def modus_ponens_formula(B, AB, A):
sA = get_value(A, tv=True).mean
cA = get_value(A, tv=True).confidence
sAB = get_value(AB, tv=True).mean
cAB = get_value(AB, tv=True).confidence
snotAB = 0.2 # Huge hack
cnotAB = 1
new_tv = TTruthValue(precise_modus_ponens_strength_formula(sA, sAB, snotAB),
min(cAB, cnotAB, cA))
cog_merge_hi_conf_tv(B, new_tv)
return B
def extract(self, result):
tmp = [get_value(x) for x in result.out]
results = []
for item in tmp:
name = tbd_helpers.filter_reg.match(item['name']).group(2)
results.append((item['attention'].sum().numpy(), name))
return results
def gt_zero_confidence(atom):
tensor_tv = get_value(atom, tv=True)
result = TruthValue(0 < tensor_tv.confidence, 1)
return result
def cog_merge_hi_conf_tv(atom, tv):
old_tv = get_value(atom, tv=True)
if old_tv.confidence < tv.confidence:
set_value(atom, tv, tv=True)
atom.tv = TruthValue(float(tv.mean), float(tv.confidence))
atomspace = AtomSpace()
initialize_opencog(atomspace)
inp = InputModule(ConceptNode("image"), torch.tensor(1.))
InheritanceLink(ConceptNode("red"), ConceptNode("color"))
InheritanceLink(ConceptNode("green"), ConceptNode("color"))
net1 = AttentionModule(ConceptNode("red"))
net2 = AttentionModule(ConceptNode("green"))
# direct execution proceeds as usual
print(net1(inp()))
# execution from Atomese
prog1 = net1.execute(inp.execute())
print(prog1)
print(get_value(execute_atom(atomspace, prog1)))
prog2 = net2.execute(inp.execute())
print(get_value(execute_atom(atomspace, prog2)))
bl = BindLink(
TypedVariableLink(VariableNode("$X"), TypeNode("ConceptNode")),
#VariableNode("$X"),
AndLink(
InheritanceLink(VariableNode("$X"), ConceptNode("color")),
evaluate(
VariableNode("$X"),
inp.execute()) #inp.execution() == execute(ConceptNode("image"))
),
VariableNode("$X"))
print(execute_atom(atomspace, bl))