vect1 = rand(1000,5)*100; % uniform
sigma2=[1,3,1,3,2]*5;
mu2=[70,60,70,55,89];
vect2 = randn(120,5);
vect2 = vect2.*repmat(sigma2,size(vect2,1),1)+repmat(mu2,size(vect2,1),1);
sigma3=[1,3,1,3,2]*5;
mu3=[20,10,15,55,9];
vect3 = randn(100,5);
vect3 = vect3.*repmat(sigma3,size(vect3,1),1)+repmat(mu3,size(vect3,1),1);
data=[vect1; vect2; vect3];
[modes, p_modes, w_modes]=fams(data,20,15,200,'5Ddata','res/','-h',20);
Load data points from matlab ...done
RunFAMS with res/pilot_200_5Ddata.txt ...
Running FAMS with K=20 L=15
Run pilot fixed bandwith...done.
Start MS iterations..........done.
Join Modes with adaptive h/2, min pt=40, jump=1
pass 1.done
pass 2..........nrel 6
0 0
1 1
2 2
3 3
4 4
5 5
done
Save convergence points ...done
Save joined convergence points ...done
Save indicies of modes ...done
FAMS done.
format short g; p_modes % this script results: % p_modes = % 1533 70.356 60.01 69.579 56.076 89.433 % 1162 19.909 9.4154 15.675 54.572 8.4813 % 48 36.851 29.007 82.868 43.807 28.804 % 47 37.235 26.553 49.035 28.731 71.776 % % here the results of meanshift_Euclidian function % % the first two vectors in <res> matrix - view like <mu2> and <mu3> vectors % % [res, num_rows,mean_vals,data_group, sample_num ] =meanshift_Euclidian(data,0.1); % %>> res(1:4,:) % % ans = % % 70.0758 59.9020 70.0746 54.5983 88.6178 % % 19.8981 10.0129 14.9481 55.1258 9.3043 % % 87.8244 37.2915 64.2528 58.1243 37.1069 % % 41.5967 27.2727 49.5976 19.0590 12.5807 % % >> size(res) % % ans = % % 535 5 % % >> num_rows(1:15,:) % % ans = % % 126 % % 107 % % 14 % % 11 % % % % =========================================== % % >> ans = % % 546 5 % % ans = % % 69.9826 59.7245 70.1298 54.9041 89.0960 % % 20.0367 9.7626 15.0001 55.1254 9.0817 % % 72.1779 16.1155 43.0180 51.8788 81.5878 % % 18.3489 57.1178 72.5199 35.9218 50.6380 % % ans = % % 127 % % 100 % % 14 % % 14
p_modes =
446 70.369 57.104 69.913 55.933 88.223
315 21.429 8.8381 14.123 56.03 8.5525
98 76.775 26.421 71.663 52.89 48.564
58 84.778 69.555 45.305 78.922 29.83
54 44.767 34.338 36.552 46.509 30.843
46 27.184 33.584 54.598 27.461 33.91