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

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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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14
GPy/kern/rbf.py
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@ -8,13 +8,13 @@ import hashlib
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::
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
:type D: int
@ -37,8 +37,7 @@ class rbf(kernpart):
if lengthscale is not None:
assert lengthscale.shape == (1,)
else:
lengthscale = np.ones(1)
lengthscale = np.ones(1)
else:
self.Nparam = self.D + 1
self.name = 'rbf_ARD'
@ -109,11 +108,10 @@ class rbf(kernpart):
self._X2 = X2
if X2 is None: X2 = X
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()):
self._params == self._get_params()
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_dist2 = np.square(self._K_dist/self.lengthscale)
self._K_dvar = np.exp(-0.5*self._K_dist2.sum(-1))
def psi0(self,Z,mu,S,target):