diff options
| -rw-r--r-- | mllib/src/main/scala/org/apache/spark/ml/classification/NaiveBayes.scala | 8 |
1 files changed, 4 insertions, 4 deletions
diff --git a/mllib/src/main/scala/org/apache/spark/ml/classification/NaiveBayes.scala b/mllib/src/main/scala/org/apache/spark/ml/classification/NaiveBayes.scala index 6775745167..e565a6fd3e 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/classification/NaiveBayes.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/classification/NaiveBayes.scala @@ -176,8 +176,8 @@ class NaiveBayes @Since("1.5.0") ( val numLabels = aggregated.length val numDocuments = aggregated.map(_._2._1).sum - val piArray = Array.fill[Double](numLabels)(0.0) - val thetaArrays = Array.fill[Double](numLabels, numFeatures)(0.0) + val piArray = new Array[Double](numLabels) + val thetaArray = new Array[Double](numLabels * numFeatures) val lambda = $(smoothing) val piLogDenom = math.log(numDocuments + numLabels * lambda) @@ -193,14 +193,14 @@ class NaiveBayes @Since("1.5.0") ( } var j = 0 while (j < numFeatures) { - thetaArrays(i)(j) = math.log(sumTermFreqs(j) + lambda) - thetaLogDenom + thetaArray(i * numFeatures + j) = math.log(sumTermFreqs(j) + lambda) - thetaLogDenom j += 1 } i += 1 } val pi = Vectors.dense(piArray) - val theta = new DenseMatrix(numLabels, thetaArrays(0).length, thetaArrays.flatten, true) + val theta = new DenseMatrix(numLabels, numFeatures, thetaArray, true) new NaiveBayesModel(uid, pi, theta) } |