Github user rnowling commented on a diff in the pull request:
https://github.com/apache/spark/pull/4087#discussion_r23142579
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/classification/NaiveBayes.scala ---
@@ -32,28 +42,42 @@ import org.apache.spark.rdd.RDD
* @param pi log of class priors, whose dimension is C, number of labels
* @param theta log of class conditional probabilities, whose dimension is
C-by-D,
* where D is number of features
+ * @param model The type of NB model to fit from the enumeration
NaiveBayesModels, can be
+ * Multinomial or Bernoulli
*/
+
class NaiveBayesModel private[mllib] (
val labels: Array[Double],
val pi: Array[Double],
- val theta: Array[Array[Double]]) extends ClassificationModel with
Serializable {
-
- private val brzPi = new BDV[Double](pi)
- private val brzTheta = new BDM[Double](theta.length, theta(0).length)
+ val theta: Array[Array[Double]],
+ val model: NaiveBayesModels) extends ClassificationModel with
Serializable {
- {
- // Need to put an extra pair of braces to prevent Scala treating `i`
as a member.
+ def populateMatrix(arrayIn: Array[Array[Double]],
+ matrixIn: BDM[Double],
+ transformation: (Double) => Double = (x) => x) = {
var i = 0
- while (i < theta.length) {
+ while (i < arrayIn.length) {
var j = 0
- while (j < theta(i).length) {
- brzTheta(i, j) = theta(i)(j)
+ while (j < arrayIn(i).length) {
+ matrixIn(i, j) = transformation(theta(i)(j))
j += 1
}
i += 1
}
}
+ private val brzPi = new BDV[Double](pi)
+ private val brzTheta = new BDM[Double](theta.length, theta(0).length)
+ populateMatrix(theta, brzTheta)
+
+ private val brzNegTheta: Option[BDM[Double]] = model match {
--- End diff --
Why use an Option if this method is only called for Bernoulli anyway?
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