def test1(N=100, trials=3, repeats=5, **kwargs):
print('\n***mds.test1()***')
cost = np.empty((repeats, trials))
for i in range(repeats):
X = misc.disk(N, 2)
D = multigraph.graph_from_coordinates(X, **kwargs)
for j in range(trials):
mds = MDS(D, dim=2, **kwargs)
mds.gd(min_step=1e-3, **kwargs)
cost[i, j] = mds.cost
print(cost)
def reliability0(N=100, trials=3, repeats=5, **kwargs):
"""\
Check number of times the mds algorithm is able to reach the optimal
solution for different random embeddings.
"""
print('\n***mds.reliability()***')
cost = np.empty((repeats, trials))
success = np.zeros(repeats)
for i in range(repeats):
X = misc.disk(N, 2)
D = multigraph.graph_from_coordinates(X, **kwargs)
for j in range(trials):
vis = mds.MDS(D, dim=2, **kwargs)
vis.gd(min_step=1e-3, **kwargs)
cost[i, j] = vis.cost
if vis.cost < 1e-3:
success[i] += 1
print(cost)
print(success)
def reliability(N=[100], edge_probability=[None], trials=10, **kwargs):
"""\
Check number of times the mds algorithm is able to reach the optimal
solution for different random embeddings.
"""
print('\n***mds.reliability()***')
lenN = len(N)
lenE = len(edge_probability)
success_count = np.zeros((lenN, lenE))
time_ave = np.zeros((lenN, lenE))
for i in range(lenN):
n = N[i]
for j in range(lenE):
p = edge_probability[j]
for k in range(trials):
X = misc.disk(n, 2)
D = multigraph.graph_from_coordinates(X, **kwargs)
vis = mds.MDS(D, dim=2, **kwargs)
vis.gd(min_step=1e-4,
edge_probability=p,
max_iters=1e6,
**kwargs)
if vis.cost < 1e-2:
success_count[i, j] += 1
time_ave[i, j] += vis.H['time']
if success_count[i, j] != 0:
time_ave[i, j] /= success_count[i, j]
print('N, prob :', n, p)
print(' success ratio :', success_count[i, j] / trials)
print(' average time :', time_ave[i, j])
success_ratio = success_count / trials
plt.figure()
df = pd.DataFrame(time_ave.T, E, N)
sn.set(font_scale=1.4)
sn.heatmap(df, annot=success_ratio.T, cbar_kws={'label': 'time'})
plt.xlabel('number of nodes')
plt.ylabel('edge probabilities')
plt.title('Success ratio')
plt.show()