def generateRandomTSPInstance(V, e):
M = gr.generateRandMatrix(V, e)
# generate a random adjacency matrix with n vertices, and a probability to be connected to each other vertex set to p
G = gr.generateRandomGraph(M,
np.shape(M)[0], 0, 0, 0)
# generate the graph with the adjacency matrix M
n = np.size(G.getAdjacencyMatrix()[0])
SP, SP_cost = np.array(sp.shortest_path(G.getAdjacencyMatrix(), n, n))
connected_adj = gr.fromSpToAdjacency(sp.connectMatrix(SP_cost))
#print("Adjacency graph of the connected matrix is\n", connected_adj);
for i in range(np.shape(connected_adj)[0]):
G.getVertex(i).setAdjacents(connected_adj[i], G)
diameter = np.matrix.max((sp.shortest_path(G.getAdjacencyMatrix(), n,
n))[1]).astype(int)
# calculate the diameter in order to set the min/max deadlines on G
G.setAllDeadlines(diameter, (2 * diameter) + 1)
return G, SP_cost
def getDensity(self):
tot = 0;
n = np.size(self.getAdjacencyMatrix()[0]);
SP, SP_cost = np.array(sp.shortest_path(self.getAdjacencyMatrix(),n,n));
for i in range(n):
for j in range(n):
if SP_cost[i,j]==1 and i!=j:
tot += 1;
return (tot)/(n*(n-1));
# testgraph.py
import graphreader
import shortestpath
import pprint
nodes, edges = graphreader.load_graph('testgraph1.txt')
assert shortestpath.shortest_path('O', 'T', nodes, edges) == ['O', 'A', 'B', 'D', 'T']
# example from "Algorithm Design and Applications" textbook pg. 402, 403
nodes, edges = graphreader.load_graph('airportgraph.txt')
pprint.pprint(shortestpath.find_shortest_paths('BWI', nodes, edges))
pprint.pprint(shortestpath.shortest_path('BWI', 'LAX', nodes, edges))
nodes, edges = graphreader.load_graph('campusgraph.txt')
pprint.pprint(shortestpath.shortest_path('RR20', 'RR12', nodes, edges))
def getDiameter(G):
n = np.size(G.getAdjacencyMatrix()[0]);
SP, SP_cost = np.array(sp.shortest_path(G.getAdjacencyMatrix(),n,n));
return np.matrix.max(SP_cost).astype(int);
def getShortestPath(G, i, j):
n = np.size(G.getAdjacencyMatrix()[0]);
SP, SP_cost = np.array(sp.shortest_path(G.getAdjacencyMatrix(),n,n));
return int(SP_cost.item(i,j));
return adj;
"""
Little testing to see if the algorithms work as expected
"""
verbose = False; # this variable controls whether the output is printed
if verbose:
M = generateRandMatrix(15, 0.15); # generate a random adjacency matrix with n vertices, and a probability to be connected to each other vertex set to p
G = generateRandomGraph(M, np.shape(M)[0], 0.2, 0, 0); # generate the graph with the adjacency matrix M
#print("\n Targets on G are:");
#print([int(i) for i in G.getTargets()]);
print("Adjacency matrix:");
print(G.getAdjacencyMatrix());
#obtain the shortest path matrix (through a classical sp algorithm)
n = np.size(G.getAdjacencyMatrix()[0]);
SP, SP_cost = np.array(sp.shortest_path(G.getAdjacencyMatrix(),n,n));
#print("\n Shortest path matrix:");
#print(SP);
print("\n Shortest Path's cost Matrix:");
print(SP_cost);
print("Density of the graph is ", G.getDensity());
connected_adj = fromSpToAdjacency(sp.connectMatrix(SP_cost));
print("Adjacency graph of the connected matrix is\n", connected_adj);
for i in range(np.shape(connected_adj)[0]):
G.getVertex(i).setAdjacents(connected_adj[i]);
increased_adj = increaseEdgeDensity(connected_adj, 0.35);
def getpath():
path = shortestpath.shortest_path(request.form);
return path