numpy中matrix的坑

在看一篇利用numpy实现简单的GCN网络的文章时，遇到了matplotlib中scatter函数一直显示“Masked arrays must be 1-D”这个错误。后面查阅资料得知是numpy中matrix的维度一定为2维

import networkx as nx
import numpy as np
from GCN import gcn_layer
import matplotlib.pyplot as plt


zkc = nx.karate_club_graph()
order = sorted(list(zkc.nodes()))
A = nx.to_numpy_matrix(zkc, nodelist=order)
I = np.eye(zkc.number_of_nodes())
A_hat = A + I
D_hat = np.array(A_hat.sum(axis=0))[0]  # D_hat.shape = (1,34)
D_hat = np.matrix(np.diag(D_hat))  # the usage of np.diag :D_hat.shape must be 1-dim
w1 = np.random.normal(loc=0, scale=1, size=(zkc.number_of_nodes(), 8))  # generate weight1 randomly (n,4)
w2 = np.random.normal(loc=0, size=(w1.shape[1], 2))  # match the shape of the first gcn_layer's output
H1 = gcn_layer(D_hat, A_hat, I, w1)
H2 = gcn_layer(D_hat, A, H1, w2)
feature_representations = {node: np.array(H2)[node] for node in zkc.nodes()}
x = H2.T[0]
y = H2.T[1]
plt.scatter(x, y)
plt.title("Feature representations of Zachary")
plt.show()

Note： It is no longer recommended to use this class, even for linear algebra. Instead use regular arrays. The class may be removed in the future.
A matrix is a specialized 2-D array that retains its 2-D nature through operations.
因此在matrix使用过程中会一直保持二维数组的特性，在scatter函数中，scatter(x,y)中x,y均为一维数组。
Solution：将其转化为array再变为1-D array即可

x = np.array(H2.T[0])
y = np.array(H2.T[1])
# 或者
y = np.asarray(H2.T[0])
y = np.asarray(H2.T[1])