Sklearn:每个簇距质心的平均距离 [英] Sklearn : Mean Distance from Centroid of each cluster
问题描述
我如何找到质心到每个群集中所有数据点的平均距离.我能够找到每个点(在我的数据集中)与每个聚类的质心的欧几里得距离.现在,我想找到质心到每个群集中所有数据点的平均距离. 计算距每个质心的平均距离的好方法是什么? 到目前为止,我已经做到了.
How can i find the mean distance from the centroid to all the data points in each cluster. I am able to find the euclidean distance of each point (in my dataset) from the centroid of each cluster. Now i want to find the mean distance from centroid to all the data points in each cluster. What is a good way of calculating mean distance from each centroid ? So far I have done this..
def k_means(self):
data = pd.read_csv('hdl_gps_APPLE_20111220_130416.csv', delimiter=',')
combined_data = data.iloc[0:, 0:4].dropna()
#print combined_data
array_convt = combined_data.values
#print array_convt
combined_data.head()
t_data=PCA(n_components=2).fit_transform(array_convt)
#print t_data
k_means=KMeans()
k_means.fit(t_data)
#------------k means fit predict method for testing purpose-----------------
clusters=k_means.fit_predict(t_data)
#print clusters.shape
cluster_0=np.where(clusters==0)
print cluster_0
X_cluster_0 = t_data[cluster_0]
#print X_cluster_0
distance = euclidean(X_cluster_0[0], k_means.cluster_centers_[0])
print distance
classified_data = k_means.labels_
#print ('all rows forst column........')
x_min = t_data[:, 0].min() - 5
x_max = t_data[:, 0].max() - 1
#print ('min is ')
#print x_min
#print ('max is ')
#print x_max
df_processed = data.copy()
df_processed['Cluster Class'] = pd.Series(classified_data, index=df_processed.index)
#print df_processed
y_min, y_max = t_data[:, 1].min(), t_data[:, 1].max() + 5
xx, yy = np.meshgrid(np.arange(x_min, x_max, 1), np.arange(y_min, y_max, 1))
#print ('the mesh grid is: ')
#print xx
Z = k_means.predict(np.c_[xx.ravel(), yy.ravel()])
Z = Z.reshape(xx.shape)
plt.figure(1)
plt.clf()
plt.imshow(Z, interpolation='nearest',
extent=(xx.min(), xx.max(), yy.min(), yy.max()),
cmap=plt.cm.Paired,
aspect='auto', origin='lower')
#print Z
plt.plot(t_data[:, 0], t_data[:, 1], 'k.', markersize=20)
centroids = k_means.cluster_centers_
inert = k_means.inertia_
plt.scatter(centroids[:, 0], centroids[:, 1],
marker='x', s=169, linewidths=3,
color='w', zorder=8)
plt.xlim(x_min, x_max)
plt.ylim(y_min, y_max)
plt.xticks(())
plt.yticks(())
plt.show()
简而言之,我想计算特定群集中所有数据点到该群集质心的平均距离,因为我需要根据该平均距离来清理数据
推荐答案
这是一种方法.如果需要除欧几里得以外的其他距离度量标准,可以在函数中用k_mean_distance()
替换另一个距离度量标准.
Here's one way. You can substitute another distance measure in the function for k_mean_distance()
if you want another distance metric other than Euclidean.
计算每个分配的聚类和聚类中心的数据点之间的距离,并返回平均值.
Calculate distance between data points for each assigned cluster and cluster centers and return the mean value.
距离计算功能:
def k_mean_distance(data, cx, cy, i_centroid, cluster_labels):
# Calculate Euclidean distance for each data point assigned to centroid
distances = [np.sqrt((x-cx)**2+(y-cy)**2) for (x, y) in data[cluster_labels == i_centroid]]
# return the mean value
return np.mean(distances)
对于每个质心,使用函数来获取平均距离:
And for each centroid, use the function to get the mean distance:
total_distance = []
for i, (cx, cy) in enumerate(centroids):
# Function from above
mean_distance = k_mean_distance(data, cx, cy, i, cluster_labels)
total_dist.append(mean_distance)
因此,根据您的问题:
def k_mean_distance(data, cx, cy, i_centroid, cluster_labels):
distances = [np.sqrt((x-cx)**2+(y-cy)**2) for (x, y) in data[cluster_labels == i_centroid]]
return np.mean(distances)
t_data=PCA(n_components=2).fit_transform(array_convt)
k_means=KMeans()
clusters=k_means.fit_predict(t_data)
centroids = km.cluster_centers_
c_mean_distances = []
for i, (cx, cy) in enumerate(centroids):
mean_distance = k_mean_distance(t_data, cx, cy, i, clusters)
c_mean_distances.append(mean_distance)
如果绘制结果plt.plot(c_mean_distances)
,您应该会看到类似这样的内容:
If you plot the results plt.plot(c_mean_distances)
you should see something like this:
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