基于随机采样获取训练、测试数据示例(Python)

import randomnPoints = 1000#随机获取x列表的数据xPlot = [(float(i)/float(nPoints) - 0.5) for i in range(nPoints + 1)]x = [[s] for s in xPlot]#运行一个随机种子random.seed(1)#在x的基础上进行数据的随机获取，每个数据在0.1范围内随机波动y = [s + numpy.random.normal(scale=0.1) for s in xPlot]#抽样数为总数的0.3倍nSample = int(nPoints * 0.30)# 随机抽样获取测试数据的索引idxTest = random.sample(range(nPoints), nSample)idxTest.sort()# 随机抽样获取训练数据的索引idxTrain = [idx for idx in range(nPoints) if not (idx in idxTest)]# 根据上述得到的索引值获取随机抽样出来的测试和训练具体数据xTrain = [x[r] for r in idxTrain]xTest = [x[r] for r in idxTest]yTrain = [y[r] for r in idxTrain]yTest = [y[r] for r in idxTest]