X-Git-Url: https://fleuret.org/cgi-bin/gitweb/gitweb.cgi?a=blobdiff_plain;f=tasks.py;h=5583fc89827d82be551db14ac9cb601f670b4233;hb=3dea181a5903a0e577e4830c66405b40f2a2df1d;hp=8b57cb2056462d8265bb38c1dffb49c4d59dc41a;hpb=e3a8032a070175ece08fc79c77312d5f2f59150e;p=picoclvr.git

diff --git a/tasks.py b/tasks.py
index 8b57cb2..5583fc8 100755
--- a/tasks.py
+++ b/tasks.py
@@ -73,8 +73,12 @@ class Problem:
 
 class ProblemByheart(Problem):
     def __init__(self):
-        pass
+        nb_seq, len_prompt, len_result = 100, 5, 5
+        self.seq = torch.randint(10, (nb_seq, len_prompt + 1 + len_result))
+        self.seq[:,len_prompt]=-1
 
+    def generate_sequences(self, nb):
+        return self.seq[torch.randint(self.seq.size(0), (nb,))]
 
 class SandBox(Task):
     def __init__(
@@ -89,13 +93,23 @@ class SandBox(Task):
 
         self.batch_size = batch_size
 
+        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
+
+        train_seq = generate_sequences(nb_train_samples)
+        test_seq = generate_sequences(nb_test_samples)
 
-        self.train_input = generate_sequences(nb_train_samples)
-        self.test_input = generate_sequences(nb_test_samples)
+        for strain, stest in zip(train_seq, test_seq):
+            s = torch.cat((strain,stest),0)
 
         self.nb_codes = max(self.train_input.max(), self.test_input.max()) + 1