def plotEmbedComparison(G, e1, e2):
e1.embed(G)
e2.embed(G)
x1 = e1.get_scaled(2)
x2 = e2.get_scaled(2)
x2p = Embed.procrustes(x1, x2)
#block = nx.get_node_attributes(G, 'block').values()
#plot.subplot(121);
plot.scatter(x1[:,0],x1[:,1],c='r')
#plot.subplot(122);
plot.scatter(x2p[:,0],x2p[:,1],c='b')
def get_rdpg_sim_res(rdpg, embed, k):
rdpg.generate_adjacency()
Xhat = Embed.procrustes(rdpg.X, embed.embed(rdpg.Adj).get_scaled())
#Y = rdpg.block_assignment
knn = KNeighborsClassifier(k)
loo = cross_validation.LeaveOneOut(rdpg.n_nodes)
X = rdpg.X
Y = (X[:,0]>X[:,1]).astype(float)
Xhat = Embed.procrustes(X,embed.embed(rdpg.Adj).get_scaled(),scale=False, center=False)
result = np.zeros(1, dtype=[('X',np.float32),('Xhat',np.float32),
('gstarX',np.float32),('gstarXhat',np.float32),('sqerr',np.float32),('n',np.int)])
result['X'] = 1-np.mean(cross_validation.cross_val_score(knn, X,Y, cv=loo))
result['Xhat'] = 1-np.mean(cross_validation.cross_val_score(knn, Xhat,Y, cv=loo))
result['gstarX'] = 1-np.mean(np.equal(X[:,0]>X[:,1],Y))
result['gstarXhat'] = 1-np.mean(np.equal(Xhat[:,0]>Xhat[:,1],Y))
result['sqerr'] = np.linalg.norm(X-Xhat,'fro')
result['n'] = rdpg.n_nodes
return result
