Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/2294#discussion_r17255317
--- Diff: mllib/src/main/scala/org/apache/spark/mllib/linalg/Matrices.scala
---
@@ -63,7 +88,162 @@ class DenseMatrix(val numRows: Int, val numCols: Int,
val values: Array[Double])
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(r: Int, c: Int): Double = values(index(r, c))
+
+ private [mllib] def index(r: Int, c: Int): Int = r + numRows * c
+
+ private [mllib] def update(r: Int, c: Int, v: Double){
+ values(index(r, c)) = v
+ }
+
+ def copy = new DenseMatrix(numRows, numCols, values.clone())
+}
+
+/**
+ * Factory methods for [[org.apache.spark.mllib.linalg.DenseMatrix]].
+ *
+ * These methods can be used to generate common matrix types such as the
Identity matrix, any
+ * diagonal matrix, zero matrix, and random matrices.
+ */
+object DenseMatrix {
+
+ /**
+ * Generate a `DenseMatrix` consisting of zeros.
+ * @param numRows number of rows of the matrix
+ * @param numCols number of columns of the matrix
+ * @return `DenseMatrix` with size `numRows` x `numCols` and values of
zeros
+ */
+ def zeros(numRows: Int, numCols: Int) =
+ new DenseMatrix(numRows, numCols, Array.fill(numRows * numCols)(0.0))
+
+ /**
+ * Generate a `DenseMatrix` consisting of ones.
+ * @param numRows number of rows of the matrix
+ * @param numCols number of columns of the matrix
+ * @return `DenseMatrix` with size `numRows` x `numCols` and values of
ones
+ */
+ def ones(numRows: Int, numCols: Int) =
+ new DenseMatrix(numRows, numCols, Array.fill(numRows * numCols)(1.0))
+
+ /**
+ * Generate an Identity Matrix in `DenseMatrix` format.
+ * @param n number of rows and columns of the matrix
+ * @return `DenseMatrix` with size `n` x `n` and values of ones on the
diagonal
+ */
+ def eye(n: Int) = {
+ val identity = DenseMatrix.zeros(n,n)
+ for (i <- 0 until n){
+ identity.update(i, i, 1.0)
+ }
+ identity
+ }
+
+ /**
+ * Generate a `DenseMatrix` consisting of i.i.d. uniform random numbers.
+ * @param numRows number of rows of the matrix
+ * @param numCols number of columns of the matrix
+ * @return `DenseMatrix` with size `numRows` x `numCols` and values in
U(0, 1)
+ */
+ def rand(numRows: Int, numCols: Int) = {
+ val rand = new scala.util.Random
--- End diff --
use `XORShiftRandom` for speed
---
If your project is set up for it, you can reply to this email and have your
reply appear on GitHub as well. If your project does not have this feature
enabled and wishes so, or if the feature is enabled but not working, please
contact infrastructure at infrastruct...@apache.org or file a JIRA ticket
with INFRA.
---
---------------------------------------------------------------------
To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org
For additional commands, e-mail: reviews-h...@spark.apache.org
