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- from math import exp, log
- from random import random
- from typing import List
-
- from gbso.program.test_case import Output, TestCase, eq_on_testcase
- from gbso.program.mutate import mutate_program
- from gbso.program.program import Program
-
- EPSILON = 0.00001
-
- DEFAULT_NUM_ITERS = 1_000_000
-
- DEFAULT_PROB_OPCODE = 0.25
- DEFAULT_PROB_OPERAND = 0.25
- DEFAULT_PROB_SWAP = 0.25
- DEFAULT_PROB_INSN = 0.25
-
- DEFAULT_PROB_INSN_UNUSED = 0.1
-
-
- def cost(orig_prgm, test_cases, outputs, prgm) -> int:
- c = prgm.perf() - orig_prgm.perf()
- # print(f"init cost: {c}")
-
- for test_case in test_cases:
- c += eq_on_testcase(orig_prgm, prgm, test_case, outputs)
- # print(f"cost after testcase: {c}")
-
- return c
-
-
- def optimize(
- prgm: Program,
- max_size: int,
- test_cases: List[TestCase],
- outputs: List[Output],
- beta: int = 0.75, # How far away in cost you are allowed to search
- num_iters: int = DEFAULT_NUM_ITERS,
- prob_opcode: float = DEFAULT_PROB_OPCODE,
- prob_operand: float = DEFAULT_PROB_OPERAND,
- prob_swap: float = DEFAULT_PROB_SWAP,
- prob_insn: float = DEFAULT_PROB_INSN,
- prob_insn_unused: float = DEFAULT_PROB_INSN_UNUSED,
- ) -> Program:
- padded_prgm = prgm.pad(max_size)
-
- last_prgm = padded_prgm
- last_cost = cost(padded_prgm, test_cases, outputs, last_prgm)
-
- for _ in range(num_iters):
- candidate_prgm = mutate_program(
- last_prgm, prob_opcode, prob_operand, prob_swap, prob_insn, prob_insn_unused
- )
- candidate_cost = cost(padded_prgm, test_cases, outputs, candidate_prgm)
-
- if candidate_cost < last_cost - log(random()) / beta:
- last_prgm = candidate_prgm
- last_cost = candidate_cost
-
- return last_prgm
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