def compare_results(block,kmA,kmL):
blockB = [[int(b==l) for b in block] for l in xrange(6)]
for l in xrange(5):
print "Block "+repr(l)+" results:"
num_diff = vn.num_diff_w_perms(blockB[l], kmA)
ari = adjusted_rand_score(blockB[l],kmA)
print "Adjacency: num error="+repr(num_diff)+" ari="+repr(ari)
num_diff = vn.num_diff_w_perms(blockB[l], kmL)
ari = adjusted_rand_score(blockB[l],kmL)
print "Laplacian: num error="+repr(num_diff)+" ari="+repr(ari)
def kmeans_analysis(G):
block = nx.get_node_attributes(G,'block').values()
xA, xL = get_embedding(G,2)
cA,kmA,_ = k_means(xA,2)
cB,kmL,_ = k_means(xL,2)
# plt.subplot(221); plt.scatter(xA[:,0],xA[:,1],c=block)
# plt.subplot(222); plt.scatter(xA[:,0],xA[:,1],c=kmA)
# plt.subplot(223); plt.scatter(xL[:,0],xL[:,1],c=block)
# plt.subplot(224); plt.scatter(xL[:,0],xL[:,1],c=kmL)
ax = plt.subplot(121); plt.scatter(xA[:,0],xA[:,1],c=block,marker='x')
ax.set_aspect('equal','datalim')
lim = plt.axis()
a = cA[0,:]-cA[1,:]
a = np.array([1, -a[0]/a[1]])
b = np.mean(cA,axis=0)
x = np.array([b+a,b-a])
plt.plot(x[:,0],x[:,1],'k--',linewidth=1)
plt.axis(lim)
ax = plt.subplot(122); plt.scatter(xL[:,0],xL[:,1],c=block,marker='x')
ax.set_aspect('equal','datalim')
lim = plt.axis()
a = cB[0,:]-cB[1,:]
a = np.array([1, -a[0]/a[1]])
b = np.mean(cB,axis=0)
x = np.array([b+a,b-a])
plt.plot(x[:,0],x[:,1],'k--',linewidth=1)
plt.axis(lim)
compare_results(block,kmA,kmL)
_,kmA,_ = k_means(xA,5)
_,kmL,_ = k_means(xL,5)
print "ALL FIVE"
num_diff = vn.num_diff_w_perms(block, kmA)
ari = adjusted_rand_score(block,kmA)
print "Adjacency: num error="+repr(num_diff)+" ari="+repr(ari)
num_diff = vn.num_diff_w_perms(block, kmL)
ari = adjusted_rand_score(block,kmL)
print "Laplacian: num error="+repr(num_diff)+" ari="+repr(ari)
