def _init_covar(self, partial_fit, n_chunks):
nsave = int(max(math.log(n_chunks, 2), 2))
# in case we do a one shot estimation, we want to re-initialize running_covar
if not hasattr(self, '_covar') or not partial_fit:
self._logger.debug("using %s moments for %i chunks" % (nsave, n_chunks))
self._covar = running_covar(xx=True, xy=False, yy=False,
remove_mean=True, symmetrize=False,
nsave=nsave)
else:
# check storage size vs. n_chunks of the new iterator
old_nsave = self._covar.storage_XX.nsave
if old_nsave < nsave or old_nsave > nsave:
self.logger.info("adopting storage size")
self._covar.storage_XX.nsave = nsave
self._covar.storage_XY.nsave = nsave
def _init_covar(self, partial_fit, n_chunks):
nsave = min(int(max(log(n_chunks, 2), 2)), self.ncov_max)
if self._rc is not None and partial_fit:
# check storage size vs. n_chunks of the new iterator
old_nsave = self.nsave
if old_nsave < nsave:
self.logger.info("adapting storage size")
self.nsave = nsave
else: # in case we do a one shot estimation, we want to re-initialize running_covar
self.logger.debug("using %s moments for %i chunks", nsave, n_chunks)
self._rc = running_covar(xx=self.c00, xy=self.c0t, yy=self.ctt,
remove_mean=self.remove_data_mean, symmetrize=self.reversible,
sparse_mode=self.sparse_mode, modify_data=self.modify_data,
column_selection=self.column_selection, diag_only=self.diag_only,
nsave=nsave)
