def test_docplex_tsp(self):
""" Docplex tsp test """
# Generating a graph of 3 nodes
n = 3
ins = tsp.random_tsp(n)
graph = nx.Graph()
graph.add_nodes_from(np.arange(0, n, 1))
num_node = ins.dim
# Create an Ising Hamiltonian with docplex.
mdl = Model(name='tsp')
x = {(i, p): mdl.binary_var(name='x_{0}_{1}'.format(i, p))
for i in range(num_node) for p in range(num_node)}
tsp_func = mdl.sum(ins.w[i, j] * x[(i, p)] * x[(j, (p + 1) % num_node)]
for i in range(num_node) for j in range(num_node)
for p in range(num_node))
mdl.minimize(tsp_func)
for i in range(num_node):
mdl.add_constraint(
mdl.sum(x[(i, p)] for p in range(num_node)) == 1)
for j in range(num_node):
mdl.add_constraint(
mdl.sum(x[(i, j)] for i in range(num_node)) == 1)
qubit_op, offset = docplex.get_operator(mdl)
e_e = NumPyMinimumEigensolver(qubit_op)
result = e_e.run()
ee_expected = NumPyMinimumEigensolver(QUBIT_OP_TSP)
expected_result = ee_expected.run()
# Compare objective
self.assertAlmostEqual(result.eigenvalue.real + offset,
expected_result.eigenvalue.real + OFFSET_TSP)
def setUp(self):
super().setUp()
self.seed = 80598
aqua_globals.random_seed = self.seed
self.num_nodes = 3
self.ins = tsp.random_tsp(self.num_nodes)
self.qubit_op, self.offset = tsp.get_operator(self.ins)
nx.draw_networkx(G,
node_color=colors,
node_size=600,
alpha=.8,
ax=default_axes,
pos=pos)
edge_labels = nx.get_edge_attributes(G, 'weight')
nx.draw_networkx_edge_labels(G, pos=pos, edge_labels=edge_labels)
exact = MinimumEigenOptimizer(NumPyMinimumEigensolver())
# setup aqua logging
# Generating a graph of 3 nodes
n = 3
num_qubits = n**2
ins = tsp.random_tsp(n, seed=123)
data = tsp.TspData()
print(data)
# Draw the graph
G = nx.Graph()
G.add_nodes_from(np.arange(0, ins.dim, 1))
colors = ['r' for node in G.nodes()]
for i in range(0, ins.dim):
for j in range(i + 1, ins.dim):
G.add_edge(i, j, weight=ins.w[i, j])
pos = {k: v for k, v in enumerate(ins.coord)}
draw_graph(G, colors, pos)
