def get_optimal_chains(digraph, ndds, edge_success_prob=1):
# Chain edges
chain_next_vv = {
e.src.id: e.tgt.id
for e in digraph.es for var in e.grb_vars if var.x > 0.1
}
optimal_chains = []
for i, ndd in enumerate(ndds):
chain_edges = []
for e in ndd.edges:
if e.edge_var.x > 0.1:
assert len(chain_edges) == 0
chain_edges.append(e)
vtx_indices = find_selected_path(e.tgt.id, chain_next_vv)
# Get weight of edge from NDD
weight = e.weight * edge_success_prob
# Add weights of edges between vertices
for j in range(len(vtx_indices) - 1):
weight += digraph.adj_mat[vtx_indices[j]][vtx_indices[
j + 1]].weight * edge_success_prob**(j + 2)
chain_edges.append(
digraph.adj_mat[vtx_indices[j]][vtx_indices[j + 1]])
optimal_chains.append(kidney_ndds.Chain(
i, vtx_indices, weight))
return optimal_chains
def get_optimal_chains(digraph, ndds, edge_success_prob=1):
# Chain edges
chain_next_vv = {e.src.id: e.tgt.id
for e in digraph.es
for var in e.grb_vars
if var.x > 0.1}
optimal_chains = []
for i, ndd in enumerate(ndds):
for e in ndd.edges:
if e.edge_var.x > 0.1:
vtx_indices = find_selected_path(e.target_v.id, chain_next_vv)
# Get score of edge from NDD
score = e.score * edge_success_prob
# Add scores of edges between vertices
for j in range(len(vtx_indices) - 1):
score += digraph.adj_mat[vtx_indices[j]][vtx_indices[j+1]].score * edge_success_prob**(j+2)
optimal_chains.append(kidney_ndds.Chain(i, vtx_indices, score))
return optimal_chains