BIGNUM *order = BN_new(), *d = BN_new();constEC_GROUP *group = GOST_KEY_get0_group(ec);intrc =0;if(order ==NULL|| d ==NULL)gotoerr;if(EC_GROUP_get_order(group, order,NULL) ==0)gotoerr;do{if(BN_rand_range(d, order) ==0) { GOSTerr(GOST_F_GOST2001_KEYGEN, GOST_R_...
}elseif(!BN_is_bit_set(range, n -2) && !BN_is_bit_set(range, n -3)) {do{if(!bn_rand(r, n +1,-1,0))return0;if(BN_cmp(r ,range) >=0) {if(!BN_sub(r, r, range))return0;if(BN_cmp(r, range) >=0)if(!BN_sub(r, r, range))return0; } }while(BN_cmp(r, ...
int BN_rand(BIGNUM *rnd, int bits, int top, int bottom); int BN_pseudo_rand(BIGNUM *rnd, int bits, int top, int bottom); int BN_rand_range(BIGNUM *rnd, BIGNUM *range); int BN_pseudo_rand_range(BIGNUM *rnd, BIGNUM *range); BIGNUM *BN_generate_prime(BIGNUM *ret, int bits,int...
int BN_pseudo_rand(BIGNUM *rnd, int bits, int top, int bottom);产生一个伪随机数,应用于某些目的。 int BN_rand_range(BIGNUM *rnd, BIGNUM *range);产生的0<rnd<range int BN_pseudo_rand_range(BIGNUM *rnd, BIGNUM *range);同上面道理 9.产生素数函数 BIGNUM *BN_generate_prime(BIGNUM *ret, ...
int BN_pseudo_rand(BIGNUM *rnd, int bits, int top, int bottom); 生成可预测的随机大数序列便于调试,成功返回1,失败返回0。 int BN_rand_range(BIGNUM *rnd, const BIGNUM *range); 生成0~range范围的大数,成功返回1,失败返回0。 int BN_pseudo_rand_range(BIGNUM *rnd, const BIGNUM *range); 生成...
foriinrange(nb_epochs): np.random.shuffle(data) forbatchinget_batches(data, batch_size=50): params_grad = evaluate_gradient(loss_function, batch, params) params = params - learning_rate * params_grad 优点: 1)减少参数更新的变化, 从而得到更加稳定的收敛 ...
if__name__=="__main__":net=Net()net.requires_grad_(False)torch.random.manual_seed(5)test_data=torch.rand(1,1,32,32)train_data=torch.rand(5,1,32,32)#print(test_data)#print(train_data[0,...])forepochinrange(2):# training phase,假设每个epoch只迭代一次 net.train()net.bn1.eva...
if (!BN_pseudo_rand_range(check, A1)) goto err; if (!BN_add_word(check, 1)) goto err; /* now 1 <= check < A */ j = witness(check, A, A1, A1_odd, k, ctx, mont); if (j == -1) goto err; if (j) { ret=0; goto err; } if(!BN_GENCB_call(cb, 1, i)) got...
train_data = torch.rand(5, 1, 32, 32) # print(test_data) # print(train_data[0, ...]) for epoch in range(2): # training phase, 假设每个epoch只迭代一次 net.train() pre = net(train_data) # 计算损失和参数更新等 # ... #...
().gen_range(0, V);letc= rand::thread_rng().gen_range(0, V);letd= rand::thread_rng().gen_range(0, V);letla= a.min(b);letra= a.max(b);letlb= c.min(d);letrb= c.max(d);letans1=nums1(&A, &B, la, ra, lb, rb);letans2=nums2(&A, &B, la, ra, lb, rb)...