def hypotheses_expected_final_entropy(action, data=None, normalized=False): #normalize p_data_action, p_theory_data not necessarily, just tell me norm=0 for d in world.possible_data(action): norm+=model.p_data_action(d,action,data) expval=0 for d in world.possible_data(action): alldata=[d] if data is None else [d]+data expval+=utils.H(lambda hs: model.p_hypotheses_data(hs,alldata),\ model.fullh_space, False)*model.p_data_action(d,action,data) return expval/norm
def hypotheses_expected_final_entropy(action, data=None, normalized=False):
#normalize p_data_action, p_theory_data not necessarily, just tell me
norm = 0
for d in world.possible_data(action):
norm += model.p_data_action(d, action, data)
expval = 0
for d in world.possible_data(action):
alldata = [d] if data is None else [d] + data
expval+=utils.H(lambda hs: model.p_hypotheses_data(hs,alldata),\
model.fullh_space, False)*model.p_data_action(d,action,data)
return expval / norm
def success_probability(self, action, prev_data=[]):
data_no, data_yes = world.possible_data(action)
p_yes = model.p_data_action(data_yes, action, prev_data)
p_no = model.p_data_action(data_no, action, prev_data)
return p_yes / (p_yes + p_no)
def success_probability(self, action, prev_data=[]): data_no, data_yes=world.possible_data(action) p_yes=model.p_data_action(data_yes, action, prev_data) p_no=model.p_data_action(data_no, action, prev_data) return p_yes/(p_yes+p_no)
def theory_expected_entropy_gain(action, data=None): #normalize p_data_action #p_theory_data SHOULD BE NORMALIZED. norm=0 for d in world.possible_data(action): norm+=model.p_data_action(d,action,data) expval=0 for d in world.possible_data(action): alldata=[d] if data is None else [d]+data #print utils.H(lambda t: model.p_theory_data(t, alldata), model.t_space) #print 'p_d_a', model.p_data_action(d,action,data) expval+=(utils.H(lambda t: model.p_theory_data(t, alldata, normalized=True)\ , model.t_space)-\ utils.H(lambda t: model.p_theory_data(t, data, normalized=True)\ , model.t_space))*\ model.p_data_action(d,action,data) return expval/norm
def theory_expected_entropy_gain(action, data=None):
#normalize p_data_action
#p_theory_data SHOULD BE NORMALIZED.
norm = 0
for d in world.possible_data(action):
norm += model.p_data_action(d, action, data)
expval = 0
for d in world.possible_data(action):
alldata = [d] if data is None else [d] + data
#print utils.H(lambda t: model.p_theory_data(t, alldata), model.t_space)
#print 'p_d_a', model.p_data_action(d,action,data)
expval+=(utils.H(lambda t: model.p_theory_data(t, alldata, normalized=True)\
, model.t_space)-\
utils.H(lambda t: model.p_theory_data(t, data, normalized=True)\
, model.t_space))*\
model.p_data_action(d,action,data)
return expval / norm
def p_data_action(datapoint, action, prev_data=None): if datapoint in world.possible_data(action): pda=0 machine=action[1] for t in t_space: for h in hf.create_all_hypotheses(machine): pda+=h.single_likelihood(datapoint)*\ t.hypothesis_likelihood(h)*t.prior() #h.unnormalized_posterior(prev_data) return pda else: return 0
def p_data_action(datapoint, action, prev_data=[]):
"""UNNORMALIZED --CHECKED"""
if datapoint in world.possible_data(action):
pda = 0
machine = action[1]
for t in t_space:
for h in singleh_space:
pda+=p_singledata_hypothesis(datapoint,h,machine)*\
p_hypothesis_theorydata(h,machine,t,prev_data)*\
p_theory_data(t,prev_data)#, normalized=True) I don't need to normalize, this gives an extra constant
return pda
else:
return 0
def p_data_action(datapoint, action, prev_data=[]): """UNNORMALIZED --CHECKED""" if datapoint in world.possible_data(action): pda=0 machine=action[1] for t in t_space: for h in singleh_space: pda+=p_singledata_hypothesis(datapoint,h,machine)*\ p_hypothesis_theorydata(h,machine,t,prev_data)*\ p_theory_data(t,prev_data)#, normalized=True) I don't need to normalize, this gives an extra constant return pda else: return 0
def p_data_action(datapoint, action, prev_data=[]):
"""UNNORMALIZED"""
if datapoint in world.possible_data(action):
pda = 0
machine = action[1]
for t in t_space:
#for h in hf.create_all_hypotheses(machine):
for h in singleh_space:
pda+=p_singledata_hypothesis(datapoint, h, machine)*\
p_hypothesis_theory(h,machine,t)*p_theory(t)
#p_hypothesis_data(h, machine, prev_data)
#pda+=h.single_likelihood(datapoint)*h.unnormalized_posterior(prev_data)
return pda #float(pda)/len(world.possible_data(action))
else:
return 0
def p_data_action(datapoint, action, prev_data=[]): """UNNORMALIZED""" if datapoint in world.possible_data(action): pda=0 machine=action[1] for t in t_space: #for h in hf.create_all_hypotheses(machine): for h in singleh_space: pda+=p_singledata_hypothesis(datapoint, h, machine)*\ p_hypothesis_theory(h,machine,t)*p_theory(t) #p_hypothesis_data(h, machine, prev_data) #pda+=h.single_likelihood(datapoint)*h.unnormalized_posterior(prev_data) return pda#float(pda)/len(world.possible_data(action)) else: return 0
# print 't: {0}, p: {1}, ppost: {2}'.format(t, model.p_theory(t),\
# model.p_theory_data(t,d0)\
# )
d0p=Datapoint.Datapoint((t1,m0), True)
d1=Datapoint.Datapoint((t1,m1), True)
#for h in model.singleh_space:
#print model.p_data_action(d0p, (t1,m0), []), model.p_data_action(d0p, (t1,m0), d0)
action=(t2,m1)
n1=0
n2=0
p1s=[]
p2s=[]
d0[0].display()
print action
for dat in world.possible_data(action):
p1=model.p_data_action(dat, (t2,m1), [])
p2=model.p_data_action(dat, (t2,m1), d0)
n1+=p1
n2+=p2
p1s.append(p1)
p2s.append(p2)
print [p/n1 for p in p1s]
print [p/n2 for p in p2s]
# for t in model.t_space:
# print 't: {0}, p: {1}, ppost: {2}'.format(t, model.p_theory(t),\
# model.p_theory_data(t,d0)\
# )
d0p = Datapoint.Datapoint((t1, m0), True)
d1 = Datapoint.Datapoint((t1, m1), True)
#for h in model.singleh_space:
#print model.p_data_action(d0p, (t1,m0), []), model.p_data_action(d0p, (t1,m0), d0)
action = (t2, m1)
n1 = 0
n2 = 0
p1s = []
p2s = []
d0[0].display()
print action
for dat in world.possible_data(action):
p1 = model.p_data_action(dat, (t2, m1), [])
p2 = model.p_data_action(dat, (t2, m1), d0)
n1 += p1
n2 += p2
p1s.append(p1)
p2s.append(p2)
print[p / n1 for p in p1s]
print[p / n2 for p in p2s]