Github user jkbradley commented on a diff in the pull request:
https://github.com/apache/spark/pull/2451#discussion_r17803218
--- Diff: mllib/src/main/scala/org/apache/spark/mllib/linalg/Matrices.scala
---
@@ -57,13 +250,709 @@ trait Matrix extends Serializable {
* @param numCols number of columns
* @param values matrix entries in column major
*/
-class DenseMatrix(val numRows: Int, val numCols: Int, val values:
Array[Double]) extends Matrix {
+class DenseMatrix(val numRows: Int, val numCols: Int, val values:
Array[Double]) extends Matrix with Serializable {
- require(values.length == numRows * numCols)
+ require(values.length == numRows * numCols, "The number of values
supplied doesn't match the " +
+ s"size of the matrix! values.length: ${values.length}, numRows *
numCols: ${numRows * numCols}")
override def toArray: Array[Double] = values
- private[mllib] override def toBreeze: BM[Double] = new
BDM[Double](numRows, numCols, values)
+ private[mllib] def toBreeze: BM[Double] = new BDM[Double](numRows,
numCols, values)
+
+ private[mllib] def apply(i: Int): Double = values(i)
+
+ private[mllib] def apply(i: Int, j: Int): Double = values(index(i, j))
+
+ private[mllib] def index(i: Int, j: Int): Int = i + numRows * j
+
+ private[mllib] def update(i: Int, j: Int, v: Double): Unit = {
+ values(index(i, j)) = v
+ }
+
+ override def copy = new DenseMatrix(numRows, numCols, values.clone())
+
+ private[mllib] def elementWiseOperateOnColumnsInPlace(
+ f: (Double, Double) => Double,
+ y: Matrix): DenseMatrix = {
+ val y_vals = y.toArray
+ val len = y_vals.length
+ require(y_vals.length == numRows)
+ var j = 0
+ while (j < numCols){
+ var i = 0
+ while (i < len){
+ val idx = index(i, j)
+ values(idx) = f(values(idx), y_vals(i))
+ i += 1
+ }
+ j += 1
+ }
+ this
+ }
+
+ private[mllib] def elementWiseOperateOnRowsInPlace(
+ f: (Double, Double) => Double,
+ y: Matrix): DenseMatrix = {
+ val y_vals = y.toArray
+ require(y_vals.length == numCols)
+ var j = 0
+ while (j < numCols){
+ var i = 0
+ while (i < numRows){
+ val idx = index(i, j)
+ values(idx) = f(values(idx), y_vals(j))
+ i += 1
+ }
+ j += 1
+ }
+ this
+ }
+
+ private[mllib] def elementWiseOperateInPlace(f: (Double, Double) =>
Double, y: Matrix): DenseMatrix = {
+ val y_val = y.toArray
+ val len = values.length
+ require(y_val.length == values.length)
+ var j = 0
+ while (j < len){
+ values(j) = f(values(j), y_val(j))
+ j += 1
+ }
+ this
+ }
+
+ private[mllib] def elementWiseOperateScalarInPlace(f: (Double, Double)
=> Double, y: Double): DenseMatrix = {
+ var j = 0
+ val len = values.length
+ while (j < len){
+ values(j) = f(values(j), y)
+ j += 1
+ }
+ this
+ }
+
+ private[mllib] def operateInPlace(f: (Double, Double) => Double, y:
Matrix): DenseMatrix = {
+ if (y.numCols==1 || y.numRows == 1){
+ require(numCols != numRows, "Operation is ambiguous. Please use
elementWiseOperateOnRows " +
+ "or elementWiseOperateOnColumns instead")
+ }
+ if (y.numCols == 1 && y.numRows == 1){
+ elementWiseOperateScalarInPlace(f, y.toArray(0))
+ } else {
+ if (y.numCols==1) {
+ elementWiseOperateOnColumnsInPlace(f, y)
+ }else if (y.numRows==1){
+ elementWiseOperateOnRowsInPlace(f, y)
+ }else{
+ elementWiseOperateInPlace(f, y)
+ }
+ }
+ }
+
+ private[mllib] def elementWiseOperateOnColumns(f: (Double, Double) =>
Double, y: Matrix): DenseMatrix = {
+ val dup = this.copy
+ dup.elementWiseOperateOnColumnsInPlace(f, y)
+ }
+
+ private[mllib] def elementWiseOperateOnRows(f: (Double, Double) =>
Double, y: Matrix): DenseMatrix = {
+ val dup = this.copy
+ dup.elementWiseOperateOnRowsInPlace(f, y)
+ }
+
+ private[mllib] def elementWiseOperate(f: (Double, Double) => Double, y:
Matrix): DenseMatrix = {
+ val dup = this.copy
+ dup.elementWiseOperateInPlace(f, y)
+ }
+
+ private[mllib] def elementWiseOperateScalar(f: (Double, Double) =>
Double, y: Double): DenseMatrix = {
+ val dup = this.copy
+ dup.elementWiseOperateScalarInPlace(f, y)
+ }
+
+ private[mllib] def operate(f: (Double, Double) => Double, y: Matrix):
DenseMatrix = {
+ val dup = this.copy
+ dup.operateInPlace(f, y)
+ }
+
+ def map(f: Double => Double) = new DenseMatrix(numRows, numCols,
values.map(f))
+
+ def update(f: Double => Double): DenseMatrix = {
+ val len = values.length
+ var i = 0
+ while (i < len) {
+ values(i) = f(values(i))
+ i += 1
+ }
+ this
+ }
+
+ def colNorms(p: Double): DenseMatrix = {
+ if (p==1.0) return colSums(true)
+ val sums = new DenseMatrix(1, numCols, Array.fill(numCols)(0.0))
+ var j = 0
+ while (j < numCols){
+ var i = 0
+ while (i < numRows){
+ val idx = index(i, j)
+ sums.update(0,j, sums(j) + math.pow(values(idx),p))
+ i += 1
+ }
+ j += 1
+ }
+ j = 0
+ while (j < numCols){
+ sums.update(0, j, math.pow(sums(j), 1/p))
--- End diff --
"1/p" --> "1 / p"
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