def simplify(self):
p = copy.deepcopy(self.productions)
uf = UnionFind(list(p.keys()))
while True:
changed = False
subs = {}
to_add = {}
to_delete = set()
for k1, r1 in p.items():
for k2, r2 in p.items():
if k1 != k2 and k1 not in subs and k2 not in subs and r1 == r2:
subs[k2] = k1
to_add[k1] = r2
to_delete.add(k2)
uf.union(k1, k2)
changed = True
p = {**p, **to_add}
for k in to_delete:
del p[k]
if not changed:
break
else:
p = {k2: r2.substitute(subs) for k2, r2 in p.items()}
return Grammar(productions=p), {k: uf.component(k) for k in p.keys()}
def sample_config(config,
eta,
N,
no_colors,
sites,
param_name,
curr_params,
uf=None,
cluster_constraints=None):
if uf == None and not only_averages:
'''Generate clusters from the assigned bonds (eta_edge)'''
uf = UnionFind(sites)
eta_edges = eta[1]
for i in range(eta_edges.shape[0]):
for j in range(eta_edges.shape[1]):
for e in range(eta_edges.shape[2]):
if eta_edges[i, j, e] == -1: continue
if eta_edges[i, j, e] == 0:
if e == 0:
uf.union(site2str((i, j)), site2str((i + 1, j)))
elif e == 1:
uf.union(site2str((i, j)), site2str((i, j + 1)))
'''For each cluster, find the site with strongest constraint (smallest eta_site)
and assign that eta_site to the entire cluster
'''
eta_sites = eta[2]
cluster_constraints = {}
cl_n = 0
cls = np.zeros((eta_sites.shape[0], eta_sites.shape[1]),
dtype=np.uint16) # up to 255x255 box
for cluster in uf.components():
cl_n += 1
min_constraint = no_colors
cluster_root = '-1,-1'
for site_str in cluster:
site = str2site(site_str)
if eta_sites[site[0], site[1]] <= min_constraint:
min_constraint = eta_sites[site[0], site[1]]
cluster_root = site_str
cls[site[0], site[1]] = cl_n
cluster_constraints[cluster_root] = min_constraint
cluster_constraints[cl_n] = min_constraint
if prt: print('clusters formed by bonds (eta_edge):')
if prt: print(cls)
# Case with no field and gamma > 0
if (curr_params['alpha'] == 0 and curr_params['gamma'] > 0):
if prt: print('Case with no field and gamma > 0')
'''Choose exactly how many colors to use in the configuration'''
max_colors = eta[0] # eta_lambda
prob_k = []
if only_averages:
no_cl = N * N
else:
no_cl = len(uf.components())
for k in range(1, max_colors + 1):
prob_k.append(P_exact_cols(no_colors, k, no_cl, S))
if prt: print('prob_k:', prob_k)
prob_k = [pk / sum(prob_k) for pk in prob_k]
prob_k = np.array(prob_k)
if prt: print('prob_k:', prob_k)
exact_k = np.random.choice((np.arange(1, max_colors + 1)), p=prob_k)
if prt: print('exact number of colors to use in configuration:')
if prt: print(exact_k)
if only_averages:
'''Only compute average in case with zero field and no interaction, but with gamma > 0,
use an arbitray partition (avoid computing Bell polynomials)
'''
chosen_colors = range(1, exact_k + 1)
if exact_k == no_colors:
chosen_partition = [exact_k]
else:
chosen_partition = [
exact_k - 1
] + [0] * max(0, no_colors - exact_k - 1) + [1]
part = []
for i in range(len(chosen_partition)):
part += ([i + 1] * chosen_partition[i])
chosen_partition = part
if prt: print('chosen partition:')
if prt: print(chosen_partition)
color_arr = []
for i in range(len(chosen_partition)):
color_arr += [chosen_colors[i]] * chosen_partition[i]
config = np.array(color_arr).reshape((N, N))
return config, None, None
'''Sample a partition of the no. of clusters into k blocks using Bell polynomials'''
partition_dict = {}
if ((no_cl, exact_k) in bell_dict):
partition_dict = bell_dict[(no_cl, exact_k)]
if prt: print("Bell found", (no_cl, exact_k))
else:
session.evaluate("subs = Array[x," + str(no_cl - exact_k + 1) +
"]")
partition_dict = session.evaluate(
"Association@CoefficientRules[BellY[" + str(no_cl) + ", " +
str(exact_k) + ", subs], subs]")
bell_dict[(no_cl, exact_k)] = partition_dict
if prt: print("Bell computed", (no_cl, exact_k))
if prt: print("partition_dict", partition_dict)
partitions = []
partition_p = []
for partition in partition_dict:
partitions.append(partition)
partition_p.append(partition_dict[partition])
if prt: print('partition_p:', partition_p)
partition_p = [pk / sum(partition_p) for pk in partition_p]
parition_p = np.array(partition_p)
if prt: print('partition_p:', partition_p)
chosen_partition = partitions[np.random.choice(
(np.arange(0, len(partitions))), p=partition_p)]
if prt: print('chosen partition:')
if prt: print(chosen_partition)
# Transform to actual partition
part = []
for i in range(len(chosen_partition)):
part += ([i + 1] * chosen_partition[i])
chosen_partition = part
if prt: print('chosen partition:')
if prt: print(chosen_partition)
'''Color each block in the partition randomly without replacement'''
# Choose the colors to be used
chosen_colors = np.random.choice((np.arange(1, no_colors + 1)),
len(chosen_partition),
replace=False)
# Choose a random permutation of the given word
color_arr = []
for i in range(len(chosen_partition)):
color_arr += [chosen_colors[i]] * chosen_partition[i]
color_arr = np.array(color_arr)
color_arr = np.random.permutation(color_arr)
if prt: print('colors for clusters:')
if prt: print(color_arr)
'''Color each cluster with the assigned color'''
i = 0
for root in cluster_constraints:
cluster_color = color_arr[i]
for site_str in uf.component(root):
site = str2site(site_str)
config[site[0], site[1]] = cluster_color
i += 1
# Case with field and gamma > 0 or case with gamma = 0
else:
'''Randomly sample a color for each cluster'''
if prt: print('Case with field and gamma > 0 or case with gamma = 0')
config = brute_force_sample(cluster_constraints, cls, uf, config)
return config, uf, cluster_constraints
