vwb = VWB(x, [x1, x2], device=device, verbose=False)
output = vwb.cluster(max_iter_h=5000, max_iter_p=1)
idx = output['idx']
xmin, xmax, ymin, ymax = -1.0, 1.0, -0.5, 0.5
for k in [33]:
plt.figure(figsize=(8, 4))
for i in range(2):
ce = np.array(plt.get_cmap('viridis')(idx[i].cpu().numpy() / (K - 1)))
utils.scatter_otsamples(vwb.data_p,
vwb.data_e[i],
size_p=30,
marker_p='o',
color_x=ce,
xmin=xmin,
xmax=xmax,
ymin=ymin,
ymax=ymax,
facecolor_p='none')
e0s = vwb.data_e[0][idx[0] == k]
e1s = vwb.data_e[1][idx[1] == k]
p = vwb.data_p[k]
for e0, e1 in zip(e0s, e1s):
x = [e1[0], p[0], e0[0]]
y = [e1[1], p[1], e0[1]]
plt.plot(x, y, c='lightgray', alpha=0.4)
# plt.savefig("ship" + str(k) + ".svg")
plt.savefig("ship" + str(k) + ".png", dpi=300, bbox_inches='tight')
vot = VOT(x, [x1, x2], verbose=False)
vot.cluster(max_iter_h=3000, max_iter_y=1)
idx = vot.idx
xmin, xmax, ymin, ymax = -1., 1., 0., 1.
for k in [21]:
plt.figure(figsize=(8, 4))
for i in range(2):
ce = np.array(plt.get_cmap('viridis')(idx[i] / (K - 1)))
utils.scatter_otsamples(vot.y,
vot.x[i],
size_p=30,
marker_p='o',
color_e=ce,
xmin=xmin,
xmax=xmax,
ymin=ymin,
ymax=ymax,
facecolor_p='none')
p = vot.y[k]
for i in range(2):
es = vot.x[i][idx[i] == k]
for e in es:
x = [p[0], e[0]]
y = [p[1], e[1]]
plt.plot(x, y, c='lightgray', alpha=0.4)
# plt.savefig("ship" + str(k) + ".svg")
vot.data_p,
fig232,
color=cp,
title='w/o reg map',
facecolor_after='none')
# ------ plot after ----- #
plt.subplot(233)
ce = np.array([utils.COLOR_LIGHT_BLUE,
utils.COLOR_LIGHT_RED])[pred_label_e.int().cpu().numpy(), :]
cp = np.array([utils.COLOR_DARK_BLUE,
utils.COLOR_RED])[vot.label_p.int().cpu().numpy(), :]
utils.scatter_otsamples(vot.data_p,
vot.data_e,
size_p=30,
marker_p='o',
color_p=cp,
color_e=ce,
title='w/o reg after',
facecolor_p='none')
# -------------------------------------- #
# --------- w/ regularization ---------- #
# -------------------------------------- #
# ------- run RWM ------- #
data_p = np.loadtxt('data/p.csv', delimiter=",")
data_p = torch.from_numpy(data_p).double().to(device)
vot_reg = VotReg(data_p[:, 1:],
data_e[:, 1:],
data_p[:, 0],
data_e[:, 0],
kmeans = KMeans(n_clusters=K, init=x).fit(x0)
label = kmeans.predict(x0)
newx = kmeans.cluster_centers_
color_map = np.array([[237, 125, 49, 255], [112, 173, 71, 255],
[91, 155, 213, 255]]) / 255
plt.figure(figsize=(4, 4))
for i in range(1):
ce = color_map[label]
utils.scatter_otsamples(newx,
x0,
size_p=30,
marker_p='o',
color_e=ce,
xmin=xmin,
xmax=xmax,
ymin=ymin,
ymax=ymax,
facecolor_p='none')
plt.axis('off')
# plt.savefig("kmeans.svg", bbox_inches='tight')
plt.savefig("kmeans.png", dpi=300, bbox_inches='tight')
use_gpu = False
if use_gpu and torch.cuda.is_available():
device = 'cuda:0'
else:
device = 'cpu'
# ---------------VWB---------------
idx, _ = vot.map(max_iter=3000)
tock = time.time()
print('total time: {0:.4f}'.format(tock - tick))
# Note: Area preserving usually requires a pre-defined boundary.
# That is beyond the scope of the demo. Missing the boundary condition,
# this area-preserving demo might not produce accurate maps near the boundary.
# This can be visualized by drawing the Voronoi diagram or Delaunay triangulation
# and one may see slight intersection near the boundary centroids.
# ----- plot before ----- #
plt.figure(figsize=(12, 8))
plt.subplot(231)
utils.scatter_otsamples(
vot.data_p_original,
vot.x,
title='before',
)
# ------ plot map ------- #
fig232 = plt.subplot(232)
utils.plot_otmap(vot.data_p_original,
vot.y,
fig232,
title='vot map',
facecolor_after='none')
# ------ plot after ----- #
ce = np.array(plt.get_cmap('viridis')(idx / (N - 1)))
plt.subplot(233)
utils.scatter_otsamples(vot.y, vot.x, color_x=ce, title='after')
kmeans = KMeans(n_clusters=K, init=y).fit(x)
label = kmeans.predict(x)
y = kmeans.cluster_centers_
color_map = np.array([[237, 125, 49, 255], [112, 173, 71, 255],
[91, 155, 213, 255]]) / 255
plt.figure(figsize=(4, 4))
for i in range(1):
ce = color_map[label]
utils.scatter_otsamples(y,
x,
size_p=30,
marker_p='o',
color_e=ce,
xmin=xmin,
xmax=xmax,
ymin=ymin,
ymax=ymax,
facecolor_p='none')
plt.axis('off')
# plt.savefig("kmeans.svg", bbox_inches='tight')
plt.savefig("kmeans.png", dpi=300, bbox_inches='tight')
# --------------- OT ---------------
y_copy = y.copy()
x_copy = x.copy()
vwb = VOT(y_copy, [x_copy], verbose=False)
output = vwb.cluster(lr=0.5,
max_iter_h=20,
