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

@@ -13,44 +13,6 @@ public class CMMatrix(public val origin: RealMatrix) : Matrix<Double> {
     public override val rowNum: Int get() = origin.rowDimension
     public override val colNum: Int get() = origin.columnDimension
 
-    @UnstableKMathAPI
-    override fun <T : Any> getFeature(type: KClass<T>): T? = when (type) {
-        DiagonalFeature::class -> if (origin is DiagonalMatrix) DiagonalFeature else null
-
-        DeterminantFeature::class, LupDecompositionFeature::class -> object :
-            DeterminantFeature<Double>,
-            LupDecompositionFeature<Double> {
-            private val lup by lazy { LUDecomposition(origin) }
-            override val determinant: Double by lazy { lup.determinant }
-            override val l: Matrix<Double> by lazy { CMMatrix(lup.l) + LFeature }
-            override val u: Matrix<Double> by lazy { CMMatrix(lup.u) + UFeature }
-            override val p: Matrix<Double> by lazy { CMMatrix(lup.p) }
-        }
-
-        CholeskyDecompositionFeature::class -> object : CholeskyDecompositionFeature<Double> {
-            override val l: Matrix<Double> by lazy {
-                val cholesky = CholeskyDecomposition(origin)
-                CMMatrix(cholesky.l) + LFeature
-            }
-        }
-
-        QRDecompositionFeature::class -> object : QRDecompositionFeature<Double> {
-            private val qr by lazy { QRDecomposition(origin) }
-            override val q: Matrix<Double> by lazy { CMMatrix(qr.q) + OrthogonalFeature }
-            override val r: Matrix<Double> by lazy { CMMatrix(qr.r) + UFeature }
-        }
-
-        SingularValueDecompositionFeature::class -> object : SingularValueDecompositionFeature<Double> {
-            private val sv by lazy { SingularValueDecomposition(origin) }
-            override val u: Matrix<Double> by lazy { CMMatrix(sv.u) }
-            override val s: Matrix<Double> by lazy { CMMatrix(sv.s) }
-            override val v: Matrix<Double> by lazy { CMMatrix(sv.v) }
-            override val singularValues: Point<Double> by lazy { RealBuffer(sv.singularValues) }
-        }
-
-        else -> null
-    }?.let(type::cast)
-
     public override operator fun get(i: Int, j: Int): Double = origin.getEntry(i, j)
 
     override fun equals(other: Any?): Boolean {
@@ -134,6 +96,50 @@ public object CMLinearSpace : LinearSpace<Double, RealField> {
 
     override fun Double.times(v: Point<Double>): CMVector =
         v * this
+
+    @UnstableKMathAPI
+    override fun <F : Any> getFeature(structure: Matrix<Double>, type: KClass<F>): F? {
+        //Return the feature if it is intrinsic to the structure
+        structure.getFeature(type)?.let { return it }
+
+        val origin = structure.toCM().origin
+        
+        return when (type) {
+            DiagonalFeature::class -> if (origin is DiagonalMatrix) DiagonalFeature else null
+
+            DeterminantFeature::class, LupDecompositionFeature::class -> object :
+                DeterminantFeature<Double>,
+                LupDecompositionFeature<Double> {
+                private val lup by lazy { LUDecomposition(origin) }
+                override val determinant: Double by lazy { lup.determinant }
+                override val l: Matrix<Double> by lazy { CMMatrix(lup.l) + LFeature }
+                override val u: Matrix<Double> by lazy { CMMatrix(lup.u) + UFeature }
+                override val p: Matrix<Double> by lazy { CMMatrix(lup.p) }
+            }
+
+            CholeskyDecompositionFeature::class -> object : CholeskyDecompositionFeature<Double> {
+                override val l: Matrix<Double> by lazy {
+                    val cholesky = CholeskyDecomposition(origin)
+                    CMMatrix(cholesky.l) + LFeature
+                }
+            }
+
+            QRDecompositionFeature::class -> object : QRDecompositionFeature<Double> {
+                private val qr by lazy { QRDecomposition(origin) }
+                override val q: Matrix<Double> by lazy { CMMatrix(qr.q) + OrthogonalFeature }
+                override val r: Matrix<Double> by lazy { CMMatrix(qr.r) + UFeature }
+            }
+
+            SingularValueDecompositionFeature::class -> object : SingularValueDecompositionFeature<Double> {
+                private val sv by lazy { SingularValueDecomposition(origin) }
+                override val u: Matrix<Double> by lazy { CMMatrix(sv.u) }
+                override val s: Matrix<Double> by lazy { CMMatrix(sv.s) }
+                override val v: Matrix<Double> by lazy { CMMatrix(sv.v) }
+                override val singularValues: Point<Double> by lazy { RealBuffer(sv.singularValues) }
+            }
+            else -> null
+        }?.let(type::cast)
+    }
 }
 
 public operator fun CMMatrix.plus(other: CMMatrix): CMMatrix =

kmath-core/src/commonMain/kotlin/space/kscience/kmath/linear/LinearSpace.kt

@@ -156,16 +156,15 @@ public interface LinearSpace<T : Any, out A : Ring<T>> {
     public operator fun T.times(v: Point<T>): Point<T> = v * this
 
     /**
-     * Gets a feature from the matrix. This function may return some additional features to
-     * [space.kscience.kmath.nd.NDStructure.getFeature].
+     * Get a feature of the structure in this scope. Structure features take precedence other context features
      *
      * @param F the type of feature.
-     * @param m the matrix.
+     * @param structure the structure.
      * @param type the [KClass] instance of [F].
      * @return a feature object or `null` if it isn't present.
      */
     @UnstableKMathAPI
-    public fun <F : Any> getFeature(m: Matrix<T>, type: KClass<F>): F? = m.getFeature(type)
+    public fun <F : Any> getFeature(structure: Matrix<T>, type: KClass<F>): F? = structure.getFeature(type)
 
     public companion object {
 
@@ -187,19 +186,17 @@ public interface LinearSpace<T : Any, out A : Ring<T>> {
     }
 }
 
-public operator fun <LS : LinearSpace<*, *>, R> LS.invoke(block: LS.() -> R): R = run(block)
-
 /**
- * Gets a feature from the matrix. This function may return some additional features to
- * [space.kscience.kmath.nd.NDStructure.getFeature].
+ * Get a feature of the structure in this scope. Structure features take precedence other context features
  *
  * @param T the type of items in the matrices.
- * @param M the type of operated matrices.
  * @param F the type of feature.
- * @receiver the [LinearSpace] of [T].
- * @param m the matrix.
  * @return a feature object or `null` if it isn't present.
  */
 @UnstableKMathAPI
-public inline fun <T : Any, reified F : Any> LinearSpace<T, *>.getFeature(m: Matrix<T>): F? = getFeature(m, F::class)
+public inline fun <T : Any, reified F : Any> LinearSpace<T, *>.getFeature(structure: Matrix<T>): F? =
+    getFeature(structure, F::class)
+
+
+public operator fun <LS : LinearSpace<*, *>, R> LS.invoke(block: LS.() -> R): R = run(block)
 

kmath-core/src/commonMain/kotlin/space/kscience/kmath/nd/NDAlgebra.kt

@@ -1,7 +1,9 @@
 package space.kscience.kmath.nd
 
+import space.kscience.kmath.misc.UnstableKMathAPI
 import space.kscience.kmath.operations.*
 import space.kscience.kmath.structures.*
+import kotlin.reflect.KClass
 
 /**
  * An exception is thrown when the expected ans actual shape of NDArray differs.
@@ -56,9 +58,32 @@ public interface NDAlgebra<T, C : Algebra<T>> {
     public operator fun Function1<T, T>.invoke(structure: NDStructure<T>): NDStructure<T> =
         structure.map { value -> this@invoke(value) }
 
+    /**
+     * Get a feature of the structure in this scope. Structure features take precedence other context features
+     *
+     * @param F the type of feature.
+     * @param structure the structure.
+     * @param type the [KClass] instance of [F].
+     * @return a feature object or `null` if it isn't present.
+     */
+    @UnstableKMathAPI
+    public fun <F : Any> getFeature(structure: NDStructure<T>, type: KClass<F>): F? = structure.getFeature(type)
+
     public companion object
 }
 
+
+/**
+ * Get a feature of the structure in this scope. Structure features take precedence other context features
+ *
+ * @param T the type of items in the matrices.
+ * @param F the type of feature.
+ * @return a feature object or `null` if it isn't present.
+ */
+@UnstableKMathAPI
+public inline fun <T : Any, reified F : Any> NDAlgebra<T, *>.getFeature(structure: NDStructure<T>): F? =
+    getFeature(structure, F::class)
+
 /**
  * Checks if given elements are consistent with this context.
  *

kmath-ejml/src/main/kotlin/space/kscience/kmath/ejml/EjmlLinearSpace.kt

@@ -1,10 +1,14 @@
 package space.kscience.kmath.ejml
 
+import org.ejml.dense.row.factory.DecompositionFactory_DDRM
 import org.ejml.simple.SimpleMatrix
 import space.kscience.kmath.linear.*
 import space.kscience.kmath.misc.UnstableKMathAPI
 import space.kscience.kmath.nd.getFeature
 import space.kscience.kmath.operations.RealField
+import space.kscience.kmath.structures.RealBuffer
+import kotlin.reflect.KClass
+import kotlin.reflect.cast
 
 /**
  * Represents context of basic operations operating with [EjmlMatrix].
@@ -83,6 +87,76 @@ public object EjmlLinearSpace : LinearSpace<Double, RealField> {
 
     override fun Double.times(v: Point<Double>): EjmlVector =
         v.toEjml().origin.scale(this).wrapVector()
+
+    @UnstableKMathAPI
+    override fun <F : Any> getFeature(structure: Matrix<Double>, type: KClass<F>): F? {
+        //Return the feature if it is intrinsic to the structure
+        structure.getFeature(type)?.let { return it }
+
+        val origin = structure.toEjml().origin
+
+        return when (type) {
+            InverseMatrixFeature::class -> object : InverseMatrixFeature<Double> {
+                override val inverse: Matrix<Double> by lazy { EjmlMatrix(origin.invert()) }
+            }
+
+            DeterminantFeature::class -> object : DeterminantFeature<Double> {
+                override val determinant: Double by lazy(origin::determinant)
+            }
+
+            SingularValueDecompositionFeature::class -> 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: Matrix<Double> by lazy { EjmlMatrix(SimpleMatrix(svd.getU(null, false))) }
+                override val s: Matrix<Double> by lazy { EjmlMatrix(SimpleMatrix(svd.getW(null))) }
+                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> {
+                private val qr by lazy {
+                    DecompositionFactory_DDRM.qr().apply { decompose(origin.ddrm.copy()) }
+                }
+
+                override val q: Matrix<Double> by lazy {
+                    EjmlMatrix(SimpleMatrix(qr.getQ(null, false))) + OrthogonalFeature
+                }
+
+                override val r: Matrix<Double> by lazy { EjmlMatrix(SimpleMatrix(qr.getR(null, false))) + UFeature }
+            }
+
+            CholeskyDecompositionFeature::class -> object : CholeskyDecompositionFeature<Double> {
+                override val l: Matrix<Double> by lazy {
+                    val cholesky =
+                        DecompositionFactory_DDRM.chol(structure.rowNum, true).apply { decompose(origin.ddrm.copy()) }
+
+                    EjmlMatrix(SimpleMatrix(cholesky.getT(null))) + LFeature
+                }
+            }
+
+            LupDecompositionFeature::class -> object : LupDecompositionFeature<Double> {
+                private val lup by lazy {
+                    DecompositionFactory_DDRM.lu(origin.numRows(), origin.numCols())
+                        .apply { decompose(origin.ddrm.copy()) }
+                }
+
+                override val l: Matrix<Double> by lazy {
+                    EjmlMatrix(SimpleMatrix(lup.getLower(null))) + LFeature
+                }
+
+                override val u: Matrix<Double> by lazy {
+                    EjmlMatrix(SimpleMatrix(lup.getUpper(null))) + UFeature
+                }
+
+                override val p: Matrix<Double> by lazy { EjmlMatrix(SimpleMatrix(lup.getRowPivot(null))) }
+            }
+
+            else -> null
+        }?.let(type::cast)
+    }
 }
 
 /**

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

@@ -1,13 +1,8 @@
 package space.kscience.kmath.ejml
 
-import org.ejml.dense.row.factory.DecompositionFactory_DDRM
 import org.ejml.simple.SimpleMatrix
-import space.kscience.kmath.linear.*
-import space.kscience.kmath.misc.UnstableKMathAPI
+import space.kscience.kmath.linear.Matrix
 import space.kscience.kmath.nd.NDStructure
-import space.kscience.kmath.structures.RealBuffer
-import kotlin.reflect.KClass
-import kotlin.reflect.cast
 
 /**
  * Represents featured matrix over EJML [SimpleMatrix].
@@ -19,68 +14,6 @@ public class EjmlMatrix(public val origin: SimpleMatrix) : Matrix<Double> {
     public override val rowNum: Int get() = origin.numRows()
     public override val colNum: Int get() = origin.numCols()
 
-    @UnstableKMathAPI
-    public override fun <T : Any> getFeature(type: KClass<T>): T? = when (type) {
-        InverseMatrixFeature::class -> object : InverseMatrixFeature<Double> {
-            override val inverse: Matrix<Double> by lazy { EjmlMatrix(origin.invert()) }
-        }
-
-        DeterminantFeature::class -> object : DeterminantFeature<Double> {
-            override val determinant: Double by lazy(origin::determinant)
-        }
-
-        SingularValueDecompositionFeature::class -> 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: Matrix<Double> by lazy { EjmlMatrix(SimpleMatrix(svd.getU(null, false))) }
-            override val s: Matrix<Double> by lazy { EjmlMatrix(SimpleMatrix(svd.getW(null))) }
-            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> {
-            private val qr by lazy {
-                DecompositionFactory_DDRM.qr().apply { decompose(origin.ddrm.copy()) }
-            }
-
-            override val q: Matrix<Double> by lazy {
-                EjmlMatrix(SimpleMatrix(qr.getQ(null, false))) + OrthogonalFeature
-            }
-
-            override val r: Matrix<Double> by lazy { EjmlMatrix(SimpleMatrix(qr.getR(null, false))) + UFeature }
-        }
-
-        CholeskyDecompositionFeature::class -> object : CholeskyDecompositionFeature<Double> {
-            override val l: Matrix<Double> by lazy {
-                val cholesky =
-                    DecompositionFactory_DDRM.chol(rowNum, true).apply { decompose(origin.ddrm.copy()) }
-
-                EjmlMatrix(SimpleMatrix(cholesky.getT(null))) + LFeature
-            }
-        }
-
-        LupDecompositionFeature::class -> object : LupDecompositionFeature<Double> {
-            private val lup by lazy {
-                DecompositionFactory_DDRM.lu(origin.numRows(), origin.numCols()).apply { decompose(origin.ddrm.copy()) }
-            }
-
-            override val l: Matrix<Double> by lazy {
-                EjmlMatrix(SimpleMatrix(lup.getLower(null))) + LFeature
-            }
-
-            override val u: Matrix<Double> by lazy {
-                EjmlMatrix(SimpleMatrix(lup.getUpper(null))) + UFeature
-            }
-
-            override val p: Matrix<Double> by lazy { EjmlMatrix(SimpleMatrix(lup.getRowPivot(null))) }
-        }
-
-        else -> null
-    }?.let(type::cast)
-
     public override operator fun get(i: Int, j: Int): Double = origin[i, j]
 
     override fun equals(other: Any?): Boolean {

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

@@ -2,10 +2,7 @@ package space.kscience.kmath.ejml
 
 import org.ejml.dense.row.factory.DecompositionFactory_DDRM
 import org.ejml.simple.SimpleMatrix
-import space.kscience.kmath.linear.DeterminantFeature
-import space.kscience.kmath.linear.LupDecompositionFeature
-import space.kscience.kmath.linear.MatrixFeature
-import space.kscience.kmath.linear.plus
+import space.kscience.kmath.linear.*
 import space.kscience.kmath.misc.UnstableKMathAPI
 import space.kscience.kmath.nd.getFeature
 import kotlin.random.Random
@@ -45,9 +42,9 @@ internal class EjmlMatrixTest {
     fun features() {
         val m = randomMatrix
         val w = EjmlMatrix(m)
-        val det = w.getFeature<DeterminantFeature<Double>>() ?: fail()
+        val det: DeterminantFeature<Double> = EjmlLinearSpace.getFeature(w) ?: fail()
         assertEquals(m.determinant(), det.determinant)
-        val lup = w.getFeature<LupDecompositionFeature<Double>>() ?: fail()
+        val lup: LupDecompositionFeature<Double> = EjmlLinearSpace.getFeature(w) ?: fail()
 
         val ludecompositionF64 = DecompositionFactory_DDRM.lu(m.numRows(), m.numCols())
             .also { it.decompose(m.ddrm.copy()) }