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

AUTHORS.txt

@@ -1,8 +1,3 @@
-James Hensman
+# This list was out of date, I've put "see contributors" for the moment. Not sure how to fix long term but don't want to be 
-Nicolo Fusi
+# embarrassed by some off the great contributors of late being omitted from a list.
-Ricardo Andrade
+See contributors.
-Nicolas Durrande
-Alan Saul
-Max Zwiessele
-Neil D. Lawrence
-Zhenwen Dai

GPy/__init__.py

@@ -21,6 +21,8 @@ from . import plotting
 from .core import Model
 from .core.parameterization import Param, Parameterized, ObsAr
 
+from .__version__ import __version__
+
 #@nottest
 try:
     #Get rid of nose dependency by only ignoring if you have nose installed

GPy/__version__.py

@@ -0,0 +1 @@
+__version__ = "0.8.3"

GPy/core/gp.py

@@ -535,7 +535,7 @@ class GP(Model):
         which_data_ycols='all', fixed_inputs=[],
         levels=20, samples=0, fignum=None, ax=None, resolution=None,
         plot_raw=False, linecol=None,fillcol=None, Y_metadata=None,
-        data_symbol='kx', predict_kw=None, plot_training_data=True):
+        data_symbol='kx', predict_kw=None, plot_training_data=True, samples_y=0, apply_link=False):
         """
         Plot the posterior of the GP.
           - In one dimension, the function is plotted with a shaded region identifying two standard deviations.
@@ -558,7 +558,7 @@ class GP(Model):
         :param levels: number of levels to plot in a contour plot.
         :param levels: for 2D plotting, the number of contour levels to use is ax is None, create a new figure
         :type levels: int
-        :param samples: the number of a posteriori samples to plot
+        :param samples: the number of a posteriori samples to plot, p(f*|y)
         :type samples: int
         :param fignum: figure to plot on.
         :type fignum: figure number
@@ -574,6 +574,10 @@ class GP(Model):
         :type data_symbol: color either as Tango.colorsHex object or character ('r' is red, 'g' is green) alongside marker type, as is standard in matplotlib.
         :param plot_training_data: whether or not to plot the training points
         :type plot_training_data: boolean
+        :param samples_y: the number of a posteriori samples to plot, p(y*|y)
+        :type samples_y: int
+        :param apply_link: if there is a link function of the likelihood, plot the link(f*) rather than f*, when plotting posterior samples f
+        :type apply_link: boolean
         """
         assert "matplotlib" in sys.modules, "matplotlib package has not been imported."
         from ..plotting.matplot_dep import models_plots
@@ -587,7 +591,7 @@ class GP(Model):
                                      levels, samples, fignum, ax, resolution,
                                      plot_raw=plot_raw, Y_metadata=Y_metadata,
                                      data_symbol=data_symbol, predict_kw=predict_kw,
-                                     plot_training_data=plot_training_data, **kw)
+                                     plot_training_data=plot_training_data, samples_y=samples_y, apply_link=apply_link, **kw)
 
 
     def plot_data(self, which_data_rows='all',
@@ -613,7 +617,7 @@ class GP(Model):
         :param levels: number of levels to plot in a contour plot.
         :param levels: for 2D plotting, the number of contour levels to use is ax is None, create a new figure
         :type levels: int
-        :param samples: the number of a posteriori samples to plot
+        :param samples: the number of a posteriori samples to plot, p(f*|y)
         :type samples: int
         :param fignum: figure to plot on.
         :type fignum: figure number

GPy/core/parameterization/priors.py

@@ -366,6 +366,7 @@ class InverseGamma(Gamma):
     def rvs(self, n):
         return 1. / np.random.gamma(scale=1. / self.b, shape=self.a, size=n)
 
+
 class DGPLVM_KFDA(Prior):
     """
     Implementation of the Discriminative Gaussian Process Latent Variable function using
@@ -512,6 +513,7 @@ class DGPLVM_KFDA(Prior):
         self.A = self.compute_A(lst_ni)
         self.x_shape = x_shape
 
+
 class DGPLVM(Prior):
     """
     Implementation of the Discriminative Gaussian Process Latent Variable model paper, by Raquel.
@@ -669,7 +671,7 @@ class DGPLVM(Prior):
         M_i = self.compute_Mi(cls)
         Sb = self.compute_Sb(cls, M_i, M_0)
         Sw = self.compute_Sw(cls, M_i)
-        # Sb_inv_N = np.linalg.inv(Sb + np.eye(Sb.shape[0]) * (np.diag(Sb).min() * 0.1))
+        # sb_N = np.linalg.inv(Sb + np.eye(Sb.shape[0]) * (np.diag(Sb).min() * 0.1))
         #Sb_inv_N = np.linalg.inv(Sb+np.eye(Sb.shape[0])*0.1)
         #Sb_inv_N = pdinv(Sb+ np.eye(Sb.shape[0]) * (np.diag(Sb).min() * 0.1))[0]
         Sb_inv_N = pdinv(Sb + np.eye(Sb.shape[0])*0.1)[0]
@@ -1198,6 +1200,7 @@ class DGPLVM_T(Prior):
 
 
 
+
 class HalfT(Prior):
     """
     Implementation of the half student t probability function, coupled with random variables.
@@ -1208,15 +1211,17 @@ class HalfT(Prior):
     """
     domain = _POSITIVE
     _instances = []
-    def __new__(cls, A, nu): # Singleton:
+
+    def __new__(cls, A, nu):  # Singleton:
         if cls._instances:
             cls._instances[:] = [instance for instance in cls._instances if instance()]
             for instance in cls._instances:
                 if instance().A == A and instance().nu == nu:
-                   return instance()
+                    return instance()
         o = super(Prior, cls).__new__(cls, A, nu)
         cls._instances.append(weakref.ref(o))
         return cls._instances[-1]()
+
     def __init__(self, A, nu):
         self.A = float(A)
         self.nu = float(nu)
@@ -1225,37 +1230,81 @@ class HalfT(Prior):
     def __str__(self):
         return "hT({:.2g}, {:.2g})".format(self.A, self.nu)
 
-    def lnpdf(self,theta):
+    def lnpdf(self, theta):
-        return (theta>0) * ( self.constant -.5*(self.nu+1) * np.log( 1.+ (1./self.nu) * (theta/self.A)**2 ) )
+        return (theta > 0) * (self.constant - .5*(self.nu + 1) * np.log(1. + (1./self.nu) * (theta/self.A)**2))
 
-        #theta = theta if isinstance(theta,np.ndarray) else np.array([theta])
+        # theta = theta if isinstance(theta,np.ndarray) else np.array([theta])
-        #lnpdfs = np.zeros_like(theta)
+        # lnpdfs = np.zeros_like(theta)
-        #theta = np.array([theta])
+        # theta = np.array([theta])
-        #above_zero = theta.flatten()>1e-6
+        # above_zero = theta.flatten()>1e-6
-        #v = self.nu
+        # v = self.nu
-        #sigma2=self.A
+        # sigma2=self.A
-        #stop
+        # stop
-        #lnpdfs[above_zero] = (+ gammaln((v + 1) * 0.5)
+        # lnpdfs[above_zero] = (+ gammaln((v + 1) * 0.5)
-        #    - gammaln(v * 0.5)
+        #     - gammaln(v * 0.5)
-        #    - 0.5*np.log(sigma2 * v * np.pi)
+        #     - 0.5*np.log(sigma2 * v * np.pi)
-        #    - 0.5*(v + 1)*np.log(1 + (1/np.float(v))*((theta[above_zero][0]**2)/sigma2))
+        #     - 0.5*(v + 1)*np.log(1 + (1/np.float(v))*((theta[above_zero][0]**2)/sigma2))
-        #)
+        # )
-        #return lnpdfs
+        # return lnpdfs
 
-    def lnpdf_grad(self,theta):
+    def lnpdf_grad(self, theta):
-        theta = theta if isinstance(theta,np.ndarray) else np.array([theta])
+        theta = theta if isinstance(theta, np.ndarray) else np.array([theta])
         grad = np.zeros_like(theta)
-        above_zero = theta>1e-6
+        above_zero = theta > 1e-6
         v = self.nu
-        sigma2=self.A
+        sigma2 = self.A
         grad[above_zero] = -0.5*(v+1)*(2*theta[above_zero])/(v*sigma2 + theta[above_zero][0]**2)
         return grad
 
     def rvs(self, n):
-         #return np.random.randn(n) * self.sigma + self.mu
+        # return np.random.randn(n) * self.sigma + self.mu
-         from scipy.stats import t
+        from scipy.stats import t
-         #[np.abs(x) for x in t.rvs(df=4,loc=0,scale=50, size=10000)])
+        # [np.abs(x) for x in t.rvs(df=4,loc=0,scale=50, size=10000)])
-         ret = t.rvs(self.nu,loc=0,scale=self.A, size=n)
+        ret = t.rvs(self.nu, loc=0, scale=self.A, size=n)
-         ret[ret<0] = 0
+        ret[ret < 0] = 0
-         return ret
+        return ret
 
+
+class Exponential(Prior):
+    """
+    Implementation of the Exponential probability function,
+    coupled with random variables.
+
+    :param l: shape parameter
+
+    """
+    domain = _POSITIVE
+    _instances = []
+
+    def __new__(cls, l):  # Singleton:
+        if cls._instances:
+            cls._instances[:] = [instance for instance in cls._instances if instance()]
+            for instance in cls._instances:
+                if instance().l == l:
+                    return instance()
+        o = super(Exponential, cls).__new__(cls, l)
+        cls._instances.append(weakref.ref(o))
+        return cls._instances[-1]()
+
+    def __init__(self, l):
+        self.l = l
+
+    def __str__(self):
+        return "Exp({:.2g})".format(self.l)
+
+    def summary(self):
+        ret = {"E[x]": 1. / self.l,
+               "E[ln x]": np.nan,
+               "var[x]": 1. / self.l**2,
+               "Entropy": 1. - np.log(self.l),
+               "Mode": 0.}
+        return ret
+
+    def lnpdf(self, x):
+        return np.log(self.l) - self.l * x
+
+    def lnpdf_grad(self, x):
+        return - self.l
+
+    def rvs(self, n):
+        return np.random.exponential(scale=self.l, size=n)

GPy/core/parameterization/transformations.py

@@ -62,7 +62,7 @@ class Transformation(object):
         import matplotlib.pyplot as plt
         from ...plotting.matplot_dep import base_plots
         x = np.linspace(-8,8)
-        base_plots.meanplot(x, self.f(x),axes=axes*args,**kw)
+        base_plots.meanplot(x, self.f(x), *args, ax=axes, **kw)
         axes = plt.gca()
         axes.set_xlabel(xlabel)
         axes.set_ylabel(ylabel)
@@ -488,7 +488,7 @@ class Logistic(Transformation):
                     return instance()
         newfunc = super(Transformation, cls).__new__
         if newfunc is object.__new__:
-            o = newfunc(cls)
+            o = newfunc(cls)  
         else:
             o = newfunc(cls, lower, upper, *args, **kwargs)
         cls._instances.append(weakref.ref(o))

GPy/inference/mcmc/__init__.py

@@ -1 +1,2 @@
 from .hmc import HMC
+from .samplers import *

GPy/inference/mcmc/samplers.py

@@ -18,11 +18,11 @@ class Metropolis_Hastings:
     def __init__(self,model,cov=None):
         """Metropolis Hastings, with tunings according to Gelman et al. """
         self.model = model
-        current = self.model._get_params_transformed()
+        current = self.model.optimizer_array
         self.D = current.size
         self.chains = []
         if cov is None:
-            self.cov = model.Laplace_covariance()
+            self.cov = np.eye(self.D)
         else:
             self.cov = cov
         self.scale = 2.4/np.sqrt(self.D)
@@ -33,20 +33,20 @@ class Metropolis_Hastings:
         if start is None:
             self.model.randomize()
         else:
-            self.model._set_params_transformed(start)
+            self.model.optimizer_array = start
 
-
+    def sample(self, Ntotal=10000, Nburn=1000, Nthin=10, tune=True, tune_throughout=False, tune_interval=400):
-
+        current = self.model.optimizer_array
-    def sample(self, Ntotal, Nburn, Nthin, tune=True, tune_throughout=False, tune_interval=400):
+        fcurrent = self.model.log_likelihood() + self.model.log_prior() + \
-        current = self.model._get_params_transformed()
+                   self.model._log_det_jacobian()
-        fcurrent = self.model.log_likelihood() + self.model.log_prior()
         accepted = np.zeros(Ntotal,dtype=np.bool)
         for it in range(Ntotal):
-            print("sample %d of %d\r"%(it,Ntotal), end=' ')
+            print("sample %d of %d\r"%(it,Ntotal),end="\t")
             sys.stdout.flush()
             prop = np.random.multivariate_normal(current, self.cov*self.scale*self.scale)
-            self.model._set_params_transformed(prop)
+            self.model.optimizer_array = prop
-            fprop = self.model.log_likelihood() + self.model.log_prior()
+            fprop = self.model.log_likelihood() + self.model.log_prior() + \
+                    self.model._log_det_jacobian()
 
             if fprop>fcurrent:#sample accepted, going 'uphill'
                 accepted[it] = True
@@ -74,10 +74,11 @@ class Metropolis_Hastings:
 
     def predict(self,function,args):
         """Make a prediction for the function, to which we will pass the additional arguments"""
-        param = self.model._get_params()
+        param = self.model.param_array
         fs = []
         for p in self.chain:
-            self.model._set_params(p)
+            self.model.param_array = p
             fs.append(function(*args))
-        self.model._set_params(param)# reset model to starting state
+        # reset model to starting state
+        self.model.param_array = param
         return fs

GPy/kern/_src/kern.py

@@ -256,8 +256,6 @@ class Kern(Parameterized):
 
         :param other: the other kernel to be added
         :type other: GPy.kern
-        :param tensor: whether or not to use the tensor space (default is false).
-        :type tensor: bool
 
         """
         assert isinstance(other, Kern), "only kernels can be multiplied to kernels..."

GPy/kern/_src/prod.py

@@ -27,8 +27,6 @@ class Prod(CombinationKernel):
 
     :param k1, k2: the kernels to multiply
     :type k1, k2: Kern
-    :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
 
     """

GPy/likelihoods/exponential.py

@@ -124,7 +124,7 @@ class Exponential(Likelihood):
         #d3lik_dlink3 = 6*y/(link_f**4) - 2./(link_f**3)
         return d3lik_dlink3
 
-    def samples(self, gp):
+    def samples(self, gp, Y_metadata=None):
         """
         Returns a set of samples of observations based on a given value of the latent variable.
 

GPy/likelihoods/likelihood.py

@@ -622,7 +622,7 @@ class Likelihood(Parameterized):
         Nf_samp = 300
         Ny_samp = 1
         s = np.random.randn(mu.shape[0], Nf_samp)*np.sqrt(var) + mu
-        ss_y = self.samples(s, Y_metadata, samples=Ny_samp)
+        ss_y = self.samples(s, Y_metadata)#, samples=Ny_samp)
         #ss_y = ss_y.reshape(mu.shape[0], mu.shape[1], Nf_samp*Ny_samp)
 
         pred_quantiles = [np.percentile(ss_y, q, axis=1)[:,None] for q in quantiles]

GPy/likelihoods/poisson.py

@@ -137,7 +137,7 @@ class Poisson(Likelihood):
         """
         return self.gp_link.transf(gp)
 
-    def samples(self, gp, Y_metadata=None, samples=1):
+    def samples(self, gp, Y_metadata=None):
         """
         Returns a set of samples of observations based on a given value of the latent variable.
 
@@ -145,5 +145,7 @@ class Poisson(Likelihood):
         """
         orig_shape = gp.shape
         gp = gp.flatten()
-        Ysim = np.random.poisson(self.gp_link.transf(gp), [samples, gp.size]).T
+        # Ysim = np.random.poisson(self.gp_link.transf(gp), [samples, gp.size]).T
-        return Ysim.reshape(orig_shape+(samples,))
+        # return Ysim.reshape(orig_shape+(samples,))
+        Ysim = np.random.poisson(self.gp_link.transf(gp))
+        return Ysim.reshape(orig_shape)

GPy/plotting/matplot_dep/models_plots.py

@@ -75,7 +75,7 @@ def plot_fit(model, plot_limits=None, which_data_rows='all',
         levels=20, samples=0, fignum=None, ax=None, resolution=None,
         plot_raw=False,
         linecol=Tango.colorsHex['darkBlue'],fillcol=Tango.colorsHex['lightBlue'], Y_metadata=None, data_symbol='kx',
-        apply_link=False, samples_f=0, plot_uncertain_inputs=True, predict_kw=None, plot_training_data=True):
+        apply_link=False, samples_y=0, plot_uncertain_inputs=True, predict_kw=None, plot_training_data=True):
     """
     Plot the posterior of the GP.
       - In one dimension, the function is plotted with a shaded region identifying two standard deviations.
@@ -93,24 +93,30 @@ def plot_fit(model, plot_limits=None, which_data_rows='all',
     :type which_data_rows: 'all' or a list of integers
     :param fixed_inputs: a list of tuple [(i,v), (i,v)...], specifying that input index i should be set to value v.
     :type fixed_inputs: a list of tuples
-    :param resolution: the number of intervals to sample the GP on. Defaults to 200 in 1D and 50 (a 50x50 grid) in 2D
+    :param levels: for 2D plotting, the number of contour levels to use is ax is None, create a new figure
-    :type resolution: int
-    :param levels: number of levels to plot in a contour plot.
     :type levels: int
-    :param samples: the number of a posteriori samples to plot p(y*|y)
+    :param samples: the number of a posteriori samples to plot p(f*|y)
     :type samples: int
     :param fignum: figure to plot on.
     :type fignum: figure number
     :param ax: axes to plot on.
     :type ax: axes handle
+    :param resolution: the number of intervals to sample the GP on. Defaults to 200 in 1D and 50 (a 50x50 grid) in 2D
+    :type resolution: int
+    :param plot_raw: Whether to plot the raw function p(f|y)
+    :type plot_raw: boolean
     :param linecol: color of line to plot.
-    :type linecol:
+    :type linecol: hex or color
     :param fillcol: color of fill
-    :param levels: for 2D plotting, the number of contour levels to use is ax is None, create a new figure
+    :type fillcol: hex or color
-    :param apply_link: apply the link function if plotting f (default false)
+    :param apply_link: apply the link function if plotting f (default false), as well as posterior samples if requested
     :type apply_link: boolean
-    :param samples_f: the number of posteriori f samples to plot p(f*|y)
+    :param samples_y: the number of posteriori f samples to plot p(y*|y)
-    :type samples_f: int
+    :type samples_y: int
+    :param plot_uncertain_inputs: plot the uncertainty of the inputs as error bars if they have uncertainty (BGPLVM etc.)
+    :type plot_uncertain_inputs: boolean
+    :param predict_kw: keyword args for _raw_predict and predict functions if required
+    :type predict_kw: dict
     :param plot_training_data: whether or not to plot the training points
     :type plot_training_data: boolean
     """
@@ -185,17 +191,17 @@ def plot_fit(model, plot_limits=None, which_data_rows='all',
 
         #optionally plot some samples
         if samples: #NOTE not tested with fixed_inputs
-            Ysim = model.posterior_samples(Xgrid, samples, Y_metadata=Y_metadata)
+            Fsim = model.posterior_samples_f(Xgrid, samples)
-            print(Ysim.shape)
+            if apply_link:
-            print(Xnew.shape)
+                Fsim = model.likelihood.gp_link.transf(Fsim)
-            for yi in Ysim.T:
-                plots['posterior_samples'] = ax.plot(Xnew, yi[:,None], '#3300FF', linewidth=0.25)
-                #ax.plot(Xnew, yi[:,None], marker='x', linestyle='--',color=Tango.colorsHex['darkBlue']) #TODO apply this line for discrete outputs.
-
-        if samples_f: #NOTE not tested with fixed_inputs
-            Fsim = model.posterior_samples_f(Xgrid, samples_f)
             for fi in Fsim.T:
-                plots['posterior_samples_f'] = ax.plot(Xnew, fi[:,None], Tango.colorsHex['darkBlue'], linewidth=0.25)
+                plots['posterior_samples'] = ax.plot(Xnew, fi[:,None], '#3300FF', linewidth=0.25)
+                #ax.plot(Xnew, fi[:,None], marker='x', linestyle='--',color=Tango.colorsHex['darkBlue']) #TODO apply this line for discrete outputs.
+
+        if samples_y: #NOTE not tested with fixed_inputs
+            Ysim = model.posterior_samples(Xgrid, samples_y, Y_metadata=Y_metadata)
+            for yi in Ysim.T:
+                plots['posterior_samples_y'] = ax.scatter(Xnew, yi[:,None], s=5, c=Tango.colorsHex['darkBlue'], marker='o', alpha=0.5)
                 #ax.plot(Xnew, yi[:,None], marker='x', linestyle='--',color=Tango.colorsHex['darkBlue']) #TODO apply this line for discrete outputs.
 
 

GPy/testing/linalg_test.py

@@ -1,7 +1,6 @@
 import numpy as np
 import scipy as sp
-from GPy.util.linalg import jitchol
+from ..util.linalg import jitchol,trace_dot, ijk_jlk_to_il, ijk_ljk_to_ilk
-import GPy
 
 class LinalgTests(np.testing.TestCase):
     def setUp(self):
@@ -37,18 +36,19 @@ class LinalgTests(np.testing.TestCase):
         except sp.linalg.LinAlgError:
             return True
 
-    def test_einsum_ijk_jlk_to_il(self):
+    def test_trace_dot(self):
-        A = np.random.randn(50, 150, 5)
+        N = 5
-        B = np.random.randn(150, 100, 5)
+        A = np.random.rand(N,N)
-        pure = np.einsum('ijk,jlk->il', A, B)
+        B = np.random.rand(N,N)
-        quick = GPy.util.linalg.ijk_jlk_to_il(A, B)
+        trace = np.trace(A.dot(B))
-        np.testing.assert_allclose(pure, quick)
+        test_trace = trace_dot(A,B)
+        np.testing.assert_allclose(trace,test_trace,atol=1e-13)
 
     def test_einsum_ij_jlk_to_ilk(self):
         A = np.random.randn(15, 150, 5)
         B = np.random.randn(150, 50, 5)
         pure = np.einsum('ijk,jlk->il', A, B)
-        quick = GPy.util.linalg.ijk_jlk_to_il(A,B)
+        quick = ijk_jlk_to_il(A,B)
         np.testing.assert_allclose(pure, quick)
 
     def test_einsum_ijk_ljk_to_ilk(self):
@@ -56,5 +56,5 @@ class LinalgTests(np.testing.TestCase):
         B = np.random.randn(150, 20, 5)
         #B = A.copy()
         pure = np.einsum('ijk,ljk->ilk', A, B)
-        quick = GPy.util.linalg.ijk_ljk_to_ilk(A,B)
+        quick = ijk_ljk_to_ilk(A,B)
         np.testing.assert_allclose(pure, quick)

GPy/testing/rv_transformation_tests.py

@@ -0,0 +1,101 @@
+# Written by Ilias Bilionis
+"""
+Test if hyperparameters in models are properly transformed.
+"""
+
+
+import unittest
+import numpy as np
+import scipy.stats as st
+import GPy
+
+
+class TestModel(GPy.core.Model):
+    """
+    A simple GPy model with one parameter.
+    """
+    def __init__(self):
+        GPy.core.Model.__init__(self, 'test_model')
+        theta = GPy.core.Param('theta', 1.)
+        self.link_parameter(theta)
+
+    def log_likelihood(self):
+        return 0.
+
+
+class RVTransformationTestCase(unittest.TestCase):
+
+    def _test_trans(self, trans):
+        m = TestModel()
+        prior = GPy.priors.LogGaussian(.5, 0.1)
+        m.theta.set_prior(prior)
+        m.theta.unconstrain()
+        m.theta.constrain(trans)
+        # The PDF of the transformed variables
+        p_phi = lambda phi : np.exp(-m._objective_grads(phi)[0])
+        # To the empirical PDF of:
+        theta_s = prior.rvs(100000)
+        phi_s = trans.finv(theta_s)
+        # which is essentially a kernel density estimation
+        kde = st.gaussian_kde(phi_s)
+        # We will compare the PDF here:
+        phi = np.linspace(phi_s.min(), phi_s.max(), 100)
+        # The transformed PDF of phi should be this:
+        pdf_phi = np.array([p_phi(p) for p in phi])
+        # UNCOMMENT TO SEE GRAPHICAL COMPARISON
+        #import matplotlib.pyplot as plt
+        #fig, ax = plt.subplots()
+        #ax.hist(phi_s, normed=True, bins=100, alpha=0.25, label='Histogram')
+        #ax.plot(phi, kde(phi), '--', linewidth=2, label='Kernel Density Estimation')
+        #ax.plot(phi, pdf_phi, ':', linewidth=2, label='Transformed PDF')
+        #ax.set_xlabel(r'transformed $\theta$', fontsize=16)
+        #ax.set_ylabel('PDF', fontsize=16)
+        #plt.legend(loc='best')
+        #plt.show(block=True)
+        # END OF PLOT
+        # The following test cannot be very accurate
+        self.assertTrue(np.linalg.norm(pdf_phi - kde(phi)) / np.linalg.norm(kde(phi)) <= 1e-1)
+        # Check the gradients at a few random points
+        for i in range(10):
+            m.theta = theta_s[i]
+            self.assertTrue(m.checkgrad(verbose=True))
+
+    def test_Logexp(self):
+        self._test_trans(GPy.constraints.Logexp())
+        self._test_trans(GPy.constraints.Exponent())
+
+
+if __name__ == '__main__':
+    unittest.main()
+    quit()
+    m = TestModel()
+    prior = GPy.priors.LogGaussian(0., .9)
+    m.theta.set_prior(prior)
+
+    # The following should return the PDF in terms of the transformed quantities
+    p_phi = lambda phi : np.exp(-m._objective_grads(phi)[0])
+
+    # Let's look at the transformation phi = log(exp(theta - 1))
+    trans = GPy.constraints.Exponent()
+    m.theta.constrain(trans)
+    # Plot the transformed probability density
+    phi = np.linspace(-8, 8, 100)
+    fig, ax = plt.subplots()
+    # Let's draw some samples of theta and transform them so that we see
+    # which one is right
+    theta_s = prior.rvs(10000)
+    # Transform it to the new variables
+    phi_s = trans.finv(theta_s)
+    # And draw their histogram
+    ax.hist(phi_s, normed=True, bins=100, alpha=0.25, label='Empirical')
+    # This is to be compared to the PDF of the model expressed in terms of these new
+    # variables
+    ax.plot(phi, [p_phi(p) for p in phi], label='Transformed PDF', linewidth=2)
+    ax.set_xlim(-3, 10)
+    ax.set_xlabel(r'transformed $\theta$', fontsize=16)
+    ax.set_ylabel('PDF', fontsize=16)
+    plt.legend(loc='best')
+    # Now let's test the gradients
+    m.checkgrad(verbose=True)
+    # And show the plot
+    plt.show(block=True)

GPy/util/linalg.py

@@ -157,7 +157,7 @@ def trace_dot(a, b):
     """
     Efficiently compute the trace of the matrix product of a and b
     """
-    return np.sum(a * b)
+    return np.einsum('ij,ji->', a, b)
 
 def mdot(*args):
     """

README.md

@@ -1,5 +1,6 @@
 # GPy
 
+
 A Gaussian processes framework in Python.
 
 * [GPy homepage](http://sheffieldml.github.io/GPy/)
@@ -10,23 +11,31 @@ A Gaussian processes framework in Python.
 
 Continuous integration status: ![CI status](https://travis-ci.org/SheffieldML/GPy.png)
 
+### Citation
+
+    @Misc{gpy2014,
+      author =   {{The GPy authors}},
+      title =    {{GPy}: A Gaussian process framework in python},
+      howpublished = {\url{http://github.com/SheffieldML/GPy}},
+      year = {2012--2015}
+    }
+
+### Pronounciation
+
+We like to pronounce it 'Gee-pie'.
+
 ### Getting started: installing with pip
 
-We are now requiring the newest version of ![scipy](http://www.scipy.org/) and thus, we strongly recommend using 
+We are now requiring the newest version (0.16) of 
-the  ![anaconda python distribution](http://continuum.io/downloads).
+[scipy](http://www.scipy.org/) and thus, we strongly recommend using 
+the  [anaconda python distribution](http://continuum.io/downloads).
 With anaconda you can install GPy by the following:
 
     conda update scipy
     pip install gpy
     
-. We've also had luck with ![enthought](http://www.enthought.com), although enthought currently (as of 8th Sep. 2015) does not support scipy 0.16.
+We've also had luck with [enthought](http://www.enthought.com), 
-
+although enthought currently (as of 8th Sep. 2015) does not support scipy 0.16.
-On a fresh install of windows 8.1, we downloaded the Anaconda python distribution, started the anaconda command prompt and typed 
-
-    conda update scipy
-    pip install GPy
-
-Everything seems to work: from here you can type `ipython` and then `import GPy; GPy.tests()`. Working as of 21/11/14
 
 If you'd like to install from source, or want to contribute to the project (e.g. by sending pull requests via github), read on.
 
@@ -55,21 +64,13 @@ The command `python3 setup.py build_ext --inplace` builds the Cython extensions.
 * All weave functions not covered by the test suite are *simply commented out*. Can add equivalents later as test functions become available
 * A set of benchmarks would be useful! 
 
-### Citation
 
-    @Misc{gpy2014,
-      author =   {The GPy authors},
-      title =    {{GPy}: A Gaussian process framework in python},
-      howpublished = {\url{http://github.com/SheffieldML/GPy}},
-      year = {2012--2014}
-    }
-
-### Pronounciation
-
-We like to pronounce it 'Gee-pie'.
 
 ### Ubuntu hackers
 
+:Note: Right now the Ubuntu package index does not include scipy 0.16.0, and thus, cannot
+be used for GPy. We hope this gets fixed soon.
+
 For the most part, the developers are using ubuntu. To install the required packages:
 
     sudo apt-get install python-numpy python-scipy python-matplotlib
@@ -83,10 +84,29 @@ clone this git repository and add it to your path:
  
 ### OSX
 
-Everything appears to work out-of-the box using ![enthought](http://www.enthought.com) on osx Mavericks. Download/clone GPy, and then add GPy to your PYTHONPATH
 
-    git clone git@github.com:SheffieldML/GPy.git ~/SheffieldML
+We were working hard to make pre-built distributions ready. 
-    echo 'PYTHONPATH=$PYTHONPATH:~/SheffieldML' >> ~/.profile
+You can now install GPy via pip on MacOSX using 
+[anaconda python distribution](http://continuum.io/downloads):
+
+    conda update scipy
+    pip install gpy
+
+If this does not work, then you need to build GPy yourself, 
+using the [development toolkits](https://developer.apple.com/xcode/). 
+Download/clone GPy and run the build process:
+
+    conda update scipy
+    git clone git@github.com:SheffieldML/GPy.git ~/GPy
+    cd ~/GPy
+    python setup.py install
+
+If you do not wish to build the C extensions (10 times speedup),
+you can run the pure python installations, by just adding GPy
+to your python path.
+
+   echo 'PYTHONPATH=$PYTHONPATH:~/SheffieldML' >> ~/.profile
+
 
 
 ### Compiling documentation:
@@ -137,7 +157,9 @@ Run nosetests from the root directory of the repository:
 or from within IPython
 
     import GPy; GPy.tests()
-		    
+
+
+
 ## Funding Acknowledgements
 
 
@@ -145,15 +167,12 @@ Current support for the GPy software is coming through the following projects.
 
 * [EU FP7-PEOPLE Project Ref 316861](http://staffwww.dcs.shef.ac.uk/people/N.Lawrence/projects/mlpm/) "MLPM2012: Machine Learning for Personalized Medicine"
 
-* [BBSRC Project No BB/K011197/1](http://staffwww.dcs.shef.ac.uk/people/N.Lawrence/projects/recombinant/) "Linking recombinant gene sequence to protein product manufacturability using CHO cell genomic resources"
-
 * MRC Special Training Fellowship "Bayesian models of expression in the transcriptome for clinical RNA-seq"
 
-* [EU FP7-KBBE Project Ref 289434](http://staffwww.dcs.shef.ac.uk/people/N.Lawrence/projects/biopredyn/) "From Data to Models: New Bioinformatics Methods and Tools for Data-Driven Predictive Dynamic Modelling in Biotechnological Applications"
-
 *  [EU FP7-ICT Project Ref 612139](http://staffwww.dcs.shef.ac.uk/people/N.Lawrence/projects/wysiwyd/) "WYSIWYD: What You Say is What You Did"
 
 Previous support for the GPy software came from the following projects:
-
+* [BBSRC Project No BB/K011197/1](http://staffwww.dcs.shef.ac.uk/people/N.Lawrence/projects/recombinant/) "Linking recombinant gene sequence to protein product manufacturability using CHO cell genomic resources"
+* [EU FP7-KBBE Project Ref 289434](http://staffwww.dcs.shef.ac.uk/people/N.Lawrence/projects/biopredyn/) "From Data to Models: New Bioinformatics Methods and Tools for Data-Driven Predictive Dynamic Modelling in Biotechnological Applications"
 * [BBSRC Project No BB/H018123/2](http://staffwww.dcs.shef.ac.uk/people/N.Lawrence/projects/iterative/) "An iterative pipeline of computational modelling and experimental design for uncovering gene regulatory networks in vertebrates"
 * [Erasysbio](http://staffwww.dcs.shef.ac.uk/people/N.Lawrence/projects/synergy/) "SYNERGY: Systems approach to gene regulation biology through nuclear receptors"

doc/log.txt

@@ -1,222 +0,0 @@
-/home/maxz/Documents/gpy/GPy/__init__.py:docstring of GPy.load:1: WARNING: Inline interpreted text or phrase reference start-string without end-string.
-/home/maxz/Documents/gpy/GPy/core/gp.py:docstring of GPy.core.gp.GP.optimize:8: ERROR: Unknown interpreted text role "module".
-/home/maxz/Documents/gpy/GPy/core/gp.py:docstring of GPy.core.gp.GP.predict_wishard_embedding:6: WARNING: Field list ends without a blank line; unexpected unindent.
-/home/maxz/Documents/gpy/GPy/core/gp.py:docstring of GPy.core.gp.GP.predictive_gradients:5: ERROR: Unexpected indentation.
-/home/maxz/Documents/gpy/GPy/core/gp.py:docstring of GPy.core.gp.GP.predictive_gradients:10: WARNING: Block quote ends without a blank line; unexpected unindent.
-/home/maxz/Documents/gpy/GPy/core/model.py:docstring of GPy.core.model.Model.optimize_restarts:29: WARNING: Explicit markup ends without a blank line; unexpected unindent.
-/home/maxz/Documents/gpy/doc/GPy.core.rst:57: WARNING: autodoc: failed to import module u'GPy.core.svigp'; the following exception was raised:
-Traceback (most recent call last):
-  File "/home/maxz/anaconda/lib/python2.7/site-packages/Sphinx-1.3.1-py2.7.egg/sphinx/ext/autodoc.py", line 385, in import_object
-    __import__(self.modname)
-ImportError: No module named svigp
-/home/maxz/Documents/gpy/doc/GPy.core.rst:65: WARNING: autodoc: failed to import module u'GPy.core.symbolic'; the following exception was raised:
-Traceback (most recent call last):
-  File "/home/maxz/anaconda/lib/python2.7/site-packages/Sphinx-1.3.1-py2.7.egg/sphinx/ext/autodoc.py", line 385, in import_object
-    __import__(self.modname)
-  File "/home/maxz/Documents/gpy/GPy/core/symbolic.py", line 10, in <module>
-    from sympy.utilities.lambdify import lambdastr, _imp_namespace, _get_namespace
-ImportError: No module named lambdify
-/home/maxz/Documents/gpy/GPy/core/parameterization/parameter_core.py:docstring of GPy.core.parameterization.parameter_core.Indexable.unset_priors:1: WARNING: Inline emphasis start-string without end-string.
-/home/maxz/Documents/gpy/GPy/core/parameterization/parameter_core.py:docstring of GPy.core.parameterization.parameter_core.Nameable.hierarchy_name:4: WARNING: Field list ends without a blank line; unexpected unindent.
-/home/maxz/Documents/gpy/GPy/core/parameterization/parameter_core.py:docstring of GPy.core.parameterization.parameter_core.Parameterizable.traverse:1: WARNING: Inline emphasis start-string without end-string.
-/home/maxz/Documents/gpy/GPy/core/parameterization/parameter_core.py:docstring of GPy.core.parameterization.parameter_core.Parameterizable.traverse:1: WARNING: Inline strong start-string without end-string.
-/home/maxz/Documents/gpy/GPy/core/parameterization/parameterized.py:docstring of GPy.core.parameterization.parameterized.Parameterized:18: ERROR: Unexpected indentation.
-/home/maxz/Documents/gpy/GPy/core/parameterization/parameterized.py:docstring of GPy.core.parameterization.parameterized.Parameterized:20: WARNING: Block quote ends without a blank line; unexpected unindent.
-/home/maxz/Documents/gpy/GPy/core/parameterization/ties_and_remappings.py:docstring of GPy.core.parameterization.ties_and_remappings.Tie:18: SEVERE: Unexpected section title or transition.
-
-================================
-/home/maxz/Documents/gpy/doc/GPy.examples.rst:50: WARNING: autodoc: failed to import module u'GPy.examples.stochastic'; the following exception was raised:
-Traceback (most recent call last):
-  File "/home/maxz/anaconda/lib/python2.7/site-packages/Sphinx-1.3.1-py2.7.egg/sphinx/ext/autodoc.py", line 385, in import_object
-    __import__(self.modname)
-ImportError: No module named stochastic
-/home/maxz/Documents/gpy/doc/GPy.examples.rst:58: WARNING: autodoc: failed to import module u'GPy.examples.tutorials'; the following exception was raised:
-Traceback (most recent call last):
-  File "/home/maxz/anaconda/lib/python2.7/site-packages/Sphinx-1.3.1-py2.7.egg/sphinx/ext/autodoc.py", line 385, in import_object
-    __import__(self.modname)
-ImportError: No module named tutorials
-/home/maxz/Documents/gpy/doc/GPy.inference.latent_function_inference.rst:82: WARNING: autodoc: failed to import module u'GPy.inference.latent_function_inference.var_dtc_gpu'; the following exception was raised:
-Traceback (most recent call last):
-  File "/home/maxz/anaconda/lib/python2.7/site-packages/Sphinx-1.3.1-py2.7.egg/sphinx/ext/autodoc.py", line 385, in import_object
-    __import__(self.modname)
-ImportError: No module named var_dtc_gpu
-/home/maxz/Documents/gpy/doc/GPy.inference.optimization.rst:42: WARNING: autodoc: failed to import module u'GPy.inference.optimization.sgd'; the following exception was raised:
-Traceback (most recent call last):
-  File "/home/maxz/anaconda/lib/python2.7/site-packages/Sphinx-1.3.1-py2.7.egg/sphinx/ext/autodoc.py", line 385, in import_object
-    __import__(self.modname)
-ImportError: No module named sgd
-/home/maxz/Documents/gpy/GPy/kern/_src/coregionalize.py:docstring of GPy.kern._src.coregionalize.Coregionalize:5: ERROR: Unexpected indentation.
-/home/maxz/Documents/gpy/doc/GPy.kern._src.rst:73: WARNING: autodoc: failed to import module u'GPy.kern._src.hierarchical'; the following exception was raised:
-Traceback (most recent call last):
-  File "/home/maxz/anaconda/lib/python2.7/site-packages/Sphinx-1.3.1-py2.7.egg/sphinx/ext/autodoc.py", line 385, in import_object
-    __import__(self.modname)
-ImportError: No module named hierarchical
-/home/maxz/Documents/gpy/GPy/kern/_src/independent_outputs.py:docstring of GPy.kern._src.independent_outputs.IndependentOutputs:9: WARNING: Field list ends without a blank line; unexpected unindent.
-/home/maxz/Documents/gpy/GPy/kern/_src/stationary.py:docstring of GPy.kern._src.stationary.Stationary:24: ERROR: Unexpected indentation.
-/home/maxz/Documents/gpy/GPy/kern/_src/stationary.py:docstring of GPy.kern._src.stationary.Stationary:22: WARNING: Inline literal start-string without end-string.
-/home/maxz/Documents/gpy/GPy/kern/_src/stationary.py:docstring of GPy.kern._src.stationary.Stationary:25: WARNING: Block quote ends without a blank line; unexpected unindent.
-/home/maxz/Documents/gpy/GPy/kern/_src/stationary.py:docstring of GPy.kern._src.stationary.Stationary:27: WARNING: Definition list ends without a blank line; unexpected unindent.
-/home/maxz/Documents/gpy/GPy/kern/_src/stationary.py:docstring of GPy.kern._src.stationary.Stationary:27: WARNING: Inline literal start-string without end-string.
-/home/maxz/Documents/gpy/GPy/kern/_src/stationary.py:docstring of GPy.kern._src.stationary.Stationary:27: WARNING: Inline interpreted text or phrase reference start-string without end-string.
-/home/maxz/Documents/gpy/doc/GPy.kern._src.rst:177: WARNING: autodoc: failed to import module u'GPy.kern._src.symbolic'; the following exception was raised:
-Traceback (most recent call last):
-  File "/home/maxz/anaconda/lib/python2.7/site-packages/Sphinx-1.3.1-py2.7.egg/sphinx/ext/autodoc.py", line 385, in import_object
-    __import__(self.modname)
-  File "/home/maxz/Documents/gpy/GPy/kern/_src/symbolic.py", line 5, in <module>
-    from ...core.symbolic import Symbolic_core
-  File "/home/maxz/Documents/gpy/GPy/core/symbolic.py", line 10, in <module>
-    from sympy.utilities.lambdify import lambdastr, _imp_namespace, _get_namespace
-ImportError: No module named lambdify
-/home/maxz/Documents/gpy/GPy/models/gp_kronecker_gaussian_regression.py:docstring of GPy.models.gp_kronecker_gaussian_regression.GPKroneckerGaussianRegression:13: ERROR: Unexpected indentation.
-/home/maxz/Documents/gpy/GPy/models/gp_kronecker_gaussian_regression.py:docstring of GPy.models.gp_kronecker_gaussian_regression.GPKroneckerGaussianRegression:18: WARNING: Block quote ends without a blank line; unexpected unindent.
-/home/maxz/Documents/gpy/doc/GPy.models.rst:66: WARNING: autodoc: failed to import module u'GPy.models.gp_multioutput_regression'; the following exception was raised:
-Traceback (most recent call last):
-  File "/home/maxz/anaconda/lib/python2.7/site-packages/Sphinx-1.3.1-py2.7.egg/sphinx/ext/autodoc.py", line 385, in import_object
-    __import__(self.modname)
-ImportError: No module named gp_multioutput_regression
-/home/maxz/Documents/gpy/GPy/models/gp_var_gauss.py:docstring of GPy.models.gp_var_gauss.GPVariationalGaussianApproximation:9: WARNING: Definition list ends without a blank line; unexpected unindent.
-/home/maxz/Documents/gpy/GPy/models/mrd.py:docstring of GPy.models.mrd.MRD:32: WARNING: Field list ends without a blank line; unexpected unindent.
-/home/maxz/Documents/gpy/GPy/models/mrd.py:docstring of GPy.models.mrd.MRD:32: WARNING: Inline interpreted text or phrase reference start-string without end-string.
-/home/maxz/Documents/gpy/GPy/models/mrd.py:docstring of GPy.models.mrd.MRD:34: WARNING: Definition list ends without a blank line; unexpected unindent.
-/home/maxz/Documents/gpy/doc/GPy.models.rst:138: WARNING: autodoc: failed to import module u'GPy.models.sparse_gp_multioutput_regression'; the following exception was raised:
-Traceback (most recent call last):
-  File "/home/maxz/anaconda/lib/python2.7/site-packages/Sphinx-1.3.1-py2.7.egg/sphinx/ext/autodoc.py", line 385, in import_object
-    __import__(self.modname)
-ImportError: No module named sparse_gp_multioutput_regression
-/home/maxz/Documents/gpy/doc/GPy.models.rst:178: WARNING: autodoc: failed to import module u'GPy.models.svigp_regression'; the following exception was raised:
-Traceback (most recent call last):
-  File "/home/maxz/anaconda/lib/python2.7/site-packages/Sphinx-1.3.1-py2.7.egg/sphinx/ext/autodoc.py", line 385, in import_object
-    __import__(self.modname)
-ImportError: No module named svigp_regression
-/home/maxz/Documents/gpy/GPy/plotting/matplot_dep/netpbmfile.py:docstring of GPy.plotting.matplot_dep.netpbmfile.imread:6: SEVERE: Unexpected section title.
-
-Examples
---------
-/home/maxz/Documents/gpy/GPy/plotting/matplot_dep/netpbmfile.py:docstring of GPy.plotting.matplot_dep.netpbmfile.imsave:4: SEVERE: Unexpected section title.
-
-Examples
---------
-/home/maxz/Documents/gpy/GPy/testing/likelihood_tests.py:docstring of GPy.testing.likelihood_tests.dparam_checkgrad:6: ERROR: Unexpected indentation.
-/home/maxz/Documents/gpy/GPy/testing/likelihood_tests.py:docstring of GPy.testing.likelihood_tests.dparam_checkgrad:7: WARNING: Block quote ends without a blank line; unexpected unindent.
-/home/maxz/Documents/gpy/GPy/testing/likelihood_tests.py:docstring of GPy.testing.likelihood_tests.dparam_partial:7: WARNING: Definition list ends without a blank line; unexpected unindent.
-/home/maxz/Documents/gpy/GPy/testing/likelihood_tests.py:docstring of GPy.testing.likelihood_tests.dparam_partial:9: ERROR: Unexpected indentation.
-docstring of GPy.util.datasets.hapmap3:7: WARNING: Block quote ends without a blank line; unexpected unindent.
-/home/maxz/Documents/gpy/doc/GPy.util.rst:74: WARNING: autodoc: failed to import module u'GPy.util.erfcx'; the following exception was raised:
-Traceback (most recent call last):
-  File "/home/maxz/anaconda/lib/python2.7/site-packages/Sphinx-1.3.1-py2.7.egg/sphinx/ext/autodoc.py", line 385, in import_object
-    __import__(self.modname)
-ImportError: No module named erfcx
-/home/maxz/Documents/gpy/doc/GPy.util.rst:146: WARNING: autodoc: failed to import module u'GPy.util.mpi'; the following exception was raised:
-Traceback (most recent call last):
-  File "/home/maxz/anaconda/lib/python2.7/site-packages/Sphinx-1.3.1-py2.7.egg/sphinx/ext/autodoc.py", line 385, in import_object
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-ImportError: No module named mpi
-/home/maxz/Documents/gpy/GPy/util/netpbmfile.py:docstring of GPy.util.netpbmfile.imread:6: SEVERE: Unexpected section title.
-
-Examples
---------
-/home/maxz/Documents/gpy/GPy/util/netpbmfile.py:docstring of GPy.util.netpbmfile.imsave:4: SEVERE: Unexpected section title.
-
-Examples
---------
-/home/maxz/Documents/gpy/doc/GPy.util.rst:2: SEVERE: Duplicate ID: "module-GPy.util.subarray_and_sorting".
-/home/maxz/Documents/gpy/GPy/util/subarray_and_sorting.py:docstring of GPy.util.subarray_and_sorting.common_subarrays:8: ERROR: Unexpected indentation.
-/home/maxz/Documents/gpy/GPy/util/subarray_and_sorting.py:docstring of GPy.util.subarray_and_sorting.common_subarrays:11: SEVERE: Unexpected section title.
-
-Examples:
-=========
-/home/maxz/Documents/gpy/GPy/util/subarray_and_sorting.py:docstring of GPy.util.subarray_and_sorting.common_subarrays:19: ERROR: Unexpected indentation.
-/home/maxz/Documents/gpy/GPy/util/subarray_and_sorting.py:docstring of GPy.util.subarray_and_sorting.common_subarrays:21: WARNING: Block quote ends without a blank line; unexpected unindent.
-/home/maxz/Documents/gpy/GPy/util/block_matrices.py:docstring of GPy.util.block_matrices.block_dot:3: ERROR: Undefined substitution referenced: "A11.B11|B12.B12".
-/home/maxz/Documents/gpy/GPy/util/block_matrices.py:docstring of GPy.util.block_matrices.block_dot:3: ERROR: Undefined substitution referenced: "A21.B21|A22.B22".
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-/home/maxz/Documents/gpy/doc/installation.rst:27: ERROR: Unexpected indentation.
-/home/maxz/Documents/gpy/doc/tuto_creating_new_kernels.rst:58: WARNING: Inline literal start-string without end-string.
-/home/maxz/Documents/gpy/doc/tuto_creating_new_models.rst:24: ERROR: Unknown target name: "parameterized".
-/home/maxz/Documents/gpy/doc/tuto_interacting_with_models.rst:83: WARNING: Title underline too short.
-
-Interacting with Parameters:
-=======================
-/home/maxz/Documents/gpy/doc/tuto_interacting_with_models.rst:83: WARNING: Title underline too short.
-
-Interacting with Parameters:
-=======================
-/home/maxz/Documents/gpy/doc/tuto_interacting_with_models.rst:109: WARNING: Title underline too short.
-
-Regular expressions
-----------------
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-
-Setting and fetching parameters `parameter_array`
-------------------------------------------
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-
-Setting and fetching parameters `parameter_array`
-------------------------------------------
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-
-Getting the model parameter's gradients
-============================
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-
-Getting the model parameter's gradients
-============================
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-
-Adjusting the model's constraints
-================================
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-
-Adjusting the model's constraints
-================================
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-
-Available Constraints
-==============
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-
-Available Constraints
-==============
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-
-Tying Parameters
-============
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-
-Tying Parameters
-============
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-
-*******************
-Parameterization handling
-*******************
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-
-Parameter handles
-==============
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-
-:py:class:`~GPy.core.parameterization.parameterized.Parameterized`
-==========
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-
-:py:class:`~GPy.core.parameterization.parameterized.Parameterized`
-==========
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-
-:py:class:`~GPy.core.parameterization.param.Param`
-===========
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-
-:py:class:`~GPy.core.parameterization.param.Param`
-===========
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doc/tuto_kernel_overview.rst

@@ -13,9 +13,9 @@ First we import the libraries we will need ::
 
 For most kernels, the dimension is the only mandatory parameter to define a kernel object. However, it is also possible to specify the values of the parameters. For example, the three following commands are valid for defining a squared exponential kernel (ie rbf or Gaussian) ::
 
-    ker1 = GPy.kern.rbf(1)  # Equivalent to ker1 = GPy.kern.rbf(input_dim=1, variance=1., lengthscale=1.)
+    ker1 = GPy.kern.RBF(1)  # Equivalent to ker1 = GPy.kern.RBF(input_dim=1, variance=1., lengthscale=1.)
-    ker2 = GPy.kern.rbf(input_dim=1, variance = .75, lengthscale=2.)
+    ker2 = GPy.kern.RBF(input_dim=1, variance = .75, lengthscale=2.)
-    ker3 = GPy.kern.rbf(1, .5, .5)
+    ker3 = GPy.kern.RBF(1, .5, .5)
 
 A ``print`` and a ``plot`` functions are implemented to represent kernel objects. The commands ::
     
@@ -52,21 +52,18 @@ Operations to combine kernels
 
 In ``GPy``, kernel objects can be added or multiplied. In both cases, two kinds of operations are possible since one can assume that the kernels to add/multiply are defined on the same space or on different subspaces. In other words, it is possible to use two kernels :math:`k_1,\ k_2` over :math:`\mathbb{R} \times \mathbb{R}` to create 
 
-    * a kernel over :math:`\mathbb{R} \times \mathbb{R}`:  :math:`k(x,y) = k_1(x,y) \times k_2(x,y)`
+    * a kernel over :math:`\mathbb{R} \times \mathbb{R}`:  :math:`k(x,y) = k_1(x,y) \times k_2(x,y)`.
-    * a kernel over :math:`\mathbb{R}^2 \times \mathbb{R}^2`:  :math:`k(\mathbf{x},\mathbf{y}) = k_1(x_1,y_1) \times k_2(x_2,y_2)`
 
-These two options are available in GPy using the flag ``tensor`` in the ``add`` and ``prod`` functions. Here is a quick example ::
+This is available in GPy via the ``add`` and ``prod`` functions. Here is a quick example ::
 
-    k1 = GPy.kern.rbf(1,1.,2.)
+    k1 = GPy.kern.RBF(1,1.,2.)
     k2 = GPy.kern.Matern32(1, 0.5, 0.2)
 
     # Product of kernels
-    k_prod = k1.prod(k2)                        # By default, tensor=False
+    k_prod = k1.prod(k2)
-    k_prodtens = k1.prod(k2,tensor=True)
 
     # Sum of kernels
-    k_add = k1.add(k2)                          # By default, tensor=False
+    k_add = k1.add(k2)
-    k_addtens = k1.add(k2,tensor=True)    
 
 ..  # plots
     pb.figure(figsize=(8,8))
@@ -75,23 +72,23 @@ These two options are available in GPy using the flag ``tensor`` in the ``add``
     pb.title('prod')
     pb.subplot(2,2,2)
     k_prodtens.plot()
-    pb.title('tensor prod')
+    pb.title('prod')
     pb.subplot(2,2,3)
     k_add.plot()
     pb.title('sum')
     pb.subplot(2,2,4)
     k_addtens.plot()
-    pb.title('tensor sum')
+    pb.title('sum')
     pb.subplots_adjust(wspace=0.3, hspace=0.3)
 
 .. figure::  Figures/tuto_kern_overview_multadd.png
     :align:  center
     :height: 500px
 
-A shortcut for ``add`` and ``prod`` (with default flag ``tensor=False``) is provided by the usual ``+`` and ``*`` operators. Here is another example where we create a periodic kernel with some decay ::
+A shortcut for ``add`` and ``prod`` is provided by the usual ``+`` and ``*`` operators. Here is another example where we create a periodic kernel with some decay ::
     
-    k1 = GPy.kern.rbf(1,1.,2)
+    k1 = GPy.kern.RBF(1,1.,2)
-    k2 = GPy.kern.periodic_Matern52(1,variance=1e3, lengthscale=1, period = 1.5, lower=-5., upper = 5)
+    k2 = GPy.kern.PeriodicMatern52(1,variance=1e3, lengthscale=1, period = 1.5, lower=-5., upper = 5)
 
     k = k1 * k2  # equivalent to k = k1.prod(k2)
     print k
@@ -116,14 +113,14 @@ A shortcut for ``add`` and ``prod`` (with default flag ``tensor=False``) is prov
 In general, ``kern`` objects can be seen as a sum of ``kernparts`` objects, where the later are covariance functions defined on the same space. For example, the following code ::
 
     k = (k1+k2)*(k1+k2)
-    print k.parts[0].name, '\n', k.parts[1].name, '\n', k.parts[2].name, '\n', k.parts[3].name
+    print k.parts[0].name, '\n', k.parts[1].name, '\n', k.parts[1].parts[0].name, '\n', k.parts[1].parts[1].name, '\n'
 
 returns ::
+  add_1 
+  add_2 
+  rbf 
+  periodic_Matern52 
 
-    rbf<times>rbf 
-    rbf<times>periodic_Mat52 
-    periodic_Mat52<times>rbf 
-    periodic_Mat52<times>periodic_Mat52
 
 Constraining the parameters
 ===========================
@@ -137,9 +134,9 @@ Various constrains can be applied to the parameters of a kernel
 
 When calling one of these functions, the parameters to constrain can either by specified by a regular expression that matches its name or by a number that corresponds to the rank of the parameter. Here is an example ::
 
-    k1 = GPy.kern.rbf(1)
+    k1 = GPy.kern.RBF(1)
     k2 = GPy.kern.Matern32(1)
-    k3 = GPy.kern.white(1)
+    k3 = GPy.kern.White(1)
 
     k = k1 + k2 + k3
     print k
@@ -182,9 +179,9 @@ In two dimensions ANOVA kernels have the following form:
 
 Let us assume that we want to define an ANOVA kernel with a Matern 3/2 kernel for :math:`k_i`. As seen previously, we can define this kernel as follows ::
 
-    k_cst = GPy.kern.bias(1,variance=1.)
+    k_cst = GPy.kern.Bias(1,variance=1.)
-    k_mat = GPy.kern.Matern52(1,variance=1., lengthscale=3)
+    k_mat = GPy.kern.Matern52(1,variance=1.,lengthscale=3)
-    Kanova = (k_cst + k_mat).prod(k_cst + k_mat,tensor=True)
+    Kanova = (k_cst + k_mat).prod(k_cst + k_mat)
     print Kanova
 
 Printing the resulting kernel outputs the following ::
@@ -257,17 +254,17 @@ The submodels can be represented with the option ``which_function`` of ``plot``:
     import GPy
     pb.ion()
 
-    ker1 = GPy.kern.rbf(D=1)  # Equivalent to ker1 = GPy.kern.rbf(D=1, variance=1., lengthscale=1.)
+    ker1 = GPy.kern.RBF(D=1)  # Equivalent to ker1 = GPy.kern.RBF(D=1, variance=1., lengthscale=1.)
-    ker2 = GPy.kern.rbf(D=1, variance = .75, lengthscale=3.)
+    ker2 = GPy.kern.RBF(D=1, variance = .75, lengthscale=3.)
-    ker3 = GPy.kern.rbf(1, .5, .25)
+    ker3 = GPy.kern.RBF(1, .5, .25)
 
     ker1.plot()
     ker2.plot()
     ker3.plot()
     #pb.savefig("Figures/tuto_kern_overview_basicdef.png")
 
-    kernels = [GPy.kern.rbf(1), GPy.kern.exponential(1), GPy.kern.Matern32(1), GPy.kern.Matern52(1),  GPy.kern.Brownian(1), GPy.kern.bias(1), GPy.kern.linear(1), GPy.kern.spline(1), GPy.kern.periodic_exponential(1), GPy.kern.periodic_Matern32(1), GPy.kern.periodic_Matern52(1), GPy.kern.white(1)]
+    kernels = [GPy.kern.RBF(1), GPy.kern.Exponential(1), GPy.kern.Matern32(1), GPy.kern.Matern52(1),  GPy.kern.Brownian(1), GPy.kern.Bias(1), GPy.kern.Linear(1), GPy.kern.PeriodicExponential(1), GPy.kern.PeriodicMatern32(1), GPy.kern.PeriodicMatern52(1), GPy.kern.White(1)]
-    kernel_names = ["GPy.kern.rbf", "GPy.kern.exponential", "GPy.kern.Matern32", "GPy.kern.Matern52", "GPy.kern.Brownian", "GPy.kern.bias", "GPy.kern.linear", "GPy.kern.spline", "GPy.kern.periodic_exponential", "GPy.kern.periodic_Matern32", "GPy.kern.periodic_Matern52", "GPy.kern.white"]
+    kernel_names = ["GPy.kern.RBF", "GPy.kern.Exponential", "GPy.kern.Matern32", "GPy.kern.Matern52", "GPy.kern.Brownian", "GPy.kern.Bias", "GPy.kern.Linear", "GPy.kern.PeriodicExponential", "GPy.kern.PeriodicMatern32", "GPy.kern.PeriodicMatern52", "GPy.kern.White"]
     
     pb.figure(figsize=(16,12))
     pb.subplots_adjust(wspace=.5, hspace=.5)

setup.cfg

@@ -0,0 +1,10 @@
+[bumpversion]
+current_version = 0.8.3
+tag = True
+commit = True
+
+[bumpversion:file:GPy/__version__.py]
+
+[bdist_wheel]
+universal = 1
+

setup.py

@@ -1,18 +1,29 @@
 #!/usr/bin/env python
 # -*- coding: utf-8 -*-
-
+from __future__ import print_function
 import os
 import sys
 from setuptools import setup, Extension
 import numpy as np
 
-# Version number
-version = '0.6.1'
 
 def read(fname):
     return open(os.path.join(os.path.dirname(__file__), fname)).read()
 
-#Mac OS X Clang doesn't support OpenMP th the current time.
+def read_to_rst(fname):
+    try:
+        import pypandoc
+        #print 'Warning in installation: For rst formatting in pypi, consider installing pypandoc for conversion'
+        return pypandoc.convert('README.md', 'rst')
+    except:
+        return read(fname)
+
+version_dummy = {}
+exec(read('GPy/__version__.py'), version_dummy)
+__version__ = version_dummy['__version__']
+del version_dummy
+
+#Mac OS X Clang doesn't support OpenMP at the current time.
 #This detects if we are building on a Mac
 def ismac():
     platform = sys.platform
@@ -23,7 +34,7 @@ def ismac():
 
 if ismac():
     compile_flags = [ '-O3', ]
-    link_args = ['']
+    link_args = []
 else:
     compile_flags = [ '-fopenmp', '-O3', ]
     link_args = ['-lgomp']
@@ -48,9 +59,9 @@ ext_mods = [Extension(name='GPy.kern._src.stationary_cython',
                       extra_compile_args=compile_flags)]
 
 setup(name = 'GPy',
-      version = version,
+      version = __version__,
       author = read('AUTHORS.txt'),
-      author_email = "james.hensman@gmail.com",
+      author_email = "gpy.authors@gmail.com",
       description = ("The Gaussian Process Toolbox"),
       license = "BSD 3-clause",
       keywords = "machine-learning gaussian-processes kernels",
@@ -72,12 +83,13 @@ setup(name = 'GPy',
       package_dir={'GPy': 'GPy'},
       package_data = {'GPy': ['defaults.cfg', 'installation.cfg',
                               'util/data_resources.json',
-                              'util/football_teams.json']},
+                              'util/football_teams.json',
+                              ]},
       include_package_data = True,
       py_modules = ['GPy.__init__'],
       test_suite = 'GPy.testing',
-      long_description=read('README.md'),
+      long_description=read_to_rst('README.md'),
-      install_requires=['numpy>=1.7', 'scipy>=0.16'],
+      install_requires=['numpy>=1.7', 'scipy>=0.16', 'six'],
       extras_require = {'docs':['matplotlib >=1.3','Sphinx','IPython']},
       classifiers=['License :: OSI Approved :: BSD License',
                    'Natural Language :: English',