def mkntype_ptype(pred, args, io, assgn, gsym, network):
T_ptype = Type_n(assgn=assgn)
T_ptype.add_apat(
network,
ActivityPattern(
[[gsym.index('ptype' + str(len(pred.arity)), network, assgn)]]))
T_rel = Type_n(assgn=assgn)
T_rel.add_apat(network,
ActivityPattern([[gsym.index('rel', network, assgn)]]))
arglabels = ['arg' + str(i) for i in range(len(pred.arity))]
argtypes = [Type_n(assgn=assgn) for l in arglabels]
for T, l in zip(argtypes, arglabels):
T.add_apat(network, ActivityPattern([[gsym.index(l, network, assgn)]]))
# argtypes = [MeetType_n(pred.nu.__getattribute__(arglabel),
# iota_assgn(arg,io,assgn))
# for arglabel,arg in zip(arglabels,args)]
T_relpred = MeetType_n(T_rel, nu(pred, assgn), assgn)
argtypesargs = [
MeetType_n(T_arg, iota_assgn(arg, io, assgn), assgn)
for T_arg, arg in zip(argtypes, args)
]
types = []
types.append(MeetType_n(T_ptype, T_relpred, assgn))
for i in range(len(argtypes)):
if i is 0:
types.append(
MeetType_n(InhibitType_n(T_relpred), argtypesargs[0], assgn))
else:
types.append(
MeetType_n(InhibitType_n(argtypesargs[i - 1]), argtypesargs[i],
assgn))
types.append(InhibitType_n(MeetType_n(T_ptype, argtypesargs[-1], assgn)))
return StringType_n(types, assgn)
def mkntype_deptype(var, domain_type, body, assgn, gsym, network):
T_lam = Type_n(assgn=assgn)
T_lam.add_apat(network,
ActivityPattern([[gsym.index('lambda', network, assgn)]]))
T_dom = Type_n(assgn=assgn)
T_dom.add_apat(network,
ActivityPattern([[gsym.index('dom', network, assgn)]]))
T_var = Type_n(assgn=assgn)
var_index = gsym.index('var', network, assgn)
T_var.add_apat(network, ActivityPattern([[var_index]]))
assgn[var] = T_var
T_start = MeetType_n(MeetType_n(T_lam, T_dom, assgn),
MeetType_n(T_var, nu(domain_type, assgn), assgn),
assgn)
T_rng = Type_n(assgn=assgn)
T_rng.add_apat(network,
ActivityPattern([[gsym.index('rng', network, assgn)]]))
T_body = Type_n(assgn=assgn)
T_body.add_apat(network, nu(body, assgn).getapat(network))
return StringType_n([
T_start,
MeetType_n(
InhibitType_n(
MeetType_n(T_dom,
MeetType_n(T_var, nu(domain_type, assgn), assgn),
assgn)), MeetType_n(T_rng, T_body, assgn), assgn),
InhibitType_n(MeetType_n(T_lam, T_rng, assgn), assgn)
], assgn)
def getapat(self, network):
inhibit_apat_list = [
i.getapat(network) for i in self.comps.base_type.to_inhibit
]
return merge_apat_list([
ActivityPattern(i.neurons, i.vals * 0) for i in inhibit_apat_list
])
def mkntype_field(fld, io, assgn, gsym, network):
#old_in_use = list(assgn['in_use'])
T_field = Type_n(assgn=assgn)
T_field.add_apat(network,
ActivityPattern([[gsym.index('field', network, assgn)]]))
T_label = Type_n(assgn=assgn)
T_label.add_apat(network,
ActivityPattern([[gsym.index('label', network, assgn)]]))
T_field0 = labels.gettype_n(fld[0])
T_label_field0 = MeetType_n(T_label, T_field0, assgn)
T_value = Type_n(assgn=assgn)
T_value.add_apat(network,
ActivityPattern([[gsym.index('value', network, assgn)]]))
T_field1 = iota_assgn(fld[1], io, assgn)
T_value_field1 = MeetType_n(T_value, T_field1, assgn)
res = StringType_n([
MeetType_n(T_field, T_label_field0, assgn),
MeetType_n(InhibitType_n(T_label_field0, assgn), T_value_field1,
assgn),
MeetType_n(InhibitType_n(T_value_field1, assgn),
InhibitType_n(T_field, assgn), assgn)
], assgn)
#assgn['in_use'] = old_in_use
return res
def getapat(self, network, gsym=gensym_n, io=iota):
if self.assgn is None:
self.assgn = {'in_use': []}
old_in_use = list(self.assgn['in_use'])
postypes = []
for fld in self.rec.__dict__.items():
t = mkntype_field(fld, io, self.assgn, gsym, network)
postypes.append(t)
negtypes = [InhibitType_n(T, self.assgn) for T in postypes]
T_rec = Type_n(assgn=self.assgn)
T_rec.add_apat(
network, ActivityPattern([[gsym.index('rec', network,
self.assgn)]]))
T_start = MeetType_n(T_rec, postypes[0], self.assgn)
T_end = MeetType_n(InhibitType_n(T_rec), negtypes[-1], self.assgn)
res = StringType_n([T_start] + [
MeetType_n(T1, T2, self.assgn)
for T1, T2 in zip(negtypes[:-1], postypes[1:])
] + [T_end], self.assgn).getapat(network)
self.assgn['in_use'] = old_in_use
return res
def add_grandmother(self, obj, network):
if obj not in self.dict:
self.dict[obj] = Type_n(obj)
network.add_neuron(obj)
self.dict[obj].add_apat(network,
ActivityPattern([[len(network.neurons) - 1]]))
def add_switch_grandmother(self, network):
n = network.add_switch_neuron(self.name)
self.apats[network.name] = ActivityPattern([[len(network.neurons) - 1]
])
return n
from pprint import pprint
from nu import Type, BType, PType, DepType, Pred, MeetType_n, FunType, InhibitType_n, StringType_n, Ty, iota, gensym_n, nu, and_n, MeetType, or_n, JoinType, labels, Rec, RecType
from neurons import Network, Neuron, Synapse, ActivityPattern
from utils import show, example
example(1)
#Types in general correspond to a pattern of activation on a given network.
T = Type('MyType')
print(show(nu(T)))
N = Network()
n1 = N.add_neuron()
h1 = ActivityPattern([[0]])
Tn = nu(T)
Tn.add_apat(N, h1)
N.ntrace()
Tn.create_n(N)
N.display_history()
print(Tn.query_n(N))
example(2)
#We can do the same for a basic type. In this example T corresponds to the
#activation of two neurons.
T = BType('MyBasicType')
print(show(nu(T)))
N = Network()
n1 = N.add_neuron()
n2 = N.add_neuron()
h1 = ActivityPattern([[0], [1]])
N.excite() print(N.history) N.inhibit() print(N.history) N.excite([1,2]) print(N.history) N.inhibit([1]) print(N.history) N.nontrace() N.inhibit() N.ntrace() h = ActivityPattern([[2]]) N.realize_apat(h) print(N.history) N.nontrace() N.inhibit() N.ntrace() h = ActivityPattern([[2],[1]]) N.realize_apat(h) print(N.history) N.nontrace() N.inhibit() N.ntrace() h = ActivityPattern([[2],[1],[1,2]],[[1],[1],[0,0]]) N.realize_apat(h)