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function fit_value=fitness(antibody,training_x,training_y)
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% 函數fitness通過輸入一個個體編碼，以及訓練樣本集，就能返回該個體的
% 網絡適應度的值。具體實現的思路是：解碼個體，構建出個體所代表的RBF網絡的結構
% 并利用LS算法構建網絡的輸出層參數，從而得到完整的網絡。然后利用訓練數據計算MG
% 序列的預測誤差。最終生成適應度的值。
%
% 關于輸入參數，當參數為3個時，執行一般的適應度計算操作；當參數為4個時，第
% 4個參數表示固定的寬度，此時執行指定寬度的適應度計算操作。
% 從antibody得到網絡有效節點的中心和寬度。count記錄了控制基因不為零的行。
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count=find(antibody(:,1));
if (size(count,1)==0) 
   fit_value=0;
else 
   centers=antibody(count,2:(size(antibody,2)-1));
   radius=antibody(count,size(antibody,2));
   cou1=find(radius<.01);
   radius(cou1)=.01;
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   % 構建網絡輸出層。這里采用newrb中計算輸出層權值和誤差的方法，調用了
   % radbas和dist兩個函數計算隱層節點的輸出。輸入參量的結構為：
   % training_x: M*N。M為輸入維數，N為樣本個數。
   % training_y: T*N。T為輸出維數，N為樣本個數，這里T=1。
   % centers: S*M。S為中心的個數，M為輸入維數（即中心矢量的維數）。
   % radius: S*1。S為中心的個數。
   %r2為S*N矩陣,其中所有的列向量均為對應的寬度矢量。
   %p1即為隱層節點的輸出，為S*N矩陣。
   r1=1./(radius*sqrt(2));
   r2=r1*ones(1,size(training_x,2));
   p1=radbas(dist(centers,training_x).*r2);
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   %根據p1和training_y計算輸出層權值weights，并計算誤差sse1。
   weights=training_y*pinv(p1);
   result=weights*p1;
   sse1=sumsqr(training_y-result);
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    % 通過輸出的值與樣本中的數值的誤差得到適應度。
   fit_value=1/(sse1);
end
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