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Some tidying in the uncollapsed GP

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James Hensman 2012-12-03 10:53:37 -08:00
parent 5f22bd00e6
commit 1a9872c187
1 changed files with 12 additions and 13 deletions
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25
GPy/models/uncollapsed_sparse_GP.py
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@ -17,6 +17,8 @@ class uncollapsed_sparse_GP(sparse_GP_regression):
:type X: np.ndarray (N x Q)
:param Y: observed data
:type Y: np.ndarray of observations (N x D)
:param q_u: canonical parameters of the distribution squasehd into a 1D array
:type q_u: np.ndarray
:param kernel : the kernel/covariance function. See link kernels
:type kernel: a GPy kernel
:param Z: inducing inputs (optional, see note)
@ -30,20 +32,15 @@ class uncollapsed_sparse_GP(sparse_GP_regression):
:type normalize_(X|Y): bool
"""
def __init__(self, X, Y, q_u=None, kernel=None, beta=100., Z=None,Zslices=None,M=10,normalize_X=False,normalize_Y=False):
def __init__(self, X, Y, q_u=None, *args, **kwargs)
D = Y.shape[1]
if q_u is None:
if Z is None:
self.q_u_mean = np.zeros((M,D))
self.q_u_cov = np.eye(M) #note: each output dist will have the same covariance...
self.q_u_prec = np.eye(M)
M = Z.shape[0]
else:
self.q_u_mean = np.zeros((Z.shape[0],D))
self.q_u_cov = np.eye(Z.shape[0])
self.q_u_prec = np.eye(Z.shape[0])
sparse_GP_regression.__init__(self, X, Y, kernel=kernel, normalize_X=normalize_X, normalize_Y=normalize_Y)
M=M
self.set_vb_param(np.hstack((np.ones(M*D)),np.eye(M).flatten()))
sparse_GP_regression.__init__(self, X, Y, *args, **kwargs)
def _computations(self):
self.V = self.beta*self.Y
@ -58,7 +55,6 @@ class uncollapsed_sparse_GP(sparse_GP_regression):
# Compute dL_dpsi
self.dL_dpsi0 = - 0.5 * self.D * self.beta * np.ones(self.N)
dC_dpsi1 =
self.dL_dpsi1 =
self.dL_dpsi2 =
@ -104,13 +100,16 @@ class uncollapsed_sparse_GP(sparse_GP_regression):
self.q_u_cov = np.dot(self.q_u_cov_L,self.q_u_cov_L.T)
self.q_u_mean = -2.*np.dot(self.q_u_cov,vb_param[:self.M*self.D].reshape(self.M,self.D))
self.q_u_expectation = (self.q_u_mean, np.dot(self.q_u_mean,self.q_u_mean.T)+self.q_u_cov)
self.q_u_canonical = (np.dot(self.q_u_prec, self.q_u_mean),-0.5*self.q_u_prec)
#TODO: computations now?
def get_vb_param(self):
"""
Return the canonical paramters of the distribution q(u)
Return the canonical parameters of the distribution q(u)
"""
return np.hstack((-0.5*np.dot(self.q_u_prec, self.q_u_mean).flatten()-0.5*self.q_u_prec.flatten()))
return np.hstack([e.flatten() for e in self.q_u_canonical])
def vb_grad_natgrad(self):
"""