def _update_inverses(self):
"""
Calculates and stores kernel, and its inverses.
"""
for j in range(self.num_latent_proc):
self.Kzz[j, :, :] = self.kernels_latent[j].K(self.Z[j, :, :])
self.chol[j, :, :] = jitchol(self.Kzz[j, :, :])
self.invZ[j, :, :] = inv_chol(self.chol[j, :, :])
self.log_detZ[j] = pddet(self.chol[j, :, :])
self.hypers_changed = False
self.inducing_changed = False
def _update_inverses(self):
"""
Calculates and stores kernel, and its inverses.
"""
for j in range(self.num_latent_proc):
self.Kzz[j, :, :] = self.kernels_latent[j].K(self.Z[j, :, :])
self.chol[j, :, :] = jitchol(self.Kzz[j, :, :])
self.invZ[j, :, :] = inv_chol(self.chol[j, :, :])
self.log_detZ[j] = pddet(self.chol[j, :, :])
self.hypers_changed = False
self.inducing_changed = False
def update_covariance(self, j, Sj):
Sj = Sj.copy()
mm = min(Sj[np.diag_indices_from(Sj)])
if mm < 0:
Sj[np.diag_indices_from(Sj)] = Sj[np.diag_indices_from(Sj)] - 1.1 * mm
for k in range(self.num_comp):
self.s[k,j] = Sj.copy()
self.L[k,j] = jitchol(Sj,10)
tmp = self.L[k,j].copy()
tmp[np.diag_indices_from(tmp)] = np.log(tmp[np.diag_indices_from(tmp)])
self.L_flatten[k,j] = tmp[np.tril_indices_from(tmp)]
self._update()
def update_covariance(self, j, Sj):
Sj = Sj.copy()
mm = min(Sj[np.diag_indices_from(Sj)])
if mm < 0:
Sj[np.diag_indices_from(
Sj)] = Sj[np.diag_indices_from(Sj)] - 1.1 * mm
for k in range(self.num_comp):
self.s[k, j] = Sj.copy()
self.L[k, j] = jitchol(Sj, 10)
tmp = self.L[k, j].copy()
tmp[np.diag_indices_from(tmp)] = np.log(
tmp[np.diag_indices_from(tmp)])
self.L_flatten[k, j] = tmp[np.tril_indices_from(tmp)]
self._update()
