def UpdateStats(stats, t0, curr_lp, K, z, c, steps, gt_z, map_z, verbose):
stats['lp'].append(curr_lp)
stats['K'].append(K)
stats['z'].append(z)
stats['c'].append(c)
curr_time = time.clock() - t0
stats['times'].append(curr_time)
if verbose:
print('Step: ' + str(steps) + ' Time: ' + str(curr_time) + ' LP: ' +
str(curr_lp) + ' K: ' + str(K))
if gt_z.size > 0:
stats['NMI'].append(StatsUtil.NMI(gt_z, map_z))
return stats
def LearnSynthForDataset(synth):
# Hyperparameters
alpha = 10;
kappa = 0.0001;
nu = 1;
sigsq = 0.01;
pass_limit = 30;
D = NormalizeConn(synth.D) # Normalize connectivity to zero mean, unit var
# Compute our ddCRP-based parcellation
Z = WardClustering.ClusterTree(D, synth.adj_list)
_,dd_stats = initdd.InitializeAndRun(Z, D, synth.adj_list, range(1,21),
alpha, kappa, nu, sigsq, pass_limit, synth.z, 0)
DC = dd_stats['NMI'][-1]
DC_K = dd_stats['K'][-1]
# Ward Clustering, using number of clusters discovered from our method
WC = StatsUtil.NMI(synth.z, WardClustering.Cluster(Z, DC_K))
return (WC,DC,DC_K)
