如何解决使用igraph python
我有一个example.cvc文件,其中包含以下值:
pos= [180 270 360 450 540 630 720 810]
mean_values= [(270,630),(540,720),(270,810),(450,(180,360),540),(360,(630,450),720)]
平均值基本上来自2d高斯混合模型。根据我的理解,标签可以表示顶点(8),mean_values可以称为边(12)。
关于数据:pos基本上由蓝色和黄色圆圈表示,而均值对应于哪个与哪个pos有关。因此,例如,在180,360,540和720个标签值中,有180个连接到360,540,720,如箭头所示,并由以下均值表示:[(180,360),(180,540),(180,720)]和类似的结果可以与其他pos,
关于如何使用igraph获得这样的结果的任何想法。我进行了几次搜索,但没有任何想法。我是igraph的新手,不胜感激。预先感谢
解决方法
首先,您需要像这样计算最接近的点集(请注意,我可以自由地从列表而不是文件中读取数据,可以相应地对其进行更改):
import numpy as np
a = np.asarray([640.,270.,450.,230.,180.,540.,360.,610.,360.])
b = np.asarray([810.,630.,810.,760.,720.,540.])
closest_mapping = []
for node in a:
# Subtract node from each element in b
# and get the absolute value
dist_list = np.absolute(np.array(b) - node)
# Find the element in b with the min. absolute value
min_element = b[np.argmin(dist_list)]
# Create a tuple of (node,min_element) and add it to list.
# This will be used to plot a graph later.
# Note that the second element is stored as a string.
closest_mapping.append((node,str(min_element)))
当我们绘制图形时,我将第二个元素存储为字符串的原因将被清除。您可以自己查看要验证的点
print(closest_mapping)
#[(640.0,'630.0'),# (270.0,'360.0'),# (450.0,'450.0'),# (230.0,# (180.0,# (540.0,'540.0'),# (360.0,# (610.0,'360.0')]
我不知道如何使用igraph绘制二部图,因此我将为此使用NetworkX:
import networkx as nx
# Create an empty graph
G = nx.Graph()
# Add the edges from the list we created
G.add_edges_from(closest_mapping)
# Create a bipartite layout
pos = nx.bipartite_layout(G,a)
# Draw the Graph
nx.draw(G,pos,with_labels=True,node_size=900,node_color='y')
左侧的节点来自A
,而右侧的节点来自B
。
如果要查找每对节点的最近距离
import numpy as np
import networkx as nx
a = np.asarray([640.,540.])
closest_mapping = []
# Find mapping for A->B
for node in a:
# Subtract node from each element in b
# and get the absolute value
dist_list = np.absolute(np.array(b) - node)
# Find the element in b with the min. absolute value
min_element = b[np.argmin(dist_list)]
# Create a tuple of (node,str(min_element)))
# Find Mapping for B->A
for node in b:
# Subtract node from each element in b
# and get the absolute value
dist_list = np.absolute(np.array(a) - node)
# Find the element in b with the min. absolute value
min_element = a[np.argmin(dist_list)]
# Create a tuple of (node,min_element) and add it to list.
# This will be used to plot a graph later.
# Note that the first element is stored as a string.
closest_mapping.append((str(node),min_element))
# Create an empty graph
G = nx.Graph()
# Add the edges from the list we created
G.add_edges_from(closest_mapping)
# Create a bipartite layout
pos = nx.bipartite_layout(G,node_color='y')
注意将列表B
中的节点存储为字符串的原因是,如果将它们保留为整数/浮点并且它们在图表A
中具有相同的值不会是二分的(即使两个节点在逻辑上都不相同,它也不会注册重复的节点,因此我将一个节点列表保留为字符串)。
更新: 根据更新后的问题,您可以使用NetworkX直接添加节点和边,如下所示:
将networkx导入为nx
pos= [180,270,360,450,540,630,720,810]
mean_values= [(270,630),(540,720),(270,810),(450,(180,360),540),(360,(630,450),720)]
G = nx.Graph()
G.add_nodes_from(pos)
G.add_edges_from(mean_values)
pos = nx.spring_layout(G)
nx.draw(G,node_size=500,node_color='y')
您可以将nx.DiGraph
用于这样的定向边缘
import networkx as nx
pos= [180,720)]
G = nx.DiGraph()
G.add_nodes_from(pos)
G.add_edges_from(mean_values)
pos = nx.spring_layout(G)
nx.draw(G,node_color='y')
参考:
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