#!/usr/bin/env python
-import math
+import math, re
import torch, torchvision
else:
return str(torch.randint(10, (1,)).item())
else:
- op = torch.randint(4, (1,)).item()
+ op = torch.randint(3, (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] != ")"):
if op == 1:
return e1 + "+" + e2
elif op == 2:
- return e1 + "+" + e2
- elif op == 3:
return e1 + "*" + e2
return s, variables
+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):
+ assert nb_variables <= 26
sequences = []
for n in range(nb):
result = None
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
import time
start_time = time.perf_counter()
- sequences = generate_sequences(1000, randomize_length=True)
+ sequences = generate_sequences(1000, length=30)
end_time = time.perf_counter()
for s in sequences[:10]:
print(s)
print(f"{len(sequences) / (end_time - start_time):.02f} samples per second")
+
+ print(extract_results(sequences[:10]))