"seedrandom": "^3.0.5", "tiny-emitter": "^2.1.0", "typed-function": "^4.2.1" }, "bin": { "mathjs": "bin/cli.js" }, "engines": { "node": ">= 18" } }, "node_modules/media-typer": { "version": "0.3.0", ...
if np.random.rand() < self.cross_rate: i_ = np.random.randint(0, self.pop_size, size=1) # select another individual from pop cross_points = np.random.randint(0, 2, self.DNA_size).astype(np.bool) # choose crossover points parent[cross_points] = pop[i_, cross_points] # mating...
( /usr/local/lib/python3.10/dist-packages/gym/utils/passive_env_checker.py:174: UserWarning: WARN: Future gym versions will require that `Env.reset` can be passed a `seed` instead of using `Env.seed` for resetting the environment random number generator. logger.warn( /usr/local/lib/...
From the earlier experience of using the eugenic algorithm for the problem of waveform design, it was realised that rather than random but multiple mutations, all the first-order mutations should be examined to pick up the best one. This is called Hamming scan, which has the advantage of ...
random_shuffle(s_HuCardAll, s_HuCardAll + 126); // 这个函数对计算有影响 Expand All @@ -77,7 +78,7 @@ void main() hu += m_cAlgorithm.CanWin_Do_Nai(stData, gui_index); } } cout << "rjc递归方法, 总数:" << 9 * MAX_COUNT/10000 << "万次, time:" << GetTickCount() - ...
>>> timeit.timeit('random.randint(1, 100)', 'import random', number=10000000) 10.020913950999784By default, the code in the string you pass to timeit.timeit() won’t be able to access the variables and the functions in the rest of the program:...
np.random.shuffle(rand_ch) self.parent[i, :] = rand_ch self.fitness[i] = self.comp_fit(rand_ch)defcomp_fit(self, one_path):""" 计算,咱们这个路径的长度,例如A-B-C-D :param one_path: :return: """res =foriinrange(self.num -): ...
np.random.randint(0, 2, size=DNA_SIZE).astype(np.bool) # choose crossover points parent[cross_points] = pop[i_, cross_points] # mating and produce one child return parent def mutate(child): for point in range(DNA_SIZE): if np.random.rand() < MUTATION_RATE: child[point] = 1 ...
random.choice(range(prob_weights.shape[1]), p=prob_weights.ravel()) return action 存储回合 之前说过,policy gradient是在一个完整的episode结束后才开始训练的,因此,在一个episode结束前,我们要存储这个episode所有的经验,即状态,动作和奖励。 def store_transition(self, s, a, r): self.ep_obs....
{ + random_shuffle(g_HuCardAll, g_HuCardAll + 130); // Õâ¸öº¯Êý¶Ô¼ÆËãÓÐÓ°Ïì + for (int i = 0; i < 9; ++i) // 136/14 -> 9 + { + BYTE* cards = &source[total++ * 34]; + memset(cards, 0, 34); + for (int j = i ...