return sum(X, 1) GENEPOOL = rand(300, 6) GENEPOOL[GENEPOOL < 0.5] = 0 GENEPOOL[GENEPOOL > 0] = 1 G_List = zeros([GENEPOOL.shape[0], 1]) N = 1000 X_Train = rand(N) * 20 - 10 Y_Train = linfun(X_Train) + rand(X_Train.size) X_Test = rand(100) * 20 - 10 Y_Test = linfun(X_Test) for GEN_I in range(GENEPOOL.shape[0]): print(GENEPOOL[GEN_I, :]) if not sum(GENEPOOL[GEN_I, :]) == 0: Reg = Regressor(GENEPOOL[GEN_I, :]) Reg.learn(X_Train, Y_Train) R = sum((Reg.eval(X_Test) - Y_Test)**2) else: R = 1E16 G_List[GEN_I] = R minind = argmin(G_List) print(G_List[minind]) print(GENEPOOL[minind, :])