diff options
Diffstat (limited to 'networkx/algorithms/flow')
| -rw-r--r-- | networkx/algorithms/flow/maxflow.py | 15 | ||||
| -rw-r--r-- | networkx/algorithms/flow/tests/test_maxflow.py | 11 |
2 files changed, 13 insertions, 13 deletions
diff --git a/networkx/algorithms/flow/maxflow.py b/networkx/algorithms/flow/maxflow.py index 78a5c675..f1cab347 100644 --- a/networkx/algorithms/flow/maxflow.py +++ b/networkx/algorithms/flow/maxflow.py @@ -62,8 +62,7 @@ def maximum_flow(G, s, t, capacity='capacity', flow_func=None, Returns ------- flow_value : integer, float - Value of the maximum flow, i.e., net outflow from the source if - value_only is True. + Value of the maximum flow, i.e., net outflow from the source. flow_dict : dict A dictionary containing the value of the flow that went through @@ -147,7 +146,7 @@ def maximum_flow(G, s, t, capacity='capacity', flow_func=None, If you want to compute all flows, you have to set to False the parameter value_only: - >>> flow_dict = nx.maximum_flow(G, 'x', 'y', value_only=False) + >>> value, flow_dict = nx.maximum_flow(G, 'x', 'y', value_only=False) >>> print(flow_dict['x']['b']) 1.0 @@ -179,7 +178,7 @@ def maximum_flow(G, s, t, capacity='capacity', flow_func=None, # Build the flow dictionary flow_dict = build_flow_dict(G, R) - return flow_dict + return (R.graph['flow_value'], flow_dict) def minimum_cut(G, s, t, capacity='capacity', flow_func=None, @@ -229,7 +228,7 @@ def minimum_cut(G, s, t, capacity='capacity', flow_func=None, Returns ------- cut_value : integer, float - Value of the minimum cut if value_only is True. + Value of the minimum cut. partition : pair of node sets A partitioning of the nodes that defines a minimum cut if @@ -304,7 +303,7 @@ def minimum_cut(G, s, t, capacity='capacity', flow_func=None, If you want node partition that defines the minimum cut, you have to set the parameter value_only to False: - >>> partition = nx.minimum_cut(G, 'x', 'y', value_only=False) + >>> cut_value, partition = nx.minimum_cut(G, 'x', 'y', value_only=False) >>> reachable, non_reachable = partition 'partition' here is a tuple with the two sets of nodes that define @@ -356,8 +355,8 @@ def minimum_cut(G, s, t, capacity='capacity', flow_func=None, # residual network form the node partition that defines # the minimum cut. non_reachable = set(nx.shortest_path_length(R, target=t)) - reachable = set(G) - non_reachable - return (reachable, non_reachable) + partition = (set(G) - non_reachable, non_reachable) + return (R.graph['flow_value'], partition) # backwards compatibility diff --git a/networkx/algorithms/flow/tests/test_maxflow.py b/networkx/algorithms/flow/tests/test_maxflow.py index 9ab7cc6f..090b1a00 100644 --- a/networkx/algorithms/flow/tests/test_maxflow.py +++ b/networkx/algorithms/flow/tests/test_maxflow.py @@ -89,12 +89,13 @@ def compare_flows_and_cuts(G, s, t, solnFlows, solnValue, capacity='capacity'): validate_flows(G, s, t, flow_dict, solnValue, capacity, flow_func) # Minimum cut if legacy: - partition = nx.minimum_cut(G, s, t, capacity=capacity, - flow_func=nx.ford_fulkerson, - value_only=False) + cut_value, partition = nx.minimum_cut(G, s, t, capacity=capacity, + flow_func=nx.ford_fulkerson, + value_only=False) else: - partition = nx.minimum_cut(G, s, t, capacity=capacity, - flow_func=flow_func, value_only=False) + cut_value, partition = nx.minimum_cut(G, s, t, capacity=capacity, + flow_func=flow_func, + value_only=False) validate_cuts(G, s, t, solnValue, partition, capacity, flow_func) |