Fixed LUP solver implementation

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

@@ -1,52 +1,28 @@
 package scientifik.kmath.linear
 
 import scientifik.kmath.operations.Field
-import scientifik.kmath.operations.RealField
 import scientifik.kmath.operations.Ring
 import scientifik.kmath.structures.*
 import scientifik.kmath.structures.MutableBuffer.Companion.boxing
 
-/**
- * Matrix LUP decomposition
- */
-interface LUPDecompositionFeature<T : Any> : DeterminantFeature<T> {
-    /**
-     * A reference to L-matrix
-     */
-    val l: Matrix<T>
-    /**
-     * A reference to u-matrix
-     */
-    val u: Matrix<T>
-    /**
-     * Pivoting points for each row
-     */
-    val pivot: IntArray
-    /**
-     * Permutation matrix based on [pivot]
-     */
-    val p: Matrix<T>
-}
 
-
+class LUPDecomposition<T : Comparable<T>>(
-private class LUPDecomposition<T : Comparable<T>, R : Ring<T>>(
+    private val elementContext: Ring<T>,
-    val context: R,
+    private val lu: NDStructure<T>,
-    val lu: NDStructure<T>,
+    val pivot: IntArray,
-    override val pivot: IntArray,
     private val even: Boolean
-) : LUPDecompositionFeature<T> {
+) : DeterminantFeature<T> {
 
     /**
      * Returns the matrix L of the decomposition.
      *
-     * L is a lower-triangular matrix
+     * L is a lower-triangular matrix with [Ring.one] in diagonal
-     * @return the L matrix (or null if decomposed matrix is singular)
      */
-    override val l: Matrix<T> = VirtualMatrix(lu.shape[0], lu.shape[1]) { i, j ->
+    val l: Matrix<T> = VirtualMatrix(lu.shape[0], lu.shape[1]) { i, j ->
         when {
             j < i -> lu[i, j]
-            j == i -> context.one
+            j == i -> elementContext.one
-            else -> context.zero
+            else -> elementContext.zero
         }
     }
 
@@ -54,26 +30,21 @@ private class LUPDecomposition<T : Comparable<T>, R : Ring<T>>(
     /**
      * Returns the matrix U of the decomposition.
      *
-     * U is an upper-triangular matrix
+     * U is an upper-triangular matrix including the diagonal
-     * @return the U matrix (or null if decomposed matrix is singular)
      */
-    override val u: Matrix<T> = VirtualMatrix(lu.shape[0], lu.shape[1]) { i, j ->
+    val u: Matrix<T> = VirtualMatrix(lu.shape[0], lu.shape[1]) { i, j ->
-        if (j >= i) lu[i, j] else context.zero
+        if (j >= i) lu[i, j] else elementContext.zero
     }
 
 
     /**
      * Returns the P rows permutation matrix.
      *
-     * P is a sparse matrix with exactly one element set to 1.0 in
+     * 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 0.0.
+     * each row and each column, all other elements being set to [Ring.zero].
-     *
-     * The positions of the 1 elements are given by the [ pivot permutation vector][.getPivot].
-     * @return the P rows permutation matrix (or null if decomposed matrix is singular)
-     * @see .getPivot
      */
-    override val p: Matrix<T> = VirtualMatrix(lu.shape[0], lu.shape[1]) { i, j ->
+    val p: Matrix<T> = VirtualMatrix(lu.shape[0], lu.shape[1]) { i, j ->
-        if (j == pivot[i]) context.one else context.zero
+        if (j == pivot[i]) elementContext.one else elementContext.zero
     }
 
 
@@ -82,7 +53,7 @@ private class LUPDecomposition<T : Comparable<T>, R : Ring<T>>(
      * @return determinant of the matrix
      */
     override val determinant: T by lazy {
-        with(context) {
+        with(elementContext) {
             (0 until lu.shape[0]).fold(if (even) one else -one) { value, i -> value * lu[i, i] }
         }
     }
@@ -90,14 +61,12 @@ private class LUPDecomposition<T : Comparable<T>, R : Ring<T>>(
 }
 
 
-/**
+class LUSolver<T : Comparable<T>, F : Field<T>>(
- * Implementation based on Apache common-maths LU-decomposition
+    override val context: MatrixContext<T, F>,
- */
-class LUPDecompositionBuilder<T : Comparable<T>, F : Field<T>>(
-    val context: F,
     val bufferFactory: MutableBufferFactory<T> = ::boxing,
     val singularityCheck: (T) -> Boolean
-) {
+) : LinearSolver<T, F> {
+
 
     /**
      * In-place transformation for [MutableNDStructure], using given transformation for each element
@@ -106,9 +75,10 @@ class LUPDecompositionBuilder<T : Comparable<T>, F : Field<T>>(
         this[intArrayOf(i, j)] = value
     }
 
-    private fun abs(value: T) = if (value > context.zero) value else with(context) { -value }
+    private fun abs(value: T) =
+        if (value > context.elementContext.zero) value else with(context.elementContext) { -value }
 
-    fun decompose(matrix: Matrix<T>): LUPDecompositionFeature<T> {
+    fun buildDecomposition(matrix: Matrix<T>): LUPDecomposition<T> {
         if (matrix.rowNum != matrix.colNum) {
             error("LU decomposition supports only square matrices")
         }
@@ -121,7 +91,7 @@ class LUPDecompositionBuilder<T : Comparable<T>, F : Field<T>>(
         }
 
 
-        with(context) {
+        with(context.elementContext) {
             // Initialize permutation array and parity
             for (row in 0 until m) {
                 pivot[row] = row
@@ -174,34 +144,30 @@ class LUPDecompositionBuilder<T : Comparable<T>, F : Field<T>>(
                     lu[row, col] = lu[row, col] / luDiag
                 }
             }
-            return LUPDecomposition(context, lu, pivot, even)
+            return LUPDecomposition(context.elementContext, lu, pivot, even)
         }
     }
 
-    companion object {
+    /**
-        val real: LUPDecompositionBuilder<Double, RealField> = LUPDecompositionBuilder(RealField) { it < 1e-11 }
+     * Produce a matrix with added decomposition feature
+     */
+    fun decompose(matrix: Matrix<T>): Matrix<T> {
+        if (matrix.hasFeature<LUPDecomposition<*>>()) {
+            return matrix
+        } else {
+            val decomposition = buildDecomposition(matrix)
+            return VirtualMatrix.wrap(matrix, decomposition)
         }
-
     }
 
 
-class LUSolver<T : Comparable<T>, F : Field<T>>(
-    override val context: MatrixContext<T, F>,
-    val bufferFactory: MutableBufferFactory<T> = ::boxing,
-    val singularityCheck: (T) -> Boolean
-) : LinearSolver<T, F> {
-
-
     override fun solve(a: Matrix<T>, b: Matrix<T>): Matrix<T> {
         if (b.rowNum != a.colNum) {
             error("Matrix dimension mismatch expected ${a.rowNum}, but got ${b.colNum}")
         }
 
         // Use existing decomposition if it is provided by matrix
-        @Suppress("UNCHECKED_CAST")
+        val decomposition = a.getFeature() ?: buildDecomposition(a)
-        val decomposition = a.features.find { it is LUPDecompositionFeature<*> }?.let {
-            it as LUPDecompositionFeature<T>
-        } ?: LUPDecompositionBuilder(context.elementContext, bufferFactory, singularityCheck).decompose(a)
 
         with(decomposition) {
             with(context.elementContext) {
@@ -219,12 +185,14 @@ class LUSolver<T : Comparable<T>, F : Field<T>>(
                     }
                 }
 
-
                 // Solve UX = Y
                 for (col in a.rowNum - 1 downTo 0) {
+                    for(j in 0 until b.colNum){
+                        bp[col,j]/= u[col,col]
+                    }
                     for (i in 0 until col) {
                         for (j in 0 until b.colNum) {
-                            bp[i, j] -= bp[col, j] / u[col, col] * u[i, col]
+                            bp[i, j] -= bp[col, j]  * u[i, col]
                         }
                     }
                 }

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

@@ -5,6 +5,7 @@ import scientifik.kmath.operations.Ring
 import scientifik.kmath.structures.*
 import scientifik.kmath.structures.Buffer.Companion.DoubleBufferFactory
 import scientifik.kmath.structures.Buffer.Companion.boxing
+import kotlin.math.sqrt
 
 
 interface MatrixContext<T : Any, R : Ring<T>> {
@@ -146,13 +147,35 @@ interface Matrix<T : Any> : NDStructure<T> {
     companion object {
         fun real(rows: Int, columns: Int, initializer: (Int, Int) -> Double) =
             MatrixContext.real.produce(rows, columns, initializer)
+
+        /**
+         * Build a square matrix from given elements.
+         */
+        fun <T : Any> build(vararg elements: T): Matrix<T> {
+            val buffer = elements.asBuffer()
+            val size: Int = sqrt(elements.size.toDouble()).toInt()
+            if (size * size != elements.size) error("The number of elements ${elements.size} is not a full square")
+            val structure = Mutable2DStructure(size, size, buffer)
+            return StructureMatrix(structure)
+        }
     }
 }
 
+/**
+ * Check if matrix has the given feature class
+ */
+inline fun <reified T : Any> Matrix<*>.hasFeature(): Boolean = features.find { T::class.isInstance(it) } != null
+
+/**
+ * Get the first feature matching given class. Does not guarantee that matrix has only one feature matching the criteria
+ */
+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
  */
-fun <T : Any, R : Ring<T>> MatrixContext<T, R>.one(rows: Int, columns: Int): Matrix<T>  = VirtualMatrix<T>(rows,columns){i, j->
+fun <T : Any, R : Ring<T>> MatrixContext<T, R>.one(rows: Int, columns: Int): Matrix<T> =
+    VirtualMatrix<T>(rows, columns) { i, j ->
         if (i == j) elementContext.one else elementContext.zero
     }
 
@@ -160,29 +183,20 @@ fun <T : Any, R : Ring<T>> MatrixContext<T, R>.one(rows: Int, columns: Int): Mat
 /**
  * A virtual matrix of zeroes
  */
-fun <T : Any, R : Ring<T>> MatrixContext<T, R>.zero(rows: Int, columns: Int): Matrix<T>  = VirtualMatrix<T>(rows,columns){i, j->
+fun <T : Any, R : Ring<T>> MatrixContext<T, R>.zero(rows: Int, columns: Int): Matrix<T> =
+    VirtualMatrix<T>(rows, columns) { i, j ->
         elementContext.zero
     }
 
-
+class TransposedFeature<T : Any>(val original: Matrix<T>) : MatrixFeature
-inline class TransposedMatrix<T : Any>(val original: Matrix<T>) : Matrix<T> {
-    override val rowNum: Int get() = original.colNum
-    override val colNum: Int get() = original.rowNum
-    override val features: Set<MatrixFeature> get() = emptySet() //TODO retain some features
-
-    override fun get(i: Int, j: Int): T = original[j, i]
-
-    override fun elements(): Sequence<Pair<IntArray, T>> =
-        original.elements().map { (key, value) -> intArrayOf(key[1], key[0]) to value }
-}
 
 /**
  * Create a virtual transposed matrix without copying anything. `A.transpose().transpose() === A`
  */
 fun <T : Any, R : Ring<T>> Matrix<T>.transpose(): Matrix<T> {
-    return if (this is TransposedMatrix) {
+    return this.getFeature<TransposedFeature<T>>()?.original ?: VirtualMatrix(
-        original
+        this.colNum,
-    } else {
+        this.rowNum,
-        TransposedMatrix(this)
+        setOf(TransposedFeature(this))
-    }
+    ) { i, j -> get(j, i) }
 }

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

@@ -1,10 +1,7 @@
 package scientifik.kmath.linear
 
 import scientifik.kmath.operations.Ring
-import scientifik.kmath.structures.BufferFactory
+import scientifik.kmath.structures.*
-import scientifik.kmath.structures.NDStructure
-import scientifik.kmath.structures.get
-import scientifik.kmath.structures.ndStructure
 
 /**
  * Basic implementation of Matrix space based on [NDStructure]
@@ -24,7 +21,7 @@ class StructureMatrixContext<T : Any, R : Ring<T>>(
 }
 
 class StructureMatrix<T : Any>(
-    val structure: NDStructure<T>,
+    val structure: NDStructure<out T>,
     override val features: Set<MatrixFeature> = emptySet()
 ) : Matrix<T> {
 
@@ -51,8 +48,7 @@ class StructureMatrix<T : Any>(
     override fun equals(other: Any?): Boolean {
         if (this === other) return true
         return when (other) {
-            is StructureMatrix<*> -> return this.structure == other.structure
+            is NDStructure<*> -> return NDStructure.equals(this, other)
-            is Matrix<*> -> elements().all { (index, value) -> value == other[index] }
             else -> false
         }
     }
@@ -63,5 +59,16 @@ class StructureMatrix<T : Any>(
         return result
     }
 
+    override fun toString(): String {
+        return if (rowNum <= 5 && colNum <= 5) {
+            "Matrix(rowsNum = $rowNum, colNum = $colNum, features=$features)\n" +
+                    rows.asSequence().joinToString(prefix = "(", postfix = ")", separator = "\n ") {
+                        it.asSequence().joinToString(separator = "\t") { it.toString() }
+                    }
+        } else {
+            "Matrix(rowsNum = $rowNum, colNum = $colNum, features=$features)"
+        }
+    }
+
 
 }

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

@@ -27,4 +27,18 @@ class VirtualMatrix<T : Any>(
     }
 
 
+    companion object {
+        /**
+         * Wrap a matrix adding additional features to it
+         */
+        fun <T : Any> wrap(matrix: Matrix<T>, vararg features: MatrixFeature): Matrix<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/scientifik/kmath/structures/NDStructure.kt

@@ -11,6 +11,16 @@ interface NDStructure<T> {
     operator fun get(index: IntArray): T
 
     fun elements(): Sequence<Pair<IntArray, T>>
+
+    companion object {
+        fun equals(st1: NDStructure<*>, st2: NDStructure<*>): Boolean {
+            return when {
+                st1 === st2 -> true
+                st1 is BufferNDStructure<*> && st2 is BufferNDStructure<*> && st1.strides == st2.strides -> st1.buffer.contentEquals(st2.buffer)
+                else -> st1.elements().all { (index, value) -> value == st2[index] }
+            }
+        }
+    }
 }
 
 operator fun <T> NDStructure<T>.get(vararg index: Int): T = get(index)
@@ -140,7 +150,7 @@ interface NDBuffer<T> : NDStructure<T> {
 /**
  * Boxing generic [NDStructure]
  */
-data class BufferNDStructure<T>(
+class BufferNDStructure<T>(
     override val strides: Strides,
     override val buffer: Buffer<T>
 ) : NDBuffer<T> {
@@ -160,7 +170,7 @@ data class BufferNDStructure<T>(
     override fun equals(other: Any?): Boolean {
         return when {
             this === other -> true
-            other is BufferNDStructure<*> -> this.strides == other.strides && this.buffer.contentEquals(other.buffer)
+            other is BufferNDStructure<*> && this.strides == other.strides -> this.buffer.contentEquals(other.buffer)
             other is NDStructure<*> -> elements().all { (index, value) -> value == other[index] }
             else -> false
         }
@@ -193,7 +203,11 @@ inline fun <T, reified R : Any> NDStructure<T>.mapToBuffer(
  *
  * Strides should be reused if possible
  */
-fun <T> ndStructure(strides: Strides, bufferFactory: BufferFactory<T> = Buffer.Companion::boxing, initializer: (IntArray) -> T) =
+fun <T> ndStructure(
+    strides: Strides,
+    bufferFactory: BufferFactory<T> = Buffer.Companion::boxing,
+    initializer: (IntArray) -> T
+) =
     BufferNDStructure(strides, bufferFactory(strides.linearSize) { i -> initializer(strides.index(i)) })
 
 /**
@@ -202,7 +216,11 @@ fun <T> ndStructure(strides: Strides, bufferFactory: BufferFactory<T> = Buffer.C
 inline fun <reified T : Any> inlineNDStructure(strides: Strides, crossinline initializer: (IntArray) -> T) =
     BufferNDStructure(strides, Buffer.auto(strides.linearSize) { i -> initializer(strides.index(i)) })
 
-fun <T> ndStructure(shape: IntArray, bufferFactory: BufferFactory<T> = Buffer.Companion::boxing, initializer: (IntArray) -> T) =
+fun <T> ndStructure(
+    shape: IntArray,
+    bufferFactory: BufferFactory<T> = Buffer.Companion::boxing,
+    initializer: (IntArray) -> T
+) =
     ndStructure(DefaultStrides(shape), bufferFactory, initializer)
 
 inline fun <reified T : Any> inlineNdStructure(shape: IntArray, crossinline initializer: (IntArray) -> T) =

kmath-core/src/commonTest/kotlin/scientifik/kmath/linear/RealLUSolverTest.kt

@@ -11,10 +11,31 @@ class RealLUSolverTest {
         assertEquals(matrix, inverted)
     }
 
-//    @Test
+    @Test
-//    fun testInvert() {
+    fun testInvert() {
-//        val matrix = realMatrix(2,2){}
+        val matrix = Matrix.build(
-//        val inverted = LUSolver.real.inverse(matrix)
+            3.0, 1.0,
-//        assertEquals(matrix, inverted)
+            1.0, 3.0
-//    }
+        )
+
+        val decomposed = LUSolver.real.decompose(matrix)
+        val decomposition = decomposed.getFeature<LUPDecomposition<Double>>()!!
+
+        //Check determinant
+        assertEquals(8.0, decomposition.determinant)
+
+        //Check decomposition
+        with(MatrixContext.real) {
+            assertEquals(decomposition.p dot matrix, decomposition.l dot decomposition.u)
+        }
+
+        val inverted = LUSolver.real.inverse(decomposed)
+
+        val expected = Matrix.build(
+            0.375, -0.125,
+            -0.125, 0.375
+        )
+
+        assertEquals(expected, inverted)
+    }
 }