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

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This commit is contained in:
Ricardo 2014-09-09 10:52:26 +01:00
commit 71b435b2ab
71 changed files with 1949 additions and 733 deletions
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2
GPy/kern/_src/ODE_UY.py
View file

@ -17,7 +17,7 @@ class ODE_UY(Kern):
self.lengthscale_Y = Param('lengthscale_Y', lengthscale_Y, Logexp())
self.lengthscale_U = Param('lengthscale_U', lengthscale_Y, Logexp())
self.add_parameters(self.variance_Y, self.variance_U, self.lengthscale_Y, self.lengthscale_U)
self.link_parameters(self.variance_Y, self.variance_U, self.lengthscale_Y, self.lengthscale_U)
def K(self, X, X2=None):
# model : a * dy/dt + b * y = U

8
GPy/kern/_src/add.py
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@ -18,7 +18,7 @@ class Add(CombinationKernel):
if isinstance(kern, Add):
del subkerns[i]
for part in kern.parts[::-1]:
kern.remove_parameter(part)
kern.unlink_parameter(part)
subkerns.insert(i, part)
super(Add, self).__init__(subkerns, name)
@ -171,10 +171,10 @@ class Add(CombinationKernel):
if isinstance(other, Add):
other_params = other.parameters[:]
for p in other_params:
other.remove_parameter(p)
self.add_parameters(*other_params)
other.unlink_parameter(p)
self.link_parameters(*other_params)
else:
self.add_parameter(other)
self.link_parameter(other)
self.input_dim, self.active_dims = self.get_input_dim_active_dims(self.parts)
return self

2
GPy/kern/_src/brownian.py
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@ -22,7 +22,7 @@ class Brownian(Kern):
super(Brownian, self).__init__(input_dim, active_dims, name)
self.variance = Param('variance', variance, Logexp())
self.add_parameters(self.variance)
self.link_parameters(self.variance)
def K(self,X,X2=None):
if X2 is None:

2
GPy/kern/_src/coregionalize.py
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@ -50,7 +50,7 @@ class Coregionalize(Kern):
else:
assert kappa.shape==(self.output_dim, )
self.kappa = Param('kappa', kappa, Logexp())
self.add_parameters(self.W, self.kappa)
self.link_parameters(self.W, self.kappa)
def parameters_changed(self):
self.B = np.dot(self.W, self.W.T) + np.diag(self.kappa)

2
GPy/kern/_src/kern.py
View file

@ -221,7 +221,7 @@ class CombinationKernel(Kern):
# initialize the kernel with the full input_dim
super(CombinationKernel, self).__init__(input_dim, active_dims, name)
self.extra_dims = extra_dims
self.add_parameters(*kernels)
self.link_parameters(*kernels)
@property
def parts(self):

6
GPy/kern/_src/linear.py
View file

@ -49,7 +49,7 @@ class Linear(Kern):
variances = np.ones(self.input_dim)
self.variances = Param('variances', variances, Logexp())
self.add_parameter(self.variances)
self.link_parameter(self.variances)
self.psicomp = PSICOMP_Linear()
@Cache_this(limit=2)
@ -103,7 +103,7 @@ class Linear(Kern):
def gradients_X_diag(self, dL_dKdiag, X):
return 2.*self.variances*dL_dKdiag[:,None]*X
def input_sensitivity(self):
def input_sensitivity(self, summarize=True):
return np.ones(self.input_dim) * self.variances
#---------------------------------------#
@ -144,7 +144,7 @@ class LinearFull(Kern):
self.W = Param('W', W)
self.kappa = Param('kappa', kappa, Logexp())
self.add_parameters(self.W, self.kappa)
self.link_parameters(self.W, self.kappa)
def K(self, X, X2=None):
P = np.dot(self.W, self.W.T) + np.diag(self.kappa)

2
GPy/kern/_src/mlp.py
View file

@ -36,7 +36,7 @@ class MLP(Kern):
self.variance = Param('variance', variance, Logexp())
self.weight_variance = Param('weight_variance', weight_variance, Logexp())
self.bias_variance = Param('bias_variance', bias_variance, Logexp())
self.add_parameters(self.variance, self.weight_variance, self.bias_variance)
self.link_parameters(self.variance, self.weight_variance, self.bias_variance)
def K(self, X, X2=None):

2
GPy/kern/_src/periodic.py
View file

@ -33,7 +33,7 @@ class Periodic(Kern):
self.variance = Param('variance', np.float64(variance), Logexp())
self.lengthscale = Param('lengthscale', np.float64(lengthscale), Logexp())
self.period = Param('period', np.float64(period), Logexp())
self.add_parameters(self.variance, self.lengthscale, self.period)
self.link_parameters(self.variance, self.lengthscale, self.period)
def _cos(self, alpha, omega, phase):
def f(x):

2
GPy/kern/_src/poly.py
View file

@ -14,7 +14,7 @@ class Poly(Kern):
def __init__(self, input_dim, variance=1., order=3., active_dims=None, name='poly'):
super(Poly, self).__init__(input_dim, active_dims, name)
self.variance = Param('variance', variance, Logexp())
self.add_parameter(self.variance)
self.link_parameter(self.variance)
self.order=order
def K(self, X, X2=None):

3
GPy/kern/_src/psi_comp/__init__.py
View file

@ -29,6 +29,9 @@ class PSICOMP_RBF(Pickleable):
else:
raise ValueError, "unknown distriubtion received for psi-statistics"
def _setup_observers(self):
pass
class PSICOMP_Linear(Pickleable):
@Cache_this(limit=2, ignore_args=(0,))

2
GPy/kern/_src/static.py
View file

@ -11,7 +11,7 @@ class Static(Kern):
def __init__(self, input_dim, variance, active_dims, name):
super(Static, self).__init__(input_dim, active_dims, name)
self.variance = Param('variance', variance, Logexp())
self.add_parameters(self.variance)
self.link_parameters(self.variance)
def Kdiag(self, X):
ret = np.empty((X.shape[0],), dtype=np.float64)

4
GPy/kern/_src/stationary.py
View file

@ -61,7 +61,7 @@ class Stationary(Kern):
self.lengthscale = Param('lengthscale', lengthscale, Logexp())
self.variance = Param('variance', variance, Logexp())
assert self.variance.size==1
self.add_parameters(self.variance, self.lengthscale)
self.link_parameters(self.variance, self.lengthscale)
def K_of_r(self, r):
raise NotImplementedError, "implement the covariance function as a fn of r to use this class"
@ -343,7 +343,7 @@ class RatQuad(Stationary):
def __init__(self, input_dim, variance=1., lengthscale=None, power=2., ARD=False, active_dims=None, name='RatQuad'):
super(RatQuad, self).__init__(input_dim, variance, lengthscale, ARD, active_dims, name)
self.power = Param('power', power, Logexp())
self.add_parameters(self.power)
self.link_parameters(self.power)
def K_of_r(self, r):
r2 = np.power(r, 2.)