Create dinic.py (#1396)

* Create dinic.py

Dinic's algorithm for maximum flow

* Update dinic.py

Changes made.

graphs/dinic.py

@@ -0,0 +1,93 @@
+INF = float("inf")
+
+class Dinic:
+    def __init__(self, n):
+        self.lvl = [0] * n
+        self.ptr = [0] * n
+        self.q = [0] * n
+        self.adj = [[] for _ in range(n)]
+
+    '''
+    Here we will add our edges containing with the following parameters:
+    vertex closest to source, vertex closest to sink and flow capacity
+    through that edge ...
+    '''
+    def add_edge(self, a, b, c, rcap=0):
+        self.adj[a].append([b, len(self.adj[b]), c, 0])
+        self.adj[b].append([a, len(self.adj[a]) - 1, rcap, 0])
+
+    #This is a sample depth first search to be used at max_flow
+    def depth_first_search(self, vertex, sink, flow):
+        if vertex == sink or not flow:
+            return flow
+
+        for i in range(self.ptr[vertex], len(self.adj[vertex])):
+            e = self.adj[vertex][i]
+            if self.lvl[e[0]] == self.lvl[vertex] + 1:
+                p = self.depth_first_search(e[0], sink, min(flow, e[2] - e[3]))
+                if p:
+                    self.adj[vertex][i][3] += p
+                    self.adj[e[0]][e[1]][3] -= p
+                    return p
+            self.ptr[vertex] = self.ptr[vertex] + 1
+        return 0
+    
+    #Here we calculate the flow that reaches the sink
+    def max_flow(self, source, sink):
+        flow, self.q[0] = 0, source
+        for l in range(31):  # l = 30 maybe faster for random data
+            while True:
+                self.lvl, self.ptr = [0] * len(self.q), [0] * len(self.q)
+                qi, qe, self.lvl[source] = 0, 1, 1
+                while qi < qe and not self.lvl[sink]:
+                    v = self.q[qi]
+                    qi += 1
+                    for e in self.adj[v]:
+                        if not self.lvl[e[0]] and (e[2] - e[3]) >> (30 - l):
+                            self.q[qe] = e[0]
+                            qe += 1
+                            self.lvl[e[0]] = self.lvl[v] + 1
+
+                p = self.depth_first_search(source, sink, INF)
+                while p:
+                    flow += p
+                    p = self.depth_first_search(source, sink, INF)
+
+                if not self.lvl[sink]:
+                    break
+
+        return flow
+
+#Example to use
+
+'''
+Will be a bipartite graph, than it has the vertices near the source(4)
+and the vertices near the sink(4)
+'''
+#Here we make a graphs with 10 vertex(source and sink includes)
+graph = Dinic(10)
+source = 0
+sink = 9
+'''
+Now we add the vertices next to the font in the font with 1 capacity in this edge
+(source -> source vertices)
+'''
+for vertex in range(1, 5):
+	graph.add_edge(source, vertex, 1)
+'''
+We will do the same thing for the vertices near the sink, but from vertex to sink
+(sink vertices -> sink)
+'''
+for vertex in range(5, 9):
+	graph.add_edge(vertex, sink, 1)
+'''
+Finally we add the verices near the sink to the vertices near the source.
+(source vertices -> sink vertices)
+'''
+for vertex in range(1, 5):
+	graph.add_edge(vertex, vertex+4, 1)
+
+#Now we can know that is the maximum flow(source -> sink)
+print(graph.max_flow(source, sink))
+
+