什么是networkx？
networkx在02年5月产生，是用python语言编写的软件包，便于用户对复杂网络进行创建、操作和学习。利用networkx可以以标准化和非标准化的数据格式存储网络、生成多种随机网络和经典网络、分析网络结构、建立网络模型、设计新的网络算法、进行网络绘制等。 ——百度百科
我们可以用networkx做什么？
Examples - NetworkX 2.1 documentation- 画图
2. 有向图，无向图，网络图……
3. 总之各种图
看到这你是不是心动了呢？今天的教程就是要教会你画出封面上的三层感知机模型图！
Let's get started!
首先导入networkx和matplotlib模块
import networkx as nx
import matplotlib.pyplot as plt
>>> import networkx as nx
>>> G = nx.Graph() 定义了一个空图
>>> G.add_node(1) 这个图中增加了1节点
>>> G.add_node('A') 增加'A'节点
>>> G.add_nodes_from([2, 3]) 同时加2和3两个节点
>>> G.add_edges_from([(1,2),(1,3),(2,4),(2,5),(3,6),(4,8),(5,8),(3,7)])
# 增加这么多条边，在下面有举例
>>> H = nx.path_graph(10)
>>> G.add_nodes_from(H)
>>> G.add_node(H)
G.add_node('a')#添加点a
G.add_node(1,1)#用坐标来添加点
G.add_edge('x','y')#添加边,起点为x，终点为y
G.add_weight_edges_from([('x','y',1.0)])#第三个输入量为权值
#也可以
list = [[('a','b',5.0),('b','c',3.0),('a','c',1.0)]
G.add_weight_edges_from([(list)])
我们来看看上面最后一句是什么意思
import matplotlib.pyplot as plt
import networkx as nx
H = nx.path_graph(10)
G.add_nodes_from(H)
nx.draw(G, with_labels=True)
plt.show()
生成了标号为0到9的十个点！别急，丑是丑了点，一会我们再给他化妆。
#再举个栗子
G=nx.Graph()
#导入所有边，每条边分别用tuple表示
G.add_edges_from([(1,2),(1,3),(2,4),(2,5),(3,6),(4,8),(5,8),(3,7)])
nx.draw(G, with_labels=True, edge_color='b', node_color='g', node_size=1000)
plt.show()
#plt.savefig('./generated_image.png') 如果你想保存图片，去除这句的注释
好了，你现在已经知道如何给图添加边和节点了，接下来是构造环：
画个圈圈
import matplotlib.pyplot as plt
import networkx as nx
# H = nx.path_graph(10)
# G.add_nodes_from(H)
G = nx.Graph()
G.add_cycle([0,1,2,3,4])
nx.draw(G, with_labels=True)
plt.show()
画个五角星
import networkx as nx
import matplotlib.pyplot as plt
#画图！
G=nx.Graph()
G.add_node(1)
G.add_nodes_from([2,3,4,5])
for i in range(5):
for j in range(i):
if (abs(i-j) not in (1,4)):
G.add_edge(i+1, j+1)
nx.draw(G,
with_labels=True, #这个选项让节点有名称
edge_color='b', # b stands for blue!
pos=nx.circular_layout(G), # 这个是选项选择点的排列方式，具体可以用 help(nx.drawing.layout) 查看
# 主要有spring_layout (default), random_layout, circle_layout, shell_layout
# 这里是环形排布，还有随机排列等其他方式
node_color='r', # r = red
node_size=1000, # 节点大小
width=3, # 边的宽度
)
plt.show()
import random
G = nx.gnp_random_graph(10,0.3)
for u,v,d in G.edges(data=True):
d['weight'] = random.random()
edges,weights = zip(*nx.get_edge_attributes(G,'weight').items())
pos = nx.spring_layout(G)
nx.draw(G, pos, node_color='b', edgelist=edges, edge_color=weights, width=10.0, edge_cmap=plt.cm.Blues)
# plt.savefig('edges.png')
plt.show()
加入权重
import matplotlib.pyplot as plt
import networkx as nx
G = nx.Graph()
G.add_edge('a', 'b', weight=0.6)
G.add_edge('a', 'c', weight=0.2)
G.add_edge('c', 'd', weight=0.1)
G.add_edge('c', 'e', weight=0.7)
G.add_edge('c', 'f', weight=0.9)
G.add_edge('a', 'd', weight=0.3)
elarge = [(u, v) for (u, v, d) in G.edges(data=True) if d['weight'] > 0.5]
esmall = [(u, v) for (u, v, d) in G.edges(data=True) if d['weight'] <= 0.5]
pos = nx.spring_layout(G) # positions for all nodes
# nodes
nx.draw_networkx_nodes(G, pos, node_size=700)
# edges
nx.draw_networkx_edges(G, pos, edgelist=elarge,
width=6)
nx.draw_networkx_edges(G, pos, edgelist=esmall,
width=6, alpha=0.5, edge_color='b', style='dashed')
# labels
nx.draw_networkx_labels(G, pos, font_size=20, font_family='sans-serif')
plt.axis('off')
plt.show()
有向图
from __future__ import division
import matplotlib.pyplot as plt
import networkx as nx
G = nx.generators.directed.random_k_out_graph(10, 3, 0.5)
pos = nx.layout.spring_layout(G)
node_sizes = [3 + 10 * i for i in range(len(G))]
M = G.number_of_edges()
edge_colors = range(2, M + 2)
edge_alphas = [(5 + i) / (M + 4) for i in range(M)]
nodes = nx.draw_networkx_nodes(G, pos, node_size=node_sizes, node_color='blue')
edges = nx.draw_networkx_edges(G, pos, node_size=node_sizes, arrowstyle='->',
arrowsize=10, edge_color=edge_colors,
edge_cmap=plt.cm.Blues, width=2)
# set alpha value for each edge
for i in range(M):
edges[i].set_alpha(edge_alphas[i])
ax = plt.gca()
ax.set_axis_off()
plt.show()
颜色渐变的节点
import matplotlib.pyplot as plt
import networkx as nx
G = nx.cycle_graph(24)
pos = nx.spring_layout(G, iterations=200)
nx.draw(G, pos, node_color=range(24), node_size=800, cmap=plt.cm.Blues)
plt.show()
颜色渐变的边
import matplotlib.pyplot as plt
import networkx as nx
G = nx.star_graph(20)
pos = nx.spring_layout(G)
colors = range(20)
nx.draw(G, pos, node_color='#A0CBE2', edge_color=colors,
width=4, edge_cmap=plt.cm.Blues, with_labels=False)
plt.show()
如何画一个多层感知机？
import matplotlib.pyplot as plt
import networkx as nx
left, right, bottom, top, layer_sizes = .1, .9, .1, .9, [4, 7, 7, 2]
# 网络离上下左右的距离
# layter_sizes可以自己调整
import random
G = nx.Graph()
v_spacing = (top - bottom)/float(max(layer_sizes))
h_spacing = (right - left)/float(len(layer_sizes) - 1)
node_count = 0
for i, v in enumerate(layer_sizes):
layer_top = v_spacing*(v-1)/2. + (top + bottom)/2.
for j in range(v):
G.add_node(node_count, pos=(left + i*h_spacing, layer_top - j*v_spacing))
node_count += 1
# 这上面的数字调整我想了好半天，汗
for x, (left_nodes, right_nodes) in enumerate(zip(layer_sizes[:-1], layer_sizes[1:])):
for i in range(left_nodes):
for j in range(right_nodes):
G.add_edge(i+sum(layer_sizes[:x]), j+sum(layer_sizes[:x+1]))
# 慢慢研究吧
pos=nx.get_node_attributes(G,'pos')
# 把每个节点中的位置pos信息导出来
nx.draw(G, pos,
node_color=range(node_count),
with_labels=True,
node_size=200,
edge_color=[random.random() for i in range(len(G.edges))],
width=3,
cmap=plt.cm.Dark2, # matplotlib的调色板，可以搜搜，很多颜色呢
edge_cmap=plt.cm.Blues
)
plt.show()
差不多就是这个效果了。
后续我会封装为一个类，加入动态演示，比如通过颜色深浅，显示神经网络在优化的时候权重变化。应该会很好玩，嘿嘿。
上面你也可以改变layer_sizes
比如改为233333
