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__(
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