X-Git-Url: https://fleuret.org/cgi-bin/gitweb/gitweb.cgi?a=blobdiff_plain;f=problems.py;h=b8fcdb34b32a07e8fd46900c88d889c3cf9761ea;hb=76e62a5782fc2509ce989fcfc0d0aedc17322b3a;hp=28e4f7b0df34afb7ced6f9b6c57228e0b00a8fe7;hpb=503298855a80bde0bf856f1a34b532079d3c7ef6;p=picoclvr.git

diff --git a/problems.py b/problems.py
index 28e4f7b..b8fcdb3 100755
--- a/problems.py
+++ b/problems.py
@@ -287,19 +287,46 @@ class ProblemAddition(Problem):
 
 
 class ProblemMixing(Problem):
-    def __init__(self, height=3, width=3, nb_time_steps=12, hard=False):
+    def __init__(self, height=4, width=4, nb_time_steps=9, hard=False):
         self.height = height
         self.width = width
         self.nb_time_steps = nb_time_steps
         self.hard = hard
 
-    def start(self, nb):
-        return (
-            torch.arange(self.height * self.width)
-            .reshape(1, 1, self.height, self.width)
-            .expand(nb, -1, -1, -1)
+    def start_random(self, nb):
+        y = torch.arange(self.height * self.width).reshape(1, -1).expand(nb, -1)
+
+        # m = (torch.rand(y.size()).sort(dim=-1).indices < y.size(1) // 2).long()
+
+        i = torch.arange(self.height).reshape(1,-1,1).expand(nb,self.height,self.width)
+        j = torch.arange(self.width).reshape(1,1,-1).expand(nb,self.height,self.width)
+
+        ri = torch.randint(self.height, (nb,)).reshape(nb,1,1)
+        rj = torch.randint(self.width, (nb,)).reshape(nb,1,1)
+
+        m = 1 - torch.logical_or(i==ri,j==rj).long().flatten(1)
+
+        y = (y * m + self.height * self.width * (1 - m)).reshape(
+            nb, self.height, self.width
         )
 
+        return y
+
+    def start_error(self, x):
+        i = torch.arange(self.height, device=x.device).reshape(1,-1,1).expand_as(x)
+        j = torch.arange(self.width, device=x.device).reshape(1,1,-1).expand_as(x)
+
+        ri = (x == self.height * self.width).long().sum(dim=-1).argmax(-1).view(-1,1,1)
+        rj = (x == self.height * self.width).long().sum(dim=-2).argmax(-1).view(-1,1,1)
+
+        m = 1 - torch.logical_or(i==ri,j==rj).long().flatten(1)
+
+        x = x.flatten(1)
+        u = torch.arange(self.height * self.width, device = x.device).reshape(1, -1)
+
+        d = (x - (m * u + (1 - m) * self.height * self.width)).abs().sum(-1)
+        return d
+
     def moves(self, x):
         y = (
             x[:, None, :, :]
@@ -323,8 +350,7 @@ class ProblemMixing(Problem):
         return y
 
     def generate_sequences(self, nb):
-        y = self.start(nb)
-        x = y[torch.arange(nb), torch.randint(y.size(1), (nb,))]
+        x = self.start_random(nb)
 
         seq = [x.flatten(1)]
 
@@ -349,8 +375,7 @@ class ProblemMixing(Problem):
 
         x = a[0]
 
-        y = self.start(result.size(0)).to(x.device)
-        d = (x[:, None] - y).abs().sum((-1, -2)).min(dim=-1).values
+        d = self.start_error(x)
 
         for t in range(self.nb_time_steps - 1):
             x0, x = a[t], a[t + 1]
@@ -365,7 +390,7 @@ class ProblemMixing(Problem):
         return " | ".join(
             [
                 " ".join(
-                    ["-".join([f"{x:02d}" for x in s]) for s in r.split(self.width)]
+                    ["-".join([f"{x:02d}" if x < self.height * self.width else "**" for x in s]) for s in r.split(self.width)]
                 )
                 for r in seq.split(self.height * self.width)
             ]
@@ -375,7 +400,7 @@ class ProblemMixing(Problem):
 ####################
 
 if __name__ == "__main__":
-    p = ProblemMixing(hard=True)
+    p = ProblemMixing()
     s, m = p.generate_sequences(10000)
     for x in s[:5]:
         print(p.seq2str(x))