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Source code for networkx.algorithms.flow.utils

# -*- coding: utf-8 -*-
Utility classes and functions for network flow algorithms.

__author__ = """ysitu <ysitu@users.noreply.github.com>"""
# Copyright (C) 2014 ysitu <ysitu@users.noreply.github.com>
# All rights reserved.
# BSD license.

from collections import deque
import networkx as nx

__all__ = ['CurrentEdge', 'Level', 'GlobalRelabelThreshold',
           'build_residual_network', 'detect_unboundedness', 'build_flow_dict']

class CurrentEdge(object):
    """Mechanism for iterating over out-edges incident to a node in a circular
    manner. StopIteration exception is raised when wraparound occurs.
    __slots__ = ('_edges', '_it', '_curr')

    def __init__(self, edges):
        self._edges = edges
        if self._edges:

    def get(self):
        return self._curr

    def move_to_next(self):
            self._curr = next(self._it)
        except StopIteration:

    def _rewind(self):
        self._it = iter(self._edges.items())
        self._curr = next(self._it)

class Level(object):
    """Active and inactive nodes in a level.
    __slots__ = ('active', 'inactive')

    def __init__(self):
        self.active = set()
        self.inactive = set()

class GlobalRelabelThreshold(object):
    """Measurement of work before the global relabeling heuristic should be

    def __init__(self, n, m, freq):
        self._threshold = (n + m) / freq if freq else float('inf')
        self._work = 0

    def add_work(self, work):
        self._work += work

    def is_reached(self):
        return self._work >= self._threshold

    def clear_work(self):
        self._work = 0

[docs]def build_residual_network(G, capacity): """Build a residual network and initialize a zero flow. The residual network :samp:`R` from an input graph :samp:`G` has the same nodes as :samp:`G`. :samp:`R` is a DiGraph that contains a pair of edges :samp:`(u, v)` and :samp:`(v, u)` iff :samp:`(u, v)` is not a self-loop, and at least one of :samp:`(u, v)` and :samp:`(v, u)` exists in :samp:`G`. For each edge :samp:`(u, v)` in :samp:`R`, :samp:`R[u][v]['capacity']` is equal to the capacity of :samp:`(u, v)` in :samp:`G` if it exists in :samp:`G` or zero otherwise. If the capacity is infinite, :samp:`R[u][v]['capacity']` will have a high arbitrary finite value that does not affect the solution of the problem. This value is stored in :samp:`R.graph['inf']`. For each edge :samp:`(u, v)` in :samp:`R`, :samp:`R[u][v]['flow']` represents the flow function of :samp:`(u, v)` and satisfies :samp:`R[u][v]['flow'] == -R[v][u]['flow']`. The flow value, defined as the total flow into :samp:`t`, the sink, is stored in :samp:`R.graph['flow_value']`. If :samp:`cutoff` is not specified, reachability to :samp:`t` using only edges :samp:`(u, v)` such that :samp:`R[u][v]['flow'] < R[u][v]['capacity']` induces a minimum :samp:`s`-:samp:`t` cut. """ if G.is_multigraph(): raise nx.NetworkXError( 'MultiGraph and MultiDiGraph not supported (yet).') R = nx.DiGraph() R.add_nodes_from(G) inf = float('inf') # Extract edges with positive capacities. Self loops excluded. edge_list = [(u, v, attr) for u, v, attr in G.edges(data=True) if u != v and attr.get(capacity, inf) > 0] # Simulate infinity with three times the sum of the finite edge capacities # or any positive value if the sum is zero. This allows the # infinite-capacity edges to be distinguished for unboundedness detection # and directly participate in residual capacity calculation. If the maximum # flow is finite, these edges cannot appear in the minimum cut and thus # guarantee correctness. Since the residual capacity of an # infinite-capacity edge is always at least 2/3 of inf, while that of an # finite-capacity edge is at most 1/3 of inf, if an operation moves more # than 1/3 of inf units of flow to t, there must be an infinite-capacity # s-t path in G. inf = 3 * sum(attr[capacity] for u, v, attr in edge_list if capacity in attr and attr[capacity] != inf) or 1 if G.is_directed(): for u, v, attr in edge_list: r = min(attr.get(capacity, inf), inf) if not R.has_edge(u, v): # Both (u, v) and (v, u) must be present in the residual # network. R.add_edge(u, v, capacity=r) R.add_edge(v, u, capacity=0) else: # The edge (u, v) was added when (v, u) was visited. R[u][v]['capacity'] = r else: for u, v, attr in edge_list: # Add a pair of edges with equal residual capacities. r = min(attr.get(capacity, inf), inf) R.add_edge(u, v, capacity=r) R.add_edge(v, u, capacity=r) # Record the value simulating infinity. R.graph['inf'] = inf return R
def detect_unboundedness(R, s, t): """Detect an infinite-capacity s-t path in R. """ q = deque([s]) seen = set([s]) inf = R.graph['inf'] while q: u = q.popleft() for v, attr in R[u].items(): if attr['capacity'] == inf and v not in seen: if v == t: raise nx.NetworkXUnbounded( 'Infinite capacity path, flow unbounded above.') seen.add(v) q.append(v) def build_flow_dict(G, R): """Build a flow dictionary from a residual network. """ flow_dict = {} for u in G: flow_dict[u] = {v: 0 for v in G[u]} flow_dict[u].update((v, attr['flow']) for v, attr in R[u].items() if attr['flow'] > 0) return flow_dict