def to_solve_k(G, locs):
un = UnionFind()
pos = 1
source = UnionNode(0)
dict = source.get_vertices()
dict[0] = 0
un.add(source)
for l in locs:
if l != 0:
source = UnionNode(l)
dict = source.get_vertices()
dict[l] = pos
un.add(source)
pos += 1
return KrusPrune(un, G, locs)
def UnionPrimTest():
G = nx.MultiGraph()
G.add_nodes_from([0, 1, 2, 3])
G.add_weighted_edges_from([(0, 1, 2), (0, 2, 1), (1, 3, 3), (2, 3, 2)])
locs = {}
locs[0] = 0
locs[3] = 1
un = UnionFind()
source = UnionNode(0)
dict = source.get_vertices()
dict[0] = 0
un.add(source)
home = UnionNode(3)
dict2 = home.get_vertices()
dict2[3] = 3
un.add(home)
vert_edges_MST = UnionPrim(un, G, locs)
print(vert_edges_MST)
def solve():
size = 99
H = 50
print("dog")
G = nx.MultiGraph()
G.add_nodes_from([i for i in range(size)])
# Add the nodes to the graph
homes = set()
while H > 0:
value = random.randint(1, size - 1)
while value in homes:
value = random.randint(1, size - 1)
homes.add(value)
H -= 1
print(homes)
locs = {}
un = UnionFind()
source = UnionNode(0)
print(type(source.get_vertices()))
dict = source.get_vertices()
unIndex = 0
dict[0] = unIndex
locs[0] = unIndex
unIndex += 1
# union find index of the elements array
un.add(source)
for a in homes:
# print(a)
loc = UnionNode(a)
dict = loc.get_vertices()
dict[a] = unIndex
locs[a] = unIndex
unIndex += 1
un.add(loc)
# print(un)
UnionPrim(un, G, locs)
# Alternative predefined Kruskals if too slow
mst = nx.minimum_spanning_edges(G, data=False) # a generator of MST edges
edgelist = list(mst)
# https://networkx.github.io/documentation/networkx-1.10/reference/generated/networkx.algorithms.mst.minimum_spanning_edges.html#networkx.algorithms.mst.minimum_spanning_edges
G.add_node(0)
weigh = 1
prev = np.array([0, 0, 0, 0, 0, 0, 0])
i = 1
while i < size:
j = 0
while j < 7:
G.add_node(i + j)
G.add_edge(i + j, prev[j], weight=weigh)
j += 1
j = 0
weigh += 1
while j < 6:
if (j == 0):
G.add_edge(i + j, i + j + 6, weight=1)
G.add_edge(i + j, i + j + 1, weight=1)
j += 1
j = 0
while j < 7:
prev[j] = i + j
j += 1
i += 7
nx.draw(G)
# nx.draw_spectral(G)
plt.show()
# A = nx.adjacency_matrix(G)
A = nx.to_numpy_matrix(G, nodelist=[i for i in range(size)])
print(A)
