如何获取类的概率在星火朴素贝叶斯分类？

问题描述

我在训练火花NaiveBayesModel，但是当我用它来predict一个新的实例，我需要得到每个类的概率。我看了看predict功能的NaiveBayesModel的code和拿出以下code：

I'm training a NaiveBayesModel in Spark, however when I'm using it to predict a new instance I need to get the probabilities for each class. I looked at the code of predict function in NaiveBayesModel and come up with the following code:

val thetaMatrix = new DenseMatrix (model.labels.length,model.theta(0).length,model.theta.flatten,true)
val piVector = new DenseVector(model.pi)
//val prob = thetaMatrix.multiply(test.features)

val x = test.map {p =>
  val prob = thetaMatrix.multiply(p.features)
  BLAS.axpy(1.0, piVector, prob)
  prob
}

正常工作的呢？行 BLAS.axpy（1.0，piVector，概率）一直给我的价值axpy'没有发现错误。

Does this work properly? The line BLAS.axpy(1.0, piVector, prob) keeps giving me an error that the value 'axpy' is not found.

推荐答案

在最近的这被添加到星火干线，将在星火1.5（关闭的）。因此，你可以叫

In a recent pull-request this was added to the Spark trunk and will be released in Spark 1.5 (closing SPARK-4362). you can therefore call

DEF predictProbabilities（TESTDATA：RDD [矢量]）：RDD [矢量]

或

DEF predictProbabilities（TESTDATA：矢量）：矢量

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