for num_c in tqdm(num_cs, desc="num_c"):
#print(f"\n### n:{n} num_c:{num_c} ###")
#print("Generation of G ...")
clusters = pd.fixed_clusters(n, num_c)
G, GT, labels = pd.custom_cluster_matrix(n, clusters, inter, 1,
intra, 0)
# epsilon parameter for the approx Alon et al
c = Codec(0.285, 0.285, 1)
c.verbose = False
#print("Generation of compressed/reduced graph ...")
tm = time.time()
k, epsilon, classes, sze_idx, reg_list, nirr = c.compress(
G, "indeg_guided")
red = c.reduced_matrix(G, k, epsilon, classes, reg_list)
t_compression = time.time() - tm
#print(f"s:{t_compression:.2f}")
# Precomputation of the eigenvalues
#print("Eigenvalues of G ... ")
tm = time.time()
G_eig, aux = metrics.eigs(G)
t_G_eig = time.time() - tm
#print(f"s:{t_G_eig:.2f}")
#print("Eigenvalues of red ... ")
tm = time.time()
red_eig, aux = metrics.eigs(red)
t_red_eig = time.time() - tm
############## Main script code ##############
##############################################
refinement = 'indeg_guided'
vals = [0.1, 0.2, 0.5, 0.7]
first = True
n = 1000
# Query graph, the bigger one
clusters = pd.random_clusters(n, 8)
inter = 0.1
intra = 0.1
oG, oGT, olabeling = pd.custom_cluster_matrix(n, clusters, inter, 1, intra, 0)
c = Codec(0.15, 0.5, 5)
k, epsilon, classes, sze_idx, reg_list, nirr = c.compress(oG, refinement)
query = c.reduced_matrix(oG, k, epsilon, classes, reg_list)
# Database
c = Codec(0.2, 0.5, 5)
num_c = 8
for r in range(5):
for i in vals:
clusters = pd.random_clusters(n, num_c)
G, GT, labeling = pd.custom_cluster_matrix(n, clusters, i, 1, i, 0)
k, epsilon, classes, sze_idx, reg_list, nirr = c.compress(
G, refinement)
red = c.reduced_matrix(G, k, epsilon, classes, reg_list)
if query.shape[0] > red.shape[0]:
tm = time.time()
clusters1 = proc_d.random_clusters(n, 8)
G, GT, labeling = proc_d.custom_cluster_matrix(n, clusters1, 0.3, 1, 0.2, 0)
tgen = time.time() -tm
#print(f"TIME: tgen:{tgen:.2f}")
# Second graph
clusters2 = proc_d.random_clusters(n, 8)
G2, GT2, labeling2 = proc_d.custom_cluster_matrix(n, clusters2, 0.6, 1, 0.1, 0)
# Compression G1
c = Codec(0.285, 0.285, 1)
c.verbose = False
tm = time.time()
k, epsilon, classes, sze_idx, reg_list, nirr = c.compress(G, 'indeg_guided')
tcompression1 = time.time() - tm
#print(f"TIME: tcompression1:{tcompression1:.2f}")
# Compression G1
c = Codec(0.285, 0.285, 1)
c.verbose = False
tm = time.time()
k2, epsilon, classes2, sze_idx, reg_list2, nirr2 = c.compress(G2, 'indeg_guided')
tcompression2 = time.time() - tm
#print(f"TIME: tcompression2:{tcompression2:.2f}")
# Reduced graph
tm = time.time()
red = c.reduced_matrix(G, k, epsilon, classes, reg_list)
red2 = c.reduced_matrix(G2, k2, epsilon, classes2, reg_list2)
