def run(do_print):
tk=0
tp=0
for i in range(100):
G,C,Gp,N,n=load_data_2('testing_graph_'+str(i)+'.txt')
start=time.time()
if do_print:
print_kruskal(C,n,str(i))
else:
kruskal(C,n)
tk=tk+time.time()-start
n=G.shape[0]
start=time.time()
if do_print:
print_prim(Gp,N,n,str(i))
else:
prim(Gp,N,n)
tp=tp+time.time()-start
tp=tp/100.0
tk=tk/100.0
print tp
print tk
f=open('runtime.txt','w')
f.write('Prim: '+str(tp)+' seconds\nKruskal: '+str(tk)+' seconds')
f.close()
return
def grid_span():
grid = full_grid()
spanning_tree1 = kruskal(grid.nodes(), sorted(grid.edges()))
spanning_tree2 = kruskal(grid.nodes(), reversed(sorted(grid.edges())))
grid.remove_edges_from(grid.edges())
grid.add_edges_from(spanning_tree1 + spanning_tree2)
grid.graph['name'] = 'grid_span'
for i in range(10):
if i != 4:
grid.add_edge((i, 5), (i + 1, 5))
grid.graph['init path'].append(((i, 5), (i + 1, 5)))
return grid
def grid_span_rand():
grid = full_grid()
edges = grid.edges()
shuffle(edges)
spanning_tree1 = kruskal(grid.nodes(), edges)
shuffle(edges)
spanning_tree2 = kruskal(grid.nodes(), edges)
grid.remove_edges_from(grid.edges())
grid.add_edges_from(spanning_tree1 + spanning_tree2)
grid.graph['name'] = 'grid_span_rand'
for i in range(10):
if i != 4:
grid.add_edge((i, 5), (i + 1, 5))
grid.graph['init path'].append(((i, 5), (i + 1, 5)))
return grid
def main():
graph = {
'A': ['B'],
'B': ['D'],
'C': ['D', 'E'],
'D': ['F', 'G'],
'E': ['J'],
'F': ['I'],
'G': ['J'],
'H': ['K'],
'I': ['K'],
'J': ['L'],
'K': [],
'L': []
}
newGraph = moralizeGraph(graph)
addEdge = minfill(newGraph)
for edge in addEdge:
a = edge[0][0]
b = edge[0][1]
newGraph[a] += [b]
newGraph[b] += [a]
clique = findMaximalClique(newGraph)
print clique
print '================================'
cliqueGraph = createCliqueGraph(clique)
print cliqueGraph
print '================================'
cliqueTree = kruskal.kruskal(cliqueGraph)
print cliqueTree
print '================================'
def test_kruskal():
input_graph = Graph(
Edge(1, 2, 1), Edge(1, 3, 4), Edge(1, 4, 3), Edge(2, 4, 2), Edge(3, 4, 5)
)
expected_graph = Graph(Edge(1, 2, 1), Edge(1, 3, 4), Edge(2, 4, 2))
actual_graph = kruskal(input_graph)
assert actual_graph == expected_graph
def Kruskal_Algorithm():
#Sets the number of vertices in the graph
n = 10
#Creates the maximum number of vertices for this graph
g = K.kruskal(n)
print("Applying a graph of ", n, " vertices.")
print("")
#Sets the edges that are present in the graph, reading from, to, and its weight value
g.addEdge(0, 1, 4)
g.addEdge(0, 4, 3)
g.addEdge(1, 4, 2)
g.addEdge(1, 5, 5)
g.addEdge(1, 2, 4)
g.addEdge(2, 5, 6)
g.addEdge(2, 6, 9)
g.addEdge(3, 6, 13)
g.addEdge(4, 7, 1)
g.addEdge(4, 5, 12)
g.addEdge(5, 7, 21)
g.addEdge(7, 8, 14)
g.addEdge(5, 8, 11)
g.addEdge(5, 6, 17)
g.addEdge(6, 8, 10)
g.addEdge(8, 9, 16)
g.addEdge(3, 9, 20)
print("")
print("Creating the new graph using Kruskal's Algorithm...")
#Sends the new formed graph to be sorted using kruskal's algorithm. The next class will print its result
g.Kruskal_al()
def main():
grafo = Grafo()
archivo = "map.osm"
depurar(archivo)
print("fin de depuracion")
archivo2 = "modificado.xml"
grafo = Grafo()
grafo = grafoOSM(archivo2)
print("fin de procesamiento de datos OSM")
# PRUEBA
arbol = kruskal(grafo)
print("fin de kruskal")
idA = encuentra_punto(grafo, -12.066159,
-77.073635) # -12.071697, -77.076262)
assert idA in grafo.dic_vertices.keys(
), "idA no se encuentra dentro del grafo"
idB = encuentra_punto(
grafo, -12.072115,
-77.072334) # -12.070534, -77.066984) # -12.086531, -77.063292)
assert idB in grafo.dic_vertices.keys(
), "idB no se encuentra dentro del grafo"
camino = dijkstra(grafo, arbol, idA, idB)
print("fin de dijkstra")
mapeoarbol(grafo, arbol, camino)
print("fin de grafico de mapa")
print("FIN")
def main():
for filename in get_test_files():
print("%s:" % filename, end=" ")
with open(filename, "r") as fhandler:
lines = fhandler.readlines()
nb_edges = int(lines[0])
edges = [
(lambda s:
(int(s[0]), int(s[1]), int(s[2])))(lines[i + 1].strip().split())
for i in range(nb_edges)
]
try:
t1 = time.perf_counter()
_, prim_c = prim(edges)
t2 = time.perf_counter()
_, kruskal_c = kruskal(edges)
t3 = time.perf_counter()
except Exception:
print("runtime error")
else:
if prim_c != kruskal_c:
print("KO (Prim: %d, Kruskal: %d)" % (prim_c, kruskal_c))
else:
print("OK (both %d)\t- %d edges\t"
"- Prim: %.3f ms, \tKruskal: %.3f ms" %
(prim_c, nb_edges, 1000 * (t2 - t1), 1000 * (t3 - t2)))
def get_all_results(from_to_cost, no_of_nodes, source, node_x_y, result, ui):
kruskal_output = kruskal.kruskal(from_to_cost, no_of_nodes)
prims_output = prims.PRIMS(from_to_cost, no_of_nodes, source)
dijkstra_output = dijkstra.DIJKSTRA(from_to_cost, no_of_nodes, source)
bellman_output = bellmanford.BELLMANFORD(from_to_cost, no_of_nodes, source)
floyd_output = FloydWarshall.FLOYDWARSHALL(from_to_cost, no_of_nodes,
source, node_x_y)
# print(kruskal_output)
# print(prims_output)
# print(dijkstra_output)
# print(bellman_output)
# print(floyd_output)
kruskal_cost = kruskal_output[1]
prims_cost = prims_output[1]
dijkstra_cost = dijkstra_output[1]
bellman_cost = bellman_output[1]
floyd_cost = floyd_output[1]
local_cluster_average = LocalCluster.LOCALCLUSTERAVERAGE(result)
# print(kruskal_cost)
# print(prims_cost)
# print(dijkstra_cost)
# print(bellman_cost)
# print(floyd_cost)
# print(local_cluster_average)
data = [
kruskal_cost, prims_cost, dijkstra_cost, bellman_cost, floyd_cost,
local_cluster_average
]
for y in range(0, len(data)):
# print('col test : ' + str(ui.tableWidget.item(0,y).text()))
ui.tableWidget.item(0, y).setText(str(data[y]))
def compute_max_spanning_tree(graph):
mst = kruskal(graph)
print(">>> [mst]")
pprint(dict(mst))
# show_graph(mst)
return mst
def heuristic_tree(points, iterations=1000, alpha=0.01):
"""heuristic algorithm for Steiner tree."""
size = points.shape[0]
adjacentcy_list = kruskal(points)
points, adjacentcy_list = add_steiner_points(points, adjacentcy_list)
points, adjacentcy_list = move_steiner_points(points,
adjacentcy_list,
size,
iterations=iterations,
alpha=alpha)
return points, adjacentcy_list
def test_kruskal_str_vertices(self):
graph = Graph()
graph.add_edge(Edge(Vertex("A"), Vertex("D"), 2))
graph.add_edge(Edge(Vertex("A"), Vertex("G"), 3))
graph.add_edge(Edge(Vertex("A"), Vertex("B"), 1))
graph.add_edge(Edge(Vertex("B"), Vertex("E"), 6))
graph.add_edge(Edge(Vertex("B"), Vertex("F"), 7))
graph.add_edge(Edge(Vertex("F"), Vertex("D"), 10))
graph.add_edge(Edge(Vertex("F"), Vertex("C"), 12))
graph.add_edge(Edge(Vertex("E"), Vertex("G"), 9))
self.assertEqual(kruskal(graph), 31)
def kruskal_graph():
graph = {}
edges_l = []
graph['vertices'] = list_vertics(vertic)
pytanie = raw_input("Czy chcesz podac wagi? (T or F) ")
for i in edge_list:
if pytanie != 'F':
waga = int(raw_input("Podaj wage wierzcholka " + str(i) + ': '))
else:
waga = 1
edg = waga, i[0], i[1]
edges_l.append(edg)
edges_l.sort()
print edges_l
graph['edges'] = edges_l
return kruskal.kruskal(graph)
def runAct(dataArray):
print "Choosing the list of samples."
#or use partition by metadatum values
sampleNameList,metadataList,interval1List,interval2List = createSampleNameList(dataArray)
n = len(sampleNameList)
print "\nAVAILABLE COMPARISON FUNCTION(S):"
fctF = printComparison()
f = raw_input("\nChoose your comparison function above those printed above.\n")
isInDatabase([f],fctF)
completeGraph = Graph(n).constructComplete(sampleNameList,dataArray[7],f)
superTree,w = kruskal(completeGraph)
#Constructing distance matrix
matrix = np.zeros((n,n))
print "\nAVAILABLE DISTANCE FUNCTION(S):"
fctD = printDistance()
d = raw_input("\nChoose your distance function above those printed above.\n")
isInDatabase([d],fctD)
valueArray = []
print "\nSUPERTREE of weight:",w
print superTree.vertices
print superTree.edges
for i in range(n):
for j in range(i,n):
#matrix is symmetric (distance)
s = applyFctD(d,superTree,i,j)
matrix[i][j] = s
matrix[j][i] = s
valueArray.append(s)
valueArray = sorted(valueArray)
valueNumber = n*n/2
quartile3 = valueNumber*3/4
valueQuartile = valueArray[quartile3]
mostDifferent = []
#Distance is symmetric
for i in range(n):
for j in range(i+1,n):
if matrix[i][j] >= valueQuartile:
mostDifferent.append((sampleNameList[i],sampleNameList[j]))
print "\nRESULTING MATRIX:"
print matrix
print "\n---\nMost different samples groups from:\n"
for sampleGroup in sampleNameList:
print sampleGroup
print "\nare:\n"
print mostDifferent
print "\n--- END OF DISPLAY\n"
def benchmark():
n = 10**4
m = 10 * n
G = Graph.random_graph(n, m, weighted=True)
begin = time.time()
A = kruskal(G)
end = time.time()
print('* Kruskal {:.5} s'.format(end - begin))
print('-- total weight:', sum(G[u][v] for u, v in A))
print()
begin = time.time()
B = prim(G)
end = time.time()
print('* Prim {:.5} s'.format(end - begin))
print('-- total weight:', sum(G[u][v] for u, v in B))
def run_equivalence_test(n, trace=False):
"""
Compare all algorithms for the same input graph
:param n: size of the test graph
:param trace: print debug info is test failed
:return True if test succeeds
"""
t = timeit.default_timer()
adj_list, vertex_list, edge_list = generate_graph(n)
if trace:
print 'Generate graph: {}'.format(timeit.default_timer() - t)
t = timeit.default_timer()
min_span_tree_boruvka = set(boruvka(adj_list))
normalize_edge_list(min_span_tree_boruvka)
if trace:
print 'Boruvka: {}'.format(timeit.default_timer() - t)
t = timeit.default_timer()
min_span_tree_kruskal = set(kruskal(vertex_list, edge_list))
if trace:
print 'Kruskal: {}'.format(timeit.default_timer() - t)
t = timeit.default_timer()
min_span_tree_prim = build_mst_edge_list(prim(adj_list), adj_list)
if trace:
print 'Prim: {}'.format(timeit.default_timer() - t)
success = min_span_tree_boruvka == min_span_tree_kruskal == min_span_tree_prim
if not success and trace and n < 100:
print 'TEST FAILED:'
print 'Adjacency list: {}'.format(adj_list)
print 'Edge and vertex list: {}'.format(edge_list)
print 'Minimum spanning tree with Boruvka: {}'.format(min_span_tree_boruvka)
print 'Minimum spanning tree with Kruskal: {}'.format(min_span_tree_kruskal)
print 'Minimum spanning tree with Prim: {}'.format(min_span_tree_prim)
print '----------------------------------'
return success
def eulerKruskal(n, s, G, input):
tracemalloc.start()
cost = 0
k = 0
v = s
MST = [[] for _ in range(n)]
T = kruskal(n, input)
for t in T:
MST[t[0].value].append([t[1].value, 0])
MST[t[1].value].append([t[0].value, 0])
to_visit = len(T) * 2
vertices = []
while to_visit > 0:
vertices.append(v)
u = -1
vi = 3
for edg in MST[v]:
if edg[1] < 2 and edg[1] < vi:
u = edg[0]
vi = edg[1]
idx = MST[v].index([u, vi])
MST[v][idx][1] += 1
idx = MST[u].index([v, vi])
MST[u][idx][1] += 1
v = u
to_visit -= 1
visited = [False for _ in range(n)]
i = 0
route = []
route.append(s)
while i < len(vertices):
if visited[vertices[i]]:
vertices.pop(i)
else:
visited[vertices[i]] = True
i += 1
for i in range(0, n - 1):
k += 1
cost += G[vertices[i]][vertices[i + 1]]
route.append(vertices[i + 1])
memory = tracemalloc.get_traced_memory()[1]
tracemalloc.stop()
return cost, memory, route, len(route)
def test_kruskal_int_vertices(self):
graph = Graph()
graph.add_edge(Edge(Vertex(0), Vertex(1), 1))
graph.add_edge(Edge(Vertex(0), Vertex(4), 1))
graph.add_edge(Edge(Vertex(1), Vertex(0), 1))
graph.add_edge(Edge(Vertex(1), Vertex(3), 1))
graph.add_edge(Edge(Vertex(1), Vertex(4), 1))
graph.add_edge(Edge(Vertex(1), Vertex(2), 1))
graph.add_edge(Edge(Vertex(2), Vertex(3), 1))
graph.add_edge(Edge(Vertex(2), Vertex(1), 1))
graph.add_edge(Edge(Vertex(3), Vertex(1), 1))
graph.add_edge(Edge(Vertex(3), Vertex(2), 1))
graph.add_edge(Edge(Vertex(3), Vertex(4), 1))
self.assertEqual(kruskal(graph), 4)
def testMST(self):
size = 50
for _ in range(100):
adjList = defaultdict(list)
edges = []
cnt = 0
weights = range(size * (size - 1) / 2)
random.shuffle(weights)
for i in range(size):
for j in range(i + 1, size):
adjList[i].append((j, weights[cnt]))
adjList[j].append((i, weights[cnt]))
edges.append((i, j, weights[cnt]))
cnt += 1
parent = prim(adjList)
edges = kruskal(size, edges)
self.assertEqual(len(edges) + 1, len(parent))
for u, v in edges:
self.assertTrue(parent[u] == v or parent[v] == u)
def clustering(dataP,
confLevel=0.95,
minRate=0.01,
_lambda=10,
KMax=50,
verbose=False,
visualization=False):
numPoints = len(dataP)
#print(numPoints)
print("********************** Clustering **********************")
# Complete Graph and Kruskal MST
distMatrix = np.zeros((numPoints, numPoints))
repEdges = np.zeros((numPoints * (numPoints - 1), 2))
repEdgeWeights = np.zeros((numPoints * (numPoints - 1), 1))
count = 0
for k in range(numPoints):
for l in range(k + 1, numPoints):
dist = np.linalg.norm(dataP[k][:] - dataP[l][:], 2)
distMatrix[k][l] = dist
distMatrix[l][k] = dist
repEdges[count][0] = k
repEdges[count][1] = l
repEdgeWeights[count][0] = dist
count += 1
repEdges[count][0] = l
repEdges[count][1] = k
repEdgeWeights[count][0] = dist
count += 1
totalWeight, ST, XSt = kruskal(repEdges, repEdgeWeights)
#print(distMatrix)
return 1
def kruskal_test(G, expected):
mst = kruskal(G)
assert len(mst) == len(G.get_vertices()) - 1
mst_cost = 0
unvisited = set(G.get_vertices())
for u, v in mst:
unvisited.discard(u)
unvisited.discard(v)
mst_cost += G.get_weight(u, v)
assert len(unvisited) == 0, f"Tkrus is not spanning: {mst}"
unvisited = set(G.get_vertices())
expt_cost = 0
for u, v in expected:
unvisited.discard(u)
unvisited.discard(v)
expt_cost += G.get_weight(u, v)
assert len(unvisited) == 0, f"Expected is not spanning: {expected}"
assert mst_cost == expt_cost
nd6 = Vertice(6)
nd6.x = 4
nd6.y = 4
grafo.agregarVertice(nd6)
nd7 = Vertice(7)
nd7.x = 12
nd7.y = 4
grafo.agregarVertice(nd7)
# ALMACENAMIENTO DE CALLES EN GRAFO
grafo.conj_aristas.add((1, 1, 2))
grafo.conj_aristas.add((5, 2, 3))
grafo.conj_aristas.add((3, 2, 4))
grafo.conj_aristas.add((4, 3, 4))
grafo.conj_aristas.add((2, 4, 5))
grafo.conj_aristas.add((2, 4, 6))
grafo.conj_aristas.add((3, 6, 7))
# PRUEBA
arbol = kruskal(grafo)
print("fin de kruskal")
camino = dijkstra(grafo, arbol)
print("fin de dijkstra")
print(camino)
mapeoarbol(grafo, arbol, camino)
print("fin de grafico de mapa")
print("FIN")
}
G = weighted_graph.Graph(len(node_numbers))
for node in node_numbers:
G.set_vertex(node_numbers[node], node)
G.add_edge(node_numbers['N1'], node_numbers['N2'], 3)
G.add_edge(node_numbers['N1'], node_numbers['N4'], 2)
G.add_edge(node_numbers['N1'], node_numbers['N3'], 3)
G.add_edge(node_numbers['N2'], node_numbers['N4'], 9)
G.add_edge(node_numbers['N2'], node_numbers['N5'], 4)
G.add_edge(node_numbers['N2'], node_numbers['N7'], 2)
G.add_edge(node_numbers['N3'], node_numbers['N4'], 4)
G.add_edge(node_numbers['N3'], node_numbers['N5'], 1)
G.add_edge(node_numbers['N3'], node_numbers['N7'], 6)
G.add_edge(node_numbers['N3'], node_numbers['N8'], 6)
G.add_edge(node_numbers['N4'], node_numbers['N6'], 1)
G.add_edge(node_numbers['N5'], node_numbers['N7'], 7)
G.print_graph()
print('Breadth First Traversal:')
G.breadth_first_traverse()
MST = kruskal.kruskal(G)
MST.print_graph()
def show_kruskal(self):
text = kruskal(self.df)
self.k_weight.setText(str(text))
import weighted_graph
import kruskal
node_numbers = {'John':0, 'Sally':1, 'George':2,
'Phil':3, 'Rose':4, 'Alice':5}
G = weighted_graph.Graph(len(node_numbers))
for node in node_numbers:
G.set_vertex(node_numbers[node],node)
G.add_edge(node_numbers['John'],node_numbers['Sally'],1)
G.add_edge(node_numbers['John'],node_numbers['George'],2)
G.add_edge(node_numbers['John'],node_numbers['Rose'],3)
G.add_edge(node_numbers['George'],node_numbers['Sally'],4)
G.add_edge(node_numbers['Phil'],node_numbers['Sally'],5)
G.add_edge(node_numbers['Rose'],node_numbers['Alice'],6)
G.print_graph()
print('Breadth First Traversal:')
G.breadth_first_traverse()
MST = kruskal.kruskal(G)
MST.print_graph()
elaspedTime = time.time() - startTime
print('elaspedTime: %f' % elaspedTime)
print('')
s_name = []
t_name = []
for i in range(0, 6):
sid = random.randint(1, VNUM)
s_name.append(sid)
tid = random.randint(1, VNUM)
t_name.append(tid)
#print(s_name)
#print(t_name)
print('---- Test on G1 by kruskal ----')
k = kruskal.kruskal()
startTime = time.time()
mst = k.kruskal(g1)
mst.collectEdges()
gmst = k.adjgraph()
s = s_name[0]
t = t_name[0]
#ssp = bfs.bfs(gmst, s, t)
ssp = bfsdfs.dfs_iter(gmst, s)
#print(ssp)
elaspedTime = time.time() - startTime
print('#1 pair elaspedTime: %f' % elaspedTime)
et_arr = [elaspedTime]
for i in range(1, 6):
#s_id = s_name[i]
def kruskal():
visualKruskal.kruskal(listofEdges, listofVertexes, window)
def kruskal(self):
kruskal.kruskal(self)
from kruskal import kruskal
vertexs = ['A', 'B', 'C', 'D', 'E', 'F']
edges = [
(5, 'A', 'B'),
(1, 'A', 'F'),
(2, 'A', 'D'),
(3, 'B', 'C'),
(3, 'B', 'E'),
(1, 'C', 'D'),
(4, 'C', 'F'),
(4, 'D', 'E'),
(2, 'E', 'F'),
]
print kruskal(vertexs, edges)
# Brazil's graph, just change the variables # 's' and 'f' in this file and run it like: # $ python work.py < inputs/BR.txt # from graph import Graph from kruskal import kruskal from dijkstra_h import path, dijkstra_h G = Graph() G.build_graph() # minimmum spanning tree mst = kruskal(G) length = sum([edge.weight for edge in mst]) print length # shortest path from city s to f s = 93; f = 112 d, p = dijkstra_h(G, s) print d[f] # shortest path tree from city s s = 104 d, p = dijkstra_h(G, s) edges = [] for v in G.vertices():
def main():
file = open("bnet", "r")
# define nodes of the graph
nodes = []
for line in file:
node, parents, values = line.split(";")
values = map(float, values.strip().split())
parents = parents.strip().split()
bn_n = {
"name": node.strip(),
"neighbors": [],
"named_neighbors": [],
"named_parent": parents,
"parent": [],
"values": values
}
nodes.append(bn_n)
define_parents_and_neighbors(nodes)
define_named_neighbors(nodes)
print "MORALIZED GRAPH"
moralize_graph(nodes)
pretty_print_graph(nodes)
print "\nTRIANGULATED GRAPH"
triangulate_graph(nodes, deepcopy(nodes))
pretty_print_graph(nodes)
print "\nMAXIMAL CLIQUES"
maximal_cliques.bron_kerbosch([], nodes, [])
cliques = maximal_cliques.cliques
str_cliques = []
clique_nodes = {}
k = 1
for clique in cliques:
str_clique = [node["name"] for node in clique]
str_cliques.append(str_clique)
maximal_cliques.print_clique(clique)
name = "c" + str(k)
clique_nodes[name] = str_clique
k += 1
edges = set()
for i in range(len(clique_nodes)):
for j in range(i + 1, len(clique_nodes)):
key1 = clique_nodes.keys()[i]
key2 = clique_nodes.keys()[j]
clique1 = clique_nodes[key1]
clique2 = clique_nodes[key2]
share = maximal_cliques.share_check(clique1, clique2)
if share:
# print clique1, clique2, len(share)
edges.add((len(share), key1, key2))
edges.add((len(share), key2, key1))
vertices = clique_nodes.keys()
# print clique_nodes
# print vertices
# print edges
my_graph = dict()
my_graph["vertices"] = vertices
my_graph["edges"] = edges
# print my_graph
mst = kruskal.kruskal(my_graph)
print "\nMAXIMUM SPANNING TREE"
print mst
# for edge in mst: print "(" + str(clique_nodes[edge[1]]) + ", " + str(clique_nodes[edge[2]]) + ") -> " + str(edge[0])
# *********** second part ***********
query = {"E": 0}
print "\nQUERY:", query
clique_tree_nodes = []
orig_clique_nodes = deepcopy(clique_nodes)
for clique in clique_nodes:
# print clique_nodes[clique], "->", clique
str_nodes = clique_nodes[clique]
# format your CPD graph representation
clique_nodes[clique] = [
get_node_values(nodes, node, clique_nodes[clique])
for node in str_nodes
]
cn = {
"name": clique,
"neighbors": [],
"children": [],
"named_children": [],
"parent": [],
"named_parent": [],
"leaf": False,
"recv_child_map": None,
"recv_parent_map": None,
"msg_child_map": None,
"msg_parent_map": None
}
cn["cpd"] = clique_nodes[clique]
clique_tree_nodes.append(cn)
# print orig_clique_nodes
# set clique nodes neighbors
for edge in mst:
clqn1 = find_node_by_name(clique_tree_nodes, edge[1])
clqn2 = find_node_by_name(clique_tree_nodes, edge[2])
clqn1["neighbors"].append(clqn2)
clqn2["neighbors"].append(clqn1)
clique1 = orig_clique_nodes[clqn1["name"]]
clique2 = orig_clique_nodes[clqn2["name"]]
share = maximal_cliques.share_check(clique1, clique2)
# print clique1, clique2, share
# set sepsets
if share:
if not "sepset" in clqn1:
clqn1["sepset"] = {clqn2["name"]: share}
else:
clqn1["sepset"][clqn2["name"]] = share
if not "sepset" in clqn2:
clqn2["sepset"] = {clqn1["name"]: share}
else:
clqn2["sepset"][clqn1["name"]] = share
print "\nSEPSETS"
for c in clique_tree_nodes:
print c["name"], c["sepset"]
for evidence in query:
incorporate_evidence(clique_tree_nodes, evidence, query[evidence])
print "\nFACTORS"
for c in clique_tree_nodes:
print c["name"], c["cpd"]
# pretty_print_graph_v1(clique_tree_nodes, "neighbors")
# root = clique_tree_nodes[random.randint(0, len(clique_tree_nodes) - 1)]
root = [node for node in clique_tree_nodes if node["name"] == "c1"][0]
global global_root
global_root = root["name"]
print "\n-> ROOT:", root["name"]
# set children/parent according to root init
set_links(root)
print "\nPARENTS:"
pretty_print_graph_v1(clique_tree_nodes, "parent")
print "\nLEAVES:"
for c in clique_tree_nodes:
if c["leaf"]:
print c["name"]
for node in clique_tree_nodes:
node["recv_child_map"] = {
children: False
for children in node["named_children"]
}
node["recv_parent_map"] = {
parent: False
for parent in node["named_parent"]
}
node["msg_child_map"] = {
children: None
for children in node["named_children"]
}
node["msg_parent_map"] = {
parent: None
for parent in node["named_parent"]
}
print "\nINITIAL STATUS OF THE CLIQUE NODES"
if 1 == 2:
for n in clique_tree_nodes:
print n["name"], "sent to parent -> ", n["recv_parent_map"]
print n["name"], "received from child -> ", n["recv_child_map"]
else:
print "INITIALIZED."
print "----------------"
print "\nSEND_MESSAGES"
print "----------------"
print "[[LEAVES -> ROOT]]\n"
leaf_threads = []
for c in clique_tree_nodes:
if c["leaf"]:
thread = Thread(target=leaf_function, kwargs=(c))
thread.setName(c["name"])
leaf_threads.append(thread)
thread.start()
non_leaf_threads = []
for c in clique_tree_nodes:
if not c["leaf"]:
thread = Thread(target=intermediary_node_function, kwargs=(c))
thread.setName(c["name"])
non_leaf_threads.append(thread)
thread.start()
for thread in leaf_threads:
thread.join()
print thread.name, "leaf finished...exiting"
for thread in non_leaf_threads:
thread.join()
print thread.name, "node finished...exiting"
print "\nALL DONE."
import networkx as nx
import random
import unittest
import kruskal
'''
for i in range(1,500,1):
for j in range(i+1,500,1):
G.add_edge(int(i),int(j),weight=random.randint(1,20))
nx.write_weighted_edgelist(G, "k500.adjlist")
'''
G = nx.read_weighted_edgelist("k500.adjlist" , nodetype=int)
print kruskal.kruskal(G)
print ('coucou')
T = nx.minimum_spanning_tree(G)
print(sorted(T.edges(data=True)))
#print nx.all_pairs_dijkstra_path_length(G)[1]
# --------------
# In order to obtain the results for
# Brazil's graph, just change the variables
# 's' and 'f' in this file and run it like:
# $ python work.py < inputs/BR.txt
#
from graph import Graph
from kruskal import kruskal
from dijkstra_h import path, dijkstra_h
G = Graph()
G.build_graph()
# minimmum spanning tree
mst = kruskal(G)
length = sum([edge.weight for edge in mst])
print length
# shortest path from city s to f
s = 93
f = 112
d, p = dijkstra_h(G, s)
print d[f]
# shortest path tree from city s
s = 104
d, p = dijkstra_h(G, s)
edges = []
for v in G.vertices():
edges += path(p, s, v)
print "Node tool Selected"
mode = [True, False, False, False]
if ButtonUndirectedPath.pressed(pos):
print "Undirected Path Tool Selected"
mode = [False, True, False, False]
if ButtonDirectedPath.pressed(pos):
print "Directed Path Tool Selected"
mode = [False, False, True, False]
if ButtonRun.pressed(pos):
print("Minimum Spanning Tree Is:")
print(kruskal.kruskal(Graph))
print("Topological Sort")
print(topsort.dfs_topsort(Graph))
print("Breadth First Search")
answer = inputbox.ask(window, "Enter Node for BFS")
answer = ord(answer) - ord('1')
print(BFS.BFS(Graph, Graph.vertexList[answer]))
print("Dijkstra Tree")
answer = inputbox.ask(window,
"Enter Node for Dijkstra Tree")
plt.plot([x[0] for x in c], [x[1] for x in c], "b--")
# m => set of edges in mst
def plot_mst(m):
for e in m:
c = e.tuple_rep()
plt.plot([x[0] for x in c], [x[1] for x in c], "b-")
if __name__ == "__main__":
# base data
graph, points = setup()
# find the mst
mst_prim = prim(graph, points, len(points))
mst_kruskal = kruskal(graph)
# metadata for Kruskal
p1 = plt.subplot(221)
p1.set_title("Kruskal")
plt.ylim([-1, 5])
plt.xlim([-1, 5])
# basic
plot_base(graph, points)
# the goods
plot_mst(mst_kruskal)
# metadata for Prim
p2 = plt.subplot(222)
from parseOsm import *
from grafos import *
from kruskal import kruskal
from grafico import mapeoarbol
from depuracion import depurar
archivo = "mapa.osm"
depurar(archivo)
print("fin de depuracion")
archivo2 = "modificado.xml"
grafo = Grafo()
grafo = grafoOSM(archivo2)
print("fin de procesamiento de datos OSM")
# PRUEBA
k = kruskal(grafo)
print("fin de kruskal")
mapeoarbol(grafo, k)
print("fin de grafico de mapa")
#otros comentarios
print("FIN")
# comentarios para probar un segundo commit
