grossberg.conf

% true oznaczałoby losowanie wag dla neuronów, weights jest wtedy ignorowane
random_weights = true;

% dane wejściowe
input_data = [

 1 1 1  0 0 0  0 0 0;
 0 0 0  1 1 1  0 0 0;
 0 0 0  0 0 0  1 1 1;

 1 0 0  1 0 0  1 0 0;
 0 1 0  0 1 0  0 1 0;
 0 0 1  0 0 1  0 0 1;

 1 0 0  0 0 0  0 0 0;
 0 0 0  0 0 0  0 0 1;
 0 0 1  0 1 0  1 0 0;
];

expected = [
 1 0 0;
 1 0 0;
 1 0 0;

 0 1 0;
 0 1 0;
 0 1 0;

 0 0 1;
 0 0 1;
 0 0 1;
];

%global epochs = [1 8000 16000 24000]

global layers = {};
learn_steps = 10000;
% pierwsza warstwa
% liczba neuronów w warstwie
layers{1}.type='kohonen';
layers{1}.neurons = 9;
layers{1}.row_count = 1;
% funkcja aktywacji
layers{1}.activation_function =@(X) sigmoid(X);
% wartość bias
layers{1}.bias = 0;
% przedział, z którego są losowane wagi
layers{1}.rand_min = -1.0;
layers{1}.rand_max = 1.0;
layers{1}.neighbourhood_width = [0 0 0 0 0];%[7.0 5.0 3.0 1.0];
layers{1}.conscience_coefficient = [1.0 0.5 0.25 0.125 0]; %[0 0 0 0];%
layers{1}.learning_coefficient = [0.06 0.03 0.015 0.0075 0];
layers{1}.learn = true;
layers{1}.random_weights = true;

layers{1}.weights = [0.9746088   0.6969666   0.7322416  -0.6771279   0.2077822   0.9740011  -0.7472979  -0.1011296  -0.5120499  -0.0876053;
   0.7576958   0.4246126  -0.0103857   0.9560081   0.2292163   0.0424290  -0.9320392   0.9851484  -0.9298358   0.5258510;
   0.0054430  -0.9519101   0.2446065   0.5692466   0.8223106   0.5376411   0.7851723   0.6604336  -0.9124292   0.4518019;
  -0.1669319  -0.2452196   0.6550497  -0.7913123   0.7743242  -0.1695199   0.1760520  -0.9078714   0.7994041   0.8622987;
   0.2897925   0.0215888   0.7536550   0.5732586  -0.7247909   0.4500806   0.6996162  -0.4742860   0.7249293   0.9737810;
  -0.0946207  -0.3548946   0.0028543  -0.3367118   0.2768645  -0.0349370  -0.6861820   0.9402029   0.8403788   0.7062659;
  -0.5128018   0.9135842   0.6764165   0.6463172   0.3561881  -0.0219371   0.5133370  -0.0237980  -0.9790999  -0.2907691;
   0.1799878   0.2019274  -0.7217509  -0.2709379   0.7752287  -0.3626045  -0.2701657   0.8085669   0.7448796  -0.8409742;
  -0.2007081  -0.4168636  -0.1575188  -0.3084180   0.7120755   0.0033763  -0.1034657  -0.3460517  -0.1063193   0.7367478;
  ]
layers{1}.epochs = [1 1000 2000 2500 3000];


layers{2}.type='grossberg';
layers{2}.neurons=3;
layers{2}.bias = 0;
layers{2}.rand_min = -1.0;
layers{2}.rand_max = 1.0;
layers{2}.activation_function = @(X) linear(X,1,0);
layers{2}.epochs = [3000 10000 15000 20000];
layers{2}.coeffs = [0.06 0.03 0.02 0.01];%[0.8 0.5 0.3 0.1];
layers{2}.epoch=0;
layers{2}.random_weights = true;
layers{2}.rule = 'delta'; %'widrow';
layers{2}.learn = true;