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Commit 5b70ef42 authored by chsieh16's avatar chsieh16
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Clean up print messages

parent 0f06ebb9
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......@@ -124,8 +124,6 @@ class DTreeLearner(LearnerBase):
def add_positive_examples(self, *args) -> None:
feature_vec_list = [self._s2f_func(sample) for sample in args]
print("Positive feature vectors:", feature_vec_list)
self._append_to_data_file(feature_vec_list, "true")
def add_negative_examples(self, *args) -> None:
......@@ -134,7 +132,6 @@ class DTreeLearner(LearnerBase):
self.count_neg_dup += 1
raise ValueError("repeated negative example: " + str(samp))
perc_samp = tuple(self._s2f_func(samp))
print(tuple(perc_samp))
if perc_samp in self.debug_neg_perc:
raise ValueError("repeated negative example: " + str(perc_samp))
self.debug_neg_perc.add(perc_samp)
......
......@@ -45,7 +45,7 @@ def test_synth_dtree():
# 0.0 <= x <= 30.0 and -1.0 <= y <= 0.9 and 0.2 <= theta <= 0.22
teacher.set_old_state_bound(lb=[0.0, -1.0, 0.2], ub=[30.0, -0.9, 0.22])
synth_dtree(positive_examples, teacher, num_max_iterations=50)
synth_dtree(positive_examples, teacher, num_max_iterations=10)
def synth_dtree(positive_examples, teacher, num_max_iterations: int = 10):
......@@ -60,6 +60,7 @@ def synth_dtree(positive_examples, teacher, num_max_iterations: int = 10):
past_candidate_list = []
for k in range(num_max_iterations):
print("="*80)
print(f"Iteration {k}:", sep='')
print("learning ....")
......@@ -71,7 +72,7 @@ def synth_dtree(positive_examples, teacher, num_max_iterations: int = 10):
# QUERYING TEACHER IF THERE ARE NEGATIVE EXAMPLES
result = teacher.check(candidate)
print(result)
print(f"Satisfiability: {result}")
if result == z3.sat:
negative_examples = teacher.model()
assert len(negative_examples) > 0
......
......@@ -34,7 +34,6 @@ class DTreeGEMStanleyGurobiTeacher(GEMStanleyGurobiTeacher):
raise RuntimeError("Only support affine expressions.")
def _set_candidate(self, conjunct: sympy.logic.boolalg.Boolean) -> None:
print(conjunct)
# Variable Aliases
m = self._gp_model
......@@ -95,7 +94,6 @@ class DTreeGEMStanleyGurobiTeacher(GEMStanleyGurobiTeacher):
self._gp_model.optimize()
if self._gp_model.status in [gp.GRB.OPTIMAL, gp.GRB.SUBOPTIMAL]:
print(f"ObjValue: {self._gp_model.getObjective().getValue()}")
cex = tuple(self._old_state.x) + tuple(self._percept.x)
self._cexs.append(cex)
elif self._gp_model.status == gp.GRB.INFEASIBLE:
......
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