def init_Population(self):
self.pop = latin(self.pop_size, self.chrom_length, self.min_value, self.max_value)
numy2 = newy[datanum - traindata:, ]
model[0].fit(numx0, numy0)
model[1].fit(numx1, numy1)
model[2].fit(numx2, numy2)
if __name__ == '__main__':
starttime = time.perf_counter()
dimension = 10
fun = fun.ellipsoid
lower_bound = -5.12
upper_bound = 5.12
datanum = 11 * dimension
x = latin(datanum, dimension, lower_bound, upper_bound)
y = fun(x)
model = [0] * 3
traindata = int(datanum / 3)
model[0] = RBFN(input_shape=dimension,
hidden_shape=int(np.sqrt(traindata)),
kernel='gaussian')
model[1] = RBFN(input_shape=dimension,
hidden_shape=int(np.sqrt(traindata)),
kernel='gaussian')
model[2] = RBFN(input_shape=dimension,
hidden_shape=int(np.sqrt(traindata)),
kernel='gaussian')
resetmodel(x, y)
max_iter = 100