def project_outer_trips(G, matrix, assign_config):
max_inner = assign_config['max_inner']
importance_conditions = assign_config['outer_importance_conditions']
inner_hash = {}
for i in range(max_inner):
inner_hash[i + 1] = 1
end_nodes = {}
for condit in importance_conditions:
for i in range(condit[0], condit[1]):
the_target = {}
for j in range(max_inner):
if matrix[i - 1, j] > 0 or matrix[j, i - 1] > 0:
the_target[j + 1] = 1
if not the_target:
continue
dist, paths = single_source_dijkstra(
G, i, assign_config['outer_impedance'], target=the_target)
for j in the_target:
for k in range(len(paths[j])):
if G[paths[j][k]][paths[j][k + 1]][
assign_config['boundary_condition']]:
break
node = paths[j][k]
newdist, newpaths, centroid = single_source_dijkstra(
G,
node,
assign_config['outer_impedance'],
target=inner_hash,
allow_centroids=True,
target_type=1)
#print string.join([str(i),str(centroid),str(j),str(matrix[i-1,j-1]),str(matrix[j-1,i-1])],'\t')
matrix[centroid - 1, j -
1] = matrix[centroid - 1, j - 1] + matrix[i - 1, j - 1]
matrix[j - 1, centroid -
1] = matrix[j - 1, centroid - 1] + matrix[j - 1, i - 1]
matrix = matrix[:max_inner, :max_inner]
return matrix
def project_outer_trips(G,matrix,assign_config):
max_inner=assign_config['max_inner']
importance_conditions=assign_config['outer_importance_conditions']
inner_hash={}
for i in range(max_inner):
inner_hash[i+1]=1
end_nodes={}
for condit in importance_conditions:
for i in range(condit[0],condit[1]):
the_target={}
for j in range(max_inner):
if matrix[i-1,j]>0 or matrix[j,i-1]>0:
the_target[j+1]=1
if not the_target:
continue
dist,paths=single_source_dijkstra(G,i,assign_config['outer_impedance'],target=the_target)
for j in the_target:
for k in range(len(paths[j])):
if G[paths[j][k]][paths[j][k+1]][assign_config['boundary_condition']]:
break
node=paths[j][k]
newdist,newpaths,centroid=single_source_dijkstra(G,node,assign_config['outer_impedance'],target=inner_hash,allow_centroids=True,target_type=1)
#print string.join([str(i),str(centroid),str(j),str(matrix[i-1,j-1]),str(matrix[j-1,i-1])],'\t')
matrix[centroid-1,j-1]=matrix[centroid-1,j-1]+matrix[i-1,j-1]
matrix[j-1,centroid-1]=matrix[j-1,centroid-1]+matrix[j-1,i-1]
matrix=matrix[:max_inner,:max_inner]
return matrix
def ds_generate_single_source(G,source,target,choice_set_config,link_randomizer,ext_bound):
config=choice_set_config
num_draws=config['ds_num_draws']
this_target=target
this_link_randomizer=link_randomizer
bounding_box=ext_bound
varcoef={}
master_sets={}
for node in target:
master_sets[node]=[]
for i in range(num_draws):
#sample random coefficients from bounding box
for key in ext_bound:
if config['log_prior']:
varcoef[key]=exp(random.uniform(log(bounding_box[key][0]),log(bounding_box[key][1])))
else:
varcoef[key]=random.uniform(bounding_box[key][0],bounding_box[key][1])
to_iter=1
if config['randomize_after']:
to_iter=config['randomize_after_iters']
for i in range(to_iter):
#perform generalized cost shortest path search
paths = single_source_dijkstra(G,source,varcoef,target=this_target,weights=config['weights'],link_randomizer=this_link_randomizer)[1]
for node in target:
if node in paths:
master_sets[node].append(paths[node])
else:
print 'WARNING: no path found from',str(source),'to',str(node)
for node in target:
master_sets[node]=filter_master(G,None,master_sets[node],choice_set_config)
return master_sets
def inverted_assignment_search_worker(work_queue,done_queue,network,master_config,ext_bound,st_dict,seed_selections):
if not master_config.choice_set_config['randomize_after'] :
print time.asctime(time.localtime()), "-", current_process().name, "- initializing link randomizer..."
link_randomizer=master_config.choice_set_config.get_link_randomizer(network,master_config)
rand_net=network.copy()
rand_net.orig_network=None
bounding_box=ext_bound
varcoef={}
for i in iter(work_queue.get,'STOP'):
if master_config.choice_set_config['inverted_nested'] and not master_config.choice_set_config['randomize_after']:
#randomize link values
for e in rand_net.edges_iter():
for key in master_config.choice_set_config['variables']:
rand_net[e[0]][e[1]][key]=link_randomizer.generate_value(network,e[0],e[1],key)
#loop over number of parameter randomizations
for j in range(master_config.choice_set_config['inverted_N_param']):
print time.asctime(time.localtime()), "-", current_process().name, "- Attr #", i, ', Param #', j
if not master_config.choice_set_config['inverted_nested'] and not master_config.choice_set_config['randomize_after']:
#randomize link values
for e in rand_net.edges_iter():
for key in master_config.choice_set_config['variables']:
rand_net[e[0]][e[1]][key]=link_randomizer.generate_value(network,e[0],e[1],key)
#sample generalized cost coefficients
for key in bounding_box:
if master_config.choice_set_config['log_prior']:
varcoef[key]=exp(random.uniform(log(bounding_box[key][0]),log(bounding_box[key][1])))
else:
varcoef[key]=random.uniform(bounding_box[key][0],bounding_box[key][1])
print varcoef
#calculate generalized costs
for e in rand_net.edges_iter():
rand_net[e[0]][e[1]]['gencost']=0
for key in varcoef:
wgt=1
if key in master_config.choice_set_config['weights']:
wgt=network[e[0]][e[1]][master_config.choice_set_config['weights'][key]]
rand_net[e[0]][e[1]]['gencost']=rand_net[e[0]][e[1]]['gencost']+varcoef[key]*rand_net[e[0]][e[1]][key]*wgt
use_cost='gencost'
to_iter=1
if master_config.choice_set_config['randomize_after']:
use_cost='usecost'
to_iter=master_config.choice_set_config['randomize_after_iters']
while to_iter:
to_iter=to_iter-1
result_paths={}
if master_config.choice_set_config['randomize_after']:
for e in rand_net.edges_iter():
rand_net[e[0]][e[1]]['usecost']=rand_net[e[0]][e[1]]['gencost']*random.uniform(1-master_config.choice_set_config['randomize_after_dev'],1+master_config.choice_set_config['randomize_after_dev'])
for source in st_dict:
if seed_selections[source][i*master_config.choice_set_config['inverted_N_param']+j]:
the_target=st_dict[source]
if source in the_target:
del the_target[source]
every_path=single_source_dijkstra(rand_net,source,use_cost,target=the_target)[1]
target_paths={}
for t in the_target:
if t in every_path:
target_paths[t]=every_path[t]
fname=os.path.join(master_config.assign_config['pickle_path'],str(source)+'_'+str(i)+'-'+str(j)+'_'+str(to_iter))
f=open(fname,'wb')
cPickle.dump(target_paths,f,1)
f.close()
done_queue.put('STOP')
return True
def inverted_assignment_search_worker(work_queue, done_queue, network,
master_config, ext_bound, st_dict,
seed_selections):
if not master_config.choice_set_config['randomize_after']:
print time.asctime(time.localtime()), "-", current_process(
).name, "- initializing link randomizer..."
link_randomizer = master_config.choice_set_config.get_link_randomizer(
network, master_config)
rand_net = network.copy()
rand_net.orig_network = None
bounding_box = ext_bound
varcoef = {}
for i in iter(work_queue.get, 'STOP'):
if master_config.choice_set_config[
'inverted_nested'] and not master_config.choice_set_config[
'randomize_after']:
#randomize link values
for e in rand_net.edges_iter():
for key in master_config.choice_set_config['variables']:
rand_net[e[0]][e[1]][key] = link_randomizer.generate_value(
network, e[0], e[1], key)
#loop over number of parameter randomizations
for j in range(master_config.choice_set_config['inverted_N_param']):
print time.asctime(time.localtime()), "-", current_process(
).name, "- Attr #", i, ', Param #', j
if not master_config.choice_set_config[
'inverted_nested'] and not master_config.choice_set_config[
'randomize_after']:
#randomize link values
for e in rand_net.edges_iter():
for key in master_config.choice_set_config['variables']:
rand_net[e[0]][
e[1]][key] = link_randomizer.generate_value(
network, e[0], e[1], key)
#sample generalized cost coefficients
for key in bounding_box:
if master_config.choice_set_config['log_prior']:
varcoef[key] = exp(
random.uniform(log(bounding_box[key][0]),
log(bounding_box[key][1])))
else:
varcoef[key] = random.uniform(bounding_box[key][0],
bounding_box[key][1])
print varcoef
#calculate generalized costs
for e in rand_net.edges_iter():
rand_net[e[0]][e[1]]['gencost'] = 0
for key in varcoef:
wgt = 1
if key in master_config.choice_set_config['weights']:
wgt = network[e[0]][e[1]][
master_config.choice_set_config['weights'][key]]
rand_net[e[0]][e[1]]['gencost'] = rand_net[e[0]][
e[1]]['gencost'] + varcoef[key] * rand_net[e[0]][
e[1]][key] * wgt
use_cost = 'gencost'
to_iter = 1
if master_config.choice_set_config['randomize_after']:
use_cost = 'usecost'
to_iter = master_config.choice_set_config[
'randomize_after_iters']
while to_iter:
to_iter = to_iter - 1
result_paths = {}
if master_config.choice_set_config['randomize_after']:
for e in rand_net.edges_iter():
rand_net[e[0]][e[1]]['usecost'] = rand_net[e[0]][
e[1]]['gencost'] * random.uniform(
1 - master_config.
choice_set_config['randomize_after_dev'],
1 + master_config.
choice_set_config['randomize_after_dev'])
for source in st_dict:
if seed_selections[source][
i *
master_config.choice_set_config['inverted_N_param']
+ j]:
the_target = st_dict[source]
if source in the_target:
del the_target[source]
every_path = single_source_dijkstra(
rand_net, source, use_cost, target=the_target)[1]
target_paths = {}
for t in the_target:
if t in every_path:
target_paths[t] = every_path[t]
fname = os.path.join(
master_config.assign_config['pickle_path'],
str(source) + '_' + str(i) + '-' + str(j) + '_' +
str(to_iter))
f = open(fname, 'wb')
cPickle.dump(target_paths, f, 1)
f.close()
done_queue.put('STOP')
return True
