rename max-flow to min-cut

ariel-research · Jul 9, 2024 · adf9974 · adf9974
1 parent 2ef6676
commit adf9974
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Showing 3 changed files with 11 additions and 11 deletions.
diff --git a/networkz/algorithms/approximation/firefighter_problem/Firefighter_Problem.py b/networkz/algorithms/approximation/firefighter_problem/Firefighter_Problem.py
@@ -23,7 +23,7 @@
 import networkx.algorithms.connectivity as algo 
 import math
 import logging
-from networkz.algorithms.approximation.firefighter_problem.max_flow_with_node_capacity import max_flow_with_node_capacity
+from networkz.algorithms.approximation.firefighter_problem.max_flow_with_node_capacity import min_cut_with_node_capacity
 from networkz.algorithms.approximation.firefighter_problem.Utils import *
 
 logger = logging.getLogger(__name__)
@@ -275,7 +275,7 @@ def non_spreading_dirlaynet_minbudget(Graph:nx.DiGraph, src:int, targets:list)->
     layers = adjust_nodes_capacity(Graph, src)
     G = create_st_graph(Graph, targets)
     #display_graph(G)
-    G_reduction_min_cut = max_flow_with_node_capacity(G, source=src, target='t')
+    G_reduction_min_cut = min_cut_with_node_capacity(G, source=src, target='t')
     N_groups = min_cut_N_groups(G_reduction_min_cut,layers)
     vacc_matrix = calculate_vaccine_matrix(layers, N_groups)
     integer_matrix = matrix_to_integers_values(vacc_matrix)

diff --git a/networkz/algorithms/approximation/firefighter_problem/max_flow_with_node_capacity.py b/networkz/algorithms/approximation/firefighter_problem/max_flow_with_node_capacity.py
@@ -27,9 +27,9 @@
 
 logger = logging.getLogger(__name__)
 
-def max_flow_with_node_capacity(graph: nx.DiGraph, source: int = None, target: int = None) -> set:
+def min_cut_with_node_capacity(graph: nx.DiGraph, source: int = None, target: int = None) -> set:
     """
-    Computes a maximum flow in the given graph, where each node has a capacity
+    Computes a minimum cut in the given graph, where each node has a capacity
 
     Parameters:
     ----------
@@ -70,7 +70,8 @@ def max_flow_with_node_capacity(graph: nx.DiGraph, source: int = None, target: i
     >>> G.add_edge(2, 3)
     >>> G.add_edge(1, 4)
     >>> s_t_G = create_st_graph(G, [2,4])
-    >>> min_cut_nodes = max_flow_with_node_capacity(s_t_G, 0, 4)
+    >>> min_cut_nodes = min_cut_with_node_capacity
+    (s_t_G, 0, 4)
     >>> sorted(min_cut_nodes)
     ['2_out', '4_out']
     """
@@ -105,7 +106,6 @@ def max_flow_with_node_capacity(graph: nx.DiGraph, source: int = None, target: i
     min_cut_nodes = algo.minimum_st_node_cut(H, f'{source}_out', 't_in')
 
     logger.info(f"Minimum Cut is: {min_cut_nodes}")  
-
     return min_cut_nodes
 
 if __name__ == "__main__":

diff --git a/networkz/algorithms/tests/test_firefighter_problem/test_non_spreading_dirlaynet_minbudget.py b/networkz/algorithms/tests/test_firefighter_problem/test_non_spreading_dirlaynet_minbudget.py
@@ -30,7 +30,7 @@
 from networkz.algorithms.approximation.firefighter_problem.Utils import adjust_nodes_capacity
 from networkz.algorithms.approximation.firefighter_problem.Utils import create_st_graph
 from networkz.algorithms.approximation.firefighter_problem.Utils import parse_json_to_networkx
-from networkz.algorithms.approximation.firefighter_problem.max_flow_with_node_capacity import max_flow_with_node_capacity
+from networkz.algorithms.approximation.firefighter_problem.max_flow_with_node_capacity import min_cut_with_node_capacity
 from networkz.algorithms.approximation.firefighter_problem.Utils import calculate_vaccine_matrix
 from networkz.algorithms.approximation.firefighter_problem.Utils import min_cut_N_groups
 from networkz.algorithms.approximation.firefighter_problem.Utils import matrix_to_integers_values
@@ -77,7 +77,7 @@ def test_source_is_target(graph_key, source, targets):
 layers_1 = adjust_nodes_capacity(graph_1, 0) # src is 0 
 targets_1 = [1, 2, 3]  # saving 1,2,3
 G1 = create_st_graph(graph_1, targets_1)
-reduction_G1_min_cut = max_flow_with_node_capacity(G1, 0)
+reduction_G1_min_cut = min_cut_with_node_capacity(G1, 0)
 N_1_groups = min_cut_N_groups(reduction_G1_min_cut,layers_1)
 matrix_1 = calculate_vaccine_matrix(layers_1, N_1_groups)
 integer_matrix_1 = matrix_to_integers_values(matrix_1) 
@@ -89,7 +89,7 @@ def test_source_is_target(graph_key, source, targets):
 layers_2 = adjust_nodes_capacity(graph_2, 0)  # src is 0
 targets_2 = [2, 4]  # saving 2,4
 G2 = create_st_graph(graph_2, targets_2)
-reduction_G2_min_cut = max_flow_with_node_capacity(G2, 0) 
+reduction_G2_min_cut = min_cut_with_node_capacity(G2, 0) 
 N_2_groups = min_cut_N_groups(reduction_G2_min_cut, layers_2)
 matrix_2 = calculate_vaccine_matrix(layers_2, N_2_groups)
 integer_matrix_2 = matrix_to_integers_values(matrix_2)
@@ -101,7 +101,7 @@ def test_source_is_target(graph_key, source, targets):
 layers_3 = adjust_nodes_capacity(graph_3, 0)  # src is 0
 targets_3 = [1, 5, 7]  # saving 1,5,7
 G3 = create_st_graph(graph_3, targets_3)
-reduction_G3_min_cut = max_flow_with_node_capacity(G3, 0)
+reduction_G3_min_cut = min_cut_with_node_capacity(G3, 0)
 N_3_groups = min_cut_N_groups(reduction_G3_min_cut, layers_3)
 matrix_3 = calculate_vaccine_matrix(layers_3, N_3_groups)
 integer_matrix_3 = matrix_to_integers_values(matrix_3)
@@ -113,7 +113,7 @@ def test_source_is_target(graph_key, source, targets):
 layers_4 = adjust_nodes_capacity(graph_4, 0)  # src is 0
 targets_4 = [4, 5, 6, 8]  # saving 4,5,6,8
 G4 = create_st_graph(graph_4, targets_4)
-reduction_G4_min_cut = max_flow_with_node_capacity(G4, 0)
+reduction_G4_min_cut = min_cut_with_node_capacity(G4, 0)
 N_4_groups = min_cut_N_groups(reduction_G4_min_cut, layers_4)
 matrix_4 = calculate_vaccine_matrix(layers_4, N_4_groups)
 integer_matrix_4 = matrix_to_integers_values(matrix_4)