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, :])
