examples/src/benchmarks/kotlin/kscience/kmath/ast/ExpressionsInterpretersBenchmark.kt

@@ -28,7 +28,7 @@ internal class ExpressionsInterpretersBenchmark {
     @Benchmark
     fun mstExpression() {
         val expr = algebra.mstInField {
-            symbol("x") * number(2.0) + number(2.0) / symbol("x") - number(16.0)
+            symbol("x") * 2.0 + 2.0 / symbol("x") - 16.0
         }
 
         invokeAndSum(expr)
@@ -37,7 +37,7 @@ internal class ExpressionsInterpretersBenchmark {
     @Benchmark
     fun asmExpression() {
         val expr = algebra.mstInField {
-            symbol("x") * number(2.0) + number(2.0) / symbol("x") - number(16.0)
+            symbol("x") * 2.0 + 2.0 / symbol("x") - 16.0
         }.compile()
 
         invokeAndSum(expr)

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

@@ -2,9 +2,8 @@ package kscience.kmath.benchmarks
 
 import kotlinx.benchmark.Benchmark
 import kscience.kmath.commons.linear.CMMatrixContext
-import kscience.kmath.commons.linear.toCM
 import kscience.kmath.ejml.EjmlMatrixContext
-import kscience.kmath.ejml.toEjml
+
 import kscience.kmath.linear.BufferMatrixContext
 import kscience.kmath.linear.RealMatrixContext
 import kscience.kmath.linear.real
@@ -26,11 +25,11 @@ 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 = matrix1.toCM()
+        val cmMatrix1 = CMMatrixContext { matrix1.toCM() }
-        val cmMatrix2 = matrix2.toCM()
+        val cmMatrix2 = CMMatrixContext { matrix2.toCM() }
 
-        val ejmlMatrix1 = matrix1.toEjml()
+        val ejmlMatrix1 = EjmlMatrixContext { matrix1.toEjml() }
-        val ejmlMatrix2 = matrix2.toEjml()
+        val ejmlMatrix2 = EjmlMatrixContext { matrix2.toEjml() }
     }
 
     @Benchmark
@@ -49,9 +48,10 @@ class DotBenchmark {
 
     @Benchmark
     fun ejmlMultiplicationwithConversion() {
+        EjmlMatrixContext {
             val ejmlMatrix1 = matrix1.toEjml()
             val ejmlMatrix2 = matrix2.toEjml()
-        EjmlMatrixContext {
+
             ejmlMatrix1 dot ejmlMatrix2
         }
     }

examples/src/benchmarks/kotlin/kscience/kmath/benchmarks/LargeNDBenchmark.kt

@@ -1,25 +0,0 @@
-package kscience.kmath.benchmarks
-
-import kscience.kmath.structures.NDField
-import org.openjdk.jmh.annotations.Benchmark
-import org.openjdk.jmh.annotations.Scope
-import org.openjdk.jmh.annotations.State
-import org.openjdk.jmh.infra.Blackhole
-import kotlin.random.Random
-
-@State(Scope.Benchmark)
-class LargeNDBenchmark {
-    val arraySize = 10000
-    val RANDOM = Random(222)
-    val src1 = DoubleArray(arraySize) { RANDOM.nextDouble() }
-    val src2 = DoubleArray(arraySize) { RANDOM.nextDouble() }
-    val field = NDField.real(arraySize)
-    val kmathArray1 = field.produce { (a) -> src1[a] }
-    val kmathArray2 = field.produce { (a) -> src2[a] }
-
-    @Benchmark
-    fun test10000(bh: Blackhole) {
-        bh.consume(field.add(kmathArray1, kmathArray2))
-    }
-
-}

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

@@ -5,10 +5,8 @@ import kotlinx.benchmark.Benchmark
 import kscience.kmath.commons.linear.CMMatrixContext
 import kscience.kmath.commons.linear.CMMatrixContext.dot
 import kscience.kmath.commons.linear.inverse
-import kscience.kmath.commons.linear.toCM
 import kscience.kmath.ejml.EjmlMatrixContext
 import kscience.kmath.ejml.inverse
-import kscience.kmath.ejml.toEjml
 import kscience.kmath.operations.invoke
 import kscience.kmath.structures.Matrix
 import org.openjdk.jmh.annotations.Scope
@@ -35,16 +33,14 @@ class LinearAlgebraBenchmark {
     @Benchmark
     fun cmLUPInversion() {
         CMMatrixContext {
-            val cm = matrix.toCM()             //avoid overhead on conversion
+            inverse(matrix)
-            inverse(cm)
         }
     }
 
     @Benchmark
     fun ejmlInverse() {
         EjmlMatrixContext {
-            val km = matrix.toEjml()      //avoid overhead on conversion
+            inverse(matrix)
-            inverse(km)
         }
     }
 }

kmath-core/src/commonMain/kotlin/kscience/kmath/expressions/FunctionalExpressionAlgebra.kt

@@ -95,8 +95,9 @@ public open class FunctionalExpressionRing<T, A : Ring<T>>(
         super<FunctionalExpressionSpace>.binaryOperationFunction(operation)
 }
 
-public open class FunctionalExpressionField<T, A : Field<T>>(algebra: A) :
+public open class FunctionalExpressionField<T, A : Field<T>>(
-    FunctionalExpressionRing<T, A>(algebra), Field<Expression<T>> {
+    algebra: A,
+) : FunctionalExpressionRing<T, A>(algebra), Field<Expression<T>> {
     /**
      * Builds an Expression of division an expression by another one.
      */

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

@@ -224,6 +224,7 @@ public inline fun <reified T : Comparable<T>, F : Field<T>> GenericMatrixContext
 ): Matrix<T> = solveWithLUP(matrix, one(matrix.rowNum, matrix.colNum), bufferFactory, checkSingular)
 
 
+@OptIn(UnstableKMathAPI::class)
 public fun RealMatrixContext.solveWithLUP(a: Matrix<Double>, b: Matrix<Double>): Matrix<Double> {
     // Use existing decomposition if it is provided by matrix
     val bufferFactory: MutableBufferFactory<Double> = MutableBuffer.Companion::real

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

@@ -3,6 +3,7 @@ package kscience.kmath.operations
 import kscience.kmath.memory.MemoryReader
 import kscience.kmath.memory.MemorySpec
 import kscience.kmath.memory.MemoryWriter
+import kscience.kmath.misc.UnstableKMathAPI
 import kscience.kmath.structures.Buffer
 import kscience.kmath.structures.MemoryBuffer
 import kscience.kmath.structures.MutableBuffer
@@ -41,6 +42,7 @@ private val PI_DIV_2 = Complex(PI / 2, 0)
 /**
  * A field of [Complex].
  */
+@OptIn(UnstableKMathAPI::class)
 public object ComplexField : ExtendedField<Complex>, Norm<Complex, Complex>, RingWithNumbers<Complex> {
     override val zero: Complex = 0.0.toComplex()
     override val one: Complex = 1.0.toComplex()

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

@@ -24,35 +24,31 @@ public class RealNDField(override val shape: IntArray) :
         return produce { d }
     }
 
-    private inline fun buildBuffer(size: Int, crossinline initializer: (Int) -> Double): Buffer<Double> =
+    @Suppress("OVERRIDE_BY_INLINE")
-        RealBuffer(DoubleArray(size) { initializer(it) })
+    override inline fun map(
-
-    /**
-     * Inline transform an NDStructure to
-     */
-    override fun map(
         arg: NDBuffer<Double>,
-        transform: RealField.(Double) -> Double
+        transform: RealField.(Double) -> Double,
     ): RealNDElement {
         check(arg)
-        val array = buildBuffer(arg.strides.linearSize) { offset -> RealField.transform(arg.buffer[offset]) }
+        val array = RealBuffer(arg.strides.linearSize) { offset -> RealField.transform(arg.buffer[offset]) }
         return BufferedNDFieldElement(this, array)
     }
 
-    override fun produce(initializer: RealField.(IntArray) -> Double): RealNDElement {
+    @Suppress("OVERRIDE_BY_INLINE")
-        val array = buildBuffer(strides.linearSize) { offset -> elementContext.initializer(strides.index(offset)) }
+    override inline fun produce(initializer: RealField.(IntArray) -> Double): RealNDElement {
+        val array = RealBuffer(strides.linearSize) { offset -> elementContext.initializer(strides.index(offset)) }
         return BufferedNDFieldElement(this, array)
     }
 
-    override fun mapIndexed(
+    @Suppress("OVERRIDE_BY_INLINE")
+    override inline fun mapIndexed(
         arg: NDBuffer<Double>,
-        transform: RealField.(index: IntArray, Double) -> Double
+        transform: RealField.(index: IntArray, Double) -> Double,
     ): RealNDElement {
         check(arg)
-
         return BufferedNDFieldElement(
             this,
-            buildBuffer(arg.strides.linearSize) { offset ->
+            RealBuffer(arg.strides.linearSize) { offset ->
                 elementContext.transform(
                     arg.strides.index(offset),
                     arg.buffer[offset]
@@ -60,16 +56,17 @@ public class RealNDField(override val shape: IntArray) :
             })
     }
 
-    override fun combine(
+    @Suppress("OVERRIDE_BY_INLINE")
+    override inline fun combine(
         a: NDBuffer<Double>,
         b: NDBuffer<Double>,
-        transform: RealField.(Double, Double) -> Double
+        transform: RealField.(Double, Double) -> Double,
     ): RealNDElement {
         check(a, b)
-        return BufferedNDFieldElement(
+        val buffer = RealBuffer(strides.linearSize) { offset ->
-            this,
+            elementContext.transform(a.buffer[offset], b.buffer[offset])
-            buildBuffer(strides.linearSize) { offset -> elementContext.transform(a.buffer[offset], b.buffer[offset]) }
+        }
-        )
+        return BufferedNDFieldElement(this, buffer)
     }
 
     override fun NDBuffer<Double>.toElement(): FieldElement<NDBuffer<Double>, *, out BufferedNDField<Double, RealField>> =

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

@@ -7,6 +7,7 @@ import kscience.kmath.structures.as2D
 import kotlin.test.Test
 import kotlin.test.assertEquals
 
+@Suppress("UNUSED_VARIABLE")
 class MatrixTest {
     @Test
     fun testTranspose() {

kmath-core/src/commonTest/kotlin/kscience/kmath/structures/NumberNDFieldTest.kt

@@ -8,6 +8,7 @@ import kotlin.math.pow
 import kotlin.test.Test
 import kotlin.test.assertEquals
 
+@Suppress("UNUSED_VARIABLE")
 class NumberNDFieldTest {
     val array1: RealNDElement = real2D(3, 3) { i, j -> (i + j).toDouble() }
     val array2: RealNDElement = real2D(3, 3) { i, j -> (i - j).toDouble() }

kmath-dimensions/src/commonTest/kotlin/kscience/dimensions/DMatrixContextTest.kt

@@ -6,6 +6,7 @@ import kscience.kmath.dimensions.DMatrixContext
 import kscience.kmath.dimensions.one
 import kotlin.test.Test
 
+@Suppress("UNUSED_VARIABLE")
 internal class DMatrixContextTest {
     @Test
     fun testDimensionSafeMatrix() {