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

@@ -17,9 +17,8 @@ interface ExpressionContext<T> {
 }
 
 internal class VariableExpression<T>(val name: String, val default: T? = null) : Expression<T> {
-    override fun invoke(arguments: Map<String, T>): T {
+    override fun invoke(arguments: Map<String, T>): T =
-        return arguments[name] ?: default ?: error("The parameter not found: $name")
+            arguments[name] ?: default ?: error("Parameter not found: $name")
-    }
 }
 
 internal class ConstantExpression<T>(val value: T) : Expression<T> {

kmath-core/src/commonMain/kotlin/scientifik/kmath/histogram/Counters.kt

@@ -5,15 +5,15 @@ package scientifik.kmath.histogram
  */
 
 
-expect class LongCounter(){
+expect class LongCounter() {
     fun decrement()
     fun increment()
     fun reset()
     fun sum(): Long
-    fun add(l:Long)
+    fun add(l: Long)
 }
 
-expect class DoubleCounter(){
+expect class DoubleCounter() {
     fun reset()
     fun sum(): Double
     fun add(d: Double)

kmath-core/src/commonMain/kotlin/scientifik/kmath/histogram/FastHistogram.kt

@@ -31,7 +31,7 @@ class FastHistogram(
         // 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 ((upper - lower).asSequence().any { it <= 0 }) error("Range for one of axis is not strictly positive")
+        if ((upper - lower).asSequence().any { it <= 0 }) error("Range for one axis is not strictly positive")
     }
 
 
@@ -41,23 +41,19 @@ class FastHistogram(
     /**
      * Get internal [NDStructure] bin index for given axis
      */
-    private fun getIndex(axis: Int, value: Double): Int {
+    private fun getIndex(axis: Int, value: Double): Int =
-        return when {
+            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 getIndex(point: Buffer<out Double>): IntArray =
+            IntArray(dimension) { getIndex(it, point[it]) }
 
-    private fun getValue(index: IntArray): Long {
+    private fun getValue(index: IntArray): Long = values[index].sum()
-        return values[index].sum()
-    }
 
-    fun getValue(point: Buffer<out Double>): Long {
+    fun getValue(point: Buffer<out Double>): Long = getValue(getIndex(point))
-        return getValue(getIndex(point))
-    }
 
     private fun getTemplate(index: IntArray): BinTemplate<Double> {
         val center = index.mapIndexed { axis, i ->
@@ -70,9 +66,7 @@ class FastHistogram(
         return BinTemplate(center, binSize)
     }
 
-    fun getTemplate(point: Buffer<out Double>): BinTemplate<Double> {
+    fun getTemplate(point: Buffer<out Double>): BinTemplate<Double> = getTemplate(getIndex(point))
-        return getTemplate(getIndex(point))
-    }
 
     override fun get(point: Buffer<out Double>): PhantomBin<Double>? {
         val index = getIndex(point)
@@ -85,16 +79,16 @@ class FastHistogram(
         values[index].increment()
     }
 
-    override fun iterator(): Iterator<PhantomBin<Double>> = values.elements().map { (index, value) ->
+    override fun iterator(): Iterator<PhantomBin<Double>> =
+            values.elements().map { (index, value) ->
                 PhantomBin(getTemplate(index), value.sum())
             }.iterator()
 
     /**
      * Convert this histogram into NDStructure containing bin values but not bin descriptions
      */
-    fun asNDStructure(): NDStructure<Number> {
+    fun asNDStructure(): NDStructure<Number> =
-        return inlineNdStructure(this.values.shape) { values[it].sum() }
+            inlineNdStructure(this.values.shape) { values[it].sum() }
-    }
 
     /**
      * Create a phantom lightweight immutable copy of this histogram
@@ -115,9 +109,8 @@ class FastHistogram(
          *)
          *```
          */
-        fun fromRanges(vararg ranges: ClosedFloatingPointRange<Double>): FastHistogram {
+        fun fromRanges(vararg ranges: ClosedFloatingPointRange<Double>): FastHistogram =
-            return FastHistogram(ranges.map { it.start }.toVector(), ranges.map { it.endInclusive }.toVector())
+                FastHistogram(ranges.map { it.start }.toVector(), ranges.map { it.endInclusive }.toVector())
-        }
 
         /**
          * Use it like
@@ -128,13 +121,12 @@ class FastHistogram(
          *)
          *```
          */
-        fun fromRanges(vararg ranges: Pair<ClosedFloatingPointRange<Double>, Int>): FastHistogram {
+        fun fromRanges(vararg ranges: Pair<ClosedFloatingPointRange<Double>, Int>): FastHistogram =
-            return FastHistogram(
+                FastHistogram(
                         ListBuffer(ranges.map { it.first.start }),
                         ListBuffer(ranges.map { it.first.endInclusive }),
                         ranges.map { it.second }.toIntArray()
                 )
     }
-    }
 
 }

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

@@ -12,7 +12,7 @@ typealias RealPoint = Buffer<Double>
  * A simple geometric domain
  * TODO move to geometry module
  */
-interface Domain<T: Any> {
+interface Domain<T : Any> {
     operator fun contains(vector: Point<out T>): Boolean
     val dimension: Int
 }
@@ -20,7 +20,7 @@ interface Domain<T: Any> {
 /**
  * The bin in the histogram. The histogram is by definition always done in the real space
  */
-interface Bin<T: Any> : Domain<T> {
+interface Bin<T : Any> : Domain<T> {
     /**
      * The value of this bin
      */
@@ -28,7 +28,7 @@ interface Bin<T: Any> : Domain<T> {
     val center: Point<T>
 }
 
-interface Histogram<T: Any, out B : Bin<T>> : Iterable<B> {
+interface Histogram<T : Any, out B : Bin<T>> : Iterable<B> {
 
     /**
      * Find existing bin, corresponding to given coordinates
@@ -42,7 +42,7 @@ interface Histogram<T: Any, out B : Bin<T>> : Iterable<B> {
 
 }
 
-interface MutableHistogram<T: Any, out B : Bin<T>>: Histogram<T,B>{
+interface MutableHistogram<T : Any, out B : Bin<T>> : Histogram<T, B> {
 
     /**
      * Increment appropriate bin
@@ -50,14 +50,14 @@ interface MutableHistogram<T: Any, out B : Bin<T>>: Histogram<T,B>{
     fun put(point: Point<out T>, weight: Double = 1.0)
 }
 
-fun <T: Any> MutableHistogram<T,*>.put(vararg point: T) = put(ArrayBuffer(point))
+fun <T : Any> MutableHistogram<T, *>.put(vararg point: T) = put(ArrayBuffer(point))
 
-fun MutableHistogram<Double,*>.put(vararg point: Number) = put(DoubleBuffer(point.map { it.toDouble() }.toDoubleArray()))
+fun MutableHistogram<Double, *>.put(vararg point: Number) = put(DoubleBuffer(point.map { it.toDouble() }.toDoubleArray()))
-fun MutableHistogram<Double,*>.put(vararg point: Double) = put(DoubleBuffer(point))
+fun MutableHistogram<Double, *>.put(vararg point: Double) = put(DoubleBuffer(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>>) = 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(buider: suspend SequenceScope<Point<T>>.() -> Unit) = fill(sequence(buider).asIterable())

kmath-core/src/commonMain/kotlin/scientifik/kmath/histogram/PhantomHistogram.kt

@@ -8,8 +8,8 @@ import scientifik.kmath.structures.asSequence
 data class BinTemplate<T : Comparable<T>>(val center: Vector<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 = center.context.run { center + sizes / 2.0}
+        val upper = center.context.run { center + sizes / 2.0 }
-        val lower =  center.context.run {center - sizes / 2.0}
+        val lower = center.context.run { center - sizes / 2.0 }
         return vector.asSequence().mapIndexed { i, value ->
             value in lower[i]..upper[i]
         }.all { it }
@@ -51,9 +51,8 @@ class PhantomHistogram<T : Comparable<T>>(
     override val dimension: Int
         get() = data.dimension
 
-    override fun iterator(): Iterator<PhantomBin<T>> {
+    override fun iterator(): Iterator<PhantomBin<T>> =
-        return bins.asSequence().map { entry -> PhantomBin(entry.key, data[entry.value]) }.iterator()
+            bins.asSequence().map { entry -> PhantomBin(entry.key, data[entry.value]) }.iterator()
-    }
 
     override fun get(point: Point<out T>): PhantomBin<T>? {
         val template = bins.keys.find { it.contains(point) }

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

@@ -86,7 +86,7 @@ abstract class LUDecomposition<T : Comparable<T>, F : Field<T>>(val matrix: Matr
         }
 
     /**
-     * In-place transformation for [MutableNDArray], using given transformation for each element
+     * In-place transformation for [MutableNDStructure], using given transformation for each element
      */
     operator fun <T> MutableNDStructure<T>.set(i: Int, j: Int, value: T) {
         this[intArrayOf(i, j)] = value
@@ -174,9 +174,7 @@ abstract class LUDecomposition<T : Comparable<T>, F : Field<T>>(val matrix: Matr
      * @return the pivot permutation vector
      * @see .getP
      */
-    fun getPivot(): IntArray {
+    fun getPivot(): IntArray = pivot.copyOf()
-        return pivot.copyOf()
-    }
 
 }
 

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

@@ -28,8 +28,8 @@ fun List<Double>.toVector() = Vector.real(this.size) { this[it] }
 /**
  * Convert matrix to vector if it is possible
  */
-fun <T : Any, F : Ring<T>> Matrix<T, F>.toVector(): Vector<T, F> {
+fun <T : Any, F : Ring<T>> Matrix<T, F>.toVector(): Vector<T, F> =
-    return if (this.numCols == 1) {
+        if (this.numCols == 1) {
 //            if (this is ArrayMatrix) {
 //                //Reuse existing underlying array
 //                ArrayVector(ArrayVectorSpace(rows, context.field, context.ndFactory), array)
@@ -39,7 +39,6 @@ fun <T : Any, F : Ring<T>> Matrix<T, F>.toVector(): Vector<T, F> {
 //            }
             Vector.generic(numRows, context.ring) { get(it, 0) }
         } else error("Can't convert matrix with more than one column to vector")
-}
 
 fun <T : Any, R : Ring<T>> Vector<T, R>.toMatrix(): Matrix<T, R> {
 //    val context = StructureMatrixContext(size, 1, context.space)
@@ -56,9 +55,8 @@ fun <T : Any, R : Ring<T>> Vector<T, R>.toMatrix(): Matrix<T, R> {
 }
 
 object VectorL2Norm : Norm<Vector<out Number, *>, Double> {
-    override fun norm(arg: Vector<out Number, *>): Double {
+    override fun norm(arg: Vector<out Number, *>): Double =
-        return kotlin.math.sqrt(arg.asSequence().sumByDouble { it.toDouble() })
+            kotlin.math.sqrt(arg.asSequence().sumByDouble { it.toDouble() })
-    }
 }
 
 typealias RealVector = Vector<Double, DoubleField>

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

@@ -33,9 +33,11 @@ interface MatrixSpace<T : Any, R : Ring<T>> : Space<Matrix<T, R>> {
 
     val one get() = produce { i, j -> if (i == j) ring.one else ring.zero }
 
-    override fun add(a: Matrix<T, R>, b: Matrix<T, R>): Matrix<T, R> = produce(rowNum, colNum) { i, j -> ring.run { a[i, j] + b[i, j] } }
+    override fun add(a: Matrix<T, R>, b: Matrix<T, R>): Matrix<T, R> =
+            produce(rowNum, colNum) { i, j -> ring.run { a[i, j] + b[i, j] } }
 
-    override fun multiply(a: Matrix<T, R>, k: Double): Matrix<T, R> = produce(rowNum, colNum) { i, j -> ring.run { a[i, j] * k } }
+    override fun multiply(a: Matrix<T, R>, k: Double): Matrix<T, R> =
+            produce(rowNum, colNum) { i, j -> ring.run { a[i, j] * k } }
 
     companion object {
         /**
@@ -120,8 +122,8 @@ data class StructureMatrixSpace<T : Any, R : Ring<T>>(
 
     private val strides = DefaultStrides(shape)
 
-    override fun produce(rows: Int, columns: Int, initializer: (i: Int, j: Int) -> T): Matrix<T, R> {
+    override fun produce(rows: Int, columns: Int, initializer: (i: Int, j: Int) -> T): Matrix<T, R> =
-        return if (rows == rowNum && columns == colNum) {
+            if (rows == rowNum && columns == colNum) {
                 val structure = NdStructure(strides, bufferFactory) { initializer(it[0], it[1]) }
                 StructureMatrix(this, structure)
             } else {
@@ -129,12 +131,15 @@ data class StructureMatrixSpace<T : Any, R : Ring<T>>(
                 val structure = NdStructure(context.strides, bufferFactory) { initializer(it[0], it[1]) }
                 StructureMatrix(context, structure)
             }
-    }
 
     override fun point(size: Int, initializer: (Int) -> T): Point<T> = bufferFactory(size, initializer)
 }
 
-data class StructureMatrix<T : Any, R : Ring<T>>(override val context: StructureMatrixSpace<T, R>, val structure: NDStructure<T>) : Matrix<T, R> {
+data class StructureMatrix<T : Any, R : Ring<T>>(
+        override val context: StructureMatrixSpace<T, R>,
+        val structure: NDStructure<T>
+) : Matrix<T, R> {
+
     init {
         if (structure.shape.size != 2 || structure.shape[0] != context.rowNum || structure.shape[1] != context.colNum) {
             error("Dimension mismatch for structure, (${context.rowNum}, ${context.colNum}) expected, but ${structure.shape} found")

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

@@ -33,9 +33,8 @@ interface VectorSpace<T : Any, S : Space<T>> : Space<Point<T>> {
         /**
          * Non-boxing double vector space
          */
-        fun real(size: Int): BufferVectorSpace<Double, DoubleField> {
+        fun real(size: Int): BufferVectorSpace<Double, DoubleField> =
-            return realSpaceCache.getOrPut(size) { BufferVectorSpace(size, DoubleField, DoubleBufferFactory) }
+                realSpaceCache.getOrPut(size) { BufferVectorSpace(size, DoubleField, DoubleBufferFactory) }
-        }
 
         /**
          * A structured vector space with custom buffer
@@ -69,16 +68,12 @@ interface Vector<T : Any, S : Space<T>> : SpaceElement<Point<T>, VectorSpace<T,
                 VectorSpace.buffered(size, field).produce(initializer)
 
         fun real(size: Int, initializer: (Int) -> Double) = VectorSpace.real(size).produce(initializer)
-        fun ofReal(vararg elements: Double) = VectorSpace.real(elements.size).produce{elements[it]}
+        fun ofReal(vararg elements: Double) = VectorSpace.real(elements.size).produce { elements[it] }
 
     }
 }
 
-data class BufferVectorSpace<T : Any, S : Space<T>>(
+data class BufferVectorSpace<T : Any, S : Space<T>>(override val size: Int, override val space: S, val bufferFactory: BufferFactory<T>) : VectorSpace<T, S> {
-        override val size: Int,
-        override val space: S,
-        val bufferFactory: BufferFactory<T>
-) : VectorSpace<T, S> {
     override fun produce(initializer: (Int) -> T): Vector<T, S> = BufferVector(this, bufferFactory(size, initializer))
 }
 
@@ -91,9 +86,7 @@ data class BufferVector<T : Any, S : Space<T>>(override val context: VectorSpace
         }
     }
 
-    override fun get(index: Int): T {
+    override fun get(index: Int): T = buffer[index]
-        return buffer[index]
-    }
 
     override val self: BufferVector<T, S> get() = this
 

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

@@ -32,28 +32,28 @@ fun <T, R> List<T>.cumulative(initial: R, operation: (T, R) -> R): List<R> = thi
 //Cumulative sum
 
 @JvmName("cumulativeSumOfDouble")
-fun Iterable<Double>.cumulativeSum() = this.cumulative(0.0){ element, sum -> sum + element}
+fun Iterable<Double>.cumulativeSum() = this.cumulative(0.0) { element, sum -> sum + element }
 
 @JvmName("cumulativeSumOfInt")
-fun Iterable<Int>.cumulativeSum() = this.cumulative(0){ element, sum -> sum + element}
+fun Iterable<Int>.cumulativeSum() = this.cumulative(0) { element, sum -> sum + element }
 
 @JvmName("cumulativeSumOfLong")
-fun Iterable<Long>.cumulativeSum() = this.cumulative(0L){ element, sum -> sum + element}
+fun Iterable<Long>.cumulativeSum() = this.cumulative(0L) { element, sum -> sum + element }
 
 @JvmName("cumulativeSumOfDouble")
-fun Sequence<Double>.cumulativeSum() = this.cumulative(0.0){ element, sum -> sum + element}
+fun Sequence<Double>.cumulativeSum() = this.cumulative(0.0) { element, sum -> sum + element }
 
 @JvmName("cumulativeSumOfInt")
-fun Sequence<Int>.cumulativeSum() = this.cumulative(0){ element, sum -> sum + element}
+fun Sequence<Int>.cumulativeSum() = this.cumulative(0) { element, sum -> sum + element }
 
 @JvmName("cumulativeSumOfLong")
-fun Sequence<Long>.cumulativeSum() = this.cumulative(0L){ element, sum -> sum + element}
+fun Sequence<Long>.cumulativeSum() = this.cumulative(0L) { element, sum -> sum + element }
 
 @JvmName("cumulativeSumOfDouble")
-fun List<Double>.cumulativeSum() = this.cumulative(0.0){ element, sum -> sum + element}
+fun List<Double>.cumulativeSum() = this.cumulative(0.0) { element, sum -> sum + element }
 
 @JvmName("cumulativeSumOfInt")
-fun List<Int>.cumulativeSum() = this.cumulative(0){ element, sum -> sum + element}
+fun List<Int>.cumulativeSum() = this.cumulative(0) { element, sum -> sum + element }
 
 @JvmName("cumulativeSumOfLong")
-fun List<Long>.cumulativeSum() = this.cumulative(0L){ element, sum -> sum + element}
+fun List<Long>.cumulativeSum() = this.cumulative(0L) { element, sum -> sum + element }

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

@@ -8,8 +8,8 @@ package scientifik.kmath.misc
  *
  * If step is negative, the same goes from upper boundary downwards
  */
-fun ClosedFloatingPointRange<Double>.toSequence(step: Double): Sequence<Double> {
+fun ClosedFloatingPointRange<Double>.toSequence(step: Double): Sequence<Double> =
-    return when {
+        when {
             step == 0.0 -> error("Zero step in double progression")
             step > 0 -> sequence {
                 var current = start
@@ -26,12 +26,11 @@ fun ClosedFloatingPointRange<Double>.toSequence(step: Double): Sequence<Double>
                 }
             }
         }
-}
 
 /**
  * Convert double range to array of evenly spaced doubles, where the size of array equals [numPoints]
  */
 fun ClosedFloatingPointRange<Double>.toGrid(numPoints: Int): DoubleArray {
-    if (numPoints < 2) error("Can't generic grid with less than two points")
+    if (numPoints < 2) error("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/operations/Algebra.kt

@@ -53,6 +53,7 @@ interface Space<T> {
 
     //TODO move to external extensions when they are available
     fun Iterable<T>.sum(): T = fold(zero) { left, right -> left + right }
+
     fun Sequence<T>.sum(): T = fold(zero) { left, right -> left + right }
 }
 

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

@@ -79,11 +79,11 @@ object DoubleField : ExtendedField<Double>, Norm<Double, Double> {
 /**
  * A field for double without boxing. Does not produce appropriate field element
  */
-object IntField : Field<Int>{
+object IntField : Field<Int> {
     override val zero: Int = 0
     override fun add(a: Int, b: Int): Int = a + b
     override fun multiply(a: Int, b: Int): Int = a * b
-    override fun multiply(a: Int, k: Double): Int = (k*a).toInt()
+    override fun multiply(a: Int, k: Double): Int = (k * a).toInt()
     override val one: Int = 1
     override fun divide(a: Int, b: Int): Int = a / b
 }

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

@@ -2,12 +2,15 @@ package scientifik.kmath.structures
 
 import scientifik.kmath.operations.Field
 
-open class BufferNDField<T, F : Field<T>>(final override val shape: IntArray, final override val field: F, val bufferFactory: BufferFactory<T>) : NDField<T, F> {
+open class BufferNDField<T, F : Field<T>>(
+        final override val shape: IntArray,
+        final override val field: F,
+        val bufferFactory: BufferFactory<T>
+) : NDField<T, F> {
     val strides = DefaultStrides(shape)
 
-    override fun produce(initializer: F.(IntArray) -> T): BufferNDElement<T, F> {
+    override fun produce(initializer: F.(IntArray) -> T): BufferNDElement<T, F> =
-        return BufferNDElement(this, bufferFactory(strides.linearSize) { offset -> field.initializer(strides.index(offset)) })
+            BufferNDElement(this, bufferFactory(strides.linearSize) { offset -> field.initializer(strides.index(offset)) })
-    }
 
     open fun produceBuffered(initializer: F.(Int) -> T) =
             BufferNDElement(this, bufferFactory(strides.linearSize) { offset -> field.initializer(offset) })
@@ -31,7 +34,10 @@ open class BufferNDField<T, F : Field<T>>(final override val shape: IntArray, fi
 //    }
 }
 
-class BufferNDElement<T, F : Field<T>>(override val context: BufferNDField<T, F>, val buffer: Buffer<T>) : NDElement<T, F> {
+class BufferNDElement<T, F : Field<T>>(
+        override val context: BufferNDField<T, F>,
+        val buffer: Buffer<T>
+) : NDElement<T, F> {
 
     override val self: NDStructure<T> get() = this
     override val shape: IntArray get() = context.shape
@@ -60,7 +66,7 @@ operator fun <T : Any, F : Field<T>> BufferNDElement<T, F>.plus(arg: T) =
 /**
  * Subtraction operation between [BufferNDElement] and single element
  */
-operator fun <T: Any, F : Field<T>> BufferNDElement<T, F>.minus(arg: T) =
+operator fun <T : Any, F : Field<T>> BufferNDElement<T, F>.minus(arg: T) =
         context.produceBuffered { i -> buffer[i] - arg }
 
 /* prod and div */
@@ -68,11 +74,11 @@ operator fun <T: Any, F : Field<T>> BufferNDElement<T, F>.minus(arg: T) =
 /**
  * Product operation for [BufferNDElement] and single element
  */
-operator fun <T: Any, F : Field<T>> BufferNDElement<T, F>.times(arg: T) =
+operator fun <T : Any, F : Field<T>> BufferNDElement<T, F>.times(arg: T) =
         context.produceBuffered { i -> buffer[i] * arg }
 
 /**
  * Division operation between [BufferNDElement] and single element
  */
-operator fun <T: Any, F : Field<T>> BufferNDElement<T, F>.div(arg: T) =
+operator fun <T : Any, F : Field<T>> BufferNDElement<T, F>.div(arg: T) =
         context.produceBuffered { i -> buffer[i] / arg }

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

@@ -139,14 +139,13 @@ inline fun <T> boxingBuffer(size: Int, initializer: (Int) -> T): Buffer<T> = Lis
  * Create most appropriate immutable buffer for given type avoiding boxing wherever possible
  */
 @Suppress("UNCHECKED_CAST")
-inline fun <reified T : Any> inlineBuffer(size: Int, initializer: (Int) -> T): Buffer<T> {
+inline fun <reified T : Any> inlineBuffer(size: Int, initializer: (Int) -> T): Buffer<T> =
-    return when (T::class) {
+        when (T::class) {
             Double::class -> DoubleBuffer(DoubleArray(size) { initializer(it) as Double }) as Buffer<T>
             Int::class -> IntBuffer(IntArray(size) { initializer(it) as Int }) as Buffer<T>
             Long::class -> LongBuffer(LongArray(size) { initializer(it) as Long }) as Buffer<T>
             else -> boxingBuffer(size, initializer)
         }
-}
 
 /**
  * Create a boxing mutable buffer of given type
@@ -157,14 +156,13 @@ inline fun <T : Any> boxingMutableBuffer(size: Int, initializer: (Int) -> T): Mu
  * Create most appropriate mutable buffer for given type avoiding boxing wherever possible
  */
 @Suppress("UNCHECKED_CAST")
-inline fun <reified T : Any> inlineMutableBuffer(size: Int, initializer: (Int) -> T): MutableBuffer<T> {
+inline fun <reified T : Any> inlineMutableBuffer(size: Int, initializer: (Int) -> T): MutableBuffer<T> =
-    return when (T::class) {
+        when (T::class) {
             Double::class -> DoubleBuffer(DoubleArray(size) { initializer(it) as Double }) 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 -> boxingMutableBuffer(size, initializer)
         }
-}
 
 typealias BufferFactory<T> = (Int, (Int) -> T) -> Buffer<T>
 typealias MutableBufferFactory<T> = (Int, (Int) -> T) -> MutableBuffer<T>

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

@@ -23,25 +23,16 @@ inline class ExtendedNDFieldWrapper<T : Any, F : ExtendedField<T>>(private val n
 
     override fun produce(initializer: F.(IntArray) -> T): NDElement<T, F> = ndField.produce(initializer)
 
-    override fun power(arg: NDStructure<T>, pow: Double): NDElement<T, F> {
+    override fun power(arg: NDStructure<T>, pow: Double): NDElement<T, F> =
-        return produce { with(field) { power(arg[it], pow) } }
+            produce { with(field) { power(arg[it], pow) } }
-    }
 
-    override fun exp(arg: NDStructure<T>): NDElement<T, F> {
+    override fun exp(arg: NDStructure<T>): NDElement<T, F> = produce { with(field) { exp(arg[it]) } }
-        return produce { with(field) { exp(arg[it]) } }
-    }
 
-    override fun ln(arg: NDStructure<T>): NDElement<T, F> {
+    override fun ln(arg: NDStructure<T>): NDElement<T, F> = produce { with(field) { ln(arg[it]) } }
-        return produce { with(field) { ln(arg[it]) } }
-    }
 
-    override fun sin(arg: NDStructure<T>): NDElement<T, F> {
+    override fun sin(arg: NDStructure<T>): NDElement<T, F> = produce { with(field) { sin(arg[it]) } }
-        return produce { with(field) { sin(arg[it]) } }
-    }
 
-    override fun cos(arg: NDStructure<T>): NDElement<T, F> {
+    override fun cos(arg: NDStructure<T>): NDElement<T, F> = produce { with(field) { cos(arg[it]) } }
-        return produce { with(field) { cos(arg[it]) } }
-    }
 }
 
 

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

@@ -11,8 +11,8 @@ class ShapeMismatchException(val expected: IntArray, val actual: IntArray) : Run
 
 /**
  * Field for n-dimensional arrays.
- * @param shape - the list of dimensions of the array
+ * @property shape - the list of dimensions of the array
- * @param field - operations field defined on individual array element
+ * @property field - operations field defined on individual array element
  * @param T the type of the element contained in NDArray
  */
 interface NDField<T, F : Field<T>> : Field<NDStructure<T>> {
@@ -33,13 +33,8 @@ interface NDField<T, F : Field<T>> : Field<NDStructure<T>> {
     /**
      * Check the shape of given NDArray and throw exception if it does not coincide with shape of the field
      */
-    fun checkShape(vararg elements: NDStructure<T>) {
+    fun checkShape(vararg elements: NDStructure<T>) =
-        elements.forEach {
+        elements.forEach { if (!shape.contentEquals(it.shape)) throw ShapeMismatchException(shape, it.shape) }
-            if (!shape.contentEquals(it.shape)) {
-                throw ShapeMismatchException(shape, it.shape)
-            }
-        }
-    }
 
     /**
      * Element-by-element addition
@@ -97,21 +92,17 @@ interface NDElement<T, F : Field<T>> : FieldElement<NDStructure<T>, NDField<T, F
         /**
          * Create a platform-optimized NDArray of doubles
          */
-        fun real(shape: IntArray, initializer: DoubleField.(IntArray) -> Double = { 0.0 }): NDElement<Double, DoubleField> {
+        fun real(shape: IntArray, initializer: DoubleField.(IntArray) -> Double = { 0.0 }): NDElement<Double, DoubleField> =
-            return NDField.real(shape).produce(initializer)
+                NDField.real(shape).produce(initializer)
-        }
 
-        fun real1D(dim: Int, initializer: (Int) -> Double = { _ -> 0.0 }): NDElement<Double, DoubleField> {
+        fun real1D(dim: Int, initializer: (Int) -> Double = { _ -> 0.0 }): NDElement<Double, DoubleField> =
-            return real(intArrayOf(dim)) { initializer(it[0]) }
+                real(intArrayOf(dim)) { initializer(it[0]) }
-        }
 
-        fun real2D(dim1: Int, dim2: Int, initializer: (Int, Int) -> Double = { _, _ -> 0.0 }): NDElement<Double, DoubleField> {
+        fun real2D(dim1: Int, dim2: Int, initializer: (Int, Int) -> Double = { _, _ -> 0.0 }): NDElement<Double, DoubleField> =
-            return real(intArrayOf(dim1, dim2)) { initializer(it[0], it[1]) }
+                real(intArrayOf(dim1, dim2)) { initializer(it[0], it[1]) }
-        }
 
-        fun real3D(dim1: Int, dim2: Int, dim3: Int, initializer: (Int, Int, Int) -> Double = { _, _, _ -> 0.0 }): NDElement<Double, DoubleField> {
+        fun real3D(dim1: Int, dim2: Int, dim3: Int, initializer: (Int, Int, Int) -> Double = { _, _, _ -> 0.0 }): NDElement<Double, DoubleField> =
-            return real(intArrayOf(dim1, dim2, dim3)) { initializer(it[0], it[1], it[2]) }
+                real(intArrayOf(dim1, dim2, dim3)) { initializer(it[0], it[1], it[2]) }
-        }
 
 //        inline fun real(shape: IntArray, block: ExtendedNDField<Double, DoubleField>.() -> NDStructure<Double>): NDElement<Double, DoubleField> {
 //            val field = NDField.real(shape)
@@ -121,13 +112,11 @@ interface NDElement<T, F : Field<T>> : FieldElement<NDStructure<T>, NDField<T, F
         /**
          * Simple boxing NDArray
          */
-        fun <T : Any, F : Field<T>> generic(shape: IntArray, field: F, initializer: F.(IntArray) -> T): NDElement<T, F> {
+        fun <T : Any, F : Field<T>> generic(shape: IntArray, field: F, initializer: F.(IntArray) -> T): NDElement<T, F> =
-            return NDField.generic(shape, field).produce(initializer)
+                NDField.generic(shape, field).produce(initializer)
-        }
 
-        inline fun <reified T : Any, F : Field<T>> inline(shape: IntArray, field: F, noinline initializer: F.(IntArray) -> T): NDElement<T, F> {
+        inline fun <reified T : Any, F : Field<T>> inline(shape: IntArray, field: F, noinline initializer: F.(IntArray) -> T): NDElement<T, F> =
-            return NDField.inline(shape, field).produce(initializer)
+                NDField.inline(shape, field).produce(initializer)
-        }
     }
 }
 

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

@@ -19,11 +19,8 @@ interface MutableNDStructure<T> : NDStructure<T> {
     operator fun set(index: IntArray, value: T)
 }
 
-fun <T> MutableNDStructure<T>.mapInPlace(action: (IntArray, T) -> T) {
+fun <T> MutableNDStructure<T>.mapInPlace(action: (IntArray, T) -> T) =
-    elements().forEach { (index, oldValue) ->
+        elements().forEach { (index, oldValue) -> this[index] = action(index, oldValue) }
-        this[index] = action(index, oldValue)
-    }
-}
 
 /**
  * A way to convert ND index to linear one and back
@@ -56,11 +53,11 @@ interface Strides {
 
     /**
      * Iterate over ND indices in a natural order
+     *
+     * TODO: introduce a fast way to calculate index of the next element?
      */
-    fun indices(): Sequence<IntArray> {
+    fun indices(): Sequence<IntArray> =
-        //TODO introduce a fast way to calculate index of the next element?
+            (0 until linearSize).asSequence().map { index(it) }
-        return (0 until linearSize).asSequence().map { index(it) }
-    }
 }
 
 class DefaultStrides private constructor(override val shape: IntArray) : Strides {
@@ -128,25 +125,21 @@ abstract class GenericNDStructure<T, B : Buffer<T>> : NDStructure<T> {
 /**
  * Boxing generic [NDStructure]
  */
-class BufferNDStructure<T>(
+class BufferNDStructure<T>(override val strides: Strides,
-        override val strides: Strides,
+                           override val buffer: Buffer<T>) : GenericNDStructure<T, Buffer<T>>() {
-        override val buffer: Buffer<T>
-) : GenericNDStructure<T, Buffer<T>>() {
-
     init {
         if (strides.linearSize != buffer.size) {
             error("Expected buffer side of ${strides.linearSize}, but found ${buffer.size}")
         }
     }
 
-    override fun equals(other: Any?): Boolean {
+    override fun equals(other: Any?): Boolean =
-        return when {
+            when {
                 this === other -> true
                 other is BufferNDStructure<*> -> this.strides == other.strides && this.buffer.contentEquals(other.buffer)
                 other is NDStructure<*> -> elements().all { (index, value) -> value == other[index] }
                 else -> false
             }
-    }
 
     override fun hashCode(): Int {
         var result = strides.hashCode()
@@ -158,14 +151,13 @@ class BufferNDStructure<T>(
 /**
  * Transform structure to a new structure using provided [BufferFactory] and optimizing if argument is [BufferNDStructure]
  */
-inline fun <T, reified R : Any> NDStructure<T>.map(factory: BufferFactory<R> = ::inlineBuffer, crossinline transform: (T) -> R): BufferNDStructure<R> {
+inline fun <T, reified R : Any> NDStructure<T>.map(factory: BufferFactory<R> = ::inlineBuffer, crossinline transform: (T) -> R): BufferNDStructure<R> =
-    return if (this is BufferNDStructure<T>) {
+        if (this is BufferNDStructure<T>) {
             BufferNDStructure(this.strides, factory.invoke(strides.linearSize) { transform(buffer[it]) })
         } else {
             val strides = DefaultStrides(shape)
             BufferNDStructure(strides, factory.invoke(strides.linearSize) { transform(get(strides.index(it))) })
         }
-}
 
 /**
  * Create a NDStructure with explicit buffer factory

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

@@ -9,14 +9,13 @@ class RealNDField(shape: IntArray) : BufferNDField<Double, DoubleField>(shape, D
     /**
      * Inline map an NDStructure to
      */
-    private inline fun NDStructure<Double>.mapInline(crossinline operation: DoubleField.(Double) -> Double): RealNDElement {
+    private inline fun NDStructure<Double>.mapInline(crossinline operation: DoubleField.(Double) -> Double): RealNDElement =
-        return if (this is BufferNDElement<Double, *>) {
+            if (this is BufferNDElement<Double, *>) {
                 val array = DoubleArray(strides.linearSize) { offset -> DoubleField.operation(buffer[offset]) }
                 BufferNDElement(this@RealNDField, DoubleBuffer(array))
             } else {
                 produce { index -> DoubleField.operation(get(index)) }
             }
-    }
 
 
     @Suppress("OVERRIDE_BY_INLINE")
@@ -58,16 +57,12 @@ inline fun BufferNDField<Double, DoubleField>.produceInline(crossinline initiali
 operator fun Function1<Double, Double>.invoke(ndElement: RealNDElement) =
         ndElement.context.produceInline { i -> invoke(ndElement.buffer[i]) }
 
-/* plus and minus */
-
 /**
  * Summation operation for [BufferNDElement] and single element
  */
-operator fun RealNDElement.plus(arg: Double) =
+operator fun RealNDElement.plus(arg: Double) = context.produceInline { i -> buffer[i] + arg }
-        context.produceInline { i -> buffer[i] + arg }
 
 /**
  * Subtraction operation between [BufferNDElement] and single element
  */
-operator fun RealNDElement.minus(arg: Double) =
+operator fun RealNDElement.minus(arg: Double) = context.produceInline { i -> buffer[i] - arg }
-        context.produceInline { i -> buffer[i] - arg }