Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/3319#discussion_r22010764
--- Diff: mllib/src/main/scala/org/apache/spark/mllib/linalg/Matrices.scala
---
@@ -123,6 +135,97 @@ class DenseMatrix(val numRows: Int, val numCols: Int,
val values: Array[Double])
}
override def copy = new DenseMatrix(numRows, numCols, values.clone())
+
+ private[mllib] def map(f: Double => Double) = new DenseMatrix(numRows,
numCols, values.map(f))
+
+ private[mllib] def update(f: Double => Double): DenseMatrix = {
+ val len = values.length
+ var i = 0
+ while (i < len) {
+ values(i) = f(values(i))
+ i += 1
+ }
+ this
+ }
+}
+
+/**
+ * Factory methods for [[org.apache.spark.mllib.linalg.DenseMatrix]].
+ */
+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): DenseMatrix =
+ new DenseMatrix(numRows, numCols, new Array[Double](numRows * numCols))
+
+ /**
+ * 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): DenseMatrix =
+ 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): DenseMatrix = {
+ val identity = DenseMatrix.zeros(n, n)
+ var i = 0
+ while (i < n) {
+ identity.update(i, i, 1.0)
+ i += 1
+ }
+ 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
+ * @param rng a random number generator
+ * @return `DenseMatrix` with size `numRows` x `numCols` and values in
U(0, 1)
+ */
+ def rand(numRows: Int, numCols: Int, rng: Random): DenseMatrix = {
+ new DenseMatrix(numRows, numCols, Array.fill(numRows *
numCols)(rng.nextDouble()))
+ }
+
+ /**
+ * Generate a `DenseMatrix` consisting of i.i.d. gaussian random numbers.
+ * @param numRows number of rows of the matrix
+ * @param numCols number of columns of the matrix
+ * @param rng a random number generator
+ * @return `DenseMatrix` with size `numRows` x `numCols` and values in
N(0, 1)
+ */
+ def randn(numRows: Int, numCols: Int, rng: Random): DenseMatrix = {
+ new DenseMatrix(numRows, numCols, Array.fill(numRows *
numCols)(rng.nextGaussian()))
+ }
+
+ /**
+ * Generate a diagonal matrix in `DenseMatrix` format from the supplied
values.
+ * @param vector a `Vector` that will form the values on the diagonal of
the matrix
+ * @return Square `DenseMatrix` with size `values.length` x
`values.length` and `values`
+ * on the diagonal
+ */
+ def diag(vector: Vector): DenseMatrix = {
+ val n = vector.size
+ val matrix = DenseMatrix.eye(n)
--- End diff --
`eye(n)` -> `zeros(n, n)`
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