AttributeError: 'SupervisedDataSet' object has no attribute '_convertToOneOfMany'

最新推荐文章于 2024-11-03 08:45:00 发布

Dillon2015

最新推荐文章于 2024-11-03 08:45:00 发布

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分类专栏： python 机器学习 pybrain 文章标签： pybrain python 神经网络

本文链接：https://blog.youkuaiyun.com/Dillon2015/article/details/61432501

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本文解决了使用PyBrain构建神经网络时出现的错误，详细介绍了如何处理由splitWithProportion方法导致的数据集类型变化问题。

使用pybrain构造神经网络，在执行官网代码时出错：

means = [(-1,0),(2,4),(3,1)]
cov = [diag([1,1]), diag([0.5,1.2]), diag([1.5,0.7])]
alldata = ClassificationDataSet(2, 1, nb_classes=3)
for n in xrange(400):
    for klass in range(3):
        input = multivariate_normal(means[klass],cov[klass])
        alldata.addSample(input, [klass])

tstdata, trndata = alldata.splitWithProportion( 0.25 )
trndata._convertToOneOfMany( )
tstdata._convertToOneOfMany( )

报错：

AttributeError: 'SupervisedDataSet' object has no attribute '_convertToOneOfMany'

在代码中alldata被定义成ClassificationDataSet,官网查看后发现这个类确实有_convertToOneOfMany()方法。

在https://github.com/pybrain/pybrain/commit/2f02b8d9e4e9d6edbc135a355ab387048a00f1af中找到原因如下：

Now splitWithProporion uses numpy array indicies with numpy.random.permutation instead of for loop, before this change on large datasets this method was very slow, now its finish almost instant.

This commit breaks polymorphism: When called on an ClassificationDataSet (as shown in the tutorials) it no longer returns ClassificationDataSets but SupervisedDataSets.

执行

splitWithProporion后alldata返回的是SupervisedDataSets而不是ClassificationDataSet，而SupervisedDataSets没有_convertToOneOfMany方法。

解决办法：

http://stackoverflow.com/questions/27887936/attributeerror-using-pybrain-splitwithportion-object-type-changed/30869317#30869317

将上面代码改为：

tstdata_temp, trndata_temp = alldata.splitWithProportion(0.25)

tstdata = ClassificationDataSet(2, 1, nb_classes=3)
for n in xrange(0, tstdata_temp.getLength()):
    tstdata.addSample( tstdata_temp.getSample(n)[0], tstdata_temp.getSample(n)[1] )

trndata = ClassificationDataSet(2, 1, nb_classes=3)
for n in xrange(0, trndata_temp.getLength()):
    trndata.addSample( trndata_temp.getSample(n)[0], trndata_temp.getSample(n)[1] )