####################
-class ProblemLenId(Problem):
- def __init__(self, len_max=10):
- self.len_max = len_max
+class ProblemTwoTargets(Problem):
+ def __init__(self, len_total=10, len_targets=3):
+ assert len_targets >= 3
+ assert len_total >= 3 * len_targets - 1
+ self.len_total = len_total
+ self.len_targets = len_targets
def generate_sequences(self, nb):
- k = torch.arange(self.len_max * 3 + 3)[None, :]
- l = torch.randint(self.len_max, (2, nb))[:, :, None] + 1
- i = torch.randint(10, (2, nb))[:, :, None]
- a = l[0]
- b = l[0] + 1 + l[1]
- c = l[0] + 1 + l[1] + 1 + l[0]
- sequences = (
- (k < a) * i[0]
- + (k == a) * 10
- + (k > a) * (k < b) * i[1]
- + (k == b) * 11
- + (k > b) * (k < c) * i[1]
- + (k >= c) * 12
+ k = torch.arange(self.len_total)[None, :]
+ s = torch.randint(10, (nb, self.len_total))
+ l = torch.rand(nb, self.len_total)
+ l = l * (k <= self.len_total - self.len_targets).long()
+ k1 = l.argmax(dim=1, keepdim=True)
+ m = (k != k1).long() * (k != k1 + self.len_targets - 1).long()
+ s = s * m + 10 * (1 - m)
+ l = l * (
+ 1
+ - (k + self.len_targets - 1 >= k1).long()
+ * (k < k1 + self.len_targets).long()
)
- ar_mask = (sequences == 11).long()
+ k2 = l.argmax(dim=1, keepdim=True)
+ m = (k != k2).long() * (k != k2 + self.len_targets - 1).long()
+ s = s * m + 11 * (1 - m)
+ a1 = s.gather(dim=1, index=k1 + 1 + torch.arange(self.len_targets - 2)[None, :])
+ a2 = s.gather(dim=1, index=k2 + 1 + torch.arange(self.len_targets - 2)[None, :])
+ sequences = torch.cat(
+ (
+ s,
+ torch.full((nb, 1), 12),
+ a1,
+ torch.full((nb, 1), 12),
+ a2,
+ torch.full((nb, 1), 12),
+ ),
+ 1,
+ )
+ ar_mask = (sequences == 12).long()
ar_mask = (ar_mask.cumsum(1) - ar_mask).clamp(max=1)
return sequences, ar_mask
def seq2str(self, seq):
- return "".join("0123456789|>_"[x.item()] for x in seq)
+ return "".join("0123456789-+|"[x.item()] for x in seq)
####################
-class ProblemLevel0(Problem):
- def __init__(self, nb_sentences=100, len_prompt=5, len_result=5):
+class ProblemByHeart(Problem):
+ def __init__(self, nb_sentences=100, len_prompt=8, len_result=8):
self.seq = torch.randint(10, (nb_sentences, len_prompt + 1 + len_result))
self.seq[:, len_prompt] = 10
####################
-class ProblemLevel1(Problem):
- def __init__(self, nb_operators=100, len_source=5, len_result=8):
+class ProblemLearnOperator(Problem):
+ def __init__(self, nb_operators=100, len_source=6, len_result=9):
self.len_source = len_source
self.len_result = len_result
self.len_nb_operator = int(math.log(nb_operators) / math.log(10)) + 1
// 10 ** torch.arange(self.len_nb_operator - 1, -1, -1)
) % 10
marker1 = torch.full((nb, 1), 10)
- # source = torch.randint(10, (nb, self.len_source))
source = torch.rand(nb, 10).sort(dim=1).indices[:, : self.len_source]
marker2 = torch.full((nb, 1), 11)
result = operators.bmm(source[:, :, None]).squeeze(-1)
####################
-class ProblemLevel2(Problem):
+class ProblemGuessOperator(Problem):
def __init__(self, len_source=5, len_result=8):
self.len_source = len_source
self.len_result = len_result
return "".join(self.id2char[x.item()] for x in seq)
-# class ProblemUnion(Problem):
-# problems = [ProblemByheart()]
-# nb_common_codes = 100
-
-# def generate_sequences(nb_samples):
-# problem_indexes = torch.randint(len(problems), (nb_samples,))
-# nb_samples_per_problem = torch.one_hot(problem_indexes).sum(0)
-# print(f"{nb_samples_per_problem}")
-# all_seq = []
-# for nb, p in zip(nb_samples_per_problem, problems):
-# all_seq.append(p.generate_sequences(nb_samples_per_problem[nb]))
-# return all_seq
-
-# for strain, stest in zip(train_seq, test_seq):
-# s = torch.cat((strain, stest), 0)
+if __name__ == "__main__":
+ p = ProblemTwoTargets(12, 4)
+ s, m = p.generate_sequences(10)
+ for x in s:
+ print(p.seq2str(x))