#!/usr/bin/env python
-import math
+# Any copyright is dedicated to the Public Domain.
+# https://creativecommons.org/publicdomain/zero/1.0/
+
+# Written by Francois Fleuret <francois@fleuret.org>
+
+import math, re
import torch, torchvision
return l[torch.randint(len(l), (1,)).item()]
-def random_expr(variables, budget):
+def random_expr(variables, operand_max, 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())
+ return str(torch.randint(operand_max + 1, (1,)).item())
else:
- op = torch.randint(4, (1,)).item()
+ op = torch.randint(3, (1,)).item()
if op == 0:
- e = random_expr(variables, budget - 2)
+ e = random_expr(variables, operand_max, 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)
+ e1 = random_expr(variables, operand_max, b)
+ e2 = random_expr(variables, operand_max, 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):
+def generate_program(nb_variables, operand_max, length):
s = ""
variables = set()
+
while len(s) < length:
v = random_var(nb_variables=nb_variables)
- s += v + "=" + random_expr(variables, budget=20) + ";"
+ s += v + "=" + random_expr(variables, operand_max, budget=20) + ";"
variables.add(v)
+
return s, variables
-def generate_sequences(nb, nb_variables=5, length=20, randomize_length=False):
+def generate_sequences(nb, nb_variables=5, length=20, operand_max=9, result_max=99):
+ assert nb_variables <= 26
sequences = []
+
for n in range(nb):
+ # We take length itself half of the time, and uniform between
+ # 1 and length otherwise. The actual length can be slightly
+ # greater
+
+ l = min(length, 1 + torch.randint(length * 2, (1,)).item())
result = None
- while result == None or max(result.values()) > 100:
- l = length
- if l > 5 and randomize_length:
- l = 5 + torch.randint(l - 5, (1,)).item()
- p, v = generate_program(nb_variables, l)
+ while result == None or max(result.values()) > result_max:
+ p, v = generate_program(nb_variables, operand_max, 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
+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
+
+
if __name__ == "__main__":
import time
start_time = time.perf_counter()
- sequences = generate_sequences(1000, randomize_length=True)
+ sequences = generate_sequences(1000, length=40)
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]))