2, 4
         X = np.random.rand(num_data, input_dim)
-        k = GPy.kern.rbf(input_dim) + GPy.kern.white(input_dim, 0.00001)
+        k = GPy.kern.RBF(input_dim) + GPy.kern.White(input_dim, 0.00001)
         K = k.K(X)
         Y = np.random.multivariate_normal(np.zeros(num_data),K,output_dim).T
-        k = GPy.kern.linear(input_dim) + GPy.kern.white(input_dim, 0.00001)
+        k = GPy.kern.Linear(input_dim) + GPy.kern.White(input_dim, 0.00001)
         m = GPy.models.GPLVM(Y, input_dim, kernel = k)
         m.randomize()
         self.assertTrue(m.checkgrad())
@@ -31,10 +31,10 @@ class GPLVMTests(unittest.TestCase):
     def test_rbf_kern(self):
         num_data, num_inducing, input_dim, output_dim = 10, 3, 2, 4
         X = np.random.rand(num_data, input_dim)
-        k = GPy.kern.rbf(input_dim) + GPy.kern.white(input_dim, 0.00001)
+        k = GPy.kern.RBF(input_dim) + GPy.kern.White(input_dim, 0.00001)
         K = k.K(X)
         Y = np.random.multivariate_normal(np.zeros(num_data),K,output_dim).T
-        k = GPy.kern.rbf(input_dim) + GPy.kern.white(input_dim, 0.00001)
+        k = GPy.kern.RBF(input_dim) + GPy.kern.White(input_dim, 0.00001)
         m = GPy.models.GPLVM(Y, input_dim, kernel = k)
         m.randomize()
         self.assertTrue(m.checkgrad())

GPy/testing/kernel_tests.py

@@ -4,6 +4,7 @@
 import unittest
 import numpy as np
 import GPy
+import sys
 
 verbose = True
 
@@ -14,106 +15,223 @@ except ImportError:
     SYMPY_AVAILABLE=False
 
 
-class KernelTests(unittest.TestCase):
-    def test_kerneltie(self):
-        K = GPy.kern.rbf(5, ARD=True)
-        K.tie_params('.*[01]')
-        K.constrain_fixed('2')
-        X = np.random.rand(5,5)
-        Y = np.ones((5,1))
-        m = GPy.models.GPRegression(X,Y,K)
-        self.assertTrue(m.checkgrad())
+class Kern_check_model(GPy.core.Model):
+    """
+    This is a dummy model class used as a base class for checking that the
+    gradients of a given kernel are implemented correctly. It enables
+    checkgrad() to be called independently on a kernel.
+    """
+    def __init__(self, kernel=None, dL_dK=None, X=None, X2=None):
+        GPy.core.Model.__init__(self, 'kernel_test_model')
+        if kernel==None:
+            kernel = GPy.kern.RBF(1)
+        if X is None:
+            X = np.random.randn(20, kernel.input_dim)
+        if dL_dK is None:
+            if X2 is None:
+                dL_dK = np.ones((X.shape[0], X.shape[0]))
+            else:
+                dL_dK = np.ones((X.shape[0], X2.shape[0]))
 
-    def test_rbfkernel(self):
-        kern = GPy.kern.rbf(5)
-        self.assertTrue(GPy.kern.kern_test(kern, verbose=verbose))
+        self.kernel = kernel
+        self.X = GPy.core.parameterization.Param('X',X)
+        self.X2 = X2
+        self.dL_dK = dL_dK
 
-    def test_rbf_sympykernel(self):
-        if SYMPY_AVAILABLE:
-            kern = GPy.kern.rbf_sympy(5)
-            self.assertTrue(GPy.kern.kern_test(kern, verbose=verbose))
+    def is_positive_definite(self):
+        v = np.linalg.eig(self.kernel.K(self.X))[0]
+        if any(v<-10*sys.float_info.epsilon):
+            return False
+        else:
+            return True
 
-    def test_eq_sympykernel(self):
-        if SYMPY_AVAILABLE:
-            kern = GPy.kern.eq_sympy(5, 3)
-            self.assertTrue(GPy.kern.kern_test(kern, output_ind=4, verbose=verbose))
+    def log_likelihood(self):
+        return np.sum(self.dL_dK*self.kernel.K(self.X, self.X2))
 
-    def test_ode1_eqkernel(self):
-        if SYMPY_AVAILABLE:
-            kern = GPy.kern.ode1_eq(3)
-            self.assertTrue(GPy.kern.kern_test(kern, output_ind=1, verbose=verbose, X_positive=True))
+class Kern_check_dK_dtheta(Kern_check_model):
+    """
+    This class allows gradient checks for the gradient of a kernel with
+    respect to parameters.
+    """
+    def __init__(self, kernel=None, dL_dK=None, X=None, X2=None):
+        Kern_check_model.__init__(self,kernel=kernel,dL_dK=dL_dK, X=X, X2=X2)
+        self.add_parameter(self.kernel)
 
-    def test_rbf_invkernel(self):
-        kern = GPy.kern.rbf_inv(5)
-        self.assertTrue(GPy.kern.kern_test(kern, verbose=verbose))
+    def parameters_changed(self):
+        return self.kernel.update_gradients_full(self.dL_dK, self.X, self.X2)
 
-    def test_Matern32kernel(self):
-        kern = GPy.kern.Matern32(5)
-        self.assertTrue(GPy.kern.kern_test(kern, verbose=verbose))
 
-    def test_Matern52kernel(self):
-        kern = GPy.kern.Matern52(5)
-        self.assertTrue(GPy.kern.kern_test(kern, verbose=verbose))
+class Kern_check_dKdiag_dtheta(Kern_check_model):
+    """
+    This class allows gradient checks of the gradient of the diagonal of a
+    kernel with respect to the parameters.
+    """
+    def __init__(self, kernel=None, dL_dK=None, X=None):
+        Kern_check_model.__init__(self,kernel=kernel,dL_dK=dL_dK, X=X, X2=None)
+        self.add_parameter(self.kernel)
 
-    def test_linearkernel(self):
-        kern = GPy.kern.linear(5)
-        self.assertTrue(GPy.kern.kern_test(kern, verbose=verbose))
+    def parameters_changed(self):
+        self.kernel.update_gradients_diag(self.dL_dK, self.X)
 
-    def test_periodic_exponentialkernel(self):
-        kern = GPy.kern.periodic_exponential(1)
-        self.assertTrue(GPy.kern.kern_test(kern, verbose=verbose))
+    def log_likelihood(self):
+        return (np.diag(self.dL_dK)*self.kernel.Kdiag(self.X)).sum()
 
-    def test_periodic_Matern32kernel(self):
-        kern = GPy.kern.periodic_Matern32(1)
-        self.assertTrue(GPy.kern.kern_test(kern, verbose=verbose))
+    def parameters_changed(self):
+        return self.kernel.update_gradients_diag(np.diag(self.dL_dK), self.X)
 
-    def test_periodic_Matern52kernel(self):
-        kern = GPy.kern.periodic_Matern52(1)
-        self.assertTrue(GPy.kern.kern_test(kern, verbose=verbose))
+class Kern_check_dK_dX(Kern_check_model):
+    """This class allows gradient checks for the gradient of a kernel with respect to X. """
+    def __init__(self, kernel=None, dL_dK=None, X=None, X2=None):
+        Kern_check_model.__init__(self,kernel=kernel,dL_dK=dL_dK, X=X, X2=X2)
+        self.add_parameter(self.X)
 
-    def test_rational_quadratickernel(self):
-        kern = GPy.kern.rational_quadratic(1)
-        self.assertTrue(GPy.kern.kern_test(kern, verbose=verbose))
+    def parameters_changed(self):
+        self.X.gradient =  self.kernel.gradients_X(self.dL_dK, self.X, self.X2)
 
-    def test_gibbskernel(self):
-        kern = GPy.kern.gibbs(5, mapping=GPy.mappings.Linear(5, 1))
-        self.assertTrue(GPy.kern.kern_test(kern, verbose=verbose))
+class Kern_check_dKdiag_dX(Kern_check_dK_dX):
+    """This class allows gradient checks for the gradient of a kernel diagonal with respect to X. """
+    def __init__(self, kernel=None, dL_dK=None, X=None, X2=None):
+        Kern_check_dK_dX.__init__(self,kernel=kernel,dL_dK=dL_dK, X=X, X2=None)
 
-    def test_heterokernel(self):
-        kern = GPy.kern.hetero(5, mapping=GPy.mappings.Linear(5, 1), transform=GPy.core.transformations.logexp())
-        self.assertTrue(GPy.kern.kern_test(kern, verbose=verbose))
+    def log_likelihood(self):
+        return (np.diag(self.dL_dK)*self.kernel.Kdiag(self.X)).sum()
 
-    def test_mlpkernel(self):
-        kern = GPy.kern.mlp(5)
-        self.assertTrue(GPy.kern.kern_test(kern, verbose=verbose))
+    def parameters_changed(self):
+        self.X.gradient =  self.kernel.gradients_X_diag(self.dL_dK, self.X)
 
-    def test_polykernel(self):
-        kern = GPy.kern.poly(5, degree=4)
-        self.assertTrue(GPy.kern.kern_test(kern, verbose=verbose))
+def kern_test(kern, X=None, X2=None, output_ind=None, verbose=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.
 
-    def test_fixedkernel(self):
-        """
-        Fixed effect kernel test
-        """
-        X = np.random.rand(30, 4)
-        K = np.dot(X, X.T)
-        kernel = GPy.kern.fixed(4, K)
-        kern = GPy.kern.poly(5, degree=4)
-        self.assertTrue(GPy.kern.kern_test(kern, verbose=verbose))
+    :param kern: the kernel to be tested.
+    :type kern: GPy.kern.Kernpart
+    :param X: X input values to test the covariance function.
+    :type X: ndarray
+    :param X2: X2 input values to test the covariance function.
+    :type X2: ndarray
 
-    # def test_coregionalization(self):
-    #     X1 = np.random.rand(50,1)*8
-    #     X2 = np.random.rand(30,1)*5
-    #     index = np.vstack((np.zeros_like(X1),np.ones_like(X2)))
-    #     X = np.hstack((np.vstack((X1,X2)),index))
-    #     Y1 = np.sin(X1) + np.random.randn(*X1.shape)*0.05
-    #     Y2 = np.sin(X2) + np.random.randn(*X2.shape)*0.05 + 2.
-    #     Y = np.vstack((Y1,Y2))
+    """
+    pass_checks = True
+    if X==None:
+        X = np.random.randn(10, kern.input_dim)
+        if output_ind is not None:
+            X[:, output_ind] = np.random.randint(kern.output_dim, X.shape[0])
+    if X2==None:
+        X2 = np.random.randn(20, kern.input_dim)
+        if output_ind is not None:
+            X2[:, output_ind] = np.random.randint(kern.output_dim, X2.shape[0])
+
+    if verbose:
+        print("Checking covariance function is positive definite.")
+    result = Kern_check_model(kern, X=X).is_positive_definite()
+    if result and verbose:
+        print("Check passed.")
+    if not result:
+        print("Positive definite check failed for " + kern.name + " covariance function.")
+        pass_checks = False
+        return False
+
+    if verbose:
+        print("Checking gradients of K(X, X) wrt theta.")
+    result = Kern_check_dK_dtheta(kern, X=X, X2=None).checkgrad(verbose=verbose)
+    if result and verbose:
+        print("Check passed.")
+    if not result:
+        print("Gradient of K(X, X) wrt theta failed for " + kern.name + " covariance function. Gradient values as follows:")
+        Kern_check_dK_dtheta(kern, X=X, X2=None).checkgrad(verbose=True)
+        pass_checks = False
+        return False
+
+    if verbose:
+        print("Checking gradients of K(X, X2) wrt theta.")
+    result = Kern_check_dK_dtheta(kern, X=X, X2=X2).checkgrad(verbose=verbose)
+    if result and verbose:
+        print("Check passed.")
+    if not result:
+        print("Gradient of K(X, X) wrt theta failed for " + kern.name + " covariance function. Gradient values as follows:")
+        Kern_check_dK_dtheta(kern, X=X, X2=X2).checkgrad(verbose=True)
+        pass_checks = False
+        return False
+
+    if verbose:
+        print("Checking gradients of Kdiag(X) wrt theta.")
+    result = Kern_check_dKdiag_dtheta(kern, X=X).checkgrad(verbose=verbose)
+    if result and verbose:
+        print("Check passed.")
+    if not result:
+        print("Gradient of Kdiag(X) wrt theta failed for " + kern.name + " covariance function. Gradient values as follows:")
+        Kern_check_dKdiag_dtheta(kern, X=X).checkgrad(verbose=True)
+        pass_checks = False
+        return False
+
+    if verbose:
+        print("Checking gradients of K(X, X) wrt X.")
+    try:
+        result = Kern_check_dK_dX(kern, X=X, X2=None).checkgrad(verbose=verbose)
+    except NotImplementedError:
+        result=True
+        if verbose:
+            print("gradients_X not implemented for " + kern.name)
+    if result and verbose:
+        print("Check passed.")
+    if not result:
+        print("Gradient of K(X, X) wrt X failed for " + kern.name + " covariance function. Gradient values as follows:")
+        Kern_check_dK_dX(kern, X=X, X2=None).checkgrad(verbose=True)
+        pass_checks = False
+        return False
+
+    if verbose:
+        print("Checking gradients of K(X, X2) wrt X.")
+    try:
+        result = Kern_check_dK_dX(kern, X=X, X2=X2).checkgrad(verbose=verbose)
+    except NotImplementedError:
+        result=True
+        if verbose:
+            print("gradients_X not implemented for " + kern.name)
+    if result and verbose:
+        print("Check passed.")
+    if not result:
+        print("Gradient of K(X, X) wrt X failed for " + kern.name + " covariance function. Gradient values as follows:")
+        Kern_check_dK_dX(kern, X=X, X2=X2).checkgrad(verbose=True)
+        pass_checks = False
+        return False
+
+    if verbose:
+        print("Checking gradients of Kdiag(X) wrt X.")
+    try:
+        result = Kern_check_dKdiag_dX(kern, X=X).checkgrad(verbose=verbose)
+    except NotImplementedError:
+        result=True
+        if verbose:
+            print("gradients_X not implemented for " + kern.name)
+    if result and verbose:
+        print("Check passed.")
+    if not result:
+        print("Gradient of Kdiag(X) wrt X failed for " + kern.name + " covariance function. Gradient values as follows:")
+        Kern_check_dKdiag_dX(kern, X=X).checkgrad(verbose=True)
+        pass_checks = False
+        return False
+
+    return pass_checks
+
+
+class KernelTestsContinuous(unittest.TestCase):
+    def setUp(self):
+        self.X = np.random.randn(100,2)
+        self.X2 = np.random.randn(110,2)
+
+    def test_Matern32(self):
+        k = GPy.kern.Matern32(2)
+        self.assertTrue(kern_test(k, X=self.X, X2=self.X2, verbose=verbose))
+
+    def test_Matern52(self):
+        k = GPy.kern.Matern52(2)
+        self.assertTrue(kern_test(k, X=self.X, X2=self.X2, verbose=verbose))
+
+    #TODO: turn off grad checkingwrt X for indexed kernels liek coregionalize
 
-    #     k1 = GPy.kern.rbf(1) + GPy.kern.bias(1)
-    #     k2 = GPy.kern.coregionalize(2,1)
-    #     kern = k1**k2
-    #     self.assertTrue(GPy.kern.kern_test(kern, verbose=verbose))
 
 
 if __name__ == "__main__":

GPy/testing/old_tests/cgd_tests.py

@@ -5,10 +5,10 @@ Created on 26 Apr 2013
 '''
 import unittest
 import numpy
-from GPy.inference.conjugate_gradient_descent import CGD, RUNNING
+from GPy.inference.optimization.conjugate_gradient_descent import CGD, RUNNING
 import pylab
 from scipy.optimize.optimize import rosen, rosen_der
-from GPy.inference.gradient_descent_update_rules import PolakRibiere
+from GPy.inference.optimization.gradient_descent_update_rules import PolakRibiere
 
 
 class Test(unittest.TestCase):
@@ -30,7 +30,7 @@ class Test(unittest.TestCase):
                 assert numpy.allclose(res[0], 0, atol=1e-5)
                 break
             except AssertionError:
-                import ipdb;ipdb.set_trace()
+                import pdb;pdb.set_trace()
                 # RESTART
                 pass
         else:
@@ -108,4 +108,3 @@ if __name__ == "__main__":
     res[0] = opt.opt(f, df, x0.copy(), callback, messages=True, maxiter=1000,
                    report_every=7, gtol=1e-12, update_rule=PolakRibiere)
 
-    pass

GPy/util/linalg.py

@@ -62,41 +62,41 @@ def force_F_ordered(A):
     print "why are your arrays not F order?"
     return np.asfortranarray(A)
 
+# def jitchol(A, maxtries=5):
+#     A = force_F_ordered_symmetric(A)
+#     L, info = lapack.dpotrf(A, lower=1)
+#     if info == 0:
+#         return L
+#     else:
+#         if maxtries==0:
+#             raise linalg.LinAlgError, "not positive definite, even with jitter."
+#         diagA = np.diag(A)
+#         if np.any(diagA <= 0.):
+#             raise linalg.LinAlgError, "not pd: non-positive diagonal elements"
+#         jitter = diagA.mean() * 1e-6
+
+#         return jitchol(A+np.eye(A.shape[0])*jitter, maxtries-1)
+
 def jitchol(A, maxtries=5):
-    A = force_F_ordered_symmetric(A)
-    L, info = lapack.dpotrf(A, lower=1)
-    if info == 0:
-        return L
-    else:
-        if maxtries==0:
-            raise linalg.LinAlgError, "not positive definite, even with jitter."
-        diagA = np.diag(A)
-        if np.any(diagA <= 0.):
-            raise linalg.LinAlgError, "not pd: non-positive diagonal elements"
-        jitter = diagA.mean() * 1e-6
+   A = np.ascontiguousarray(A)
+   L, info = lapack.dpotrf(A, lower=1)
+   if info == 0:
+       return L
+   else:
+       diagA = np.diag(A)
+       if np.any(diagA <= 0.):
+           raise linalg.LinAlgError, "not pd: non-positive diagonal elements"
+       jitter = diagA.mean() * 1e-6
+       while maxtries > 0 and np.isfinite(jitter):
+           print 'Warning: adding jitter of {:.10e}'.format(jitter)
+           try:
+               return linalg.cholesky(A + np.eye(A.shape[0]).T * jitter, lower=True)
+           except:
+               jitter *= 10
+           finally:
+               maxtries -= 1
+       raise linalg.LinAlgError, "not positive definite, even with jitter."
 
-        return jitchol(A+np.eye(A.shape[0])*jitter, maxtries-1)
-
-#def jitchol(A, maxtries=5):
-#    A = np.ascontiguousarray(A)
-#    L, info = lapack.dpotrf(A, lower=1)
-#    if info == 0:
-#        return L
-#    else:
-#        diagA = np.diag(A)
-#        if np.any(diagA <= 0.):
-#            raise linalg.LinAlgError, "not pd: non-positive diagonal elements"
-#        jitter = diagA.mean() * 1e-6
-#        while maxtries > 0 and np.isfinite(jitter):
-#            print 'Warning: adding jitter of {:.10e}'.format(jitter)
-#            try:
-#                return linalg.cholesky(A + np.eye(A.shape[0]).T * jitter, lower=True)
-#            except:
-#                jitter *= 10
-#            finally:
-#                maxtries -= 1
-#        raise linalg.LinAlgError, "not positive definite, even with jitter."
-#