nit

Author: zhengruifeng

mllib/src/main/scala/org/apache/spark/ml/clustering/GaussianMixture.scala

@@ -819,7 +819,7 @@ private class BlockExpectationAggregator(
   @transient private lazy val newMeansMat = DenseMatrix.zeros(numFeatures, k)
   @transient private lazy val newCovsMat = DenseMatrix.zeros(covSize, k)
   @transient private lazy val auxiliaryProbMat = DenseMatrix.zeros(blockSize, k)
-  @transient private lazy val auxiliaryMat = DenseMatrix.zeros(blockSize, numFeatures)
+  @transient private lazy val auxiliaryPDFMat = DenseMatrix.zeros(blockSize, numFeatures)
   @transient private lazy val auxiliaryCovVec = Vectors.zeros(covSize).toDense
 
   @transient private lazy val gaussians = {
@@ -852,20 +852,36 @@ private class BlockExpectationAggregator(
     val size = matrix.numRows
     require(weights.length == size)
 
+    val blas1 = BLAS.getBLAS(size)
+    val blas2 = BLAS.getBLAS(k)
+
     val probMat = if (blockSize == size) auxiliaryProbMat else DenseMatrix.zeros(size, k)
     require(!probMat.isTransposed)
     java.util.Arrays.fill(probMat.values, EPSILON)
 
-    val mat = if (blockSize == size) auxiliaryMat else DenseMatrix.zeros(size, numFeatures)
+    val pdfMat = if (blockSize == size) auxiliaryPDFMat else DenseMatrix.zeros(size, numFeatures)
+    val probSumVec = Vectors.zeros(size).toDense
     var j = 0
-    val blas1 = BLAS.getBLAS(size)
     while (j < k) {
-      val pdfVec = gaussians(j).pdf(matrix, mat)
-      blas1.daxpy(size, bcWeights.value(j), pdfVec.values, 0, 1,
-        probMat.values, j * size, 1)
+      val pdfVec = gaussians(j).pdf(matrix, pdfMat)
+      val w = bcWeights.value(j)
+      blas1.daxpy(size, w, pdfVec.values, 1, probSumVec.values, 1)
+      blas1.daxpy(size, w, pdfVec.values, 0, 1, probMat.values, j * size, 1)
       j += 1
     }
 
+    var i = 0
+    while (i < size) {
+      val probSum = probSumVec(i)
+      val weight = weights(i)
+      blas2.dscal(k, weight / probSum, probMat.values, i, size)
+      blas2.daxpy(k, 1.0, probMat.values, i, size, newWeights, 0, 1)
+      newLogLikelihood += math.log(probSum) * weight
+      i += 1
+    }
+
+    BLAS.gemm(1.0, matrix.transpose, probMat, 1.0, newMeansMat)
+
     // compute the cov vector for each row vector
     val covVec = auxiliaryCovVec
     val covVecIter = matrix match {
@@ -886,18 +902,11 @@ private class BlockExpectationAggregator(
         }
     }
 
-    val blas2 = BLAS.getBLAS(k)
-    covVecIter.zip(weights.iterator).zipWithIndex.foreach {
-      case ((covVec, weight), i) =>
-        val probSum = blas2.dasum(k, probMat.values, i, size)
-        blas2.dscal(k, weight / probSum, probMat.values, i, size)
-        blas2.daxpy(k, 1.0, probMat.values, i, size, newWeights, 0, 1)
-        BLAS.nativeBLAS.dger(covSize, k, 1.0, covVec.values, 0, 1,
-          probMat.values, i, size, newCovsMat.values, 0, covSize)
-        newLogLikelihood += math.log(probSum) * weight
+    covVecIter.zipWithIndex.foreach { case (covVec, i) =>
+      BLAS.nativeBLAS.dger(covSize, k, 1.0, covVec.values, 0, 1,
+        probMat.values, i, size, newCovsMat.values, 0, covSize)
     }
 
-    BLAS.gemm(1.0, matrix.transpose, probMat, 1.0, newMeansMat)
     totalCnt += size
 
     this