diff options
Diffstat (limited to 'mllib-local/src/main/scala/org/apache/spark/ml/linalg/Matrices.scala')
| -rw-r--r-- | mllib-local/src/main/scala/org/apache/spark/ml/linalg/Matrices.scala | 71 |
1 files changed, 61 insertions, 10 deletions
diff --git a/mllib-local/src/main/scala/org/apache/spark/ml/linalg/Matrices.scala b/mllib-local/src/main/scala/org/apache/spark/ml/linalg/Matrices.scala index 8204b5af02..a47526d36f 100644 --- a/mllib-local/src/main/scala/org/apache/spark/ml/linalg/Matrices.scala +++ b/mllib-local/src/main/scala/org/apache/spark/ml/linalg/Matrices.scala @@ -24,21 +24,28 @@ import scala.collection.mutable.{ArrayBuffer, ArrayBuilder => MArrayBuilder, Has import breeze.linalg.{CSCMatrix => BSM, DenseMatrix => BDM, Matrix => BM} import com.github.fommil.netlib.BLAS.{getInstance => blas} +import org.apache.spark.annotation.Since + /** * Trait for a local matrix. */ +@Since("2.0.0") sealed trait Matrix extends Serializable { /** Number of rows. */ + @Since("2.0.0") def numRows: Int /** Number of columns. */ + @Since("2.0.0") def numCols: Int /** Flag that keeps track whether the matrix is transposed or not. False by default. */ + @Since("2.0.0") val isTransposed: Boolean = false /** Converts to a dense array in column major. */ + @Since("2.0.0") def toArray: Array[Double] = { val newArray = new Array[Double](numRows * numCols) foreachActive { (i, j, v) => @@ -51,18 +58,21 @@ sealed trait Matrix extends Serializable { * Returns an iterator of column vectors. * This operation could be expensive, depending on the underlying storage. */ + @Since("2.0.0") def colIter: Iterator[Vector] /** * Returns an iterator of row vectors. * This operation could be expensive, depending on the underlying storage. */ + @Since("2.0.0") def rowIter: Iterator[Vector] = this.transpose.colIter /** Converts to a breeze matrix. */ private[ml] def toBreeze: BM[Double] /** Gets the (i, j)-th element. */ + @Since("2.0.0") def apply(i: Int, j: Int): Double /** Return the index for the (i, j)-th element in the backing array. */ @@ -72,12 +82,15 @@ sealed trait Matrix extends Serializable { private[ml] def update(i: Int, j: Int, v: Double): Unit /** Get a deep copy of the matrix. */ + @Since("2.0.0") def copy: Matrix /** Transpose the Matrix. Returns a new `Matrix` instance sharing the same underlying data. */ + @Since("2.0.0") def transpose: Matrix /** Convenience method for `Matrix`-`DenseMatrix` multiplication. */ + @Since("2.0.0") def multiply(y: DenseMatrix): DenseMatrix = { val C: DenseMatrix = DenseMatrix.zeros(numRows, y.numCols) BLAS.gemm(1.0, this, y, 0.0, C) @@ -85,11 +98,13 @@ sealed trait Matrix extends Serializable { } /** Convenience method for `Matrix`-`DenseVector` multiplication. For binary compatibility. */ + @Since("2.0.0") def multiply(y: DenseVector): DenseVector = { multiply(y.asInstanceOf[Vector]) } /** Convenience method for `Matrix`-`Vector` multiplication. */ + @Since("2.0.0") def multiply(y: Vector): DenseVector = { val output = new DenseVector(new Array[Double](numRows)) BLAS.gemv(1.0, this, y, 0.0, output) @@ -100,6 +115,7 @@ sealed trait Matrix extends Serializable { override def toString: String = toBreeze.toString() /** A human readable representation of the matrix with maximum lines and width */ + @Since("2.0.0") def toString(maxLines: Int, maxLineWidth: Int): String = toBreeze.toString(maxLines, maxLineWidth) /** @@ -129,11 +145,13 @@ sealed trait Matrix extends Serializable { /** * Find the number of non-zero active values. */ + @Since("2.0.0") def numNonzeros: Int /** * Find the number of values stored explicitly. These values can be zero as well. */ + @Since("2.0.0") def numActives: Int } @@ -154,10 +172,11 @@ sealed trait Matrix extends Serializable { * @param isTransposed whether the matrix is transposed. If true, `values` stores the matrix in * row major. */ -class DenseMatrix ( - val numRows: Int, - val numCols: Int, - val values: Array[Double], +@Since("2.0.0") +class DenseMatrix @Since("2.0.0") ( + @Since("2.0.0") val numRows: Int, + @Since("2.0.0") val numCols: Int, + @Since("2.0.0") val values: Array[Double], override val isTransposed: Boolean) extends Matrix { require(values.length == numRows * numCols, "The number of values supplied doesn't match the " + @@ -178,6 +197,7 @@ class DenseMatrix ( * @param numCols number of columns * @param values matrix entries in column major */ + @Since("2.0.0") def this(numRows: Int, numCols: Int, values: Array[Double]) = this(numRows, numCols, values, false) @@ -266,6 +286,7 @@ class DenseMatrix ( * Generate a `SparseMatrix` from the given `DenseMatrix`. The new matrix will have isTransposed * set to false. */ + @Since("2.0.0") def toSparse: SparseMatrix = { val spVals: MArrayBuilder[Double] = new MArrayBuilder.ofDouble val colPtrs: Array[Int] = new Array[Int](numCols + 1) @@ -307,6 +328,7 @@ class DenseMatrix ( /** * Factory methods for [[org.apache.spark.ml.linalg.DenseMatrix]]. */ +@Since("2.0.0") object DenseMatrix { /** @@ -315,6 +337,7 @@ object DenseMatrix { * @param numCols number of columns of the matrix * @return `DenseMatrix` with size `numRows` x `numCols` and values of zeros */ + @Since("2.0.0") def zeros(numRows: Int, numCols: Int): DenseMatrix = { require(numRows.toLong * numCols <= Int.MaxValue, s"$numRows x $numCols dense matrix is too large to allocate") @@ -327,6 +350,7 @@ object DenseMatrix { * @param numCols number of columns of the matrix * @return `DenseMatrix` with size `numRows` x `numCols` and values of ones */ + @Since("2.0.0") def ones(numRows: Int, numCols: Int): DenseMatrix = { require(numRows.toLong * numCols <= Int.MaxValue, s"$numRows x $numCols dense matrix is too large to allocate") @@ -338,6 +362,7 @@ object DenseMatrix { * @param n number of rows and columns of the matrix * @return `DenseMatrix` with size `n` x `n` and values of ones on the diagonal */ + @Since("2.0.0") def eye(n: Int): DenseMatrix = { val identity = DenseMatrix.zeros(n, n) var i = 0 @@ -355,6 +380,7 @@ object DenseMatrix { * @param rng a random number generator * @return `DenseMatrix` with size `numRows` x `numCols` and values in U(0, 1) */ + @Since("2.0.0") def rand(numRows: Int, numCols: Int, rng: Random): DenseMatrix = { require(numRows.toLong * numCols <= Int.MaxValue, s"$numRows x $numCols dense matrix is too large to allocate") @@ -368,6 +394,7 @@ object DenseMatrix { * @param rng a random number generator * @return `DenseMatrix` with size `numRows` x `numCols` and values in N(0, 1) */ + @Since("2.0.0") def randn(numRows: Int, numCols: Int, rng: Random): DenseMatrix = { require(numRows.toLong * numCols <= Int.MaxValue, s"$numRows x $numCols dense matrix is too large to allocate") @@ -380,6 +407,7 @@ object DenseMatrix { * @return Square `DenseMatrix` with size `values.length` x `values.length` and `values` * on the diagonal */ + @Since("2.0.0") def diag(vector: Vector): DenseMatrix = { val n = vector.size val matrix = DenseMatrix.zeros(n, n) @@ -415,12 +443,13 @@ object DenseMatrix { * Compressed Sparse Row (CSR) format, where `colPtrs` behaves as rowPtrs, * and `rowIndices` behave as colIndices, and `values` are stored in row major. */ -class SparseMatrix ( - val numRows: Int, - val numCols: Int, - val colPtrs: Array[Int], - val rowIndices: Array[Int], - val values: Array[Double], +@Since("2.0.0") +class SparseMatrix @Since("2.0.0") ( + @Since("2.0.0") val numRows: Int, + @Since("2.0.0") val numCols: Int, + @Since("2.0.0") val colPtrs: Array[Int], + @Since("2.0.0") val rowIndices: Array[Int], + @Since("2.0.0") val values: Array[Double], override val isTransposed: Boolean) extends Matrix { require(values.length == rowIndices.length, "The number of row indices and values don't match! " + @@ -451,6 +480,7 @@ class SparseMatrix ( * order for each column * @param values non-zero matrix entries in column major */ + @Since("2.0.0") def this( numRows: Int, numCols: Int, @@ -550,6 +580,7 @@ class SparseMatrix ( * Generate a `DenseMatrix` from the given `SparseMatrix`. The new matrix will have isTransposed * set to false. */ + @Since("2.0.0") def toDense: DenseMatrix = { new DenseMatrix(numRows, numCols, toArray) } @@ -594,6 +625,7 @@ class SparseMatrix ( /** * Factory methods for [[org.apache.spark.ml.linalg.SparseMatrix]]. */ +@Since("2.0.0") object SparseMatrix { /** @@ -605,6 +637,7 @@ object SparseMatrix { * @param entries Array of (i, j, value) tuples * @return The corresponding `SparseMatrix` */ + @Since("2.0.0") def fromCOO(numRows: Int, numCols: Int, entries: Iterable[(Int, Int, Double)]): SparseMatrix = { val sortedEntries = entries.toSeq.sortBy(v => (v._2, v._1)) val numEntries = sortedEntries.size @@ -653,6 +686,7 @@ object SparseMatrix { * @param n number of rows and columns of the matrix * @return `SparseMatrix` with size `n` x `n` and values of ones on the diagonal */ + @Since("2.0.0") def speye(n: Int): SparseMatrix = { new SparseMatrix(n, n, (0 to n).toArray, (0 until n).toArray, Array.fill(n)(1.0)) } @@ -722,6 +756,7 @@ object SparseMatrix { * @param rng a random number generator * @return `SparseMatrix` with size `numRows` x `numCols` and values in U(0, 1) */ + @Since("2.0.0") def sprand(numRows: Int, numCols: Int, density: Double, rng: Random): SparseMatrix = { val mat = genRandMatrix(numRows, numCols, density, rng) mat.update(i => rng.nextDouble()) @@ -735,6 +770,7 @@ object SparseMatrix { * @param rng a random number generator * @return `SparseMatrix` with size `numRows` x `numCols` and values in N(0, 1) */ + @Since("2.0.0") def sprandn(numRows: Int, numCols: Int, density: Double, rng: Random): SparseMatrix = { val mat = genRandMatrix(numRows, numCols, density, rng) mat.update(i => rng.nextGaussian()) @@ -746,6 +782,7 @@ object SparseMatrix { * @return Square `SparseMatrix` with size `values.length` x `values.length` and non-zero * `values` on the diagonal */ + @Since("2.0.0") def spdiag(vector: Vector): SparseMatrix = { val n = vector.size vector match { @@ -762,6 +799,7 @@ object SparseMatrix { /** * Factory methods for [[org.apache.spark.ml.linalg.Matrix]]. */ +@Since("2.0.0") object Matrices { /** @@ -771,6 +809,7 @@ object Matrices { * @param numCols number of columns * @param values matrix entries in column major */ + @Since("2.0.0") def dense(numRows: Int, numCols: Int, values: Array[Double]): Matrix = { new DenseMatrix(numRows, numCols, values) } @@ -784,6 +823,7 @@ object Matrices { * @param rowIndices the row index of the entry * @param values non-zero matrix entries in column major */ + @Since("2.0.0") def sparse( numRows: Int, numCols: Int, @@ -825,6 +865,7 @@ object Matrices { * @param numCols number of columns of the matrix * @return `Matrix` with size `numRows` x `numCols` and values of zeros */ + @Since("2.0.0") def zeros(numRows: Int, numCols: Int): Matrix = DenseMatrix.zeros(numRows, numCols) /** @@ -833,6 +874,7 @@ object Matrices { * @param numCols number of columns of the matrix * @return `Matrix` with size `numRows` x `numCols` and values of ones */ + @Since("2.0.0") def ones(numRows: Int, numCols: Int): Matrix = DenseMatrix.ones(numRows, numCols) /** @@ -840,6 +882,7 @@ object Matrices { * @param n number of rows and columns of the matrix * @return `Matrix` with size `n` x `n` and values of ones on the diagonal */ + @Since("2.0.0") def eye(n: Int): Matrix = DenseMatrix.eye(n) /** @@ -847,6 +890,7 @@ object Matrices { * @param n number of rows and columns of the matrix * @return `Matrix` with size `n` x `n` and values of ones on the diagonal */ + @Since("2.0.0") def speye(n: Int): Matrix = SparseMatrix.speye(n) /** @@ -856,6 +900,7 @@ object Matrices { * @param rng a random number generator * @return `Matrix` with size `numRows` x `numCols` and values in U(0, 1) */ + @Since("2.0.0") def rand(numRows: Int, numCols: Int, rng: Random): Matrix = DenseMatrix.rand(numRows, numCols, rng) @@ -867,6 +912,7 @@ object Matrices { * @param rng a random number generator * @return `Matrix` with size `numRows` x `numCols` and values in U(0, 1) */ + @Since("2.0.0") def sprand(numRows: Int, numCols: Int, density: Double, rng: Random): Matrix = SparseMatrix.sprand(numRows, numCols, density, rng) @@ -877,6 +923,7 @@ object Matrices { * @param rng a random number generator * @return `Matrix` with size `numRows` x `numCols` and values in N(0, 1) */ + @Since("2.0.0") def randn(numRows: Int, numCols: Int, rng: Random): Matrix = DenseMatrix.randn(numRows, numCols, rng) @@ -888,6 +935,7 @@ object Matrices { * @param rng a random number generator * @return `Matrix` with size `numRows` x `numCols` and values in N(0, 1) */ + @Since("2.0.0") def sprandn(numRows: Int, numCols: Int, density: Double, rng: Random): Matrix = SparseMatrix.sprandn(numRows, numCols, density, rng) @@ -897,6 +945,7 @@ object Matrices { * @return Square `Matrix` with size `values.length` x `values.length` and `values` * on the diagonal */ + @Since("2.0.0") def diag(vector: Vector): Matrix = DenseMatrix.diag(vector) /** @@ -906,6 +955,7 @@ object Matrices { * @param matrices array of matrices * @return a single `Matrix` composed of the matrices that were horizontally concatenated */ + @Since("2.0.0") def horzcat(matrices: Array[Matrix]): Matrix = { if (matrices.isEmpty) { return new DenseMatrix(0, 0, Array[Double]()) @@ -964,6 +1014,7 @@ object Matrices { * @param matrices array of matrices * @return a single `Matrix` composed of the matrices that were vertically concatenated */ + @Since("2.0.0") def vertcat(matrices: Array[Matrix]): Matrix = { if (matrices.isEmpty) { return new DenseMatrix(0, 0, Array[Double]()) |