CHANGELOG.md

@@ -20,11 +20,14 @@
 - Norm support for `Complex`
 
 ### Changed
+- `readAsMemory` now has `throws IOException` in JVM signature.
+- Several functions taking functional types were made `inline`.
+- Several functions taking functional types now have `callsInPlace` contracts.
 - BigInteger and BigDecimal algebra: JBigDecimalField has companion object with default math context; minor optimizations
 - `power(T, Int)` extension function has preconditions and supports `Field<T>`
 - Memory objects have more preconditions (overflow checking)
 - `tg` function is renamed to `tan` (https://github.com/mipt-npm/kmath/pull/114)
-- Gradle version: 6.3 -> 6.5.1
+- Gradle version: 6.3 -> 6.6
 - Moved probability distributions to commons-rng and to `kmath-prob`
 
 ### Fixed

examples/build.gradle.kts

@@ -56,9 +56,16 @@ benchmark {
     }
 }
 
+kotlin.sourceSets.all {
+    with(languageSettings) {
+        useExperimentalAnnotation("kotlin.contracts.ExperimentalContracts")
+        useExperimentalAnnotation("kotlin.ExperimentalUnsignedTypes")
+    }
+}
 
 tasks.withType<KotlinCompile> {
     kotlinOptions {
         jvmTarget = Scientifik.JVM_TARGET.toString()
+        freeCompilerArgs = freeCompilerArgs + "-Xopt-in=kotlin.RequiresOptIn"
     }
 }

examples/src/benchmarks/kotlin/scientifik/kmath/structures/NDFieldBenchmark.kt

@@ -4,46 +4,38 @@ import org.openjdk.jmh.annotations.Benchmark
 import org.openjdk.jmh.annotations.Scope
 import org.openjdk.jmh.annotations.State
 import scientifik.kmath.operations.RealField
+import scientifik.kmath.operations.invoke
 
 @State(Scope.Benchmark)
 class NDFieldBenchmark {
-
     @Benchmark
     fun autoFieldAdd() {
-        bufferedField.run {
+        bufferedField {
             var res: NDBuffer<Double> = one
-            repeat(n) {
-                res += one
-            }
+            repeat(n) { res += one }
         }
     }
 
     @Benchmark
     fun autoElementAdd() {
         var res = genericField.one
-        repeat(n) {
-            res += 1.0
-        }
+        repeat(n) { res += 1.0 }
     }
 
     @Benchmark
     fun specializedFieldAdd() {
-        specializedField.run {
+        specializedField {
             var res: NDBuffer<Double> = one
-            repeat(n) {
-                res += 1.0
-            }
+            repeat(n) { res += 1.0 }
         }
     }
 
 
     @Benchmark
     fun boxingFieldAdd() {
-        genericField.run {
+        genericField {
             var res: NDBuffer<Double> = one
-            repeat(n) {
-                res += one
-            }
+            repeat(n) { res += one }
         }
     }
 

examples/src/benchmarks/kotlin/scientifik/kmath/structures/ViktorBenchmark.kt

@@ -5,23 +5,22 @@ import org.openjdk.jmh.annotations.Benchmark
 import org.openjdk.jmh.annotations.Scope
 import org.openjdk.jmh.annotations.State
 import scientifik.kmath.operations.RealField
+import scientifik.kmath.operations.invoke
 import scientifik.kmath.viktor.ViktorNDField
 
-
 @State(Scope.Benchmark)
 class ViktorBenchmark {
     final val dim = 1000
     final val n = 100
 
     // automatically build context most suited for given type.
-    final val autoField = NDField.auto(RealField, dim, dim)
-    final val realField = NDField.real(dim, dim)
-
-    final val viktorField = ViktorNDField(intArrayOf(dim, dim))
+    final val autoField: BufferedNDField<Double, RealField> = NDField.auto(RealField, dim, dim)
+    final val realField: RealNDField = NDField.real(dim, dim)
+    final val viktorField: ViktorNDField = ViktorNDField(intArrayOf(dim, dim))
 
     @Benchmark
     fun automaticFieldAddition() {
-        autoField.run {
+        autoField {
             var res = one
             repeat(n) { res += one }
         }
@@ -29,7 +28,7 @@ class ViktorBenchmark {
 
     @Benchmark
     fun viktorFieldAddition() {
-        viktorField.run {
+        viktorField {
             var res = one
             repeat(n) { res += one }
         }
@@ -44,7 +43,7 @@ class ViktorBenchmark {
 
     @Benchmark
     fun realdFieldLog() {
-        realField.run {
+        realField {
             val fortyTwo = produce { 42.0 }
             var res = one
             repeat(n) { res = ln(fortyTwo) }

examples/src/benchmarks/kotlin/scientifik/kmath/utils/utils.kt

@@ -1,8 +1,11 @@
 package scientifik.kmath.utils
 
+import kotlin.contracts.InvocationKind
+import kotlin.contracts.contract
 import kotlin.system.measureTimeMillis
 
 internal inline fun measureAndPrint(title: String, block: () -> Unit) {
+    contract { callsInPlace(block, InvocationKind.EXACTLY_ONCE) }
     val time = measureTimeMillis(block)
     println("$title completed in $time millis")
 }

examples/src/main/kotlin/scientifik/kmath/linear/LinearAlgebraBenchmark.kt

@@ -5,6 +5,7 @@ import scientifik.kmath.commons.linear.CMMatrixContext
 import scientifik.kmath.commons.linear.inverse
 import scientifik.kmath.commons.linear.toCM
 import scientifik.kmath.operations.RealField
+import scientifik.kmath.operations.invoke
 import scientifik.kmath.structures.Matrix
 import kotlin.contracts.ExperimentalContracts
 import kotlin.random.Random
@@ -21,29 +22,18 @@ fun main() {
 
     val n = 5000 // iterations
 
-    MatrixContext.real.run {
-
-        repeat(50) {
-            val res = inverse(matrix)
-        }
-
-        val inverseTime = measureTimeMillis {
-            repeat(n) {
-                val res = inverse(matrix)
-            }
-        }
-
+    MatrixContext.real {
+        repeat(50) { val res = inverse(matrix) }
+        val inverseTime = measureTimeMillis { repeat(n) { val res = inverse(matrix) } }
         println("[kmath] Inversion of $n matrices $dim x $dim finished in $inverseTime millis")
     }
 
     //commons-math
 
     val commonsTime = measureTimeMillis {
-        CMMatrixContext.run {
+        CMMatrixContext {
             val cm = matrix.toCM()             //avoid overhead on conversion
-            repeat(n) {
-                val res = inverse(cm)
-            }
+            repeat(n) { val res = inverse(cm) }
         }
     }
 
@@ -53,7 +43,7 @@ fun main() {
     //koma-ejml
 
     val komaTime = measureTimeMillis {
-        KomaMatrixContext(EJMLMatrixFactory(), RealField).run {
+        (KomaMatrixContext(EJMLMatrixFactory(), RealField)) {
             val km = matrix.toKoma()      //avoid overhead on conversion
             repeat(n) {
                 val res = inverse(km)

examples/src/main/kotlin/scientifik/kmath/linear/MultiplicationBenchmark.kt

@@ -4,6 +4,7 @@ import koma.matrix.ejml.EJMLMatrixFactory
 import scientifik.kmath.commons.linear.CMMatrixContext
 import scientifik.kmath.commons.linear.toCM
 import scientifik.kmath.operations.RealField
+import scientifik.kmath.operations.invoke
 import scientifik.kmath.structures.Matrix
 import kotlin.random.Random
 import kotlin.system.measureTimeMillis
@@ -18,7 +19,7 @@ fun main() {
 //    //warmup
 //    matrix1 dot matrix2
 
-    CMMatrixContext.run {
+    CMMatrixContext {
         val cmMatrix1 = matrix1.toCM()
         val cmMatrix2 = matrix2.toCM()
 
@@ -29,8 +30,7 @@ fun main() {
         println("CM implementation time: $cmTime")
     }
 
-
-    KomaMatrixContext(EJMLMatrixFactory(), RealField).run {
+    (KomaMatrixContext(EJMLMatrixFactory(), RealField)) {
         val komaMatrix1 = matrix1.toKoma()
         val komaMatrix2 = matrix2.toKoma()
 

examples/src/main/kotlin/scientifik/kmath/operations/ComplexDemo.kt

@@ -9,13 +9,11 @@ fun main() {
         Complex(index[0].toDouble() - index[1].toDouble(), index[0].toDouble() + index[1].toDouble())
     }
 
-
-    val compute = NDField.complex(8).run {
+    val compute = (NDField.complex(8)) {
         val a = produce { (it) -> i * it - it.toDouble() }
         val b = 3
         val c = Complex(1.0, 1.0)
 
         (a pow b) + c
     }
-
 }

examples/src/main/kotlin/scientifik/kmath/structures/ComplexND.kt

@@ -13,9 +13,8 @@ fun main() {
     val realField = NDField.real(dim, dim)
     val complexField = NDField.complex(dim, dim)
 
-
     val realTime = measureTimeMillis {
-        realField.run {
+        realField {
             var res: NDBuffer<Double> = one
             repeat(n) {
                 res += 1.0
@@ -26,18 +25,15 @@ fun main() {
     println("Real addition completed in $realTime millis")
 
     val complexTime = measureTimeMillis {
-        complexField.run {
+        complexField {
             var res: NDBuffer<Complex> = one
-            repeat(n) {
-                res += 1.0
-            }
+            repeat(n) { res += 1.0 }
         }
     }
 
     println("Complex addition completed in $complexTime millis")
 }
 
-
 fun complexExample() {
     //Create a context for 2-d structure with complex values
     ComplexField {
@@ -46,10 +42,7 @@ fun complexExample() {
             val x = one * 2.5
             operator fun Number.plus(other: Complex) = Complex(this.toDouble() + other.re, other.im)
             //a structure generator specific to this context
-            val matrix = produce { (k, l) ->
-                k + l * i
-            }
-
+            val matrix = produce { (k, l) -> k + l * i }
             //Perform sum
             val sum = matrix + x + 1.0
 

examples/src/main/kotlin/scientifik/kmath/structures/NDField.kt

@@ -2,14 +2,18 @@ package scientifik.kmath.structures
 
 import kotlinx.coroutines.GlobalScope
 import scientifik.kmath.operations.RealField
+import scientifik.kmath.operations.invoke
+import kotlin.contracts.ExperimentalContracts
+import kotlin.contracts.InvocationKind
+import kotlin.contracts.contract
 import kotlin.system.measureTimeMillis
 
 internal inline fun measureAndPrint(title: String, block: () -> Unit) {
+    contract { callsInPlace(block, InvocationKind.EXACTLY_ONCE) }
     val time = measureTimeMillis(block)
     println("$title completed in $time millis")
 }
 
-
 fun main() {
     val dim = 1000
     val n = 1000
@@ -22,27 +26,21 @@ fun main() {
     val genericField = NDField.boxing(RealField, dim, dim)
 
     measureAndPrint("Automatic field addition") {
-        autoField.run {
+        autoField {
             var res: NDBuffer<Double> = one
-            repeat(n) {
-                res += number(1.0)
-            }
+            repeat(n) { res += number(1.0) }
         }
     }
 
     measureAndPrint("Element addition") {
         var res = genericField.one
-        repeat(n) {
-            res += 1.0
-        }
+        repeat(n) { res += 1.0 }
     }
 
     measureAndPrint("Specialized addition") {
-        specializedField.run {
+        specializedField {
             var res: NDBuffer<Double> = one
-            repeat(n) {
-                res += 1.0
-            }
+            repeat(n) { res += 1.0 }
         }
     }
 
@@ -60,12 +58,11 @@ fun main() {
 
     measureAndPrint("Generic addition") {
         //genericField.run(action)
-        genericField.run {
+        genericField {
             var res: NDBuffer<Double> = one
             repeat(n) {
-                res += one // con't avoid using `one` due to resolution ambiguity
+                res += one // couldn't avoid using `one` due to resolution ambiguity }
             }
         }
     }
-
 }

examples/src/main/kotlin/scientifik/kmath/structures/typeSafeDimensions.kt

@@ -23,13 +23,10 @@ fun DMatrixContext<Double, RealField>.custom() {
     val m1 = produce<D2, D5> { i, j -> (i + j).toDouble() }
     val m2 = produce<D5, D2> { i, j -> (i - j).toDouble() }
     val m3 = produce<D2, D2> { i, j -> (i - j).toDouble() }
-
     (m1 dot m2) + m3
 }
 
-fun main() {
-    DMatrixContext.real.run {
-        simple()
-        custom()
-    }
+fun main(): Unit = with(DMatrixContext.real) {
+    simple()
+    custom()
 }

kmath-ast/build.gradle.kts

@@ -1,11 +1,8 @@
 plugins { id("scientifik.mpp") }
 
 kotlin.sourceSets {
-//    all {
-//       languageSettings.apply{
-//            enableLanguageFeature("NewInference")
-//       }
-//    }
+    all { languageSettings.useExperimentalAnnotation("kotlin.contracts.ExperimentalContracts") }
+
     commonMain {
         dependencies {
             api(project(":kmath-core"))

kmath-ast/src/commonMain/kotlin/scientifik/kmath/ast/MstExpression.kt

@@ -2,6 +2,9 @@ package scientifik.kmath.ast
 
 import scientifik.kmath.expressions.*
 import scientifik.kmath.operations.*
+import kotlin.contracts.ExperimentalContracts
+import kotlin.contracts.InvocationKind
+import kotlin.contracts.contract
 
 /**
  * The expression evaluates MST on-flight. Should be much faster than functional expression, but slower than
@@ -24,7 +27,7 @@ class MstExpression<T>(val algebra: Algebra<T>, val mst: MST) : Expression<T> {
             error("Numeric nodes are not supported by $this")
     }
 
-    override fun invoke(arguments: Map<String, T>): T = InnerAlgebra(arguments).evaluate(mst)
+    override operator fun invoke(arguments: Map<String, T>): T = InnerAlgebra(arguments).evaluate(mst)
 }
 
 /**
@@ -38,51 +41,63 @@ inline fun <reified T : Any, A : Algebra<T>, E : Algebra<MST>> A.mst(
 /**
  * Builds [MstExpression] over [Space].
  */
-inline fun <reified T : Any> Space<T>.mstInSpace(block: MstSpace.() -> MST): MstExpression<T> =
-    MstExpression(this, MstSpace.block())
+inline fun <reified T : Any> Space<T>.mstInSpace(block: MstSpace.() -> MST): MstExpression<T> {
+    contract { callsInPlace(block, InvocationKind.EXACTLY_ONCE) }
+    return MstExpression(this, MstSpace.block())
+}
 
 /**
  * Builds [MstExpression] over [Ring].
  */
-inline fun <reified T : Any> Ring<T>.mstInRing(block: MstRing.() -> MST): MstExpression<T> =
-    MstExpression(this, MstRing.block())
+inline fun <reified T : Any> Ring<T>.mstInRing(block: MstRing.() -> MST): MstExpression<T> {
+    contract { callsInPlace(block, InvocationKind.EXACTLY_ONCE) }
+    return MstExpression(this, MstRing.block())
+}
 
 /**
  * Builds [MstExpression] over [Field].
  */
-inline fun <reified T : Any> Field<T>.mstInField(block: MstField.() -> MST): MstExpression<T> =
-    MstExpression(this, MstField.block())
+inline fun <reified T : Any> Field<T>.mstInField(block: MstField.() -> MST): MstExpression<T> {
+    contract { callsInPlace(block, InvocationKind.EXACTLY_ONCE) }
+    return MstExpression(this, MstField.block())
+}
 
 /**
  * Builds [MstExpression] over [ExtendedField].
  */
-inline fun <reified T : Any> Field<T>.mstInExtendedField(block: MstExtendedField.() -> MST): MstExpression<T> =
-    MstExpression(this, MstExtendedField.block())
+inline fun <reified T : Any> Field<T>.mstInExtendedField(block: MstExtendedField.() -> MST): MstExpression<T> {
+    contract { callsInPlace(block, InvocationKind.EXACTLY_ONCE) }
+    return MstExpression(this, MstExtendedField.block())
+}
 
 /**
  * Builds [MstExpression] over [FunctionalExpressionSpace].
  */
-inline fun <reified T : Any, A : Space<T>> FunctionalExpressionSpace<T, A>.mstInSpace(
-    block: MstSpace.() -> MST
-): MstExpression<T> = algebra.mstInSpace(block)
+inline fun <reified T : Any, A : Space<T>> FunctionalExpressionSpace<T, A>.mstInSpace(block: MstSpace.() -> MST): MstExpression<T> {
+    contract { callsInPlace(block, InvocationKind.EXACTLY_ONCE) }
+    return algebra.mstInSpace(block)
+}
 
 /**
  * Builds [MstExpression] over [FunctionalExpressionRing].
  */
-inline fun <reified T : Any, A : Ring<T>> FunctionalExpressionRing<T, A>.mstInRing(
-    block: MstRing.() -> MST
-): MstExpression<T> = algebra.mstInRing(block)
+inline fun <reified T : Any, A : Ring<T>> FunctionalExpressionRing<T, A>.mstInRing(block: MstRing.() -> MST): MstExpression<T> {
+    contract { callsInPlace(block, InvocationKind.EXACTLY_ONCE) }
+    return algebra.mstInRing(block)
+}
 
 /**
  * Builds [MstExpression] over [FunctionalExpressionField].
  */
-inline fun <reified T : Any, A : Field<T>> FunctionalExpressionField<T, A>.mstInField(
-    block: MstField.() -> MST
-): MstExpression<T> = algebra.mstInField(block)
+inline fun <reified T : Any, A : Field<T>> FunctionalExpressionField<T, A>.mstInField(block: MstField.() -> MST): MstExpression<T> {
+    contract { callsInPlace(block, InvocationKind.EXACTLY_ONCE) }
+    return algebra.mstInField(block)
+}
 
 /**
  * Builds [MstExpression] over [FunctionalExpressionExtendedField].
  */
-inline fun <reified T : Any, A : ExtendedField<T>> FunctionalExpressionExtendedField<T, A>.mstInExtendedField(
-    block: MstExtendedField.() -> MST
-): MstExpression<T> = algebra.mstInExtendedField(block)
+inline fun <reified T : Any, A : ExtendedField<T>> FunctionalExpressionExtendedField<T, A>.mstInExtendedField(block: MstExtendedField.() -> MST): MstExpression<T> {
+    contract { callsInPlace(block, InvocationKind.EXACTLY_ONCE) }
+    return algebra.mstInExtendedField(block)
+}

kmath-ast/src/jvmMain/kotlin/scientifik/kmath/asm/internal/codegenUtils.kt

@@ -7,6 +7,9 @@ import scientifik.kmath.ast.MST
 import scientifik.kmath.expressions.Expression
 import scientifik.kmath.operations.Algebra
 import java.lang.reflect.Method
+import kotlin.contracts.ExperimentalContracts
+import kotlin.contracts.InvocationKind
+import kotlin.contracts.contract
 import kotlin.reflect.KClass
 
 private val methodNameAdapters: Map<Pair<String, Int>, String> by lazy {
@@ -26,8 +29,10 @@ internal val KClass<*>.asm: Type
 /**
  * Returns singleton array with this value if the [predicate] is true, returns empty array otherwise.
  */
-internal inline fun <reified T> T.wrapToArrayIf(predicate: (T) -> Boolean): Array<T> =
-    if (predicate(this)) arrayOf(this) else emptyArray()
+internal inline fun <reified T> T.wrapToArrayIf(predicate: (T) -> Boolean): Array<T> {
+    contract { callsInPlace(predicate, InvocationKind.EXACTLY_ONCE) }
+    return if (predicate(this)) arrayOf(this) else emptyArray()
+}
 
 /**
  * Creates an [InstructionAdapter] from this [MethodVisitor].
@@ -37,8 +42,10 @@ private fun MethodVisitor.instructionAdapter(): InstructionAdapter = Instruction
 /**
  * Creates an [InstructionAdapter] from this [MethodVisitor] and applies [block] to it.
  */
-internal fun MethodVisitor.instructionAdapter(block: InstructionAdapter.() -> Unit): InstructionAdapter =
-    instructionAdapter().apply(block)
+internal inline fun MethodVisitor.instructionAdapter(block: InstructionAdapter.() -> Unit): InstructionAdapter {
+    contract { callsInPlace(block, InvocationKind.EXACTLY_ONCE) }
+    return instructionAdapter().apply(block)
+}
 
 /**
  * Constructs a [Label], then applies it to this visitor.
@@ -64,8 +71,10 @@ internal tailrec fun buildName(mst: MST, collision: Int = 0): String {
 }
 
 @Suppress("FunctionName")
-internal inline fun ClassWriter(flags: Int, block: ClassWriter.() -> Unit): ClassWriter =
-    ClassWriter(flags).apply(block)
+internal inline fun ClassWriter(flags: Int, block: ClassWriter.() -> Unit): ClassWriter {
+    contract { callsInPlace(block, InvocationKind.EXACTLY_ONCE) }
+    return ClassWriter(flags).apply(block)
+}
 
 internal inline fun ClassWriter.visitField(
     access: Int,
@@ -74,7 +83,10 @@ internal inline fun ClassWriter.visitField(
     signature: String?,
     value: Any?,
     block: FieldVisitor.() -> Unit
-): FieldVisitor = visitField(access, name, descriptor, signature, value).apply(block)
+): FieldVisitor {
+    contract { callsInPlace(block, InvocationKind.EXACTLY_ONCE) }
+    return visitField(access, name, descriptor, signature, value).apply(block)
+}
 
 private fun <T> AsmBuilder<T>.findSpecific(context: Algebra<T>, name: String, parameterTypes: Array<MstType>): Method? =
     context.javaClass.methods.find { method ->
@@ -158,6 +170,7 @@ internal inline fun <T> AsmBuilder<T>.buildAlgebraOperationCall(
     parameterTypes: Array<MstType>,
     parameters: AsmBuilder<T>.() -> Unit
 ) {
+    contract { callsInPlace(parameters, InvocationKind.EXACTLY_ONCE) }
     val arity = parameterTypes.size
     loadAlgebra()
     if (!buildExpectationStack(context, name, parameterTypes)) loadStringConstant(name)

kmath-commons/build.gradle.kts

@@ -1,7 +1,4 @@
-plugins {
-    id("scientifik.jvm")
-}
-
+plugins { id("scientifik.jvm") }
 description = "Commons math binding for kmath"
 
 dependencies {
@@ -11,3 +8,5 @@ dependencies {
     api(project(":kmath-functions"))
     api("org.apache.commons:commons-math3:3.6.1")
 }
+
+kotlin.sourceSets.all { languageSettings.useExperimentalAnnotation("kotlin.contracts.ExperimentalContracts") }

kmath-commons/src/main/kotlin/scientifik/kmath/commons/expressions/DiffExpression.kt

@@ -5,6 +5,7 @@ import scientifik.kmath.expressions.Expression
 import scientifik.kmath.expressions.ExpressionAlgebra
 import scientifik.kmath.operations.ExtendedField
 import scientifik.kmath.operations.Field
+import scientifik.kmath.operations.invoke
 import kotlin.properties.ReadOnlyProperty
 import kotlin.reflect.KProperty
 
@@ -15,7 +16,6 @@ class DerivativeStructureField(
     val order: Int,
     val parameters: Map<String, Double>
 ) : ExtendedField<DerivativeStructure> {
-
     override val zero: DerivativeStructure by lazy { DerivativeStructure(order, parameters.size) }
     override val one: DerivativeStructure by lazy { DerivativeStructure(order, parameters.size, 1.0) }
 
@@ -23,17 +23,15 @@ class DerivativeStructureField(
         DerivativeStructure(parameters.size, order, parameters.keys.indexOf(key), value)
     }
 
-    val variable = object : ReadOnlyProperty<Any?, DerivativeStructure> {
-        override fun getValue(thisRef: Any?, property: KProperty<*>): DerivativeStructure {
-            return variables[property.name] ?: error("A variable with name ${property.name} does not exist")
-        }
+    val variable: ReadOnlyProperty<Any?, DerivativeStructure> = object : ReadOnlyProperty<Any?, DerivativeStructure> {
+        override fun getValue(thisRef: Any?, property: KProperty<*>): DerivativeStructure =
+            variables[property.name] ?: error("A variable with name ${property.name} does not exist")
     }
 
     fun variable(name: String, default: DerivativeStructure? = null): DerivativeStructure =
         variables[name] ?: default ?: error("A variable with name $name does not exist")
 
-
-    fun Number.const() = DerivativeStructure(order, parameters.size, toDouble())
+    fun Number.const(): DerivativeStructure = DerivativeStructure(order, parameters.size, toDouble())
 
     fun DerivativeStructure.deriv(parName: String, order: Int = 1): Double {
         return deriv(mapOf(parName to order))
@@ -83,16 +81,15 @@ class DerivativeStructureField(
 
     override operator fun DerivativeStructure.plus(b: Number): DerivativeStructure = add(b.toDouble())
     override operator fun DerivativeStructure.minus(b: Number): DerivativeStructure = subtract(b.toDouble())
-    override operator fun Number.plus(b: DerivativeStructure) = b + this
-    override operator fun Number.minus(b: DerivativeStructure) = b - this
+    override operator fun Number.plus(b: DerivativeStructure): DerivativeStructure = b + this
+    override operator fun Number.minus(b: DerivativeStructure): DerivativeStructure = b - this
 }
 
 /**
  * A constructs that creates a derivative structure with required order on-demand
  */
 class DiffExpression(val function: DerivativeStructureField.() -> DerivativeStructure) : Expression<Double> {
-
-    override fun invoke(arguments: Map<String, Double>): Double = DerivativeStructureField(
+    override operator fun invoke(arguments: Map<String, Double>): Double = DerivativeStructureField(
         0,
         arguments
     ).run(function).value
@@ -101,45 +98,40 @@ class DiffExpression(val function: DerivativeStructureField.() -> DerivativeStru
      * Get the derivative expression with given orders
      * TODO make result [DiffExpression]
      */
-    fun derivative(orders: Map<String, Int>): Expression<Double> {
-        return object : Expression<Double> {
-            override fun invoke(arguments: Map<String, Double>): Double =
-                DerivativeStructureField(orders.values.max() ?: 0, arguments)
-                    .run {
-                        function().deriv(orders)
-                    }
-        }
+    fun derivative(orders: Map<String, Int>): Expression<Double> = object : Expression<Double> {
+        override operator fun invoke(arguments: Map<String, Double>): Double =
+            (DerivativeStructureField(orders.values.max() ?: 0, arguments)) { function().deriv(orders) }
     }
 
     //TODO add gradient and maybe other vector operators
 }
 
-fun DiffExpression.derivative(vararg orders: Pair<String, Int>) = derivative(mapOf(*orders))
-fun DiffExpression.derivative(name: String) = derivative(name to 1)
+fun DiffExpression.derivative(vararg orders: Pair<String, Int>): Expression<Double> = derivative(mapOf(*orders))
+fun DiffExpression.derivative(name: String): Expression<Double> = derivative(name to 1)
 
 /**
  * A context for [DiffExpression] (not to be confused with [DerivativeStructure])
  */
 object DiffExpressionAlgebra : ExpressionAlgebra<Double, DiffExpression>, Field<DiffExpression> {
-    override fun variable(name: String, default: Double?) =
+    override fun variable(name: String, default: Double?): DiffExpression =
         DiffExpression { variable(name, default?.const()) }
 
     override fun const(value: Double): DiffExpression =
         DiffExpression { value.const() }
 
-    override fun add(a: DiffExpression, b: DiffExpression) =
+    override fun add(a: DiffExpression, b: DiffExpression): DiffExpression =
         DiffExpression { a.function(this) + b.function(this) }
 
-    override val zero = DiffExpression { 0.0.const() }
+    override val zero: DiffExpression = DiffExpression { 0.0.const() }
 
-    override fun multiply(a: DiffExpression, k: Number) =
+    override fun multiply(a: DiffExpression, k: Number): DiffExpression =
         DiffExpression { a.function(this) * k }
 
-    override val one = DiffExpression { 1.0.const() }
+    override val one: DiffExpression = DiffExpression { 1.0.const() }
 
-    override fun multiply(a: DiffExpression, b: DiffExpression) =
+    override fun multiply(a: DiffExpression, b: DiffExpression): DiffExpression =
         DiffExpression { a.function(this) * b.function(this) }
 
-    override fun divide(a: DiffExpression, b: DiffExpression) =
+    override fun divide(a: DiffExpression, b: DiffExpression): DiffExpression =
         DiffExpression { a.function(this) / b.function(this) }
 }

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

@@ -1,8 +1,6 @@
 package scientifik.kmath.commons.linear
 
 import org.apache.commons.math3.linear.*
-import org.apache.commons.math3.linear.RealMatrix
-import org.apache.commons.math3.linear.RealVector
 import scientifik.kmath.linear.*
 import scientifik.kmath.structures.Matrix
 import scientifik.kmath.structures.NDStructure
@@ -14,12 +12,12 @@ class CMMatrix(val origin: RealMatrix, features: Set<MatrixFeature>? = null) :
 
     override val features: Set<MatrixFeature> = features ?: sequence<MatrixFeature> {
         if (origin is DiagonalMatrix) yield(DiagonalFeature)
-    }.toSet()
+    }.toHashSet()
 
-    override fun suggestFeature(vararg features: MatrixFeature) =
+    override fun suggestFeature(vararg features: MatrixFeature): CMMatrix =
         CMMatrix(origin, this.features + features)
 
-    override fun get(i: Int, j: Int): Double = origin.getEntry(i, j)
+    override operator fun get(i: Int, j: Int): Double = origin.getEntry(i, j)
 
     override fun equals(other: Any?): Boolean {
         return NDStructure.equals(this, other as? NDStructure<*> ?: return false)
@@ -40,24 +38,22 @@ fun Matrix<Double>.toCM(): CMMatrix = if (this is CMMatrix) {
     CMMatrix(Array2DRowRealMatrix(array))
 }
 
-fun RealMatrix.asMatrix() = CMMatrix(this)
+fun RealMatrix.asMatrix(): CMMatrix = CMMatrix(this)
 
 class CMVector(val origin: RealVector) : Point<Double> {
     override val size: Int get() = origin.dimension
 
-    override fun get(index: Int): Double = origin.getEntry(index)
+    override operator fun get(index: Int): Double = origin.getEntry(index)
 
-    override fun iterator(): Iterator<Double> = origin.toArray().iterator()
+    override operator fun iterator(): Iterator<Double> = origin.toArray().iterator()
 }
 
-fun Point<Double>.toCM(): CMVector = if (this is CMVector) {
-    this
-} else {
+fun Point<Double>.toCM(): CMVector = if (this is CMVector) this else {
     val array = DoubleArray(size) { this[it] }
     CMVector(ArrayRealVector(array))
 }
 
-fun RealVector.toPoint() = CMVector(this)
+fun RealVector.toPoint(): CMVector = CMVector(this)
 
 object CMMatrixContext : MatrixContext<Double> {
     override fun produce(rows: Int, columns: Int, initializer: (i: Int, j: Int) -> Double): CMMatrix {
@@ -65,30 +61,31 @@ object CMMatrixContext : MatrixContext<Double> {
         return CMMatrix(Array2DRowRealMatrix(array))
     }
 
-    override fun Matrix<Double>.dot(other: Matrix<Double>) =
+    override fun Matrix<Double>.dot(other: Matrix<Double>): CMMatrix =
         CMMatrix(this.toCM().origin.multiply(other.toCM().origin))
 
     override fun Matrix<Double>.dot(vector: Point<Double>): CMVector =
         CMVector(this.toCM().origin.preMultiply(vector.toCM().origin))
 
-    override fun Matrix<Double>.unaryMinus(): CMMatrix =
+    override operator fun Matrix<Double>.unaryMinus(): CMMatrix =
         produce(rowNum, colNum) { i, j -> -get(i, j) }
 
-    override fun add(a: Matrix<Double>, b: Matrix<Double>) =
+    override fun add(a: Matrix<Double>, b: Matrix<Double>): CMMatrix =
         CMMatrix(a.toCM().origin.multiply(b.toCM().origin))
 
-    override fun Matrix<Double>.minus(b: Matrix<Double>) =
+    override operator fun Matrix<Double>.minus(b: Matrix<Double>): CMMatrix =
         CMMatrix(this.toCM().origin.subtract(b.toCM().origin))
 
-    override fun multiply(a: Matrix<Double>, k: Number) =
+    override fun multiply(a: Matrix<Double>, k: Number): CMMatrix =
         CMMatrix(a.toCM().origin.scalarMultiply(k.toDouble()))
 
-    override fun Matrix<Double>.times(value: Double): Matrix<Double>  =
+    override operator fun Matrix<Double>.times(value: Double): Matrix<Double> =
         produce(rowNum, colNum) { i, j -> get(i, j) * value }
 }
 
 operator fun CMMatrix.plus(other: CMMatrix): CMMatrix =
     CMMatrix(this.origin.add(other.origin))
+
 operator fun CMMatrix.minus(other: CMMatrix): CMMatrix =
     CMMatrix(this.origin.subtract(other.origin))
 

kmath-commons/src/main/kotlin/scientifik/kmath/commons/random/CMRandomGeneratorWrapper.kt

@@ -4,10 +4,9 @@ import scientifik.kmath.prob.RandomGenerator
 
 class CMRandomGeneratorWrapper(val factory: (IntArray) -> RandomGenerator) :
     org.apache.commons.math3.random.RandomGenerator {
-    private var generator = factory(intArrayOf())
+    private var generator: RandomGenerator = factory(intArrayOf())
 
     override fun nextBoolean(): Boolean = generator.nextBoolean()
-
     override fun nextFloat(): Float = generator.nextDouble().toFloat()
 
     override fun setSeed(seed: Int) {
@@ -27,12 +26,8 @@ class CMRandomGeneratorWrapper(val factory: (IntArray) -> RandomGenerator) :
     }
 
     override fun nextInt(): Int = generator.nextInt()
-
     override fun nextInt(n: Int): Int = generator.nextInt(n)
-
     override fun nextGaussian(): Double = TODO()
-
     override fun nextDouble(): Double = generator.nextDouble()
-
     override fun nextLong(): Long = generator.nextLong()
 }

kmath-commons/src/test/kotlin/scientifik/kmath/commons/expressions/AutoDiffTest.kt

@@ -1,11 +1,15 @@
 package scientifik.kmath.commons.expressions
 
 import scientifik.kmath.expressions.invoke
+import kotlin.contracts.InvocationKind
+import kotlin.contracts.contract
 import kotlin.test.Test
 import kotlin.test.assertEquals
 
-inline fun <R> diff(order: Int, vararg parameters: Pair<String, Double>, block: DerivativeStructureField.() -> R) =
-    DerivativeStructureField(order, mapOf(*parameters)).run(block)
+inline fun <R> diff(order: Int, vararg parameters: Pair<String, Double>, block: DerivativeStructureField.() -> R): R {
+    contract { callsInPlace(block, InvocationKind.EXACTLY_ONCE) }
+    return DerivativeStructureField(order, mapOf(*parameters)).run(block)
+}
 
 class AutoDiffTest {
     @Test

kmath-core/build.gradle.kts

@@ -1,7 +1,6 @@
 plugins { id("scientifik.mpp") }
 
 kotlin.sourceSets {
-    commonMain {
-        dependencies { api(project(":kmath-memory")) }
-    }
+    all { languageSettings.useExperimentalAnnotation("kotlin.contracts.ExperimentalContracts") }
+    commonMain { dependencies { api(project(":kmath-memory")) } }
 }

kmath-core/src/commonMain/kotlin/scientifik/kmath/expressions/Builders.kt

@@ -4,28 +4,38 @@ import scientifik.kmath.operations.ExtendedField
 import scientifik.kmath.operations.Field
 import scientifik.kmath.operations.Ring
 import scientifik.kmath.operations.Space
+import kotlin.contracts.ExperimentalContracts
+import kotlin.contracts.InvocationKind
+import kotlin.contracts.contract
 
 /**
  * Creates a functional expression with this [Space].
  */
-fun <T> Space<T>.spaceExpression(block: FunctionalExpressionSpace<T, Space<T>>.() -> Expression<T>): Expression<T> =
-    FunctionalExpressionSpace(this).run(block)
+inline fun <T> Space<T>.spaceExpression(block: FunctionalExpressionSpace<T, Space<T>>.() -> Expression<T>): Expression<T> {
+    contract { callsInPlace(block, InvocationKind.EXACTLY_ONCE) }
+    return FunctionalExpressionSpace(this).block()
+}
 
 /**
  * Creates a functional expression with this [Ring].
  */
-fun <T> Ring<T>.ringExpression(block: FunctionalExpressionRing<T, Ring<T>>.() -> Expression<T>): Expression<T> =
-    FunctionalExpressionRing(this).run(block)
+inline fun <T> Ring<T>.ringExpression(block: FunctionalExpressionRing<T, Ring<T>>.() -> Expression<T>): Expression<T> {
+    contract { callsInPlace(block, InvocationKind.EXACTLY_ONCE) }
+    return FunctionalExpressionRing(this).block()
+}
 
 /**
  * Creates a functional expression with this [Field].
  */
-fun <T> Field<T>.fieldExpression(block: FunctionalExpressionField<T, Field<T>>.() -> Expression<T>): Expression<T> =
-    FunctionalExpressionField(this).run(block)
+inline fun <T> Field<T>.fieldExpression(block: FunctionalExpressionField<T, Field<T>>.() -> Expression<T>): Expression<T> {
+    contract { callsInPlace(block, InvocationKind.EXACTLY_ONCE) }
+    return FunctionalExpressionField(this).block()
+}
 
 /**
  * Creates a functional expression with this [ExtendedField].
  */
-fun <T> ExtendedField<T>.fieldExpression(
-    block: FunctionalExpressionExtendedField<T, ExtendedField<T>>.() -> Expression<T>
-): Expression<T> = FunctionalExpressionExtendedField(this).run(block)
+inline fun <T> ExtendedField<T>.extendedFieldExpression(block: FunctionalExpressionExtendedField<T, ExtendedField<T>>.() -> Expression<T>): Expression<T> {
+    contract { callsInPlace(block, InvocationKind.EXACTLY_ONCE) }
+    return FunctionalExpressionExtendedField(this).block()
+}

kmath-core/src/commonMain/kotlin/scientifik/kmath/expressions/Expression.kt

@@ -22,7 +22,7 @@ interface Expression<T> {
  */
 fun <T> Algebra<T>.expression(block: Algebra<T>.(arguments: Map<String, T>) -> T): Expression<T> =
     object : Expression<T> {
-        override fun invoke(arguments: Map<String, T>): T = block(arguments)
+        override operator fun invoke(arguments: Map<String, T>): T = block(arguments)
     }
 
 /**

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

@@ -4,7 +4,7 @@ import scientifik.kmath.operations.*
 
 internal class FunctionalUnaryOperation<T>(val context: Algebra<T>, val name: String, private val expr: Expression<T>) :
     Expression<T> {
-    override fun invoke(arguments: Map<String, T>): T = context.unaryOperation(name, expr.invoke(arguments))
+    override operator fun invoke(arguments: Map<String, T>): T = context.unaryOperation(name, expr.invoke(arguments))
 }
 
 internal class FunctionalBinaryOperation<T>(
@@ -13,17 +13,17 @@ internal class FunctionalBinaryOperation<T>(
     val first: Expression<T>,
     val second: Expression<T>
 ) : Expression<T> {
-    override fun invoke(arguments: Map<String, T>): T =
+    override operator fun invoke(arguments: Map<String, T>): T =
         context.binaryOperation(name, first.invoke(arguments), second.invoke(arguments))
 }
 
 internal class FunctionalVariableExpression<T>(val name: String, val default: T? = null) : Expression<T> {
-    override fun invoke(arguments: Map<String, T>): T =
+    override operator fun invoke(arguments: Map<String, T>): T =
         arguments[name] ?: default ?: error("Parameter not found: $name")
 }
 
 internal class FunctionalConstantExpression<T>(val value: T) : Expression<T> {
-    override fun invoke(arguments: Map<String, T>): T = value
+    override operator fun invoke(arguments: Map<String, T>): T = value
 }
 
 internal class FunctionalConstProductExpression<T>(
@@ -31,7 +31,7 @@ internal class FunctionalConstProductExpression<T>(
     private val expr: Expression<T>,
     val const: Number
 ) : Expression<T> {
-    override fun invoke(arguments: Map<String, T>): T = context.multiply(expr.invoke(arguments), const)
+    override operator fun invoke(arguments: Map<String, T>): T = context.multiply(expr.invoke(arguments), const)
 }
 
 /**

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

@@ -53,16 +53,12 @@ class BufferMatrix<T : Any>(
     override fun suggestFeature(vararg features: MatrixFeature): BufferMatrix<T> =
         BufferMatrix(rowNum, colNum, buffer, this.features + features)
 
-    override fun get(index: IntArray): T = get(index[0], index[1])
+    override operator fun get(index: IntArray): T = get(index[0], index[1])
 
-    override fun get(i: Int, j: Int): T = buffer[i * colNum + j]
+    override operator fun get(i: Int, j: Int): T = buffer[i * colNum + j]
 
     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))
-            }
-        }
+        for (i in 0 until rowNum) for (j in 0 until colNum) yield(intArrayOf(i, j) to get(i, j))
     }
 
     override fun equals(other: Any?): Boolean {
@@ -95,7 +91,7 @@ class BufferMatrix<T : Any>(
  * Optimized dot product for real matrices
  */
 infix fun BufferMatrix<Double>.dot(other: BufferMatrix<Double>): BufferMatrix<Double> {
-    if (this.colNum != other.rowNum) error("Matrix dot operation dimension mismatch: ($rowNum, $colNum) x (${other.rowNum}, ${other.colNum})")
+    require(colNum == other.rowNum) { "Matrix dot operation dimension mismatch: ($rowNum, $colNum) x (${other.rowNum}, ${other.colNum})" }
 
     val array = DoubleArray(this.rowNum * other.colNum)
 

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

@@ -4,6 +4,8 @@ import scientifik.kmath.operations.Ring
 import scientifik.kmath.structures.Matrix
 import scientifik.kmath.structures.Structure2D
 import scientifik.kmath.structures.asBuffer
+import kotlin.contracts.ExperimentalContracts
+import kotlin.contracts.contract
 import kotlin.math.sqrt
 
 /**
@@ -26,15 +28,17 @@ interface FeaturedMatrix<T : Any> : Matrix<T> {
     companion object
 }
 
-fun Structure2D.Companion.real(rows: Int, columns: Int, initializer: (Int, Int) -> Double): Matrix<Double> =
-    MatrixContext.real.produce(rows, columns, initializer)
+inline fun Structure2D.Companion.real(rows: Int, columns: Int, initializer: (Int, Int) -> Double): Matrix<Double> {
+    contract { callsInPlace(initializer) }
+    return MatrixContext.real.produce(rows, columns, initializer)
+}
 
 /**
  * Build a square matrix from given elements.
  */
 fun <T : Any> Structure2D.Companion.square(vararg elements: T): FeaturedMatrix<T> {
     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")
+    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)
 }

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

@@ -3,6 +3,7 @@ package scientifik.kmath.linear
 import scientifik.kmath.operations.Field
 import scientifik.kmath.operations.RealField
 import scientifik.kmath.operations.Ring
+import scientifik.kmath.operations.invoke
 import scientifik.kmath.structures.BufferAccessor2D
 import scientifik.kmath.structures.Matrix
 import scientifik.kmath.structures.Structure2D
@@ -60,15 +61,13 @@ class LUPDecomposition<T : Any>(
      * @return determinant of the matrix
      */
     override val determinant: T by lazy {
-        with(elementContext) {
-            (0 until lu.shape[0]).fold(if (even) one else -one) { value, i -> value * lu[i, i] }
-        }
+        elementContext { (0 until lu.shape[0]).fold(if (even) one else -one) { value, i -> value * lu[i, i] } }
     }
 
 }
 
 fun <T : Comparable<T>, F : Field<T>> GenericMatrixContext<T, F>.abs(value: T): T =
-    if (value > elementContext.zero) value else with(elementContext) { -value }
+    if (value > elementContext.zero) value else elementContext { -value }
 
 
 /**
@@ -88,43 +87,34 @@ fun <T : Comparable<T>, F : Field<T>> GenericMatrixContext<T, F>.lup(
 
     //TODO just waits for KEEP-176
     BufferAccessor2D(type, matrix.rowNum, matrix.colNum).run {
-        elementContext.run {
-
+        elementContext {
             val lu = create(matrix)
 
             // Initialize permutation array and parity
-            for (row in 0 until m) {
-                pivot[row] = row
-            }
+            for (row in 0 until m) pivot[row] = row
             var even = true
 
             // Initialize permutation array and parity
-            for (row in 0 until m) {
-                pivot[row] = row
-            }
+            for (row in 0 until m) pivot[row] = row
 
             // Loop over columns
             for (col in 0 until m) {
-
                 // upper
                 for (row in 0 until col) {
                     val luRow = lu.row(row)
                     var sum = luRow[col]
-                    for (i in 0 until row) {
-                        sum -= luRow[i] * lu[i, col]
-                    }
+                    for (i in 0 until row) sum -= luRow[i] * lu[i, col]
                     luRow[col] = sum
                 }
 
                 // lower
                 var max = col // permutation row
                 var largest = -one
+
                 for (row in col until m) {
                     val luRow = lu.row(row)
                     var sum = luRow[col]
-                    for (i in 0 until col) {
-                        sum -= luRow[i] * lu[i, col]
-                    }
+                    for (i in 0 until col) sum -= luRow[i] * lu[i, col]
                     luRow[col] = sum
 
                     // maintain best permutation choice
@@ -135,19 +125,19 @@ fun <T : Comparable<T>, F : Field<T>> GenericMatrixContext<T, F>.lup(
                 }
 
                 // Singularity check
-                if (checkSingular(this@lup.abs(lu[max, col]))) {
-                    error("The matrix is singular")
-                }
+                check(!checkSingular(this@lup.abs(lu[max, col]))) { "The matrix is singular" }
 
                 // Pivot if necessary
                 if (max != col) {
                     val luMax = lu.row(max)
                     val luCol = lu.row(col)
+
                     for (i in 0 until m) {
                         val tmp = luMax[i]
                         luMax[i] = luCol[i]
                         luCol[i] = tmp
                     }
+
                     val temp = pivot[max]
                     pivot[max] = pivot[col]
                     pivot[col] = temp
@@ -156,9 +146,7 @@ fun <T : Comparable<T>, F : Field<T>> GenericMatrixContext<T, F>.lup(
 
                 // Divide the lower elements by the "winning" diagonal elt.
                 val luDiag = lu[col, col]
-                for (row in col + 1 until m) {
-                    lu[row, col] /= luDiag
-                }
+                for (row in col + 1 until m) lu[row, col] /= luDiag
             }
 
             return LUPDecomposition(this@lup, lu.collect(), pivot, even)
@@ -175,28 +163,23 @@ fun GenericMatrixContext<Double, RealField>.lup(matrix: Matrix<Double>): LUPDeco
     lup(Double::class, matrix) { it < 1e-11 }
 
 fun <T : Any> LUPDecomposition<T>.solve(type: KClass<T>, matrix: Matrix<T>): Matrix<T> {
-
-    if (matrix.rowNum != pivot.size) {
-        error("Matrix dimension mismatch. Expected ${pivot.size}, but got ${matrix.colNum}")
-    }
+    require(matrix.rowNum == pivot.size) { "Matrix dimension mismatch. Expected ${pivot.size}, but got ${matrix.colNum}" }
 
     BufferAccessor2D(type, matrix.rowNum, matrix.colNum).run {
-        elementContext.run {
-
+        elementContext {
             // Apply permutations to b
             val bp = create { _, _ -> zero }
 
             for (row in pivot.indices) {
                 val bpRow = bp.row(row)
                 val pRow = pivot[row]
-                for (col in 0 until matrix.colNum) {
-                    bpRow[col] = matrix[pRow, col]
-                }
+                for (col in 0 until matrix.colNum) bpRow[col] = matrix[pRow, col]
             }
 
             // Solve LY = b
             for (col in pivot.indices) {
                 val bpCol = bp.row(col)
+
                 for (i in col + 1 until pivot.size) {
                     val bpI = bp.row(i)
                     val luICol = lu[i, col]
@@ -210,17 +193,15 @@ fun <T : Any> LUPDecomposition<T>.solve(type: KClass<T>, matrix: Matrix<T>): Mat
             for (col in pivot.size - 1 downTo 0) {
                 val bpCol = bp.row(col)
                 val luDiag = lu[col, col]
-                for (j in 0 until matrix.colNum) {
-                    bpCol[j] /= luDiag
-                }
+                for (j in 0 until matrix.colNum) bpCol[j] /= luDiag
+
                 for (i in 0 until col) {
                     val bpI = bp.row(i)
                     val luICol = lu[i, col]
-                    for (j in 0 until matrix.colNum) {
-                        bpI[j] -= bpCol[j] * luICol
-                    }
+                    for (j in 0 until matrix.colNum) bpI[j] -= bpCol[j] * luICol
                 }
             }
+
             return context.produce(pivot.size, matrix.colNum) { i, j -> bp[i, j] }
         }
     }

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

@@ -7,7 +7,7 @@ import scientifik.kmath.structures.asBuffer
 
 class MatrixBuilder(val rows: Int, val columns: Int) {
     operator fun <T : Any> invoke(vararg elements: T): FeaturedMatrix<T> {
-        if (rows * columns != elements.size) error("The number of elements ${elements.size} is not equal $rows * $columns")
+        require(rows * columns == elements.size) { "The number of elements ${elements.size} is not equal $rows * $columns" }
         val buffer = elements.asBuffer()
         return BufferMatrix(rows, columns, buffer)
     }

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

@@ -2,6 +2,7 @@ package scientifik.kmath.linear
 
 import scientifik.kmath.operations.Ring
 import scientifik.kmath.operations.SpaceOperations
+import scientifik.kmath.operations.invoke
 import scientifik.kmath.operations.sum
 import scientifik.kmath.structures.Buffer
 import scientifik.kmath.structures.BufferFactory
@@ -37,8 +38,7 @@ interface MatrixContext<T : Any> : SpaceOperations<Matrix<T>> {
         fun <T : Any, R : Ring<T>> buffered(
             ring: R,
             bufferFactory: BufferFactory<T> = Buffer.Companion::boxing
-        ): GenericMatrixContext<T, R> =
-            BufferMatrixContext(ring, bufferFactory)
+        ): GenericMatrixContext<T, R> = BufferMatrixContext(ring, bufferFactory)
 
         /**
          * Automatic buffered matrix, unboxed if it is possible
@@ -61,45 +61,49 @@ interface GenericMatrixContext<T : Any, R : Ring<T>> : MatrixContext<T> {
 
     override infix fun Matrix<T>.dot(other: Matrix<T>): Matrix<T> {
         //TODO add typed error
-        if (this.colNum != other.rowNum) error("Matrix dot operation dimension mismatch: ($rowNum, $colNum) x (${other.rowNum}, ${other.colNum})")
+        require(colNum == other.rowNum) { "Matrix dot operation dimension mismatch: ($rowNum, $colNum) x (${other.rowNum}, ${other.colNum})" }
+
         return produce(rowNum, other.colNum) { i, j ->
             val row = rows[i]
             val column = other.columns[j]
-            with(elementContext) {
-                sum(row.asSequence().zip(column.asSequence(), ::multiply))
-            }
+            elementContext { sum(row.asSequence().zip(column.asSequence(), ::multiply)) }
         }
     }
 
     override infix fun Matrix<T>.dot(vector: Point<T>): Point<T> {
         //TODO add typed error
-        if (this.colNum != vector.size) error("Matrix dot vector operation dimension mismatch: ($rowNum, $colNum) x (${vector.size})")
+        require(colNum == vector.size) { "Matrix dot vector operation dimension mismatch: ($rowNum, $colNum) x (${vector.size})" }
+
         return point(rowNum) { i ->
             val row = rows[i]
-            with(elementContext) {
-                sum(row.asSequence().zip(vector.asSequence(), ::multiply))
-            }
+            elementContext { sum(row.asSequence().zip(vector.asSequence(), ::multiply)) }
         }
     }
 
     override operator fun Matrix<T>.unaryMinus(): Matrix<T> =
-        produce(rowNum, colNum) { i, j -> elementContext.run { -get(i, j) } }
+        produce(rowNum, colNum) { i, j -> elementContext { -get(i, j) } }
 
     override fun add(a: Matrix<T>, b: Matrix<T>): Matrix<T> {
-        if (a.rowNum != b.rowNum || a.colNum != b.colNum) error("Matrix operation dimension mismatch. [${a.rowNum},${a.colNum}] + [${b.rowNum},${b.colNum}]")
-        return produce(a.rowNum, a.colNum) { i, j -> elementContext.run { a[i, j] + b[i, j] } }
+        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] } }
     }
 
     override operator fun Matrix<T>.minus(b: Matrix<T>): Matrix<T> {
-        if (rowNum != b.rowNum || colNum != b.colNum) error("Matrix operation dimension mismatch. [$rowNum,$colNum] - [${b.rowNum},${b.colNum}]")
-        return produce(rowNum, colNum) { i, j -> elementContext.run { get(i, j) + b[i, j] } }
+        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] } }
     }
 
     override fun multiply(a: Matrix<T>, k: Number): Matrix<T> =
-        produce(a.rowNum, a.colNum) { i, j -> elementContext.run { a[i, j] * k } }
+        produce(a.rowNum, a.colNum) { i, j -> elementContext { a[i, j] * k } }
 
     operator fun Number.times(matrix: FeaturedMatrix<T>): Matrix<T> = matrix * this
 
-    override fun Matrix<T>.times(value: T): Matrix<T> =
-        produce(rowNum, colNum) { i, j -> elementContext.run { get(i, j) * value } }
+    override operator fun Matrix<T>.times(value: T): Matrix<T> =
+        produce(rowNum, colNum) { i, j -> elementContext { get(i, j) * value } }
 }

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

@@ -2,6 +2,7 @@ package scientifik.kmath.linear
 
 import scientifik.kmath.operations.RealField
 import scientifik.kmath.operations.Space
+import scientifik.kmath.operations.invoke
 import scientifik.kmath.structures.Buffer
 import scientifik.kmath.structures.BufferFactory
 
@@ -10,10 +11,9 @@ import scientifik.kmath.structures.BufferFactory
  * Could be used on any point-like structure
  */
 interface VectorSpace<T : Any, S : Space<T>> : Space<Point<T>> {
-
     val size: Int
-
     val space: S
+    override val zero: Point<T> get() = produce { space.zero }
 
     fun produce(initializer: (Int) -> T): Point<T>
 
@@ -22,29 +22,24 @@ interface VectorSpace<T : Any, S : Space<T>> : Space<Point<T>> {
      */
     //fun produceElement(initializer: (Int) -> T): Vector<T, S>
 
-    override val zero: Point<T> get() = produce { space.zero }
+    override fun add(a: Point<T>, b: Point<T>): Point<T> = produce { space { a[it] + b[it] } }
 
-    override fun add(a: Point<T>, b: Point<T>): Point<T> = produce { with(space) { a[it] + b[it] } }
-
-    override fun multiply(a: Point<T>, k: Number): Point<T> = produce { with(space) { a[it] * k } }
+    override fun multiply(a: Point<T>, k: Number): Point<T> = produce { space { a[it] * k } }
 
     //TODO add basis
 
     companion object {
-
-        private val realSpaceCache = HashMap<Int, BufferVectorSpace<Double, RealField>>()
+        private val realSpaceCache: MutableMap<Int, BufferVectorSpace<Double, RealField>> = hashMapOf()
 
         /**
          * Non-boxing double vector space
          */
-        fun real(size: Int): BufferVectorSpace<Double, RealField> {
-            return realSpaceCache.getOrPut(size) {
-                BufferVectorSpace(
-                    size,
-                    RealField,
-                    Buffer.Companion::auto
-                )
-            }
+        fun real(size: Int): BufferVectorSpace<Double, RealField> = realSpaceCache.getOrPut(size) {
+            BufferVectorSpace(
+                size,
+                RealField,
+                Buffer.Companion::auto
+            )
         }
 
         /**

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

@@ -18,7 +18,7 @@ class VirtualMatrix<T : Any>(
 
     override val shape: IntArray get() = intArrayOf(rowNum, colNum)
 
-    override fun get(i: Int, j: Int): T = generator(i, j)
+    override operator fun get(i: Int, j: Int): T = generator(i, j)
 
     override fun suggestFeature(vararg features: MatrixFeature): VirtualMatrix<T> =
         VirtualMatrix(rowNum, colNum, this.features + features, generator)

kmath-core/src/commonMain/kotlin/scientifik/kmath/misc/AutoDiff.kt

@@ -3,8 +3,12 @@ package scientifik.kmath.misc
 import scientifik.kmath.linear.Point
 import scientifik.kmath.operations.ExtendedField
 import scientifik.kmath.operations.Field
+import scientifik.kmath.operations.invoke
 import scientifik.kmath.operations.sum
 import scientifik.kmath.structures.asBuffer
+import kotlin.contracts.ExperimentalContracts
+import kotlin.contracts.InvocationKind
+import kotlin.contracts.contract
 
 /*
  * Implementation of backward-mode automatic differentiation.
@@ -27,15 +31,14 @@ class DerivationResult<T : Any>(
     /**
      * compute divergence
      */
-    fun div(): T = context.run { sum(deriv.values) }
+    fun div(): T = context { sum(deriv.values) }
 
     /**
      * Compute a gradient for variables in given order
      */
-    fun grad(vararg variables: Variable<T>): Point<T> = if (variables.isEmpty()) {
-        error("Variable order is not provided for gradient construction")
-    } else {
-        variables.map(::deriv).asBuffer()
+    fun grad(vararg variables: Variable<T>): Point<T> {
+        check(variables.isNotEmpty()) { "Variable order is not provided for gradient construction" }
+        return variables.map(::deriv).asBuffer()
     }
 }
 
@@ -52,19 +55,27 @@ class DerivationResult<T : Any>(
  * assertEquals(9.0, x.d)  // dy/dx
  * ```
  */
-fun <T : Any, F : Field<T>> F.deriv(body: AutoDiffField<T, F>.() -> Variable<T>): DerivationResult<T> =
-    AutoDiffContext(this).run {
+inline fun <T : Any, F : Field<T>> F.deriv(body: AutoDiffField<T, F>.() -> Variable<T>): DerivationResult<T> {
+    contract { callsInPlace(body, InvocationKind.EXACTLY_ONCE) }
+
+    return (AutoDiffContext(this)) {
         val result = body()
-        result.d = context.one// computing derivative w.r.t result
+        result.d = context.one // computing derivative w.r.t result
         runBackwardPass()
         DerivationResult(result.value, derivatives, this@deriv)
     }
+}
 
 
 abstract class AutoDiffField<T : Any, F : Field<T>> : Field<Variable<T>> {
-
     abstract val context: F
 
+    /**
+     * A variable accessing inner state of derivatives.
+     * Use this function in inner builders to avoid creating additional derivative bindings
+     */
+    abstract var Variable<T>.d: T
+
     /**
      * Performs update of derivative after the rest of the formula in the back-pass.
      *
@@ -78,12 +89,6 @@ abstract class AutoDiffField<T : Any, F : Field<T>> : Field<Variable<T>> {
      */
     abstract fun <R> derive(value: R, block: F.(R) -> Unit): R
 
-    /**
-     * A variable accessing inner state of derivatives.
-     * Use this function in inner builders to avoid creating additional derivative bindings
-     */
-    abstract var Variable<T>.d: T
-
     abstract fun variable(value: T): Variable<T>
 
     inline fun variable(block: F.() -> T): Variable<T> = variable(context.block())
@@ -98,46 +103,35 @@ abstract class AutoDiffField<T : Any, F : Field<T>> : Field<Variable<T>> {
     override operator fun Variable<T>.plus(b: Number): Variable<T> = b.plus(this)
 
     override operator fun Number.minus(b: Variable<T>): Variable<T> =
-        derive(variable { this@minus.toDouble() * one - b.value }) { z ->
-            b.d -= z.d
-        }
+        derive(variable { this@minus.toDouble() * one - b.value }) { z -> b.d -= z.d }
 
     override operator fun Variable<T>.minus(b: Number): Variable<T> =
-        derive(variable { this@minus.value - one * b.toDouble() }) { z ->
-            this@minus.d += z.d
-        }
+        derive(variable { this@minus.value - one * b.toDouble() }) { z -> this@minus.d += z.d }
 }
 
 /**
  * Automatic Differentiation context class.
  */
-private class AutoDiffContext<T : Any, F : Field<T>>(override val context: F) : AutoDiffField<T, F>() {
-
+@PublishedApi
+internal class AutoDiffContext<T : Any, F : Field<T>>(override val context: F) : AutoDiffField<T, F>() {
     // this stack contains pairs of blocks and values to apply them to
-    private var stack = arrayOfNulls<Any?>(8)
-    private var sp = 0
-
-    internal val derivatives = HashMap<Variable<T>, T>()
-
+    private var stack: Array<Any?> = arrayOfNulls<Any?>(8)
+    private var sp: Int = 0
+    val derivatives: MutableMap<Variable<T>, T> = hashMapOf()
+    override val zero: Variable<T> get() = Variable(context.zero)
+    override val one: Variable<T> get() = Variable(context.one)
 
     /**
      * A variable coupled with its derivative. For internal use only
      */
     private class VariableWithDeriv<T : Any>(x: T, var d: T) : Variable<T>(x)
 
-
     override fun variable(value: T): Variable<T> =
         VariableWithDeriv(value, context.zero)
 
     override var Variable<T>.d: T
         get() = (this as? VariableWithDeriv)?.d ?: derivatives[this] ?: context.zero
-        set(value) {
-            if (this is VariableWithDeriv) {
-                d = value
-            } else {
-                derivatives[this] = value
-            }
-        }
+        set(value) = if (this is VariableWithDeriv) d = value else derivatives[this] = value
 
     @Suppress("UNCHECKED_CAST")
     override fun <R> derive(value: R, block: F.(R) -> Unit): R {
@@ -160,67 +154,49 @@ private class AutoDiffContext<T : Any, F : Field<T>>(override val context: F) :
     // Basic math (+, -, *, /)
 
 
-    override fun add(a: Variable<T>, b: Variable<T>): Variable<T> =
-        derive(variable { a.value + b.value }) { z ->
-            a.d += z.d
-            b.d += z.d
-        }
+    override fun add(a: Variable<T>, b: Variable<T>): Variable<T> = derive(variable { a.value + b.value }) { z ->
+        a.d += z.d
+        b.d += z.d
+    }
 
-    override fun multiply(a: Variable<T>, b: Variable<T>): Variable<T> =
-        derive(variable { a.value * b.value }) { z ->
-            a.d += z.d * b.value
-            b.d += z.d * a.value
-        }
+    override fun multiply(a: Variable<T>, b: Variable<T>): Variable<T> = derive(variable { a.value * b.value }) { z ->
+        a.d += z.d * b.value
+        b.d += z.d * a.value
+    }
 
-    override fun divide(a: Variable<T>, b: Variable<T>): Variable<T> =
-        derive(variable { a.value / b.value }) { z ->
-            a.d += z.d / b.value
-            b.d -= z.d * a.value / (b.value * b.value)
-        }
+    override fun divide(a: Variable<T>, b: Variable<T>): Variable<T> = derive(variable { a.value / b.value }) { z ->
+        a.d += z.d / b.value
+        b.d -= z.d * a.value / (b.value * b.value)
+    }
 
-    override fun multiply(a: Variable<T>, k: Number): Variable<T> =
-        derive(variable { k.toDouble() * a.value }) { z ->
-            a.d += z.d * k.toDouble()
-        }
-
-    override val zero: Variable<T> get() = Variable(context.zero)
-    override val one: Variable<T> get() = Variable(context.one)
+    override fun multiply(a: Variable<T>, k: Number): Variable<T> = derive(variable { k.toDouble() * a.value }) { z ->
+        a.d += z.d * k.toDouble()
+    }
 }
 
 // Extensions for differentiation of various basic mathematical functions
 
 // x ^ 2
 fun <T : Any, F : Field<T>> AutoDiffField<T, F>.sqr(x: Variable<T>): Variable<T> =
-    derive(variable { x.value * x.value }) { z ->
-        x.d += z.d * 2 * x.value
-    }
+    derive(variable { x.value * x.value }) { z -> x.d += z.d * 2 * x.value }
 
 // x ^ 1/2
 fun <T : Any, F : ExtendedField<T>> AutoDiffField<T, F>.sqrt(x: Variable<T>): Variable<T> =
-    derive(variable { sqrt(x.value) }) { z ->
-        x.d += z.d * 0.5 / z.value
-    }
+    derive(variable { sqrt(x.value) }) { z -> x.d += z.d * 0.5 / z.value }
 
 // x ^ y (const)
 fun <T : Any, F : ExtendedField<T>> AutoDiffField<T, F>.pow(x: Variable<T>, y: Double): Variable<T> =
-    derive(variable { power(x.value, y) }) { z ->
-        x.d += z.d * y * power(x.value, y - 1)
-    }
+    derive(variable { power(x.value, y) }) { z -> x.d += z.d * y * power(x.value, y - 1) }
 
 fun <T : Any, F : ExtendedField<T>> AutoDiffField<T, F>.pow(x: Variable<T>, y: Int): Variable<T> = pow(x, y.toDouble())
 
 // exp(x)
 fun <T : Any, F : ExtendedField<T>> AutoDiffField<T, F>.exp(x: Variable<T>): Variable<T> =
-    derive(variable { exp(x.value) }) { z ->
-        x.d += z.d * z.value
-    }
+    derive(variable { exp(x.value) }) { z -> x.d += z.d * z.value }
 
 // ln(x)
-fun <T : Any, F : ExtendedField<T>> AutoDiffField<T, F>.ln(x: Variable<T>): Variable<T> = derive(
-    variable { ln(x.value) }
-) { z ->
-    x.d += z.d / x.value
-}
+fun <T : Any, F : ExtendedField<T>> AutoDiffField<T, F>.ln(x: Variable<T>): Variable<T> =
+    derive(variable { ln(x.value) }) { z -> x.d += z.d / x.value }
 
 // x ^ y (any)
 fun <T : Any, F : ExtendedField<T>> AutoDiffField<T, F>.pow(x: Variable<T>, y: Variable<T>): Variable<T> =
@@ -228,12 +204,8 @@ fun <T : Any, F : ExtendedField<T>> AutoDiffField<T, F>.pow(x: Variable<T>, y: V
 
 // sin(x)
 fun <T : Any, F : ExtendedField<T>> AutoDiffField<T, F>.sin(x: Variable<T>): Variable<T> =
-    derive(variable { sin(x.value) }) { z ->
-        x.d += z.d * cos(x.value)
-    }
+    derive(variable { sin(x.value) }) { z -> x.d += z.d * cos(x.value) }
 
 // cos(x)
 fun <T : Any, F : ExtendedField<T>> AutoDiffField<T, F>.cos(x: Variable<T>): Variable<T> =
-    derive(variable { cos(x.value) }) { z ->
-        x.d -= z.d * sin(x.value)
-    }
+    derive(variable { cos(x.value) }) { z -> x.d -= z.d * sin(x.value) }

kmath-core/src/commonMain/kotlin/scientifik/kmath/misc/Grids.kt

@@ -41,6 +41,6 @@ fun ClosedFloatingPointRange<Double>.toSequenceWithPoints(numPoints: Int): Seque
  */
 @Deprecated("Replace by 'toSequenceWithPoints'")
 fun ClosedFloatingPointRange<Double>.toGrid(numPoints: Int): DoubleArray {
-    if (numPoints < 2) error("Can't create generic grid with less than two points")
+    require(numPoints >= 2) { "Can't create generic grid with less than two points" }
     return DoubleArray(numPoints) { i -> start + (endInclusive - start) / (numPoints - 1) * i }
 }

kmath-core/src/commonMain/kotlin/scientifik/kmath/misc/cumulative.kt

@@ -2,6 +2,8 @@ package scientifik.kmath.misc
 
 import scientifik.kmath.operations.Space
 import scientifik.kmath.operations.invoke
+import kotlin.contracts.ExperimentalContracts
+import kotlin.contracts.contract
 import kotlin.jvm.JvmName
 
 /**
@@ -11,67 +13,68 @@ import kotlin.jvm.JvmName
  * @param R the type of resulting iterable.
  * @param initial lazy evaluated.
  */
-fun <T, R> Iterator<T>.cumulative(initial: R, operation: (R, T) -> R): Iterator<R> = object : Iterator<R> {
-    var state: R = initial
-    override fun hasNext(): Boolean = this@cumulative.hasNext()
+inline fun <T, R> Iterator<T>.cumulative(initial: R, crossinline operation: (R, T) -> R): Iterator<R> {
+    contract { callsInPlace(operation) }
 
-    override fun next(): R {
-        state = operation(state, this@cumulative.next())
-        return state
+    return object : Iterator<R> {
+        var state: R = initial
+
+        override fun hasNext(): Boolean = this@cumulative.hasNext()
+
+        override fun next(): R {
+            state = operation(state, this@cumulative.next())
+            return state
+        }
     }
 }
 
-fun <T, R> Iterable<T>.cumulative(initial: R, operation: (R, T) -> R): Iterable<R> = object : Iterable<R> {
-    override fun iterator(): Iterator<R> = this@cumulative.iterator().cumulative(initial, operation)
-}
+inline fun <T, R> Iterable<T>.cumulative(initial: R, crossinline operation: (R, T) -> R): Iterable<R> =
+    Iterable { this@cumulative.iterator().cumulative(initial, operation) }
 
-fun <T, R> Sequence<T>.cumulative(initial: R, operation: (R, T) -> R): Sequence<R> = object : Sequence<R> {
-    override fun iterator(): Iterator<R> = this@cumulative.iterator().cumulative(initial, operation)
+inline fun <T, R> Sequence<T>.cumulative(initial: R, crossinline operation: (R, T) -> R): Sequence<R> = Sequence {
+    this@cumulative.iterator().cumulative(initial, operation)
 }
 
 fun <T, R> List<T>.cumulative(initial: R, operation: (R, T) -> R): List<R> =
-    this.iterator().cumulative(initial, operation).asSequence().toList()
+    iterator().cumulative(initial, operation).asSequence().toList()
 
 //Cumulative sum
 
 /**
  * Cumulative sum with custom space
  */
-fun <T> Iterable<T>.cumulativeSum(space: Space<T>): Iterable<T> = space {
-    cumulative(zero) { element: T, sum: T -> sum + element }
-}
+fun <T> Iterable<T>.cumulativeSum(space: Space<T>): Iterable<T> =
+    space { cumulative(zero) { element: T, sum: T -> sum + element } }
 
 @JvmName("cumulativeSumOfDouble")
-fun Iterable<Double>.cumulativeSum(): Iterable<Double> = this.cumulative(0.0) { element, sum -> sum + element }
+fun Iterable<Double>.cumulativeSum(): Iterable<Double> = cumulative(0.0) { element, sum -> sum + element }
 
 @JvmName("cumulativeSumOfInt")
-fun Iterable<Int>.cumulativeSum(): Iterable<Int> = this.cumulative(0) { element, sum -> sum + element }
+fun Iterable<Int>.cumulativeSum(): Iterable<Int> = cumulative(0) { element, sum -> sum + element }
 
 @JvmName("cumulativeSumOfLong")
-fun Iterable<Long>.cumulativeSum(): Iterable<Long> = this.cumulative(0L) { element, sum -> sum + element }
+fun Iterable<Long>.cumulativeSum(): Iterable<Long> = cumulative(0L) { element, sum -> sum + element }
 
-fun <T> Sequence<T>.cumulativeSum(space: Space<T>): Sequence<T> = with(space) {
-    cumulative(zero) { element: T, sum: T -> sum + element }
-}
+fun <T> Sequence<T>.cumulativeSum(space: Space<T>): Sequence<T> =
+    space { cumulative(zero) { element: T, sum: T -> sum + element } }
 
 @JvmName("cumulativeSumOfDouble")
-fun Sequence<Double>.cumulativeSum(): Sequence<Double> = this.cumulative(0.0) { element, sum -> sum + element }
+fun Sequence<Double>.cumulativeSum(): Sequence<Double> = cumulative(0.0) { element, sum -> sum + element }
 
 @JvmName("cumulativeSumOfInt")
-fun Sequence<Int>.cumulativeSum(): Sequence<Int> = this.cumulative(0) { element, sum -> sum + element }
+fun Sequence<Int>.cumulativeSum(): Sequence<Int> = cumulative(0) { element, sum -> sum + element }
 
 @JvmName("cumulativeSumOfLong")
-fun Sequence<Long>.cumulativeSum(): Sequence<Long> = this.cumulative(0L) { element, sum -> sum + element }
+fun Sequence<Long>.cumulativeSum(): Sequence<Long> = cumulative(0L) { element, sum -> sum + element }
 
-fun <T> List<T>.cumulativeSum(space: Space<T>): List<T> = with(space) {
-    cumulative(zero) { element: T, sum: T -> sum + element }
-}
+fun <T> List<T>.cumulativeSum(space: Space<T>): List<T> =
+    space { cumulative(zero) { element: T, sum: T -> sum + element } }
 
 @JvmName("cumulativeSumOfDouble")
-fun List<Double>.cumulativeSum(): List<Double> = this.cumulative(0.0) { element, sum -> sum + element }
+fun List<Double>.cumulativeSum(): List<Double> = cumulative(0.0) { element, sum -> sum + element }
 
 @JvmName("cumulativeSumOfInt")
-fun List<Int>.cumulativeSum(): List<Int> = this.cumulative(0) { element, sum -> sum + element }
+fun List<Int>.cumulativeSum(): List<Int> = cumulative(0) { element, sum -> sum + element }
 
 @JvmName("cumulativeSumOfLong")
-fun List<Long>.cumulativeSum(): List<Long> = this.cumulative(0L) { element, sum -> sum + element }
+fun List<Long>.cumulativeSum(): List<Long> = cumulative(0L) { element, sum -> sum + element }

kmath-core/src/commonMain/kotlin/scientifik/kmath/operations/BigInt.kt

@@ -3,12 +3,13 @@ package scientifik.kmath.operations
 import scientifik.kmath.operations.BigInt.Companion.BASE
 import scientifik.kmath.operations.BigInt.Companion.BASE_SIZE
 import scientifik.kmath.structures.*
+import kotlin.contracts.ExperimentalContracts
+import kotlin.contracts.contract
 import kotlin.math.log2
 import kotlin.math.max
 import kotlin.math.min
 import kotlin.math.sign
 
-
 typealias Magnitude = UIntArray
 typealias TBase = ULong
 
@@ -431,8 +432,8 @@ fun ULong.toBigInt(): BigInt = BigInt(
  * Create a [BigInt] with this array of magnitudes with protective copy
  */
 fun UIntArray.toBigInt(sign: Byte): BigInt {
-    if (sign == 0.toByte() && isNotEmpty()) error("")
-    return BigInt(sign, this.copyOf())
+    require(sign != 0.toByte() || !isNotEmpty())
+    return BigInt(sign, copyOf())
 }
 
 val hexChToInt: MutableMap<Char, Int> = hashMapOf(
@@ -485,11 +486,15 @@ fun String.parseBigInteger(): BigInt? {
     return res * sign
 }
 
-inline fun Buffer.Companion.bigInt(size: Int, initializer: (Int) -> BigInt): Buffer<BigInt> =
-    boxing(size, initializer)
+inline fun Buffer.Companion.bigInt(size: Int, initializer: (Int) -> BigInt): Buffer<BigInt> {
+    contract { callsInPlace(initializer) }
+    return boxing(size, initializer)
+}
 
-inline fun MutableBuffer.Companion.bigInt(size: Int, initializer: (Int) -> BigInt): MutableBuffer<BigInt> =
-    boxing(size, initializer)
+inline fun MutableBuffer.Companion.bigInt(size: Int, initializer: (Int) -> BigInt): MutableBuffer<BigInt> {
+    contract { callsInPlace(initializer) }
+    return boxing(size, initializer)
+}
 
 fun NDAlgebra.Companion.bigInt(vararg shape: Int): BoxingNDRing<BigInt, BigIntField> =
     BoxingNDRing(shape, BigIntField, Buffer.Companion::bigInt)
@@ -497,5 +502,4 @@ fun NDAlgebra.Companion.bigInt(vararg shape: Int): BoxingNDRing<BigInt, BigIntFi
 fun NDElement.Companion.bigInt(
     vararg shape: Int,
     initializer: BigIntField.(IntArray) -> BigInt
-): BufferedNDRingElement<BigInt, BigIntField> =
-    NDAlgebra.bigInt(*shape).produce(initializer)
+): BufferedNDRingElement<BigInt, BigIntField> = NDAlgebra.bigInt(*shape).produce(initializer)

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

@@ -6,6 +6,8 @@ import scientifik.kmath.structures.MutableBuffer
 import scientifik.memory.MemoryReader
 import scientifik.memory.MemorySpec
 import scientifik.memory.MemoryWriter
+import kotlin.contracts.ExperimentalContracts
+import kotlin.contracts.contract
 import kotlin.math.*
 
 /**
@@ -197,9 +199,11 @@ data class Complex(val re: Double, val im: Double) : FieldElement<Complex, Compl
 fun Number.toComplex(): Complex = Complex(this, 0.0)
 
 inline fun Buffer.Companion.complex(size: Int, crossinline init: (Int) -> Complex): Buffer<Complex> {
+    contract { callsInPlace(init) }
     return MemoryBuffer.create(Complex, size, init)
 }
 
 inline fun MutableBuffer.Companion.complex(size: Int, crossinline init: (Int) -> Complex): Buffer<Complex> {
+    contract { callsInPlace(init) }
     return MemoryBuffer.create(Complex, size, init)
 }

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

@@ -8,19 +8,17 @@ class BoxingNDField<T, F : Field<T>>(
     override val elementContext: F,
     val bufferFactory: BufferFactory<T>
 ) : BufferedNDField<T, F> {
-
+    override val zero: BufferedNDFieldElement<T, F> by lazy { produce { zero } }
+    override val one: BufferedNDFieldElement<T, F> by lazy { produce { one } }
     override val strides: Strides = DefaultStrides(shape)
 
     fun buildBuffer(size: Int, initializer: (Int) -> T): Buffer<T> =
         bufferFactory(size, initializer)
 
     override fun check(vararg elements: NDBuffer<T>) {
-        if (!elements.all { it.strides == this.strides }) error("Element strides are not the same as context strides")
+        check(elements.all { it.strides == strides }) { "Element strides are not the same as context strides" }
     }
 
-    override val zero: BufferedNDFieldElement<T, F> by lazy { produce { zero } }
-    override val one: BufferedNDFieldElement<T, F> by lazy { produce { one } }
-
     override fun produce(initializer: F.(IntArray) -> T): BufferedNDFieldElement<T, F> =
         BufferedNDFieldElement(
             this,
@@ -28,6 +26,7 @@ class BoxingNDField<T, F : Field<T>>(
 
     override fun map(arg: NDBuffer<T>, transform: F.(T) -> T): BufferedNDFieldElement<T, F> {
         check(arg)
+
         return BufferedNDFieldElement(
             this,
             buildBuffer(arg.strides.linearSize) { offset -> elementContext.transform(arg.buffer[offset]) })

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

@@ -8,19 +8,16 @@ class BoxingNDRing<T, R : Ring<T>>(
     override val elementContext: R,
     val bufferFactory: BufferFactory<T>
 ) : BufferedNDRing<T, R> {
-
     override val strides: Strides = DefaultStrides(shape)
-
-    fun buildBuffer(size: Int, initializer: (Int) -> T): Buffer<T> =
-        bufferFactory(size, initializer)
-
-    override fun check(vararg elements: NDBuffer<T>) {
-        if (!elements.all { it.strides == this.strides }) error("Element strides are not the same as context strides")
-    }
-
     override val zero: BufferedNDRingElement<T, R> by lazy { produce { zero } }
     override val one: BufferedNDRingElement<T, R> by lazy { produce { one } }
 
+    fun buildBuffer(size: Int, initializer: (Int) -> T): Buffer<T> = bufferFactory(size, initializer)
+
+    override fun check(vararg elements: NDBuffer<T>) {
+        require(elements.all { it.strides == strides }) { "Element strides are not the same as context strides" }
+    }
+
     override fun produce(initializer: R.(IntArray) -> T): BufferedNDRingElement<T, R> =
         BufferedNDRingElement(
             this,

kmath-core/src/commonMain/kotlin/scientifik/kmath/structures/BufferAccessor2D.kt

@@ -6,7 +6,6 @@ import kotlin.reflect.KClass
  * A context that allows to operate on a [MutableBuffer] as on 2d array
  */
 class BufferAccessor2D<T : Any>(val type: KClass<T>, val rowNum: Int, val colNum: Int) {
-
     operator fun Buffer<T>.get(i: Int, j: Int): T = get(i + colNum * j)
 
     operator fun MutableBuffer<T>.set(i: Int, j: Int, value: T) {
@@ -26,15 +25,14 @@ class BufferAccessor2D<T : Any>(val type: KClass<T>, val rowNum: Int, val colNum
     inner class Row(val buffer: MutableBuffer<T>, val rowIndex: Int) : MutableBuffer<T> {
         override val size: Int get() = colNum
 
-        override fun get(index: Int): T = buffer[rowIndex, index]
+        override operator fun get(index: Int): T = buffer[rowIndex, index]
 
-        override fun set(index: Int, value: T) {
+        override operator fun set(index: Int, value: T) {
             buffer[rowIndex, index] = value
         }
 
         override fun copy(): MutableBuffer<T> = MutableBuffer.auto(type, colNum) { get(it) }
-
-        override fun iterator(): Iterator<T> = (0 until colNum).map(::get).iterator()
+        override operator fun iterator(): Iterator<T> = (0 until colNum).map(::get).iterator()
 
     }
 

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

@@ -5,9 +5,8 @@ import scientifik.kmath.operations.*
 interface BufferedNDAlgebra<T, C> : NDAlgebra<T, C, NDBuffer<T>> {
     val strides: Strides
 
-    override fun check(vararg elements: NDBuffer<T>) {
-        if (!elements.all { it.strides == this.strides }) error("Strides mismatch")
-    }
+    override fun check(vararg elements: NDBuffer<T>): Unit =
+        require(elements.all { it.strides == strides }) { ("Strides mismatch") }
 
     /**
      * Convert any [NDStructure] to buffered structure using strides from this context.

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

@@ -30,7 +30,6 @@ class BufferedNDRingElement<T, R : Ring<T>>(
     override val context: BufferedNDRing<T, R>,
     override val buffer: Buffer<T>
 ) : BufferedNDElement<T, R>(), RingElement<NDBuffer<T>, BufferedNDRingElement<T, R>, BufferedNDRing<T, R>> {
-
     override fun unwrap(): NDBuffer<T> = this
 
     override fun NDBuffer<T>.wrap(): BufferedNDRingElement<T, R> {
@@ -43,7 +42,6 @@ class BufferedNDFieldElement<T, F : Field<T>>(
     override val context: BufferedNDField<T, F>,
     override val buffer: Buffer<T>
 ) : BufferedNDElement<T, F>(), FieldElement<NDBuffer<T>, BufferedNDFieldElement<T, F>, BufferedNDField<T, F>> {
-
     override fun unwrap(): NDBuffer<T> = this
 
     override fun NDBuffer<T>.wrap(): BufferedNDFieldElement<T, F> {

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

@@ -2,6 +2,8 @@ package scientifik.kmath.structures
 
 import scientifik.kmath.operations.Complex
 import scientifik.kmath.operations.complex
+import kotlin.contracts.ExperimentalContracts
+import kotlin.contracts.contract
 import kotlin.reflect.KClass
 
 /**
@@ -117,15 +119,14 @@ interface MutableBuffer<T> : Buffer<T> {
             MutableListBuffer(MutableList(size, initializer))
 
         @Suppress("UNCHECKED_CAST")
-        inline fun <T : Any> auto(type: KClass<out T>, size: Int, initializer: (Int) -> T): MutableBuffer<T> {
-            return when (type) {
+        inline fun <T : Any> auto(type: KClass<out T>, size: Int, initializer: (Int) -> T): MutableBuffer<T> =
+            when (type) {
                 Double::class -> RealBuffer(DoubleArray(size) { initializer(it) as Double }) as MutableBuffer<T>
                 Short::class -> ShortBuffer(ShortArray(size) { initializer(it) as Short }) as MutableBuffer<T>
                 Int::class -> IntBuffer(IntArray(size) { initializer(it) as Int }) as MutableBuffer<T>
                 Long::class -> LongBuffer(LongArray(size) { initializer(it) as Long }) as MutableBuffer<T>
                 else -> boxing(size, initializer)
             }
-        }
 
         /**
          * Create most appropriate mutable buffer for given type avoiding boxing wherever possible
@@ -150,9 +151,8 @@ inline class ListBuffer<T>(val list: List<T>) : Buffer<T> {
     override val size: Int
         get() = list.size
 
-    override fun get(index: Int): T = list[index]
-
-    override fun iterator(): Iterator<T> = list.iterator()
+    override operator fun get(index: Int): T = list[index]
+    override operator fun iterator(): Iterator<T> = list.iterator()
 }
 
 /**
@@ -167,7 +167,10 @@ fun <T> List<T>.asBuffer(): ListBuffer<T> = ListBuffer(this)
  * The function [init] is called for each array element sequentially starting from the first one.
  * It should return the value for an array element given its index.
  */
-inline fun <T> ListBuffer(size: Int, init: (Int) -> T): ListBuffer<T> = List(size, init).asBuffer()
+inline fun <T> ListBuffer(size: Int, init: (Int) -> T): ListBuffer<T> {
+    contract { callsInPlace(init) }
+    return List(size, init).asBuffer()
+}
 
 /**
  * [MutableBuffer] implementation over [MutableList].
@@ -176,17 +179,16 @@ inline fun <T> ListBuffer(size: Int, init: (Int) -> T): ListBuffer<T> = List(siz
  * @property list The underlying list.
  */
 inline class MutableListBuffer<T>(val list: MutableList<T>) : MutableBuffer<T> {
-
     override val size: Int
         get() = list.size
 
-    override fun get(index: Int): T = list[index]
+    override operator fun get(index: Int): T = list[index]
 
-    override fun set(index: Int, value: T) {
+    override operator fun set(index: Int, value: T) {
         list[index] = value
     }
 
-    override fun iterator(): Iterator<T> = list.iterator()
+    override operator fun iterator(): Iterator<T> = list.iterator()
     override fun copy(): MutableBuffer<T> = MutableListBuffer(ArrayList(list))
 }
 
@@ -201,14 +203,13 @@ class ArrayBuffer<T>(private val array: Array<T>) : MutableBuffer<T> {
     override val size: Int
         get() = array.size
 
-    override fun get(index: Int): T = array[index]
+    override operator fun get(index: Int): T = array[index]
 
-    override fun set(index: Int, value: T) {
+    override operator fun set(index: Int, value: T) {
         array[index] = value
     }
 
-    override fun iterator(): Iterator<T> = array.iterator()
-
+    override operator fun iterator(): Iterator<T> = array.iterator()
     override fun copy(): MutableBuffer<T> = ArrayBuffer(array.copyOf())
 }
 
@@ -226,9 +227,9 @@ fun <T> Array<T>.asBuffer(): ArrayBuffer<T> = ArrayBuffer(this)
 inline class ReadOnlyBuffer<T>(val buffer: MutableBuffer<T>) : Buffer<T> {
     override val size: Int get() = buffer.size
 
-    override fun get(index: Int): T = buffer[index]
+    override operator fun get(index: Int): T = buffer[index]
 
-    override fun iterator(): Iterator<T> = buffer.iterator()
+    override operator fun iterator(): Iterator<T> = buffer.iterator()
 }
 
 /**
@@ -238,12 +239,12 @@ inline class ReadOnlyBuffer<T>(val buffer: MutableBuffer<T>) : Buffer<T> {
  * @param T the type of elements provided by the buffer.
  */
 class VirtualBuffer<T>(override val size: Int, private val generator: (Int) -> T) : Buffer<T> {
-    override fun get(index: Int): T {
+    override operator fun get(index: Int): T {
         if (index < 0 || index >= size) throw IndexOutOfBoundsException("Expected index from 0 to ${size - 1}, but found $index")
         return generator(index)
     }
 
-    override fun iterator(): Iterator<T> = (0 until size).asSequence().map(generator).iterator()
+    override operator fun iterator(): Iterator<T> = (0 until size).asSequence().map(generator).iterator()
 
     override fun contentEquals(other: Buffer<*>): Boolean {
         return if (other is VirtualBuffer) {

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

@@ -4,6 +4,9 @@ import scientifik.kmath.operations.Complex
 import scientifik.kmath.operations.ComplexField
 import scientifik.kmath.operations.FieldElement
 import scientifik.kmath.operations.complex
+import kotlin.contracts.ExperimentalContracts
+import kotlin.contracts.InvocationKind
+import kotlin.contracts.contract
 
 typealias ComplexNDElement = BufferedNDFieldElement<Complex, ComplexField>
 
@@ -110,6 +113,7 @@ inline fun ComplexNDElement.mapIndexed(crossinline transform: ComplexField.(inde
  * Map one [ComplexNDElement] using function without indices.
  */
 inline fun ComplexNDElement.map(crossinline transform: ComplexField.(Complex) -> Complex): ComplexNDElement {
+    contract { callsInPlace(transform) }
     val buffer = Buffer.complex(strides.linearSize) { offset -> ComplexField.transform(buffer[offset]) }
     return BufferedNDFieldElement(context, buffer)
 }
@@ -149,5 +153,6 @@ fun NDElement.Companion.complex(vararg shape: Int, initializer: ComplexField.(In
  * Produce a context for n-dimensional operations inside this real field
  */
 inline fun <R> ComplexField.nd(vararg shape: Int, action: ComplexNDField.() -> R): R {
-    return NDField.complex(*shape).run(action)
+    contract { callsInPlace(action, InvocationKind.EXACTLY_ONCE) }
+    return NDField.complex(*shape).action()
 }

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

@@ -1,5 +1,7 @@
 package scientifik.kmath.structures
 
+import kotlin.contracts.ExperimentalContracts
+import kotlin.contracts.contract
 import kotlin.experimental.and
 
 /**
@@ -57,17 +59,18 @@ class FlaggedRealBuffer(val values: DoubleArray, val flags: ByteArray) : Flagged
 
     override val size: Int get() = values.size
 
-    override fun get(index: Int): Double? = if (isValid(index)) values[index] else null
+    override operator fun get(index: Int): Double? = if (isValid(index)) values[index] else null
 
-    override fun iterator(): Iterator<Double?> = values.indices.asSequence().map {
+    override operator fun iterator(): Iterator<Double?> = values.indices.asSequence().map {
         if (isValid(it)) values[it] else null
     }.iterator()
 }
 
 inline fun FlaggedRealBuffer.forEachValid(block: (Double) -> Unit) {
-    for (i in indices) {
-        if (isValid(i)) {
-            block(values[i])
-        }
-    }
+    contract { callsInPlace(block) }
+
+    indices
+        .asSequence()
+        .filter(::isValid)
+        .forEach { block(values[it]) }
 }

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

@@ -1,5 +1,8 @@
 package scientifik.kmath.structures
 
+import kotlin.contracts.ExperimentalContracts
+import kotlin.contracts.contract
+
 /**
  * Specialized [MutableBuffer] implementation over [FloatArray].
  *
@@ -8,13 +11,13 @@ package scientifik.kmath.structures
 inline class FloatBuffer(val array: FloatArray) : MutableBuffer<Float> {
     override val size: Int get() = array.size
 
-    override fun get(index: Int): Float = array[index]
+    override operator fun get(index: Int): Float = array[index]
 
-    override fun set(index: Int, value: Float) {
+    override operator fun set(index: Int, value: Float) {
         array[index] = value
     }
 
-    override fun iterator(): FloatIterator = array.iterator()
+    override operator fun iterator(): FloatIterator = array.iterator()
 
     override fun copy(): MutableBuffer<Float> =
         FloatBuffer(array.copyOf())
@@ -27,7 +30,10 @@ inline class FloatBuffer(val array: FloatArray) : MutableBuffer<Float> {
  * The function [init] is called for each array element sequentially starting from the first one.
  * It should return the value for an buffer element given its index.
  */
-inline fun FloatBuffer(size: Int, init: (Int) -> Float): FloatBuffer = FloatBuffer(FloatArray(size) { init(it) })
+inline fun FloatBuffer(size: Int, init: (Int) -> Float): FloatBuffer {
+    contract { callsInPlace(init) }
+    return FloatBuffer(FloatArray(size) { init(it) })
+}
 
 /**
  * Returns a new [FloatBuffer] of given elements.

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

@@ -1,5 +1,9 @@
 package scientifik.kmath.structures
 
+import kotlin.contracts.ExperimentalContracts
+import kotlin.contracts.InvocationKind
+import kotlin.contracts.contract
+
 /**
  * Specialized [MutableBuffer] implementation over [IntArray].
  *
@@ -8,17 +12,16 @@ package scientifik.kmath.structures
 inline class IntBuffer(val array: IntArray) : MutableBuffer<Int> {
     override val size: Int get() = array.size
 
-    override fun get(index: Int): Int = array[index]
+    override operator fun get(index: Int): Int = array[index]
 
-    override fun set(index: Int, value: Int) {
+    override operator fun set(index: Int, value: Int) {
         array[index] = value
     }
 
-    override fun iterator(): IntIterator = array.iterator()
+    override operator fun iterator(): IntIterator = array.iterator()
 
     override fun copy(): MutableBuffer<Int> =
         IntBuffer(array.copyOf())
-
 }
 
 /**
@@ -28,7 +31,10 @@ inline class IntBuffer(val array: IntArray) : MutableBuffer<Int> {
  * The function [init] is called for each array element sequentially starting from the first one.
  * It should return the value for an buffer element given its index.
  */
-inline fun IntBuffer(size: Int, init: (Int) -> Int): IntBuffer = IntBuffer(IntArray(size) { init(it) })
+inline fun IntBuffer(size: Int, init: (Int) -> Int): IntBuffer {
+    contract { callsInPlace(init) }
+    return IntBuffer(IntArray(size) { init(it) })
+}
 
 /**
  * Returns a new [IntBuffer] of given elements.

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

@@ -1,5 +1,8 @@
 package scientifik.kmath.structures
 
+import kotlin.contracts.ExperimentalContracts
+import kotlin.contracts.contract
+
 /**
  * Specialized [MutableBuffer] implementation over [LongArray].
  *
@@ -8,13 +11,13 @@ package scientifik.kmath.structures
 inline class LongBuffer(val array: LongArray) : MutableBuffer<Long> {
     override val size: Int get() = array.size
 
-    override fun get(index: Int): Long = array[index]
+    override operator fun get(index: Int): Long = array[index]
 
-    override fun set(index: Int, value: Long) {
+    override operator fun set(index: Int, value: Long) {
         array[index] = value
     }
 
-    override fun iterator(): LongIterator = array.iterator()
+    override operator fun iterator(): LongIterator = array.iterator()
 
     override fun copy(): MutableBuffer<Long> =
         LongBuffer(array.copyOf())
@@ -28,7 +31,10 @@ inline class LongBuffer(val array: LongArray) : MutableBuffer<Long> {
  * The function [init] is called for each array element sequentially starting from the first one.
  * It should return the value for an buffer element given its index.
  */
-inline fun LongBuffer(size: Int, init: (Int) -> Long): LongBuffer = LongBuffer(LongArray(size) { init(it) })
+inline fun LongBuffer(size: Int, init: (Int) -> Long): LongBuffer {
+    contract { callsInPlace(init) }
+    return LongBuffer(LongArray(size) { init(it) })
+}
 
 /**
  * Returns a new [LongBuffer] of given elements.

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

@@ -14,10 +14,8 @@ open class MemoryBuffer<T : Any>(protected val memory: Memory, protected val spe
 
     private val reader: MemoryReader = memory.reader()
 
-    override fun get(index: Int): T = reader.read(spec, spec.objectSize * index)
-
-    override fun iterator(): Iterator<T> = (0 until size).asSequence().map { get(it) }.iterator()
-
+    override operator fun get(index: Int): T = reader.read(spec, spec.objectSize * index)
+    override operator fun iterator(): Iterator<T> = (0 until size).asSequence().map { get(it) }.iterator()
 
     companion object {
         fun <T : Any> create(spec: MemorySpec<T>, size: Int): MemoryBuffer<T> =
@@ -48,8 +46,7 @@ class MutableMemoryBuffer<T : Any>(memory: Memory, spec: MemorySpec<T>) : Memory
 
     private val writer: MemoryWriter = memory.writer()
 
-    override fun set(index: Int, value: T): Unit = writer.write(spec, spec.objectSize * index, value)
-
+    override operator fun set(index: Int, value: T): Unit = writer.write(spec, spec.objectSize * index, value)
     override fun copy(): MutableBuffer<T> = MutableMemoryBuffer(memory.copy(), spec)
 
     companion object {

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

@@ -26,19 +26,20 @@ interface NDElement<T, C, N : NDStructure<T>> : NDStructure<T> {
         fun real(shape: IntArray, initializer: RealField.(IntArray) -> Double = { 0.0 }): RealNDElement =
             NDField.real(*shape).produce(initializer)
 
-
-        fun real1D(dim: Int, initializer: (Int) -> Double = { _ -> 0.0 }): RealNDElement =
+        inline fun real1D(dim: Int, crossinline initializer: (Int) -> Double = { _ -> 0.0 }): RealNDElement =
             real(intArrayOf(dim)) { initializer(it[0]) }
 
+        inline fun real2D(
+            dim1: Int,
+            dim2: Int,
+            crossinline initializer: (Int, Int) -> Double = { _, _ -> 0.0 }
+        ): RealNDElement = real(intArrayOf(dim1, dim2)) { initializer(it[0], it[1]) }
 
-        fun real2D(dim1: Int, dim2: Int, initializer: (Int, Int) -> Double = { _, _ -> 0.0 }): RealNDElement =
-            real(intArrayOf(dim1, dim2)) { initializer(it[0], it[1]) }
-
-        fun real3D(
+        inline fun real3D(
             dim1: Int,
             dim2: Int,
             dim3: Int,
-            initializer: (Int, Int, Int) -> Double = { _, _, _ -> 0.0 }
+            crossinline initializer: (Int, Int, Int) -> Double = { _, _, _ -> 0.0 }
         ): RealNDElement = real(intArrayOf(dim1, dim2, dim3)) { initializer(it[0], it[1], it[2]) }
 
 
@@ -72,7 +73,6 @@ fun <T, C, N : NDStructure<T>> NDElement<T, C, N>.mapIndexed(transform: C.(index
 fun <T, C, N : NDStructure<T>> NDElement<T, C, N>.map(transform: C.(T) -> T): NDElement<T, C, N> =
     context.map(unwrap(), transform).wrap()
 
-
 /**
  * Element by element application of any operation on elements to the whole [NDElement]
  */
@@ -107,7 +107,6 @@ operator fun <T, R : Ring<T>, N : NDStructure<T>> NDElement<T, R, N>.times(arg:
 operator fun <T, F : Field<T>, N : NDStructure<T>> NDElement<T, F, N>.div(arg: T): NDElement<T, F, N> =
     map { value -> arg / value }
 
-
 //    /**
 //     * Reverse sum operation
 //     */

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

@@ -1,5 +1,7 @@
 package scientifik.kmath.structures
 
+import kotlin.contracts.ExperimentalContracts
+import kotlin.contracts.contract
 import kotlin.jvm.JvmName
 import kotlin.reflect.KClass
 
@@ -139,9 +141,8 @@ interface MutableNDStructure<T> : NDStructure<T> {
 }
 
 inline fun <T> MutableNDStructure<T>.mapInPlace(action: (IntArray, T) -> T) {
-    elements().forEach { (index, oldValue) ->
-        this[index] = action(index, oldValue)
-    }
+    contract { callsInPlace(action) }
+    elements().forEach { (index, oldValue) -> this[index] = action(index, oldValue) }
 }
 
 /**
@@ -200,14 +201,12 @@ class DefaultStrides private constructor(override val shape: IntArray) : Strides
         }.toList()
     }
 
-    override fun offset(index: IntArray): Int {
-        return index.mapIndexed { i, value ->
-            if (value < 0 || value >= this.shape[i]) {
-                throw RuntimeException("Index $value out of shape bounds: (0,${this.shape[i]})")
-            }
-            value * strides[i]
-        }.sum()
-    }
+    override fun offset(index: IntArray): Int = index.mapIndexed { i, value ->
+        if (value < 0 || value >= this.shape[i])
+            throw IndexOutOfBoundsException("Index $value out of shape bounds: (0,${this.shape[i]})")
+
+        value * strides[i]
+    }.sum()
 
     override fun index(offset: Int): IntArray {
         val res = IntArray(shape.size)
@@ -259,7 +258,7 @@ abstract class NDBuffer<T> : NDStructure<T> {
      */
     abstract val strides: Strides
 
-    override fun get(index: IntArray): T = buffer[strides.offset(index)]
+    override operator fun get(index: IntArray): T = buffer[strides.offset(index)]
 
     override val shape: IntArray get() = strides.shape
 
@@ -319,13 +318,13 @@ class MutableBufferNDStructure<T>(
         }
     }
 
-    override fun set(index: IntArray, value: T): Unit = buffer.set(strides.offset(index), value)
+    override operator fun set(index: IntArray, value: T): Unit = buffer.set(strides.offset(index), value)
 }
 
 inline fun <reified T : Any> NDStructure<T>.combine(
     struct: NDStructure<T>,
     crossinline block: (T, T) -> T
 ): NDStructure<T> {
-    if (!this.shape.contentEquals(struct.shape)) error("Shape mismatch in structure combination")
+    require(shape.contentEquals(struct.shape)) { "Shape mismatch in structure combination" }
     return NDStructure.auto(shape) { block(this[it], struct[it]) }
 }

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

@@ -1,5 +1,8 @@
 package scientifik.kmath.structures
 
+import kotlin.contracts.ExperimentalContracts
+import kotlin.contracts.contract
+
 /**
  * Specialized [MutableBuffer] implementation over [DoubleArray].
  *
@@ -8,13 +11,13 @@ package scientifik.kmath.structures
 inline class RealBuffer(val array: DoubleArray) : MutableBuffer<Double> {
     override val size: Int get() = array.size
 
-    override fun get(index: Int): Double = array[index]
+    override operator fun get(index: Int): Double = array[index]
 
-    override fun set(index: Int, value: Double) {
+    override operator fun set(index: Int, value: Double) {
         array[index] = value
     }
 
-    override fun iterator(): DoubleIterator = array.iterator()
+    override operator fun iterator(): DoubleIterator = array.iterator()
 
     override fun copy(): MutableBuffer<Double> =
         RealBuffer(array.copyOf())
@@ -27,7 +30,10 @@ inline class RealBuffer(val array: DoubleArray) : MutableBuffer<Double> {
  * The function [init] is called for each array element sequentially starting from the first one.
  * It should return the value for an buffer element given its index.
  */
-inline fun RealBuffer(size: Int, init: (Int) -> Double): RealBuffer = RealBuffer(DoubleArray(size) { init(it) })
+inline fun RealBuffer(size: Int, init: (Int) -> Double): RealBuffer {
+    contract { callsInPlace(init) }
+    return RealBuffer(DoubleArray(size) { init(it) })
+}
 
 /**
  * Returns a new [RealBuffer] of given elements.

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

@@ -1,5 +1,8 @@
 package scientifik.kmath.structures
 
+import kotlin.contracts.ExperimentalContracts
+import kotlin.contracts.contract
+
 /**
  * Specialized [MutableBuffer] implementation over [ShortArray].
  *
@@ -8,17 +11,16 @@ package scientifik.kmath.structures
 inline class ShortBuffer(val array: ShortArray) : MutableBuffer<Short> {
     override val size: Int get() = array.size
 
-    override fun get(index: Int): Short = array[index]
+    override operator fun get(index: Int): Short = array[index]
 
-    override fun set(index: Int, value: Short) {
+    override operator fun set(index: Int, value: Short) {
         array[index] = value
     }
 
-    override fun iterator(): ShortIterator = array.iterator()
+    override operator fun iterator(): ShortIterator = array.iterator()
 
     override fun copy(): MutableBuffer<Short> =
         ShortBuffer(array.copyOf())
-
 }
 
 /**
@@ -28,7 +30,10 @@ inline class ShortBuffer(val array: ShortArray) : MutableBuffer<Short> {
  * The function [init] is called for each array element sequentially starting from the first one.
  * It should return the value for an buffer element given its index.
  */
-inline fun ShortBuffer(size: Int, init: (Int) -> Short): ShortBuffer = ShortBuffer(ShortArray(size) { init(it) })
+inline fun ShortBuffer(size: Int, init: (Int) -> Short): ShortBuffer {
+    contract { callsInPlace(init) }
+    return ShortBuffer(ShortArray(size) { init(it) })
+}
 
 /**
  * Returns a new [ShortBuffer] of given elements.

kmath-core/src/commonMain/kotlin/scientifik/kmath/structures/Structure1D.kt

@@ -6,12 +6,12 @@ package scientifik.kmath.structures
 interface Structure1D<T> : NDStructure<T>, Buffer<T> {
     override val dimension: Int get() = 1
 
-    override fun get(index: IntArray): T {
-        if (index.size != 1) error("Index dimension mismatch. Expected 1 but found ${index.size}")
+    override operator fun get(index: IntArray): T {
+        require(index.size == 1) { "Index dimension mismatch. Expected 1 but found ${index.size}" }
         return get(index[0])
     }
 
-    override fun iterator(): Iterator<T> = (0 until size).asSequence().map { get(it) }.iterator()
+    override operator fun iterator(): Iterator<T> = (0 until size).asSequence().map { get(it) }.iterator()
 }
 
 /**
@@ -22,7 +22,7 @@ private inline class Structure1DWrapper<T>(val structure: NDStructure<T>) : Stru
     override val shape: IntArray get() = structure.shape
     override val size: Int get() = structure.shape[0]
 
-    override fun get(index: Int): T = structure[index]
+    override operator fun get(index: Int): T = structure[index]
 
     override fun elements(): Sequence<Pair<IntArray, T>> = structure.elements()
 }
@@ -39,7 +39,7 @@ private inline class Buffer1DWrapper<T>(val buffer: Buffer<T>) : Structure1D<T>
     override fun elements(): Sequence<Pair<IntArray, T>> =
         asSequence().mapIndexed { index, value -> intArrayOf(index) to value }
 
-    override fun get(index: Int): T = buffer[index]
+    override operator fun get(index: Int): T = buffer[index]
 }
 
 /**

kmath-core/src/commonMain/kotlin/scientifik/kmath/structures/Structure2D.kt

@@ -9,8 +9,8 @@ interface Structure2D<T> : NDStructure<T> {
 
     operator fun get(i: Int, j: Int): T
 
-    override fun get(index: IntArray): T {
-        if (index.size != 2) error("Index dimension mismatch. Expected 2 but found ${index.size}")
+    override operator fun get(index: IntArray): T {
+        require(index.size == 2) { "Index dimension mismatch. Expected 2 but found ${index.size}" }
         return get(index[0], index[1])
     }
 
@@ -39,10 +39,10 @@ interface Structure2D<T> : NDStructure<T> {
  * A 2D wrapper for nd-structure
  */
 private inline class Structure2DWrapper<T>(val structure: NDStructure<T>) : Structure2D<T> {
-    override fun get(i: Int, j: Int): T = structure[i, j]
-
     override val shape: IntArray get() = structure.shape
 
+    override operator fun get(i: Int, j: Int): T = structure[i, j]
+
     override fun elements(): Sequence<Pair<IntArray, T>> = structure.elements()
 }
 

kmath-core/src/commonTest/kotlin/scientifik/kmath/expressions/ExpressionFieldTest.kt

@@ -3,6 +3,7 @@ package scientifik.kmath.expressions
 import scientifik.kmath.operations.Complex
 import scientifik.kmath.operations.ComplexField
 import scientifik.kmath.operations.RealField
+import scientifik.kmath.operations.invoke
 import kotlin.test.Test
 import kotlin.test.assertEquals
 
@@ -10,10 +11,12 @@ class ExpressionFieldTest {
     @Test
     fun testExpression() {
         val context = FunctionalExpressionField(RealField)
-        val expression = with(context) {
+
+        val expression = context {
             val x = variable("x", 2.0)
             x * x + 2 * x + one
         }
+
         assertEquals(expression("x" to 1.0), 4.0)
         assertEquals(expression(), 9.0)
     }
@@ -21,10 +24,12 @@ class ExpressionFieldTest {
     @Test
     fun testComplex() {
         val context = FunctionalExpressionField(ComplexField)
-        val expression = with(context) {
+
+        val expression = context {
             val x = variable("x", Complex(2.0, 0.0))
             x * x + 2 * x + one
         }
+
         assertEquals(expression("x" to Complex(1.0, 0.0)), Complex(4.0, 0.0))
         assertEquals(expression(), Complex(9.0, 0.0))
     }

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

@@ -7,7 +7,6 @@ import kotlin.test.Test
 import kotlin.test.assertEquals
 
 class MatrixTest {
-
     @Test
     fun testTranspose() {
         val matrix = MatrixContext.real.one(3, 3)
@@ -51,6 +50,7 @@ class MatrixTest {
     fun test2DDot() {
         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 {
 //            val firstMatrix = produce(2, 3) { i, j -> (i + j).toDouble() }
 //            val secondMatrix = produce(3, 2) { i, j -> (i + j).toDouble() }

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

@@ -1,6 +1,7 @@
 package scientifik.kmath.structures
 
 import scientifik.kmath.operations.Norm
+import scientifik.kmath.operations.invoke
 import scientifik.kmath.structures.NDElement.Companion.real2D
 import kotlin.math.abs
 import kotlin.math.pow
@@ -56,17 +57,12 @@ class NumberNDFieldTest {
     }
 
     object L2Norm : Norm<NDStructure<out Number>, Double> {
-        override fun norm(arg: NDStructure<out Number>): Double {
-            return kotlin.math.sqrt(arg.elements().sumByDouble { it.second.toDouble() })
-        }
+        override fun norm(arg: NDStructure<out Number>): Double =
+            kotlin.math.sqrt(arg.elements().sumByDouble { it.second.toDouble() })
     }
 
     @Test
     fun testInternalContext() {
-        NDField.real(*array1.shape).run {
-            with(L2Norm) {
-                1 + norm(array1) + exp(array2)
-            }
-        }
+        (NDField.real(*array1.shape)) { with(L2Norm) { 1 + norm(array1) + exp(array2) } }
     }
 }

kmath-core/src/jvmMain/kotlin/scientifik/kmath/operations/BigNumbers.kt

@@ -17,10 +17,10 @@ object JBigIntegerField : Field<BigInteger> {
     override fun number(value: Number): BigInteger = BigInteger.valueOf(value.toLong())
     override fun divide(a: BigInteger, b: BigInteger): BigInteger = a.div(b)
     override fun add(a: BigInteger, b: BigInteger): BigInteger = a.add(b)
-    override fun BigInteger.minus(b: BigInteger): BigInteger = this.subtract(b)
+    override operator fun BigInteger.minus(b: BigInteger): BigInteger = subtract(b)
     override fun multiply(a: BigInteger, k: Number): BigInteger = a.multiply(k.toInt().toBigInteger())
     override fun multiply(a: BigInteger, b: BigInteger): BigInteger = a.multiply(b)
-    override fun BigInteger.unaryMinus(): BigInteger = negate()
+    override operator fun BigInteger.unaryMinus(): BigInteger = negate()
 }
 
 /**
@@ -38,7 +38,7 @@ abstract class JBigDecimalFieldBase internal constructor(val mathContext: MathCo
         get() = BigDecimal.ONE
 
     override fun add(a: BigDecimal, b: BigDecimal): BigDecimal = a.add(b)
-    override fun BigDecimal.minus(b: BigDecimal): BigDecimal = subtract(b)
+    override operator fun BigDecimal.minus(b: BigDecimal): BigDecimal = subtract(b)
     override fun number(value: Number): BigDecimal = BigDecimal.valueOf(value.toDouble())
 
     override fun multiply(a: BigDecimal, k: Number): BigDecimal =
@@ -48,8 +48,7 @@ abstract class JBigDecimalFieldBase internal constructor(val mathContext: MathCo
     override fun divide(a: BigDecimal, b: BigDecimal): BigDecimal = a.divide(b, mathContext)
     override fun power(arg: BigDecimal, pow: Number): BigDecimal = arg.pow(pow.toInt(), mathContext)
     override fun sqrt(arg: BigDecimal): BigDecimal = arg.sqrt(mathContext)
-    override fun BigDecimal.unaryMinus(): BigDecimal = negate(mathContext)
-
+    override operator fun BigDecimal.unaryMinus(): BigDecimal = negate(mathContext)
 }
 
 /**

kmath-coroutines/build.gradle.kts

@@ -4,20 +4,27 @@ plugins {
 }
 
 kotlin.sourceSets {
+    all {
+        with(languageSettings) {
+            useExperimentalAnnotation("kotlin.contracts.ExperimentalContracts")
+            useExperimentalAnnotation("kotlinx.coroutines.InternalCoroutinesApi")
+            useExperimentalAnnotation("kotlinx.coroutines.ExperimentalCoroutinesApi")
+            useExperimentalAnnotation("kotlinx.coroutines.FlowPreview")
+        }
+    }
+
     commonMain {
         dependencies {
             api(project(":kmath-core"))
             api("org.jetbrains.kotlinx:kotlinx-coroutines-core-common:${Scientifik.coroutinesVersion}")
         }
     }
+
     jvmMain {
-        dependencies {
-            api("org.jetbrains.kotlinx:kotlinx-coroutines-core:${Scientifik.coroutinesVersion}")
-        }
+        dependencies { api("org.jetbrains.kotlinx:kotlinx-coroutines-core:${Scientifik.coroutinesVersion}") }
     }
+
     jsMain {
-        dependencies {
-            api("org.jetbrains.kotlinx:kotlinx-coroutines-core-js:${Scientifik.coroutinesVersion}")
-        }
+        dependencies { api("org.jetbrains.kotlinx:kotlinx-coroutines-core-js:${Scientifik.coroutinesVersion}") }
     }
 }

kmath-coroutines/src/commonMain/kotlin/scientifik/kmath/chains/Chain.kt

@@ -16,9 +16,9 @@
 
 package scientifik.kmath.chains
 
-import kotlinx.coroutines.InternalCoroutinesApi
 import kotlinx.coroutines.flow.Flow
 import kotlinx.coroutines.flow.FlowCollector
+import kotlinx.coroutines.flow.flow
 import kotlinx.coroutines.sync.Mutex
 import kotlinx.coroutines.sync.withLock
 
@@ -37,14 +37,8 @@ interface Chain<out R> : Flow<R> {
      */
     fun fork(): Chain<R>
 
-    @OptIn(InternalCoroutinesApi::class)
-    override suspend fun collect(collector: FlowCollector<R>) {
-        kotlinx.coroutines.flow.flow {
-            while (true) {
-                emit(next())
-            }
-        }.collect(collector)
-    }
+    override suspend fun collect(collector: FlowCollector<R>): Unit =
+        flow { while (true) emit(next()) }.collect(collector)
 
     companion object
 }
@@ -139,9 +133,10 @@ fun <T, R> Chain<T>.map(func: suspend (T) -> R): Chain<R> = object : Chain<R> {
 fun <T> Chain<T>.filter(block: (T) -> Boolean): Chain<T> = object : Chain<T> {
     override suspend fun next(): T {
         var next: T
-        do {
-            next = this@filter.next()
-        } while (!block(next))
+
+        do next = this@filter.next()
+        while (!block(next))
+
         return next
     }
 
@@ -159,6 +154,7 @@ fun <T, R> Chain<T>.collect(mapper: suspend (Chain<T>) -> R): Chain<R> = object
 fun <T, S, R> Chain<T>.collectWithState(state: S, stateFork: (S) -> S, mapper: suspend S.(Chain<T>) -> R): Chain<R> =
     object : Chain<R> {
         override suspend fun next(): R = state.mapper(this@collectWithState)
+
         override fun fork(): Chain<R> =
             this@collectWithState.fork().collectWithState(stateFork(state), stateFork, mapper)
     }
@@ -168,6 +164,5 @@ fun <T, S, R> Chain<T>.collectWithState(state: S, stateFork: (S) -> S, mapper: s
  */
 fun <T, U, R> Chain<T>.zip(other: Chain<U>, block: suspend (T, U) -> R): Chain<R> = object : Chain<R> {
     override suspend fun next(): R = block(this@zip.next(), other.next())
-
     override fun fork(): Chain<R> = this@zip.fork().zip(other.fork(), block)
 }

kmath-coroutines/src/commonMain/kotlin/scientifik/kmath/chains/flowExtra.kt

@@ -7,15 +7,15 @@ import kotlinx.coroutines.flow.scan
 import kotlinx.coroutines.flow.scanReduce
 import scientifik.kmath.operations.Space
 import scientifik.kmath.operations.SpaceOperations
-
+import scientifik.kmath.operations.invoke
 
 @ExperimentalCoroutinesApi
-fun <T> Flow<T>.cumulativeSum(space: SpaceOperations<T>): Flow<T> = with(space) {
+fun <T> Flow<T>.cumulativeSum(space: SpaceOperations<T>): Flow<T> = space {
     scanReduce { sum: T, element: T -> sum + element }
 }
 
 @ExperimentalCoroutinesApi
-fun <T> Flow<T>.mean(space: Space<T>): Flow<T> = with(space) {
+fun <T> Flow<T>.mean(space: Space<T>): Flow<T> = space {
     class Accumulator(var sum: T, var num: Int)
 
     scan(Accumulator(zero, 0)) { sum, element ->

kmath-coroutines/src/commonMain/kotlin/scientifik/kmath/coroutines/coroutinesExtra.kt

@@ -3,6 +3,7 @@ package scientifik.kmath.coroutines
 import kotlinx.coroutines.*
 import kotlinx.coroutines.channels.produce
 import kotlinx.coroutines.flow.*
+import kotlin.contracts.contract
 
 val Dispatchers.Math: CoroutineDispatcher
     get() = Default
@@ -23,15 +24,11 @@ internal class LazyDeferred<T>(val dispatcher: CoroutineDispatcher, val block: s
 }
 
 class AsyncFlow<T> internal constructor(internal val deferredFlow: Flow<LazyDeferred<T>>) : Flow<T> {
-    @InternalCoroutinesApi
     override suspend fun collect(collector: FlowCollector<T>) {
-        deferredFlow.collect {
-            collector.emit((it.await()))
-        }
+        deferredFlow.collect { collector.emit((it.await())) }
     }
 }
 
-@FlowPreview
 fun <T, R> Flow<T>.async(
     dispatcher: CoroutineDispatcher = Dispatchers.Default,
     block: suspend CoroutineScope.(T) -> R
@@ -42,7 +39,6 @@ fun <T, R> Flow<T>.async(
     return AsyncFlow(flow)
 }
 
-@FlowPreview
 fun <T, R> AsyncFlow<T>.map(action: (T) -> R): AsyncFlow<R> =
     AsyncFlow(deferredFlow.map { input ->
         //TODO add function composition
@@ -52,10 +48,9 @@ fun <T, R> AsyncFlow<T>.map(action: (T) -> R): AsyncFlow<R> =
         }
     })
 
-@ExperimentalCoroutinesApi
-@FlowPreview
 suspend fun <T> AsyncFlow<T>.collect(concurrency: Int, collector: FlowCollector<T>) {
     require(concurrency >= 1) { "Buffer size should be more than 1, but was $concurrency" }
+
     coroutineScope {
         //Starting up to N deferred coroutines ahead of time
         val channel = produce(capacity = concurrency - 1) {
@@ -81,21 +76,18 @@ suspend fun <T> AsyncFlow<T>.collect(concurrency: Int, collector: FlowCollector<
     }
 }
 
-@ExperimentalCoroutinesApi
-@FlowPreview
-suspend fun <T> AsyncFlow<T>.collect(concurrency: Int, action: suspend (value: T) -> Unit) {
+suspend inline fun <T> AsyncFlow<T>.collect(concurrency: Int, crossinline action: suspend (value: T) -> Unit) {
+    contract { callsInPlace(action) }
+
     collect(concurrency, object : FlowCollector<T> {
         override suspend fun emit(value: T): Unit = action(value)
     })
 }
 
-@ExperimentalCoroutinesApi
-@FlowPreview
-fun <T, R> Flow<T>.mapParallel(
+inline fun <T, R> Flow<T>.mapParallel(
     dispatcher: CoroutineDispatcher = Dispatchers.Default,
-    transform: suspend (T) -> R
+    crossinline transform: suspend (T) -> R
 ): Flow<R> {
-    return flatMapMerge { value ->
-        flow { emit(transform(value)) }
-    }.flowOn(dispatcher)
+    contract { callsInPlace(transform) }
+    return flatMapMerge { value -> flow { emit(transform(value)) } }.flowOn(dispatcher)
 }

kmath-coroutines/src/commonMain/kotlin/scientifik/kmath/streaming/RingBuffer.kt

@@ -20,7 +20,7 @@ class RingBuffer<T>(
     override var size: Int = size
         private set
 
-    override fun get(index: Int): T {
+    override operator fun get(index: Int): T {
         require(index >= 0) { "Index must be positive" }
         require(index < size) { "Index $index is out of circular buffer size $size" }
         return buffer[startIndex.forward(index)] as T
@@ -31,15 +31,13 @@ class RingBuffer<T>(
     /**
      * Iterator could provide wrong results if buffer is changed in initialization (iteration is safe)
      */
-    override fun iterator(): Iterator<T> = object : AbstractIterator<T>() {
+    override operator fun iterator(): Iterator<T> = object : AbstractIterator<T>() {
         private var count = size
         private var index = startIndex
         val copy = buffer.copy()
 
         override fun computeNext() {
-            if (count == 0) {
-                done()
-            } else {
+            if (count == 0) done() else {
                 setNext(copy[index] as T)
                 index = index.forward(1)
                 count--

kmath-coroutines/src/jvmMain/kotlin/scientifik/kmath/chains/ChainExt.kt

@@ -1,7 +1,6 @@
 package scientifik.kmath.chains
 
 import kotlinx.coroutines.runBlocking
-import kotlin.sequences.Sequence
 
 /**
  * Represent a chain as regular iterator (uses blocking calls)
@@ -15,6 +14,4 @@ operator fun <R> Chain<R>.iterator(): Iterator<R> = object : Iterator<R> {
 /**
  * Represent a chain as a sequence
  */
-fun <R> Chain<R>.asSequence(): Sequence<R> = object : Sequence<R> {
-    override fun iterator(): Iterator<R> = this@asSequence.iterator()
-}
+fun <R> Chain<R>.asSequence(): Sequence<R> = Sequence { this@asSequence.iterator() }

kmath-coroutines/src/jvmMain/kotlin/scientifik/kmath/structures/LazyNDStructure.kt

@@ -18,7 +18,7 @@ class LazyNDStructure<T>(
 
     suspend fun await(index: IntArray): T = deferred(index).await()
 
-    override fun get(index: IntArray): T = runBlocking {
+    override operator fun get(index: IntArray): T = runBlocking {
         deferred(index).await()
     }
 
@@ -52,10 +52,12 @@ suspend fun <T> NDStructure<T>.await(index: IntArray): T =
 /**
  * PENDING would benefit from KEEP-176
  */
-fun <T, R> NDStructure<T>.mapAsyncIndexed(
+inline fun <T, R> NDStructure<T>.mapAsyncIndexed(
     scope: CoroutineScope,
-    function: suspend (T, index: IntArray) -> R
+    crossinline function: suspend (T, index: IntArray) -> R
 ): LazyNDStructure<R> = LazyNDStructure(scope, shape) { index -> function(get(index), index) }
 
-fun <T, R> NDStructure<T>.mapAsync(scope: CoroutineScope, function: suspend (T) -> R): LazyNDStructure<R> =
-    LazyNDStructure(scope, shape) { index -> function(get(index)) }
+inline fun <T, R> NDStructure<T>.mapAsync(
+    scope: CoroutineScope,
+    crossinline function: suspend (T) -> R
+): LazyNDStructure<R> = LazyNDStructure(scope, shape) { index -> function(get(index)) }

kmath-dimensions/src/commonMain/kotlin/scientifik/kmath/dimensions/Wrappers.kt

@@ -4,7 +4,9 @@ import scientifik.kmath.linear.GenericMatrixContext
 import scientifik.kmath.linear.MatrixContext
 import scientifik.kmath.linear.Point
 import scientifik.kmath.linear.transpose
+import scientifik.kmath.operations.RealField
 import scientifik.kmath.operations.Ring
+import scientifik.kmath.operations.invoke
 import scientifik.kmath.structures.Matrix
 import scientifik.kmath.structures.Structure2D
 
@@ -42,7 +44,7 @@ inline class DMatrixWrapper<T, R : Dimension, C : Dimension>(
     val structure: Structure2D<T>
 ) : DMatrix<T, R, C> {
     override val shape: IntArray get() = structure.shape
-    override fun get(i: Int, j: Int): T = structure[i, j]
+    override operator fun get(i: Int, j: Int): T = structure[i, j]
 }
 
 /**
@@ -70,9 +72,9 @@ inline class DPointWrapper<T, D : Dimension>(val point: Point<T>) :
     DPoint<T, D> {
     override val size: Int get() = point.size
 
-    override fun get(index: Int): T = point[index]
+    override operator fun get(index: Int): T = point[index]
 
-    override fun iterator(): Iterator<T> = point.iterator()
+    override operator fun iterator(): Iterator<T> = point.iterator()
 }
 
 
@@ -82,12 +84,14 @@ inline class DPointWrapper<T, D : Dimension>(val point: Point<T>) :
 inline class DMatrixContext<T : Any, Ri : Ring<T>>(val context: GenericMatrixContext<T, Ri>) {
 
     inline fun <reified R : Dimension, reified C : Dimension> Matrix<T>.coerce(): DMatrix<T, R, C> {
-        if (rowNum != Dimension.dim<R>().toInt()) {
-            error("Row number mismatch: expected ${Dimension.dim<R>()} but found $rowNum")
-        }
-        if (colNum != Dimension.dim<C>().toInt()) {
-            error("Column number mismatch: expected ${Dimension.dim<C>()} but found $colNum")
-        }
+        check(
+            rowNum == Dimension.dim<R>().toInt()
+        ) { "Row number mismatch: expected ${Dimension.dim<R>()} but found $rowNum" }
+
+        check(
+            colNum == Dimension.dim<C>().toInt()
+        ) { "Column number mismatch: expected ${Dimension.dim<C>()} but found $colNum" }
+
         return DMatrix.coerceUnsafe(this)
     }
 
@@ -97,11 +101,12 @@ inline class DMatrixContext<T : Any, Ri : Ring<T>>(val context: GenericMatrixCon
     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.produce(rows.toInt(), cols.toInt(), initializer).coerce<R, C>()
     }
 
     inline fun <reified D : Dimension> point(noinline initializer: (Int) -> T): DPoint<T, D> {
         val size = Dimension.dim<D>()
+
         return DPoint.coerceUnsafe(
             context.point(
                 size.toInt(),
@@ -112,37 +117,28 @@ inline class DMatrixContext<T : Any, Ri : Ring<T>>(val context: GenericMatrixCon
 
     inline infix fun <reified R1 : Dimension, reified C1 : Dimension, reified C2 : Dimension> DMatrix<T, R1, C1>.dot(
         other: DMatrix<T, C1, C2>
-    ): DMatrix<T, R1, C2> {
-        return context.run { this@dot dot other }.coerce()
-    }
+    ): DMatrix<T, R1, C2> = context { this@dot dot other }.coerce()
 
-    inline infix fun <reified R : Dimension, reified C : Dimension> DMatrix<T, R, C>.dot(vector: DPoint<T, C>): DPoint<T, R> {
-        return DPoint.coerceUnsafe(context.run { this@dot dot vector })
-    }
+    inline infix fun <reified R : Dimension, reified C : Dimension> DMatrix<T, R, C>.dot(vector: DPoint<T, C>): DPoint<T, R> =
+        DPoint.coerceUnsafe(context { this@dot dot vector })
 
-    inline operator fun <reified R : Dimension, reified C : Dimension> DMatrix<T, R, C>.times(value: T): DMatrix<T, R, C> {
-        return context.run { this@times.times(value) }.coerce()
-    }
+    inline operator fun <reified R : Dimension, reified C : Dimension> DMatrix<T, R, C>.times(value: T): DMatrix<T, R, C> =
+        context { this@times.times(value) }.coerce()
 
     inline operator fun <reified R : Dimension, reified C : Dimension> T.times(m: DMatrix<T, R, C>): DMatrix<T, R, C> =
         m * this
 
+    inline operator fun <reified R : Dimension, reified C : Dimension> DMatrix<T, C, R>.plus(other: DMatrix<T, C, R>): DMatrix<T, C, R> =
+        context { this@plus + other }.coerce()
 
-    inline operator fun <reified R : Dimension, reified C : Dimension> DMatrix<T, C, R>.plus(other: DMatrix<T, C, R>): DMatrix<T, C, R> {
-        return context.run { this@plus + other }.coerce()
-    }
+    inline operator fun <reified R : Dimension, reified C : Dimension> DMatrix<T, C, R>.minus(other: DMatrix<T, C, R>): DMatrix<T, C, R> =
+        context { this@minus + other }.coerce()
 
-    inline operator fun <reified R : Dimension, reified C : Dimension> DMatrix<T, C, R>.minus(other: DMatrix<T, C, R>): DMatrix<T, C, R> {
-        return context.run { this@minus + other }.coerce()
-    }
+    inline operator fun <reified R : Dimension, reified C : Dimension> DMatrix<T, C, R>.unaryMinus(): DMatrix<T, C, R> =
+        context { this@unaryMinus.unaryMinus() }.coerce()
 
-    inline operator fun <reified R : Dimension, reified C : Dimension> DMatrix<T, C, R>.unaryMinus(): DMatrix<T, C, R> {
-        return context.run { this@unaryMinus.unaryMinus() }.coerce()
-    }
-
-    inline fun <reified R : Dimension, reified C : Dimension> DMatrix<T, C, R>.transpose(): DMatrix<T, R, C> {
-        return context.run { (this@transpose as Matrix<T>).transpose() }.coerce()
-    }
+    inline fun <reified R : Dimension, reified C : Dimension> DMatrix<T, C, R>.transpose(): DMatrix<T, R, C> =
+        context { (this@transpose as Matrix<T>).transpose() }.coerce()
 
     /**
      * A square unit matrix
@@ -156,6 +152,6 @@ inline class DMatrixContext<T : Any, Ri : Ring<T>>(val context: GenericMatrixCon
     }
 
     companion object {
-        val real = DMatrixContext(MatrixContext.real)
+        val real: DMatrixContext<Double, RealField> = DMatrixContext(MatrixContext.real)
     }
 }

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

@@ -5,11 +5,10 @@ import scientifik.kmath.dimensions.D3
 import scientifik.kmath.dimensions.DMatrixContext
 import kotlin.test.Test
 
-
 class DMatrixContextTest {
     @Test
     fun testDimensionSafeMatrix() {
-        val res = DMatrixContext.real.run {
+        val res = with(DMatrixContext.real) {
             val m = produce<D2, D2> { i, j -> (i + j).toDouble() }
 
             //The dimension of `one()` is inferred from type
@@ -19,7 +18,7 @@ class DMatrixContextTest {
 
     @Test
     fun testTypeCheck() {
-        val res = DMatrixContext.real.run {
+        val res = with(DMatrixContext.real) {
             val m1 = produce<D2, D3> { i, j -> (i + j).toDouble() }
             val m2 = produce<D3, D2> { i, j -> (i + j).toDouble() }
 

kmath-for-real/build.gradle.kts

@@ -1,11 +1,6 @@
-plugins {
-    id("scientifik.mpp")
-}
+plugins { id("scientifik.mpp") }
 
 kotlin.sourceSets {
-    commonMain {
-        dependencies {
-            api(project(":kmath-core"))
-        }
-    }
+    all { languageSettings.useExperimentalAnnotation("kotlin.contracts.ExperimentalContracts") }
+    commonMain { dependencies { api(project(":kmath-core")) } }
 }

kmath-for-real/src/commonMain/kotlin/scientifik/kmath/real/RealVector.kt

@@ -14,8 +14,8 @@ import kotlin.math.sqrt
 
 typealias RealPoint = Point<Double>
 
-fun DoubleArray.asVector() = RealVector(this.asBuffer())
-fun List<Double>.asVector() = RealVector(this.asBuffer())
+fun DoubleArray.asVector(): RealVector = RealVector(this.asBuffer())
+fun List<Double>.asVector(): RealVector = RealVector(this.asBuffer())
 
 object VectorL2Norm : Norm<Point<out Number>, Double> {
     override fun norm(arg: Point<out Number>): Double = sqrt(arg.asIterable().sumByDouble { it.toDouble() })
@@ -32,15 +32,14 @@ inline class RealVector(private val point: Point<Double>) :
 
     override val size: Int get() = point.size
 
-    override fun get(index: Int): Double = point[index]
+    override operator fun get(index: Int): Double = point[index]
 
-    override fun iterator(): Iterator<Double> = point.iterator()
+    override operator fun iterator(): Iterator<Double> = point.iterator()
 
     companion object {
+        private val spaceCache: MutableMap<Int, BufferVectorSpace<Double, RealField>> = hashMapOf()
 
-        private val spaceCache = HashMap<Int, BufferVectorSpace<Double, RealField>>()
-
-        inline operator fun invoke(dim: Int, initializer: (Int) -> Double) =
+        inline operator fun invoke(dim: Int, initializer: (Int) -> Double): RealVector =
             RealVector(RealBuffer(dim, initializer))
 
         operator fun invoke(vararg values: Double): RealVector = values.asVector()

kmath-for-real/src/commonMain/kotlin/scientifik/kmath/real/realMatrix.kt

@@ -3,11 +3,14 @@ package scientifik.kmath.real
 import scientifik.kmath.linear.MatrixContext
 import scientifik.kmath.linear.RealMatrixContext.elementContext
 import scientifik.kmath.linear.VirtualMatrix
+import scientifik.kmath.operations.invoke
 import scientifik.kmath.operations.sum
 import scientifik.kmath.structures.Buffer
 import scientifik.kmath.structures.Matrix
 import scientifik.kmath.structures.RealBuffer
 import scientifik.kmath.structures.asIterable
+import kotlin.contracts.ExperimentalContracts
+import kotlin.contracts.contract
 import kotlin.math.pow
 
 /*
@@ -27,7 +30,7 @@ typealias RealMatrix = Matrix<Double>
 fun realMatrix(rowNum: Int, colNum: Int, initializer: (i: Int, j: Int) -> Double): RealMatrix =
     MatrixContext.real.produce(rowNum, colNum, initializer)
 
-fun Array<DoubleArray>.toMatrix(): RealMatrix{
+fun Array<DoubleArray>.toMatrix(): RealMatrix {
     return MatrixContext.real.produce(size, this[0].size) { row, col -> this[row][col] }
 }
 
@@ -117,13 +120,16 @@ operator fun Matrix<Double>.minus(other: Matrix<Double>): RealMatrix =
  *  Operations on columns
  */
 
-inline fun Matrix<Double>.appendColumn(crossinline mapper: (Buffer<Double>) -> Double) =
-    MatrixContext.real.produce(rowNum, colNum + 1) { row, col ->
+inline fun Matrix<Double>.appendColumn(crossinline mapper: (Buffer<Double>) -> Double): Matrix<Double> {
+    contract { callsInPlace(mapper) }
+
+    return MatrixContext.real.produce(rowNum, colNum + 1) { row, col ->
         if (col < colNum)
             this[row, col]
         else
             mapper(rows[row])
     }
+}
 
 fun Matrix<Double>.extractColumns(columnRange: IntRange): RealMatrix =
     MatrixContext.real.produce(rowNum, columnRange.count()) { row, col ->
@@ -135,17 +141,15 @@ fun Matrix<Double>.extractColumn(columnIndex: Int): RealMatrix =
 
 fun Matrix<Double>.sumByColumn(): RealBuffer = RealBuffer(colNum) { j ->
     val column = columns[j]
-    with(elementContext) {
-        sum(column.asIterable())
-    }
+    elementContext { sum(column.asIterable()) }
 }
 
 fun Matrix<Double>.minByColumn(): RealBuffer = RealBuffer(colNum) { j ->
-    columns[j].asIterable().min() ?: throw Exception("Cannot produce min on empty column")
+    columns[j].asIterable().min() ?: error("Cannot produce min on empty column")
 }
 
 fun Matrix<Double>.maxByColumn(): RealBuffer = RealBuffer(colNum) { j ->
-    columns[j].asIterable().max() ?: throw Exception("Cannot produce min on empty column")
+    columns[j].asIterable().max() ?: error("Cannot produce min on empty column")
 }
 
 fun Matrix<Double>.averageByColumn(): RealBuffer = RealBuffer(colNum) { j ->
@@ -156,10 +160,7 @@ fun Matrix<Double>.averageByColumn(): RealBuffer = RealBuffer(colNum) { j ->
  * Operations processing all elements
  */
 
-fun Matrix<Double>.sum() = elements().map { (_, value) -> value }.sum()
-
-fun Matrix<Double>.min() = elements().map { (_, value) -> value }.min()
-
-fun Matrix<Double>.max() = elements().map { (_, value) -> value }.max()
-
-fun Matrix<Double>.average() = elements().map { (_, value) -> value }.average()
+fun Matrix<Double>.sum(): Double = elements().map { (_, value) -> value }.sum()
+fun Matrix<Double>.min(): Double? = elements().map { (_, value) -> value }.min()
+fun Matrix<Double>.max(): Double? = elements().map { (_, value) -> value }.max()
+fun Matrix<Double>.average(): Double = elements().map { (_, value) -> value }.average()

kmath-for-real/src/commonTest/kotlin/scientifik/kmath/linear/VectorTest.kt

@@ -1,5 +1,6 @@
 package scientifik.kmath.linear
 
+import scientifik.kmath.operations.invoke
 import scientifik.kmath.real.RealVector
 import kotlin.test.Test
 import kotlin.test.assertEquals
@@ -24,14 +25,10 @@ class VectorTest {
     fun testDot() {
         val vector1 = RealVector(5) { it.toDouble() }
         val vector2 = RealVector(5) { 5 - it.toDouble() }
-
         val matrix1 = vector1.asMatrix()
         val matrix2 = vector2.asMatrix().transpose()
-        val product = MatrixContext.real.run { matrix1 dot matrix2 }
-
-
+        val product = MatrixContext.real { matrix1 dot matrix2 }
         assertEquals(5.0, product[1, 0])
         assertEquals(6.0, product[2, 2])
     }
-
 }

kmath-functions/build.gradle.kts

@@ -1,11 +1,6 @@
-plugins {
-    id("scientifik.mpp")
-}
+plugins { id("scientifik.mpp") }
 
 kotlin.sourceSets {
-    commonMain {
-        dependencies {
-            api(project(":kmath-core"))
-        }
-    }
+    all { languageSettings.useExperimentalAnnotation("kotlin.contracts.ExperimentalContracts") }
+    commonMain { dependencies { api(project(":kmath-core")) } }
 }

kmath-functions/src/commonMain/kotlin/scientifik/kmath/functions/Polynomial.kt

@@ -2,6 +2,10 @@ package scientifik.kmath.functions
 
 import scientifik.kmath.operations.Ring
 import scientifik.kmath.operations.Space
+import scientifik.kmath.operations.invoke
+import kotlin.contracts.ExperimentalContracts
+import kotlin.contracts.InvocationKind
+import kotlin.contracts.contract
 import kotlin.math.max
 import kotlin.math.pow
 
@@ -13,20 +17,21 @@ inline class Polynomial<T : Any>(val coefficients: List<T>) {
     constructor(vararg coefficients: T) : this(coefficients.toList())
 }
 
-fun Polynomial<Double>.value() =
+fun Polynomial<Double>.value(): Double =
     coefficients.reduceIndexed { index: Int, acc: Double, d: Double -> acc + d.pow(index) }
 
-
-fun <T : Any, C : Ring<T>> Polynomial<T>.value(ring: C, arg: T): T = ring.run {
-    if (coefficients.isEmpty()) return@run zero
+fun <T : Any, C : Ring<T>> Polynomial<T>.value(ring: C, arg: T): T = ring {
+    if (coefficients.isEmpty()) return@ring zero
     var res = coefficients.first()
     var powerArg = arg
+
     for (index in 1 until coefficients.size) {
         res += coefficients[index] * powerArg
         //recalculating power on each step to avoid power costs on long polynomials
         powerArg *= arg
     }
-    return@run res
+
+    res
 }
 
 /**
@@ -34,7 +39,7 @@ fun <T : Any, C : Ring<T>> Polynomial<T>.value(ring: C, arg: T): T = ring.run {
  */
 fun <T : Any, C : Ring<T>> Polynomial<T>.asMathFunction(): MathFunction<T, out C, T> = object :
     MathFunction<T, C, T> {
-    override fun C.invoke(arg: T): T = value(this, arg)
+    override operator fun C.invoke(arg: T): T = value(this, arg)
 }
 
 /**
@@ -49,18 +54,16 @@ class PolynomialSpace<T : Any, C : Ring<T>>(val ring: C) : Space<Polynomial<T>>
 
     override fun add(a: Polynomial<T>, b: Polynomial<T>): Polynomial<T> {
         val dim = max(a.coefficients.size, b.coefficients.size)
-        ring.run {
-            return Polynomial(List(dim) { index ->
+
+        return ring {
+            Polynomial(List(dim) { index ->
                 a.coefficients.getOrElse(index) { zero } + b.coefficients.getOrElse(index) { zero }
             })
         }
     }
 
-    override fun multiply(a: Polynomial<T>, k: Number): Polynomial<T> {
-        ring.run {
-            return Polynomial(List(a.coefficients.size) { index -> a.coefficients[index] * k })
-        }
-    }
+    override fun multiply(a: Polynomial<T>, k: Number): Polynomial<T> =
+        ring { Polynomial(List(a.coefficients.size) { index -> a.coefficients[index] * k }) }
 
     override val zero: Polynomial<T> =
         Polynomial(emptyList())
@@ -68,6 +71,7 @@ class PolynomialSpace<T : Any, C : Ring<T>>(val ring: C) : Space<Polynomial<T>>
     operator fun Polynomial<T>.invoke(arg: T): T = value(ring, arg)
 }
 
-fun <T : Any, C : Ring<T>, R> C.polynomial(block: PolynomialSpace<T, C>.() -> R): R {
-    return PolynomialSpace(this).run(block)
+inline fun <T : Any, C : Ring<T>, R> C.polynomial(block: PolynomialSpace<T, C>.() -> R): R {
+    contract { callsInPlace(block, InvocationKind.EXACTLY_ONCE) }
+    return PolynomialSpace(this).block()
 }

kmath-functions/src/commonMain/kotlin/scientifik/kmath/interpolation/LinearInterpolator.kt

@@ -4,13 +4,13 @@ import scientifik.kmath.functions.OrderedPiecewisePolynomial
 import scientifik.kmath.functions.PiecewisePolynomial
 import scientifik.kmath.functions.Polynomial
 import scientifik.kmath.operations.Field
+import scientifik.kmath.operations.invoke
 
 /**
  * Reference JVM implementation: https://github.com/apache/commons-math/blob/master/src/main/java/org/apache/commons/math4/analysis/interpolation/LinearInterpolator.java
  */
 class LinearInterpolator<T : Comparable<T>>(override val algebra: Field<T>) : PolynomialInterpolator<T> {
-
-    override fun interpolatePolynomials(points: XYPointSet<T, T>): PiecewisePolynomial<T> = algebra.run {
+    override fun interpolatePolynomials(points: XYPointSet<T, T>): PiecewisePolynomial<T> = algebra {
         require(points.size > 0) { "Point array should not be empty" }
         insureSorted(points)
 

kmath-functions/src/commonMain/kotlin/scientifik/kmath/interpolation/SplineInterpolator.kt

@@ -4,6 +4,7 @@ import scientifik.kmath.functions.OrderedPiecewisePolynomial
 import scientifik.kmath.functions.PiecewisePolynomial
 import scientifik.kmath.functions.Polynomial
 import scientifik.kmath.operations.Field
+import scientifik.kmath.operations.invoke
 import scientifik.kmath.structures.MutableBufferFactory
 
 /**
@@ -17,7 +18,7 @@ class SplineInterpolator<T : Comparable<T>>(
 
     //TODO possibly optimize zeroed buffers
 
-    override fun interpolatePolynomials(points: XYPointSet<T, T>): PiecewisePolynomial<T> = algebra.run {
+    override fun interpolatePolynomials(points: XYPointSet<T, T>): PiecewisePolynomial<T> = algebra {
         if (points.size < 3) {
             error("Can't use spline interpolator with less than 3 points")
         }

kmath-functions/src/commonMain/kotlin/scientifik/kmath/interpolation/XYPointSet.kt

@@ -14,9 +14,7 @@ interface XYZPointSet<X, Y, Z> : XYPointSet<X, Y> {
 }
 
 internal fun <T : Comparable<T>> insureSorted(points: XYPointSet<T, *>) {
-    for (i in 0 until points.size - 1) {
-        if (points.x[i + 1] <= points.x[i]) error("Input data is not sorted at index $i")
-    }
+    for (i in 0 until points.size - 1) require(points.x[i + 1] > points.x[i]) { "Input data is not sorted at index $i" }
 }
 
 class NDStructureColumn<T>(val structure: Structure2D<T>, val column: Int) : Buffer<T> {
@@ -26,9 +24,9 @@ class NDStructureColumn<T>(val structure: Structure2D<T>, val column: Int) : Buf
 
     override val size: Int get() = structure.rowNum
 
-    override fun get(index: Int): T = structure[index, column]
+    override operator fun get(index: Int): T = structure[index, column]
 
-    override fun iterator(): Iterator<T> = sequence {
+    override operator fun iterator(): Iterator<T> = sequence {
         repeat(size) {
             yield(get(it))
         }

kmath-geometry/src/commonMain/kotlin/scientifik/kmath/geometry/Euclidean2DSpace.kt

@@ -9,25 +9,21 @@ import kotlin.math.sqrt
 interface Vector2D : Point<Double>, Vector, SpaceElement<Vector2D, Vector2D, Euclidean2DSpace> {
     val x: Double
     val y: Double
-
+    override val context: Euclidean2DSpace get() = Euclidean2DSpace
     override val size: Int get() = 2
 
-    override fun get(index: Int): Double = when (index) {
+    override operator fun get(index: Int): Double = when (index) {
         1 -> x
         2 -> y
         else -> error("Accessing outside of point bounds")
     }
 
-    override fun iterator(): Iterator<Double> = listOf(x, y).iterator()
-
-    override val context: Euclidean2DSpace get() = Euclidean2DSpace
-
+    override operator fun iterator(): Iterator<Double> = listOf(x, y).iterator()
     override fun unwrap(): Vector2D = this
-
     override fun Vector2D.wrap(): Vector2D = this
 }
 
-val Vector2D.r: Double get() = Euclidean2DSpace.run { sqrt(norm()) }
+val Vector2D.r: Double get() = Euclidean2DSpace { sqrt(norm()) }
 
 @Suppress("FunctionName")
 fun Vector2D(x: Double, y: Double): Vector2D = Vector2DImpl(x, y)

kmath-geometry/src/commonMain/kotlin/scientifik/kmath/geometry/Euclidean3DSpace.kt

@@ -2,6 +2,7 @@ package scientifik.kmath.geometry
 
 import scientifik.kmath.linear.Point
 import scientifik.kmath.operations.SpaceElement
+import scientifik.kmath.operations.invoke
 import kotlin.math.sqrt
 
 
@@ -9,19 +10,17 @@ interface Vector3D : Point<Double>, Vector, SpaceElement<Vector3D, Vector3D, Euc
     val x: Double
     val y: Double
     val z: Double
-
+    override val context: Euclidean3DSpace get() = Euclidean3DSpace
     override val size: Int get() = 3
 
-    override fun get(index: Int): Double = when (index) {
+    override operator fun get(index: Int): Double = when (index) {
         1 -> x
         2 -> y
         3 -> z
         else -> error("Accessing outside of point bounds")
     }
 
-    override fun iterator(): Iterator<Double> = listOf(x, y, z).iterator()
-
-    override val context: Euclidean3DSpace get() = Euclidean3DSpace
+    override operator fun iterator(): Iterator<Double> = listOf(x, y, z).iterator()
 
     override fun unwrap(): Vector3D = this
 
@@ -31,7 +30,7 @@ interface Vector3D : Point<Double>, Vector, SpaceElement<Vector3D, Vector3D, Euc
 @Suppress("FunctionName")
 fun Vector3D(x: Double, y: Double, z: Double): Vector3D = Vector3DImpl(x, y, z)
 
-val Vector3D.r: Double get() = Euclidean3DSpace.run { sqrt(norm()) }
+val Vector3D.r: Double get() = Euclidean3DSpace { sqrt(norm()) }
 
 private data class Vector3DImpl(
     override val x: Double,

kmath-histograms/src/commonMain/kotlin/scientifik/kmath/histogram/Histogram.kt

@@ -4,6 +4,9 @@ import scientifik.kmath.domains.Domain
 import scientifik.kmath.linear.Point
 import scientifik.kmath.structures.ArrayBuffer
 import scientifik.kmath.structures.RealBuffer
+import kotlin.contracts.ExperimentalContracts
+import kotlin.contracts.InvocationKind
+import kotlin.contracts.contract
 
 /**
  * The bin in the histogram. The histogram is by definition always done in the real space
@@ -37,20 +40,20 @@ interface MutableHistogram<T : Any, out B : Bin<T>> : Histogram<T, B> {
      */
     fun putWithWeight(point: Point<out T>, weight: Double)
 
-    fun put(point: Point<out T>) = putWithWeight(point, 1.0)
+    fun put(point: Point<out T>): Unit = putWithWeight(point, 1.0)
 }
 
-fun <T : Any> MutableHistogram<T, *>.put(vararg point: T) = put(ArrayBuffer(point))
+fun <T : Any> MutableHistogram<T, *>.put(vararg point: T): Unit = put(ArrayBuffer(point))
 
-fun MutableHistogram<Double, *>.put(vararg point: Number) =
+fun MutableHistogram<Double, *>.put(vararg point: Number): Unit =
     put(RealBuffer(point.map { it.toDouble() }.toDoubleArray()))
 
-fun MutableHistogram<Double, *>.put(vararg point: Double) = put(RealBuffer(point))
+fun MutableHistogram<Double, *>.put(vararg point: Double): Unit = put(RealBuffer(point))
 
-fun <T : Any> MutableHistogram<T, *>.fill(sequence: Iterable<Point<T>>) = sequence.forEach { put(it) }
+fun <T : Any> MutableHistogram<T, *>.fill(sequence: Iterable<Point<T>>): Unit = sequence.forEach { put(it) }
 
 /**
  * Pass a sequence builder into histogram
  */
-fun <T : Any> MutableHistogram<T, *>.fill(buider: suspend SequenceScope<Point<T>>.() -> Unit) =
-    fill(sequence(buider).asIterable())
+fun <T : Any> MutableHistogram<T, *>.fill(block: suspend SequenceScope<Point<T>>.() -> Unit): Unit =
+    fill(sequence(block).asIterable())

kmath-histograms/src/commonMain/kotlin/scientifik/kmath/histogram/RealHistogram.kt

@@ -2,6 +2,7 @@ package scientifik.kmath.histogram
 
 import scientifik.kmath.linear.Point
 import scientifik.kmath.operations.SpaceOperations
+import scientifik.kmath.operations.invoke
 import scientifik.kmath.real.asVector
 import scientifik.kmath.structures.*
 import kotlin.math.floor
@@ -9,19 +10,16 @@ import kotlin.math.floor
 
 data class BinDef<T : Comparable<T>>(val space: SpaceOperations<Point<T>>, val center: Point<T>, val sizes: Point<T>) {
     fun contains(vector: Point<out T>): Boolean {
-        if (vector.size != center.size) error("Dimension mismatch for input vector. Expected ${center.size}, but found ${vector.size}")
-        val upper = space.run { center + sizes / 2.0 }
-        val lower = space.run { center - sizes / 2.0 }
-        return vector.asSequence().mapIndexed { i, value ->
-            value in lower[i]..upper[i]
-        }.all { it }
+        require(vector.size == center.size) { "Dimension mismatch for input vector. Expected ${center.size}, but found ${vector.size}" }
+        val upper = space { center + sizes / 2.0 }
+        val lower = space { center - sizes / 2.0 }
+        return vector.asSequence().mapIndexed { i, value -> value in lower[i]..upper[i] }.all { it }
     }
 }
 
 
 class MultivariateBin<T : Comparable<T>>(val def: BinDef<T>, override val value: Number) : Bin<T> {
-
-    override fun contains(point: Point<T>): Boolean = def.contains(point)
+    override operator fun contains(point: Point<T>): Boolean = def.contains(point)
 
     override val dimension: Int
         get() = def.center.size
@@ -39,47 +37,34 @@ class RealHistogram(
     private val upper: Buffer<Double>,
     private val binNums: IntArray = IntArray(lower.size) { 20 }
 ) : MutableHistogram<Double, MultivariateBin<Double>> {
-
-
     private val strides = DefaultStrides(IntArray(binNums.size) { binNums[it] + 2 })
-
     private val values: NDStructure<LongCounter> = NDStructure.auto(strides) { LongCounter() }
-
     private val weights: NDStructure<DoubleCounter> = NDStructure.auto(strides) { DoubleCounter() }
-
     override val dimension: Int get() = lower.size
-
-
     private val binSize = RealBuffer(dimension) { (upper[it] - lower[it]) / binNums[it] }
 
     init {
         // argument checks
-        if (lower.size != upper.size) error("Dimension mismatch in histogram lower and upper limits.")
-        if (lower.size != binNums.size) error("Dimension mismatch in bin count.")
-        if ((0 until dimension).any { upper[it] - lower[it] < 0 }) error("Range for one of axis is not strictly positive")
+        require(lower.size == upper.size) { "Dimension mismatch in histogram lower and upper limits." }
+        require(lower.size == binNums.size) { "Dimension mismatch in bin count." }
+        require(!(0 until dimension).any { upper[it] - lower[it] < 0 }) { "Range for one of axis is not strictly positive" }
     }
 
 
     /**
      * Get internal [NDStructure] bin index for given axis
      */
-    private fun getIndex(axis: Int, value: Double): Int {
-        return when {
-            value >= upper[axis] -> binNums[axis] + 1 // overflow
-            value < lower[axis] -> 0 // underflow
-            else -> floor((value - lower[axis]) / binSize[axis]).toInt() + 1
-        }
+    private fun getIndex(axis: Int, value: Double): Int = when {
+        value >= upper[axis] -> binNums[axis] + 1 // overflow
+        value < lower[axis] -> 0 // underflow
+        else -> floor((value - lower[axis]) / binSize[axis]).toInt() + 1
     }
 
     private fun getIndex(point: Buffer<out Double>): IntArray = IntArray(dimension) { getIndex(it, point[it]) }
 
-    private fun getValue(index: IntArray): Long {
-        return values[index].sum()
-    }
+    private fun getValue(index: IntArray): Long = values[index].sum()
 
-    fun getValue(point: Buffer<out Double>): Long {
-        return getValue(getIndex(point))
-    }
+    fun getValue(point: Buffer<out Double>): Long = getValue(getIndex(point))
 
     private fun getDef(index: IntArray): BinDef<Double> {
         val center = index.mapIndexed { axis, i ->
@@ -89,14 +74,13 @@ class RealHistogram(
                 else -> lower[axis] + (i.toDouble() - 0.5) * binSize[axis]
             }
         }.asBuffer()
+
         return BinDef(RealBufferFieldOperations, center, binSize)
     }
 
-    fun getDef(point: Buffer<out Double>): BinDef<Double> {
-        return getDef(getIndex(point))
-    }
+    fun getDef(point: Buffer<out Double>): BinDef<Double> = getDef(getIndex(point))
 
-    override fun get(point: Buffer<out Double>): MultivariateBin<Double>? {
+    override operator fun get(point: Buffer<out Double>): MultivariateBin<Double>? {
         val index = getIndex(point)
         return MultivariateBin(getDef(index), getValue(index))
     }
@@ -112,26 +96,21 @@ class RealHistogram(
         weights[index].add(weight)
     }
 
-    override fun iterator(): Iterator<MultivariateBin<Double>> = weights.elements().map { (index, value) ->
+    override operator fun iterator(): Iterator<MultivariateBin<Double>> = weights.elements().map { (index, value) ->
         MultivariateBin(getDef(index), value.sum())
     }.iterator()
 
     /**
      * Convert this histogram into NDStructure containing bin values but not bin descriptions
      */
-    fun values(): NDStructure<Number> {
-        return NDStructure.auto(values.shape) { values[it].sum() }
-    }
+    fun values(): NDStructure<Number> = NDStructure.auto(values.shape) { values[it].sum() }
 
     /**
      * Sum of weights
      */
-    fun weights():NDStructure<Double>{
-        return NDStructure.auto(weights.shape) { weights[it].sum() }
-    }
+    fun weights(): NDStructure<Double> = NDStructure.auto(weights.shape) { weights[it].sum() }
 
     companion object {
-
         /**
          * Use it like
          * ```
@@ -141,12 +120,10 @@ class RealHistogram(
          *)
          *```
          */
-        fun fromRanges(vararg ranges: ClosedFloatingPointRange<Double>): RealHistogram {
-            return RealHistogram(
-                ranges.map { it.start }.asVector(),
-                ranges.map { it.endInclusive }.asVector()
-            )
-        }
+        fun fromRanges(vararg ranges: ClosedFloatingPointRange<Double>): RealHistogram = RealHistogram(
+            ranges.map { it.start }.asVector(),
+            ranges.map { it.endInclusive }.asVector()
+        )
 
         /**
          * Use it like
@@ -157,13 +134,10 @@ class RealHistogram(
          *)
          *```
          */
-        fun fromRanges(vararg ranges: Pair<ClosedFloatingPointRange<Double>, Int>): RealHistogram {
-            return RealHistogram(
-                ListBuffer(ranges.map { it.first.start }),
-                ListBuffer(ranges.map { it.first.endInclusive }),
-                ranges.map { it.second }.toIntArray()
-            )
-        }
+        fun fromRanges(vararg ranges: Pair<ClosedFloatingPointRange<Double>, Int>): RealHistogram = RealHistogram(
+            ListBuffer(ranges.map { it.first.start }),
+            ListBuffer(ranges.map { it.first.endInclusive }),
+            ranges.map { it.second }.toIntArray()
+        )
     }
-
 }

kmath-histograms/src/jvmMain/kotlin/scientifik/kmath/histogram/UnivariateHistogram.kt

@@ -46,11 +46,11 @@ class UnivariateHistogram private constructor(private val factory: (Double) -> U
         synchronized(this) { bins.put(it.position, it) }
     }
 
-    override fun get(point: Buffer<out Double>): UnivariateBin? = get(point[0])
+    override operator fun get(point: Buffer<out Double>): UnivariateBin? = get(point[0])
 
     override val dimension: Int get() = 1
 
-    override fun iterator(): Iterator<UnivariateBin> = bins.values.iterator()
+    override operator fun iterator(): Iterator<UnivariateBin> = bins.values.iterator()
 
     /**
      * Thread safe put operation
@@ -65,15 +65,14 @@ class UnivariateHistogram private constructor(private val factory: (Double) -> U
     }
 
     companion object {
-        fun uniform(binSize: Double, start: Double = 0.0): UnivariateHistogram {
-            return UnivariateHistogram { value ->
-                val center = start + binSize * floor((value - start) / binSize + 0.5)
-                UnivariateBin(center, binSize)
-            }
+        fun uniform(binSize: Double, start: Double = 0.0): UnivariateHistogram = UnivariateHistogram { value ->
+            val center = start + binSize * floor((value - start) / binSize + 0.5)
+            UnivariateBin(center, binSize)
         }
 
         fun custom(borders: DoubleArray): UnivariateHistogram {
             val sorted = borders.sortedArray()
+
             return UnivariateHistogram { value ->
                 when {
                     value < sorted.first() -> UnivariateBin(

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

@@ -3,16 +3,16 @@ package scientifik.kmath.linear
 import koma.extensions.fill
 import koma.matrix.MatrixFactory
 import scientifik.kmath.operations.Space
+import scientifik.kmath.operations.invoke
 import scientifik.kmath.structures.Matrix
 import scientifik.kmath.structures.NDStructure
 
 class KomaMatrixContext<T : Any>(
     private val factory: MatrixFactory<koma.matrix.Matrix<T>>,
     private val space: Space<T>
-) :
-    MatrixContext<T> {
+) : MatrixContext<T> {
 
-    override fun produce(rows: Int, columns: Int, initializer: (i: Int, j: Int) -> T) =
+    override fun produce(rows: Int, columns: Int, initializer: (i: Int, j: Int) -> T): KomaMatrix<T> =
         KomaMatrix(factory.zeros(rows, columns).fill(initializer))
 
     fun Matrix<T>.toKoma(): KomaMatrix<T> = if (this is KomaMatrix) {
@@ -28,31 +28,28 @@ class KomaMatrixContext<T : Any>(
     }
 
 
-    override fun Matrix<T>.dot(other: Matrix<T>) =
-        KomaMatrix(this.toKoma().origin * other.toKoma().origin)
+    override fun Matrix<T>.dot(other: Matrix<T>): KomaMatrix<T> =
+        KomaMatrix(toKoma().origin * other.toKoma().origin)
 
-    override fun Matrix<T>.dot(vector: Point<T>) =
-        KomaVector(this.toKoma().origin * vector.toKoma().origin)
+    override fun Matrix<T>.dot(vector: Point<T>): KomaVector<T> =
+        KomaVector(toKoma().origin * vector.toKoma().origin)
 
-    override fun Matrix<T>.unaryMinus() =
-        KomaMatrix(this.toKoma().origin.unaryMinus())
+    override operator fun Matrix<T>.unaryMinus(): KomaMatrix<T> =
+        KomaMatrix(toKoma().origin.unaryMinus())
 
-    override fun add(a: Matrix<T>, b: Matrix<T>) =
+    override fun add(a: Matrix<T>, b: Matrix<T>): KomaMatrix<T> =
         KomaMatrix(a.toKoma().origin + b.toKoma().origin)
 
-    override fun Matrix<T>.minus(b: Matrix<T>) =
-        KomaMatrix(this.toKoma().origin - b.toKoma().origin)
+    override operator fun Matrix<T>.minus(b: Matrix<T>): KomaMatrix<T> =
+        KomaMatrix(toKoma().origin - b.toKoma().origin)
 
     override fun multiply(a: Matrix<T>, k: Number): Matrix<T> =
-        produce(a.rowNum, a.colNum) { i, j -> space.run { a[i, j] * k } }
+        produce(a.rowNum, a.colNum) { i, j -> space { a[i, j] * k } }
 
-    override fun Matrix<T>.times(value: T) =
-        KomaMatrix(this.toKoma().origin * value)
-
-    companion object {
-
-    }
+    override operator fun Matrix<T>.times(value: T): KomaMatrix<T> =
+        KomaMatrix(toKoma().origin * value)
 
+    companion object
 }
 
 fun <T : Any> KomaMatrixContext<T>.solve(a: Matrix<T>, b: Matrix<T>) =
@@ -70,10 +67,11 @@ class KomaMatrix<T : Any>(val origin: koma.matrix.Matrix<T>, features: Set<Matri
 
     override val shape: IntArray get() = intArrayOf(origin.numRows(), origin.numCols())
 
-    override val features: Set<MatrixFeature> = features ?: setOf(
+    override val features: Set<MatrixFeature> = features ?: hashSetOf(
         object : DeterminantFeature<T> {
             override val determinant: T get() = origin.det()
         },
+
         object : LUPDecompositionFeature<T> {
             private val lup by lazy { origin.LU() }
             override val l: FeaturedMatrix<T> get() = KomaMatrix(lup.second)
@@ -85,7 +83,7 @@ class KomaMatrix<T : Any>(val origin: koma.matrix.Matrix<T>, features: Set<Matri
     override fun suggestFeature(vararg features: MatrixFeature): FeaturedMatrix<T> =
         KomaMatrix(this.origin, this.features + features)
 
-    override fun get(i: Int, j: Int): T = origin.getGeneric(i, j)
+    override operator fun get(i: Int, j: Int): T = origin.getGeneric(i, j)
 
     override fun equals(other: Any?): Boolean {
         return NDStructure.equals(this, other as? NDStructure<*> ?: return false)
@@ -101,14 +99,12 @@ class KomaMatrix<T : Any>(val origin: koma.matrix.Matrix<T>, features: Set<Matri
 }
 
 class KomaVector<T : Any> internal constructor(val origin: koma.matrix.Matrix<T>) : Point<T> {
-    init {
-        if (origin.numCols() != 1) error("Only single column matrices are allowed")
-    }
-
     override val size: Int get() = origin.numRows()
 
-    override fun get(index: Int): T = origin.getGeneric(index)
+    init {
+        require(origin.numCols() == 1) { error("Only single column matrices are allowed") }
+    }
 
-    override fun iterator(): Iterator<T> = origin.toIterable().iterator()
+    override operator fun get(index: Int): T = origin.getGeneric(index)
+    override operator fun iterator(): Iterator<T> = origin.toIterable().iterator()
 }
-

kmath-memory/build.gradle.kts

@@ -1,3 +1,2 @@
-plugins {
-    id("scientifik.mpp")
-}
+plugins { id("scientifik.mpp") }
+kotlin.sourceSets.all { languageSettings.useExperimentalAnnotation("kotlin.contracts.ExperimentalContracts") }

kmath-memory/src/commonMain/kotlin/scientifik/memory/Memory.kt

@@ -1,5 +1,8 @@
 package scientifik.memory
 
+import kotlin.contracts.InvocationKind
+import kotlin.contracts.contract
+
 /**
  * Represents a display of certain memory structure.
  */
@@ -81,6 +84,7 @@ interface MemoryReader {
  * Uses the memory for read then releases the reader.
  */
 inline fun Memory.read(block: MemoryReader.() -> Unit) {
+    contract { callsInPlace(block, InvocationKind.EXACTLY_ONCE) }
     reader().apply(block).release()
 }
 
@@ -133,6 +137,7 @@ interface MemoryWriter {
  * Uses the memory for write then releases the writer.
  */
 inline fun Memory.write(block: MemoryWriter.() -> Unit) {
+    contract { callsInPlace(block, InvocationKind.EXACTLY_ONCE) }
     writer().apply(block).release()
 }
 

kmath-memory/src/commonMain/kotlin/scientifik/memory/MemorySpec.kt

@@ -38,11 +38,7 @@ fun <T : Any> MemoryWriter.write(spec: MemorySpec<T>, offset: Int, value: T): Un
  * Reads array of [size] objects mapped by [spec] at certain [offset].
  */
 inline fun <reified T : Any> MemoryReader.readArray(spec: MemorySpec<T>, offset: Int, size: Int): Array<T> =
-    Array(size) { i ->
-        spec.run {
-            read(offset + i * objectSize)
-        }
-    }
+    Array(size) { i -> with(spec) { read(offset + i * objectSize) } }
 
 /**
  * Writes [array] of objects mapped by [spec] at certain [offset].

kmath-memory/src/jvmMain/kotlin/scientifik/memory/ByteBufferMemory.kt

@@ -1,12 +1,17 @@
 package scientifik.memory
 
+import java.io.IOException
 import java.nio.ByteBuffer
 import java.nio.channels.FileChannel
 import java.nio.file.Files
 import java.nio.file.Path
 import java.nio.file.StandardOpenOption
+import kotlin.contracts.ExperimentalContracts
+import kotlin.contracts.InvocationKind
+import kotlin.contracts.contract
 
-private class ByteBufferMemory(
+@PublishedApi
+internal class ByteBufferMemory(
     val buffer: ByteBuffer,
     val startOffset: Int = 0,
     override val size: Int = buffer.limit()
@@ -112,7 +117,11 @@ fun ByteBuffer.asMemory(startOffset: Int = 0, size: Int = limit()): Memory =
 /**
  * Uses direct memory-mapped buffer from file to read something and close it afterwards.
  */
-fun <R> Path.readAsMemory(position: Long = 0, size: Long = Files.size(this), block: Memory.() -> R): R =
-    FileChannel.open(this, StandardOpenOption.READ).use {
-        ByteBufferMemory(it.map(FileChannel.MapMode.READ_ONLY, position, size)).block()
-    }
+@Throws(IOException::class)
+inline fun <R> Path.readAsMemory(position: Long = 0, size: Long = Files.size(this), block: Memory.() -> R): R {
+    contract { callsInPlace(block, InvocationKind.EXACTLY_ONCE) }
+
+    return FileChannel
+        .open(this, StandardOpenOption.READ)
+        .use { ByteBufferMemory(it.map(FileChannel.MapMode.READ_ONLY, position, size)).block() }
+}

kmath-prob/src/commonMain/kotlin/scientifik/kmath/prob/SamplerAlgebra.kt

@@ -5,6 +5,7 @@ import scientifik.kmath.chains.ConstantChain
 import scientifik.kmath.chains.map
 import scientifik.kmath.chains.zip
 import scientifik.kmath.operations.Space
+import scientifik.kmath.operations.invoke
 
 class BasicSampler<T : Any>(val chainBuilder: (RandomGenerator) -> Chain<T>) : Sampler<T> {
     override fun sample(generator: RandomGenerator): Chain<T> = chainBuilder(generator)
@@ -22,10 +23,10 @@ class SamplerSpace<T : Any>(val space: Space<T>) : Space<Sampler<T>> {
     override val zero: Sampler<T> = ConstantSampler(space.zero)
 
     override fun add(a: Sampler<T>, b: Sampler<T>): Sampler<T> = BasicSampler { generator ->
-        a.sample(generator).zip(b.sample(generator)) { aValue, bValue -> space.run { aValue + bValue } }
+        a.sample(generator).zip(b.sample(generator)) { aValue, bValue -> space { aValue + bValue } }
     }
 
     override fun multiply(a: Sampler<T>, k: Number): Sampler<T> = BasicSampler { generator ->
-        a.sample(generator).map { space.run { it * k.toDouble() } }
+        a.sample(generator).map { space { it * k.toDouble() } }
     }
 }

kmath-prob/src/commonMain/kotlin/scientifik/kmath/prob/Statistic.kt

@@ -29,8 +29,10 @@ interface Statistic<T, R> {
 interface ComposableStatistic<T, I, R> : Statistic<T, R> {
     //compute statistic on a single block
     suspend fun computeIntermediate(data: Buffer<T>): I
+
     //Compose two blocks
     suspend fun composeIntermediate(first: I, second: I): I
+
     //Transform block to result
     suspend fun toResult(intermediate: I): R
 
@@ -58,26 +60,26 @@ private fun <T, I, R> ComposableStatistic<T, I, R>.flowIntermediate(
 fun <T, I, R> ComposableStatistic<T, I, R>.flow(
     flow: Flow<Buffer<T>>,
     dispatcher: CoroutineDispatcher = Dispatchers.Default
-): Flow<R> = flowIntermediate(flow,dispatcher).map(::toResult)
+): Flow<R> = flowIntermediate(flow, dispatcher).map(::toResult)
 
 /**
  * Arithmetic mean
  */
 class Mean<T>(val space: Space<T>) : ComposableStatistic<T, Pair<T, Int>, T> {
     override suspend fun computeIntermediate(data: Buffer<T>): Pair<T, Int> =
-        space.run { sum(data.asIterable()) } to data.size
+        space { sum(data.asIterable()) } to data.size
 
     override suspend fun composeIntermediate(first: Pair<T, Int>, second: Pair<T, Int>): Pair<T, Int> =
-        space.run { first.first + second.first } to (first.second + second.second)
+        space { first.first + second.first } to (first.second + second.second)
 
     override suspend fun toResult(intermediate: Pair<T, Int>): T =
-        space.run { intermediate.first / intermediate.second }
+        space { intermediate.first / intermediate.second }
 
     companion object {
         //TODO replace with optimized version which respects overflow
-        val real = Mean(RealField)
-        val int = Mean(IntRing)
-        val long = Mean(LongRing)
+        val real: Mean<Double> = Mean(RealField)
+        val int: Mean<Int> = Mean(IntRing)
+        val long: Mean<Long> = Mean(LongRing)
     }
 }
 
@@ -85,11 +87,10 @@ class Mean<T>(val space: Space<T>) : ComposableStatistic<T, Pair<T, Int>, T> {
  * Non-composable median
  */
 class Median<T>(private val comparator: Comparator<T>) : Statistic<T, T> {
-    override suspend fun invoke(data: Buffer<T>): T {
-        return data.asSequence().sortedWith(comparator).toList()[data.size / 2] //TODO check if this is correct
-    }
+    override suspend fun invoke(data: Buffer<T>): T =
+        data.asSequence().sortedWith(comparator).toList()[data.size / 2] //TODO check if this is correct
 
     companion object {
-        val real = Median(Comparator { a: Double, b: Double -> a.compareTo(b) })
+        val real: Median<Double> = Median(Comparator { a: Double, b: Double -> a.compareTo(b) })
     }
 }

kmath-viktor/src/main/kotlin/scientifik/kmath/viktor/ViktorBuffer.kt

@@ -16,5 +16,5 @@ inline class ViktorBuffer(val flatArray: F64FlatArray) : MutableBuffer<Double> {
         return ViktorBuffer(flatArray.copy().flatten())
     }
 
-    override fun iterator(): Iterator<Double> = flatArray.data.iterator()
+    override operator fun iterator(): Iterator<Double> = flatArray.data.iterator()
 }