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

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Iaroslav 2020-09-23 21:15:54 +07:00
parent 68845b7a03
commit 12b599fff2
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26 changed files with 103 additions and 72 deletions
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6
build.gradle.kts
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@ -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 {

21
examples/src/benchmarks/kotlin/kscience/kmath/structures/ArrayBenchmark.kt
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@ -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) }
}
}

2
examples/src/benchmarks/kotlin/kscience/kmath/structures/BufferBenchmark.kt
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@ -28,6 +28,6 @@ class BufferBenchmark {
}
companion object {
const val size = 100
const val size: Int = 100
}
}

11
examples/src/benchmarks/kotlin/kscience/kmath/structures/NDFieldBenchmark.kt
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@ -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)
}
}

4
examples/src/benchmarks/kotlin/kscience/kmath/structures/ViktorBenchmark.kt
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@ -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)

2
examples/src/main/kotlin/kscience/kmath/ast/ExpressionsInterpretersBenchmark.kt
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@ -1,4 +1,4 @@
//package kscience.kmath.ast
package kscience.kmath.ast
//
//import kscience.kmath.asm.compile
//import kscience.kmath.expressions.Expression

5
examples/src/main/kotlin/kscience/kmath/commons/prob/DistributionBenchmark.kt
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@ -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()}")
}
}

8
examples/src/main/kotlin/kscience/kmath/structures/StructureReadBenchmark.kt
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@ -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")

2
kmath-ast/src/jvmMain/kotlin/kscience/kmath/asm/internal/AsmBuilder.kt
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@ -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)
}
/**

2
kmath-commons/src/main/kotlin/kscience/kmath/commons/expressions/DiffExpression.kt
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@ -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

33
kmath-core/src/commonMain/kotlin/kscience/kmath/expressions/FunctionalExpressionAlgebra.kt
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@ -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> =

6
kmath-core/src/commonMain/kotlin/kscience/kmath/linear/FeaturedMatrix.kt
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@ -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)
/**

34
kmath-core/src/commonMain/kotlin/kscience/kmath/misc/AutoDiff.kt
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@ -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())

4
kmath-core/src/commonMain/kotlin/kscience/kmath/operations/BigInt.kt
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@ -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
}

2
kmath-core/src/commonMain/kotlin/kscience/kmath/operations/Complex.kt
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@ -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.

3
kmath-core/src/commonMain/kotlin/kscience/kmath/structures/FlaggedBuffer.kt
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@ -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" }
}

2
kmath-core/src/commonMain/kotlin/kscience/kmath/structures/Structure1D.kt
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@ -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]

3
kmath-coroutines/src/commonMain/kotlin/kscience/kmath/coroutines/coroutinesExtra.kt
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@ -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(

7
kmath-histograms/src/commonTest/kotlin/scietifik/kmath/histogram/MultivariateHistogramTest.kt
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@ -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)) }

0
kmath-memory/src/commonMain/kotlin/scientifik/memory/Memory.kt → kmath-memory/src/commonMain/kotlin/kscience/memory/Memory.kt
View File

3
kmath-memory/src/commonMain/kotlin/scientifik/memory/MemorySpec.kt → kmath-memory/src/commonMain/kotlin/kscience/memory/MemorySpec.kt
View File

@ -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].

0
kmath-memory/src/jsMain/kotlin/scientifik/memory/DataViewMemory.kt → kmath-memory/src/jsMain/kotlin/kscience/memory/DataViewMemory.kt
View File

3
kmath-memory/src/jvmMain/kotlin/scientifik/memory/ByteBufferMemory.kt → kmath-memory/src/jvmMain/kotlin/kscience/memory/ByteBufferMemory.kt
View File

@ -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.