5 import torch, torchvision
8 from torch.nn import functional as F
10 def random_var(nb_variables=None, variables=None):
12 return chr(ord('A') + torch.randint(nb_variables, (1,)).item())
15 return l[torch.randint(len(l), (1,)).item()]
17 def random_expr(variables, budget):
19 op=torch.randint(2, (1,)).item()
20 if op == 0 and len(variables) > 0:
21 return random_var(variables=variables)
23 return str(torch.randint(10, (1,)).item())
25 op=torch.randint(4, (1,)).item()
27 e=random_expr(variables,budget-2)
28 if ("+" in e or "-" in e or "*" in e) and (e[0]!="(" or e[-1]!=")"):
33 b = 2 + torch.randint(budget-5, (1,)).item()
34 e1=random_expr(variables,b)
35 e2=random_expr(variables,budget-b-1)
43 def generate_program(nb_variables, length):
46 while len(s) < length:
47 v = random_var(nb_variables=nb_variables)
48 s += v+"="+random_expr(variables,budget = min(20,length-3-len(s)))+";"
52 def generate_sequences(nb, nb_variables = 5, length=20):
56 while result==None or max(result.values())>100:
57 p,v=generate_program(nb_variables, length)
58 v=", ".join([ "\""+v+"\": "+v for v in v ])
60 exec(p+"result={"+v+"}",globals(),ldict)
61 result=ldict["result"]
65 sequences.append(p+" "+";".join([v+":"+str(result[v]) for v in k]))
69 if __name__ == "__main__":
71 start_time = time.perf_counter()
72 sequences=generate_sequences(1000)
73 end_time = time.perf_counter()
74 for s in sequences[:10]:
76 print(f"{len(sequences) / (end_time - start_time):.02f} samples per second")