问题描述
我在高维数据库中应用了平分kMeans聚类，并想要显示派生的聚类组的大小，例如集群1 = 2000个元素;簇2 = 3489个元素，依此类推。
我需要哪些功能才能显示尺寸？可视化是可能的，如在二维图上所示：
def函数如下所示：
def convert_to_2d_array(points):
"""
Converts `points` to a 2-D numpy array.
"""
points = np.array(points)
if len(points.shape) == 1:
points = np.expand_dims(points,-1)
return points
def visualize_clusters(clusters):
"""
Visualizes the first 2 dimensions of the data as a 2-D scatter plot.
"""
plt.figure()
for cluster in clusters:
points = convert_to_2d_array(cluster)
if points.shape[1] < 2:
points = np.hstack([points,np.zeros_like(points)])
plt.plot(points[:,0],points[:,1],'o')
plt.show()
def SSE(points):
"""
Calculates the sum of squared errors for the given list of data points.
"""
points = convert_to_2d_array(points)
centroid = np.mean(points,0)
errors = np.linalg.norm(points-centroid,ord=2,axis=1)
return np.sum(errors)
def kmeans(points,k=2,epochs=10,max_iter=100,verbose=False):
"""
Clusters the list of points into `k` clusters using k-means clustering
algorithm.
"""
points = convert_to_2d_array(points)
assert len(points) >= k,"Number of data points can't be less than k"
best_sse = np.inf
for ep in range(epochs):
# Randomly initialize k centroids
np.random.shuffle(points)
centroids = points[0:k,:]
last_sse = np.inf
for it in range(max_iter):
# Cluster assignment
clusters = [None] * k
for p in points:
index = np.argmin(np.linalg.norm(centroids-p,2,1))
if clusters[index] is None:
clusters[index] = np.expand_dims(p,0)
else:
clusters[index] = np.vstack((clusters[index],p))
# Centroid update
centroids = [np.mean(c,0) for c in clusters]
# SSE calculation
sse = np.sum([SSE(c) for c in clusters])
gain = last_sse - sse
if verbose:
print((f'Epoch: {ep:3d},Iter: {it:4d},'
f'SSE: {sse:12.4f},Gain: {gain:12.4f}'))
# Check for improvement
if sse < best_sse:
best_clusters,best_sse = clusters,sse
# Epoch termination condition
if np.isclose(gain,atol=0.00001):
break
last_sse = sse
return best_clusters
def bisecting_kmeans(points,verbose=False):
"""
Clusters the list of points into `k` clusters using bisecting k-means
clustering algorithm. Internally,it uses the standard k-means with k=2 in
each iteration.
"""
points = convert_to_2d_array(points)
clusters = [points]
while len(clusters) < k:
max_sse_i = np.argmax([SSE(c) for c in clusters])
cluster = clusters.pop(max_sse_i)
two_clusters = kmeans(
cluster,epochs=epochs,max_iter=max_iter,verbose=verbose)
clusters.extend(two_clusters)
return clusters
在此先感谢您的帮助！
最诚挚的问候，
疲劳
解决方法
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