X-Git-Url: https://fleuret.org/cgi-bin/gitweb/gitweb.cgi?a=blobdiff_plain;f=problems.py;h=22b651795b0501331c69fd24c3ea499779576e48;hb=0d86d8ca945722438d3c85cd01b3740269ed3546;hp=7aa59bea856c205d232ec1bf49c80f18ef20bed1;hpb=16e7952b7cc32ca21498fa3a12fb79f679ea8c21;p=picoclvr.git

diff --git a/problems.py b/problems.py
index 7aa59be..22b6517 100755
--- a/problems.py
+++ b/problems.py
@@ -22,37 +22,51 @@ class Problem:
         nb_correct = ((result == input).long() * ar_mask).sum().item()
         return nb_total, nb_correct
 
+
 ####################
 
 
 class ProblemDegradation(Problem):
-    def __init__(self, nb_state_tokens=7, nb_time_steps=10, value_max=100, hard=False):
+    def __init__(self, nb_state_tokens=5, nb_time_steps=12, value_max=25, hard=False):
+        assert value_max // nb_state_tokens >= 2
         self.nb_state_tokens = nb_state_tokens
         self.nb_time_steps = nb_time_steps
         self.value_max = value_max
         self.hard = hard
 
-    def generate_sequences(self,nb):
-
-        x = (torch.rand(nb,self.nb_state_tokens).sort(dim=-1).indices == 0).long() * self.value_max
+    def generate_sequences(self, nb):
+        x = (
+            torch.rand(nb, self.nb_state_tokens).sort(dim=-1).indices == 0
+        ).long() * self.value_max
         seq = [x]
 
-        for t in range(self.nb_time_steps-1):
-            v = torch.rand(x.size()) * (x > 0).float()
-            u = (v.max(dim=-1,keepdim=True).values == v).long()
-            n = (u*x*torch.rand(x.size())).long().sum(dim=-1,keepdim=True) // 2
-            x = x + n * (u.roll(shifts=-1,dims=-1) - 2 * u + u.roll(shifts=1,dims=-1))
+        for t in range(self.nb_time_steps - 1):
+            v = (torch.rand(x.size()).sort(dim=-1).indices + 1) * (x >= 2).long()
+            u = (v.max(dim=-1, keepdim=True).values == v).long()
+            n = (
+                (u * x)
+                .minimum(2 + torch.randint(self.value_max // 4 - 2, x.size()))
+                .sum(dim=-1, keepdim=True)
+            )
+            m = 1 + ((n - 1) * torch.rand(n.size())).long()
+            x = (
+                x
+                + m * u.roll(shifts=-1, dims=-1)
+                - n * u
+                + (n - m) * u.roll(shifts=1, dims=-1)
+            )
             seq.append(x)
 
-        if self.hard: seq.reverse()
+        if self.hard:
+            seq.reverse()
 
-        seq = torch.cat(seq,dim=1)
-        return seq,seq.new_full(seq.size(), 1, dtype=torch.int64)
+        seq = torch.cat(seq, dim=1)
+        return seq, seq.new_full(seq.size(), 1, dtype=torch.int64)
 
     def compute_nb_correct(self, input, ar_mask, result):
         nb_total = result.size(0)
         nb_correct = 0
-        e=result.new_zeros(self.nb_state_tokens)
+        e = result.new_zeros(self.nb_state_tokens)
 
         for seq in result:
             states = list(seq.split(self.nb_state_tokens))
@@ -60,27 +74,38 @@ class ProblemDegradation(Problem):
                 states.reverse()
 
             d = states[0]
-            j=d.sort(descending=True).indices[0]
+            j = d.sort(descending=True).indices[0]
             e.zero_()
-            e[j]=self.value_max
-            if (d-e).abs().sum() == 0:
+            e[j] = self.value_max
+            if (d - e).abs().sum() == 0:
                 nb_errors = 0
-                for k in range(len(states)-1):
-                    d=states[k]-states[k+1]
-                    j=d.sort(descending=True).indices[0]
-                    e.zero_()
-                    e[j]=d[j]
-                    e[(j+1)%e.size(0)]=-d[j]//2
-                    e[(j-1)%e.size(0)]=-d[j]//2
-                    if (d-e).abs().sum() > 0:
+                for k in range(len(states) - 1):
+                    d = states[k + 1] - states[k]
+                    j = d.sort(descending=False).indices[0]
+                    if (
+                        d[j] == 0
+                        or d[j] > self.value_max // 4
+                        or d[(j + 1) % e.size(0)] <= 0
+                        or d[(j + 1) % e.size(0)] >= -d[j]
+                    ):
                         nb_errors += 1
+                    else:
+                        e.zero_()
+                        e[j] = d[j]
+                        e[(j + 1) % e.size(0)] = d[(j + 1) % e.size(0)]
+                        e[(j - 1) % e.size(0)] = -d[(j + 1) % e.size(0)] - d[j]
+                        if (d - e).abs().sum() > 0:
+                            nb_errors += 1
                 if nb_errors == 0:
                     nb_correct += 1
 
         return nb_total, nb_correct
 
     def seq2str(self, seq):
-        return " | ".join( [ " ".join([f"{x:02d}" for x in s ]) for s in seq.split(self.nb_state_tokens) ] )
+        return " | ".join(
+            [" ".join([f"{x:02d}" for x in s]) for s in seq.split(self.nb_state_tokens)]
+        )
+
 
 ####################