Fix some packaging issues, add some KDoc comments, minor refactor

build.gradle.kts

@@ -3,9 +3,9 @@ plugins {
     id("org.jetbrains.changelog") version "0.4.0"
 }
 
-val kmathVersion by extra("0.2.0-dev-1")
-val bintrayRepo by extra("kscience")
-val githubProject by extra("kmath")
+val kmathVersion: String by extra("0.2.0-dev-1")
+val bintrayRepo: String by extra("kscience")
+val githubProject: String by extra("kmath")
 
 allprojects {
     repositories {

examples/src/benchmarks/kotlin/kscience/kmath/structures/ArrayBenchmark.kt

@@ -10,30 +10,29 @@ class ArrayBenchmark {
     @Benchmark
     fun benchmarkArrayRead() {
         var res = 0
-        for (i in 1.._root_ide_package_.kscience.kmath.structures.ArrayBenchmark.Companion.size) res += _root_ide_package_.kscience.kmath.structures.ArrayBenchmark.Companion.array[_root_ide_package_.kscience.kmath.structures.ArrayBenchmark.Companion.size - i]
+        for (i in 1..size) res += array[size - i]
     }
 
     @Benchmark
     fun benchmarkBufferRead() {
         var res = 0
-        for (i in 1.._root_ide_package_.kscience.kmath.structures.ArrayBenchmark.Companion.size) res += _root_ide_package_.kscience.kmath.structures.ArrayBenchmark.Companion.arrayBuffer.get(
-            _root_ide_package_.kscience.kmath.structures.ArrayBenchmark.Companion.size - i)
+        for (i in 1..size) res += arrayBuffer.get(
+            size - i
+        )
     }
 
     @Benchmark
     fun nativeBufferRead() {
         var res = 0
-        for (i in 1.._root_ide_package_.kscience.kmath.structures.ArrayBenchmark.Companion.size) res += _root_ide_package_.kscience.kmath.structures.ArrayBenchmark.Companion.nativeBuffer.get(
-            _root_ide_package_.kscience.kmath.structures.ArrayBenchmark.Companion.size - i)
+        for (i in 1..size) res += nativeBuffer.get(
+            size - i
+        )
     }
 
     companion object {
         const val size: Int = 1000
-        val array: IntArray = IntArray(_root_ide_package_.kscience.kmath.structures.ArrayBenchmark.Companion.size) { it }
-        val arrayBuffer: IntBuffer = IntBuffer.wrap(_root_ide_package_.kscience.kmath.structures.ArrayBenchmark.Companion.array)
-
-        val nativeBuffer: IntBuffer = IntBuffer.allocate(_root_ide_package_.kscience.kmath.structures.ArrayBenchmark.Companion.size).also {
-            for (i in 0 until _root_ide_package_.kscience.kmath.structures.ArrayBenchmark.Companion.size) it.put(i, i)
-        }
+        val array: IntArray = IntArray(size) { it }
+        val arrayBuffer: IntBuffer = IntBuffer.wrap(array)
+        val nativeBuffer: IntBuffer = IntBuffer.allocate(size).also { for (i in 0 until size) it.put(i, i) }
     }
 }

examples/src/benchmarks/kotlin/kscience/kmath/structures/BufferBenchmark.kt

@@ -11,7 +11,7 @@ class BufferBenchmark {
 
     @Benchmark
     fun genericRealBufferReadWrite() {
-        val buffer = RealBuffer(size){it.toDouble()}
+        val buffer = RealBuffer(size) { it.toDouble() }
 
         (0 until size).forEach {
             buffer[it]
@@ -20,7 +20,7 @@ class BufferBenchmark {
 
     @Benchmark
     fun complexBufferReadWrite() {
-        val buffer = MutableBuffer.complex(size / 2){Complex(it.toDouble(), -it.toDouble())}
+        val buffer = MutableBuffer.complex(size / 2) { Complex(it.toDouble(), -it.toDouble()) }
 
         (0 until size / 2).forEach {
             buffer[it]
@@ -28,6 +28,6 @@ class BufferBenchmark {
     }
 
     companion object {
-        const val size = 100
+        const val size: Int = 100
     }
 }

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

@@ -40,11 +40,10 @@ class NDFieldBenchmark {
     }
 
     companion object {
-        val dim = 1000
-        val n = 100
-
-        val bufferedField = NDField.auto(RealField, dim, dim)
-        val specializedField = NDField.real(dim, dim)
-        val genericField = NDField.boxing(RealField, dim, dim)
+        const val dim: Int = 1000
+        const val n: Int = 100
+        val bufferedField: BufferedNDField<Double, RealField> = NDField.auto(RealField, dim, dim)
+        val specializedField: RealNDField = NDField.real(dim, dim)
+        val genericField: BoxingNDField<Double, RealField> = NDField.boxing(RealField, dim, dim)
     }
 }

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

@@ -10,8 +10,8 @@ import org.openjdk.jmh.annotations.State
 
 @State(Scope.Benchmark)
 class ViktorBenchmark {
-    final val dim = 1000
-    final val n = 100
+    final val dim: Int = 1000
+    final val n: Int = 100
 
     // automatically build context most suited for given type.
     final val autoField: BufferedNDField<Double, RealField> = NDField.auto(RealField, dim, dim)

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

@@ -1,4 +1,4 @@
-//package kscience.kmath.ast
+package kscience.kmath.ast
 //
 //import kscience.kmath.asm.compile
 //import kscience.kmath.expressions.Expression

examples/src/main/kotlin/kscience/kmath/commons/prob/DistributionBenchmark.kt

@@ -10,10 +10,8 @@ import org.apache.commons.rng.simple.RandomSource
 import java.time.Duration
 import java.time.Instant
 
-
-private suspend fun runChain(): Duration {
+private fun runChain(): Duration {
     val generator = RandomGenerator.fromSource(RandomSource.MT, 123L)
-
     val normal = Distribution.normal(NormalSamplerMethod.Ziggurat)
     val chain = normal.sample(generator) as BlockingRealChain
 
@@ -67,5 +65,4 @@ fun main() {
         println("Chain: ${chainJob.await()}")
         println("Direct: ${directJob.await()}")
     }
-
 }

examples/src/main/kotlin/kscience/kmath/structures/StructureReadBenchmark.kt

@@ -12,24 +12,24 @@ fun main() {
     val structure = BufferNDStructure(strides, buffer)
 
     measureTimeMillis {
-        var res: Double = 0.0
+        var res = 0.0
         strides.indices().forEach { res = structure[it] }
     } // warmup
 
     val time1 = measureTimeMillis {
-        var res: Double = 0.0
+        var res = 0.0
         strides.indices().forEach { res = structure[it] }
     }
     println("Structure reading finished in $time1 millis")
 
     val time2 = measureTimeMillis {
-        var res: Double = 0.0
+        var res = 0.0
         strides.indices().forEach { res = buffer[strides.offset(it)] }
     }
     println("Buffer reading finished in $time2 millis")
 
     val time3 = measureTimeMillis {
-        var res: Double = 0.0
+        var res = 0.0
         strides.indices().forEach { res = array[strides.offset(it)] }
     }
     println("Array reading finished in $time3 millis")

kmath-ast/src/jvmMain/kotlin/kscience/kmath/asm/internal/AsmBuilder.kt

@@ -32,7 +32,7 @@ internal class AsmBuilder<T> internal constructor(
      * Internal classloader of [AsmBuilder] with alias to define class from byte array.
      */
     private class ClassLoader(parent: java.lang.ClassLoader) : java.lang.ClassLoader(parent) {
-        internal fun defineClass(name: String?, b: ByteArray): Class<*> = defineClass(name, b, 0, b.size)
+        fun defineClass(name: String?, b: ByteArray): Class<*> = defineClass(name, b, 0, b.size)
     }
 
     /**

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

@@ -94,7 +94,7 @@ public class DiffExpression(public val function: DerivativeStructureField.() ->
      * TODO make result [DiffExpression]
      */
     public fun derivative(orders: Map<String, Int>): Expression<Double> = Expression { arguments ->
-        (DerivativeStructureField(orders.values.max() ?: 0, arguments)) { function().deriv(orders) }
+        (DerivativeStructureField(orders.values.maxOrNull() ?: 0, arguments)) { function().deriv(orders) }
     }
 
     //TODO add gradient and maybe other vector operators

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

@@ -4,7 +4,7 @@ import kscience.kmath.operations.*
 
 internal class FunctionalUnaryOperation<T>(val context: Algebra<T>, val name: String, private val expr: Expression<T>) :
     Expression<T> {
-    public override operator fun invoke(arguments: Map<String, T>): T =
+    override operator fun invoke(arguments: Map<String, T>): T =
         context.unaryOperation(name, expr.invoke(arguments))
 }
 
@@ -14,17 +14,17 @@ internal class FunctionalBinaryOperation<T>(
     val first: Expression<T>,
     val second: Expression<T>
 ) : Expression<T> {
-    public override operator 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> {
-    public override operator 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> {
-    public override operator fun invoke(arguments: Map<String, T>): T = value
+    override operator fun invoke(arguments: Map<String, T>): T = value
 }
 
 internal class FunctionalConstProductExpression<T>(
@@ -32,7 +32,7 @@ internal class FunctionalConstProductExpression<T>(
     private val expr: Expression<T>,
     val const: Number
 ) : Expression<T> {
-    public override operator 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)
 }
 
 /**
@@ -139,16 +139,27 @@ public open class FunctionalExpressionField<T, A>(algebra: A) :
 public open class FunctionalExpressionExtendedField<T, A>(algebra: A) :
     FunctionalExpressionField<T, A>(algebra),
     ExtendedField<Expression<T>> where A : ExtendedField<T>, A : NumericAlgebra<T> {
-    public override fun sin(arg: Expression<T>): Expression<T> = unaryOperation(TrigonometricOperations.SIN_OPERATION, arg)
-    public override fun cos(arg: Expression<T>): Expression<T> = unaryOperation(TrigonometricOperations.COS_OPERATION, arg)
-    public override fun asin(arg: Expression<T>): Expression<T> = unaryOperation(TrigonometricOperations.ASIN_OPERATION, arg)
-    public override fun acos(arg: Expression<T>): Expression<T> = unaryOperation(TrigonometricOperations.ACOS_OPERATION, arg)
-    public override fun atan(arg: Expression<T>): Expression<T> = unaryOperation(TrigonometricOperations.ATAN_OPERATION, arg)
+    public override fun sin(arg: Expression<T>): Expression<T> =
+        unaryOperation(TrigonometricOperations.SIN_OPERATION, arg)
+
+    public override fun cos(arg: Expression<T>): Expression<T> =
+        unaryOperation(TrigonometricOperations.COS_OPERATION, arg)
+
+    public override fun asin(arg: Expression<T>): Expression<T> =
+        unaryOperation(TrigonometricOperations.ASIN_OPERATION, arg)
+
+    public override fun acos(arg: Expression<T>): Expression<T> =
+        unaryOperation(TrigonometricOperations.ACOS_OPERATION, arg)
+
+    public override fun atan(arg: Expression<T>): Expression<T> =
+        unaryOperation(TrigonometricOperations.ATAN_OPERATION, arg)
 
     public override fun power(arg: Expression<T>, pow: Number): Expression<T> =
         binaryOperation(PowerOperations.POW_OPERATION, arg, number(pow))
 
-    public override fun exp(arg: Expression<T>): Expression<T> = unaryOperation(ExponentialOperations.EXP_OPERATION, arg)
+    public override fun exp(arg: Expression<T>): Expression<T> =
+        unaryOperation(ExponentialOperations.EXP_OPERATION, arg)
+
     public override fun ln(arg: Expression<T>): Expression<T> = unaryOperation(ExponentialOperations.LN_OPERATION, arg)
 
     public override fun unaryOperation(operation: String, arg: Expression<T>): Expression<T> =

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

@@ -24,7 +24,11 @@ public interface FeaturedMatrix<T : Any> : Matrix<T> {
     public companion object
 }
 
-public inline fun Structure2D.Companion.real(rows: Int, columns: Int, initializer: (Int, Int) -> Double): Matrix<Double> =
+public inline fun Structure2D.Companion.real(
+    rows: Int,
+    columns: Int,
+    initializer: (Int, Int) -> Double
+): Matrix<Double> =
     MatrixContext.real.produce(rows, columns, initializer)
 
 /**

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

@@ -1,10 +1,7 @@
 package kscience.kmath.misc
 
 import kscience.kmath.linear.Point
-import kscience.kmath.operations.ExtendedField
-import kscience.kmath.operations.Field
-import kscience.kmath.operations.invoke
-import kscience.kmath.operations.sum
+import kscience.kmath.operations.*
 import kscience.kmath.structures.asBuffer
 import kotlin.contracts.InvocationKind
 import kotlin.contracts.contract
@@ -17,23 +14,37 @@ import kotlin.contracts.contract
 /**
  * Differentiable variable with value and derivative of differentiation ([deriv]) result
  * with respect to this variable.
+ *
+ * @param T the non-nullable type of value.
+ * @property value The value of this variable.
  */
 public open class Variable<T : Any>(public val value: T)
 
+/**
+ * Represents result of [deriv] call.
+ *
+ * @param T the non-nullable type of value.
+ * @param value the value of result.
+ * @property deriv The mapping of differentiated variables to their derivatives.
+ * @property context The field over [T].
+ */
 public class DerivationResult<T : Any>(
     value: T,
     public val deriv: Map<Variable<T>, T>,
     public val context: Field<T>
 ) : Variable<T>(value) {
+    /**
+     * Returns derivative of [variable] or returns [Ring.zero] in [context].
+     */
     public fun deriv(variable: Variable<T>): T = deriv[variable] ?: context.zero
 
     /**
-     * compute divergence
+     * Computes the divergence.
      */
     public fun div(): T = context { sum(deriv.values) }
 
     /**
-     * Compute a gradient for variables in given order
+     * Computes the gradient for variables in given order.
      */
     public fun grad(vararg variables: Variable<T>): Point<T> {
         check(variables.isNotEmpty()) { "Variable order is not provided for gradient construction" }
@@ -53,6 +64,9 @@ public class DerivationResult<T : Any>(
  * assertEquals(17.0, y.x) // the value of result (y)
  * assertEquals(9.0, x.d)  // dy/dx
  * ```
+ *
+ * @param body the action in [AutoDiffField] context returning [Variable] to differentiate with respect to.
+ * @return the result of differentiation.
  */
 public inline fun <T : Any, F : Field<T>> F.deriv(body: AutoDiffField<T, F>.() -> Variable<T>): DerivationResult<T> {
     contract { callsInPlace(body, InvocationKind.EXACTLY_ONCE) }
@@ -65,12 +79,15 @@ public inline fun <T : Any, F : Field<T>> F.deriv(body: AutoDiffField<T, F>.() -
     }
 }
 
+/**
+ * Represents field in context of which functions can be derived.
+ */
 public abstract class AutoDiffField<T : Any, F : Field<T>> : Field<Variable<T>> {
     public abstract val context: F
 
     /**
      * A variable accessing inner state of derivatives.
-     * Use this function in inner builders to avoid creating additional derivative bindings
+     * Use this value in inner builders to avoid creating additional derivative bindings.
      */
     public abstract var Variable<T>.d: T
 
@@ -87,6 +104,9 @@ public abstract class AutoDiffField<T : Any, F : Field<T>> : Field<Variable<T>>
      */
     public abstract fun <R> derive(value: R, block: F.(R) -> Unit): R
 
+    /**
+     *
+     */
     public abstract fun variable(value: T): Variable<T>
 
     public inline fun variable(block: F.() -> T): Variable<T> = variable(context.block())

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

@@ -299,7 +299,7 @@ public class BigInt internal constructor(
 
             for (i in mag.indices) {
                 val cur: ULong = carry + mag[i].toULong() * x.toULong()
-                result[i] = (cur and BASE.toULong()).toUInt()
+                result[i] = (cur and BASE).toUInt()
                 carry = cur shr BASE_SIZE
             }
             result[resultLength - 1] = (carry and BASE).toUInt()
@@ -316,7 +316,7 @@ public class BigInt internal constructor(
 
                 for (j in mag2.indices) {
                     val cur: ULong = result[i + j].toULong() + mag1[i].toULong() * mag2[j].toULong() + carry
-                    result[i + j] = (cur and BASE.toULong()).toUInt()
+                    result[i + j] = (cur and BASE).toUInt()
                     carry = cur shr BASE_SIZE
                 }
 

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

@@ -159,7 +159,7 @@ public object ComplexField : ExtendedField<Complex>, Norm<Complex, Complex> {
 }
 
 /**
- * Represents complex number.
+ * Represents `double`-based complex number.
  *
  * @property re The real part.
  * @property im The imaginary part.

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

@@ -48,7 +48,8 @@ public fun FlaggedBuffer<*>.isMissing(index: Int): Boolean = hasFlag(index, Valu
 /**
  * A real buffer which supports flags for each value like NaN or Missing
  */
-public class FlaggedRealBuffer(public val values: DoubleArray, public val flags: ByteArray) : FlaggedBuffer<Double?>, Buffer<Double?> {
+public class FlaggedRealBuffer(public val values: DoubleArray, public val flags: ByteArray) : FlaggedBuffer<Double?>,
+    Buffer<Double?> {
     init {
         require(values.size == flags.size) { "Values and flags must have the same dimensions" }
     }

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

@@ -17,7 +17,7 @@ public interface Structure1D<T> : NDStructure<T>, Buffer<T> {
 /**
  * A 1D wrapper for nd-structure
  */
-private inline class Structure1DWrapper<T>(public val structure: NDStructure<T>) : Structure1D<T> {
+private inline class Structure1DWrapper<T>(val structure: NDStructure<T>) : Structure1D<T> {
     override val shape: IntArray get() = structure.shape
     override val size: Int get() = structure.shape[0]
 

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

@@ -21,7 +21,8 @@ internal class LazyDeferred<T>(val dispatcher: CoroutineDispatcher, val block: s
 }
 
 public class AsyncFlow<T> internal constructor(internal val deferredFlow: Flow<LazyDeferred<T>>) : Flow<T> {
-    override suspend fun collect(collector: FlowCollector<T>): Unit = deferredFlow.collect { collector.emit((it.await())) }
+    override suspend fun collect(collector: FlowCollector<T>): Unit =
+        deferredFlow.collect { collector.emit((it.await())) }
 }
 
 public fun <T, R> Flow<T>.async(

kmath-geometry/src/commonMain/kotlin/kscience/kmath/geometry/GeometrySpace.kt

@@ -4,7 +4,7 @@ import kscience.kmath.operations.Space
 
 public interface Vector
 
-public interface GeometrySpace<V: Vector>: Space<V> {
+public interface GeometrySpace<V : Vector> : Space<V> {
     /**
      * L2 distance
      */

kmath-histograms/src/commonTest/kotlin/scietifik/kmath/histogram/MultivariateHistogramTest.kt

@@ -5,10 +5,7 @@ import kscience.kmath.histogram.fill
 import kscience.kmath.histogram.put
 import kscience.kmath.real.RealVector
 import kotlin.random.Random
-import kotlin.test.Test
-import kotlin.test.assertEquals
-import kotlin.test.assertFalse
-import kotlin.test.assertTrue
+import kotlin.test.*
 
 internal class MultivariateHistogramTest {
     @Test
@@ -18,7 +15,7 @@ internal class MultivariateHistogramTest {
             (-1.0..1.0)
         )
         histogram.put(0.55, 0.55)
-        val bin = histogram.find { it.value.toInt() > 0 }!!
+        val bin = histogram.find { it.value.toInt() > 0 } ?: fail()
         assertTrue { bin.contains(RealVector(0.55, 0.55)) }
         assertTrue { bin.contains(RealVector(0.6, 0.5)) }
         assertFalse { bin.contains(RealVector(-0.55, 0.55)) }

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

@@ -32,7 +32,8 @@ public fun <T : Any> MemoryReader.read(spec: MemorySpec<T>, offset: Int): T = wi
 /**
  * Writes the object [value] with [spec] starting from [offset].
  */
-public fun <T : Any> MemoryWriter.write(spec: MemorySpec<T>, offset: Int, value: T): Unit = with(spec) { write(offset, value) }
+public fun <T : Any> MemoryWriter.write(spec: MemorySpec<T>, offset: Int, value: T): Unit =
+    with(spec) { write(offset, value) }
 
 /**
  * Reads array of [size] objects mapped by [spec] at certain [offset].

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

@@ -100,7 +100,8 @@ public actual fun Memory.Companion.allocate(length: Int): Memory =
  * Wraps a [Memory] around existing [ByteArray]. This operation is unsafe since the array is not copied
  * and could be mutated independently from the resulting [Memory].
  */
-public actual fun Memory.Companion.wrap(array: ByteArray): Memory = ByteBufferMemory(checkNotNull(ByteBuffer.wrap(array)))
+public actual fun Memory.Companion.wrap(array: ByteArray): Memory =
+    ByteBufferMemory(checkNotNull(ByteBuffer.wrap(array)))
 
 /**
  * Wraps this [ByteBuffer] to [Memory] object.

kmath-prob/src/commonMain/kotlin/kscience/kmath/prob/Distribution.kt

@@ -62,7 +62,7 @@ public fun <T : Any> Sampler<T>.sampleBuffer(
         //clear list from previous run
         tmp.clear()
         //Fill list
-        repeat(size){
+        repeat(size) {
             tmp.add(chain.next())
         }
         //return new buffer with elements from tmp

kmath-prob/src/jvmTest/kotlin/kscience/kmath/prob/SamplerTest.kt

@@ -6,7 +6,7 @@ import kotlin.test.Test
 class SamplerTest {
 
     @Test
-    fun bufferSamplerTest(){
+    fun bufferSamplerTest() {
         val sampler: Sampler<Double> =
             BasicSampler { it.chain { nextDouble() } }
         val data = sampler.sampleBuffer(RandomGenerator.default, 100)