def plot(Xs, labels, K, clusters):
labelsNo = np.max(labels)
markers = [] # get the different markers
while len(markers) < labelsNo:
markers.extend(list(matplotlib.markers.MarkerStyle.filled_markers))
colors = plt.cm.rainbow(np.linspace(0, 1, K+1))
if Xs.shape[1] == 2:
x = Xs[:,0]
y = Xs[:,1]
for (_x, _y, _c, _l) in zip(x, y, clusters, labels):
plt.scatter(_x, _y, s=200, c=colors[_c], marker=markers[_l])
plt.show()
elif Xs.shape[1] == 3:
x = Xs[:,0]
y = Xs[:,1]
z = Xs[:,2]
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
for (_x, _y, _z, _c, _l) in zip(x, y, z, clusters, labels):
ax.scatter(_x, _y, _z, s=200, c=colors[_c], marker=markers[_l])
plt.show()
else:
for i in range(N1):
print(i, ": ", clusters[i], " ~ ", labels[i])
def plotClusters(Xs, labels, centroids, clusters):
""" Plot the data with the true labels alongside the centroids and the
predicted cluster.
If the elements from the dataset are not 2 or 3 dimensional then print
the index, predicted cluster and true label.
Args:
Xs (numpy array): dataset
labels (numpy array): real/true labels
centroids (numpy array): positions for the centroids
clusters (numpy array): predicted labels
"""
labelsNo = np.max(labels)
K = centroids.shape[0]
markers = []
while len(markers) < labelsNo:
markers.extend(list(matplotlib.markers.MarkerStyle.filled_markers))
colors = plt.cm.rainbow(np.linspace(0, 1, K + 1))
if Xs.shape[1] == 2:
x = Xs[:, 0]
y = Xs[:, 1]
for (_x, _y, _c, _l) in zip(x, y, clusters, labels):
plt.scatter(_x, _y, s=500, c=[colors[_c]], marker=markers[_l])
plt.scatter(centroids[:, 0],
centroids[:, 1],
s=800,
c=[colors[K]],
marker=markers[labelsNo])
plt.show()
elif Xs.shape[1] == 3:
x = Xs[:, 0]
y = Xs[:, 1]
z = Xs[:, 2]
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
for (_x, _y, _z, _c, _l) in zip(x, y, z, clusters, labels):
ax.scatter(_x, _y, _z, s=200, c=[colors[_c]], marker=markers[_l])
ax.scatter(centroids[:, 0],
centroids[:, 1],
centroids[:, 2],
s=400,
c=[colors[K]],
marker=markers[labelsNo])
plt.show()
else:
for i in range(Xs.shape[0]):
print(f"{i} : {clusters[i]} ~ {labels[i]}")
