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[SPARK-8920] [MLlib] Add @since tags to mllib.linalg #7729

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[SPARK-8920] [MLlib] Add @since tags to mllib.linalg #7729

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6c4f7a4
Add @since tag to mllib.linalg.*
Jul 28, 2015
2e5ebd6
Add @since tag to mllib.linalg.*
Jul 28, 2015
3be09e9
Remove @since tag from sealed traits Vector and Matrix
Aug 1, 2015
3012864
Fixed various tagging issues per review by @mechcoder
Aug 4, 2015
09aad77
Remove @since tag from toString and confirmed alternate constructor h…
Aug 14, 2015
68b3ed9
Correct @since tag versions
Aug 17, 2015
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63 changes: 63 additions & 0 deletions mllib/src/main/scala/org/apache/spark/mllib/linalg/Matrices.scala
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Original file line number Diff line number Diff line change
Expand Up @@ -229,6 +229,7 @@ private[spark] class MatrixUDT extends UserDefinedType[Matrix] {
* @param values matrix entries in column major if not transposed or in row major otherwise
* @param isTransposed whether the matrix is transposed. If true, `values` stores the matrix in
* row major.
* @since 1.0.0
*/
@SQLUserDefinedType(udt = classOf[MatrixUDT])
class DenseMatrix(
Expand All @@ -254,6 +255,7 @@ class DenseMatrix(
* @param numRows number of rows
* @param numCols number of columns
* @param values matrix entries in column major
* @since 1.3.0
*/
def this(numRows: Int, numCols: Int, values: Array[Double]) =
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@MechCoder MechCoder Aug 6, 2015

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Here?

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@MechCoder MechCoder Aug 16, 2015

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ping

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@sabhyankar sabhyankar Aug 17, 2015

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Hi @MechCoder I sincerely apologize for my ignorance here! I am a little new to Scala and am not sure what I am missing here. I will be happy to fix it if you can provide some clarification. I have fixed the other errors that you identified below and will update the PR.

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@MechCoder MechCoder Aug 17, 2015

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Oh never mind, I could notice that from the diff view :( . Looks all right to me.

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@sabhyankar sabhyankar Aug 17, 2015

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:) Thanks!

this(numRows, numCols, values, false)
Expand All @@ -279,6 +281,9 @@ class DenseMatrix(

private[mllib] def apply(i: Int): Double = values(i)

/**
* @since 1.3.0
*/
override def apply(i: Int, j: Int): Double = values(index(i, j))

private[mllib] def index(i: Int, j: Int): Int = {
Expand All @@ -289,6 +294,9 @@ class DenseMatrix(
values(index(i, j)) = v
}

/**
* @since 1.4.0
*/
override def copy: DenseMatrix = new DenseMatrix(numRows, numCols, values.clone())

private[spark] def map(f: Double => Double) = new DenseMatrix(numRows, numCols, values.map(f),
Expand All @@ -304,6 +312,9 @@ class DenseMatrix(
this
}

/**
* @since 1.3.0
*/
override def transpose: DenseMatrix = new DenseMatrix(numCols, numRows, values, !isTransposed)

private[spark] override def foreachActive(f: (Int, Int, Double) => Unit): Unit = {
Expand Down Expand Up @@ -334,13 +345,20 @@ class DenseMatrix(
}
}

/**
* @since 1.5.0
*/
override def numNonzeros: Int = values.count(_ != 0)

/**
* @since 1.5.0
*/
override def numActives: Int = values.length

/**
* Generate a `SparseMatrix` from the given `DenseMatrix`. The new matrix will have isTransposed
* set to false.
* @since 1.3.0
*/
def toSparse: SparseMatrix = {
val spVals: MArrayBuilder[Double] = new MArrayBuilder.ofDouble
Expand Down Expand Up @@ -368,6 +386,7 @@ class DenseMatrix(

/**
* Factory methods for [[org.apache.spark.mllib.linalg.DenseMatrix]].
* @since 1.3.0
*/
object DenseMatrix {

Expand All @@ -376,6 +395,7 @@ object DenseMatrix {
* @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
* @since 1.3.0
*/
def zeros(numRows: Int, numCols: Int): DenseMatrix = {
require(numRows.toLong * numCols <= Int.MaxValue,
Expand All @@ -388,6 +408,7 @@ object DenseMatrix {
* @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
* @since 1.3.0
*/
def ones(numRows: Int, numCols: Int): DenseMatrix = {
require(numRows.toLong * numCols <= Int.MaxValue,
Expand All @@ -399,6 +420,7 @@ object DenseMatrix {
* 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
* @since 1.3.0
*/
def eye(n: Int): DenseMatrix = {
val identity = DenseMatrix.zeros(n, n)
Expand All @@ -416,6 +438,7 @@ object DenseMatrix {
* @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)
* @since 1.3.0
*/
def rand(numRows: Int, numCols: Int, rng: Random): DenseMatrix = {
require(numRows.toLong * numCols <= Int.MaxValue,
Expand All @@ -429,6 +452,7 @@ object DenseMatrix {
* @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)
* @since 1.3.0
*/
def randn(numRows: Int, numCols: Int, rng: Random): DenseMatrix = {
require(numRows.toLong * numCols <= Int.MaxValue,
Expand All @@ -441,6 +465,7 @@ object DenseMatrix {
* @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
* @since 1.3.0
*/
def diag(vector: Vector): DenseMatrix = {
val n = vector.size
Expand Down Expand Up @@ -476,6 +501,7 @@ object DenseMatrix {
* @param isTransposed whether the matrix is transposed. If true, the matrix can be considered
* Compressed Sparse Row (CSR) format, where `colPtrs` behaves as rowPtrs,
* and `rowIndices` behave as colIndices, and `values` are stored in row major.
* @since 1.2.0
*/
@SQLUserDefinedType(udt = classOf[MatrixUDT])
class SparseMatrix(
Expand Down Expand Up @@ -513,6 +539,7 @@ class SparseMatrix(
* @param rowIndices the row index of the entry. They must be in strictly increasing
* order for each column
* @param values non-zero matrix entries in column major
* @since 1.3.0
*/
def this(
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Here as well.

numRows: Int,
Expand All @@ -530,6 +557,9 @@ class SparseMatrix(
}
}

/**
* @since 1.3.0
*/
override def apply(i: Int, j: Int): Double = {
val ind = index(i, j)
if (ind < 0) 0.0 else values(ind)
Expand All @@ -553,6 +583,9 @@ class SparseMatrix(
}
}

/**
* @since 1.4.0
*/
override def copy: SparseMatrix = {
new SparseMatrix(numRows, numCols, colPtrs, rowIndices, values.clone())
}
Expand All @@ -570,6 +603,9 @@ class SparseMatrix(
this
}

/**
* @since 1.3.0
*/
override def transpose: SparseMatrix =
new SparseMatrix(numCols, numRows, colPtrs, rowIndices, values, !isTransposed)

Expand Down Expand Up @@ -603,19 +639,27 @@ class SparseMatrix(
/**
* Generate a `DenseMatrix` from the given `SparseMatrix`. The new matrix will have isTransposed
* set to false.
* @since 1.3.0
*/
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@MechCoder MechCoder Aug 2, 2015

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The tags for numNonzeros and numActives are missing.

def toDense: DenseMatrix = {
new DenseMatrix(numRows, numCols, toArray)
}

/**
* @since 1.5.0
*/
override def numNonzeros: Int = values.count(_ != 0)

/**
* @since 1.5.0
*/
override def numActives: Int = values.length

}

/**
* Factory methods for [[org.apache.spark.mllib.linalg.SparseMatrix]].
* @since 1.3.0
*/
object SparseMatrix {

Expand All @@ -627,6 +671,7 @@ object SparseMatrix {
* @param numCols number of columns of the matrix
* @param entries Array of (i, j, value) tuples
* @return The corresponding `SparseMatrix`
* @since 1.3.0
*/
def fromCOO(numRows: Int, numCols: Int, entries: Iterable[(Int, Int, Double)]): SparseMatrix = {
val sortedEntries = entries.toSeq.sortBy(v => (v._2, v._1))
Expand Down Expand Up @@ -675,6 +720,7 @@ object SparseMatrix {
* Generate an Identity Matrix in `SparseMatrix` format.
* @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 1.3.0
*/
def speye(n: Int): SparseMatrix = {
new SparseMatrix(n, n, (0 to n).toArray, (0 until n).toArray, Array.fill(n)(1.0))
Expand Down Expand Up @@ -744,6 +790,7 @@ object SparseMatrix {
* @param density the desired density for the matrix
* @param rng a random number generator
* @return `SparseMatrix` with size `numRows` x `numCols` and values in U(0, 1)
* @since 1.3.0
*/
def sprand(numRows: Int, numCols: Int, density: Double, rng: Random): SparseMatrix = {
val mat = genRandMatrix(numRows, numCols, density, rng)
Expand All @@ -757,6 +804,7 @@ object SparseMatrix {
* @param density the desired density for the matrix
* @param rng a random number generator
* @return `SparseMatrix` with size `numRows` x `numCols` and values in N(0, 1)
* @since 1.3.0
*/
def sprandn(numRows: Int, numCols: Int, density: Double, rng: Random): SparseMatrix = {
val mat = genRandMatrix(numRows, numCols, density, rng)
Expand All @@ -768,6 +816,7 @@ object SparseMatrix {
* @param vector a `Vector` that will form the values on the diagonal of the matrix
* @return Square `SparseMatrix` with size `values.length` x `values.length` and non-zero
* `values` on the diagonal
* @since 1.3.0
*/
def spdiag(vector: Vector): SparseMatrix = {
val n = vector.size
Expand All @@ -784,6 +833,7 @@ object SparseMatrix {

/**
* Factory methods for [[org.apache.spark.mllib.linalg.Matrix]].
* @since 1.0.0
*/
object Matrices {

Expand All @@ -793,6 +843,7 @@ object Matrices {
* @param numRows number of rows
* @param numCols number of columns
* @param values matrix entries in column major
* @since 1.0.0
*/
def dense(numRows: Int, numCols: Int, values: Array[Double]): Matrix = {
new DenseMatrix(numRows, numCols, values)
Expand All @@ -806,6 +857,7 @@ object Matrices {
* @param colPtrs the index corresponding to the start of a new column
* @param rowIndices the row index of the entry
* @param values non-zero matrix entries in column major
* @since 1.2.0
*/
def sparse(
numRows: Int,
Expand Down Expand Up @@ -839,6 +891,7 @@ object Matrices {
* @param numRows number of rows of the matrix
* @param numCols number of columns of the matrix
* @return `Matrix` with size `numRows` x `numCols` and values of zeros
* @since 1.2.0
*/
def zeros(numRows: Int, numCols: Int): Matrix = DenseMatrix.zeros(numRows, numCols)

Expand All @@ -847,20 +900,23 @@ object Matrices {
* @param numRows number of rows of the matrix
* @param numCols number of columns of the matrix
* @return `Matrix` with size `numRows` x `numCols` and values of ones
* @since 1.2.0
*/
def ones(numRows: Int, numCols: Int): Matrix = DenseMatrix.ones(numRows, numCols)

/**
* Generate a dense 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
* @since 1.2.0
*/
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
* @since 1.3.0
*/
def speye(n: Int): Matrix = SparseMatrix.speye(n)

Expand All @@ -870,6 +926,7 @@ object Matrices {
* @param numCols number of columns of the matrix
* @param rng a random number generator
* @return `Matrix` with size `numRows` x `numCols` and values in U(0, 1)
* @since 1.2.0
*/
def rand(numRows: Int, numCols: Int, rng: Random): Matrix =
DenseMatrix.rand(numRows, numCols, rng)
Expand All @@ -881,6 +938,7 @@ object Matrices {
* @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)
* @since 1.3.0
*/
def sprand(numRows: Int, numCols: Int, density: Double, rng: Random): Matrix =
SparseMatrix.sprand(numRows, numCols, density, rng)
Expand All @@ -891,6 +949,7 @@ object Matrices {
* @param numCols number of columns of the matrix
* @param rng a random number generator
* @return `Matrix` with size `numRows` x `numCols` and values in N(0, 1)
* @since 1.2.0
*/
def randn(numRows: Int, numCols: Int, rng: Random): Matrix =
DenseMatrix.randn(numRows, numCols, rng)
Expand All @@ -902,6 +961,7 @@ object Matrices {
* @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)
* @since 1.3.0
*/
def sprandn(numRows: Int, numCols: Int, density: Double, rng: Random): Matrix =
SparseMatrix.sprandn(numRows, numCols, density, rng)
Expand All @@ -911,6 +971,7 @@ object Matrices {
* @param vector a `Vector` that will form the values on the diagonal of the matrix
* @return Square `Matrix` with size `values.length` x `values.length` and `values`
* on the diagonal
* @since 1.2.0
*/
def diag(vector: Vector): Matrix = DenseMatrix.diag(vector)

Expand All @@ -920,6 +981,7 @@ object Matrices {
* a sparse matrix. If the Array is empty, an empty `DenseMatrix` will be returned.
* @param matrices array of matrices
* @return a single `Matrix` composed of the matrices that were horizontally concatenated
* @since 1.3.0
*/
def horzcat(matrices: Array[Matrix]): Matrix = {
if (matrices.isEmpty) {
Expand Down Expand Up @@ -978,6 +1040,7 @@ object Matrices {
* a sparse matrix. If the Array is empty, an empty `DenseMatrix` will be returned.
* @param matrices array of matrices
* @return a single `Matrix` composed of the matrices that were vertically concatenated
* @since 1.3.0
*/
def vertcat(matrices: Array[Matrix]): Matrix = {
if (matrices.isEmpty) {
Expand Down
1 change: 1 addition & 0 deletions mllib/src/main/scala/org/apache/spark/mllib/linalg/SingularValueDecomposition.scala
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Original file line number Diff line number Diff line change
Expand Up @@ -22,6 +22,7 @@ import org.apache.spark.annotation.Experimental
/**
* :: Experimental ::
* Represents singular value decomposition (SVD) factors.
* @since 1.0.0
*/
@Experimental
case class SingularValueDecomposition[UType, VType](U: UType, s: Vector, V: VType)
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