Features refactoring.

CHANGELOG.md

@@ -33,6 +33,7 @@
 - EjmlMatrix context is an object
 - Matrix LUP `inverse` renamed to `inverseWithLUP`
 - `NumericAlgebra` moved outside of regular algebra chain (`Ring` no longer implements it).
+- Features moved to NDStructure and became transparent.
 
 ### Deprecated
 

kmath-commons/src/main/kotlin/kscience/kmath/commons/linear/CMMatrix.kt

@@ -1,42 +1,28 @@
 package kscience.kmath.commons.linear
 
-import kscience.kmath.linear.*
+import kscience.kmath.linear.DiagonalFeature
+import kscience.kmath.linear.MatrixContext
+import kscience.kmath.linear.MatrixWrapper
+import kscience.kmath.linear.Point
+import kscience.kmath.misc.UnstableKMathAPI
 import kscience.kmath.structures.Matrix
-import kscience.kmath.structures.NDStructure
 import org.apache.commons.math3.linear.*
+import kotlin.reflect.KClass
+import kotlin.reflect.cast
 
-public class CMMatrix(public val origin: RealMatrix, features: Set<MatrixFeature>? = null) : FeaturedMatrix<Double> {
+public inline class CMMatrix(public val origin: RealMatrix) : Matrix<Double> {
     public override val rowNum: Int get() = origin.rowDimension
     public override val colNum: Int get() = origin.columnDimension
 
-    public override val features: Set<MatrixFeature> = features ?: sequence<MatrixFeature> {
+    @UnstableKMathAPI
-        if (origin is DiagonalMatrix) yield(DiagonalFeature)
+    override fun <T : Any> getFeature(type: KClass<T>): T? = when (type) {
-    }.toHashSet()
+        DiagonalFeature::class -> if (origin is DiagonalMatrix) DiagonalFeature else null
-
+        else -> null
-    public override fun suggestFeature(vararg features: MatrixFeature): CMMatrix =
+    }?.let { type.cast(it) }
-        CMMatrix(origin, this.features + features)
 
     public override operator fun get(i: Int, j: Int): Double = origin.getEntry(i, j)
-
-    public override fun equals(other: Any?): Boolean {
-        return NDStructure.equals(this, other as? NDStructure<*> ?: return false)
-    }
-
-    public override fun hashCode(): Int {
-        var result = origin.hashCode()
-        result = 31 * result + features.hashCode()
-        return result
-    }
 }
 
-//TODO move inside context
-public fun Matrix<Double>.toCM(): CMMatrix = if (this is CMMatrix) {
-    this
-} else {
-    //TODO add feature analysis
-    val array = Array(rowNum) { i -> DoubleArray(colNum) { j -> get(i, j) } }
-    CMMatrix(Array2DRowRealMatrix(array))
-}
 
 public fun RealMatrix.asMatrix(): CMMatrix = CMMatrix(this)
 
@@ -61,6 +47,16 @@ public object CMMatrixContext : MatrixContext<Double, CMMatrix> {
         return CMMatrix(Array2DRowRealMatrix(array))
     }
 
+    public fun Matrix<Double>.toCM(): CMMatrix = when {
+        this is CMMatrix -> this
+        this is MatrixWrapper && matrix is CMMatrix -> matrix as CMMatrix
+        else -> {
+            //TODO add feature analysis
+            val array = Array(rowNum) { i -> DoubleArray(colNum) { j -> get(i, j) } }
+            CMMatrix(Array2DRowRealMatrix(array))
+        }
+    }
+
     public override fun Matrix<Double>.dot(other: Matrix<Double>): CMMatrix =
         CMMatrix(toCM().origin.multiply(other.toCM().origin))
 

kmath-core/src/commonMain/kotlin/kscience/kmath/linear/BufferMatrix.kt

@@ -1,10 +1,7 @@
 package kscience.kmath.linear
 
 import kscience.kmath.operations.Ring
-import kscience.kmath.structures.Buffer
+import kscience.kmath.structures.*
-import kscience.kmath.structures.BufferFactory
-import kscience.kmath.structures.NDStructure
-import kscience.kmath.structures.asSequence
 
 /**
  * Basic implementation of Matrix space based on [NDStructure]
@@ -27,8 +24,7 @@ public class BufferMatrix<T : Any>(
     public override val rowNum: Int,
     public override val colNum: Int,
     public val buffer: Buffer<out T>,
-    public override val features: Set<MatrixFeature> = emptySet(),
+) : Matrix<T> {
-) : FeaturedMatrix<T> {
 
     init {
         require(buffer.size == rowNum * colNum) { "Dimension mismatch for matrix structure" }
@@ -36,9 +32,6 @@ public class BufferMatrix<T : Any>(
 
     override val shape: IntArray get() = intArrayOf(rowNum, colNum)
 
-    public override fun suggestFeature(vararg features: MatrixFeature): BufferMatrix<T> =
-        BufferMatrix(rowNum, colNum, buffer, this.features + features)
-
     public override operator fun get(index: IntArray): T = get(index[0], index[1])
     public override operator fun get(i: Int, j: Int): T = buffer[i * colNum + j]
 
@@ -50,23 +43,26 @@ public class BufferMatrix<T : Any>(
         if (this === other) return true
 
         return when (other) {
-            is NDStructure<*> -> return NDStructure.equals(this, other)
+            is NDStructure<*> -> NDStructure.equals(this, other)
             else -> false
         }
     }
 
-    public override fun hashCode(): Int {
+    override fun hashCode(): Int {
-        var result = buffer.hashCode()
+        var result = rowNum
-        result = 31 * result + features.hashCode()
+        result = 31 * result + colNum
+        result = 31 * result + buffer.hashCode()
         return result
     }
 
     public override fun toString(): String {
         return if (rowNum <= 5 && colNum <= 5)
-            "Matrix(rowsNum = $rowNum, colNum = $colNum, features=$features)\n" +
+            "Matrix(rowsNum = $rowNum, colNum = $colNum)\n" +
                     rows.asSequence().joinToString(prefix = "(", postfix = ")", separator = "\n ") { buffer ->
                         buffer.asSequence().joinToString(separator = "\t") { it.toString() }
                     }
-        else "Matrix(rowsNum = $rowNum, colNum = $colNum, features=$features)"
+        else "Matrix(rowsNum = $rowNum, colNum = $colNum)"
     }
+
+
 }

kmath-core/src/commonMain/kotlin/kscience/kmath/linear/LupDecomposition.kt

@@ -1,13 +1,14 @@
 package kscience.kmath.linear
 
+import kscience.kmath.misc.UnstableKMathAPI
 import kscience.kmath.operations.*
 import kscience.kmath.structures.*
 
 /**
  * Common implementation of [LupDecompositionFeature].
  */
-public class LUPDecomposition<T : Any>(
+public class LupDecomposition<T : Any>(
-    public val context: MatrixContext<T, FeaturedMatrix<T>>,
+    public val context: MatrixContext<T, Matrix<T>>,
     public val elementContext: Field<T>,
     public val lu: Matrix<T>,
     public val pivot: IntArray,
@@ -18,13 +19,13 @@ public class LUPDecomposition<T : Any>(
      *
      * L is a lower-triangular matrix with [Ring.one] in diagonal
      */
-    override val l: FeaturedMatrix<T> = VirtualMatrix(lu.shape[0], lu.shape[1], setOf(LFeature)) { i, j ->
+    override val l: Matrix<T> = VirtualMatrix(lu.shape[0], lu.shape[1]) { i, j ->
         when {
             j < i -> lu[i, j]
             j == i -> elementContext.one
             else -> elementContext.zero
         }
-    }
+    } + LFeature
 
 
     /**
@@ -32,9 +33,9 @@ public class LUPDecomposition<T : Any>(
      *
      * U is an upper-triangular matrix including the diagonal
      */
-    override val u: FeaturedMatrix<T> = VirtualMatrix(lu.shape[0], lu.shape[1], setOf(UFeature)) { i, j ->
+    override val u: Matrix<T> = VirtualMatrix(lu.shape[0], lu.shape[1]) { i, j ->
         if (j >= i) lu[i, j] else elementContext.zero
-    }
+    } + UFeature
 
     /**
      * Returns the P rows permutation matrix.
@@ -42,7 +43,7 @@ public class LUPDecomposition<T : Any>(
      * P is a sparse matrix with exactly one element set to [Ring.one] in
      * each row and each column, all other elements being set to [Ring.zero].
      */
-    override val p: FeaturedMatrix<T> = VirtualMatrix(lu.shape[0], lu.shape[1]) { i, j ->
+    override val p: Matrix<T> = VirtualMatrix(lu.shape[0], lu.shape[1]) { i, j ->
         if (j == pivot[i]) elementContext.one else elementContext.zero
     }
 
@@ -63,12 +64,12 @@ internal fun <T : Comparable<T>, F : Field<T>> GenericMatrixContext<T, F, *>.abs
 /**
  * Create a lup decomposition of generic matrix.
  */
-public fun <T : Comparable<T>> MatrixContext<T, FeaturedMatrix<T>>.lup(
+public fun <T : Comparable<T>> MatrixContext<T, Matrix<T>>.lup(
     factory: MutableBufferFactory<T>,
     elementContext: Field<T>,
     matrix: Matrix<T>,
     checkSingular: (T) -> Boolean,
-): LUPDecomposition<T> {
+): LupDecomposition<T> {
     require(matrix.rowNum == matrix.colNum) { "LU decomposition supports only square matrices" }
     val m = matrix.colNum
     val pivot = IntArray(matrix.rowNum)
@@ -137,23 +138,23 @@ public fun <T : Comparable<T>> MatrixContext<T, FeaturedMatrix<T>>.lup(
                 for (row in col + 1 until m) lu[row, col] /= luDiag
             }
 
-            return LUPDecomposition(this@lup, elementContext, lu.collect(), pivot, even)
+            return LupDecomposition(this@lup, elementContext, lu.collect(), pivot, even)
         }
     }
 }
 
-public inline fun <reified T : Comparable<T>, F : Field<T>> GenericMatrixContext<T, F, FeaturedMatrix<T>>.lup(
+public inline fun <reified T : Comparable<T>, F : Field<T>> GenericMatrixContext<T, F, Matrix<T>>.lup(
     matrix: Matrix<T>,
     noinline checkSingular: (T) -> Boolean,
-): LUPDecomposition<T> = lup(MutableBuffer.Companion::auto, elementContext, matrix, checkSingular)
+): LupDecomposition<T> = lup(MutableBuffer.Companion::auto, elementContext, matrix, checkSingular)
 
-public fun MatrixContext<Double, FeaturedMatrix<Double>>.lup(matrix: Matrix<Double>): LUPDecomposition<Double> =
+public fun MatrixContext<Double, Matrix<Double>>.lup(matrix: Matrix<Double>): LupDecomposition<Double> =
     lup(Buffer.Companion::real, RealField, matrix) { it < 1e-11 }
 
-public fun <T : Any> LUPDecomposition<T>.solveWithLUP(
+public fun <T : Any> LupDecomposition<T>.solveWithLUP(
     factory: MutableBufferFactory<T>,
-    matrix: Matrix<T>
+    matrix: Matrix<T>,
-): FeaturedMatrix<T> {
+): Matrix<T> {
     require(matrix.rowNum == pivot.size) { "Matrix dimension mismatch. Expected ${pivot.size}, but got ${matrix.colNum}" }
 
     BufferAccessor2D(matrix.rowNum, matrix.colNum, factory).run {
@@ -198,25 +199,40 @@ public fun <T : Any> LUPDecomposition<T>.solveWithLUP(
     }
 }
 
-public inline fun <reified T : Any> LUPDecomposition<T>.solveWithLUP(matrix: Matrix<T>): Matrix<T> =
+public inline fun <reified T : Any> LupDecomposition<T>.solveWithLUP(matrix: Matrix<T>): Matrix<T> =
     solveWithLUP(MutableBuffer.Companion::auto, matrix)
 
 /**
  * Solve a linear equation **a*x = b** using LUP decomposition
  */
-public inline fun <reified T : Comparable<T>, F : Field<T>> GenericMatrixContext<T, F, FeaturedMatrix<T>>.solveWithLUP(
+@OptIn(UnstableKMathAPI::class)
+public inline fun <reified T : Comparable<T>, F : Field<T>> GenericMatrixContext<T, F, Matrix<T>>.solveWithLUP(
     a: Matrix<T>,
     b: Matrix<T>,
     noinline bufferFactory: MutableBufferFactory<T> = MutableBuffer.Companion::auto,
     noinline checkSingular: (T) -> Boolean,
-): FeaturedMatrix<T> {
+): Matrix<T> {
     // Use existing decomposition if it is provided by matrix
     val decomposition = a.getFeature() ?: lup(bufferFactory, elementContext, a, checkSingular)
     return decomposition.solveWithLUP(bufferFactory, b)
 }
 
-public inline fun <reified T : Comparable<T>, F : Field<T>> GenericMatrixContext<T, F, FeaturedMatrix<T>>.inverseWithLUP(
+public inline fun <reified T : Comparable<T>, F : Field<T>> GenericMatrixContext<T, F, Matrix<T>>.inverseWithLUP(
     matrix: Matrix<T>,
     noinline bufferFactory: MutableBufferFactory<T> = MutableBuffer.Companion::auto,
     noinline checkSingular: (T) -> Boolean,
-): FeaturedMatrix<T> = solveWithLUP(matrix, one(matrix.rowNum, matrix.colNum), bufferFactory, checkSingular)
+): Matrix<T> = solveWithLUP(matrix, one(matrix.rowNum, matrix.colNum), bufferFactory, checkSingular)
+
+
+public fun RealMatrixContext.solveWithLUP(a: Matrix<Double>, b: Matrix<Double>): Matrix<Double> {
+    // Use existing decomposition if it is provided by matrix
+    val bufferFactory: MutableBufferFactory<Double> = MutableBuffer.Companion::real
+    val decomposition: LupDecomposition<Double> = a.getFeature() ?: lup(bufferFactory, RealField, a) { it < 1e-11 }
+    return decomposition.solveWithLUP(bufferFactory, b)
+}
+
+/**
+ * Inverses a square matrix using LUP decomposition. Non square matrix will throw a error.
+ */
+public fun RealMatrixContext.inverseWithLUP(matrix: Matrix<Double>): Matrix<Double> =
+    solveWithLUP(matrix, one(matrix.rowNum, matrix.colNum))

kmath-core/src/commonMain/kotlin/kscience/kmath/linear/MatrixBuilder.kt

@@ -1,12 +1,9 @@
 package kscience.kmath.linear
 
-import kscience.kmath.structures.Buffer
+import kscience.kmath.structures.*
-import kscience.kmath.structures.BufferFactory
-import kscience.kmath.structures.Structure2D
-import kscience.kmath.structures.asBuffer
 
 public class MatrixBuilder(public val rows: Int, public val columns: Int) {
-    public operator fun <T : Any> invoke(vararg elements: T): FeaturedMatrix<T> {
+    public operator fun <T : Any> invoke(vararg elements: T): Matrix<T> {
         require(rows * columns == elements.size) { "The number of elements ${elements.size} is not equal $rows * $columns" }
         val buffer = elements.asBuffer()
         return BufferMatrix(rows, columns, buffer)
@@ -17,7 +14,7 @@ public class MatrixBuilder(public val rows: Int, public val columns: Int) {
 
 public fun Structure2D.Companion.build(rows: Int, columns: Int): MatrixBuilder = MatrixBuilder(rows, columns)
 
-public fun <T : Any> Structure2D.Companion.row(vararg values: T): FeaturedMatrix<T> {
+public fun <T : Any> Structure2D.Companion.row(vararg values: T): Matrix<T> {
     val buffer = values.asBuffer()
     return BufferMatrix(1, values.size, buffer)
 }
@@ -26,12 +23,12 @@ public inline fun <reified T : Any> Structure2D.Companion.row(
     size: Int,
     factory: BufferFactory<T> = Buffer.Companion::auto,
     noinline builder: (Int) -> T
-): FeaturedMatrix<T> {
+): Matrix<T> {
     val buffer = factory(size, builder)
     return BufferMatrix(1, size, buffer)
 }
 
-public fun <T : Any> Structure2D.Companion.column(vararg values: T): FeaturedMatrix<T> {
+public fun <T : Any> Structure2D.Companion.column(vararg values: T): Matrix<T> {
     val buffer = values.asBuffer()
     return BufferMatrix(values.size, 1, buffer)
 }
@@ -40,7 +37,7 @@ public inline fun <reified T : Any> Structure2D.Companion.column(
     size: Int,
     factory: BufferFactory<T> = Buffer.Companion::auto,
     noinline builder: (Int) -> T
-): FeaturedMatrix<T> {
+): Matrix<T> {
     val buffer = factory(size, builder)
     return BufferMatrix(size, 1, buffer)
 }

kmath-core/src/commonMain/kotlin/kscience/kmath/linear/MatrixContext.kt

@@ -133,8 +133,6 @@ public interface GenericMatrixContext<T : Any, R : Ring<T>, out M : Matrix<T>> :
     public override fun multiply(a: Matrix<T>, k: Number): M =
         produce(a.rowNum, a.colNum) { i, j -> elementContext { a[i, j] * k } }
 
-    public operator fun Number.times(matrix: FeaturedMatrix<T>): M = multiply(matrix, this)
-
     public override operator fun Matrix<T>.times(value: T): M =
         produce(rowNum, colNum) { i, j -> elementContext { get(i, j) * value } }
 }

kmath-core/src/commonMain/kotlin/kscience/kmath/linear/MatrixFeatures.kt

@@ -11,17 +11,19 @@ public interface MatrixFeature
 /**
  * Matrices with this feature are considered to have only diagonal non-null elements.
  */
-public object DiagonalFeature : MatrixFeature
+public interface DiagonalFeature : MatrixFeature{
+    public companion object: DiagonalFeature
+}
 
 /**
  * Matrices with this feature have all zero elements.
  */
-public object ZeroFeature : MatrixFeature
+public object ZeroFeature : DiagonalFeature
 
 /**
  * Matrices with this feature have unit elements on diagonal and zero elements in all other places.
  */
-public object UnitFeature : MatrixFeature
+public object UnitFeature : DiagonalFeature
 
 /**
  * Matrices with this feature can be inverted: [inverse] = `a`<sup>-1</sup> where `a` is the owning matrix.
@@ -76,17 +78,17 @@ public interface LupDecompositionFeature<T : Any> : MatrixFeature {
     /**
      * The lower triangular matrix in this decomposition. It may have [LFeature].
      */
-    public val l: FeaturedMatrix<T>
+    public val l: Matrix<T>
 
     /**
      * The upper triangular matrix in this decomposition. It may have [UFeature].
      */
-    public val u: FeaturedMatrix<T>
+    public val u: Matrix<T>
 
     /**
      * The permutation matrix in this decomposition.
      */
-    public val p: FeaturedMatrix<T>
+    public val p: Matrix<T>
 }
 
 /**
@@ -104,12 +106,12 @@ public interface QRDecompositionFeature<T : Any> : MatrixFeature {
     /**
      * The orthogonal matrix in this decomposition. It may have [OrthogonalFeature].
      */
-    public val q: FeaturedMatrix<T>
+    public val q: Matrix<T>
 
     /**
      * The upper triangular matrix in this decomposition. It may have [UFeature].
      */
-    public val r: FeaturedMatrix<T>
+    public val r: Matrix<T>
 }
 
 /**
@@ -122,7 +124,7 @@ public interface CholeskyDecompositionFeature<T : Any> : MatrixFeature {
     /**
      * The triangular matrix in this decomposition. It may have either [UFeature] or [LFeature].
      */
-    public val l: FeaturedMatrix<T>
+    public val l: Matrix<T>
 }
 
 /**
@@ -135,17 +137,17 @@ public interface SingularValueDecompositionFeature<T : Any> : MatrixFeature {
     /**
      * The matrix in this decomposition. It is unitary, and it consists from left singular vectors.
      */
-    public val u: FeaturedMatrix<T>
+    public val u: Matrix<T>
 
     /**
      * The matrix in this decomposition. Its main diagonal elements are singular values.
      */
-    public val s: FeaturedMatrix<T>
+    public val s: Matrix<T>
 
     /**
      * The matrix in this decomposition. It is unitary, and it consists from right singular vectors.
      */
-    public val v: FeaturedMatrix<T>
+    public val v: Matrix<T>
 
     /**
      * The buffer of singular values of this SVD.

kmath-core/src/commonMain/kotlin/kscience/kmath/linear/MatrixWrapper.kt

@@ -1,35 +1,57 @@
 package kscience.kmath.linear
 
+import kscience.kmath.misc.UnstableKMathAPI
 import kscience.kmath.operations.Ring
 import kscience.kmath.structures.Matrix
 import kscience.kmath.structures.Structure2D
 import kscience.kmath.structures.asBuffer
+import kscience.kmath.structures.getFeature
 import kotlin.math.sqrt
+import kotlin.reflect.KClass
+import kotlin.reflect.safeCast
 
 /**
  * A [Matrix] that holds [MatrixFeature] objects.
  *
  * @param T the type of items.
  */
-public interface FeaturedMatrix<T : Any> : Matrix<T> {
+public class MatrixWrapper<T : Any>(
-    public override val shape: IntArray get() = intArrayOf(rowNum, colNum)
+    public val matrix: Matrix<T>,
+    public val features: Set<MatrixFeature>,
+) : Matrix<T> by matrix {
 
     /**
-     * The set of features this matrix possesses.
+     * Get the first feature matching given class. Does not guarantee that matrix has only one feature matching the criteria
      */
-    public val features: Set<MatrixFeature>
+    @UnstableKMathAPI
+    override fun <T : Any> getFeature(type: KClass<T>): T? = type.safeCast(features.find { type.isInstance(it) })
 
-    /**
+    override fun equals(other: Any?): Boolean = matrix == other
-     * Suggest new feature for this matrix. The result is the new matrix that may or may not reuse existing data structure.
+    override fun hashCode(): Int = matrix.hashCode()
-     *
+    override fun toString(): String {
-     * The implementation does not guarantee to check that matrix actually have the feature, so one should be careful to
+        return "MatrixWrapper(matrix=$matrix, features=$features)"
-     * add only those features that are valid.
+    }
-     */
-    public fun suggestFeature(vararg features: MatrixFeature): FeaturedMatrix<T>
-
-    public companion object
 }
 
+/**
+ * Add a single feature to a [Matrix]
+ */
+public operator fun <T : Any> Matrix<T>.plus(newFeature: MatrixFeature): MatrixWrapper<T> = if (this is MatrixWrapper) {
+    MatrixWrapper(matrix, features + newFeature)
+} else {
+    MatrixWrapper(this, setOf(newFeature))
+}
+
+/**
+ * Add a collection of features to a [Matrix]
+ */
+public operator fun <T : Any> Matrix<T>.plus(newFeatures: Collection<MatrixFeature>): MatrixWrapper<T> =
+    if (this is MatrixWrapper) {
+        MatrixWrapper(matrix, features + newFeatures)
+    } else {
+        MatrixWrapper(this, newFeatures.toSet())
+    }
+
 public inline fun Structure2D.Companion.real(
     rows: Int,
     columns: Int,
@@ -39,51 +61,37 @@ public inline fun Structure2D.Companion.real(
 /**
  * Build a square matrix from given elements.
  */
-public fun <T : Any> Structure2D.Companion.square(vararg elements: T): FeaturedMatrix<T> {
+public fun <T : Any> Structure2D.Companion.square(vararg elements: T): Matrix<T> {
     val size: Int = sqrt(elements.size.toDouble()).toInt()
     require(size * size == elements.size) { "The number of elements ${elements.size} is not a full square" }
     val buffer = elements.asBuffer()
     return BufferMatrix(size, size, buffer)
 }
 
-public val Matrix<*>.features: Set<MatrixFeature> get() = (this as? FeaturedMatrix)?.features ?: emptySet()
-
-/**
- * Check if matrix has the given feature class
- */
-public inline fun <reified T : Any> Matrix<*>.hasFeature(): Boolean =
-    features.find { it is T } != null
-
-/**
- * Get the first feature matching given class. Does not guarantee that matrix has only one feature matching the criteria
- */
-public inline fun <reified T : Any> Matrix<*>.getFeature(): T? =
-    features.filterIsInstance<T>().firstOrNull()
-
 /**
  * Diagonal matrix of ones. The matrix is virtual no actual matrix is created
  */
-public fun <T : Any, R : Ring<T>> GenericMatrixContext<T, R, *>.one(rows: Int, columns: Int): FeaturedMatrix<T> =
+public fun <T : Any, R : Ring<T>> GenericMatrixContext<T, R, *>.one(rows: Int, columns: Int): Matrix<T> =
-    VirtualMatrix(rows, columns, DiagonalFeature) { i, j ->
+    VirtualMatrix(rows, columns) { i, j ->
         if (i == j) elementContext.one else elementContext.zero
-    }
+    } + UnitFeature
 
 
 /**
  * A virtual matrix of zeroes
  */
-public fun <T : Any, R : Ring<T>> GenericMatrixContext<T, R, *>.zero(rows: Int, columns: Int): FeaturedMatrix<T> =
+public fun <T : Any, R : Ring<T>> GenericMatrixContext<T, R, *>.zero(rows: Int, columns: Int): Matrix<T> =
-    VirtualMatrix(rows, columns) { _, _ -> elementContext.zero }
+    VirtualMatrix(rows, columns) { _, _ -> elementContext.zero } + ZeroFeature
 
 public class TransposedFeature<T : Any>(public val original: Matrix<T>) : MatrixFeature
 
 /**
  * Create a virtual transposed matrix without copying anything. `A.transpose().transpose() === A`
  */
+@OptIn(UnstableKMathAPI::class)
 public fun <T : Any> Matrix<T>.transpose(): Matrix<T> {
     return getFeature<TransposedFeature<T>>()?.original ?: VirtualMatrix(
         colNum,
         rowNum,
-        setOf(TransposedFeature(this))
+    ) { i, j -> get(j, i) } + TransposedFeature(this)
-    ) { i, j -> get(j, i) }
 }

kmath-core/src/commonMain/kotlin/kscience/kmath/linear/RealMatrixContext.kt

@@ -1,9 +1,6 @@
 package kscience.kmath.linear
 
-import kscience.kmath.operations.RealField
 import kscience.kmath.structures.Matrix
-import kscience.kmath.structures.MutableBuffer
-import kscience.kmath.structures.MutableBufferFactory
 import kscience.kmath.structures.RealBuffer
 
 @Suppress("OVERRIDE_BY_INLINE")
@@ -22,9 +19,9 @@ public object RealMatrixContext : MatrixContext<Double, BufferMatrix<Double>> {
         produce(rowNum, colNum) { i, j -> get(i, j) }
     }
 
-    public fun one(rows: Int, columns: Int): FeaturedMatrix<Double> = VirtualMatrix(rows, columns, DiagonalFeature) { i, j ->
+    public fun one(rows: Int, columns: Int): Matrix<Double> = VirtualMatrix(rows, columns) { i, j ->
         if (i == j) 1.0 else 0.0
-    }
+    } + DiagonalFeature
 
     public override infix fun Matrix<Double>.dot(other: Matrix<Double>): BufferMatrix<Double> {
         require(colNum == other.rowNum) { "Matrix dot operation dimension mismatch: ($rowNum, $colNum) x (${other.rowNum}, ${other.colNum})" }
@@ -69,16 +66,3 @@ public object RealMatrixContext : MatrixContext<Double, BufferMatrix<Double>> {
  * Partially optimized real-valued matrix
  */
 public val MatrixContext.Companion.real: RealMatrixContext get() = RealMatrixContext
-
-public fun RealMatrixContext.solveWithLUP(a: Matrix<Double>, b: Matrix<Double>): FeaturedMatrix<Double> {
-    // Use existing decomposition if it is provided by matrix
-    val bufferFactory: MutableBufferFactory<Double> = MutableBuffer.Companion::real
-    val decomposition = a.getFeature() ?: lup(bufferFactory, RealField, a) { it < 1e-11 }
-    return decomposition.solveWithLUP(bufferFactory, b)
-}
-
-/**
- * Inverses a square matrix using LUP decomposition. Non square matrix will throw a error.
- */
-public fun RealMatrixContext.inverseWithLUP(matrix: Matrix<Double>): FeaturedMatrix<Double> =
-    solveWithLUP(matrix, one(matrix.rowNum, matrix.colNum))

kmath-core/src/commonMain/kotlin/kscience/kmath/linear/VirtualMatrix.kt

@@ -5,31 +5,16 @@ import kscience.kmath.structures.Matrix
 public class VirtualMatrix<T : Any>(
     override val rowNum: Int,
     override val colNum: Int,
-    override val features: Set<MatrixFeature> = emptySet(),
     public val generator: (i: Int, j: Int) -> T
-) : FeaturedMatrix<T> {
+) : Matrix<T> {
-    public constructor(
-        rowNum: Int,
-        colNum: Int,
-        vararg features: MatrixFeature,
-        generator: (i: Int, j: Int) -> T
-    ) : this(
-        rowNum,
-        colNum,
-        setOf(*features),
-        generator
-    )
 
     override val shape: IntArray get() = intArrayOf(rowNum, colNum)
 
     override operator fun get(i: Int, j: Int): T = generator(i, j)
 
-    override fun suggestFeature(vararg features: MatrixFeature): VirtualMatrix<T> =
-        VirtualMatrix(rowNum, colNum, this.features + features, generator)
-
     override fun equals(other: Any?): Boolean {
         if (this === other) return true
-        if (other !is FeaturedMatrix<*>) return false
+        if (other !is Matrix<*>) return false
 
         if (rowNum != other.rowNum) return false
         if (colNum != other.colNum) return false
@@ -40,21 +25,9 @@ public class VirtualMatrix<T : Any>(
     override fun hashCode(): Int {
         var result = rowNum
         result = 31 * result + colNum
-        result = 31 * result + features.hashCode()
         result = 31 * result + generator.hashCode()
         return result
     }
 
 
-    public companion object {
-        /**
-         * Wrap a matrix adding additional features to it
-         */
-        public fun <T : Any> wrap(matrix: Matrix<T>, vararg features: MatrixFeature): FeaturedMatrix<T> {
-            return if (matrix is VirtualMatrix)
-                VirtualMatrix(matrix.rowNum, matrix.colNum, matrix.features + features, matrix.generator)
-            else
-                VirtualMatrix(matrix.rowNum, matrix.colNum, matrix.features + features) { i, j -> matrix[i, j] }
-        }
-    }
 }

kmath-core/src/commonMain/kotlin/kscience/kmath/operations/numbers.kt

@@ -179,13 +179,15 @@ public object FloatField : ExtendedField<Float>, Norm<Float, Float> {
  * A field for [Int] without boxing. Does not produce corresponding ring element.
  */
 @Suppress("EXTENSION_SHADOWED_BY_MEMBER", "OVERRIDE_BY_INLINE", "NOTHING_TO_INLINE")
-public object IntRing : Ring<Int>, Norm<Int, Int> {
+public object IntRing : Ring<Int>, Norm<Int, Int>, NumericAlgebra<Int> {
     public override val zero: Int
         get() = 0
 
     public override val one: Int
         get() = 1
 
+    override fun number(value: Number): Int = value.toInt()
+
     public override inline fun add(a: Int, b: Int): Int = a + b
     public override inline fun multiply(a: Int, k: Number): Int = k.toInt() * a
 
@@ -203,13 +205,15 @@ public object IntRing : Ring<Int>, Norm<Int, Int> {
  * A field for [Short] without boxing. Does not produce appropriate ring element.
  */
 @Suppress("EXTENSION_SHADOWED_BY_MEMBER", "OVERRIDE_BY_INLINE", "NOTHING_TO_INLINE")
-public object ShortRing : Ring<Short>, Norm<Short, Short> {
+public object ShortRing : Ring<Short>, Norm<Short, Short>, NumericAlgebra<Short> {
     public override val zero: Short
         get() = 0
 
     public override val one: Short
         get() = 1
 
+    override fun number(value: Number): Short = value.toShort()
+
     public override inline fun add(a: Short, b: Short): Short = (a + b).toShort()
     public override inline fun multiply(a: Short, k: Number): Short = (a * k.toShort()).toShort()
 
@@ -227,13 +231,15 @@ public object ShortRing : Ring<Short>, Norm<Short, Short> {
  * A field for [Byte] without boxing. Does not produce appropriate ring element.
  */
 @Suppress("EXTENSION_SHADOWED_BY_MEMBER", "OVERRIDE_BY_INLINE", "NOTHING_TO_INLINE")
-public object ByteRing : Ring<Byte>, Norm<Byte, Byte> {
+public object ByteRing : Ring<Byte>, Norm<Byte, Byte>, NumericAlgebra<Byte> {
     public override val zero: Byte
         get() = 0
 
     public override val one: Byte
         get() = 1
 
+    override fun number(value: Number): Byte = value.toByte()
+
     public override inline fun add(a: Byte, b: Byte): Byte = (a + b).toByte()
     public override inline fun multiply(a: Byte, k: Number): Byte = (a * k.toByte()).toByte()
 
@@ -251,13 +257,15 @@ public object ByteRing : Ring<Byte>, Norm<Byte, Byte> {
  * A field for [Double] without boxing. Does not produce appropriate ring element.
  */
 @Suppress("EXTENSION_SHADOWED_BY_MEMBER", "OVERRIDE_BY_INLINE", "NOTHING_TO_INLINE")
-public object LongRing : Ring<Long>, Norm<Long, Long> {
+public object LongRing : Ring<Long>, Norm<Long, Long>, NumericAlgebra<Long> {
     public override val zero: Long
         get() = 0L
 
     public override val one: Long
         get() = 1L
 
+    override fun number(value: Number): Long = value.toLong()
+
     public override inline fun add(a: Long, b: Long): Long = a + b
     public override inline fun multiply(a: Long, k: Number): Long = a * k.toLong()
 

kmath-core/src/commonMain/kotlin/kscience/kmath/structures/NDStructure.kt

@@ -1,5 +1,6 @@
 package kscience.kmath.structures
 
+import kscience.kmath.misc.UnstableKMathAPI
 import kotlin.jvm.JvmName
 import kotlin.native.concurrent.ThreadLocal
 import kotlin.reflect.KClass
@@ -42,6 +43,13 @@ public interface NDStructure<T> {
     public override fun equals(other: Any?): Boolean
     public override fun hashCode(): Int
 
+    /**
+     * Feature is additional property or hint that does not directly affect the structure, but could in some cases help
+     * optimize operations and performance. If the feature is not present, null is defined.
+     */
+    @UnstableKMathAPI
+    public fun <T : Any> getFeature(type: KClass<T>): T? = null
+
     public companion object {
         /**
          * Indicates whether some [NDStructure] is equal to another one.
@@ -121,6 +129,9 @@ public interface NDStructure<T> {
  */
 public operator fun <T> NDStructure<T>.get(vararg index: Int): T = get(index)
 
+@UnstableKMathAPI
+public inline fun <reified T : Any> NDStructure<*>.getFeature(): T? = getFeature(T::class)
+
 /**
  * Represents mutable [NDStructure].
  */

kmath-core/src/commonMain/kotlin/kscience/kmath/structures/Structure2D.kt

@@ -9,12 +9,14 @@ public interface Structure2D<T> : NDStructure<T> {
     /**
      * The number of rows in this structure.
      */
-    public val rowNum: Int get() = shape[0]
+    public val rowNum: Int
 
     /**
      * The number of columns in this structure.
      */
-    public val colNum: Int get() = shape[1]
+    public val colNum: Int
+
+    public override val shape: IntArray get() = intArrayOf(rowNum, colNum)
 
     /**
      * The buffer of rows of this structure. It gets elements from the structure dynamically.
@@ -56,6 +58,9 @@ public interface Structure2D<T> : NDStructure<T> {
 private inline class Structure2DWrapper<T>(val structure: NDStructure<T>) : Structure2D<T> {
     override val shape: IntArray get() = structure.shape
 
+    override val rowNum: Int get() = shape[0]
+    override val colNum: Int get() = shape[1]
+
     override operator fun get(i: Int, j: Int): T = structure[i, j]
 
     override fun elements(): Sequence<Pair<IntArray, T>> = structure.elements()

kmath-dimensions/src/commonMain/kotlin/kscience/kmath/dimensions/Wrappers.kt

@@ -40,6 +40,8 @@ public inline class DMatrixWrapper<T, R : Dimension, C : Dimension>(
     private val structure: Structure2D<T>,
 ) : DMatrix<T, R, C> {
     override val shape: IntArray get() = structure.shape
+    override val rowNum: Int get() = shape[0]
+    override val colNum: Int get() = shape[1]
     override operator fun get(i: Int, j: Int): T = structure[i, j]
 }
 
@@ -147,6 +149,7 @@ public inline fun <reified D : Dimension> DMatrixContext<Double>.one(): DMatrix<
     if (i == j) 1.0 else 0.0
 }
 
-public inline fun <reified R : Dimension, reified C : Dimension> DMatrixContext<Double>.zero(): DMatrix<Double, R, C> = produce { _, _ ->
+public inline fun <reified R : Dimension, reified C : Dimension> DMatrixContext<Double>.zero(): DMatrix<Double, R, C> =
-    0.0
+    produce { _, _ ->
-}
+        0.0
+    }

kmath-ejml/src/main/kotlin/kscience/kmath/ejml/EjmlMatrix.kt

@@ -1,10 +1,13 @@
 package kscience.kmath.ejml
 
 import kscience.kmath.linear.*
-import kscience.kmath.structures.NDStructure
+import kscience.kmath.misc.UnstableKMathAPI
+import kscience.kmath.structures.Matrix
 import kscience.kmath.structures.RealBuffer
 import org.ejml.dense.row.factory.DecompositionFactory_DDRM
 import org.ejml.simple.SimpleMatrix
+import kotlin.reflect.KClass
+import kotlin.reflect.cast
 
 /**
  * Represents featured matrix over EJML [SimpleMatrix].
@@ -12,85 +15,65 @@ import org.ejml.simple.SimpleMatrix
  * @property origin the underlying [SimpleMatrix].
  * @author Iaroslav Postovalov
  */
-public class EjmlMatrix(
+public inline class EjmlMatrix(
     public val origin: SimpleMatrix,
-    features: Set<MatrixFeature> = emptySet()
+) : Matrix<Double> {
-) :    FeaturedMatrix<Double> {
+    public override val rowNum: Int get() = origin.numRows()
-    public override val rowNum: Int
-        get() = origin.numRows()
 
-    public override val colNum: Int
+    public override val colNum: Int get() = origin.numCols()
-        get() = origin.numCols()
 
-    public override val shape: IntArray by lazy { intArrayOf(rowNum, colNum) }
+    @UnstableKMathAPI
-
+    override fun <T : Any> getFeature(type: KClass<T>): T? = when (type) {
-    public override val features: Set<MatrixFeature> = hashSetOf(
+        InverseMatrixFeature::class -> object : InverseMatrixFeature<Double> {
-        object : InverseMatrixFeature<Double> {
+            override val inverse: Matrix<Double> by lazy { EjmlMatrix(origin.invert()) }
-            override val inverse: FeaturedMatrix<Double> by lazy { EjmlMatrix(origin.invert()) }
+        }
-        },
+        DeterminantFeature::class -> object : DeterminantFeature<Double> {
-
-        object : DeterminantFeature<Double> {
             override val determinant: Double by lazy(origin::determinant)
-        },
+        }
-
+        SingularValueDecompositionFeature::class -> object : SingularValueDecompositionFeature<Double> {
-        object : SingularValueDecompositionFeature<Double> {
             private val svd by lazy {
                 DecompositionFactory_DDRM.svd(origin.numRows(), origin.numCols(), true, true, false)
                     .apply { decompose(origin.ddrm.copy()) }
             }
 
-            override val u: FeaturedMatrix<Double> by lazy { EjmlMatrix(SimpleMatrix(svd.getU(null, false))) }
+            override val u: Matrix<Double> by lazy { EjmlMatrix(SimpleMatrix(svd.getU(null, false))) }
-            override val s: FeaturedMatrix<Double> by lazy { EjmlMatrix(SimpleMatrix(svd.getW(null))) }
+            override val s: Matrix<Double> by lazy { EjmlMatrix(SimpleMatrix(svd.getW(null))) }
-            override val v: FeaturedMatrix<Double> by lazy { EjmlMatrix(SimpleMatrix(svd.getV(null, false))) }
+            override val v: Matrix<Double> by lazy { EjmlMatrix(SimpleMatrix(svd.getV(null, false))) }
             override val singularValues: Point<Double> by lazy { RealBuffer(svd.singularValues) }
-        },
+        }
-
+        QRDecompositionFeature::class -> object : QRDecompositionFeature<Double> {
-        object : QRDecompositionFeature<Double> {
             private val qr by lazy {
                 DecompositionFactory_DDRM.qr().apply { decompose(origin.ddrm.copy()) }
             }
 
-            override val q: FeaturedMatrix<Double> by lazy { EjmlMatrix(SimpleMatrix(qr.getQ(null, false))) }
+            override val q: Matrix<Double> by lazy { EjmlMatrix(SimpleMatrix(qr.getQ(null, false))) }
-            override val r: FeaturedMatrix<Double> by lazy { EjmlMatrix(SimpleMatrix(qr.getR(null, false))) }
+            override val r: Matrix<Double> by lazy { EjmlMatrix(SimpleMatrix(qr.getR(null, false))) }
-        },
+        }
-
+        CholeskyDecompositionFeature::class ->         object : CholeskyDecompositionFeature<Double> {
-        object : CholeskyDecompositionFeature<Double> {
+            override val l: Matrix<Double> by lazy {
-            override val l: FeaturedMatrix<Double> by lazy {
                 val cholesky =
                     DecompositionFactory_DDRM.chol(rowNum, true).apply { decompose(origin.ddrm.copy()) }
 
-                EjmlMatrix(SimpleMatrix(cholesky.getT(null)), setOf(LFeature))
+                EjmlMatrix(SimpleMatrix(cholesky.getT(null))) + LFeature
             }
-        },
+        }
-
+        LupDecompositionFeature::class ->         object : LupDecompositionFeature<Double> {
-        object : LupDecompositionFeature<Double> {
             private val lup by lazy {
                 DecompositionFactory_DDRM.lu(origin.numRows(), origin.numCols()).apply { decompose(origin.ddrm.copy()) }
             }
 
-            override val l: FeaturedMatrix<Double> by lazy {
+            override val l: Matrix<Double> by lazy {
-                EjmlMatrix(SimpleMatrix(lup.getLower(null)), setOf(LFeature))
+                EjmlMatrix(SimpleMatrix(lup.getLower(null))) + LFeature
             }
 
-            override val u: FeaturedMatrix<Double> by lazy {
+            override val u: Matrix<Double> by lazy {
-                EjmlMatrix(SimpleMatrix(lup.getUpper(null)), setOf(UFeature))
+                EjmlMatrix(SimpleMatrix(lup.getUpper(null))) + UFeature
             }
 
-            override val p: FeaturedMatrix<Double> by lazy { EjmlMatrix(SimpleMatrix(lup.getRowPivot(null))) }
+            override val p: Matrix<Double> by lazy { EjmlMatrix(SimpleMatrix(lup.getRowPivot(null))) }
-        },
+        }
-    ) union features
+        else -> null
-
+    }?.let{type.cast(it)}
-    public override fun suggestFeature(vararg features: MatrixFeature): EjmlMatrix =
-        EjmlMatrix(origin, this.features + features)
 
     public override operator fun get(i: Int, j: Int): Double = origin[i, j]
-
-    public override fun equals(other: Any?): Boolean {
-        if (other is EjmlMatrix) return origin.isIdentical(other.origin, 0.0)
-        return NDStructure.equals(this, other as? NDStructure<*> ?: return false)
-    }
-
-    public override fun hashCode(): Int = origin.hashCode()
-
-    public override fun toString(): String = "EjmlMatrix($origin)"
 }

kmath-ejml/src/main/kotlin/kscience/kmath/ejml/EjmlMatrixContext.kt

@@ -2,23 +2,29 @@ package kscience.kmath.ejml
 
 import kscience.kmath.linear.InverseMatrixFeature
 import kscience.kmath.linear.MatrixContext
+import kscience.kmath.linear.MatrixWrapper
 import kscience.kmath.linear.Point
-import kscience.kmath.linear.getFeature
+import kscience.kmath.misc.UnstableKMathAPI
 import kscience.kmath.structures.Matrix
+import kscience.kmath.structures.getFeature
 import org.ejml.simple.SimpleMatrix
 
-/**
- * Converts this matrix to EJML one.
- */
-public fun Matrix<Double>.toEjml(): EjmlMatrix =
-    if (this is EjmlMatrix) this else EjmlMatrixContext.produce(rowNum, colNum) { i, j -> get(i, j) }
-
 /**
  * Represents context of basic operations operating with [EjmlMatrix].
  *
  * @author Iaroslav Postovalov
  */
 public object EjmlMatrixContext : MatrixContext<Double, EjmlMatrix> {
+
+    /**
+     * Converts this matrix to EJML one.
+     */
+    public fun Matrix<Double>.toEjml(): EjmlMatrix = when {
+        this is EjmlMatrix -> this
+        this is MatrixWrapper && matrix is EjmlMatrix -> matrix as EjmlMatrix
+        else -> produce(rowNum, colNum) { i, j -> get(i, j) }
+    }
+
     /**
      * Converts this vector to EJML one.
      */
@@ -80,6 +86,7 @@ public fun EjmlMatrixContext.solve(a: Matrix<Double>, b: Matrix<Double>): EjmlMa
 public fun EjmlMatrixContext.solve(a: Matrix<Double>, b: Point<Double>): EjmlVector =
     EjmlVector(a.toEjml().origin.solve(b.toEjml().origin))
 
+@OptIn(UnstableKMathAPI::class)
 public fun EjmlMatrix.inverted(): EjmlMatrix = getFeature<InverseMatrixFeature<Double>>()!!.inverse as EjmlMatrix
 
 public fun EjmlMatrixContext.inverse(matrix: Matrix<Double>): Matrix<Double> = matrix.toEjml().inverted()

kmath-ejml/src/test/kotlin/kscience/kmath/ejml/EjmlMatrixTest.kt

@@ -3,7 +3,8 @@ package kscience.kmath.ejml
 import kscience.kmath.linear.DeterminantFeature
 import kscience.kmath.linear.LupDecompositionFeature
 import kscience.kmath.linear.MatrixFeature
-import kscience.kmath.linear.getFeature
+import kscience.kmath.linear.plus
+import kscience.kmath.structures.getFeature
 import org.ejml.dense.row.factory.DecompositionFactory_DDRM
 import org.ejml.simple.SimpleMatrix
 import kotlin.random.Random
@@ -58,7 +59,7 @@ internal class EjmlMatrixTest {
 
     @Test
     fun suggestFeature() {
-        assertNotNull(EjmlMatrix(randomMatrix).suggestFeature(SomeFeature).getFeature<SomeFeature>())
+        assertNotNull((EjmlMatrix(randomMatrix) + SomeFeature).getFeature<SomeFeature>())
     }
 
     @Test

kmath-for-real/src/commonMain/kotlin/kscience/kmath/real/RealMatrix.kt

@@ -1,8 +1,12 @@
 package kscience.kmath.real
 
-import kscience.kmath.linear.*
+import kscience.kmath.linear.MatrixContext
+import kscience.kmath.linear.VirtualMatrix
+import kscience.kmath.linear.inverseWithLUP
+import kscience.kmath.linear.real
 import kscience.kmath.misc.UnstableKMathAPI
 import kscience.kmath.structures.Buffer
+import kscience.kmath.structures.Matrix
 import kscience.kmath.structures.RealBuffer
 import kscience.kmath.structures.asIterable
 import kotlin.math.pow
@@ -19,7 +23,7 @@ import kotlin.math.pow
  *  Functions that help create a real (Double) matrix
  */
 
-public typealias RealMatrix = FeaturedMatrix<Double>
+public typealias RealMatrix = Matrix<Double>
 
 public fun realMatrix(rowNum: Int, colNum: Int, initializer: (i: Int, j: Int) -> Double): RealMatrix =
     MatrixContext.real.produce(rowNum, colNum, initializer)

kmath-for-real/src/commonTest/kotlin/kaceince/kmath/real/RealMatrixTest.kt

@@ -1,6 +1,5 @@
 package kaceince.kmath.real
 
-import kscience.kmath.linear.VirtualMatrix
 import kscience.kmath.linear.build
 import kscience.kmath.real.*
 import kscience.kmath.structures.Matrix
@@ -42,7 +41,7 @@ internal class RealMatrixTest {
             1.0, 0.0, 0.0,
             0.0, 1.0, 2.0
         )
-        assertEquals(VirtualMatrix.wrap(matrix2), matrix1.repeatStackVertical(3))
+        assertEquals(matrix2, matrix1.repeatStackVertical(3))
     }
 
     @Test