--- /dev/null
+#!/usr/bin/env python
+
+import math
+
+import torch, torchvision
+
+from torch import nn
+from torch.nn import functional as F
+
+def random_var(nb_variables=None, variables=None):
+ if variables is None:
+ return chr(ord('A') + torch.randint(nb_variables, (1,)).item())
+ else:
+ l = list(variables)
+ return l[torch.randint(len(l), (1,)).item()]
+
+def random_expr(variables, budget):
+ if budget <= 5:
+ op=torch.randint(2, (1,)).item()
+ if op == 0 and len(variables) > 0:
+ return random_var(variables=variables)
+ else:
+ return str(torch.randint(10, (1,)).item())
+ else:
+ op=torch.randint(4, (1,)).item()
+ if op == 0:
+ e=random_expr(variables,budget-2)
+ if ("+" in e or "-" in e or "*" in e) and (e[0]!="(" or e[-1]!=")"):
+ return "("+e+")"
+ else:
+ return e
+ else:
+ b = 2 + torch.randint(budget-5, (1,)).item()
+ e1=random_expr(variables,b)
+ e2=random_expr(variables,budget-b-1)
+ if op == 1:
+ return e1+"+"+e2
+ elif op == 2:
+ return e1+"+"+e2
+ elif op == 3:
+ return e1+"*"+e2
+
+def generate_program(nb_variables, length):
+ s = ""
+ variables = set()
+ while len(s) < length:
+ v = random_var(nb_variables=nb_variables)
+ s += v+"="+random_expr(variables,budget = min(20,length-3-len(s)))+";"
+ variables.add(v)
+ return s, variables
+
+def generate_sequences(nb, nb_variables = 5, length=20):
+ sequences=[]
+ for n in range(nb):
+ result = None
+ while result==None or max(result.values())>100:
+ p,v=generate_program(nb_variables, length)
+ v=", ".join([ "\""+v+"\": "+v for v in v ])
+ ldict={}
+ exec(p+"result={"+v+"}",globals(),ldict)
+ result=ldict["result"]
+
+ k=list(result.keys())
+ k.sort()
+ sequences.append(p+" "+";".join([v+":"+str(result[v]) for v in k]))
+
+ return sequences
+
+if __name__ == "__main__":
+ import time
+ start_time = time.perf_counter()
+ sequences=generate_sequences(1000)
+ end_time = time.perf_counter()
+ for s in sequences[:10]:
+ print(s)
+ print(f"{len(sequences) / (end_time - start_time):.02f} samples per second")
+