我正在尝试制作一个与聚合层次聚类相关的树状图,并需要距离矩阵。我从以下内容开始:
import numpy as np
import pandas as pd
from scipy import ndimage
from scipy.cluster import hierarchy
from scipy.spatial import distance_matrix
from matplotlib import pyplot as plt
from sklearn import manifold, datasets
from sklearn.cluster import AgglomerativeClustering
from sklearn.datasets.samples_generator import make_blobs
%matplotlib inline
X1, y1 = make_blobs(n_samples=50, centers=[[4,4], [-2, -1], [1, 1], [10,4]], cluster_std=0.9)
plt.scatter(X1[:, 0], X1[:, 1], marker='o')
agglom = AgglomerativeClustering(n_clusters = 4, linkage = 'average')
agglom.fit(X1,y1)
# Create a figure of size 6 inches by 4 inches.
plt.figure(figsize=(6,4))
# These two lines of code are used to scale the data points down,
# Or else the data points will be scattered very far apart.
# Create a minimum and maximum range of X1.
x_min, x_max = np.min(X1, axis=0), np.max(X1, axis=0)
# Get the average distance for X1.
X1 = (X1 - x_min) / (x_max - x_min)
# This loop displays all of the datapoints.
for i in range(X1.shape[0]):
# Replace the data points with their respective cluster value
# (ex. 0) and is color coded with a colormap (plt.cm.spectral)
plt.text(X1[i, 0], X1[i, 1], str(y1[i]),
color=plt.cm.nipy_spectral(agglom.labels_[i] / 10.),
fontdict={'weight': 'bold', 'size': 9})
# Remove the x ticks, y ticks, x and y axis
plt.xticks([])
plt.yticks([])
#plt.axis('off')
# Display the plot of the original data before clustering
plt.scatter(X1[:, 0], X1[:, 1], marker='.')
# Display the plot
plt.show()
dist_matrix = distance_matrix(X1,X1)
print(dist_matrix)
当我写下这个时,出现了错误:
Z = hierarchy.linkage(dist_matrix, 'complete')
/home/jupyterlab/conda/envs/python/lib/python3.6/site-packages/ipykernel_launcher.py:1: 集群警告: scipy.cluster: 对称的非负空洞观察矩阵看起来很像未压缩的距离矩阵 """入口点启动IPython内核。
首先,这是什么意思,我该如何解决它?谢谢