def _forward_dataset(self, ds):
targ = np.copy(ds.targets)
mapped = None
X = None
i,j = ds.shape
if self._stack=='v':
chunks = np.unique(ds.sa[self._chunks_attr].value)
else:
chunks = np.unique(ds.fa[self._chunks_attr].value)
for ch,chunk in enumerate(chunks):
if self._stack == 'v':
table = ds[ds.sa[self._chunks_attr].value==chunk,:]
if self._stack == 'h':
table = ds[:,ds.fa[self._chunks_attr].value==chunk]
table = self.center_and_norm_table(table,
col_mean=self._subtable_stats[ch]['col_mean'],
col_norm=self._subtable_stats[ch]['col_norm'],
table_norm = self._subtable_stats[ch]['table_norm'])[0]
if self._stack =='v':
# Assume features align, use average Q
Q_ = None
for subtab in np.unique(self.subtable_idx):
if Q_ is None:
Q_ = self.Q[self.subtable_idx==subtab,:]
else:
Q_ = Q_ + self.Q[self.subtable_idx==subtab,:]
Q_ = Q_ / len(np.unique(self.subtable_idx))
part = Dataset(np.dot(table.samples,Q_))
part.sa = table.sa
if self._stack =='h':
# Assume same number of features as in X
part = Dataset(np.dot(table,self.Q[self.subtable_idx==chunk,:]))
part.sa = table.sa
part.sa['chunks'] = [chunk]*part.shape[0]
if mapped is None:
mapped = part.copy()
X = table
else:
mapped.append(part.copy())
X.append(table,'h')
mapped.a['X'] = X
if self.keep_dims == 'all':
self.keep_dims = range(mapped.shape[1])
return mapped[:,self.keep_dims]
