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, 10 insertions, 61 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 a47526d36f..8204b5af02 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 @@ -24,28 +24,21 @@ 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) => @@ -58,21 +51,18 @@ 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. */ @@ -82,15 +72,12 @@ 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) @@ -98,13 +85,11 @@ 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) @@ -115,7 +100,6 @@ 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) /** @@ -145,13 +129,11 @@ 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 } @@ -172,11 +154,10 @@ sealed trait Matrix extends Serializable { * @param isTransposed whether the matrix is transposed. If true, `values` stores the matrix in * row major. */ -@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], +class DenseMatrix ( + val numRows: Int, + val numCols: Int, + val values: Array[Double], override val isTransposed: Boolean) extends Matrix { require(values.length == numRows * numCols, "The number of values supplied doesn't match the " + @@ -197,7 +178,6 @@ class DenseMatrix @Since("2.0.0") ( * @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) @@ -286,7 +266,6 @@ class DenseMatrix @Since("2.0.0") ( * 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) @@ -328,7 +307,6 @@ class DenseMatrix @Since("2.0.0") ( /** * Factory methods for [[org.apache.spark.ml.linalg.DenseMatrix]]. */ -@Since("2.0.0") object DenseMatrix { /** @@ -337,7 +315,6 @@ 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 $numCols dense matrix is too large to allocate") @@ -350,7 +327,6 @@ object DenseMatrix { * @param numCols number of columns of the matrix * @return `DenseMatrix` with size `numRows` x `numCols` and values of ones */ - @Since("2.0.0") def ones(numRows: Int, numCols: Int): DenseMatrix = { require(numRows.toLong * numCols <= Int.MaxValue, s"$numRows x $numCols dense matrix is too large to allocate") @@ -362,7 +338,6 @@ object DenseMatrix { * @param n number of rows and columns of the matrix * @return `DenseMatrix` with size `n` x `n` and values of ones on the diagonal */ - @Since("2.0.0") def eye(n: Int): DenseMatrix = { val identity = DenseMatrix.zeros(n, n) var i = 0 @@ -380,7 +355,6 @@ object DenseMatrix { * @param rng a random number generator * @return `DenseMatrix` with size `numRows` x `numCols` and values in U(0, 1) */ - @Since("2.0.0") def rand(numRows: Int, numCols: Int, rng: Random): DenseMatrix = { require(numRows.toLong * numCols <= Int.MaxValue, s"$numRows x $numCols dense matrix is too large to allocate") @@ -394,7 +368,6 @@ object DenseMatrix { * @param rng a random number generator * @return `DenseMatrix` with size `numRows` x `numCols` and values in N(0, 1) */ - @Since("2.0.0") def randn(numRows: Int, numCols: Int, rng: Random): DenseMatrix = { require(numRows.toLong * numCols <= Int.MaxValue, s"$numRows x $numCols dense matrix is too large to allocate") @@ -407,7 +380,6 @@ 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) @@ -443,13 +415,12 @@ object DenseMatrix { * Compressed Sparse Row (CSR) format, where `colPtrs` behaves as rowPtrs, * and `rowIndices` behave as colIndices, and `values` are stored in row major. */ -@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], +class SparseMatrix ( + val numRows: Int, + val numCols: Int, + val colPtrs: Array[Int], + val rowIndices: Array[Int], + 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! " + @@ -480,7 +451,6 @@ class SparseMatrix @Since("2.0.0") ( * order for each column * @param values non-zero matrix entries in column major */ - @Since("2.0.0") def this( numRows: Int, numCols: Int, @@ -580,7 +550,6 @@ class SparseMatrix @Since("2.0.0") ( * 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) } @@ -625,7 +594,6 @@ class SparseMatrix @Since("2.0.0") ( /** * Factory methods for [[org.apache.spark.ml.linalg.SparseMatrix]]. */ -@Since("2.0.0") object SparseMatrix { /** @@ -637,7 +605,6 @@ 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 @@ -686,7 +653,6 @@ 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)) } @@ -756,7 +722,6 @@ 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()) @@ -770,7 +735,6 @@ 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()) @@ -782,7 +746,6 @@ 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 { @@ -799,7 +762,6 @@ object SparseMatrix { /** * Factory methods for [[org.apache.spark.ml.linalg.Matrix]]. */ -@Since("2.0.0") object Matrices { /** @@ -809,7 +771,6 @@ 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) } @@ -823,7 +784,6 @@ 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, @@ -865,7 +825,6 @@ 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) /** @@ -874,7 +833,6 @@ 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) /** @@ -882,7 +840,6 @@ 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) /** @@ -890,7 +847,6 @@ 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) /** @@ -900,7 +856,6 @@ 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) @@ -912,7 +867,6 @@ 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) @@ -923,7 +877,6 @@ 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) @@ -935,7 +888,6 @@ 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) @@ -945,7 +897,6 @@ 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) /** @@ -955,7 +906,6 @@ 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]()) @@ -1014,7 +964,6 @@ 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]()) |