Merge branch 'master' into SGD

2026-05-12 21:42:39 +02:00 · 2013-01-31 11:06:44 +00:00 · 2013-01-31 11:06:44 +00:00 · 31b6c06a72
commit 31b6c06a72
parent 49d2e4e4f6 d2aeb6461e
41 changed files with 13284 additions and 30 deletions
--- a/GPy/core/parameterised.py
+++ b/GPy/core/parameterised.py
@ -94,7 +94,7 @@ class parameterised(object):
        Other objects are passed through - i.e. integers which were'nt meant for grepping
        """

-        if type(expr) is str:
+        if type(expr) in [str, np.string_, np.str]:
            expr = re.compile(expr)
            return np.nonzero([expr.search(name) for name in self._get_param_names()])[0]
        elif type(expr) is re._pattern_type:
--- a/GPy/kern/Matern52.py
+++ b/GPy/kern/Matern52.py
@ -31,14 +31,14 @@ class Matern52(kernpart):
        self.ARD = ARD
        if ARD == False:
            self.Nparam = 2
-            self.name = 'Mat32'
+            self.name = 'Mat52'
            if lengthscale is not None:
                assert lengthscale.shape == (1,)
            else:
                lengthscale = np.ones(1)
        else:
            self.Nparam = self.D + 1
-            self.name = 'Mat32_ARD'
+            self.name = 'Mat52_ARD'
            if lengthscale is not None:
                assert lengthscale.shape == (self.D,)
            else:
--- a/GPy/kern/init.py
+++ b/GPy/kern/init.py
@ -2,5 +2,5 @@
 # Licensed under the BSD 3-clause license (see LICENSE.txt)


-from constructors import rbf, Matern32, Matern52, exponential, linear, white, bias, finite_dimensional, spline, Brownian, rbf_sympy, sympykern, periodic_exponential, periodic_Matern32, periodic_Matern52
+from constructors import rbf, Matern32, Matern52, exponential, linear, white, bias, finite_dimensional, spline, Brownian, rbf_sympy, sympykern, periodic_exponential, periodic_Matern32, periodic_Matern52, product_orthogonal
 from kern import kern
--- a/GPy/kern/constructors.py
+++ b/GPy/kern/constructors.py
@ -18,7 +18,7 @@ from Brownian import Brownian as Brownianpart
 from periodic_exponential import periodic_exponential as periodic_exponentialpart
 from periodic_Matern32 import periodic_Matern32 as periodic_Matern32part
 from periodic_Matern52 import periodic_Matern52 as periodic_Matern52part
-
+from product_orthogonal import product_orthogonal as product_orthogonalpart
 #TODO these s=constructors are not as clean as we'd like. Tidy the code up
 #using meta-classes to make the objects construct properly wthout them.

@ -241,3 +241,14 @@ def periodic_Matern52(D,variance=1., lengthscale=None, period=2*np.pi,n_freq=10,
    """
    part = periodic_Matern52part(D,variance, lengthscale, period, n_freq, lower, upper)
    return kern(D, [part])
+
+def product_orthogonal(k1,k2):
+    """
+     Construct a product kernel
+     
+    :param k1, k2: the kernels to multiply
+    :type k1, k2: kernpart
+    :rtype: kernel object
+    """
+    part = product_orthogonalpart(k1,k2)
+    return kern(k1.D+k2.D, [part])
--- a/GPy/kern/kern.py
+++ b/GPy/kern/kern.py
@ -4,9 +4,9 @@

 import numpy as np
 from ..core.parameterised import parameterised
-from functools import partial
 from kernpart import kernpart
 import itertools
+from product_orthogonal import product_orthogonal 

 class kern(parameterised):
    def __init__(self,D,parts=[], input_slices=None):
@ -45,7 +45,6 @@ class kern(parameterised):
        for p in self.parts:
            assert isinstance(p,kernpart), "bad kernel part"

-
        self.compute_param_slices()

        parameterised.__init__(self)
@ -133,6 +132,67 @@ class kern(parameterised):
        newkern.tied_indices = self.tied_indices + [self.Nparam + x for x in other.tied_indices]
        return newkern

+    def __mul__(self,other):
+        """
+        Shortcut for `prod_orthogonal`. Note that `+` assumes that we sum 2 kernels defines on the same space whereas `*` assumes that the kernels are defined on different subspaces.
+        """
+        return self.prod_orthogonal(other)
+
+    def prod_orthogonal(self,other):
+        """
+        multiply two kernels. Both kernels are defined on separate spaces. Note that the constrains on the parameters of the kernels to multiply will be lost.
+        :param other: the other kernel to be added
+        :type other: GPy.kern
+        """
+        K1 = self.copy()
+        K2 = other.copy()
+        K1.unconstrain('')
+        K2.unconstrain('')
+
+        prev_ties = K1.tied_indices + [arr + K1.Nparam for arr in K2.tied_indices]
+        K1.untie_everything()
+        K2.untie_everything()
+
+        D = K1.D + K2.D
+
+        newkernparts = [product_orthogonal(k1,k2).parts[0] for k1, k2 in itertools.product(K1.parts,K2.parts)]
+
+        slices = []
+        for sl1, sl2 in itertools.product(K1.input_slices,K2.input_slices):
+            s1, s2 = [False]*K1.D, [False]*K2.D
+            s1[sl1], s2[sl2] = [True], [True]
+            slices += [s1+s2]
+
+        newkern = kern(D, newkernparts, slices)
+
+        # create the ties
+        K1_param = []
+        n = 0
+        for k1 in K1.parts:
+            K1_param += [range(n,n+k1.Nparam)]
+            n += k1.Nparam
+        n = 0
+        K2_param = []
+        for k2 in K2.parts:
+            K2_param += [range(K1.Nparam+n,K1.Nparam+n+k2.Nparam)]
+            n += k2.Nparam
+        index_param = []
+        for p1 in K1_param:
+            for p2 in K2_param:
+                index_param += [0] + p1[1:] + p2[1:]
+        index_param = np.array(index_param)
+
+        # follow the previous ties
+        for arr in prev_ties:
+            for j in arr:
+                index_param[np.where(index_param==j)[0]] = arr[0]
+
+        # tie
+        for i in np.unique(index_param)[1:]:
+            newkern.tie_param(np.where(index_param==i)[0])
+
+        return newkern
+
    def _get_params(self):
        return np.hstack([p._get_params() for p in self.parts])

--- a/GPy/kern/product_orthogonal.py
+++ b/GPy/kern/product_orthogonal.py
@ -0,0 +1,91 @@
+# Copyright (c) 2012, GPy authors (see AUTHORS.txt).
+# Licensed under the BSD 3-clause license (see LICENSE.txt)
+
+from kernpart import kernpart
+import numpy as np
+import hashlib
+from scipy import integrate
+
+class product_orthogonal(kernpart):
+    """
+    Computes the product of 2 kernels
+
+    :param k1, k2: the kernels to multiply
+    :type k1, k2: kernpart
+    :rtype: kernel object
+
+    """
+    def __init__(self,k1,k2):
+        assert k1._get_param_names()[0] == 'variance' and k2._get_param_names()[0] == 'variance', "Error: The multipication of kernels is only defined when the first parameters of the kernels to multiply is the variance."
+        self.D = k1.D + k2.D
+        self.Nparam = k1.Nparam + k2.Nparam - 1
+        self.name = k1.name + '<times>' + k2.name
+        self.k1 = k1
+        self.k2 = k2
+        self._set_params(np.hstack((k1._get_params()[0]*k2._get_params()[0], k1._get_params()[1:],k2._get_params()[1:])))
+
+    def _get_params(self):
+        """return the value of the parameters."""
+        return self.params
+
+    def _set_params(self,x):
+        """set the value of the parameters."""
+        self.k1._set_params(np.hstack((1.,x[1:self.k1.Nparam])))
+        self.k2._set_params(np.hstack((1.,x[self.k1.Nparam:])))
+        self.params = x
+
+    def _get_param_names(self):
+        """return parameter names."""
+        return ['variance']+[self.k1.name + '_' + self.k1._get_param_names()[i+1] for i in range(self.k1.Nparam-1)] +  [self.k2.name + '_' + self.k2._get_param_names()[i+1] for i in range(self.k2.Nparam-1)]
+
+    def K(self,X,X2,target):
+        """Compute the covariance matrix between X and X2."""
+        if X2 is None: X2 = X
+        target1 = np.zeros((X.shape[0],X2.shape[0]))
+        target2 = np.zeros((X.shape[0],X2.shape[0]))
+        self.k1.K(X[:,0:self.k1.D],X2[:,0:self.k1.D],target1)
+        self.k2.K(X[:,self.k1.D:],X2[:,self.k1.D:],target2)
+        target += self.params[0]*target1 * target2
+
+    def Kdiag(self,X,target):
+        """Compute the diagonal of the covariance matrix associated to X."""
+        target1 = np.zeros((X.shape[0],))
+        target2 = np.zeros((X.shape[0],))
+        self.k1.Kdiag(X[:,0:self.k1.D],target1)
+        self.k2.Kdiag(X[:,self.k1.D:],target2)
+        target += self.params[0]*target1 * target2
+
+    def dK_dtheta(self,partial,X,X2,target):
+        """derivative of the covariance matrix with respect to the parameters."""
+        if X2 is None: X2 = X
+        K1 = np.zeros((X.shape[0],X2.shape[0]))
+        K2 = np.zeros((X.shape[0],X2.shape[0]))
+        self.k1.K(X[:,0:self.k1.D],X2[:,0:self.k1.D],K1)
+        self.k2.K(X[:,self.k1.D:],X2[:,self.k1.D:],K2)
+
+        k1_target = np.zeros(self.k1.Nparam)
+        k2_target = np.zeros(self.k2.Nparam)
+        self.k1.dK_dtheta(partial*K2, X[:,:self.k1.D], X2[:,:self.k1.D], k1_target)
+        self.k2.dK_dtheta(partial*K1, X[:,self.k1.D:], X2[:,self.k1.D:], k2_target)
+
+        target[0] += np.sum(K1*K2*partial)
+        target[1:self.k1.Nparam] += self.params[0]* k1_target[1:]
+        target[self.k1.Nparam:] += self.params[0]* k2_target[1:]
+        
+    def dKdiag_dtheta(self,partial,X,target):
+        """derivative of the diagonal of the covariance matrix with respect to the parameters."""
+        target[0] += 1
+
+    def dK_dX(self,partial,X,X2,target):
+        """derivative of the covariance matrix with respect to X."""
+        if X2 is None: X2 = X
+        K1 = np.zeros((X.shape[0],X2.shape[0]))
+        K2 = np.zeros((X.shape[0],X2.shape[0]))
+        self.k1.K(X[:,0:self.k1.D],X2[:,0:self.k1.D],K1)
+        self.k2.K(X[:,self.k1.D:],X2[:,self.k1.D:],K2)
+
+        self.k1.dK_dX(partial*K2, X[:,:self.k1.D], X2[:,:self.k1.D], target)
+        self.k2.dK_dX(partial*K1, X[:,self.k1.D:], X2[:,self.k1.D:], target)
+
+    def dKdiag_dX(self,X,target):
+        pass
--- a/GPy/kern/rbf.py
+++ b/GPy/kern/rbf.py
@ -26,28 +26,29 @@ class rbf(kernpart):
    :type ARD: Boolean
    :rtype: kernel object

-    .. Note: for rbf with different lengthscale on each dimension, see rbf_ARD
+    .. Note: this object implements both the ARD and 'spherical' version of the function
    """

    def __init__(self,D,variance=1.,lengthscale=None,ARD=False):
        self.D = D
+        self.name = 'rbf'
        self.ARD = ARD
        if not ARD:
            self.Nparam = 2
-            self.name = 'rbf'
            if lengthscale is not None:
-                assert lengthscale.shape == (1,)
+                lengthscale = np.asarray(lengthscale)
+                assert lengthscale.size == 1, "Only one lengthscale needed for non-ARD kernel"
            else:
                lengthscale = np.ones(1)
        else:
            self.Nparam = self.D + 1
-            self.name = 'rbf_ARD'
            if lengthscale is not None:
-                assert lengthscale.shape == (self.D,)
+                lengthscale = np.asarray(lengthscale)
+                assert lengthscale.size == self.D, "bad number of lengthscales"
            else:
                lengthscale = np.ones(self.D)

-        self._set_params(np.hstack((variance,lengthscale)))
+        self._set_params(np.hstack((variance,lengthscale.flatten())))

        #initialize cache
        self._Z, self._mu, self._S = np.empty(shape=(3,1))
--- a/GPy/util/datasets.py
+++ b/GPy/util/datasets.py
@ -116,7 +116,7 @@ def toy_linear_1d_classification(seed=default_seed):
    return {'X': X, 'Y':  sample_class(2.*X), 'F': 2.*X}

 def rogers_girolami_olympics():
-    olympic_data = scipy.io.loadmat('/home/neil/public_html/olympics.mat')['male100']
+    olympic_data = scipy.io.loadmat(os.path.join(data_path, 'olympics.mat'))['male100']
    X = olympic_data[:, 0][:, None]
    Y= olympic_data[:, 1][:, None]
    return {'X': X, 'Y': Y, 'info': "Olympic sprint times for 100 m men from 1896 until 2008. Example is from Rogers and Girolami's First Course in Machine Learning."}
--- a/GPy/util/datasets/COPYRIGHT
+++ b/GPy/util/datasets/COPYRIGHT
@ -0,0 +1 @@
+These datasets are reproduced for educational purposes only. No copyright infringement intended!
--- a/GPy/util/datasets/DellaGattadata.mat
+++ b/GPy/util/datasets/DellaGattadata.mat
--- a/GPy/util/datasets/banana.txt
+++ b/GPy/util/datasets/banana.txt
--- a/GPy/util/datasets/column_2C
+++ b/GPy/util/datasets/column_2C
@ -0,0 +1,310 @@
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+57.29 15.15 64 42.14 116.74 30.34 1
+41.19 5.79 42.87 35.39 103.35 27.66 1
+66.8 14.55 72.08 52.25 82.46 41.69 1
+79.48 26.73 70.65 52.74 118.59 61.7 1
+44.22 1.51 46.11 42.71 108.63 42.81 1
+57.04 0.35 49.2 56.69 103.05 52.17 1
+64.27 12.51 68.7 51.77 95.25 39.41 1
+92.03 35.39 77.42 56.63 115.72 58.06 1
+67.26 7.19 51.7 60.07 97.8 42.14 1
+118.14 38.45 50.84 79.7 81.02 74.04 1
+115.92 37.52 76.8 78.41 104.7 81.2 1
+53.94 9.31 43.1 44.64 124.4 25.08 1
+83.7 20.27 77.11 63.43 125.48 69.28 1
+56.99 6.87 57.01 50.12 109.98 36.81 1
+72.34 16.42 59.87 55.92 70.08 12.07 1
+95.38 24.82 95.16 70.56 89.31 57.66 1
+44.25 1.1 38 43.15 98.27 23.91 1
+64.81 15.17 58.84 49.64 111.68 21.41 1
+78.4 14.04 79.69 64.36 104.73 12.39 1
+56.67 13.46 43.77 43.21 93.69 21.11 1
+50.83 9.06 56.3 41.76 79 23.04 1
+61.41 25.38 39.1 36.03 103.4 21.84 1
+56.56 8.96 52.58 47.6 98.78 50.7 1
+67.03 13.28 66.15 53.75 100.72 33.99 1
+80.82 19.24 61.64 61.58 89.47 44.17 1
+80.65 26.34 60.9 54.31 120.1 52.47 1
+68.72 49.43 68.06 19.29 125.02 54.69 1
+37.9 4.48 24.71 33.42 157.85 33.61 1
+64.62 15.23 67.63 49.4 90.3 31.33 1
+75.44 31.54 89.6 43.9 106.83 54.97 1
+71 37.52 84.54 33.49 125.16 67.77 1
+81.06 20.8 91.78 60.26 125.43 38.18 1
+91.47 24.51 84.62 66.96 117.31 52.62 1
+81.08 21.26 78.77 59.83 90.07 49.16 1
+60.42 5.27 59.81 55.15 109.03 30.27 1
+85.68 38.65 82.68 47.03 120.84 61.96 1
+82.41 29.28 77.05 53.13 117.04 62.77 1
+43.72 9.81 52 33.91 88.43 40.88 1
+86.47 40.3 61.14 46.17 97.4 55.75 1
+74.47 33.28 66.94 41.19 146.47 124.98 1
+70.25 10.34 76.37 59.91 119.24 32.67 1
+72.64 18.93 68 53.71 116.96 25.38 1
+71.24 5.27 86 65.97 110.7 38.26 1
+63.77 12.76 65.36 51.01 89.82 56 1
+58.83 37.58 125.74 21.25 135.63 117.31 1
+74.85 13.91 62.69 60.95 115.21 33.17 1
+75.3 16.67 61.3 58.63 118.88 31.58 1
+63.36 20.02 67.5 43.34 131 37.56 1
+67.51 33.28 96.28 34.24 145.6 88.3 1
+76.31 41.93 93.28 34.38 132.27 101.22 1
+73.64 9.71 63 63.92 98.73 26.98 1
+56.54 14.38 44.99 42.16 101.72 25.77 1
+80.11 33.94 85.1 46.17 125.59 100.29 1
+95.48 46.55 59 48.93 96.68 77.28 1
+74.09 18.82 76.03 55.27 128.41 73.39 1
+87.68 20.37 93.82 67.31 120.94 76.73 1
+48.26 16.42 36.33 31.84 94.88 28.34 1
+38.51 16.96 35.11 21.54 127.63 7.99 -1
+54.92 18.97 51.6 35.95 125.85 2 -1
+44.36 8.95 46.9 35.42 129.22 4.99 -1
+48.32 17.45 48 30.87 128.98 -0.91 -1
+45.7 10.66 42.58 35.04 130.18 -3.39 -1
+30.74 13.35 35.9 17.39 142.41 -2.01 -1
+50.91 6.68 30.9 44.24 118.15 -1.06 -1
+38.13 6.56 50.45 31.57 132.11 6.34 -1
+51.62 15.97 35 35.66 129.39 1.01 -1
+64.31 26.33 50.96 37.98 106.18 3.12 -1
+44.49 21.79 31.47 22.7 113.78 -0.28 -1
+54.95 5.87 53 49.09 126.97 -0.63 -1
+56.1 13.11 62.64 43 116.23 31.17 -1
+69.4 18.9 75.97 50.5 103.58 -0.44 -1
+89.83 22.64 90.56 67.2 100.5 3.04 -1
+59.73 7.72 55.34 52 125.17 3.24 -1
+63.96 16.06 63.12 47.9 142.36 6.3 -1
+61.54 19.68 52.89 41.86 118.69 4.82 -1
+38.05 8.3 26.24 29.74 123.8 3.89 -1
+43.44 10.1 36.03 33.34 137.44 -3.11 -1
+65.61 23.14 62.58 42.47 124.13 -4.08 -1
+53.91 12.94 39 40.97 118.19 5.07 -1
+43.12 13.82 40.35 29.3 128.52 0.97 -1
+40.68 9.15 31.02 31.53 139.12 -2.51 -1
+37.73 9.39 42 28.35 135.74 13.68 -1
+63.93 19.97 40.18 43.96 113.07 -11.06 -1
+61.82 13.6 64 48.22 121.78 1.3 -1
+62.14 13.96 58 48.18 133.28 4.96 -1
+69 13.29 55.57 55.71 126.61 10.83 -1
+56.45 19.44 43.58 37 139.19 -1.86 -1
+41.65 8.84 36.03 32.81 116.56 -6.05 -1
+51.53 13.52 35 38.01 126.72 13.93 -1
+39.09 5.54 26.93 33.55 131.58 -0.76 -1
+34.65 7.51 43 27.14 123.99 -4.08 -1
+63.03 27.34 51.61 35.69 114.51 7.44 -1
+47.81 10.69 54 37.12 125.39 -0.4 -1
+46.64 15.85 40 30.78 119.38 9.06 -1
+49.83 16.74 28 33.09 121.44 1.91 -1
+47.32 8.57 35.56 38.75 120.58 1.63 -1
+50.75 20.24 37 30.52 122.34 2.29 -1
+36.16 -0.81 33.63 36.97 135.94 -2.09 -1
+40.75 1.84 50 38.91 139.25 0.67 -1
+42.92 -5.85 58 48.76 121.61 -3.36 -1
+63.79 21.35 66 42.45 119.55 12.38 -1
+72.96 19.58 61.01 53.38 111.23 0.81 -1
+67.54 14.66 58 52.88 123.63 25.97 -1
+54.75 9.75 48 45 123.04 8.24 -1
+50.16 -2.97 42 53.13 131.8 -8.29 -1
+40.35 10.19 37.97 30.15 128.01 0.46 -1
+63.62 16.93 49.35 46.68 117.09 -0.36 -1
+54.14 11.94 43 42.21 122.21 0.15 -1
+74.98 14.92 53.73 60.05 105.65 1.59 -1
+42.52 14.38 25.32 28.14 128.91 0.76 -1
+33.79 3.68 25.5 30.11 128.33 -1.78 -1
+54.5 6.82 47 47.68 111.79 -4.41 -1
+48.17 9.59 39.71 38.58 135.62 5.36 -1
+46.37 10.22 42.7 36.16 121.25 -0.54 -1
+52.86 9.41 46.99 43.45 123.09 1.86 -1
+57.15 16.49 42.84 40.66 113.81 5.02 -1
+37.14 16.48 24 20.66 125.01 7.37 -1
+51.31 8.88 57 42.44 126.47 -2.14 -1
+42.52 16.54 42 25.97 120.63 7.88 -1
+39.36 7.01 37 32.35 117.82 1.9 -1
+35.88 1.11 43.46 34.77 126.92 -1.63 -1
+43.19 9.98 28.94 33.22 123.47 1.74 -1
+67.29 16.72 51 50.57 137.59 4.96 -1
+51.33 13.63 33.26 37.69 131.31 1.79 -1
+65.76 13.21 44 52.55 129.39 -1.98 -1
+40.41 -1.33 30.98 41.74 119.34 -6.17 -1
+48.8 18.02 52 30.78 139.15 10.44 -1
+50.09 13.43 34.46 36.66 119.13 3.09 -1
+64.26 14.5 43.9 49.76 115.39 5.95 -1
+53.68 13.45 41.58 40.24 113.91 2.74 -1
+49 13.11 51.87 35.88 126.4 0.54 -1
+59.17 14.56 43.2 44.6 121.04 2.83 -1
+67.8 16.55 43.26 51.25 119.69 4.87 -1
+61.73 17.11 46.9 44.62 120.92 3.09 -1
+33.04 -0.32 19.07 33.37 120.39 9.35 -1
+74.57 15.72 58.62 58.84 105.42 0.6 -1
+44.43 14.17 32.24 30.26 131.72 -3.6 -1
+36.42 13.88 20.24 22.54 126.08 0.18 -1
+51.08 14.21 35.95 36.87 115.8 6.91 -1
+34.76 2.63 29.5 32.12 127.14 -0.46 -1
+48.9 5.59 55.5 43.32 137.11 19.85 -1
+46.24 10.06 37 36.17 128.06 -5.1 -1
+46.43 6.62 48.1 39.81 130.35 2.45 -1
+39.66 16.21 36.67 23.45 131.92 -4.97 -1
+45.58 18.76 33.77 26.82 116.8 3.13 -1
+66.51 20.9 31.73 45.61 128.9 1.52 -1
+82.91 29.89 58.25 53.01 110.71 6.08 -1
+50.68 6.46 35 44.22 116.59 -0.21 -1
+89.01 26.08 69.02 62.94 111.48 6.06 -1
+54.6 21.49 29.36 33.11 118.34 -1.47 -1
+34.38 2.06 32.39 32.32 128.3 -3.37 -1
+45.08 12.31 44.58 32.77 147.89 -8.94 -1
+47.9 13.62 36 34.29 117.45 -4.25 -1
+53.94 20.72 29.22 33.22 114.37 -0.42 -1
+61.45 22.69 46.17 38.75 125.67 -2.71 -1
+45.25 8.69 41.58 36.56 118.55 0.21 -1
+33.84 5.07 36.64 28.77 123.95 -0.2 -1
--- a/GPy/util/datasets/crabs.dat
+++ b/GPy/util/datasets/crabs.dat
@ -0,0 +1,201 @@
+sp sex index FL RW CL CW BD
+B M  1  8.1  6.7 16.1 19.0  7.0
+B M  2  8.8  7.7 18.1 20.8  7.4
+B M  3  9.2  7.8 19.0 22.4  7.7
+B M  4  9.6  7.9 20.1 23.1  8.2
+B M  5  9.8  8.0 20.3 23.0  8.2
+B M  6 10.8  9.0 23.0 26.5  9.8
+B M  7 11.1  9.9 23.8 27.1  9.8
+B M  8 11.6  9.1 24.5 28.4 10.4
+B M  9 11.8  9.6 24.2 27.8  9.7
+B M 10 11.8 10.5 25.2 29.3 10.3
+B M 11 12.2 10.8 27.3 31.6 10.9
+B M 12 12.3 11.0 26.8 31.5 11.4
+B M 13 12.6 10.0 27.7 31.7 11.4
+B M 14 12.8 10.2 27.2 31.8 10.9
+B M 15 12.8 10.9 27.4 31.5 11.0
+B M 16 12.9 11.0 26.8 30.9 11.4
+B M 17 13.1 10.6 28.2 32.3 11.0
+B M 18 13.1 10.9 28.3 32.4 11.2
+B M 19 13.3 11.1 27.8 32.3 11.3
+B M 20 13.9 11.1 29.2 33.3 12.1
+B M 21 14.3 11.6 31.3 35.5 12.7
+B M 22 14.6 11.3 31.9 36.4 13.7
+B M 23 15.0 10.9 31.4 36.4 13.2
+B M 24 15.0 11.5 32.4 37.0 13.4
+B M 25 15.0 11.9 32.5 37.2 13.6
+B M 26 15.2 12.1 32.3 36.7 13.6
+B M 27 15.4 11.8 33.0 37.5 13.6
+B M 28 15.7 12.6 35.8 40.3 14.5
+B M 29 15.9 12.7 34.0 38.9 14.2
+B M 30 16.1 11.6 33.8 39.0 14.4
+B M 31 16.1 12.8 34.9 40.7 15.7
+B M 32 16.2 13.3 36.0 41.7 15.4
+B M 33 16.3 12.7 35.6 40.9 14.9
+B M 34 16.4 13.0 35.7 41.8 15.2
+B M 35 16.6 13.5 38.1 43.4 14.9
+B M 36 16.8 12.8 36.2 41.8 14.9
+B M 37 16.9 13.2 37.3 42.7 15.6
+B M 38 17.1 12.6 36.4 42.0 15.1
+B M 39 17.1 12.7 36.7 41.9 15.6
+B M 40 17.2 13.5 37.6 43.9 16.1
+B M 41 17.7 13.6 38.7 44.5 16.0
+B M 42 17.9 14.1 39.7 44.6 16.8
+B M 43 18.0 13.7 39.2 44.4 16.2
+B M 44 18.8 15.8 42.1 49.0 17.8
+B M 45 19.3 13.5 41.6 47.4 17.8
+B M 46 19.3 13.8 40.9 46.5 16.8
+B M 47 19.7 15.3 41.9 48.5 17.8
+B M 48 19.8 14.2 43.2 49.7 18.6
+B M 49 19.8 14.3 42.4 48.9 18.3
+B M 50 21.3 15.7 47.1 54.6 20.0
+B F  1  7.2  6.5 14.7 17.1  6.1
+B F  2  9.0  8.5 19.3 22.7  7.7
+B F  3  9.1  8.1 18.5 21.6  7.7
+B F  4  9.1  8.2 19.2 22.2  7.7
+B F  5  9.5  8.2 19.6 22.4  7.8
+B F  6  9.8  8.9 20.4 23.9  8.8
+B F  7 10.1  9.3 20.9 24.4  8.4
+B F  8 10.3  9.5 21.3 24.7  8.9
+B F  9 10.4  9.7 21.7 25.4  8.3
+B F 10 10.8  9.5 22.5 26.3  9.1
+B F 11 11.0  9.8 22.5 25.7  8.2
+B F 12 11.2 10.0 22.8 26.9  9.4
+B F 13 11.5 11.0 24.7 29.2 10.1
+B F 14 11.6 11.0 24.6 28.5 10.4
+B F 15 11.6 11.4 23.7 27.7 10.0
+B F 16 11.7 10.6 24.9 28.5 10.4
+B F 17 11.9 11.4 26.0 30.1 10.9
+B F 18 12.0 10.7 24.6 28.9 10.5
+B F 19 12.0 11.1 25.4 29.2 11.0
+B F 20 12.6 12.2 26.1 31.6 11.2
+B F 21 12.8 11.7 27.1 31.2 11.9
+B F 22 12.8 12.2 26.7 31.1 11.1
+B F 23 12.8 12.2 27.9 31.9 11.5
+B F 24 13.0 11.4 27.3 31.8 11.3
+B F 25 13.1 11.5 27.6 32.6 11.1
+B F 26 13.2 12.2 27.9 32.1 11.5
+B F 27 13.4 11.8 28.4 32.7 11.7
+B F 28 13.7 12.5 28.6 33.8 11.9
+B F 29 13.9 13.0 30.0 34.9 13.1
+B F 30 14.7 12.5 30.1 34.7 12.5
+B F 31 14.9 13.2 30.1 35.6 12.0
+B F 32 15.0 13.8 31.7 36.9 14.0
+B F 33 15.0 14.2 32.8 37.4 14.0
+B F 34 15.1 13.3 31.8 36.3 13.5
+B F 35 15.1 13.5 31.9 37.0 13.8
+B F 36 15.1 13.8 31.7 36.6 13.0
+B F 37 15.2 14.3 33.9 38.5 14.7
+B F 38 15.3 14.2 32.6 38.3 13.8
+B F 39 15.4 13.3 32.4 37.6 13.8
+B F 40 15.5 13.8 33.4 38.7 14.7
+B F 41 15.6 13.9 32.8 37.9 13.4
+B F 42 15.6 14.7 33.9 39.5 14.3
+B F 43 15.7 13.9 33.6 38.5 14.1
+B F 44 15.8 15.0 34.5 40.3 15.3
+B F 45 16.2 15.2 34.5 40.1 13.9
+B F 46 16.4 14.0 34.2 39.8 15.2
+B F 47 16.7 16.1 36.6 41.9 15.4
+B F 48 17.4 16.9 38.2 44.1 16.6
+B F 49 17.5 16.7 38.6 44.5 17.0
+B F 50 19.2 16.5 40.9 47.9 18.1
+O M  1  9.1  6.9 16.7 18.6  7.4
+O M  2 10.2  8.2 20.2 22.2  9.0
+O M  3 10.7  8.6 20.7 22.7  9.2
+O M  4 11.4  9.0 22.7 24.8 10.1
+O M  5 12.5  9.4 23.2 26.0 10.8
+O M  6 12.5  9.4 24.2 27.0 11.2
+O M  7 12.7 10.4 26.0 28.8 12.1
+O M  8 13.2 11.0 27.1 30.4 12.2
+O M  9 13.4 10.1 26.6 29.6 12.0
+O M 10 13.7 11.0 27.5 30.5 12.2
+O M 11 14.0 11.5 29.2 32.2 13.1
+O M 12 14.1 10.4 28.9 31.8 13.5
+O M 13 14.1 10.5 29.1 31.6 13.1
+O M 14 14.1 10.7 28.7 31.9 13.3 
+O M 15 14.2 10.6 28.7 31.7 12.9
+O M 16 14.2 10.7 27.8 30.9 12.7
+O M 17 14.2 11.3 29.2 32.2 13.5
+O M 18 14.6 11.3 29.9 33.5 12.8
+O M 19 14.7 11.1 29.0 32.1 13.1
+O M 20 15.1 11.4 30.2 33.3 14.0
+O M 21 15.1 11.5 30.9 34.0 13.9
+O M 22 15.4 11.1 30.2 33.6 13.5
+O M 23 15.7 12.2 31.7 34.2 14.2
+O M 24 16.2 11.8 32.3 35.3 14.7
+O M 25 16.3 11.6 31.6 34.2 14.5
+O M 26 17.1 12.6 35.0 38.9 15.7
+O M 27 17.4 12.8 36.1 39.5 16.2
+O M 28 17.5 12.0 34.4 37.3 15.3
+O M 29 17.5 12.7 34.6 38.4 16.1
+O M 30 17.8 12.5 36.0 39.8 16.7
+O M 31 17.9 12.9 36.9 40.9 16.5
+O M 32 18.0 13.4 36.7 41.3 17.1
+O M 33 18.2 13.7 38.8 42.7 17.2
+O M 34 18.4 13.4 37.9 42.2 17.7
+O M 35 18.6 13.4 37.8 41.9 17.3
+O M 36 18.6 13.5 36.9 40.2 17.0
+O M 37 18.8 13.4 37.2 41.1 17.5
+O M 38 18.8 13.8 39.2 43.3 17.9
+O M 39 19.4 14.1 39.1 43.2 17.8
+O M 40 19.4 14.4 39.8 44.3 17.9
+O M 41 20.1 13.7 40.6 44.5 18.0
+O M 42 20.6 14.4 42.8 46.5 19.6
+O M 43 21.0 15.0 42.9 47.2 19.4
+O M 44 21.5 15.5 45.5 49.7 20.9
+O M 45 21.6 15.4 45.7 49.7 20.6
+O M 46 21.6 14.8 43.4 48.2 20.1
+O M 47 21.9 15.7 45.4 51.0 21.1
+O M 48 22.1 15.8 44.6 49.6 20.5
+O M 49 23.0 16.8 47.2 52.1 21.5
+O M 50 23.1 15.7 47.6 52.8 21.6
+O F  1 10.7  9.7 21.4 24.0  9.8
+O F  2 11.4  9.2 21.7 24.1  9.7
+O F  3 12.5 10.0 24.1 27.0 10.9
+O F  4 12.6 11.5 25.0 28.1 11.5
+O F  5 12.9 11.2 25.8 29.1 11.9
+O F  6 14.0 11.9 27.0 31.4 12.6
+O F  7 14.0 12.8 28.8 32.4 12.7
+O F  8 14.3 12.2 28.1 31.8 12.5
+O F  9 14.7 13.2 29.6 33.4 12.9
+O F 10 14.9 13.0 30.0 33.7 13.3
+O F 11 15.0 12.3 30.1 33.3 14.0
+O F 12 15.6 13.5 31.2 35.1 14.1
+O F 13 15.6 14.0 31.6 35.3 13.8
+O F 14 15.6 14.1 31.0 34.5 13.8
+O F 15 15.7 13.6 31.0 34.8 13.8
+O F 16 16.1 13.6 31.6 36.0 14.0
+O F 17 16.1 13.7 31.4 36.1 13.9
+O F 18 16.2 14.0 31.6 35.6 13.7
+O F 19 16.7 14.3 32.3 37.0 14.7
+O F 20 17.1 14.5 33.1 37.2 14.6
+O F 21 17.5 14.3 34.5 39.6 15.6
+O F 22 17.5 14.4 34.5 39.0 16.0
+O F 23 17.5 14.7 33.3 37.6 14.6
+O F 24 17.6 14.0 34.0 38.6 15.5
+O F 25 18.0 14.9 34.7 39.5 15.7
+O F 26 18.0 16.3 37.9 43.0 17.2
+O F 27 18.3 15.7 35.1 40.5 16.1
+O F 28 18.4 15.5 35.6 40.0 15.9
+O F 29 18.4 15.7 36.5 41.6 16.4
+O F 30 18.5 14.6 37.0 42.0 16.6
+O F 31 18.6 14.5 34.7 39.4 15.0
+O F 32 18.8 15.2 35.8 40.5 16.6
+O F 33 18.9 16.7 36.3 41.7 15.3
+O F 34 19.1 16.0 37.8 42.3 16.8
+O F 35 19.1 16.3 37.9 42.6 17.2
+O F 36 19.7 16.7 39.9 43.6 18.2
+O F 37 19.9 16.6 39.4 43.9 17.9
+O F 38 19.9 17.9 40.1 46.4 17.9
+O F 39 20.0 16.7 40.4 45.1 17.7
+O F 40 20.1 17.2 39.8 44.1 18.6
+O F 41 20.3 16.0 39.4 44.1 18.0
+O F 42 20.5 17.5 40.0 45.5 19.2
+O F 43 20.6 17.5 41.5 46.2 19.2
+O F 44 20.9 16.5 39.9 44.7 17.5
+O F 45 21.3 18.4 43.8 48.4 20.0
+O F 46 21.4 18.0 41.2 46.2 18.7
+O F 47 21.7 17.1 41.7 47.2 19.6
+O F 48 21.9 17.2 42.6 47.4 19.5
+O F 49 22.5 17.2 43.0 48.7 19.8
+O F 50 23.1 20.2 46.2 52.5 21.1
--- a/GPy/util/datasets/diabetes.txt
+++ b/GPy/util/datasets/diabetes.txt
@ -0,0 +1,769 @@
+6,148,72,35,0,33.6,0.627,50,1
+1,85,66,29,0,26.6,0.351,31,0
+8,183,64,0,0,23.3,0.672,32,1
+1,89,66,23,94,28.1,0.167,21,0
+0,137,40,35,168,43.1,2.288,33,1
+5,116,74,0,0,25.6,0.201,30,0
+3,78,50,32,88,31.0,0.248,26,1
+10,115,0,0,0,35.3,0.134,29,0
+2,197,70,45,543,30.5,0.158,53,1
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--- a/GPy/util/datasets/heart.dat
+++ b/GPy/util/datasets/heart.dat
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+50.0 1.0 3.0 140.0 233.0 0.0 0.0 163.0 0.0 0.6 2.0 1.0 7.0 2
+51.0 1.0 1.0 125.0 213.0 0.0 2.0 125.0 1.0 1.4 1.0 1.0 3.0 1
+54.0 1.0 2.0 192.0 283.0 0.0 2.0 195.0 0.0 0.0 1.0 1.0 7.0 2
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+58.0 1.0 3.0 132.0 224.0 0.0 2.0 173.0 0.0 3.2 1.0 2.0 7.0 2
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+64.0 0.0 4.0 130.0 303.0 0.0 0.0 122.0 0.0 2.0 2.0 2.0 3.0 1
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+55.0 1.0 4.0 140.0 217.0 0.0 0.0 111.0 1.0 5.6 3.0 0.0 7.0 2
+45.0 1.0 2.0 128.0 308.0 0.0 2.0 170.0 0.0 0.0 1.0 0.0 3.0 1
+56.0 1.0 1.0 120.0 193.0 0.0 2.0 162.0 0.0 1.9 2.0 0.0 7.0 1
+66.0 0.0 4.0 178.0 228.0 1.0 0.0 165.0 1.0 1.0 2.0 2.0 7.0 2
+38.0 1.0 1.0 120.0 231.0 0.0 0.0 182.0 1.0 3.8 2.0 0.0 7.0 2
+62.0 0.0 4.0 150.0 244.0 0.0 0.0 154.0 1.0 1.4 2.0 0.0 3.0 2
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+58.0 1.0 4.0 128.0 259.0 0.0 2.0 130.0 1.0 3.0 2.0 2.0 7.0 2
+43.0 1.0 4.0 110.0 211.0 0.0 0.0 161.0 0.0 0.0 1.0 0.0 7.0 1
+64.0 0.0 4.0 180.0 325.0 0.0 0.0 154.0 1.0 0.0 1.0 0.0 3.0 1
+50.0 0.0 4.0 110.0 254.0 0.0 2.0 159.0 0.0 0.0 1.0 0.0 3.0 1
+53.0 1.0 3.0 130.0 197.0 1.0 2.0 152.0 0.0 1.2 3.0 0.0 3.0 1
+45.0 0.0 4.0 138.0 236.0 0.0 2.0 152.0 1.0 0.2 2.0 0.0 3.0 1
+65.0 1.0 1.0 138.0 282.0 1.0 2.0 174.0 0.0 1.4 2.0 1.0 3.0 2
+69.0 1.0 1.0 160.0 234.0 1.0 2.0 131.0 0.0 0.1 2.0 1.0 3.0 1
+69.0 1.0 3.0 140.0 254.0 0.0 2.0 146.0 0.0 2.0 2.0 3.0 7.0 2
+67.0 1.0 4.0 100.0 299.0 0.0 2.0 125.0 1.0 0.9 2.0 2.0 3.0 2
+68.0 0.0 3.0 120.0 211.0 0.0 2.0 115.0 0.0 1.5 2.0 0.0 3.0 1
+34.0 1.0 1.0 118.0 182.0 0.0 2.0 174.0 0.0 0.0 1.0 0.0 3.0 1
+62.0 0.0 4.0 138.0 294.0 1.0 0.0 106.0 0.0 1.9 2.0 3.0 3.0 2
+51.0 1.0 4.0 140.0 298.0 0.0 0.0 122.0 1.0 4.2 2.0 3.0 7.0 2
+46.0 1.0 3.0 150.0 231.0 0.0 0.0 147.0 0.0 3.6 2.0 0.0 3.0 2
+67.0 1.0 4.0 125.0 254.0 1.0 0.0 163.0 0.0 0.2 2.0 2.0 7.0 2
+50.0 1.0 3.0 129.0 196.0 0.0 0.0 163.0 0.0 0.0 1.0 0.0 3.0 1
+42.0 1.0 3.0 120.0 240.0 1.0 0.0 194.0 0.0 0.8 3.0 0.0 7.0 1
+56.0 0.0 4.0 134.0 409.0 0.0 2.0 150.0 1.0 1.9 2.0 2.0 7.0 2
+41.0 1.0 4.0 110.0 172.0 0.0 2.0 158.0 0.0 0.0 1.0 0.0 7.0 2
+42.0 0.0 4.0 102.0 265.0 0.0 2.0 122.0 0.0 0.6 2.0 0.0 3.0 1
+53.0 1.0 3.0 130.0 246.0 1.0 2.0 173.0 0.0 0.0 1.0 3.0 3.0 1
+43.0 1.0 3.0 130.0 315.0 0.0 0.0 162.0 0.0 1.9 1.0 1.0 3.0 1
+56.0 1.0 4.0 132.0 184.0 0.0 2.0 105.0 1.0 2.1 2.0 1.0 6.0 2
+52.0 1.0 4.0 108.0 233.0 1.0 0.0 147.0 0.0 0.1 1.0 3.0 7.0 1
+62.0 0.0 4.0 140.0 394.0 0.0 2.0 157.0 0.0 1.2 2.0 0.0 3.0 1
+70.0 1.0 3.0 160.0 269.0 0.0 0.0 112.0 1.0 2.9 2.0 1.0 7.0 2
+54.0 1.0 4.0 140.0 239.0 0.0 0.0 160.0 0.0 1.2 1.0 0.0 3.0 1
+70.0 1.0 4.0 145.0 174.0 0.0 0.0 125.0 1.0 2.6 3.0 0.0 7.0 2
+54.0 1.0 2.0 108.0 309.0 0.0 0.0 156.0 0.0 0.0 1.0 0.0 7.0 1
+35.0 1.0 4.0 126.0 282.0 0.0 2.0 156.0 1.0 0.0 1.0 0.0 7.0 2
+48.0 1.0 3.0 124.0 255.0 1.0 0.0 175.0 0.0 0.0 1.0 2.0 3.0 1
+55.0 0.0 2.0 135.0 250.0 0.0 2.0 161.0 0.0 1.4 2.0 0.0 3.0 1
+58.0 0.0 4.0 100.0 248.0 0.0 2.0 122.0 0.0 1.0 2.0 0.0 3.0 1
+54.0 0.0 3.0 110.0 214.0 0.0 0.0 158.0 0.0 1.6 2.0 0.0 3.0 1
+69.0 0.0 1.0 140.0 239.0 0.0 0.0 151.0 0.0 1.8 1.0 2.0 3.0 1
+77.0 1.0 4.0 125.0 304.0 0.0 2.0 162.0 1.0 0.0 1.0 3.0 3.0 2
+68.0 1.0 3.0 118.0 277.0 0.0 0.0 151.0 0.0 1.0 1.0 1.0 7.0 1
+58.0 1.0 4.0 125.0 300.0 0.0 2.0 171.0 0.0 0.0 1.0 2.0 7.0 2
+60.0 1.0 4.0 125.0 258.0 0.0 2.0 141.0 1.0 2.8 2.0 1.0 7.0 2
+51.0 1.0 4.0 140.0 299.0 0.0 0.0 173.0 1.0 1.6 1.0 0.0 7.0 2
+55.0 1.0 4.0 160.0 289.0 0.0 2.0 145.0 1.0 0.8 2.0 1.0 7.0 2
+52.0 1.0 1.0 152.0 298.0 1.0 0.0 178.0 0.0 1.2 2.0 0.0 7.0 1
+60.0 0.0 3.0 102.0 318.0 0.0 0.0 160.0 0.0 0.0 1.0 1.0 3.0 1
+58.0 1.0 3.0 105.0 240.0 0.0 2.0 154.0 1.0 0.6 2.0 0.0 7.0 1
+64.0 1.0 3.0 125.0 309.0 0.0 0.0 131.0 1.0 1.8 2.0 0.0 7.0 2
+37.0 1.0 3.0 130.0 250.0 0.0 0.0 187.0 0.0 3.5 3.0 0.0 3.0 1
+59.0 1.0 1.0 170.0 288.0 0.0 2.0 159.0 0.0 0.2 2.0 0.0 7.0 2
+51.0 1.0 3.0 125.0 245.0 1.0 2.0 166.0 0.0 2.4 2.0 0.0 3.0 1
+43.0 0.0 3.0 122.0 213.0 0.0 0.0 165.0 0.0 0.2 2.0 0.0 3.0 1
+58.0 1.0 4.0 128.0 216.0 0.0 2.0 131.0 1.0 2.2 2.0 3.0 7.0 2
+29.0 1.0 2.0 130.0 204.0 0.0 2.0 202.0 0.0 0.0 1.0 0.0 3.0 1
+41.0 0.0 2.0 130.0 204.0 0.0 2.0 172.0 0.0 1.4 1.0 0.0 3.0 1
+63.0 0.0 3.0 135.0 252.0 0.0 2.0 172.0 0.0 0.0 1.0 0.0 3.0 1
+51.0 1.0 3.0 94.0 227.0 0.0 0.0 154.0 1.0 0.0 1.0 1.0 7.0 1
+54.0 1.0 3.0 120.0 258.0 0.0 2.0 147.0 0.0 0.4 2.0 0.0 7.0 1
+44.0 1.0 2.0 120.0 220.0 0.0 0.0 170.0 0.0 0.0 1.0 0.0 3.0 1
+54.0 1.0 4.0 110.0 239.0 0.0 0.0 126.0 1.0 2.8 2.0 1.0 7.0 2
+65.0 1.0 4.0 135.0 254.0 0.0 2.0 127.0 0.0 2.8 2.0 1.0 7.0 2
+57.0 1.0 3.0 150.0 168.0 0.0 0.0 174.0 0.0 1.6 1.0 0.0 3.0 1
+63.0 1.0 4.0 130.0 330.0 1.0 2.0 132.0 1.0 1.8 1.0 3.0 7.0 2
+35.0 0.0 4.0 138.0 183.0 0.0 0.0 182.0 0.0 1.4 1.0 0.0 3.0 1
+41.0 1.0 2.0 135.0 203.0 0.0 0.0 132.0 0.0 0.0 2.0 0.0 6.0 1
+62.0 0.0 3.0 130.0 263.0 0.0 0.0 97.0 0.0 1.2 2.0 1.0 7.0 2
+43.0 0.0 4.0 132.0 341.0 1.0 2.0 136.0 1.0 3.0 2.0 0.0 7.0 2
+58.0 0.0 1.0 150.0 283.0 1.0 2.0 162.0 0.0 1.0 1.0 0.0 3.0 1
+52.0 1.0 1.0 118.0 186.0 0.0 2.0 190.0 0.0 0.0 2.0 0.0 6.0 1
+61.0 0.0 4.0 145.0 307.0 0.0 2.0 146.0 1.0 1.0 2.0 0.0 7.0 2
+39.0 1.0 4.0 118.0 219.0 0.0 0.0 140.0 0.0 1.2 2.0 0.0 7.0 2
+45.0 1.0 4.0 115.0 260.0 0.0 2.0 185.0 0.0 0.0 1.0 0.0 3.0 1
+52.0 1.0 4.0 128.0 255.0 0.0 0.0 161.0 1.0 0.0 1.0 1.0 7.0 2
+62.0 1.0 3.0 130.0 231.0 0.0 0.0 146.0 0.0 1.8 2.0 3.0 7.0 1
+62.0 0.0 4.0 160.0 164.0 0.0 2.0 145.0 0.0 6.2 3.0 3.0 7.0 2
+53.0 0.0 4.0 138.0 234.0 0.0 2.0 160.0 0.0 0.0 1.0 0.0 3.0 1
+43.0 1.0 4.0 120.0 177.0 0.0 2.0 120.0 1.0 2.5 2.0 0.0 7.0 2
+47.0 1.0 3.0 138.0 257.0 0.0 2.0 156.0 0.0 0.0 1.0 0.0 3.0 1
+52.0 1.0 2.0 120.0 325.0 0.0 0.0 172.0 0.0 0.2 1.0 0.0 3.0 1
+68.0 1.0 3.0 180.0 274.0 1.0 2.0 150.0 1.0 1.6 2.0 0.0 7.0 2
+39.0 1.0 3.0 140.0 321.0 0.0 2.0 182.0 0.0 0.0 1.0 0.0 3.0 1
+53.0 0.0 4.0 130.0 264.0 0.0 2.0 143.0 0.0 0.4 2.0 0.0 3.0 1
+62.0 0.0 4.0 140.0 268.0 0.0 2.0 160.0 0.0 3.6 3.0 2.0 3.0 2
+51.0 0.0 3.0 140.0 308.0 0.0 2.0 142.0 0.0 1.5 1.0 1.0 3.0 1
+60.0 1.0 4.0 130.0 253.0 0.0 0.0 144.0 1.0 1.4 1.0 1.0 7.0 2
+65.0 1.0 4.0 110.0 248.0 0.0 2.0 158.0 0.0 0.6 1.0 2.0 6.0 2
+65.0 0.0 3.0 155.0 269.0 0.0 0.0 148.0 0.0 0.8 1.0 0.0 3.0 1
+60.0 1.0 3.0 140.0 185.0 0.0 2.0 155.0 0.0 3.0 2.0 0.0 3.0 2
+60.0 1.0 4.0 145.0 282.0 0.0 2.0 142.0 1.0 2.8 2.0 2.0 7.0 2
+54.0 1.0 4.0 120.0 188.0 0.0 0.0 113.0 0.0 1.4 2.0 1.0 7.0 2
+44.0 1.0 2.0 130.0 219.0 0.0 2.0 188.0 0.0 0.0 1.0 0.0 3.0 1
+44.0 1.0 4.0 112.0 290.0 0.0 2.0 153.0 0.0 0.0 1.0 1.0 3.0 2
+51.0 1.0 3.0 110.0 175.0 0.0 0.0 123.0 0.0 0.6 1.0 0.0 3.0 1
+59.0 1.0 3.0 150.0 212.0 1.0 0.0 157.0 0.0 1.6 1.0 0.0 3.0 1
+71.0 0.0 2.0 160.0 302.0 0.0 0.0 162.0 0.0 0.4 1.0 2.0 3.0 1
+61.0 1.0 3.0 150.0 243.0 1.0 0.0 137.0 1.0 1.0 2.0 0.0 3.0 1
+55.0 1.0 4.0 132.0 353.0 0.0 0.0 132.0 1.0 1.2 2.0 1.0 7.0 2
+64.0 1.0 3.0 140.0 335.0 0.0 0.0 158.0 0.0 0.0 1.0 0.0 3.0 2
+43.0 1.0 4.0 150.0 247.0 0.0 0.0 171.0 0.0 1.5 1.0 0.0 3.0 1
+58.0 0.0 3.0 120.0 340.0 0.0 0.0 172.0 0.0 0.0 1.0 0.0 3.0 1
+60.0 1.0 4.0 130.0 206.0 0.0 2.0 132.0 1.0 2.4 2.0 2.0 7.0 2
+58.0 1.0 2.0 120.0 284.0 0.0 2.0 160.0 0.0 1.8 2.0 0.0 3.0 2
+49.0 1.0 2.0 130.0 266.0 0.0 0.0 171.0 0.0 0.6 1.0 0.0 3.0 1
+48.0 1.0 2.0 110.0 229.0 0.0 0.0 168.0 0.0 1.0 3.0 0.0 7.0 2
+52.0 1.0 3.0 172.0 199.0 1.0 0.0 162.0 0.0 0.5 1.0 0.0 7.0 1
+44.0 1.0 2.0 120.0 263.0 0.0 0.0 173.0 0.0 0.0 1.0 0.0 7.0 1
+56.0 0.0 2.0 140.0 294.0 0.0 2.0 153.0 0.0 1.3 2.0 0.0 3.0 1
+57.0 1.0 4.0 140.0 192.0 0.0 0.0 148.0 0.0 0.4 2.0 0.0 6.0 1
+67.0 1.0 4.0 160.0 286.0 0.0 2.0 108.0 1.0 1.5 2.0 3.0 3.0 2
+
--- a/GPy/util/datasets/image.dat
+++ b/GPy/util/datasets/image.dat
--- a/GPy/util/datasets/mocap/ankur/ankurDataPoseSilhouette.mat
+++ b/GPy/util/datasets/mocap/ankur/ankurDataPoseSilhouette.mat
--- a/GPy/util/datasets/oil/DataTrn.txt
+++ b/GPy/util/datasets/oil/DataTrn.txt
--- a/GPy/util/datasets/oil/DataTrnLbls.txt
+++ b/GPy/util/datasets/oil/DataTrnLbls.txt
--- a/GPy/util/datasets/oil_flow_3classes.pickle
+++ b/GPy/util/datasets/oil_flow_3classes.pickle
--- a/GPy/util/datasets/olympics.mat
+++ b/GPy/util/datasets/olympics.mat
--- a/GPy/util/datasets/pumadyn-32nm/Dataset.data.gz
+++ b/GPy/util/datasets/pumadyn-32nm/Dataset.data.gz
--- a/GPy/util/datasets/pumadyn-32nm/Dataset.spec
+++ b/GPy/util/datasets/pumadyn-32nm/Dataset.spec
@ -0,0 +1,44 @@
+#
+# Puma forward dynamics -- 32nm = 32 inputs, high nonlinearity, med noise
+#
+#
+Origin: simulated
+
+Usage: assessment
+
+Order: uninformative
+
+Attributes:
+  1  theta1	      u  [-3.1416,3.1416]	# ang position of joint 1 in radians
+  2  theta2	      u  [-3.1416,3.1416]	# ang position of joint 2 in radians
+  3  theta3	      u  [-3.1416,3.1416]	# ang position of joint 3 in radians
+  4  theta4	      u  [-3.1416,3.1416]	# ang position of joint 4 in radians
+  5  theta5	      u  [-3.1416,3.1416]	# ang position of joint 5 in radians
+  6  theta6	      u  [-3.1416,3.1416]	# ang position of joint 6 in radians
+  7  thetad1      u  (-Inf,Inf)	# ang vel of joint 1 in rad/sec
+  8  thetad2      u  (-Inf,Inf)	# ang vel of joint 2 in rad/sec
+  9  thetad3      u  (-Inf,Inf)	# ang vel of joint 3 in rad/sec
+  10 thetad4      u  (-Inf,Inf)	# ang vel of joint 4 in rad/sec
+  11 thetad5      u  (-Inf,Inf)	# ang vel of joint 5 in rad/sec
+  12 thetad6      u  (-Inf,Inf)	# ang vel of joint 6 in rad/sec
+  13 tau1     u  (-Inf,Inf)	# torque on jt 1 
+  14 tau2     u  (-Inf,Inf)	# torque on jt 2
+  15 tau3     u  (-Inf,Inf)	# torque on jt 3
+  16 tau4     u  (-Inf,Inf)	# torque on jt 4
+  17 tau5     u  (-Inf,Inf)	# torque on jt 5
+  18 dm1   u	 [0,Inf)	# proportion change in mass of link 1
+  19 dm2   u	 [0,Inf)	# prop change in mass of link 2
+  20 dm3   u	 [0,Inf)	# prop change in mass of link 3
+  21 dm4   u	 [0,Inf)	# prop change in mass of link 4
+  22 dm5   u	 [0,Inf)	# prop change in mass of link 5
+  23 da1    u	 [0,Inf)	# prop change in length of link 1
+  24 da2    u	 [0,Inf)	# prop change in length of link 2
+  25 da3    u	 [0,Inf)	# prop change in length of link 3
+  26 da4    u	 [0,Inf)	# prop change in length of link 4
+  27 da5    u	 [0,Inf)	# prop change in length of link 5
+  28 db1      u	 [0,Inf)	# prop change in visc friction of link 1
+  29 db2      u	 [0,Inf)	# prop change in visc friction of link 2
+  30 db3      u	 [0,Inf)	# prop change in visc friction of link 3
+  31 db4      u	 [0,Inf)	# prop change in visc friction of link 4
+  32 db5      u	 [0,Inf)	# prop change in visc friction of link 5
+  33 thetadd6     u  (-Inf,Inf)	# ang acceleration of joint 6
--- a/GPy/util/datasets/pumadyn-32nm/accel/Prototask.data.gz
+++ b/GPy/util/datasets/pumadyn-32nm/accel/Prototask.data.gz
--- a/GPy/util/datasets/pumadyn-32nm/accel/Prototask.spec
+++ b/GPy/util/datasets/pumadyn-32nm/accel/Prototask.spec
@ -0,0 +1,12 @@
+#
+# Prototask.spec
+#
+Cases: all
+Origin: simulated 
+Inputs: 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32
+Order: retain
+Targets: 33
+Test-Set-Size: 4096
+Training-Set-Sizes: 64 128 256 512 1024
+Test-Set-Selection: hierarchical
+Maximum-Number-Of-Instances: 8
--- a/GPy/util/datasets/pumadyn-32nm/accel/std.prior
+++ b/GPy/util/datasets/pumadyn-32nm/accel/std.prior
@ -0,0 +1,33 @@
+  1   NLMH   real
+  2   NLMH   real
+  3   NLMH   real
+  4   NLMH   real
+  5   NLMH   real 
+  6   NLMH   real
+  7   NLMH   real
+  8   NLMH   real
+  9   NLMH   real
+ 10   NLMH   real
+ 11   NLMH   real
+ 12   NLMH   real
+ 13   NLMH   real
+ 14   NLMH   real
+ 15   NLMH   real
+ 16   NLMH   real
+ 17   NLMH   real
+ 18   NLMH   real
+ 19   NLMH   real
+ 20   NLMH   real
+ 21   NLMH   real
+ 22   NLMH   real
+ 23   NLMH   real
+ 24   NLMH   real
+ 25   NLMH   real
+ 26   NLMH   real
+ 27   NLMH   real
+ 28   NLMH   real
+ 29   NLMH   real
+ 30   NLMH   real
+ 31   NLMH   real
+ 32   NLMH   real
+ 33   NLMH   real
--- a/GPy/util/datasets/sonar_data
+++ b/GPy/util/datasets/sonar_data
@ -0,0 +1,209 @@
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+
--- a/GPy/util/datasets/swiss_roll_data.mat
+++ b/GPy/util/datasets/swiss_roll_data.mat
--- a/GPy/util/datasets/synth.te
+++ b/GPy/util/datasets/synth.te
--- a/GPy/util/datasets/synth.tr
+++ b/GPy/util/datasets/synth.tr
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--- a/GPy/util/datasets/thyroid_gland.dat
+++ b/GPy/util/datasets/thyroid_gland.dat
@ -0,0 +1,216 @@
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+3,102,5.3,1.4,1.3,6.7
+
--- a/doc/Figures/tuto_GP_regression_m1.png
+++ b/doc/Figures/tuto_GP_regression_m1.png
--- a/doc/Figures/tuto_GP_regression_m2.png
+++ b/doc/Figures/tuto_GP_regression_m2.png
--- a/doc/Figures/tuto_GP_regression_m3.png
+++ b/doc/Figures/tuto_GP_regression_m3.png
--- a/doc/GPy.examples.rst
+++ b/doc/GPy.examples.rst
@ -0,0 +1,83 @@
+examples Package
+================
+
+:mod:`examples` Package
+-----------------------
+
+.. automodule:: GPy.examples
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
+:mod:`BGPLVM_demo` Module
+-------------------------
+
+.. automodule:: GPy.examples.BGPLVM_demo
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
+:mod:`classification` Module
+----------------------------
+
+.. automodule:: GPy.examples.classification
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
+:mod:`oil_flow_demo` Module
+---------------------------
+
+.. automodule:: GPy.examples.oil_flow_demo
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
+:mod:`regression` Module
+------------------------
+
+.. automodule:: GPy.examples.regression
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
+:mod:`sparse_GPLVM_demo` Module
+-------------------------------
+
+.. automodule:: GPy.examples.sparse_GPLVM_demo
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
+:mod:`sparse_GP_regression_demo` Module
+---------------------------------------
+
+.. automodule:: GPy.examples.sparse_GP_regression_demo
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
+:mod:`uncertain_input_GP_regression_demo` Module
+------------------------------------------------
+
+.. automodule:: GPy.examples.uncertain_input_GP_regression_demo
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
+:mod:`uncollapsed_GP_demo` Module
+---------------------------------
+
+.. automodule:: GPy.examples.uncollapsed_GP_demo
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
+:mod:`unsupervised` Module
+--------------------------
+
+.. automodule:: GPy.examples.unsupervised
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
--- a/doc/GPy.kern.rst
+++ b/doc/GPy.kern.rst
@ -89,18 +89,34 @@ kern Package
    :undoc-members:
    :show-inheritance:

-:mod:`linear_ARD` Module
------------------------
+:mod:`periodic_Matern32` Module
+-------------------------------

-.. automodule:: GPy.kern.linear_ARD
+.. automodule:: GPy.kern.periodic_Matern32
    :members:
    :undoc-members:
    :show-inheritance:

-:mod:`rbf-testing` Module
-------------------------
+:mod:`periodic_Matern52` Module
+-------------------------------

-.. automodule:: GPy.kern.rbf-testing
+.. automodule:: GPy.kern.periodic_Matern52
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
+:mod:`periodic_exponential` Module
+----------------------------------
+
+.. automodule:: GPy.kern.periodic_exponential
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
+:mod:`product_orthogonal` Module
+--------------------------------
+
+.. automodule:: GPy.kern.product_orthogonal
    :members:
    :undoc-members:
    :show-inheritance:
--- a/doc/GPy.models.rst
+++ b/doc/GPy.models.rst
@ -9,6 +9,14 @@ models Package
    :undoc-members:
    :show-inheritance:

+:mod:`BGPLVM` Module
+--------------------
+
+.. automodule:: GPy.models.BGPLVM
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
 :mod:`GPLVM` Module
 -------------------

--- a/doc/GPy.rst
+++ b/doc/GPy.rst
@ -15,6 +15,7 @@ Subpackages
 .. toctree::

    GPy.core
+    GPy.examples
    GPy.inference
    GPy.kern
    GPy.models
--- a/doc/conf.py
+++ b/doc/conf.py
@ -101,6 +101,9 @@ pygments_style = 'sphinx'
 # a list of builtin themes.
 html_theme = 'default'

+# This is to revert to the default theme on readthedocs
+html_style = '/default.css'
+
 # Theme options are theme-specific and customize the look and feel of a theme
 # further.  For a list of options available for each theme, see the
 # documentation.
@ -291,4 +294,3 @@ epub_copyright = u'2013, Author'

 # Allow duplicate toc entries.
 #epub_tocdup = True
-
--- a/doc/index.rst
+++ b/doc/index.rst
@ -5,6 +5,14 @@

 Welcome to GPy's documentation!
 ===============================
+For a quick start, you can have a look at one of the tutorials:
+
+* `Basic Gaussian process regression <tuto_GP_regression.html>`_  
+* Advanced GP regression (Forthcoming)
+* Kernel manipulation (Forthcoming)
+* Writting kernels (Forthcoming)
+
+You may also be interested by some examples in the GPy/examples folder.

 Contents:

--- a/doc/tuto_GP_regression.rst
+++ b/doc/tuto_GP_regression.rst
@ -3,7 +3,7 @@
 Gaussian process regression tutorial
 *************************************

-We will see in this tutorial the basics for building a 1 dimensional and a 2 dimensional Gaussian process model, also known as a kriging model.
+We will see in this tutorial the basics for building a 1 dimensional and a 2 dimensional Gaussian process regression model, also known as a kriging model.

 We first import the libraries we will need: ::

@ -28,7 +28,7 @@ The first step is to define the covariance kernel we want to use for the model.
    noise = GPy.kern.white(D=1)
    kernel = Gaussian + noise

-The parameter D stands for the dimension of the input space. Note that many other kernels are implemented such as:
+The parameter ``D`` stands for the dimension of the input space. Note that many other kernels are implemented such as:

 * linear (``GPy.kern.linear``)
 * exponential kernel (``GPy.kern.exponential``)
@ -41,12 +41,27 @@ The inputs required for building the model are the observations and the kernel::

    m = GPy.models.GP_regression(X,Y,kernel)

-The functions ``print`` and ``plot`` can help us understand the model we have just build::
+The functions ``print`` and ``plot`` give an insight of the model we have just build. The code::

    print m
    m.plot()

-The default values of the kernel parameters may not be relevant for the current data (for example, the confidence intervals seems too wide on the previous figure). A common approach is find the values of the parameters that maximize the likelihood of the data. There are two steps for doing that with GPy:
+gives the following output: ::
+
+    Marginal log-likelihood: -2.281e+01
+           Name        |  Value   |  Constraints  |  Ties  |  Prior  
+    -----------------------------------------------------------------
+       rbf_variance    |  1.0000  |               |        |         
+      rbf_lengthscale  |  1.0000  |               |        |         
+      white_variance   |  1.0000  |               |        |         
+
+.. figure::  Figures/tuto_GP_regression_m1.png
+    :align:   center
+    :height: 350px
+
+    GP regression model before optimization of the parameters. The shaded region corresponds to 95% confidence intervals (ie +/- 2 standard deviation).
+
+The default values of the kernel parameters may not be relevant for the current data (for example, the confidence intervals seems too wide on the previous figure). A common approach is to find the values of the parameters that maximize the likelihood of the data. There are two steps for doing that with GPy:

 * Constrain the parameters of the kernel to ensure the kernel will always be a valid covariance structure (For example, we don\'t want some variances to be negative!).
 * Run the optimization
@ -57,20 +72,34 @@ There are various ways to constrain the parameters of the kernel. The most basic

 but it is also possible to set a range on to constrain one parameter to be fixed. The parameter of ``m.constrain_positive`` is a regular expression that matches the name of the parameters to be constrained (as seen in ``print m``). For example, if we want the variance to be positive, the lengthscale to be in [1,10] and the noise variance to be fixed we can write::

-    #m.unconstrain('')                            # Required if the model has been previously constrained
+    m.unconstrain('')                            # Required to remove the previous constrains
    m.constrain_positive('rbf_variance')
    m.constrain_bounded('lengthscale',1.,10. )
    m.constrain_fixed('white',0.0025)

-Once the constrains have bee imposed, the model can be optimized::
+Once the constrains have been imposed, the model can be optimized::

    m.optimize()

 If we want to perform some restarts to try to improve the result of the optimization, we can use the optimize_restart function::

    m.optimize_restarts(Nrestarts = 10)
-    m.plot()
-    print(m)
+
+Once again, we can use ``print(m)`` and ``m.plot()`` to look at the resulting model  resulting model::
+
+    Marginal log-likelihood: 2.001e+01
+           Name        |  Value   |  Constraints  |  Ties  |  Prior  
+    -----------------------------------------------------------------
+       rbf_variance    |  0.8033  |     (+ve)     |        |         
+      rbf_lengthscale  |  1.8033  |  (1.0, 10.0)  |        |         
+      white_variance   |  0.0025  |     Fixed     |        |               
+
+.. figure::  Figures/tuto_GP_regression_m2.png
+    :align:   center
+    :height: 350px
+
+    GP regression model after optimization of the parameters.
+

 2 dimensional example
 =====================
@ -102,4 +131,18 @@ Here is a 2 dimensional example::
    m.plot()
    print(m)

-The flag ``ARD=True`` in the definition of the Matern kernel specifies that we want one lengthscale parameter per dimension (ie the GP is not isotropic).
+The flag ``ARD=True`` in the definition of the Matern kernel specifies that we want one lengthscale parameter per dimension (ie the GP is not isotropic). The output of the last 2 lines is::
+
+    Marginal log-likelihood: 2.893e+01
+               Name            |  Value   |  Constraints  |  Ties  |  Prior  
+    -------------------------------------------------------------------------
+        Mat52_ARD_variance     |  0.4094  |     (+ve)     |        |         
+      Mat52_ARD_lengthscale_0  |  2.1060  |     (+ve)     |        |         
+      Mat52_ARD_lengthscale_1  |  2.0546  |     (+ve)     |        |         
+          white_variance       |  0.0012  |     (+ve)     |        |         
+
+.. figure::  Figures/tuto_GP_regression_m3.png
+    :align:   center
+    :height: 350px
+
+    Contour plot of the best predictor (posterior mean).
				`@ -0,0 +1 @@`
				`These datasets are reproduced for educational purposes only. No copyright infringement intended!`