forked from kscience/kmath
Refactoring
This commit is contained in:
parent
f6b576071d
commit
d62bc66d4a
@ -6,31 +6,38 @@
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package space.kscience.kmath.benchmarks
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import kotlinx.benchmark.Benchmark
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import kotlinx.benchmark.Blackhole
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import kotlinx.benchmark.Scope
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import kotlinx.benchmark.State
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import space.kscience.kmath.complex.Complex
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import space.kscience.kmath.complex.ComplexField
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import space.kscience.kmath.complex.complex
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import space.kscience.kmath.operations.invoke
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import space.kscience.kmath.structures.Buffer
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import space.kscience.kmath.structures.DoubleBuffer
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import space.kscience.kmath.structures.MutableBuffer
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@State(Scope.Benchmark)
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internal class BufferBenchmark {
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@Benchmark
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fun genericDoubleBufferReadWrite() {
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fun genericDoubleBufferReadWrite(blackhole: Blackhole) {
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val buffer = DoubleBuffer(size) { it.toDouble() }
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var res = 0.0
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(0 until size).forEach {
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buffer[it]
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res += buffer[it]
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}
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blackhole.consume(res)
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}
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@Benchmark
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fun complexBufferReadWrite() {
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val buffer = MutableBuffer.complex(size / 2) { Complex(it.toDouble(), -it.toDouble()) }
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fun complexBufferReadWrite(blackhole: Blackhole) = ComplexField {
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val buffer = Buffer.complex(size / 2) { Complex(it.toDouble(), -it.toDouble()) }
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var res = zero
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(0 until size / 2).forEach {
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buffer[it]
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res += buffer[it]
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}
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blackhole.consume(res)
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}
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private companion object {
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@ -16,10 +16,10 @@ import space.kscience.kmath.optimization.FunctionOptimizationTarget
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import space.kscience.kmath.optimization.optimizeWith
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import space.kscience.kmath.optimization.resultPoint
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import space.kscience.kmath.optimization.resultValue
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import space.kscience.kmath.random.RandomGenerator
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import space.kscience.kmath.real.DoubleVector
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import space.kscience.kmath.real.map
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import space.kscience.kmath.real.step
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import space.kscience.kmath.stat.RandomGenerator
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import space.kscience.kmath.structures.asIterable
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import space.kscience.kmath.structures.toList
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import space.kscience.plotly.*
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@ -17,9 +17,9 @@ import space.kscience.kmath.optimization.QowOptimizer
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import space.kscience.kmath.optimization.chiSquaredOrNull
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import space.kscience.kmath.optimization.fitWith
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import space.kscience.kmath.optimization.resultPoint
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import space.kscience.kmath.random.RandomGenerator
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import space.kscience.kmath.real.map
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import space.kscience.kmath.real.step
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import space.kscience.kmath.stat.RandomGenerator
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import space.kscience.kmath.structures.asIterable
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import space.kscience.kmath.structures.toList
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import space.kscience.plotly.*
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@ -10,6 +10,7 @@ import kotlinx.coroutines.async
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import kotlinx.coroutines.runBlocking
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import org.apache.commons.rng.sampling.distribution.BoxMullerNormalizedGaussianSampler
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import org.apache.commons.rng.simple.RandomSource
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import space.kscience.kmath.random.RandomGenerator
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import space.kscience.kmath.samplers.GaussianSampler
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import java.time.Duration
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import java.time.Instant
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@ -9,6 +9,7 @@ import kotlinx.coroutines.runBlocking
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import space.kscience.kmath.chains.Chain
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import space.kscience.kmath.chains.collectWithState
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import space.kscience.kmath.distributions.NormalDistribution
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import space.kscience.kmath.random.RandomGenerator
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private data class AveragingChainState(var num: Int = 0, var value: Double = 0.0)
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@ -8,9 +8,9 @@ package space.kscience.kmath.commons.random
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import kotlinx.coroutines.runBlocking
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import space.kscience.kmath.misc.PerformancePitfall
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import space.kscience.kmath.misc.toIntExact
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import space.kscience.kmath.random.RandomGenerator
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import space.kscience.kmath.samplers.GaussianSampler
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import space.kscience.kmath.samplers.next
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import space.kscience.kmath.stat.RandomGenerator
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public class CMRandomGeneratorWrapper(
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@ -14,7 +14,7 @@ import space.kscience.kmath.expressions.Symbol.Companion.y
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import space.kscience.kmath.expressions.chiSquaredExpression
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import space.kscience.kmath.expressions.symbol
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import space.kscience.kmath.optimization.*
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import space.kscience.kmath.stat.RandomGenerator
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import space.kscience.kmath.random.RandomGenerator
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import space.kscience.kmath.structures.DoubleBuffer
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import space.kscience.kmath.structures.asBuffer
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import space.kscience.kmath.structures.map
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@ -21,11 +21,6 @@ public interface BufferAlgebra<T, out A : Algebra<T>> : Algebra<Buffer<T>> {
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public val elementAlgebra: A
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public val bufferFactory: BufferFactory<T>
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public fun buffer(size: Int, vararg elements: T): Buffer<T> {
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require(elements.size == size) { "Expected $size elements but found ${elements.size}" }
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return bufferFactory(size) { elements[it] }
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}
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//TODO move to multi-receiver inline extension
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public fun Buffer<T>.map(block: A.(T) -> T): Buffer<T> = mapInline(this, block)
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@ -49,12 +44,15 @@ public interface BufferAlgebra<T, out A : Algebra<T>> : Algebra<Buffer<T>> {
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}
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}
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public fun <T, A : Algebra<T>> BufferAlgebra<T, A>.buffer(vararg elements: T): Buffer<T> =
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bufferFactory(elements.size) { elements[it] }
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/**
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* Inline map
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*/
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private inline fun <T, A : Algebra<T>> BufferAlgebra<T, A>.mapInline(
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buffer: Buffer<T>,
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crossinline block: A.(T) -> T
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crossinline block: A.(T) -> T,
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): Buffer<T> = bufferFactory(buffer.size) { elementAlgebra.block(buffer[it]) }
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/**
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@ -62,7 +60,7 @@ private inline fun <T, A : Algebra<T>> BufferAlgebra<T, A>.mapInline(
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*/
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private inline fun <T, A : Algebra<T>> BufferAlgebra<T, A>.mapIndexedInline(
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buffer: Buffer<T>,
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crossinline block: A.(index: Int, arg: T) -> T
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crossinline block: A.(index: Int, arg: T) -> T,
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): Buffer<T> = bufferFactory(buffer.size) { elementAlgebra.block(it, buffer[it]) }
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/**
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@ -71,7 +69,7 @@ private inline fun <T, A : Algebra<T>> BufferAlgebra<T, A>.mapIndexedInline(
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private inline fun <T, A : Algebra<T>> BufferAlgebra<T, A>.zipInline(
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l: Buffer<T>,
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r: Buffer<T>,
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crossinline block: A.(l: T, r: T) -> T
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crossinline block: A.(l: T, r: T) -> T,
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): Buffer<T> {
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require(l.size == r.size) { "Incompatible buffer sizes. left: ${l.size}, right: ${r.size}" }
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return bufferFactory(l.size) { elementAlgebra.block(l[it], r[it]) }
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@ -128,13 +126,13 @@ public fun <T, A : ExponentialOperations<T>> BufferAlgebra<T, A>.atanh(arg: Buff
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mapInline(arg) { atanh(it) }
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public fun <T, A : PowerOperations<T>> BufferAlgebra<T, A>.pow(arg: Buffer<T>, pow: Number): Buffer<T> =
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mapInline(arg) {it.pow(pow) }
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mapInline(arg) { it.pow(pow) }
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public open class BufferRingOps<T, A: Ring<T>>(
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public open class BufferRingOps<T, A : Ring<T>>(
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override val elementAlgebra: A,
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override val bufferFactory: BufferFactory<T>,
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) : BufferAlgebra<T, A>, RingOps<Buffer<T>>{
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) : BufferAlgebra<T, A>, RingOps<Buffer<T>> {
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override fun add(left: Buffer<T>, right: Buffer<T>): Buffer<T> = zipInline(left, right) { l, r -> l + r }
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override fun multiply(left: Buffer<T>, right: Buffer<T>): Buffer<T> = zipInline(left, right) { l, r -> l * r }
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@ -155,7 +153,8 @@ public val ShortRing.bufferAlgebra: BufferRingOps<Short, ShortRing>
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public open class BufferFieldOps<T, A : Field<T>>(
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elementAlgebra: A,
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bufferFactory: BufferFactory<T>,
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) : BufferRingOps<T, A>(elementAlgebra, bufferFactory), BufferAlgebra<T, A>, FieldOps<Buffer<T>>, ScaleOperations<Buffer<T>> {
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) : BufferRingOps<T, A>(elementAlgebra, bufferFactory), BufferAlgebra<T, A>, FieldOps<Buffer<T>>,
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ScaleOperations<Buffer<T>> {
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override fun add(left: Buffer<T>, right: Buffer<T>): Buffer<T> = zipInline(left, right) { l, r -> l + r }
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override fun multiply(left: Buffer<T>, right: Buffer<T>): Buffer<T> = zipInline(left, right) { l, r -> l * r }
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@ -172,7 +171,7 @@ public open class BufferFieldOps<T, A : Field<T>>(
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public class BufferField<T, A : Field<T>>(
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elementAlgebra: A,
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bufferFactory: BufferFactory<T>,
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override val size: Int
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override val size: Int,
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) : BufferFieldOps<T, A>(elementAlgebra, bufferFactory), Field<Buffer<T>>, WithSize {
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override val zero: Buffer<T> = bufferFactory(size) { elementAlgebra.zero }
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@ -195,4 +194,4 @@ public fun BufferField<Double, *>.buffer(vararg elements: Number): Buffer<Double
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public fun <T, A : Field<T>> A.bufferAlgebra(bufferFactory: BufferFactory<T>): BufferFieldOps<T, A> =
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BufferFieldOps(this, bufferFactory)
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public val DoubleField.bufferAlgebra: DoubleBufferOps get() = DoubleBufferOps
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public val DoubleField.bufferAlgebra: DoubleBufferOps get() = DoubleBufferOps
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@ -135,6 +135,7 @@ public abstract class DoubleBufferOps : BufferAlgebra<Double, DoubleField>, Exte
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override fun scale(a: Buffer<Double>, value: Double): DoubleBuffer = a.mapInline { it * value }
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public companion object : DoubleBufferOps() {
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public inline fun Buffer<Double>.mapInline(block: (Double) -> Double): DoubleBuffer =
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if (this is DoubleBuffer) {
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DoubleArray(size) { block(array[it]) }.asBuffer()
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@ -144,6 +145,14 @@ public abstract class DoubleBufferOps : BufferAlgebra<Double, DoubleField>, Exte
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}
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}
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public inline fun BufferAlgebra<Double, DoubleField>.buffer(size: Int, init: (Int) -> Double): DoubleBuffer {
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val res = DoubleArray(size)
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for (i in 0 until size) {
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res[i] = init(i)
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}
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return res.asBuffer()
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}
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public object DoubleL2Norm : Norm<Point<Double>, Double> {
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override fun norm(arg: Point<Double>): Double = sqrt(arg.fold(0.0) { acc: Double, d: Double -> acc + d.pow(2) })
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}
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@ -6,8 +6,8 @@
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package space.kscience.kmath.distributions
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import space.kscience.kmath.chains.Chain
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import space.kscience.kmath.random.RandomGenerator
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import space.kscience.kmath.samplers.Sampler
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import space.kscience.kmath.stat.RandomGenerator
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/**
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* A distribution of typed objects.
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@ -7,7 +7,7 @@ package space.kscience.kmath.distributions
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import space.kscience.kmath.chains.Chain
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import space.kscience.kmath.chains.SimpleChain
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import space.kscience.kmath.stat.RandomGenerator
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import space.kscience.kmath.random.RandomGenerator
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/**
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* A multivariate distribution that takes a map of parameters.
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@ -6,11 +6,11 @@
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package space.kscience.kmath.distributions
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import space.kscience.kmath.chains.Chain
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import space.kscience.kmath.random.RandomGenerator
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import space.kscience.kmath.samplers.GaussianSampler
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import space.kscience.kmath.samplers.InternalErf
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import space.kscience.kmath.samplers.NormalizedGaussianSampler
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import space.kscience.kmath.samplers.ZigguratNormalizedGaussianSampler
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import space.kscience.kmath.stat.RandomGenerator
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import kotlin.math.*
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/**
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@ -7,7 +7,7 @@ package space.kscience.kmath.distributions
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import space.kscience.kmath.chains.Chain
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import space.kscience.kmath.chains.SimpleChain
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import space.kscience.kmath.stat.RandomGenerator
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import space.kscience.kmath.random.RandomGenerator
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public class UniformDistribution(public val range: ClosedFloatingPointRange<Double>) : UnivariateDistribution<Double> {
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private val length: Double = range.endInclusive - range.start
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@ -22,6 +22,10 @@ public class MCScope(
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public val random: RandomGenerator,
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)
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public fun MCScope.asCoroutineScope(): CoroutineScope = object : CoroutineScope {
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override val coroutineContext: CoroutineContext get() = this@asCoroutineScope.coroutineContext
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}
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/**
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* Launches a supervised Monte-Carlo scope
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*/
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@ -6,7 +6,7 @@
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package space.kscience.kmath.samplers
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import space.kscience.kmath.chains.BlockingDoubleChain
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import space.kscience.kmath.stat.RandomGenerator
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import space.kscience.kmath.random.RandomGenerator
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import space.kscience.kmath.structures.DoubleBuffer
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import kotlin.math.ln
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import kotlin.math.pow
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@ -6,8 +6,8 @@
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package space.kscience.kmath.samplers
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import space.kscience.kmath.chains.Chain
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import space.kscience.kmath.stat.RandomGenerator
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import space.kscience.kmath.stat.chain
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import space.kscience.kmath.random.RandomGenerator
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import space.kscience.kmath.random.chain
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import kotlin.math.*
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/**
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@ -23,14 +23,14 @@ import kotlin.math.*
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*/
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public class AhrensDieterMarsagliaTsangGammaSampler private constructor(
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alpha: Double,
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theta: Double
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theta: Double,
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) : Sampler<Double> {
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private val delegate: BaseGammaSampler =
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if (alpha < 1) AhrensDieterGammaSampler(alpha, theta) else MarsagliaTsangGammaSampler(alpha, theta)
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private abstract class BaseGammaSampler internal constructor(
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protected val alpha: Double,
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protected val theta: Double
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protected val theta: Double,
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) : Sampler<Double> {
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init {
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require(alpha > 0) { "alpha is not strictly positive: $alpha" }
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@ -117,7 +117,7 @@ public class AhrensDieterMarsagliaTsangGammaSampler private constructor(
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public companion object {
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public fun of(
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alpha: Double,
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theta: Double
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theta: Double,
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): Sampler<Double> = AhrensDieterMarsagliaTsangGammaSampler(alpha, theta)
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}
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}
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@ -6,8 +6,8 @@
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package space.kscience.kmath.samplers
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import space.kscience.kmath.chains.Chain
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import space.kscience.kmath.stat.RandomGenerator
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import space.kscience.kmath.stat.chain
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import space.kscience.kmath.random.RandomGenerator
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import space.kscience.kmath.random.chain
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import kotlin.math.ceil
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import kotlin.math.max
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import kotlin.math.min
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@ -6,7 +6,7 @@
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package space.kscience.kmath.samplers
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import space.kscience.kmath.chains.BlockingDoubleChain
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import space.kscience.kmath.stat.RandomGenerator
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import space.kscience.kmath.random.RandomGenerator
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import space.kscience.kmath.structures.DoubleBuffer
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import kotlin.math.*
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@ -7,7 +7,7 @@ package space.kscience.kmath.samplers
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import space.kscience.kmath.chains.BlockingDoubleChain
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import space.kscience.kmath.chains.map
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import space.kscience.kmath.stat.RandomGenerator
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import space.kscience.kmath.random.RandomGenerator
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/**
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* Sampling from a Gaussian distribution with given mean and standard deviation.
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@ -21,7 +21,7 @@ import space.kscience.kmath.stat.RandomGenerator
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public class GaussianSampler(
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public val mean: Double,
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public val standardDeviation: Double,
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private val normalized: NormalizedGaussianSampler = BoxMullerSampler
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private val normalized: NormalizedGaussianSampler = BoxMullerSampler,
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) : BlockingDoubleSampler {
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init {
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@ -6,7 +6,7 @@
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package space.kscience.kmath.samplers
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import space.kscience.kmath.chains.BlockingIntChain
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import space.kscience.kmath.stat.RandomGenerator
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import space.kscience.kmath.random.RandomGenerator
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import space.kscience.kmath.structures.IntBuffer
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import kotlin.math.exp
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@ -6,7 +6,7 @@
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package space.kscience.kmath.samplers
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import space.kscience.kmath.chains.BlockingDoubleChain
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import space.kscience.kmath.stat.RandomGenerator
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import space.kscience.kmath.random.RandomGenerator
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import space.kscience.kmath.structures.DoubleBuffer
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import kotlin.math.ln
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import kotlin.math.sqrt
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@ -6,9 +6,9 @@
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package space.kscience.kmath.samplers
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import space.kscience.kmath.chains.BlockingDoubleChain
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import space.kscience.kmath.stat.RandomGenerator
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import space.kscience.kmath.random.RandomGenerator
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public interface BlockingDoubleSampler: Sampler<Double>{
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public interface BlockingDoubleSampler : Sampler<Double> {
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override fun sample(generator: RandomGenerator): BlockingDoubleChain
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}
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@ -17,6 +17,6 @@ public interface BlockingDoubleSampler: Sampler<Double>{
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* Marker interface for a sampler that generates values from an N(0,1)
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* [Gaussian distribution](https://en.wikipedia.org/wiki/Normal_distribution).
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*/
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public fun interface NormalizedGaussianSampler : BlockingDoubleSampler{
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public fun interface NormalizedGaussianSampler : BlockingDoubleSampler {
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public companion object
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}
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@ -7,7 +7,7 @@ package space.kscience.kmath.samplers
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import space.kscience.kmath.chains.BlockingIntChain
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import space.kscience.kmath.misc.toIntExact
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import space.kscience.kmath.stat.RandomGenerator
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import space.kscience.kmath.random.RandomGenerator
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import space.kscience.kmath.structures.IntBuffer
|
||||
import kotlin.math.*
|
||||
|
||||
|
@ -8,7 +8,7 @@ package space.kscience.kmath.samplers
|
||||
import kotlinx.coroutines.flow.first
|
||||
import space.kscience.kmath.chains.Chain
|
||||
import space.kscience.kmath.chains.collect
|
||||
import space.kscience.kmath.stat.RandomGenerator
|
||||
import space.kscience.kmath.random.RandomGenerator
|
||||
import space.kscience.kmath.structures.Buffer
|
||||
import space.kscience.kmath.structures.BufferFactory
|
||||
import space.kscience.kmath.structures.DoubleBuffer
|
||||
|
@ -12,7 +12,7 @@ import space.kscience.kmath.chains.zip
|
||||
import space.kscience.kmath.operations.Group
|
||||
import space.kscience.kmath.operations.ScaleOperations
|
||||
import space.kscience.kmath.operations.invoke
|
||||
import space.kscience.kmath.stat.RandomGenerator
|
||||
import space.kscience.kmath.random.RandomGenerator
|
||||
|
||||
/**
|
||||
* Implements [Sampler] by sampling only certain [value].
|
||||
|
@ -6,8 +6,7 @@
|
||||
package space.kscience.kmath.samplers
|
||||
|
||||
import space.kscience.kmath.chains.BlockingDoubleChain
|
||||
import space.kscience.kmath.misc.toIntExact
|
||||
import space.kscience.kmath.stat.RandomGenerator
|
||||
import space.kscience.kmath.random.RandomGenerator
|
||||
import space.kscience.kmath.structures.DoubleBuffer
|
||||
import kotlin.math.*
|
||||
|
||||
|
@ -7,13 +7,17 @@ package space.kscience.kmath.stat
|
||||
|
||||
import org.apache.commons.rng.UniformRandomProvider
|
||||
import org.apache.commons.rng.simple.RandomSource
|
||||
import space.kscience.kmath.random.RandomGenerator
|
||||
|
||||
/**
|
||||
* Implements [RandomGenerator] by delegating all operations to [RandomSource].
|
||||
*
|
||||
* @property source the underlying [RandomSource] object.
|
||||
*/
|
||||
public class RandomSourceGenerator internal constructor(public val source: RandomSource, seed: Long?) : RandomGenerator {
|
||||
public class RandomSourceGenerator internal constructor(
|
||||
public val source: RandomSource,
|
||||
seed: Long?,
|
||||
) : RandomGenerator {
|
||||
internal val random: UniformRandomProvider = seed?.let { RandomSource.create(source, seed) }
|
||||
?: RandomSource.create(source)
|
||||
|
||||
|
@ -8,6 +8,7 @@ package space.kscience.kmath.stat
|
||||
import kotlinx.coroutines.runBlocking
|
||||
import org.junit.jupiter.api.Assertions
|
||||
import org.junit.jupiter.api.Test
|
||||
import space.kscience.kmath.random.RandomGenerator
|
||||
import space.kscience.kmath.samplers.GaussianSampler
|
||||
|
||||
internal class CommonsDistributionsTest {
|
||||
|
@ -6,20 +6,22 @@
|
||||
package space.kscience.kmath.stat
|
||||
|
||||
import kotlinx.coroutines.*
|
||||
import space.kscience.kmath.random.launch
|
||||
import space.kscience.kmath.random.mcScope
|
||||
import java.util.*
|
||||
import kotlin.test.Test
|
||||
import kotlin.test.assertEquals
|
||||
|
||||
data class RandomResult(val branch: String, val order: Int, val value: Int)
|
||||
|
||||
typealias ATest = suspend CoroutineScope.() -> Set<RandomResult>
|
||||
internal typealias ATest = suspend () -> Set<RandomResult>
|
||||
|
||||
class MCScopeTest {
|
||||
internal class MCScopeTest {
|
||||
val simpleTest: ATest = {
|
||||
mcScope(1111) {
|
||||
val res = Collections.synchronizedSet(HashSet<RandomResult>())
|
||||
|
||||
launch {
|
||||
launch{
|
||||
//println(random)
|
||||
repeat(10) {
|
||||
delay(10)
|
||||
|
@ -6,6 +6,8 @@
|
||||
package space.kscience.kmath.stat
|
||||
|
||||
import kotlinx.coroutines.runBlocking
|
||||
import space.kscience.kmath.random.RandomGenerator
|
||||
import space.kscience.kmath.random.chain
|
||||
import space.kscience.kmath.samplers.Sampler
|
||||
import space.kscience.kmath.samplers.sampleBuffer
|
||||
import kotlin.test.Test
|
||||
|
@ -9,6 +9,8 @@ import kotlinx.coroutines.flow.first
|
||||
import kotlinx.coroutines.flow.last
|
||||
import kotlinx.coroutines.flow.take
|
||||
import kotlinx.coroutines.runBlocking
|
||||
import space.kscience.kmath.random.RandomGenerator
|
||||
import space.kscience.kmath.random.chain
|
||||
import space.kscience.kmath.streaming.chunked
|
||||
import kotlin.test.Test
|
||||
import kotlin.test.assertEquals
|
||||
|
@ -6,8 +6,8 @@
|
||||
package space.kscience.kmath.tensors.core.internal
|
||||
|
||||
import space.kscience.kmath.nd.as1D
|
||||
import space.kscience.kmath.random.RandomGenerator
|
||||
import space.kscience.kmath.samplers.GaussianSampler
|
||||
import space.kscience.kmath.stat.RandomGenerator
|
||||
import space.kscience.kmath.structures.*
|
||||
import space.kscience.kmath.tensors.core.BufferedTensor
|
||||
import space.kscience.kmath.tensors.core.DoubleTensor
|
||||
|
Loading…
Reference in New Issue
Block a user