def __init__(self, filename, fileosservazioni):
fi = json.load(open(filename))
self.var = fi['variables']
self.elenco_terapie = fi['therapies']
self.collisioni = fi['false']
self.patients = json.load(open(fileosservazioni))
self.var_dict = dict(
) ### dizionario variabili con distrubuzioni e descrizione
self.prob_dict = dict() ### dizionario probability table
var_list = list() ### lista delle variabili da dare in pasto alla BN
prob_list = list() ### lista probability table da inseriore nella BN
for v in self.var:
self.var_dict[v] = Variable(
v, self.var[v][1]
) ### costruisce una variabile da inserire nella BN dal dizionario variabili
var_list.append(self.var_dict[v])
for v in self.var:
nod = self.var_dict[v]
gen = list()
lis = self.var[v][3] ### lista genitori della singola variabile
for l in lis:
gen.append(self.var_dict[l])
self.prob_dict[v] = Prob(
nod, gen, self.var[v][4]
) ### costruzione probability table: variabile, genitore, distribuzioni di probabilità
prob_list.append(
self.prob_dict[v]
) ### append in lista probability table da inseriore in BN
print(self.prob_dict[v])
self.bn = Belief_network(var_list, prob_list)
def __init__(self, name, domain, parents):
Variable.__init__(self, name, domain)
self.parents = parents
self.all_vars = set(parents) | {self}
best_elt = elt
self.values[index] = max_val
self.decision_fun.values[index] = best_elt
return max_val
class Factor_DF(Factor_stored):
"""A decision function"""
def __init__(self, dvar, vars, values):
Factor_stored.__init__(self, vars, values)
self.dvar = dvar
self.name = str(dvar) # Used in printing
boolean = [False, True]
Al = Variable("Alarm", boolean)
Fi = Variable("Fire", boolean)
Le = Variable("Leaving", boolean)
Re = Variable("Report", boolean)
Sm = Variable("Smoke", boolean)
Ta = Variable("Tamper", boolean)
SS = Variable("See Sm", boolean)
CS = DecisionVariable("Ch Sm", boolean, {Re})
Call = DecisionVariable("Call", boolean, {SS, CS, Re})
f_ta = Prob(Ta, [], [0.98, 0.02])
f_fi = Prob(Fi, [], [0.99, 0.01])
f_sm = Prob(Sm, [Fi], [0.99, 0.01, 0.1, 0.9])
f_al = Prob(Al, [Fi, Ta], [0.9999, 0.0001, 0.15, 0.85, 0.01, 0.99, 0.5, 0.5])
f_lv = Prob(Le, [Al], [0.999, 0.001, 0.12, 0.88])
f_re = Prob(Re, [Le], [0.99, 0.01, 0.25, 0.75])
from utilities import Displayable
class Inference_method(Displayable):
"""The abstract class of graphical model inference methods"""
def query(self, qvar, obs={}):
raise NotImplementedError("Inference_method query") # abstract method
from probVariables import Variable
from probFactors import Prob
boolean = [False, True]
A = Variable("A", boolean)
B = Variable("B", boolean)
C = Variable("C", boolean)
f_a = Prob(A, [], [0.4, 0.6])
f_b = Prob(B, [A], [0.9, 0.1, 0.2, 0.8])
f_c = Prob(C, [B], [0.5, 0.5, 0.3, 0.7])
bn1 = Belief_network([A, B, C], [f_a, f_b, f_c])
# Bayesian network report of leaving example from
# Poole and Mackworth, Artificial Intelligence, 2010 http://artint.info
# This is Example 6.10 (page 236) shown in Figure 6.1
Al = Variable("Alarm", boolean)
Fi = Variable("Fire", boolean)
fi = json.load(open("cardiologia.json"))
var = fi['variables']
elenco_terapie = fi['therapies']
patients = json.load(open("osservazioni.json"))
var_dict = dict() ### dizionario variabili con distrubuzioni e descrizione
var_list = list() ### lista delle variabili da dare in pasto alla BN
for v in var:
var_dict[v] = Variable(
v, var[v][1]
) ### costruisce una variabile da inserire nella BN dal dizionario variabili
var_list.append(var_dict[v])
prob_dict = dict() ### dizionario probability table
prob_list = list() ### lista probability table da inseriore nella BN
for v in var:
nod = var_dict[v]
gen = list()
lis = var[v][3] ### lista genitori della singola variabile
for l in lis:
gen.append(var_dict[l])
def __init__(self, name, domain=[False, True], index=1):
Variable.__init__(self, name, domain)
self.index = index
self.previous = None
from probGraphicalModels import Belief_network
from probVariables import Variable
from probFactors import Prob
from probMCMC import Gibbs_sampling
boolean = [False, True]
nodi = dict()
temp = Variable('infarto', boolean)
nodi['infarto'] = ('infarto del miocardio', 'patologia', temp, (temp, [],
[0.2, 0.8]))
temp = Variable('dolore', boolean)
nodi['dolore'] = ('dolore al petto', 'sintomo', temp,
(temp, [nodi['infarto'][2]], [0.2, 0.8, 0.8, 0.2]))
propertytable = dict()
propertytable['infarto'] = Prob(nodi['infarto'][2], [], nodi['infarto'][3][2])
propertytable['dolore'] = Prob(nodi['dolore'][2], [nodi['infarto'][2]],
nodi['dolore'][3][2])
lista_nodi = list()
for n in nodi:
lista_nodi.append(nodi[n][2])
lista_prop = list()