view_init(elev,azim)函数的使用

转换视角进行观察，通过设置view_init(elev,azim)两个参数变化时，观察图像的变化

# #############################################################################
# Compute clustering
print("Compute unstructured hierarchical clustering...")
st = time.time()

#  指定分成的簇的个数为6
n_clusters=6
ward = AgglomerativeClustering(n_clusters=n_clusters, linkage='ward').fit(X)
elapsed_time = time.time() - st
label = ward.labels_
print("unstructured hierarchical clusters:: %i" % n_clusters)
print("Elapsed time: %.2fs" % elapsed_time)
print("Number of points: %i" % label.size)

# #############################################################################
# Plot result

fig = plt.figure(figsize=(14, 14))

ax = p3.Axes3D(fig)
ax.view_init(7, -80)
for l in np.unique(label):
    ax.scatter(X[label == l, 0], X[label == l, 1], X[label == l, 2],
               color=plt.cm.jet(np.float(l) / np.max(label + 1)),
               s=24, edgecolor='k')
plt.title('Without connectivity constraints (time %.2fs)' % elapsed_time)


# #############################################################################
# Define the structure A of the data. Here a 10 nearest neighbors
from sklearn.neighbors import kneighbors_graph
connectivity = kneighbors_graph(X, n_neighbors=10, include_self=False)

# #############################################################################
# Compute clustering
print("Compute structured hierarchical clustering...")
st = time.time()
ward = AgglomerativeClustering(n_clusters=n_clusters, connectivity=connectivity,
                               linkage='ward').fit(X)
elapsed_time = time.time() - st
label = ward.labels_
print("structured hierarchical clusters: %i" % n_clusters)
print("Elapsed time: %.2fs" % elapsed_time)
print("Number of points: %i" % label.size)

# #############################################################################
# Plot result
fig = plt.figure(figsize=(14, 14))

ax = p3.Axes3D(fig)
ax.view_init(7, -80)
for l in np.unique(label):
    ax.scatter(X[label == l, 0], X[label == l, 1], X[label == l, 2],
               color=plt.cm.jet(float(l) / np.max(label + 1)),
               s=24, edgecolor='k')
plt.title('With connectivity constraints (time %.2fs)' % elapsed_time)

plt.show()

将角度设置为45,0，再次进行观察
ax.view_init(45, 0)

ax.view_init(45, 45)