Oups

[picoclvr.git] / problems.py

diff --git a/problems.py b/problems.py
index 28e4f7b..d7dbc54 100755 (executable)
--- a/problems.py
+++ b/problems.py
@@ -24,6 +24,8 @@ class Problem:
 
 
 ####################
+
+
 class ProblemDegradation(Problem):
     def __init__(self, nb_state_tokens=5, nb_time_steps=12, value_max=25, hard=False):
         assert value_max // nb_state_tokens >= 2
@@ -108,6 +110,48 @@ class ProblemDegradation(Problem):
 ####################
 
 
+class ProblemMemory(Problem):
+    def __init__(self, len_total=25):
+        self.len_total = len_total
+        self.max_len_pattern = 5
+        self.nb_noise_tokens = 10
+        self.start_pattern_token = 0
+        self.end_pattern_token = 1
+        self.start_result_token = 2
+        self.end_result_token = 3
+        self.token_string = "[]<>" + "".join(
+            [chr(ord("a") + k) for k in range(self.nb_noise_tokens)]
+        )
+
+    def generate_sequences(self, nb):
+        sequences = (
+            torch.randint(self.nb_noise_tokens, (nb, self.len_total))
+            + self.end_result_token
+            + 1
+        )
+        len_patterns = torch.randint(self.max_len_pattern, (nb,)) + 1
+        pattern_positions = torch.randint(
+            self.len_total - (5 + 2 * self.max_len_pattern), (nb,)
+        )
+        k = self.len_total - (3 + self.max_len_pattern)
+        for i in range(nb):
+            l = len_patterns[i]
+            j = pattern_positions[i]
+            sequences[i, j] = self.start_pattern_token
+            sequences[i, j + l + 2] = self.end_pattern_token
+            sequences[i, k] = self.start_result_token
+            sequences[i, k + l + 2] = self.end_result_token
+            sequences[i, k + 1 : k + 2 + l] = sequences[i, j + 1 : j + 2 + l]
+
+        j = torch.arange(self.len_total)[None, :]
+        ar_mask = (j > k).long() * (j <= k + 1 + len_patterns[:, None]).long()
+
+        return sequences, ar_mask
+
+    def seq2str(self, seq):
+        return "".join(self.token_string[x.item()] for x in seq)
+
+
 class ProblemTwoTargets(Problem):
     def __init__(self, len_total=10, len_targets=3):
         assert len_targets >= 3
@@ -287,18 +331,75 @@ 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, random_start=True
+    ):
         self.height = height
         self.width = width
         self.nb_time_steps = nb_time_steps
         self.hard = hard
+        self.random_start = random_start
 
-    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)
+
+        if self.random_start:
+            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)
+
+        y = y.reshape(nb, self.height, self.width)
+
+        return y
+
+    def start_error(self, x):
+        if self.random_start:
+            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)
+        else:
+            m = 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 = (
@@ -323,8 +424,7 @@ 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 +449,7 @@ 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 +464,15 @@ 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 +482,8 @@ class ProblemMixing(Problem):
 ####################
 
 if __name__ == "__main__":
-    p = ProblemMixing(hard=True)
+    p = ProblemMixing(height=3, width=3, random_start=False)
+
     s, m = p.generate_sequences(10000)
     for x in s[:5]:
         print(p.seq2str(x))