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mucho changes to linear.py

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This commit is contained in:
James Hensman 2014-02-26 08:24:03 +00:00
parent e9957ed896
commit ec6e85b1c1
4 changed files with 32 additions and 32 deletions
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53
GPy/kern/_src/linear.py
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@ -22,22 +22,25 @@ class Linear(Kern):
:param input_dim: the number of input dimensions
:type input_dim: int
:param variances: the vector of variances :math:`\sigma^2_i`
:type variances: array or list of the appropriate size (or float if there is only one variance parameter)
:param ARD: Auto Relevance Determination. If equal to "False", the kernel has only one variance parameter \sigma^2, otherwise there is one variance parameter per dimension.
:type variances: array or list of the appropriate size (or float if there
is only one variance parameter)
:param ARD: Auto Relevance Determination. If False, the kernel has only one
variance parameter \sigma^2, otherwise there is one variance
parameter per dimension.
:type ARD: Boolean
:rtype: kernel object
"""
def __init__(self, input_dim, variances=None, ARD=False, name='linear'):
super(Linear, self).__init__(input_dim, name)
self.ARD = ARD
if ARD == False:
if not ARD:
if variances is not None:
variances = np.asarray(variances)
assert variances.size == 1, "Only one variance needed for non-ARD kernel"
else:
variances = np.ones(1)
self._Xcache, self._X2cache = np.empty(shape=(2,))
else:
if variances is not None:
variances = np.asarray(variances)
@ -103,7 +106,6 @@ class Linear(Kern):
#---------------------------------------#
# PSI statistics #
# variational #
#---------------------------------------#
def psi0(self, Z, variational_posterior):
@ -118,18 +120,16 @@ class Linear(Kern):
return np.dot(ZAinner, ZA.T)
def update_gradients_expectations(self, dL_dpsi0, dL_dpsi1, dL_dpsi2, Z, variational_posterior):
mu, S = variational_posterior.mean, variational_posterior.variance
#psi1
self.update_gradients_full(dL_dpsi1, variational_posterior.mean, Z)
# psi0:
tmp = dL_dpsi0[:, None] * self._mu2S(variational_posterior)
if self.ARD: grad = tmp.sum(0)
else: grad = np.atleast_1d(tmp.sum())
#psi1
self.update_gradients_full(dL_dpsi1, mu, Z)
grad += self.variances.gradient
if self.ARD: self.variances.gradient += tmp.sum(0)
else: self.variances.gradient += tmp.sum()
#psi2
tmp = dL_dpsi2[:, :, :, None] * (self._ZAinner(variational_posterior, Z)[:, :, None, :] * (2. * Z)[None, None, :, :])
if self.ARD: grad += tmp.sum(0).sum(0).sum(0)
else: grad += tmp.sum()
if self.ARD: self.variances.gradient += tmp.sum(0).sum(0).sum(0)
else: self.variances.gradient += tmp.sum()
def gradients_Z_expectations(self, dL_dpsi1, dL_dpsi2, Z, variational_posterior):
#psi1
@ -155,7 +155,7 @@ class Linear(Kern):
#--------------------------------------------------#
def _weave_dpsi2_dmuS(self, dL_dpsi2, Z, pv, target_mu, target_S):
def _weave_dpsi2_dmuS(self, dL_dpsi2, Z, vp, target_mu, target_S):
# Think N,num_inducing,num_inducing,input_dim
ZA = Z * self.variances
AZZA = ZA.T[:, None, :, None] * ZA[None, :, None, :]
@ -198,16 +198,15 @@ class Linear(Kern):
weave_options = {'headers' : ['<omp.h>'],
'extra_compile_args': ['-fopenmp -O3'], #-march=native'],
'extra_link_args' : ['-lgomp']}
mu = pv.mean
mu = vp.mean
N,num_inducing,input_dim,mu = mu.shape[0],Z.shape[0],mu.shape[1],param_to_array(mu)
weave.inline(code, support_code=support_code, libraries=['gomp'],
arg_names=['N','num_inducing','input_dim','mu','AZZA','AZZA_2','target_mu','target_S','dL_dpsi2'],
type_converters=weave.converters.blitz,**weave_options)
def _weave_dpsi2_dZ(self, dL_dpsi2, Z, pv, target):
AZA = self.variances*self._ZAinner(pv, Z)
def _weave_dpsi2_dZ(self, dL_dpsi2, Z, vp, target):
AZA = self.variances*self._ZAinner(vp, Z)
code="""
int n,m,mm,q;
#pragma omp parallel for private(n,mm,q)
@ -229,23 +228,23 @@ class Linear(Kern):
'extra_compile_args': ['-fopenmp -O3'], #-march=native'],
'extra_link_args' : ['-lgomp']}
N,num_inducing,input_dim = pv.mean.shape[0],Z.shape[0],pv.mean.shape[1]
mu = param_to_array(pv.mean)
N,num_inducing,input_dim = vp.mean.shape[0],Z.shape[0],vp.mean.shape[1]
mu = param_to_array(vp.mean)
weave.inline(code, support_code=support_code, libraries=['gomp'],
arg_names=['N','num_inducing','input_dim','AZA','target','dL_dpsi2'],
type_converters=weave.converters.blitz,**weave_options)
def _mu2S(self, pv):
return np.square(pv.mean) + pv.variance
def _mu2S(self, vp):
return np.square(vp.mean) + vp.variance
def _ZAinner(self, pv, Z):
def _ZAinner(self, vp, Z):
ZA = Z*self.variances
inner = (pv.mean[:, None, :] * pv.mean[:, :, None])
diag_indices = np.diag_indices(pv.mean.shape[1], 2)
inner[:, diag_indices[0], diag_indices[1]] += pv.variance
inner = (vp.mean[:, None, :] * vp.mean[:, :, None])
diag_indices = np.diag_indices(vp.mean.shape[1], 2)
inner[:, diag_indices[0], diag_indices[1]] += vp.variance
return np.dot(ZA, inner).swapaxes(0, 1) # NOTE: self.ZAinner \in [num_inducing x N x input_dim]!
return np.dot(ZA, inner).swapaxes(0, 1) # NOTE: self.ZAinner \in [num_inducing x num_data x input_dim]!
def input_sensitivity(self):
if self.ARD: return self.variances