Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/3319#discussion_r22010793
--- Diff: mllib/src/main/scala/org/apache/spark/mllib/linalg/Matrices.scala
---
@@ -256,72 +524,297 @@ object Matrices {
* 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
+ * @return `Matrix` with size `numRows` x `numCols` and values of zeros
*/
- def zeros(numRows: Int, numCols: Int): Matrix =
- new DenseMatrix(numRows, numCols, new Array[Double](numRows * numCols))
+ def zeros(numRows: Int, numCols: Int): Matrix =
DenseMatrix.zeros(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
+ * @return `Matrix` with size `numRows` x `numCols` and values of ones
*/
- def ones(numRows: Int, numCols: Int): Matrix =
- new DenseMatrix(numRows, numCols, Array.fill(numRows * numCols)(1.0))
+ def ones(numRows: Int, numCols: Int): Matrix = DenseMatrix.ones(numRows,
numCols)
/**
- * Generate an Identity Matrix in `DenseMatrix` format.
+ * Generate a dense Identity Matrix in `Matrix` 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
+ * @return `Matrix` with size `n` x `n` and values of ones on the
diagonal
*/
- def eye(n: Int): Matrix = {
- val identity = Matrices.zeros(n, n)
- var i = 0
- while (i < n){
- identity.update(i, i, 1.0)
- i += 1
- }
- identity
- }
+ def eye(n: Int): Matrix = DenseMatrix.eye(n)
+
+ /**
+ * Generate a sparse Identity Matrix in `Matrix` format.
+ * @param n number of rows and columns of the matrix
+ * @return `Matrix` with size `n` x `n` and values of ones on the
diagonal
+ */
+ def speye(n: Int): Matrix = SparseMatrix.speye(n)
/**
* 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)
+ * @return `Matrix` with size `numRows` x `numCols` and values in U(0, 1)
*/
- def rand(numRows: Int, numCols: Int, rng: Random): Matrix = {
- new DenseMatrix(numRows, numCols, Array.fill(numRows *
numCols)(rng.nextDouble()))
- }
+ def rand(numRows: Int, numCols: Int, rng: Random): Matrix =
+ DenseMatrix.rand(numRows, numCols, rng)
+
+ /**
+ * Generate a `SparseMatrix` 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 density the desired density for the matrix
+ * @param rng a random number generator
+ * @return `Matrix` with size `numRows` x `numCols` and values in U(0, 1)
+ */
+ def sprand(numRows: Int, numCols: Int, density: Double, rng: Random):
Matrix =
+ SparseMatrix.sprand(numRows, numCols, density, rng)
/**
* 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)
+ * @return `Matrix` with size `numRows` x `numCols` and values in N(0, 1)
*/
- def randn(numRows: Int, numCols: Int, rng: Random): Matrix = {
- new DenseMatrix(numRows, numCols, Array.fill(numRows *
numCols)(rng.nextGaussian()))
- }
+ def randn(numRows: Int, numCols: Int, rng: Random): Matrix =
+ DenseMatrix.randn(numRows, numCols, rng)
+
+ /**
+ * Generate a `SparseMatrix` 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 density the desired density for the matrix
+ * @param rng a random number generator
+ * @return `Matrix` with size `numRows` x `numCols` and values in N(0, 1)
+ */
+ def sprandn(numRows: Int, numCols: Int, density: Double, rng: Random):
Matrix =
+ SparseMatrix.sprandn(numRows, numCols, density, rng)
/**
* Generate a diagonal matrix in `DenseMatrix` format from the supplied
values.
* @param vector a `Vector` tat will form the values on the diagonal of
the matrix
- * @return Square `DenseMatrix` with size `values.length` x
`values.length` and `values`
+ * @return Square `Matrix` with size `values.length` x `values.length`
and `values`
* on the diagonal
*/
- def diag(vector: Vector): Matrix = {
- val n = vector.size
- val matrix = Matrices.eye(n)
- val values = vector.toArray
- var i = 0
- while (i < n) {
- matrix.update(i, i, values(i))
- i += 1
+ def diag(vector: Vector): Matrix = DenseMatrix.diag(vector)
+
+ /**
+ * Horizontally concatenate a sequence of matrices. The returned matrix
will be in the format
+ * the matrices are supplied in. Supplying a mix of dense and sparse
matrices will result in
+ * a sparse matrix.
+ * @param matrices array of matrices
+ * @return a single `Matrix` composed of the matrices that were
horizontally concatenated
+ */
+ def horzcat(matrices: Array[Matrix]): Matrix = {
--- End diff --
What if the input is an empty array?
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