def unmerge(self, cutoff=1e-10):
"""
Separate the cores in our MergedInput and return an InputRegion
The length of the resultant InputRegion will be identical to our
original MergedInput (same number of inputs), but its core_len will
be doubled (twice as many individual cores)
"""
bond_str = 'slrij'
tensor = self.tensor
svd_string = 'lrij->lui,urj'
max_D = tensor.size(1)
# Split every one of the cores into two and add them both to core_list
core_list, bond_list, sv_list = [], [-1], [-1]
for merged_core in tensor:
sv_vec = torch.empty(max_D)
left_core, right_core, bond_dim = svd_flex(merged_core,
svd_string,
max_D,
cutoff,
sv_vec=sv_vec)
core_list += [left_core, right_core]
bond_list += [bond_dim, -1]
sv_list += [sv_vec, -1]
# Collate the split cores into one tensor and return as an InputRegion
tensor = torch.stack(core_list)
return [InputRegion(tensor)], bond_list, sv_list
def unmerge(self, cutoff=1e-10):
"""
Split our MergedOutput into an OutputSite and an InputSite
The non-zero entries of our tensors are dynamically sized according to
the SVD cutoff, but will generally be padded with zeros to give the
new index a regular size.
"""
bond_str = 'olri'
tensor = self.tensor
left_output = self.left_output
if left_output:
svd_string = 'olri->olu,uri'
max_D = tensor.size(2)
sv_vec = torch.empty(max_D)
output_core, input_core, bond_dim = svd_flex(tensor,
svd_string,
max_D,
cutoff,
sv_vec=sv_vec)
return ([OutputSite(output_core),
InputSite(input_core)], [-1, bond_dim,
-1], [-1, sv_vec, -1])
else:
svd_string = 'olri->our,lui'
max_D = tensor.size(1)
sv_vec = torch.empty(max_D)
output_core, input_core, bond_dim = svd_flex(tensor,
svd_string,
max_D,
cutoff,
sv_vec=sv_vec)
return ([InputSite(input_core),
OutputSite(output_core)], [-1, bond_dim,
-1], [-1, sv_vec, -1])