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

@@ -4,11 +4,16 @@ import org.apache.commons.math3.linear.*
 import org.apache.commons.math3.linear.RealMatrix
 import org.apache.commons.math3.linear.RealVector
 
-inline class CMMatrix(val origin: RealMatrix) : Matrix<Double> {
+class CMMatrix(val origin: RealMatrix, features: Set<MatrixFeature>? = null) : Matrix<Double> {
     override val rowNum: Int get() = origin.rowDimension
     override val colNum: Int get() = origin.columnDimension
 
-    override val features: Set<MatrixFeature> get() = emptySet()
+    override val features: Set<MatrixFeature> = features ?: sequence<MatrixFeature> {
+        if(origin is DiagonalMatrix) yield(DiagonalFeature)
+    }.toSet()
+
+    override fun suggestFeature(vararg features: MatrixFeature) =
+        CMMatrix(origin, this.features + features)
 
     override fun get(i: Int, j: Int): Double = origin.getEntry(i, j)
 }
@@ -23,7 +28,7 @@ fun Matrix<Double>.toCM(): CMMatrix = if (this is CMMatrix) {
 
 fun RealMatrix.toMatrix() = CMMatrix(this)
 
-inline class CMVector(val origin: RealVector) : Point<Double> {
+class CMVector(val origin: RealVector) : Point<Double> {
     override val size: Int get() = origin.dimension
 
     override fun get(index: Int): Double = origin.getEntry(index)

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

@@ -35,6 +35,9 @@ class BufferMatrix<T : Any>(
 
     override val shape: IntArray get() = intArrayOf(rowNum, colNum)
 
+    override fun suggestFeature(vararg features: MatrixFeature) =
+        BufferMatrix(rowNum, colNum, buffer, this.features + features)
+
     override fun get(index: IntArray): T = get(index[0], index[1])
 
     override fun get(i: Int, j: Int): T = buffer[i * colNum + j]

kmath-core/src/commonMain/kotlin/scientifik/kmath/linear/LUPDecomposition.kt

@@ -8,20 +8,19 @@ import scientifik.kmath.structures.MutableBufferFactory
 import scientifik.kmath.structures.NDStructure
 import scientifik.kmath.structures.get
 
-
 class LUPDecomposition<T : Comparable<T>>(
     private val elementContext: Ring<T>,
     internal val lu: NDStructure<T>,
     val pivot: IntArray,
     private val even: Boolean
-) : DeterminantFeature<T> {
+) : LUPDecompositionFeature<T>, DeterminantFeature<T> {
 
     /**
      * Returns the matrix L of the decomposition.
      *
      * L is a lower-triangular matrix with [Ring.one] in diagonal
      */
-    val l: Matrix<T> = VirtualMatrix(lu.shape[0], lu.shape[1]) { i, j ->
+    override val l: Matrix<T> = VirtualMatrix(lu.shape[0], lu.shape[1], setOf(LFeature)) { i, j ->
         when {
             j < i -> lu[i, j]
             j == i -> elementContext.one
@@ -35,7 +34,7 @@ class LUPDecomposition<T : Comparable<T>>(
      *
      * U is an upper-triangular matrix including the diagonal
      */
-    val u: Matrix<T> = VirtualMatrix(lu.shape[0], lu.shape[1]) { i, j ->
+    override val u: Matrix<T> = VirtualMatrix(lu.shape[0], lu.shape[1], setOf(UFeature)) { i, j ->
         if (j >= i) lu[i, j] else elementContext.zero
     }
 
@@ -46,7 +45,7 @@ class LUPDecomposition<T : Comparable<T>>(
      * 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].
      */
-    val p: Matrix<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
     }
 

kmath-core/src/commonMain/kotlin/scientifik/kmath/linear/Matrix.kt

@@ -116,6 +116,14 @@ interface Matrix<T : Any> : NDStructure<T> {
 
     val features: Set<MatrixFeature>
 
+    /**
+     * Suggest new feature for this matrix. The result is the new matrix that may or may not reuse existing data structure.
+     *
+     * The implementation does not guarantee to check that matrix actually have the feature, so one should be careful to
+     * add only those features that are valid.
+     */
+    fun suggestFeature(vararg features: MatrixFeature): Matrix<T>
+
     operator fun get(i: Int, j: Int): T
 
     override fun get(index: IntArray): T = get(index[0], index[1])

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

@@ -12,7 +12,7 @@ interface MatrixFeature
 object DiagonalFeature : MatrixFeature
 
 /**
- * Matix with this feature has all zero elements
+ * Matrix with this feature has all zero elements
  */
 object ZeroFeature : MatrixFeature
 
@@ -21,10 +21,42 @@ object ZeroFeature : MatrixFeature
  */
 object UnitFeature : MatrixFeature
 
+/**
+ * Inverted matrix feature
+ */
 interface InverseMatrixFeature<T : Any> : MatrixFeature {
     val inverse: Matrix<T>
 }
 
+/**
+ * A determinant container
+ */
 interface DeterminantFeature<T : Any> : MatrixFeature {
     val determinant: T
-}
+}
+
+@Suppress("FunctionName")
+fun <T: Any> DeterminantFeature(determinant: T) = object: DeterminantFeature<T>{
+    override val determinant: T = determinant
+}
+
+/**
+ * Lower triangular matrix
+ */
+object LFeature: MatrixFeature
+
+/**
+ * Upper triangular feature
+ */
+object UFeature: MatrixFeature
+
+/**
+ * TODO add documentation
+ */
+interface LUPDecompositionFeature<T : Any> : MatrixFeature {
+    val l: Matrix<T>
+    val u: Matrix<T>
+    val p: Matrix<T>
+}
+
+//TODO add sparse matrix feature

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

@@ -8,6 +8,9 @@ class VirtualMatrix<T : Any>(
 ) : Matrix<T> {
     override fun get(i: Int, j: Int): T = generator(i, j)
 
+    override fun suggestFeature(vararg features: MatrixFeature) =
+        VirtualMatrix(rowNum, colNum, this.features + features, generator)
+
     override fun equals(other: Any?): Boolean {
         if (this === other) return true
         if (other !is Matrix<*>) return false

kmath-core/src/commonMain/kotlin/scientifik/kmath/structures/ShortNDRing.kt

@@ -15,6 +15,7 @@ class ShortNDRing(override val shape: IntArray) :
     override val zero by lazy { produce { ShortRing.zero } }
     override val one by lazy { produce { ShortRing.one } }
 
+    @Suppress("OVERRIDE_BY_INLINE")
     override inline fun buildBuffer(size: Int, crossinline initializer: (Int) -> Short): Buffer<Short> =
         ShortBuffer(ShortArray(size) { initializer(it) })
 

kmath-koma/src/commonMain/kotlin/scientifik.kmath.linear/KomaMatrix.kt

@@ -48,10 +48,25 @@ class KomaMatrixContext<T : Any>(val factory: MatrixFactory<koma.matrix.Matrix<T
         KomaMatrix(a.toKoma().origin.inv())
 }
 
-inline class KomaMatrix<T : Any>(val origin: koma.matrix.Matrix<T>) : Matrix<T> {
+class KomaMatrix<T : Any>(val origin: koma.matrix.Matrix<T>, features: Set<MatrixFeature>? = null) :
+    Matrix<T> {
     override val rowNum: Int get() = origin.numRows()
     override val colNum: Int get() = origin.numCols()
-    override val features: Set<MatrixFeature> get() = emptySet()
+
+    override val features: Set<MatrixFeature> = features ?: setOf(
+        object : DeterminantFeature<T> {
+            override val determinant: T get() = origin.det()
+        },
+        object : LUPDecompositionFeature<T> {
+            private val lup by lazy { origin.LU() }
+            override val l: Matrix<T> get() = KomaMatrix(lup.second)
+            override val u: Matrix<T> get() = KomaMatrix(lup.third)
+            override val p: Matrix<T> get() = KomaMatrix(lup.first)
+        }
+    )
+
+    override fun suggestFeature(vararg features: MatrixFeature): Matrix<T> =
+        KomaMatrix(this.origin, this.features + features)
 
     override fun get(i: Int, j: Int): T = origin.getGeneric(i, j)
 }