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integrated sparse GP regression and BGPLVM classes

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Nicolo Fusi 2013-01-30 15:51:36 +00:00
parent 079a20620a
commit 2493e2d336
3 changed files with 25 additions and 23 deletions
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32
GPy/models/sparse_GP_regression.py
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@ -107,6 +107,20 @@ class sparse_GP_regression(GP_regression):
self.dL_dKmm += -0.5 * self.D * (- self.C/sf2 - 2.*mdot(self.C, self.psi2_beta_scaled, self.Kmmi) + self.Kmmi) # dC
self.dL_dKmm += np.dot(np.dot(self.E*sf2, self.psi2_beta_scaled) - np.dot(self.C, self.psi1VVpsi1), self.Kmmi) + 0.5*self.E # dD
def _set_params(self, p):
self.Z = p[:self.M*self.Q].reshape(self.M, self.Q)
self.beta = p[self.M*self.Q]
self.kern._set_params(p[self.Z.size + 1:])
self._computations()
def _get_params(self):
return np.hstack([self.Z.flatten(),self.beta,self.kern._get_params_transformed()])
def _get_param_names(self):
return sum([['iip_%i_%i'%(i,j) for i in range(self.Z.shape[0])] for j in range(self.Z.shape[1])],[]) + ['noise_precision']+self.kern._get_param_names_transformed()
def log_likelihood(self):
""" Compute the (lower bound on the) log marginal likelihood """
sf2 = self.scale_factor**2
@ -116,18 +130,9 @@ class sparse_GP_regression(GP_regression):
D = +0.5*np.sum(self.psi1VVpsi1 * self.C)
return A+B+C+D
def set_param(self, p):
self.Z = p[:self.M*self.Q].reshape(self.M, self.Q)
self.beta = p[self.M*self.Q]
self.kern.set_param(p[self.Z.size + 1:])
self._computations()
def get_param(self):
return np.hstack([self.Z.flatten(),self.beta,self.kern.extract_param()])
def get_param_names(self):
return sum([['iip_%i_%i'%(i,j) for i in range(self.Z.shape[0])] for j in range(self.Z.shape[1])],[]) + ['noise_precision']+self.kern.extract_param_names()
def _log_likelihood_gradients(self):
return np.hstack([self.dL_dZ().flatten(), self.dL_dbeta(), self.dL_dtheta()])
def dL_dbeta(self):
"""
Compute the gradient of the log likelihood wrt beta.
@ -172,9 +177,6 @@ class sparse_GP_regression(GP_regression):
dL_dZ += self.kern.dK_dX(dL_dpsi1,self.Z,self.X)
return dL_dZ
def log_likelihood_gradients(self):
return np.hstack([self.dL_dZ().flatten(), self.dL_dbeta(), self.dL_dtheta()])
def _raw_predict(self, Xnew, slices, full_cov=False):
"""Internal helper function for making predictions, does not account for normalisation"""