import torch
import torch.nn as nn
import torch.nn.functional as F

import networkx as nx


def normalize(A , symmetric=True):
	# A = A+I
	A = A + torch.eye(A.size(0))
	# 所有节点的度
	d = A.sum(1)
	if symmetric:
		#D = D^-1/2
		D = torch.diag(torch.pow(d , -0.5))
		return D.mm(A).mm(D)
	else :
		# D=D^-1
		D =torch.diag(torch.pow(d,-1))
		return D.mm(A)


class GCN(nn.Module):
	'''  Z = AXW  '''
	def __init__(self , A, dim_in , dim_out):
		super(GCN,self).__init__()
		self.A = A
		self.fc1 = nn.Linear(dim_in ,dim_in,bias=False)
		self.fc2 = nn.Linear(dim_in,dim_in//2,bias=False)
		self.fc3 = nn.Linear(dim_in//2,dim_out,bias=False)

	def forward(self,X):
		'''  计算三层gcn  '''
		X = F.relu(self.fc1(self.A.mm(X)))
		X = F.relu(self.fc2(self.A.mm(X)))
		return self.fc3(self.A.mm(X))

#获得空手道俱乐部数据
G = nx.karate_club_graph()
A = nx.adjacency_matrix(G).todense()
#A需要正规化
A_normed = normalize(torch.FloatTensor(A),True)

N = len(A)
X_dim = N

# 没有节点的特征，简单用一个单位矩阵表示所有节点
X = torch.eye(N,X_dim)
# 正确结果
Y = torch.zeros(N,1).long()
# 计算loss的时候要去掉没有标记的样本
Y_mask = torch.zeros(N,1,dtype=torch.uint8)
# 一个分类给一个样本
Y[0][0]=0
Y[N-1][0]=1
#有样本的地方设置为1
Y_mask[0][0]=1
Y_mask[N-1][0]=1

#真实的空手道俱乐部的分类数据
Real = torch.zeros(34 , dtype=torch.long)
for i in [1,2,3,4,5,6,7,8,11,12,13,14,17,18,20,22] :
	Real[i-1] = 0
for i in [9,10,15,16,19,21,23,24,25,26,27,28,29,30,31,32,33,34] :
	Real[i-1] = 1

# 我们的GCN模型
gcn = GCN(A_normed ,X_dim,2)
#选择adam优化器
gd = torch.optim.Adam(gcn.parameters())

for i in range(300):
	#转换到概率空间
	y_pred =F.softmax(gcn(X),dim=1)
	#下面两行计算cross entropy
	loss = (-y_pred.log().gather(1,Y.view(-1,1)))
	#仅保留有标记的样本
	loss = loss.masked_select(Y_mask).mean()

	#梯度下降
	#清空前面的导数缓存
	gd.zero_grad()
	#求导
	loss.backward()
	#一步更新
	gd.step()

	if i%20==0 :
		_,mi = y_pred.max(1)
		print(mi)
		#计算精确度
		print((mi == Real).float().mean())