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Added a hack fix as suggested by max, zeroing any negative values (should really be numerically negative values on diagonal)

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Alan Saul 2014-03-14 15:23:32 +00:00
parent a56928e11a
commit 4757265b24
3 changed files with 30 additions and 20 deletions
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28
GPy/kern/_src/stationary.py
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@ -15,21 +15,21 @@ class Stationary(Kern):
"""
Stationary kernels (covariance functions).
Stationary covariance fucntion depend only on r, where r is defined as
Stationary covariance fucntion depend only on r, where r is defined as
r = \sqrt{ \sum_{q=1}^Q (x_q - x'_q)^2 }
The covariance function k(x, x' can then be written k(r).
The covariance function k(x, x' can then be written k(r).
In this implementation, r is scaled by the lengthscales parameter(s):
r = \sqrt{ \sum_{q=1}^Q \frac{(x_q - x'_q)^2}{\ell_q^2} }.
r = \sqrt{ \sum_{q=1}^Q \frac{(x_q - x'_q)^2}{\ell_q^2} }.
By default, there's only one lengthscale: seaprate lengthscales for each
dimension can be enables by setting ARD=True.
dimension can be enables by setting ARD=True.
To implement a stationary covariance function using this class, one need
only define the covariance function k(r), and it derivative.
only define the covariance function k(r), and it derivative.
...
def K_of_r(self, r):
@ -37,10 +37,10 @@ class Stationary(Kern):
def dK_dr(self, r):
return bar
The lengthscale(s) and variance parameters are added to the structure automatically.
The lengthscale(s) and variance parameters are added to the structure automatically.
"""
def __init__(self, input_dim, variance, lengthscale, ARD, active_dims, name):
super(Stationary, self).__init__(input_dim, active_dims, name)
self.ARD = ARD
@ -57,7 +57,7 @@ class Stationary(Kern):
if lengthscale.size != input_dim:
lengthscale = np.ones(input_dim)*lengthscale
else:
lengthscale = np.ones(self.input_dim)
lengthscale = np.ones(self.input_dim)
self.lengthscale = Param('lengthscale', lengthscale, Logexp())
self.variance = Param('variance', variance, Logexp())
assert self.variance.size==1
@ -95,7 +95,9 @@ class Stationary(Kern):
#X2, = self._slice_X(X2)
X1sq = np.sum(np.square(X),1)
X2sq = np.sum(np.square(X2),1)
return np.sqrt(-2.*np.dot(X, X2.T) + (X1sq[:,None] + X2sq[None,:]))
r2 = -2.*np.dot(X, X2.T) + X1sq[:,None] + X2sq[None,:]
r2[r2<0] = 0. # A bit hacky
return np.sqrt(r2)
@Cache_this(limit=5, ignore_args=())
def _scaled_dist(self, X, X2=None):
@ -133,7 +135,7 @@ class Stationary(Kern):
if self.ARD:
#rinv = self._inv_dis# this is rather high memory? Should we loop instead?t(X, X2)
#d = X[:, None, :] - X2[None, :, :]
#x_xl3 = np.square(d)
#x_xl3 = np.square(d)
#self.lengthscale.gradient = -((dL_dr*rinv)[:,:,None]*x_xl3).sum(0).sum(0)/self.lengthscale**3
tmp = dL_dr*self._inv_dist(X, X2)
if X2 is None: X2 = X
@ -247,7 +249,7 @@ class Matern52(Stationary):
.. math::
k(r) = \sigma^2 (1 + \sqrt{5} r + \\frac53 r^2) \exp(- \sqrt{5} r)
k(r) = \sigma^2 (1 + \sqrt{5} r + \\frac53 r^2) \exp(- \sqrt{5} r)
"""
def __init__(self, input_dim, variance=1., lengthscale=None, ARD=False, active_dims=None, name='Mat52'):
super(Matern52, self).__init__(input_dim, variance, lengthscale, ARD, active_dims, name)