misfit[i] = f(data[i, :])
title('neighbourhood algoritthm with ns=' + str(ns) + ' nr=' + str(nr))
# scatter(data[:,x_idx],data[:,y_idx])
# show()
# exit()
#选择nr个样本点，生成ns个样本
datasum = data.copy()
for itr in xrange(1, n_itr + 1):
print('iteration number is ' + str(itr))
if itr != 1:
data = oridata.copy()
for i in xrange(size(data, 0)):
misfit[i] = f(data[i, :])
#在前一次迭代生成的ns个样本中选择misfit最小的nr个
bdcdatasum = sc.broadcast(datasum)
chdata = sort(misfit)
chindex = argsort(misfit)
oridata = []
print('data: ', str(data[chindex[0], :]))
print('misfit is ', str(chdata[0]))
par_data = data[chindex[:nr, :]].copy() #test
par_value = sc.parallelise(
par_data, par_num).flatmap(lambda input_data: findData(
input_data, bdcdatasum, ns, nr, n_par, lowb, upperb))
oridata = asarry(par_value.collect()) #must test
datasum = asarray(list(datasum) + list(oridata)) #生成的总样本数
scatter(datasum[:, x_idx], datasum[:, y_idx])
show()
