#!/usr/bin/env python
-import math
+import math, re
import torch, torchvision
return s, variables
-def generate_sequences(nb, nb_variables=5, length=20):
+def extract_results(seq):
+ f = lambda a: (a[0], -1 if a[1] == "" else int(a[1]))
+ results = [
+ dict([f(tuple(x.split(":"))) for x in re.findall("[A-Z]:[0-9]*", s)])
+ for s in seq
+ ]
+ return results
+
+
+def generate_sequences(nb, nb_variables=5, length=20, randomize_length=False):
sequences = []
for n in range(nb):
result = None
while result == None or max(result.values()) > 100:
- p, v = generate_program(nb_variables, length)
+ l = length
+ if l > 5 and randomize_length:
+ l = 5 + torch.randint(l - 5, (1,)).item()
+ p, v = generate_program(nb_variables, l)
v = ", ".join(['"' + v + '": ' + v for v in v])
ldict = {}
exec(p + "result={" + v + "}", globals(), ldict)
k = list(result.keys())
k.sort()
- sequences.append(p + " " + ";".join([v + ":" + str(result[v]) for v in k]))
+ sequences.append(p + " " + "".join([v + ":" + str(result[v]) + ";" for v in k]))
return sequences
for s in sequences[:10]:
print(s)
print(f"{len(sequences) / (end_time - start_time):.02f} samples per second")
+
+ print(extract_results(sequences[:10]))