X-Git-Url: https://fleuret.org/cgi-bin/gitweb/gitweb.cgi?a=blobdiff_plain;f=problems.py;h=aa3acf038559195f4e68d7fd7e1625ffb41e332d;hb=687d5b2d9f465577665991b84faec7c789685271;hp=516158795d047002ef70163bea7e03b68b9b9181;hpb=16cb07f99cf770fb4e97824f874a68cbddd4c1cf;p=picoclvr.git

diff --git a/problems.py b/problems.py
index 5161587..aa3acf0 100755
--- a/problems.py
+++ b/problems.py
@@ -21,6 +21,75 @@ class Problem:
 ####################
 
 
+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_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()
+        )
+        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), 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)
+
+
+####################
+
+
+class ProblemLenId(Problem):
+    def __init__(self, len_max=10):
+        self.len_max = len_max
+
+    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
+        )
+        ar_mask = (sequences == 11).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)
+
+
+####################
+
+
 class ProblemLevel0(Problem):
     def __init__(self, nb_sentences=100, len_prompt=5, len_result=5):
         self.seq = torch.randint(10, (nb_sentences, len_prompt + 1 + len_result))
@@ -32,6 +101,12 @@ class ProblemLevel0(Problem):
         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)
+
+
+####################
+
 
 class ProblemLevel1(Problem):
     def __init__(self, nb_operators=100, len_source=5, len_result=8):
@@ -64,6 +139,9 @@ class ProblemLevel1(Problem):
         return "".join("0123456789|>"[x.item()] for x in seq)
 
 
+####################
+
+
 class ProblemLevel2(Problem):
     def __init__(self, len_source=5, len_result=8):
         self.len_source = len_source
@@ -141,18 +219,8 @@ class ProblemAddition(Problem):
         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))