WIP vector space refactor

examples/src/benchmarks/kotlin/space/kscience/kmath/benchmarks/DotBenchmark.kt

@@ -4,11 +4,11 @@ import kotlinx.benchmark.Benchmark
 import kotlinx.benchmark.Blackhole
 import kotlinx.benchmark.Scope
 import kotlinx.benchmark.State
-import space.kscience.kmath.commons.linear.CMMatrixContext
+import space.kscience.kmath.commons.linear.CMLinearSpace
-import space.kscience.kmath.ejml.EjmlMatrixContext
+import space.kscience.kmath.ejml.EjmlLinearSpace
-import space.kscience.kmath.linear.BufferMatrixContext
+import space.kscience.kmath.linear.BufferLinearSpace
 import space.kscience.kmath.linear.Matrix
-import space.kscience.kmath.linear.RealMatrixContext
+import space.kscience.kmath.linear.RealLinearSpace
 import space.kscience.kmath.operations.RealField
 import space.kscience.kmath.operations.invoke
 import space.kscience.kmath.structures.Buffer
@@ -24,44 +24,44 @@ internal class DotBenchmark {
         val matrix1 = Matrix.real(dim, dim) { i, j -> if (i <= j) random.nextDouble() else 0.0 }
         val matrix2 = Matrix.real(dim, dim) { i, j -> if (i <= j) random.nextDouble() else 0.0 }
 
-        val cmMatrix1 = CMMatrixContext { matrix1.toCM() }
+        val cmMatrix1 = CMLinearSpace { matrix1.toCM() }
-        val cmMatrix2 = CMMatrixContext { matrix2.toCM() }
+        val cmMatrix2 = CMLinearSpace { matrix2.toCM() }
 
-        val ejmlMatrix1 = EjmlMatrixContext { matrix1.toEjml() }
+        val ejmlMatrix1 = EjmlLinearSpace { matrix1.toEjml() }
-        val ejmlMatrix2 = EjmlMatrixContext { matrix2.toEjml() }
+        val ejmlMatrix2 = EjmlLinearSpace { matrix2.toEjml() }
     }
 
     @Benchmark
     fun cmDot(blackhole: Blackhole) {
-        CMMatrixContext {
+        CMLinearSpace {
             blackhole.consume(cmMatrix1 dot cmMatrix2)
         }
     }
 
     @Benchmark
     fun ejmlDot(blackhole: Blackhole) {
-        EjmlMatrixContext {
+        EjmlLinearSpace {
             blackhole.consume(ejmlMatrix1 dot ejmlMatrix2)
         }
     }
 
     @Benchmark
     fun ejmlDotWithConversion(blackhole: Blackhole) {
-        EjmlMatrixContext {
+        EjmlLinearSpace {
             blackhole.consume(matrix1 dot matrix2)
         }
     }
 
     @Benchmark
     fun bufferedDot(blackhole: Blackhole) {
-        BufferMatrixContext(RealField, Buffer.Companion::real).invoke {
+        BufferLinearSpace(RealField, Buffer.Companion::real).invoke {
             blackhole.consume(matrix1 dot matrix2)
         }
     }
 
     @Benchmark
     fun realDot(blackhole: Blackhole) {
-        RealMatrixContext {
+        RealLinearSpace {
             blackhole.consume(matrix1 dot matrix2)
         }
     }

examples/src/benchmarks/kotlin/space/kscience/kmath/benchmarks/LinearAlgebraBenchmark.kt

@@ -4,13 +4,12 @@ import kotlinx.benchmark.Benchmark
 import kotlinx.benchmark.Blackhole
 import kotlinx.benchmark.Scope
 import kotlinx.benchmark.State
-import space.kscience.kmath.commons.linear.CMMatrixContext
+import space.kscience.kmath.commons.linear.CMLinearSpace
-import space.kscience.kmath.commons.linear.CMMatrixContext.dot
 import space.kscience.kmath.commons.linear.inverse
-import space.kscience.kmath.ejml.EjmlMatrixContext
+import space.kscience.kmath.ejml.EjmlLinearSpace
 import space.kscience.kmath.ejml.inverse
+import space.kscience.kmath.linear.LinearSpace
 import space.kscience.kmath.linear.Matrix
-import space.kscience.kmath.linear.MatrixContext
 import space.kscience.kmath.linear.inverseWithLup
 import space.kscience.kmath.linear.real
 import kotlin.random.Random
@@ -29,19 +28,19 @@ internal class LinearAlgebraBenchmark {
 
     @Benchmark
     fun kmathLupInversion(blackhole: Blackhole) {
-        blackhole.consume(MatrixContext.real.inverseWithLup(matrix))
+        blackhole.consume(LinearSpace.real.inverseWithLup(matrix))
     }
 
     @Benchmark
     fun cmLUPInversion(blackhole: Blackhole) {
-        with(CMMatrixContext) {
+        with(CMLinearSpace) {
             blackhole.consume(inverse(matrix))
         }
     }
 
     @Benchmark
     fun ejmlInverse(blackhole: Blackhole) {
-        with(EjmlMatrixContext) {
+        with(EjmlLinearSpace) {
             blackhole.consume(inverse(matrix))
         }
     }

examples/src/main/kotlin/space/kscience/kmath/structures/StructureWriteBenchmark.kt

@@ -7,7 +7,7 @@ import kotlin.system.measureTimeMillis
 @Suppress("UNUSED_VARIABLE")
 fun main() {
     val n = 6000
-    val structure = NDStructure.build(intArrayOf(n, n), Buffer.Companion::auto) { 1.0 }
+    val structure = NDStructure.buffered(intArrayOf(n, n), Buffer.Companion::auto) { 1.0 }
     structure.mapToBuffer { it + 1 } // warm-up
     val time1 = measureTimeMillis { val res = structure.mapToBuffer { it + 1 } }
     println("Structure mapping finished in $time1 millis")

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

@@ -3,6 +3,7 @@ package space.kscience.kmath.commons.linear
 import org.apache.commons.math3.linear.*
 import space.kscience.kmath.linear.*
 import space.kscience.kmath.misc.UnstableKMathAPI
+import space.kscience.kmath.operations.RealField
 import space.kscience.kmath.structures.RealBuffer
 import kotlin.reflect.KClass
 import kotlin.reflect.cast
@@ -55,7 +56,7 @@ public inline class CMMatrix(public val origin: RealMatrix) : Matrix<Double> {
 
 public fun RealMatrix.asMatrix(): CMMatrix = CMMatrix(this)
 
-public class CMVector(public val origin: RealVector) : Point<Double> {
+public class CMVector(public val origin: RealVector) : Vector<Double> {
     public override val size: Int get() = origin.dimension
 
     public override operator fun get(index: Int): Double = origin.getEntry(index)
@@ -70,8 +71,8 @@ public fun Point<Double>.toCM(): CMVector = if (this is CMVector) this else {
 
 public fun RealVector.toPoint(): CMVector = CMVector(this)
 
-public object CMMatrixContext : MatrixContext<Double, CMMatrix> {
+public object CMLinearSpace : LinearSpace<Double, RealField> {
-    public override fun produce(rows: Int, columns: Int, initializer: (i: Int, j: Int) -> Double): CMMatrix {
+    public override fun buildMatrix(rows: Int, columns: Int, initializer: (i: Int, j: Int) -> Double): CMMatrix {
         val array = Array(rows) { i -> DoubleArray(columns) { j -> initializer(i, j) } }
         return CMMatrix(Array2DRowRealMatrix(array))
     }
@@ -86,17 +87,15 @@ public object CMMatrixContext : MatrixContext<Double, CMMatrix> {
         }
     }
 
-    override fun scale(a: Matrix<Double>, value: Double): Matrix<Double> = a.toCM().times(value)
-
 
     public override fun Matrix<Double>.dot(other: Matrix<Double>): CMMatrix =
         CMMatrix(toCM().origin.multiply(other.toCM().origin))
 
-    public override fun Matrix<Double>.dot(vector: Point<Double>): CMVector =
+    public override fun Matrix<Double>.dot(vector: Vector<Double>): CMVector =
         CMVector(toCM().origin.preMultiply(vector.toCM().origin))
 
     public override operator fun Matrix<Double>.unaryMinus(): CMMatrix =
-        produce(rowNum, colNum) { i, j -> -get(i, j) }
+        buildMatrix(rowNum, colNum) { i, j -> -get(i, j) }
 
     public override fun add(a: Matrix<Double>, b: Matrix<Double>): CMMatrix =
         CMMatrix(a.toCM().origin.multiply(b.toCM().origin))
@@ -108,7 +107,7 @@ public object CMMatrixContext : MatrixContext<Double, CMMatrix> {
 //        CMMatrix(a.toCM().origin.scalarMultiply(k.toDouble()))
 
     public override operator fun Matrix<Double>.times(value: Double): CMMatrix =
-        produce(rowNum, colNum) { i, j -> get(i, j) * value }
+        buildMatrix(rowNum, colNum) { i, j -> get(i, j) * value }
 }
 
 public operator fun CMMatrix.plus(other: CMMatrix): CMMatrix =

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

@@ -12,7 +12,7 @@ public enum class CMDecomposition {
     CHOLESKY
 }
 
-public fun CMMatrixContext.solver(
+public fun CMLinearSpace.solver(
     a: Matrix<Double>,
     decomposition: CMDecomposition = CMDecomposition.LUP
 ): DecompositionSolver = when (decomposition) {
@@ -23,19 +23,19 @@ public fun CMMatrixContext.solver(
     CMDecomposition.CHOLESKY -> CholeskyDecomposition(a.toCM().origin).solver
 }
 
-public fun CMMatrixContext.solve(
+public fun CMLinearSpace.solve(
     a: Matrix<Double>,
     b: Matrix<Double>,
     decomposition: CMDecomposition = CMDecomposition.LUP
 ): CMMatrix = solver(a, decomposition).solve(b.toCM().origin).asMatrix()
 
-public fun CMMatrixContext.solve(
+public fun CMLinearSpace.solve(
     a: Matrix<Double>,
     b: Point<Double>,
     decomposition: CMDecomposition = CMDecomposition.LUP
 ): CMVector = solver(a, decomposition).solve(b.toCM().origin).toPoint()
 
-public fun CMMatrixContext.inverse(
+public fun CMLinearSpace.inverse(
     a: Matrix<Double>,
     decomposition: CMDecomposition = CMDecomposition.LUP
 ): CMMatrix = solver(a, decomposition).inverse.asMatrix()

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

@@ -1,144 +0,0 @@
-package space.kscience.kmath.linear
-
-import space.kscience.kmath.nd.NDStructure
-import space.kscience.kmath.nd.Structure2D
-import space.kscience.kmath.operations.Ring
-import space.kscience.kmath.operations.ScaleOperations
-import space.kscience.kmath.operations.invoke
-import space.kscience.kmath.structures.Buffer
-import space.kscience.kmath.structures.BufferFactory
-import space.kscience.kmath.structures.asSequence
-
-/**
- * Alias for [Structure2D] with more familiar name.
- *
- * @param T the type of items.
- */
-public typealias Matrix<T> = Structure2D<T>
-
-/**
- * Basic implementation of Matrix space based on [NDStructure]
- */
-public class BufferMatrixContext<T : Any, A>(
-    public override val elementContext: A,
-    private val bufferFactory: BufferFactory<T>,
-) : GenericMatrixContext<T, A, BufferMatrix<T>> where A : Ring<T>, A : ScaleOperations<T> {
-
-    public override fun produce(rows: Int, columns: Int, initializer: (i: Int, j: Int) -> T): BufferMatrix<T> {
-        val buffer = bufferFactory(rows * columns) { offset -> initializer(offset / columns, offset % columns) }
-        return BufferMatrix(rows, columns, buffer)
-    }
-
-    override fun scale(a: Matrix<T>, value: Double): Matrix<T> = elementContext {
-        produce(a.rowNum, a.colNum) { i, j ->
-            a[i, j] * value
-        }
-    }
-
-    public override fun point(size: Int, initializer: (Int) -> T): Point<T> = bufferFactory(size, initializer)
-
-    private fun Matrix<T>.toBufferMatrix(): BufferMatrix<T> = if (this is BufferMatrix) this else {
-        produce(rowNum, colNum) { i, j -> get(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<T>.dot(other: Matrix<T>): BufferMatrix<T> {
-        require(colNum == other.rowNum) { "Matrix dot operation dimension mismatch: ($rowNum, $colNum) x (${other.rowNum}, ${other.colNum})" }
-        val bufferMatrix = toBufferMatrix()
-        val otherBufferMatrix = other.toBufferMatrix()
-        return elementContext {
-            produce(rowNum, other.colNum) { i, j ->
-                var res = one
-                for (l in 0 until colNum) {
-                    res += bufferMatrix[i, l] * otherBufferMatrix[l, j]
-                }
-                res
-            }
-        }
-    }
-
-    public override infix fun Matrix<T>.dot(vector: Point<T>): Point<T> {
-        require(colNum == vector.size) { "Matrix dot vector operation dimension mismatch: ($rowNum, $colNum) x (${vector.size})" }
-        val bufferMatrix = toBufferMatrix()
-        return elementContext {
-            bufferFactory(rowNum) { i ->
-                var res = one
-                for (j in 0 until colNum) {
-                    res += bufferMatrix[i, j] * vector[j]
-                }
-                res
-            }
-        }
-    }
-
-    override fun add(a: Matrix<T>, b: Matrix<T>): BufferMatrix<T> {
-        require(a.rowNum == b.rowNum) { "Row number mismatch in matrix addition. Left side: ${a.rowNum}, right side: ${b.rowNum}" }
-        require(a.colNum == b.colNum) { "Column number mismatch in matrix addition. Left side: ${a.colNum}, right side: ${b.colNum}" }
-        val aBufferMatrix = a.toBufferMatrix()
-        val bBufferMatrix = b.toBufferMatrix()
-        return elementContext {
-            produce(a.rowNum, a.colNum) { i, j ->
-                aBufferMatrix[i, j] + bBufferMatrix[i, j]
-            }
-        }
-    }
-
-//    override fun multiply(a: Matrix<T>, k: Number): BufferMatrix<T> {
-//        val aBufferMatrix = a.toBufferMatrix()
-//        return elementContext {
-//            produce(a.rowNum, a.colNum) { i, j -> aBufferMatrix[i, j] * k.toDouble() }
-//        }
-//    }
-
-    public companion object
-}
-
-public class BufferMatrix<T : Any>(
-    public override val rowNum: Int,
-    public override val colNum: Int,
-    public val buffer: Buffer<T>,
-) : Matrix<T> {
-
-    init {
-        require(buffer.size == rowNum * colNum) { "Dimension mismatch for matrix structure" }
-    }
-
-    override val shape: IntArray get() = intArrayOf(rowNum, colNum)
-
-    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]
-
-    public override fun elements(): Sequence<Pair<IntArray, T>> = sequence {
-        for (i in 0 until rowNum) for (j in 0 until colNum) yield(intArrayOf(i, j) to get(i, j))
-    }
-
-    public override fun equals(other: Any?): Boolean {
-        if (this === other) return true
-
-        return when (other) {
-            is NDStructure<*> -> NDStructure.contentEquals(this, other)
-            else -> false
-        }
-    }
-
-    override fun hashCode(): Int {
-        var result = rowNum
-        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)\n" +
-                    rows.asSequence().joinToString(prefix = "(", postfix = ")", separator = "\n ") { buffer ->
-                        buffer.asSequence().joinToString(separator = "\t") { it.toString() }
-                    }
-        else "Matrix(rowsNum = $rowNum, colNum = $colNum)"
-    }
-
-
-}

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

@@ -1,7 +1,7 @@
 package space.kscience.kmath.linear
 
+import space.kscience.kmath.nd.as1D
 import space.kscience.kmath.structures.Buffer
-import space.kscience.kmath.structures.VirtualBuffer
 
 public typealias Point<T> = Buffer<T>
 
@@ -10,16 +10,16 @@ public typealias Point<T> = Buffer<T>
  */
 public interface LinearSolver<T : Any> {
     public fun solve(a: Matrix<T>, b: Matrix<T>): Matrix<T>
-    public fun solve(a: Matrix<T>, b: Point<T>): Point<T> = solve(a, b.asMatrix()).asPoint()
+    public fun solve(a: Matrix<T>, b: Point<T>): Point<T> = solve(a, b.asMatrix()).asVector()
     public fun inverse(a: Matrix<T>): Matrix<T>
 }
 
 /**
  * Convert matrix to vector if it is possible
  */
-public fun <T : Any> Matrix<T>.asPoint(): Point<T> =
+public fun <T : Any> Matrix<T>.asVector(): Vector<T> =
     if (this.colNum == 1)
-        VirtualBuffer(rowNum) { get(it, 0) }
+        as1D()
     else
         error("Can't convert matrix with more than one column to vector")
 

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

@@ -0,0 +1,213 @@
+package space.kscience.kmath.linear
+
+import space.kscience.kmath.misc.UnstableKMathAPI
+import space.kscience.kmath.nd.*
+import space.kscience.kmath.operations.*
+import space.kscience.kmath.structures.Buffer
+import space.kscience.kmath.structures.BufferFactory
+import kotlin.reflect.KClass
+
+/**
+ * Alias for [Structure2D] with more familiar name.
+ *
+ * @param T the type of items.
+ */
+public typealias Matrix<T> = Structure2D<T>
+
+/**
+ * Alias for [Structure1D] with more familiar name.
+ *
+ * @param T the type of items.
+ */
+public typealias Vector<T> = Structure1D<T>
+
+/**
+ * Basic operations on matrices and vectors. Operates on [Matrix].
+ *
+ * @param T the type of items in the matrices.
+ * @param M the type of operated matrices.
+ */
+public interface LinearSpace<T : Any, A : Ring<T>> {
+    public val elementAlgebra: A
+
+    /**
+     * Produces a matrix with this context and given dimensions.
+     */
+    public fun buildMatrix(rows: Int, columns: Int, initializer: A.(i: Int, j: Int) -> T): Matrix<T>
+
+    /**
+     * Produces a point compatible with matrix space (and possibly optimized for it).
+     */
+    public fun buildVector(size: Int, initializer: A.(Int) -> T): Vector<T> =
+        buildMatrix(1, size) { _, j -> initializer(j) }.as1D()
+
+    public operator fun Matrix<T>.unaryMinus(): Matrix<T> = buildMatrix(rowNum, colNum) { i, j ->
+        -get(i, j)
+    }
+
+    public operator fun Vector<T>.unaryMinus(): Vector<T> = buildVector(size) {
+        -get(it)
+    }
+
+    /**
+     * Matrix sum
+     */
+    public operator fun Matrix<T>.plus(other: Matrix<T>): Matrix<T> = buildMatrix(rowNum, colNum) { i, j ->
+        get(i, j) + other[i, j]
+    }
+
+
+    /**
+     * Vector sum
+     */
+    public operator fun Vector<T>.plus(other: Vector<T>): Vector<T> = buildVector(size) {
+        get(it) + other[it]
+    }
+
+    /**
+     * Matrix subtraction
+     */
+    public operator fun Matrix<T>.minus(other: Matrix<T>): Matrix<T> = buildMatrix(rowNum, colNum) { i, j ->
+        get(i, j) - other[i, j]
+    }
+
+    /**
+     * Vector subtraction
+     */
+    public operator fun Vector<T>.minus(other: Vector<T>): Vector<T> = buildVector(size) {
+        get(it) - other[it]
+    }
+
+
+    /**
+     * Computes the dot product of this matrix and another one.
+     *
+     * @receiver the multiplicand.
+     * @param other the multiplier.
+     * @return the dot product.
+     */
+    public infix fun Matrix<T>.dot(other: Matrix<T>): Matrix<T> {
+        require(colNum == other.rowNum) { "Matrix dot operation dimension mismatch: ($rowNum, $colNum) x (${other.rowNum}, ${other.colNum})" }
+        return elementAlgebra {
+            buildMatrix(rowNum, other.colNum) { i, j ->
+                var res = zero
+                for (l in 0 until colNum) {
+                    res += this@dot[i, l] * other[l, j]
+                }
+                res
+            }
+        }
+    }
+
+    /**
+     * Computes the dot product of this matrix and a vector.
+     *
+     * @receiver the multiplicand.
+     * @param vector the multiplier.
+     * @return the dot product.
+     */
+    public infix fun Matrix<T>.dot(vector: Vector<T>): Vector<T> {
+        require(colNum == vector.size) { "Matrix dot vector operation dimension mismatch: ($rowNum, $colNum) x (${vector.size})" }
+        return elementAlgebra {
+            buildVector(rowNum) { i ->
+                var res = one
+                for (j in 0 until colNum) {
+                    res += this@dot[i, j] * vector[j]
+                }
+                res
+            }
+        }
+    }
+
+    /**
+     * Multiplies a matrix by its element.
+     *
+     * @receiver the multiplicand.
+     * @param value the multiplier.
+     * @receiver the product.
+     */
+    public operator fun Matrix<T>.times(value: T): Matrix<T> =
+        buildMatrix(rowNum, colNum) { i, j -> get(i, j) * value }
+
+    /**
+     * Multiplies an element by a matrix of it.
+     *
+     * @receiver the multiplicand.
+     * @param m the multiplier.
+     * @receiver the product.
+     */
+    public operator fun T.times(m: Matrix<T>): Matrix<T> = m * this
+
+    /**
+     * Multiplies a vector by its element.
+     *
+     * @receiver the multiplicand.
+     * @param value the multiplier.
+     * @receiver the product.
+     */
+    public operator fun Vector<T>.times(value: T): Vector<T> =
+        buildVector(size) { i -> get(i) * value }
+
+    /**
+     * Multiplies an element by a vector of it.
+     *
+     * @receiver the multiplicand.
+     * @param v the multiplier.
+     * @receiver the product.
+     */
+    public operator fun T.times(v: Vector<T>): Vector<T> = v * this
+
+    /**
+     * Gets a feature from the matrix. This function may return some additional features to
+     * [space.kscience.kmath.nd.NDStructure.getFeature].
+     *
+     * @param F the type of feature.
+     * @param m the matrix.
+     * @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 companion object {
+
+        /**
+         * A structured matrix with custom buffer
+         */
+        public fun <T : Any, A : Ring<T>> buffered(
+            algebra: A,
+            bufferFactory: BufferFactory<T> = Buffer.Companion::boxing,
+        ): LinearSpace<T, A> = object : LinearSpace<T, A> {
+            override val elementAlgebra: A = algebra
+
+            override fun buildMatrix(
+                rows: Int, columns: Int,
+                initializer: A.(i: Int, j: Int) -> T,
+            ): Matrix<T> = NDStructure.buffered(intArrayOf(rows, columns)) { (i, j) ->
+                algebra.initializer(i, j)
+            }.as2D()
+
+        }
+
+        /**
+         * Automatic buffered matrix, unboxed if it is possible
+         */
+        public inline fun <reified T : Any, A : Ring<T>> auto(ring: A): LinearSpace<T, A> =
+            buffered(ring, Buffer.Companion::auto)
+    }
+}
+
+/**
+ * Gets a feature from the matrix. This function may return some additional features to
+ * [space.kscience.kmath.nd.NDStructure.getFeature].
+ *
+ * @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)
+

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

@@ -12,7 +12,7 @@ import space.kscience.kmath.structures.MutableBufferFactory
  * Common implementation of [LupDecompositionFeature].
  */
 public class LupDecomposition<T : Any>(
-    public val context: MatrixContext<T, Matrix<T>>,
+    public val context: LinearSpace<T, *>,
     public val elementContext: Field<T>,
     public val lu: Matrix<T>,
     public val pivot: IntArray,
@@ -62,15 +62,14 @@ public class LupDecomposition<T : Any>(
 }
 
 @PublishedApi
-internal fun <T : Comparable<T>, F : Field<T>> GenericMatrixContext<T, F, *>.abs(value: T): T =
+internal fun <T : Comparable<T>> LinearSpace<T, Ring<T>>.abs(value: T): T =
-    if (value > elementContext.zero) value else elementContext { -value }
+    if (value > elementAlgebra.zero) value else elementAlgebra { -value }
 
 /**
  * Create a lup decomposition of generic matrix.
  */
-public fun <T : Comparable<T>> MatrixContext<T, Matrix<T>>.lup(
+public fun <T : Comparable<T>> LinearSpace<T, Field<T>>.lup(
     factory: MutableBufferFactory<T>,
-    elementContext: Field<T>,
     matrix: Matrix<T>,
     checkSingular: (T) -> Boolean,
 ): LupDecomposition<T> {
@@ -80,7 +79,7 @@ public fun <T : Comparable<T>> MatrixContext<T, Matrix<T>>.lup(
 
     //TODO just waits for KEEP-176
     BufferAccessor2D(matrix.rowNum, matrix.colNum, factory).run {
-        elementContext {
+        elementAlgebra {
             val lu = create(matrix)
 
             // Initialize permutation array and parity
@@ -142,18 +141,18 @@ public fun <T : Comparable<T>> MatrixContext<T, Matrix<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, elementAlgebra, lu.collect(), pivot, even)
         }
     }
 }
 
-public inline fun <reified T : Comparable<T>, F : Field<T>> GenericMatrixContext<T, F, Matrix<T>>.lup(
+public inline fun <reified T : Comparable<T>> LinearSpace<T, Field<T>>.lup(
     matrix: Matrix<T>,
     noinline checkSingular: (T) -> Boolean,
-): LupDecomposition<T> = lup(MutableBuffer.Companion::auto, elementContext, matrix, checkSingular)
+): LupDecomposition<T> = lup(MutableBuffer.Companion::auto, matrix, checkSingular)
 
-public fun MatrixContext<Double, Matrix<Double>>.lup(matrix: Matrix<Double>): LupDecomposition<Double> =
+public fun LinearSpace<Double, RealField>.lup(matrix: Matrix<Double>): LupDecomposition<Double> =
-    lup(Buffer.Companion::real, RealField, matrix) { it < 1e-11 }
+    lup(Buffer.Companion::real, matrix) { it < 1e-11 }
 
 public fun <T : Any> LupDecomposition<T>.solveWithLup(
     factory: MutableBufferFactory<T>,
@@ -198,7 +197,7 @@ public fun <T : Any> LupDecomposition<T>.solveWithLup(
                 }
             }
 
-            return context.produce(pivot.size, matrix.colNum) { i, j -> bp[i, j] }
+            return context.buildMatrix(pivot.size, matrix.colNum) { i, j -> bp[i, j] }
         }
     }
 }
@@ -210,18 +209,18 @@ public inline fun <reified T : Any> LupDecomposition<T>.solveWithLup(matrix: Mat
  * Solves a system of linear equations *ax = b** using LUP decomposition.
  */
 @OptIn(UnstableKMathAPI::class)
-public inline fun <reified T : Comparable<T>, F : Field<T>> GenericMatrixContext<T, F, Matrix<T>>.solveWithLup(
+public inline fun <reified T : Comparable<T>> LinearSpace<T, Field<T>>.solveWithLup(
     a: Matrix<T>,
     b: Matrix<T>,
     noinline bufferFactory: MutableBufferFactory<T> = MutableBuffer.Companion::auto,
     noinline checkSingular: (T) -> Boolean,
 ): Matrix<T> {
     // Use existing decomposition if it is provided by matrix
-    val decomposition = a.getFeature() ?: lup(bufferFactory, elementContext, a, checkSingular)
+    val decomposition = a.getFeature() ?: lup(bufferFactory, a, checkSingular)
     return decomposition.solveWithLup(bufferFactory, b)
 }
 
-public inline fun <reified T : Comparable<T>, F : Field<T>> GenericMatrixContext<T, F, Matrix<T>>.inverseWithLup(
+public inline fun <reified T : Comparable<T>> LinearSpace<T, Field<T>>.inverseWithLup(
     matrix: Matrix<T>,
     noinline bufferFactory: MutableBufferFactory<T> = MutableBuffer.Companion::auto,
     noinline checkSingular: (T) -> Boolean,
@@ -229,15 +228,15 @@ public inline fun <reified T : Comparable<T>, F : Field<T>> GenericMatrixContext
 
 
 @OptIn(UnstableKMathAPI::class)
-public fun RealMatrixContext.solveWithLup(a: Matrix<Double>, b: Matrix<Double>): Matrix<Double> {
+public fun RealLinearSpace.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 }
+    val decomposition: LupDecomposition<Double> = a.getFeature() ?: lup(bufferFactory, 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> =
+public fun RealLinearSpace.inverseWithLup(matrix: Matrix<Double>): Matrix<Double> =
     solveWithLup(matrix, one(matrix.rowNum, matrix.colNum))

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

@@ -1,46 +1,30 @@
 package space.kscience.kmath.linear
 
-import space.kscience.kmath.nd.Structure2D
+import space.kscience.kmath.misc.UnstableKMathAPI
-import space.kscience.kmath.structures.Buffer
+import space.kscience.kmath.operations.Ring
-import space.kscience.kmath.structures.BufferFactory
-import space.kscience.kmath.structures.asBuffer
 
-public class MatrixBuilder(public val rows: Int, public val columns: Int) {
+
-    public operator fun <T : Any> invoke(vararg elements: T): Matrix<T> {
+@UnstableKMathAPI
+public fun <T : Any> LinearSpace<T, Ring<T>>.matrix(rows: Int, columns: Int, 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 buildMatrix(rows, columns) { i, j -> elements[i * columns + j] }
-        return BufferMatrix(rows, columns, buffer)
 }
 
-    //TODO add specific matrix builder functions like diagonal, etc
+@UnstableKMathAPI
+public fun <T : Any> LinearSpace<T, Ring<T>>.vector(vararg elements: T): Vector<T> {
+    return buildVector(elements.size, elements::get)
 }
 
-public fun Structure2D.Companion.build(rows: Int, columns: Int): MatrixBuilder = MatrixBuilder(rows, columns)
+public inline fun <T : Any> LinearSpace<T, Ring<T>>.row(
-
-public fun <T : Any> Structure2D.Companion.row(vararg values: T): Matrix<T> {
-    val buffer = values.asBuffer()
-    return BufferMatrix(1, values.size, buffer)
-}
-
-public inline fun <reified T : Any> Structure2D.Companion.row(
     size: Int,
-    factory: BufferFactory<T> = Buffer.Companion::auto,
+    crossinline builder: (Int) -> T,
-    noinline builder: (Int) -> T,
+): Matrix<T> = buildMatrix(1, size) { _, j -> builder(j) }
-): Matrix<T> {
-    val buffer = factory(size, builder)
-    return BufferMatrix(1, size, buffer)
-}
 
-public fun <T : Any> Structure2D.Companion.column(vararg values: T): Matrix<T> {
+public fun <T : Any> LinearSpace<T, Ring<T>>.row(vararg values: T): Matrix<T> = row(values.size, values::get)
-    val buffer = values.asBuffer()
-    return BufferMatrix(values.size, 1, buffer)
-}
 
-public inline fun <reified T : Any> Structure2D.Companion.column(
+public inline fun <T : Any> LinearSpace<T, Ring<T>>.column(
     size: Int,
-    factory: BufferFactory<T> = Buffer.Companion::auto,
+    crossinline builder: (Int) -> T,
-    noinline builder: (Int) -> T,
+): Matrix<T> = buildMatrix(size, 1) { i, _ -> builder(i) }
-): Matrix<T> {
+
-    val buffer = factory(size, builder)
+public fun <T : Any> LinearSpace<T, Ring<T>>.column(vararg values: T): Matrix<T> = column(values.size, values::get)
-    return BufferMatrix(size, 1, buffer)
-}

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

@@ -1,173 +0,0 @@
-package space.kscience.kmath.linear
-
-import space.kscience.kmath.misc.UnstableKMathAPI
-import space.kscience.kmath.operations.*
-import space.kscience.kmath.structures.Buffer
-import space.kscience.kmath.structures.BufferFactory
-import space.kscience.kmath.structures.asSequence
-import kotlin.reflect.KClass
-
-/**
- * Basic operations on matrices. Operates on [Matrix].
- *
- * @param T the type of items in the matrices.
- * @param M the type of operated matrices.
- */
-public interface MatrixContext<T : Any, out M : Matrix<T>> : GroupOperations<Matrix<T>>, ScaleOperations<Matrix<T>> {
-    /**
-     * Produces a matrix with this context and given dimensions.
-     */
-    public fun produce(rows: Int, columns: Int, initializer: (i: Int, j: Int) -> T): M
-
-    /**
-     * Produces a point compatible with matrix space (and possibly optimized for it).
-     */
-    public fun point(size: Int, initializer: (Int) -> T): Point<T> = Buffer.boxing(size, initializer)
-
-    @Suppress("UNCHECKED_CAST")
-    public override fun binaryOperationFunction(operation: String): (left: Matrix<T>, right: Matrix<T>) -> M =
-        when (operation) {
-            "dot" -> { left, right -> left dot right }
-            else -> super<GroupOperations>.binaryOperationFunction(operation) as (Matrix<T>, Matrix<T>) -> M
-        }
-
-    /**
-     * Computes the dot product of this matrix and another one.
-     *
-     * @receiver the multiplicand.
-     * @param other the multiplier.
-     * @return the dot product.
-     */
-    public infix fun Matrix<T>.dot(other: Matrix<T>): M
-
-    /**
-     * Computes the dot product of this matrix and a vector.
-     *
-     * @receiver the multiplicand.
-     * @param vector the multiplier.
-     * @return the dot product.
-     */
-    public infix fun Matrix<T>.dot(vector: Point<T>): Point<T>
-
-    /**
-     * Multiplies a matrix by its element.
-     *
-     * @receiver the multiplicand.
-     * @param value the multiplier.
-     * @receiver the product.
-     */
-    public operator fun Matrix<T>.times(value: T): M
-
-    /**
-     * Multiplies an element by a matrix of it.
-     *
-     * @receiver the multiplicand.
-     * @param m the multiplier.
-     * @receiver the product.
-     */
-    public operator fun T.times(m: Matrix<T>): M = m * this
-
-    /**
-     * Gets a feature from the matrix. This function may return some additional features to
-     * [kscience.kmath.nd.NDStructure.getFeature].
-     *
-     * @param F the type of feature.
-     * @param m the matrix.
-     * @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 companion object {
-
-        /**
-         * A structured matrix with custom buffer
-         */
-        public fun <T : Any, A> buffered(
-            ring: A,
-            bufferFactory: BufferFactory<T> = Buffer.Companion::boxing,
-        ): GenericMatrixContext<T, A, BufferMatrix<T>> where A : Ring<T>, A: ScaleOperations<T> = BufferMatrixContext(ring, bufferFactory)
-
-        /**
-         * Automatic buffered matrix, unboxed if it is possible
-         */
-        public inline fun <reified T : Any, A> auto(ring: A): GenericMatrixContext<T, A, BufferMatrix<T>> where A : Ring<T>, A: ScaleOperations<T> =
-            buffered(ring, Buffer.Companion::auto)
-    }
-}
-
-/**
- * Gets a feature from the matrix. This function may return some additional features to
- * [kscience.kmath.nd.NDStructure.getFeature].
- *
- * @param T the type of items in the matrices.
- * @param M the type of operated matrices.
- * @param F the type of feature.
- * @receiver the [MatrixContext] 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> MatrixContext<T, *>.getFeature(m: Matrix<T>): F? =
-    getFeature(m, F::class)
-
-/**
- * Partial implementation of [MatrixContext] for matrices of [Ring].
- *
- * @param T the type of items in the matrices.
- * @param A the type of ring of matrix elements.
- * @param M the type of operated matrices.
- */
-public interface GenericMatrixContext<T : Any, A, out M : Matrix<T>> : MatrixContext<T, M> where A : Ring<T>, A : ScaleOperations<T>{
-    /**
-     * The ring over matrix elements.
-     */
-    public val elementContext: A
-
-    public override infix fun Matrix<T>.dot(other: Matrix<T>): M {
-        //TODO add typed error
-        require(colNum == other.rowNum) { "Matrix dot operation dimension mismatch: ($rowNum, $colNum) x (${other.rowNum}, ${other.colNum})" }
-
-        return produce(rowNum, other.colNum) { i, j ->
-            val row = rows[i]
-            val column = other.columns[j]
-            elementContext { sum(row.asSequence().zip(column.asSequence(), ::multiply)) }
-        }
-    }
-
-    public override infix fun Matrix<T>.dot(vector: Point<T>): Point<T> {
-        //TODO add typed error
-        require(colNum == vector.size) { "Matrix dot vector operation dimension mismatch: ($rowNum, $colNum) x (${vector.size})" }
-
-        return point(rowNum) { i ->
-            val row = rows[i]
-            elementContext { sum(row.asSequence().zip(vector.asSequence(), ::multiply)) }
-        }
-    }
-
-    public override operator fun Matrix<T>.unaryMinus(): M =
-        produce(rowNum, colNum) { i, j -> elementContext { -get(i, j) } }
-
-    public override fun add(a: Matrix<T>, b: Matrix<T>): M {
-        require(a.rowNum == b.rowNum && a.colNum == b.colNum) {
-            "Matrix operation dimension mismatch. [${a.rowNum},${a.colNum}] + [${b.rowNum},${b.colNum}]"
-        }
-
-        return produce(a.rowNum, a.colNum) { i, j -> elementContext { a[i, j] + b[i, j] } }
-    }
-
-    public override operator fun Matrix<T>.minus(b: Matrix<T>): M {
-        require(rowNum == b.rowNum && colNum == b.colNum) {
-            "Matrix operation dimension mismatch. [$rowNum,$colNum] - [${b.rowNum},${b.colNum}]"
-        }
-
-        return produce(rowNum, colNum) { i, j -> elementContext { get(i, j) + b[i, j] } }
-    }
-//
-//    public override fun multiply(a: Matrix<T>, k: Number): M =
-//        produce(a.rowNum, a.colNum) { i, j -> elementContext { a[i, j] * k } }
-
-    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/space/kscience/kmath/linear/MatrixWrapper.kt

@@ -1,14 +1,9 @@
 package space.kscience.kmath.linear
 
 import space.kscience.kmath.misc.UnstableKMathAPI
-import space.kscience.kmath.nd.Structure2D
 import space.kscience.kmath.nd.getFeature
 import space.kscience.kmath.operations.Ring
-import space.kscience.kmath.operations.ScaleOperations
-import space.kscience.kmath.structures.asBuffer
-import kotlin.math.sqrt
 import kotlin.reflect.KClass
-import kotlin.reflect.safeCast
 
 /**
  * A [Matrix] that holds [MatrixFeature] objects.
@@ -24,7 +19,8 @@ public class MatrixWrapper<T : Any> internal constructor(
      * Get the first feature matching given class. Does not guarantee that matrix has only one feature matching the criteria
      */
     @UnstableKMathAPI
-    override fun <T : Any> getFeature(type: KClass<T>): T? = type.safeCast(features.find { type.isInstance(it) })
+    @Suppress("UNCHECKED_CAST")
+    override fun <T : Any> getFeature(type: KClass<T>): T? = features.singleOrNull { type.isInstance(it) } as? T
         ?: origin.getFeature(type)
 
     override fun equals(other: Any?): Boolean = origin == other
@@ -61,35 +57,25 @@ public operator fun <T : Any> Matrix<T>.plus(newFeatures: Collection<MatrixFeatu
         MatrixWrapper(this, newFeatures.toSet())
     }
 
-/**
- * Build a square matrix from given elements.
- */
-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)
-}
-
 /**
  * Diagonal matrix of ones. The matrix is virtual no actual matrix is created
  */
-public fun <T : Any, A> GenericMatrixContext<T, A, *>.one(
+public fun <T : Any> LinearSpace<T, Ring<T>>.one(
     rows: Int,
     columns: Int,
-): Matrix<T> where A : Ring<T>, A : ScaleOperations<T> = VirtualMatrix(rows, columns) { i, j ->
+): Matrix<T> = VirtualMatrix(rows, columns) { i, j ->
-    if (i == j) elementContext.one else elementContext.zero
+    if (i == j) elementAlgebra.one else elementAlgebra.zero
 } + UnitFeature
 
 
 /**
  * A virtual matrix of zeroes
  */
-public fun <T : Any, A> GenericMatrixContext<T, A, *>.zero(
+public fun <T : Any> LinearSpace<T, Ring<T>>.zero(
     rows: Int,
     columns: Int,
-): Matrix<T> where A : Ring<T>, A : ScaleOperations<T> = VirtualMatrix(rows, columns) { _, _ ->
+): Matrix<T> = VirtualMatrix(rows, columns) { _, _ ->
-    elementContext.zero
+    elementAlgebra.zero
 } + ZeroFeature
 
 public class TransposedFeature<T : Any>(public val original: Matrix<T>) : MatrixFeature

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

@@ -1,34 +1,37 @@
 package space.kscience.kmath.linear
 
+import space.kscience.kmath.operations.RealField
 import space.kscience.kmath.operations.ScaleOperations
 import space.kscience.kmath.structures.RealBuffer
 
-public object RealMatrixContext : MatrixContext<Double, BufferMatrix<Double>>, ScaleOperations<Matrix<Double>> {
+public object RealLinearSpace : LinearSpace<Double, RealField>, ScaleOperations<Matrix<Double>> {
 
-    public override fun produce(
+    override val elementAlgebra: RealField get() = RealField
+
+    public override fun buildMatrix(
         rows: Int,
         columns: Int,
         initializer: (i: Int, j: Int) -> Double,
-    ): BufferMatrix<Double> {
+    ): Matrix<Double> {
         val buffer = RealBuffer(rows * columns) { offset -> initializer(offset / columns, offset % columns) }
         return  BufferMatrix(rows, columns, buffer)
     }
 
     public fun Matrix<Double>.toBufferMatrix(): BufferMatrix<Double> = if (this is BufferMatrix) this else {
-        produce(rowNum, colNum) { i, j -> get(i, j) }
+        buildMatrix(rowNum, colNum) { i, j -> get(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
 
-    override fun Matrix<Double>.unaryMinus(): Matrix<Double> = produce(rowNum, colNum) { i, j -> -get(i, j) }
+    override fun Matrix<Double>.unaryMinus(): Matrix<Double> = buildMatrix(rowNum, colNum) { i, j -> -get(i, j) }
 
     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})" }
         val bufferMatrix = toBufferMatrix()
         val otherBufferMatrix = other.toBufferMatrix()
-        return produce(rowNum, other.colNum) { i, j ->
+        return buildMatrix(rowNum, other.colNum) { i, j ->
             var res = 0.0
             for (l in 0 until colNum) {
                 res += bufferMatrix[i, l] * otherBufferMatrix[l, j]
@@ -54,14 +57,14 @@ public object RealMatrixContext : MatrixContext<Double, BufferMatrix<Double>>, S
         require(a.colNum == b.colNum) { "Column number mismatch in matrix addition. Left side: ${a.colNum}, right side: ${b.colNum}" }
         val aBufferMatrix = a.toBufferMatrix()
         val bBufferMatrix = b.toBufferMatrix()
-        return produce(a.rowNum, a.colNum) { i, j ->
+        return buildMatrix(a.rowNum, a.colNum) { i, j ->
             aBufferMatrix[i, j] + bBufferMatrix[i, j]
         }
     }
 
     override fun scale(a: Matrix<Double>, value: Double): BufferMatrix<Double> {
         val bufferMatrix = a.toBufferMatrix()
-        return produce(a.rowNum, a.colNum) { i, j -> bufferMatrix[i, j] * value }
+        return buildMatrix(a.rowNum, a.colNum) { i, j -> bufferMatrix[i, j] * value }
     }
 
     override fun Matrix<Double>.times(value: Double): BufferMatrix<Double> = scale(this, value)
@@ -82,4 +85,4 @@ public object RealMatrixContext : MatrixContext<Double, BufferMatrix<Double>>, S
 /**
  * Partially optimized real-valued matrix
  */
-public val MatrixContext.Companion.real: RealMatrixContext get() = RealMatrixContext
+public val LinearSpace.Companion.real: RealLinearSpace get() = RealLinearSpace

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

@@ -1,72 +0,0 @@
-package space.kscience.kmath.linear
-
-import space.kscience.kmath.operations.Group
-import space.kscience.kmath.operations.RealField
-import space.kscience.kmath.operations.ScaleOperations
-import space.kscience.kmath.operations.invoke
-import space.kscience.kmath.structures.Buffer
-import space.kscience.kmath.structures.BufferFactory
-
-/**
- * A linear space for vectors.
- * Could be used on any point-like structure
- */
-public interface VectorSpace<T : Any, A> : Group<Point<T>>, ScaleOperations<Point<T>>
-        where A : Group<T>, A : ScaleOperations<T> {
-    public val size: Int
-    public val algebra: A
-    override val zero: Point<T> get() = produce { algebra.zero }
-
-    public fun produce(initializer: A.(Int) -> T): Point<T>
-
-    override fun add(a: Point<T>, b: Point<T>): Point<T> = produce { algebra { a[it] + b[it] } }
-
-    override fun scale(a: Point<T>, value: Double): Point<T> = produce { algebra.scale(a[it], value) }
-
-    override fun Point<T>.unaryMinus(): Point<T> = produce { -get(it) }
-
-    //TODO add basis
-
-    public companion object {
-        private val realSpaceCache: MutableMap<Int, BufferVectorSpace<Double, RealField>> = hashMapOf()
-
-        /**
-         * Non-boxing double vector space
-         */
-        public fun real(size: Int): BufferVectorSpace<Double, RealField> = realSpaceCache.getOrPut(size) {
-            BufferVectorSpace(
-                size,
-                RealField,
-                Buffer.Companion::auto
-            )
-        }
-
-        /**
-         * A structured vector space with custom buffer
-         */
-        public fun <T : Any, A> buffered(
-            size: Int,
-            space: A,
-            bufferFactory: BufferFactory<T> = Buffer.Companion::boxing,
-        ): BufferVectorSpace<T, A> where A : Group<T>, A : ScaleOperations<T> =
-            BufferVectorSpace(size, space, bufferFactory)
-
-        /**
-         * Automatic buffered vector, unboxed if it is possible
-         */
-        public inline fun <reified T : Any, A> auto(
-            size: Int,
-            space: A,
-        ): VectorSpace<T, A> where A : Group<T>, A : ScaleOperations<T> =
-            buffered(size, space, Buffer.Companion::auto)
-    }
-}
-
-
-public class BufferVectorSpace<T : Any, A>(
-    override val size: Int,
-    override val algebra: A,
-    public val bufferFactory: BufferFactory<T>,
-) : VectorSpace<T, A> where A : Group<T>, A : ScaleOperations<T> {
-    override fun produce(initializer: A.(Int) -> T): Buffer<T> = bufferFactory(size) { algebra.initializer(it) }
-}

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

@@ -31,6 +31,4 @@ public class VirtualMatrix<T : Any>(
         result = 31 * result + generator.hashCode()
         return result
     }
-
-
 }

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

@@ -74,7 +74,7 @@ public interface NDStructure<T> {
          *
          * Strides should be reused if possible.
          */
-        public fun <T> build(
+        public fun <T> buffered(
             strides: Strides,
             bufferFactory: BufferFactory<T> = Buffer.Companion::boxing,
             initializer: (IntArray) -> T,
@@ -94,11 +94,11 @@ public interface NDStructure<T> {
             crossinline initializer: (IntArray) -> T,
         ): NDBuffer<T> = NDBuffer(strides, Buffer.auto(type, strides.linearSize) { i -> initializer(strides.index(i)) })
 
-        public fun <T> build(
+        public fun <T> buffered(
             shape: IntArray,
             bufferFactory: BufferFactory<T> = Buffer.Companion::boxing,
             initializer: (IntArray) -> T,
-        ): NDBuffer<T> = build(DefaultStrides(shape), bufferFactory, initializer)
+        ): NDBuffer<T> = buffered(DefaultStrides(shape), bufferFactory, initializer)
 
         public inline fun <reified T : Any> auto(
             shape: IntArray,

kmath-core/src/commonMain/kotlin/space/kscience/kmath/nd/Structure1D.kt

@@ -46,7 +46,11 @@ private inline class Buffer1DWrapper<T>(val buffer: Buffer<T>) : Structure1D<T>
  * Represent a [NDStructure] as [Structure1D]. Throw error in case of dimension mismatch
  */
 public fun <T> NDStructure<T>.as1D(): Structure1D<T> = if (shape.size == 1) {
-    if (this is NDBuffer) Buffer1DWrapper(this.buffer) else Structure1DWrapper(this)
+    when (this) {
+        is Structure1DWrapper -> this
+        is NDBuffer -> Buffer1DWrapper(this.buffer)
+        else -> Structure1DWrapper(this)
+    }
 } else
     error("Can't create 1d-structure from ${shape.size}d-structure")
 

kmath-core/src/commonMain/kotlin/space/kscience/kmath/nd/Structure2D.kt

@@ -1,7 +1,5 @@
 package space.kscience.kmath.nd
 
-import space.kscience.kmath.linear.BufferMatrix
-import space.kscience.kmath.linear.RealMatrixContext
 import space.kscience.kmath.structures.Buffer
 import space.kscience.kmath.structures.VirtualBuffer
 
@@ -54,15 +52,7 @@ public interface Structure2D<T> : NDStructure<T> {
             for (j in 0 until colNum) yield(intArrayOf(i, j) to get(i, j))
     }
 
-    public companion object {
+    public companion object
-        public inline fun real(
-            rows: Int,
-            columns: Int,
-            crossinline init: (i: Int, j: Int) -> Double,
-        ): BufferMatrix<Double> = RealMatrixContext.produce(rows,columns) { i, j ->
-            init(i, j)
-        }
-    }
 }
 
 /**

kmath-core/src/commonMain/kotlin/space/kscience/kmath/structures/BufferAccessor2D.kt

@@ -25,7 +25,7 @@ internal class BufferAccessor2D<T : Any>(
     public fun create(mat: Structure2D<T>): MutableBuffer<T> = create { i, j -> mat[i, j] }
 
     //TODO optimize wrapper
-    public fun MutableBuffer<T>.collect(): Structure2D<T> = NDStructure.build(
+    public fun MutableBuffer<T>.collect(): Structure2D<T> = NDStructure.buffered(
         DefaultStrides(intArrayOf(rowNum, colNum)),
         factory
     ) { (i, j) ->

kmath-core/src/commonTest/kotlin/space/kscience/kmath/linear/MatrixTest.kt

@@ -10,7 +10,7 @@ import kotlin.test.assertEquals
 class MatrixTest {
     @Test
     fun testTranspose() {
-        val matrix = MatrixContext.real.one(3, 3)
+        val matrix = LinearSpace.real.one(3, 3)
         val transposed = matrix.transpose()
         assertEquals(matrix, transposed)
     }
@@ -39,7 +39,7 @@ class MatrixTest {
         infix fun Matrix<Double>.pow(power: Int): Matrix<Double> {
             var res = this
             repeat(power - 1) {
-                res = RealMatrixContext.invoke { res dot this@pow }
+                res = RealLinearSpace.invoke { res dot this@pow }
             }
             return res
         }
@@ -52,7 +52,7 @@ class MatrixTest {
         val firstMatrix = NDStructure.auto(2, 3) { (i, j) -> (i + j).toDouble() }.as2D()
         val secondMatrix = NDStructure.auto(3, 2) { (i, j) -> (i + j).toDouble() }.as2D()
 
-        MatrixContext.real.run {
+        LinearSpace.real.run {
 //            val firstMatrix = produce(2, 3) { i, j -> (i + j).toDouble() }
 //            val secondMatrix = produce(3, 2) { i, j -> (i + j).toDouble() }
             val result = firstMatrix dot secondMatrix

kmath-core/src/commonTest/kotlin/space/kscience/kmath/linear/RealLUSolverTest.kt

@@ -7,8 +7,8 @@ class RealLUSolverTest {
 
     @Test
     fun testInvertOne() {
-        val matrix = MatrixContext.real.one(2, 2)
+        val matrix = LinearSpace.real.one(2, 2)
-        val inverted = MatrixContext.real.inverseWithLup(matrix)
+        val inverted = LinearSpace.real.inverseWithLup(matrix)
         assertEquals(matrix, inverted)
     }
 
@@ -19,7 +19,7 @@ class RealLUSolverTest {
             1.0, 3.0
         )
 
-        MatrixContext.real.run {
+        LinearSpace.real.run {
             val lup = lup(matrix)
 
             //Check determinant
@@ -36,7 +36,7 @@ class RealLUSolverTest {
             1.0, 3.0
         )
 
-        val inverted = MatrixContext.real.inverseWithLup(matrix)
+        val inverted = LinearSpace.real.inverseWithLup(matrix)
 
         val expected = Matrix.square(
             0.375, -0.125,

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

@@ -77,7 +77,7 @@ public inline class DPointWrapper<T, D : Dimension>(public val point: Point<T>)
 /**
  * Basic operations on dimension-safe matrices. Operates on [Matrix]
  */
-public inline class DMatrixContext<T : Any>(public val context: MatrixContext<T, Matrix<T>>) {
+public inline class DMatrixContext<T : Any>(public val context: LinearSpace<T, Matrix<T>>) {
     public inline fun <reified R : Dimension, reified C : Dimension> Matrix<T>.coerce(): DMatrix<T, R, C> {
         require(rowNum == Dimension.dim<R>().toInt()) {
             "Row number mismatch: expected ${Dimension.dim<R>()} but found $rowNum"
@@ -96,14 +96,14 @@ public inline class DMatrixContext<T : Any>(public val context: MatrixContext<T,
     public inline fun <reified R : Dimension, reified C : Dimension> produce(noinline initializer: (i: Int, j: Int) -> T): DMatrix<T, R, C> {
         val rows = Dimension.dim<R>()
         val cols = Dimension.dim<C>()
-        return context.produce(rows.toInt(), cols.toInt(), initializer).coerce<R, C>()
+        return context.buildMatrix(rows.toInt(), cols.toInt(), initializer).coerce<R, C>()
     }
 
     public inline fun <reified D : Dimension> point(noinline initializer: (Int) -> T): DPoint<T, D> {
         val size = Dimension.dim<D>()
 
         return DPoint.coerceUnsafe(
-            context.point(
+            context.buildVector(
                 size.toInt(),
                 initializer
             )
@@ -136,7 +136,7 @@ public inline class DMatrixContext<T : Any>(public val context: MatrixContext<T,
         context { (this@transpose as Matrix<T>).transpose() }.coerce()
 
     public companion object {
-        public val real: DMatrixContext<Double> = DMatrixContext(MatrixContext.real)
+        public val real: DMatrixContext<Double> = DMatrixContext(LinearSpace.real)
     }
 }
 

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

@@ -11,7 +11,7 @@ import space.kscience.kmath.operations.ScaleOperations
  *
  * @author Iaroslav Postovalov
  */
-public object EjmlMatrixContext : MatrixContext<Double, EjmlMatrix>, ScaleOperations<Matrix<Double>> {
+public object EjmlLinearSpace : LinearSpace<Double, EjmlMatrix>, ScaleOperations<Matrix<Double>> {
 
     /**
      * Converts this matrix to EJML one.
@@ -19,7 +19,7 @@ public object EjmlMatrixContext : MatrixContext<Double, EjmlMatrix>, ScaleOperat
     @OptIn(UnstableKMathAPI::class)
     public fun Matrix<Double>.toEjml(): EjmlMatrix = when (val matrix = origin) {
         is EjmlMatrix -> matrix
-        else -> produce(rowNum, colNum) { i, j -> get(i, j) }
+        else -> buildMatrix(rowNum, colNum) { i, j -> get(i, j) }
     }
 
     /**
@@ -30,14 +30,14 @@ public object EjmlMatrixContext : MatrixContext<Double, EjmlMatrix>, ScaleOperat
             (0 until it.numRows()).forEach { row -> it[row, 0] = get(row) }
         })
 
-    override fun produce(rows: Int, columns: Int, initializer: (i: Int, j: Int) -> Double): EjmlMatrix =
+    override fun buildMatrix(rows: Int, columns: Int, initializer: (i: Int, j: Int) -> Double): EjmlMatrix =
         EjmlMatrix(SimpleMatrix(rows, columns).also {
             (0 until rows).forEach { row ->
                 (0 until columns).forEach { col -> it[row, col] = initializer(row, col) }
             }
         })
 
-    override fun point(size: Int, initializer: (Int) -> Double): Point<Double> =
+    override fun buildVector(size: Int, initializer: (Int) -> Double): Point<Double> =
         EjmlVector(SimpleMatrix(size, 1).also {
             (0 until it.numRows()).forEach { row -> it[row, 0] = initializer(row) }
         })
@@ -58,7 +58,7 @@ public object EjmlMatrixContext : MatrixContext<Double, EjmlMatrix>, ScaleOperat
         EjmlMatrix(toEjml().origin - b.toEjml().origin)
 
     public override fun scale(a: Matrix<Double>, value: Double): EjmlMatrix =
-        produce(a.rowNum, a.colNum) { i, j -> a[i, j] * value }
+        buildMatrix(a.rowNum, a.colNum) { i, j -> a[i, j] * value }
 
     public override operator fun Matrix<Double>.times(value: Double): EjmlMatrix =
         EjmlMatrix(toEjml().origin.scale(value))
@@ -72,7 +72,7 @@ public object EjmlMatrixContext : MatrixContext<Double, EjmlMatrix>, ScaleOperat
  * @return the solution for 'x' that is n by p.
  * @author Iaroslav Postovalov
  */
-public fun EjmlMatrixContext.solve(a: Matrix<Double>, b: Matrix<Double>): EjmlMatrix =
+public fun EjmlLinearSpace.solve(a: Matrix<Double>, b: Matrix<Double>): EjmlMatrix =
     EjmlMatrix(a.toEjml().origin.solve(b.toEjml().origin))
 
 /**
@@ -83,10 +83,10 @@ public fun EjmlMatrixContext.solve(a: Matrix<Double>, b: Matrix<Double>): EjmlMa
  * @return the solution for 'x' that is n by p.
  * @author Iaroslav Postovalov
  */
-public fun EjmlMatrixContext.solve(a: Matrix<Double>, b: Point<Double>): EjmlVector =
+public fun EjmlLinearSpace.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()
+public fun EjmlLinearSpace.inverse(matrix: Matrix<Double>): Matrix<Double> = matrix.toEjml().inverted()

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

@@ -22,14 +22,14 @@ import kotlin.math.pow
 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)
+    LinearSpace.real.buildMatrix(rowNum, colNum, initializer)
 
 public fun Array<DoubleArray>.toMatrix(): RealMatrix {
-    return MatrixContext.real.produce(size, this[0].size) { row, col -> this[row][col] }
+    return LinearSpace.real.buildMatrix(size, this[0].size) { row, col -> this[row][col] }
 }
 
 public fun Sequence<DoubleArray>.toMatrix(): RealMatrix = toList().let {
-    MatrixContext.real.produce(it.size, it[0].size) { row, col -> it[row][col] }
+    LinearSpace.real.buildMatrix(it.size, it[0].size) { row, col -> it[row][col] }
 }
 
 public fun RealMatrix.repeatStackVertical(n: Int): RealMatrix =
@@ -42,37 +42,37 @@ public fun RealMatrix.repeatStackVertical(n: Int): RealMatrix =
  */
 
 public operator fun RealMatrix.times(double: Double): RealMatrix =
-    MatrixContext.real.produce(rowNum, colNum) { row, col ->
+    LinearSpace.real.buildMatrix(rowNum, colNum) { row, col ->
         this[row, col] * double
     }
 
 public operator fun RealMatrix.plus(double: Double): RealMatrix =
-    MatrixContext.real.produce(rowNum, colNum) { row, col ->
+    LinearSpace.real.buildMatrix(rowNum, colNum) { row, col ->
         this[row, col] + double
     }
 
 public operator fun RealMatrix.minus(double: Double): RealMatrix =
-    MatrixContext.real.produce(rowNum, colNum) { row, col ->
+    LinearSpace.real.buildMatrix(rowNum, colNum) { row, col ->
         this[row, col] - double
     }
 
 public operator fun RealMatrix.div(double: Double): RealMatrix =
-    MatrixContext.real.produce(rowNum, colNum) { row, col ->
+    LinearSpace.real.buildMatrix(rowNum, colNum) { row, col ->
         this[row, col] / double
     }
 
 public operator fun Double.times(matrix: RealMatrix): RealMatrix =
-    MatrixContext.real.produce(matrix.rowNum, matrix.colNum) { row, col ->
+    LinearSpace.real.buildMatrix(matrix.rowNum, matrix.colNum) { row, col ->
         this * matrix[row, col]
     }
 
 public operator fun Double.plus(matrix: RealMatrix): RealMatrix =
-    MatrixContext.real.produce(matrix.rowNum, matrix.colNum) { row, col ->
+    LinearSpace.real.buildMatrix(matrix.rowNum, matrix.colNum) { row, col ->
         this + matrix[row, col]
     }
 
 public operator fun Double.minus(matrix: RealMatrix): RealMatrix =
-    MatrixContext.real.produce(matrix.rowNum, matrix.colNum) { row, col ->
+    LinearSpace.real.buildMatrix(matrix.rowNum, matrix.colNum) { row, col ->
         this - matrix[row, col]
     }
 
@@ -87,20 +87,20 @@ public operator fun Double.minus(matrix: RealMatrix): RealMatrix =
 
 @UnstableKMathAPI
 public operator fun RealMatrix.times(other: RealMatrix): RealMatrix =
-    MatrixContext.real.produce(rowNum, colNum) { row, col -> this[row, col] * other[row, col] }
+    LinearSpace.real.buildMatrix(rowNum, colNum) { row, col -> this[row, col] * other[row, col] }
 
 public operator fun RealMatrix.plus(other: RealMatrix): RealMatrix =
-    MatrixContext.real.add(this, other)
+    LinearSpace.real.add(this, other)
 
 public operator fun RealMatrix.minus(other: RealMatrix): RealMatrix =
-    MatrixContext.real.produce(rowNum, colNum) { row, col -> this[row, col] - other[row, col] }
+    LinearSpace.real.buildMatrix(rowNum, colNum) { row, col -> this[row, col] - other[row, col] }
 
 /*
  *  Operations on columns
  */
 
 public inline fun RealMatrix.appendColumn(crossinline mapper: (Buffer<Double>) -> Double): RealMatrix =
-    MatrixContext.real.produce(rowNum, colNum + 1) { row, col ->
+    LinearSpace.real.buildMatrix(rowNum, colNum + 1) { row, col ->
         if (col < colNum)
             this[row, col]
         else
@@ -108,7 +108,7 @@ public inline fun RealMatrix.appendColumn(crossinline mapper: (Buffer<Double>) -
     }
 
 public fun RealMatrix.extractColumns(columnRange: IntRange): RealMatrix =
-    MatrixContext.real.produce(rowNum, columnRange.count()) { row, col ->
+    LinearSpace.real.buildMatrix(rowNum, columnRange.count()) { row, col ->
         this[row, columnRange.first + col]
     }
 
@@ -141,14 +141,14 @@ public fun RealMatrix.max(): Double? = elements().map { (_, value) -> value }.ma
 public fun RealMatrix.average(): Double = elements().map { (_, value) -> value }.average()
 
 public inline fun RealMatrix.map(crossinline transform: (Double) -> Double): RealMatrix =
-    MatrixContext.real.produce(rowNum, colNum) { i, j ->
+    LinearSpace.real.buildMatrix(rowNum, colNum) { i, j ->
         transform(get(i, j))
     }
 
 /**
  * Inverse a square real matrix using LUP decomposition
  */
-public fun RealMatrix.inverseWithLup(): RealMatrix = MatrixContext.real.inverseWithLup(this)
+public fun RealMatrix.inverseWithLup(): RealMatrix = LinearSpace.real.inverseWithLup(this)
 
 //extended operations
 

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

@@ -1,6 +1,6 @@
 package kaceince.kmath.real
 
-import space.kscience.kmath.linear.MatrixContext
+import space.kscience.kmath.linear.LinearSpace
 import space.kscience.kmath.linear.asMatrix
 import space.kscience.kmath.linear.real
 import space.kscience.kmath.linear.transpose
@@ -32,7 +32,7 @@ internal class RealVectorTest {
         val vector2 =  Buffer.real(5) { 5 - it.toDouble() }
         val matrix1 = vector1.asMatrix()
         val matrix2 = vector2.asMatrix().transpose()
-        val product = MatrixContext.real { matrix1 dot matrix2 }
+        val product = LinearSpace.real { matrix1 dot matrix2 }
         assertEquals(5.0, product[1, 0])
         assertEquals(6.0, product[2, 2])
     }