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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