Github user jkbradley commented on a diff in the pull request:
https://github.com/apache/spark/pull/2451#discussion_r17808020
--- Diff: mllib/src/main/scala/org/apache/spark/mllib/linalg/Matrices.scala
---
@@ -37,11 +44,197 @@ trait Matrix extends Serializable {
private[mllib] def toBreeze: BM[Double]
/** Gets the (i, j)-th element. */
- private[mllib] def apply(i: Int, j: Int): Double = toBreeze(i, j)
+ private[mllib] def apply(i: Int, j: Int): Double
+
+ /** Return the index for the (i, j)-th element in the backing array. */
+ private[mllib] def index(i: Int, j: Int): Int
+
+ /** Update element at (i, j) */
+ private[mllib] def update(i: Int, j: Int, v: Double): Unit
+
+ /** Get a deep copy of the matrix. */
+ def copy: Matrix
+ /** Convenience method for `Matrix`-`Matrix` multiplication.
+ * Note: `SparseMatrix`-`SparseMatrix` multiplication is not supported
*/
+ def multiply(y: Matrix): DenseMatrix = {
+ val C: DenseMatrix = DenseMatrix.zeros(numRows, y.numCols)
+ BLAS.gemm(false, false, 1.0, this, y, 0.0, C)
+ C
+ }
+
+ /** Convenience method for `Matrix`-`DenseVector` multiplication. */
+ def multiply(y: DenseVector): DenseVector = {
+ val output = new DenseVector(new Array[Double](numRows))
+ BLAS.gemv(1.0, this, y, 0.0, output)
+ output
+ }
+
+ /** Convenience method for `Matrix`^T^-`Matrix` multiplication.
+ * Note: `SparseMatrix`-`SparseMatrix` multiplication is not supported
*/
+ def transposeMultiply(y: Matrix): DenseMatrix = {
+ val C: DenseMatrix = DenseMatrix.zeros(numCols, y.numCols)
+ BLAS.gemm(true, false, 1.0, this, y, 0.0, C)
+ C
+ }
+
+ /** Convenience method for `Matrix`^T^-`DenseVector` multiplication. */
+ def transposeMultiply(y: DenseVector): DenseVector = {
+ val output = new DenseVector(new Array[Double](numCols))
+ BLAS.gemv(true, 1.0, this, y, 0.0, output)
+ output
+ }
+
+ /** A human readable representation of the matrix */
override def toString: String = toBreeze.toString()
+
+ private[mllib] def map(f: Double => Double): Matrix
+
+ private[mllib] def update(f: Double => Double): Matrix
+
+ private[mllib] def elementWiseOperateOnColumnsInPlace(f: (Double,
Double) => Double,
+ y: Matrix): Matrix
+
+ private[mllib] def elementWiseOperateOnRowsInPlace(f: (Double, Double)
=> Double,
+ y: Matrix): Matrix
+
+ private[mllib] def elementWiseOperateInPlace(f: (Double, Double) =>
Double, y: Matrix): Matrix
+
+ private[mllib] def elementWiseOperateScalarInPlace(f: (Double, Double)
=> Double,
+ y: Double): Matrix
+
+ private[mllib] def operateInPlace(f: (Double, Double) => Double, y:
Matrix): Matrix
+
+ private[mllib] def elementWiseOperateOnColumns(f: (Double, Double) =>
Double, y: Matrix): Matrix
+
+ private[mllib] def elementWiseOperateOnRows(f: (Double, Double) =>
Double, y: Matrix): Matrix
+
+ private[mllib] def elementWiseOperate(f: (Double, Double) => Double, y:
Matrix): Matrix
+
+ private[mllib] def elementWiseOperateScalar(f: (Double, Double) =>
Double, y: Double): Matrix
+
+ private[mllib] def operate(f: (Double, Double) => Double, y: Matrix):
Matrix
+
+ private[mllib] def *=(y: Matrix) = operateInPlace(_ * _, y)
+
+ private[mllib] def *(y: Matrix) = operate(_ * _, y)
+
+ private[mllib] def +=(y: Matrix) = operateInPlace(_ + _, y)
+
+ private[mllib] def +(y: Matrix) = operate(_ + _, y)
+
+ private[mllib] def -=(y: Matrix) = operateInPlace(_ - _, y)
+
+ private[mllib] def -(y: Matrix) = operate(_ - _, y)
+
+ private[mllib] def /=(y: Matrix) = operateInPlace(_ / _, y)
+
+ private[mllib] def /(y: Matrix) = operate(_ / _, y)
+
+ private[mllib] def *=(y: Double) = elementWiseOperateScalarInPlace(_ *
_, y)
+
+ private[mllib] def +=(y: Double) = elementWiseOperateScalarInPlace(_ +
_, y)
+
+ private[mllib] def -=(y: Double) = elementWiseOperateScalarInPlace(_ -
_, y)
+
+ private[mllib] def /=(y: Double) = elementWiseOperateScalarInPlace(_ /
_, y)
+
+ private[mllib] def *(y: Double) = elementWiseOperateScalar(_ * _, y)
+
+ private[mllib] def +(y: Double) = elementWiseOperateScalar(_ + _, y)
+
+ private[mllib] def -(y: Double) = elementWiseOperateScalar(_ - _, y)
+
+ private[mllib] def /(y: Double) = elementWiseOperateScalar(_ / _, y)
+
+ private[mllib] def neg: Matrix
+
+ private[mllib] def negInPlace: Matrix
+
+ /** Less-than-or-equal-to check. Outputs binary `DenseMatrix` */
+ private[mllib] def compare(v: Double, f: (Double, Double) => Boolean):
DenseMatrix
+
+ /** Returns the p-th norm for each column */
+ private[mllib] def colNorms(p: Double): Matrix
+
+ private[mllib] def colSums: DenseMatrix = colSums(false)
+
+ private[mllib] def colSums(absolute: Boolean, skipRows: DenseMatrix =
null): DenseMatrix = {
+ val sums = new DenseMatrix(1, numCols, Array.fill(numCols)(0.0))
+ var j = 0
+ this match {
+ case sparse: SparseMatrix =>
+ while (j < numCols){
+ var i = sparse.colPtrs(j)
+ val indEnd = sparse.colPtrs(j + 1)
+ while (i < indEnd){
+ var v = sparse.values(i)
+ if (absolute) v = math.abs(v)
+ sums.values(j) += v
+ i += 1
+ }
+ j += 1
+ }
+ case dense: DenseMatrix =>
+ while (j < numCols){
+ var i = 0
+ while (i < numRows){
+ if (skipRows == null) {
+ var v = dense.values(index(i, j))
--- End diff --
This line is a good reason to implement this in DenseMatrix: You could
avoid the expensive index (multiplication), and just iterate through counts.
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