def GraficoFase(self, id_fase): tipos = DBSession.query(TipoItem.id_tipoitem).filter(TipoItem.id_fase==id_fase).all() itemfase = [] # todos los items que estan en la fase for j in tipos: item = DBSession.query(Item.id_item).filter(Item.id_tipoitem==j.id_tipoitem) itemfase.extend(item) listitems = [] grafo = digraph() for nodo in itemfase: grafo.add_nodes([nodo.id_item]) #~ buscar relaciones de los nodos... (items) for nodo in itemfase: listitems = listitems + [nodo.id_item] #agrega el id del item a la lista relaciones = DBSession.query(Relacion).\ filter(Relacion.tiporelacion=="Padre Hijo").\ filter(Relacion.id_item1.in_(listitems)).\ filter(Relacion.id_item2.in_(listitems)).all() for relacion in relaciones: grafo.add_edge((relacion.id_item1,relacion.id_item2)) return grafo
def convert_transition_system_into_digraph(ts, starting_player, alphabet,
mp_parameters, options):
(tool, opt, critical, verbosity, nbw_constr, chk_method, chk_dir, k_start,
k_bound, k_step, unrea, set_of_winning_strategies, path,
filename) = options
(inputs, outputs, dimension, values_I, values_O, values_not_I,
values_not_O, nu, c_start, c_bound, c_step) = mp_parameters
# If the transition system is small enough, simplify labels of transitions
label_simplification = False
if ts.contents.size_states_PO <= 20:
label_simplification = True
if starting_player == P_O:
alphabet_starting_p = alphabet.contents.output
alphabet_starting_p_size = alphabet.contents.output_size
alphabet_other_p = alphabet.contents.input
alphabet_other_p_size = alphabet.contents.input_size
first_state_index = 0
last_state_index = ts.contents.size_states_PO
else:
alphabet_starting_p = alphabet.contents.input
alphabet_starting_p_size = alphabet.contents.input_size
alphabet_other_p = alphabet.contents.output
alphabet_other_p_size = alphabet.contents.output_size
first_state_index = ts.contents.size_states_PO
last_state_index = ts.contents.size_states_PO + ts.contents.size_states_PI
alphabet = (alphabet_starting_p, alphabet_starting_p_size,
alphabet_other_p, alphabet_other_p_size)
# Initialize the digraph
g = digraph()
g.__init__()
if label_simplification == True:
for state_index in range(first_state_index, last_state_index):
add_state_to_digraph_with_formula_simplification(
g, ts, state_index, alphabet, starting_player)
else:
for state_index in range(first_state_index, last_state_index):
add_state_to_digraph(g, ts, state_index, alphabet)
initial_states = convert_c_array_to_python_list(
ts.contents.initial_states, ts.contents.nb_initial_states)
for initial_state in initial_states:
g.add_node_attribute(initial_state, ('initial', True))
return g
def GraficoProyecto(self, id_proyecto):
fases = DBSession.query(Fase).filter(Fase.id_proyecto==id_proyecto).all()
grafo = digraph()
tipos = []
items = []
listitem = []
###Aca empieza lo que cambie ######
colores = ['blue','cyan','green','yellow','orange','purple','gray', 'black']
indexColor = 0
for i in fases: # todos los items que estan en la fase
tipo = DBSession.query(TipoItem.id_tipoitem).filter(TipoItem.id_fase==i.id_fase)
#tipos.extend(tipo)
for j in tipos:
item = DBSession.query(Item.id_item).filter(Item.id_tipoitem==j.id_tipoitem)
#items.extend(item)
for k in items: # por cada item que este en la fase agregamos los nodos del mismo color
#Agrego el nodo
grafo.add_node(int(k.id_item),attrs=[('color', colores[indexColor])])
#Agrego a mi lista grobal de items, para luego sacar las relaciones
listitem = listitem + [k.id_item]
# Cambio el color para la siguiente fase
if indexColor ==7:
indexColor = 0
else:
indexColor = indexColor + 1
###Aca termina lo que cambie ######
# de la lista final de items obtiene las relaciones
relaciones = DBSession.query(Relacion).\
filter((Relacion.id_item1).in_(listitem)).all()
for r in relaciones: #agrega la aristas al grafo
grafo.add_edge((int(r.id_item1), int(r.id_item2)))
return grafo
def convert_transition_system_into_digraph(ts, starting_player, alphabet, mp_parameters, options):
(tool, opt, critical, verbosity, nbw_constr, chk_method, chk_dir, k_start, k_bound, k_step, unrea, set_of_winning_strategies, path, filename) = options
(inputs, outputs, dimension, values_I, values_O, values_not_I, values_not_O, nu, c_start, c_bound, c_step) = mp_parameters
# If the transition system is small enough, simplify labels of transitions
label_simplification = False
if ts.contents.size_states_PO <= 20:
label_simplification = True
if starting_player == P_O:
alphabet_starting_p = alphabet.contents.output
alphabet_starting_p_size = alphabet.contents.output_size
alphabet_other_p = alphabet.contents.input
alphabet_other_p_size = alphabet.contents.input_size
first_state_index = 0
last_state_index = ts.contents.size_states_PO
else:
alphabet_starting_p = alphabet.contents.input
alphabet_starting_p_size = alphabet.contents.input_size
alphabet_other_p = alphabet.contents.output
alphabet_other_p_size = alphabet.contents.output_size
first_state_index = ts.contents.size_states_PO
last_state_index = ts.contents.size_states_PO + ts.contents.size_states_PI
alphabet = (alphabet_starting_p, alphabet_starting_p_size, alphabet_other_p, alphabet_other_p_size)
# Initialize the digraph
g = digraph()
g.__init__()
if label_simplification == True:
for state_index in range(first_state_index, last_state_index):
add_state_to_digraph_with_formula_simplification(g, ts, state_index, alphabet, starting_player)
else:
for state_index in range(first_state_index, last_state_index):
add_state_to_digraph(g, ts, state_index, alphabet)
initial_states = convert_c_array_to_python_list(ts.contents.initial_states, ts.contents.nb_initial_states)
for initial_state in initial_states:
g.add_node_attribute(initial_state, ('initial', True))
return g
estados = ["q0", "q1", "q2", "q3"]
estado_final = ["q0", "q3"]
estado_inicial = "q0"
alfabeto = ["a", "b"]
print("------------------------")
print("estados:")
print(estados)
print("------------------------")
print("estado inicial: " + estado_inicial)
print("------------------------")
print("estados finais: ")
print(estado_final)
print("------------------------")
grafo = digraph()
grafo.add_nodes(estados)
grafo.add_edge(("q0", "q1"), label=alfabeto[0])
grafo.add_edge(("q0", "q2"), label=alfabeto[1])
grafo.add_edge(("q1", "q3"), label=alfabeto[0])
grafo.add_edge(("q1", "q0"), label=alfabeto[1])
grafo.add_edge(("q2", "q0"), label=alfabeto[0])
grafo.add_edge(("q2", "q3"), label=alfabeto[1])
grafo.add_edge(("q3", "q2"), label=alfabeto[0])
grafo.add_edge(("q3", "q1"), label=alfabeto[1])
estado_atual = estado_inicial
while True:
def sampleLink(self, index, list_index, ll):
s = SamplerStateTracker.samplerStates[SamplerStateTracker.current_iter]
table_id = s.get_t(index, list_index)
customersAtTable = s.getCustomersAtTable(table_id, list_index)
orig_table_members = []
g = digraph()
ug = graph()
for i in customersAtTable:
orig_table_members.append(i)
if not g.has_node(i):
ug.add_node(i)
g.add_node(i)
j = s.getC(i, list_index)
if not g.has_node(j):
ug.add_node(j)
g.add_node(j)
g.add_edge((i,j))
ug.add_edge((i,j))
cycles = find_cycle(g)
isCyclePresent = False
if index in cycles:
isCyclePresent = True
if not isCyclePresent:
# obs to sample moval will split the table into 2
ug.del_edge((index, s.getC(index, list_index)))
temp, new_table_members = breadth_first_search(ug, index)
orig_table_members = new_table_members
temp, old_table_members = breadth_first_search(ug, s.getC(index, list_index))
s.setT(s.getT() + 1)
s.setC(None, index, list_index)
new_table_id = self.emptyTables[list_index].remove(0)
for l in new_table_members:
s.set_t(new_table_id, l , list_index)
old_table_id = table_id
s.setCustomersAtTable(set(old_table_members), old_table_id, list_index)
table_id = new_table_id
s.setCustomersAtTable(set(new_table_members), new_table_id, list_index)
distanceMatrices = Data.getDistanceMatrices()
distance_matrix = distanceMatrices[list_index]
priors = distance_matrix[index]
priors[index] = ll.getHyperParameters().getSelfLinkProb()
sum_p = sum(priors)
priors = priors/sum_p
posterior = []
indexes = []
for i in range(len(priors)):
if priors[i] != 0:
indexes.append(i)
table_proposed = s.get_t(i, list_index)
if table_proposed == table_id:
posterior.append(priors[i])
else:
proposedTableMembersSet = s.getCustomersAtTable(table_proposed, list_index)
proposed_table_members = list(proposedTableMembersSet)
change_in_log_likelihood = self.compute_change_in_likelihood(ll, orig_table_members,proposed_table_members, list_index)
posterior.append(exp(log(priors[i]+change_in_log_likelihood)))
sample = Util.sample(posterior)
customer_assignment_index = indexes[sample]
assigned_table = s.get_t(customer_assignment_index, list_index)
s.setC(customer_assignment_index, index, list_index)
if assigned_table != table_id:
s.setT(s.getT()-1)
for members in orig_table_members:
s.set_t(assigned_table, members, list_index)
hs_orig_members_in_new_table = set(s.getCustomersAtTable(assigned_table, list_index))
for i in range(len(orig_table_members)):
hs_orig_members_in_new_table.add(orig_table_members[i])
s.setCustomersAtTable(hs_orig_members_in_new_table, assigned_table, list_index)
s.setCustomersAtTable(None, table_id, list_index)
self.emptyTables[index].append(table_id)
# Definicion de un grafo y uso del algoritmo de Dijkstra
# para encontrar la ruta mas corta entre un origen y todos
# los demas vertices
# Import pygraph
from pygraph.classes.graph import *
from pygraph.classes.digraph import *
from pygraph.algorithms.minmax import shortest_path
import unittest
from pygraph.algorithms.pagerank import pagerank
# Graph creation
gr = digraph()
# Add nodes and edges
gr.add_nodes(["a", "b", "c", "d", "e", "f"])
# notar que los vertices o edge, ahora tienen definido un peso
gr.add_edge(("a", "b"), 3)
gr.add_edge(("a", "f"), 5)
gr.add_edge(("a", "d"), 4)
gr.add_edge(("b", "f"), 1)
gr.add_edge(("b", "c"), 1)
gr.add_edge(("f", "d"), 2)
gr.add_edge(("c", "d"), 2)
gr.add_edge(("d", "b"), 3)
gr.add_edge(("e", "f"), 2)
gr.add_edge(("e", "d"), 3)
#mostrar el grafo
print "\nEl grafo: \n"
print gr
