5 import torch, torchvision
8 from torch.nn import functional as F
11 def random_var(nb_variables=None, variables=None):
13 return chr(ord("A") + torch.randint(nb_variables, (1,)).item())
16 return l[torch.randint(len(l), (1,)).item()]
19 def random_expr(variables, budget):
21 op = torch.randint(2, (1,)).item()
22 if op == 0 and len(variables) > 0:
23 return random_var(variables=variables)
25 return str(torch.randint(10, (1,)).item())
27 op = torch.randint(4, (1,)).item()
29 e = random_expr(variables, budget - 2)
30 if ("+" in e or "-" in e or "*" in e) and (e[0] != "(" or e[-1] != ")"):
35 b = 2 + torch.randint(budget - 5, (1,)).item()
36 e1 = random_expr(variables, b)
37 e2 = random_expr(variables, budget - b - 1)
46 def generate_program(nb_variables, length):
49 while len(s) < length:
50 v = random_var(nb_variables=nb_variables)
51 s += v + "=" + random_expr(variables, budget=20) + ";"
56 def generate_sequences(nb, nb_variables=5, length=20):
60 while result == None or max(result.values()) > 100:
61 p, v = generate_program(nb_variables, length)
62 v = ", ".join(['"' + v + '": ' + v for v in v])
64 exec(p + "result={" + v + "}", globals(), ldict)
65 result = ldict["result"]
67 k = list(result.keys())
69 sequences.append(p + " " + ";".join([v + ":" + str(result[v]) for v in k]))
74 if __name__ == "__main__":
77 start_time = time.perf_counter()
78 sequences = generate_sequences(1000)
79 end_time = time.perf_counter()
80 for s in sequences[:10]:
82 print(f"{len(sequences) / (end_time - start_time):.02f} samples per second")