diary(log_file); % set random seed prev_rng = seed_rand(conf.rng_seed); caffe.set_random_seed(conf.rng_seed); % set gpu/cpuifconf.use_gpu caffe.set_mode_gpu();elsecaffe.set_mode_cpu();enddisp('conf:'); disp(conf); disp('opts:'); disp(opts); 2 making tran/val data,将bbs...
% Set display options DEParams.infoIterations = 1; DEParams.infoPeriod = 10; % in seconds % Do not send E-mails emailParams = []; % Set random state in order to always use the same population members here setrandomseed(1); % Start differential evolution [bestmem, bestval, bestFctPar...
gamma2,p]; % Initialize output function, specify Ito, set random seed opts = sdeset('OutputFUN...
(seed); % Set random seed % Containers for parameters par_u = bsxfun(@plus, lb, bsxfun(@times, rand(Nu, M), (ub-lb))); par_c = bsxfun(@plus, lb, bsxfun(@times, rand(M*Nc*n, length(lb)), (ub-lb))); % Create the conditioned samples for ind=1:M for ind2=1:n x...
T=500; %Set the maximum number of iterations seed=1; rand('state', seed ); randn('state',seed ); %setthe random seed theta= zeros( L , T ); % Init storage spaceforour samples theta(:,1) = randperm( L ); %Random ordering to start with ...
Pytorch随机种子设置 import numpy as np import random import os import torch def seed_torch(seed=...
[m, n] = size(img);%获取图像大小% 以875689为种子初始化随机数生成器random_seed = 875689;rng(random_seed);% 产生与img相同维数的矩阵，每个元素的值为0~255之间的uint8类型整数key_mat = randi([0, 255], m, n, 'uint8');% 再次初始化随机数生成器rng(random_seed);% 对1~64...
np.random.seed(1) np.random.shuffle(y_t) tf.random.set_seed(1) 1. 2. 3. 4. 5. 区分数据集中的训练集与验证集且两者不相交: x_train = x_d[:-30] # 使用切片,使前120组数据作为训练集,后30组数据作为验证集 x_test = x_d[-30:] ...
set(SignalController, 'AttValue', 'ProgNo', new_signal_programm_number); %%设置仿真参数 Random_Seed = 42; set(Vissim.Simulation, 'AttValue', 'RandSeed', Random_Seed); % To start a simulation you can run a single step: Vissim.Simulation.RunSingleStep; ...
The installer now supports Matlab 7.5.randomseedis a new function that allows you to set the seed for lightspeed's random number generator.cholprojis a new function that provides an approximate cholesky decomposition even when a matrix is not exactly positive definite, for example due to roundoff...