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Merge branch 'master' of github.com:SheffieldML/GPy

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This commit is contained in:
Alan Saul 2013-01-18 17:44:31 +00:00
commit 6d1b93faf3
2 changed files with 7 additions and 9 deletions
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10
GPy/kern/rbf.py
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@ -8,13 +8,13 @@ import hashlib
class rbf(kernpart): class rbf(kernpart):
""" """
Radial Basis Function kernel, aka squared-exponential, exponentiated quadratic or Gaussian kernel. Radial Basis Function kernel, aka squared-exponential, exponentiated quadratic or Gaussian kernel:
.. math:: .. math::
k(r) = \sigma^2 \exp(- \frac{r^2}{2\ell}) \qquad \qquad \\text{ where } r = \sqrt{\frac{\sum_{i=1}^d (x_i-x^\prime_i)^2}{\ell^2}} k(r) = \sigma^2 \exp(- \frac{1}{2}r^2) \qquad \qquad \\text{ where } r^2 = \sum_{i=1}^d \frac{ (x_i-x^\prime_i)^2}{\ell_i^2}}
where \ell is the lengthscale, \alpha the smoothness, \sigma^2 the variance and d the dimensionality of the input. where \ell_i is the lengthscale, \sigma^2 the variance and d the dimensionality of the input.
:param D: the number of input dimensions :param D: the number of input dimensions
:type D: int :type D: int
@ -38,7 +38,6 @@ class rbf(kernpart):
assert lengthscale.shape == (1,) assert lengthscale.shape == (1,)
else: else:
lengthscale = np.ones(1) lengthscale = np.ones(1)
else: else:
self.Nparam = self.D + 1 self.Nparam = self.D + 1
self.name = 'rbf_ARD' self.name = 'rbf_ARD'
@ -109,11 +108,10 @@ class rbf(kernpart):
self._X2 = X2 self._X2 = X2
if X2 is None: X2 = X if X2 is None: X2 = X
self._K_dist = X[:,None,:]-X2[None,:,:] # this can be computationally heavy self._K_dist = X[:,None,:]-X2[None,:,:] # this can be computationally heavy
self._params = np.empty(shape=(1,0))#ensure the next section gets called self._params = np.empty(shape=(1,0)) #ensure the next section gets called
if not np.all(self._params == self._get_params()): if not np.all(self._params == self._get_params()):
self._params == self._get_params() self._params == self._get_params()
self._K_dist2 = np.square(self._K_dist/self.lengthscale) self._K_dist2 = np.square(self._K_dist/self.lengthscale)
#self._K_exponent = -0.5*self._K_dist2.sum(-1) #ND: commented out because seems not to be used
self._K_dvar = np.exp(-0.5*self._K_dist2.sum(-1)) self._K_dvar = np.exp(-0.5*self._K_dist2.sum(-1))
def psi0(self,Z,mu,S,target): def psi0(self,Z,mu,S,target):

2
GPy/util/datasets.py
View file

@ -90,7 +90,7 @@ def toy_rbf_1d(seed=default_seed):
N = 500 N = 500
X = np.random.uniform(low=-1.0, high=1.0, size=(N, numIn)) X = np.random.uniform(low=-1.0, high=1.0, size=(N, numIn))
X.sort(axis=0) X.sort(axis=0)
rbf = GPy.kern.rbf(numIn, variance=1., lengthscale=0.25) rbf = GPy.kern.rbf(numIn, variance=1., lengthscale=np.array((0.25,)))
white = GPy.kern.white(numIn, variance=1e-2) white = GPy.kern.white(numIn, variance=1e-2)
kernel = rbf + white kernel = rbf + white
K = kernel.K(X) K = kernel.K(X)