def test_eval_feature():
print('III. Test evaluate example with given feature')
print('==================\n')
print('*******Feature: ')
f = LL.PlTerm([
'my_op([1],[1],[1,0])', 'my_op([0],[0],[0])', 'my_op([1],[0],[1])',
'my_op([0],[1],[1])'
])
print(f)
print('*******Examples: ')
ex1 = LL.PlTerm([1, 1, '+', 1, 1, '=', 1, 1])
ex2 = LL.PlTerm([1, 1, '+', 1, 1, '=', 1, 1, 0])
ex3 = LL.PlTerm(['A', 'A', 'B', 'A', 'A', 'C', 'A', 'A', 'D'])
re1 = LL.evalInstFeature(ex1, f)
re2 = LL.evalInstFeature(ex2, f)
re3 = LL.evalInstFeature(ex3, f)
print('****example 1 & result: ')
print(ex1)
print(re1)
print('****example 2 & result: ')
print(ex2)
print(re2)
print('****example 3 & result: ')
print(ex3)
print(re3)
print('----------END------------\n')
def get_mlp_vector(equation, model, rules, abduced_map, shape=(28, 28, 1)):
h = shape[0]
w = shape[1]
d = shape[2]
model_output = np.argmax(model.predict(equation.reshape(-1, h, w, d)), axis=1)
model_labels = []
for out in model_output:
model_labels.append(abduced_map[out])
#print(model_labels)
vector = []
for rule in rules:
ex = LL.PlTerm(model_labels)
f = LL.PlTerm(rule)
if LL.evalInstFeature(ex, f):
vector.append(1)
else:
vector.append(0)
return vector
def eval(self, ex): # apply mapping ex_symbs_n = apply_mapping(ex, self.mapping) (ex_symbs, n_pos) = remove_nulls(ex_symbs_n) ex_term = LL.PlTerm(ex_symbs) # LL term return LL.evalInstFeature(ex_term, LL.PlTerm(self.rules))