Home > General-Functions > Statistics > init_rand_seed.m

init_rand_seed

PURPOSE

% init_rand_seed: initialize random number generation by specific seed

SYNOPSIS

function seed = init_rand_seed(seed)

DESCRIPTION

% init_rand_seed: initialize random number generation by specific seed
 to ensure repeatability of experiments

 seed = init_rand_seed(seed)
 seed = int, seed for initialization of rand and randn
        default 42
        if seed = 0 rand is initialized by timestamp

 Susane Wenzel 09/16
 wenzel@igg.uni-bonn.de

CROSS-REFERENCE INFORMATION

This function calls:

This function is called by:

fig_2_11_demo_scatter_histogram % Fig. 2.11 page 56
demo_GHM_2D_line % Demo nonlinear GHM without constraints
demo_GHM_similarity % GHM with similarity
demos_GMM_regression % test GMM with linear regression with check
fig_4_11_test_sensitivity_factors_GMM_similarity % Fig. 4.11 test sensitivity factors in GMM with similarity
demo_estimate_2D_homography_from_point_pairs % demo_estimate_2D_homography_from_point_pairs
demo_direct_plane_fit % demo plane fitting direct method
demo_estimate_Point_2D_from_Lines % demo_estimate_Point_2D_from_Lines
demo_estimate_two_symmetric_planes_from_points % test_two_symmetric_planes_from_points
demo_ALG_17_estimate_Projection_3D_2D_from_point_pairs % Demo estimate 3D projection from 2D point pairs, ALG 17
demo_estimate_Projection_3D_2D_from_point_pairs_single % Demo estimate 3D projection from 2D point pairs
demo_estimate_E_Matrix_from_point_pairs % test_estimate_E_Matrix_from_point_pairs
demo_triangulation_spherical % demo triangulation
fig_16_13_profile_reconstruction_demo_precision % Fig 16.13 page 750
fig_16_14_test_scale % Fig. 16.14 page 754
fig_16_15_symmetric_outliers % Fig. 16.15 page 755
fig_16_17_asymmetric_outliers % Fig. 16.17 page 756
fig_16_18_step_break % Fig. 15.18 page 758
fig_16_3_16_4_example_demo_profile % Fig. 16.3 page 731 and 16.4 page 732
test_smooth_dem_robust_bilinear % test dem smoothing
test_smooth_dem_robust_bilinear_square % test dem smoothing
eq_15_120_theoretical_accuracy % eq_15_120_theoretical accuracy
fig_16_20_demo_surface_reconstruction % Fig. 16.20 page 761
fig_16_21_quality_surface_reconstruction % Fig. 16.21 theoretical quality of surface reconstruction
fig_16_22_demo_robust_surface_reconstruction % Fig. 16.22 page 762
fig_16_23_aurelo_result % Fig. 16.23 page 764
fig_16_9b_demo_surface_reconstruction % Fig. 16.9 bottom page 740

SOURCE CODE

0001 %% init_rand_seed: initialize random number generation by specific seed
0002 % to ensure repeatability of experiments
0003 %
0004 % seed = init_rand_seed(seed)
0005 % seed = int, seed for initialization of rand and randn
0006 %        default 42
0007 %        if seed = 0 rand is initialized by timestamp
0008 %
0009 % Susane Wenzel 09/16
0010 % wenzel@igg.uni-bonn.de
0011 
0012 function seed = init_rand_seed(seed)
0013 
0014 if nargin<1
0015     seed = 42;   
0016 end
0017 if seed == 0
0018     seed =  round( sum(10000*clock) );
0019 end;
0020 
0021 disp(['Random seed = ', num2str(seed)])
0022 
0023 if verLessThan('matlab', '15')
0024     rand('state',seed); %#ok<*RAND>
0025     randn('state',seed);
0026 else
0027     rng(seed,'v5uniform');
0028 end
0029 
0030 
0031 
0032