kscience/kmath
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Duplicate repositories declared in main build script, fix errors

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This commit is contained in:
Iaroslav Postovalov 2020-11-02 01:08:55 +07:00
parent 658a1703ed
commit 33d23c8d28
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12 changed files with 74 additions and 44 deletions
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16
README.md
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@ -211,7 +211,15 @@ Release artifacts are accessible from bintray with following configuration (see
```kotlin
repositories {
jcenter()
maven("https://clojars.org/repo")
maven("https://dl.bintray.com/egor-bogomolov/astminer/")
maven("https://dl.bintray.com/hotkeytlt/maven")
maven("https://dl.bintray.com/kotlin/kotlin-eap")
maven("https://dl.bintray.com/kotlin/kotlinx")
maven("https://dl.bintray.com/mipt-npm/kscience")
maven("https://jitpack.io")
mavenCentral()
}
dependencies {
@ -228,7 +236,15 @@ Development builds are uploaded to the separate repository:
```kotlin
repositories {
jcenter()
maven("https://clojars.org/repo")
maven("https://dl.bintray.com/egor-bogomolov/astminer/")
maven("https://dl.bintray.com/hotkeytlt/maven")
maven("https://dl.bintray.com/kotlin/kotlin-eap")
maven("https://dl.bintray.com/kotlin/kotlinx")
maven("https://dl.bintray.com/mipt-npm/dev")
maven("https://jitpack.io")
mavenCentral()
}
```

5
build.gradle.kts
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@ -1,3 +1,5 @@
import ru.mipt.npm.gradle.KSciencePublishPlugin
plugins {
id("ru.mipt.npm.project")
}
@ -17,6 +19,7 @@ allprojects {
maven("https://dl.bintray.com/mipt-npm/dev")
maven("https://dl.bintray.com/mipt-npm/kscience")
maven("https://jitpack.io")
maven("http://logicrunch.research.it.uu.se/maven/")
mavenCentral()
}
@ -25,7 +28,7 @@ allprojects {
}
subprojects {
if (name.startsWith("kmath")) apply<ru.mipt.npm.gradle.KSciencePublishPlugin>()
if (name.startsWith("kmath")) apply<KSciencePublishPlugin>()
}
readme {

14
examples/build.gradle.kts
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@ -10,6 +10,20 @@ plugins {
allOpen.annotation("org.openjdk.jmh.annotations.State")
sourceSets.register("benchmarks")
repositories {
jcenter()
maven("https://clojars.org/repo")
maven("https://dl.bintray.com/egor-bogomolov/astminer/")
maven("https://dl.bintray.com/hotkeytlt/maven")
maven("https://dl.bintray.com/kotlin/kotlin-eap")
maven("https://dl.bintray.com/kotlin/kotlinx")
maven("https://dl.bintray.com/mipt-npm/dev")
maven("https://dl.bintray.com/mipt-npm/kscience")
maven("https://jitpack.io")
maven("http://logicrunch.research.it.uu.se/maven/")
mavenCentral()
}
dependencies {
implementation(project(":kmath-ast"))
implementation(project(":kmath-kotlingrad"))

2
examples/src/main/kotlin/kscience/kmath/ast/KotlingradSupport.kt
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@ -1,9 +1,9 @@
package kscience.kmath.ast
import kscience.kmath.asm.compile
import kscience.kmath.expressions.derivative
import kscience.kmath.expressions.invoke
import kscience.kmath.expressions.symbol
import kscience.kmath.kotlingrad.derivative
import kscience.kmath.kotlingrad.differentiable
import kscience.kmath.operations.RealField

7
kmath-commons/src/main/kotlin/kscience/kmath/commons/optimization/CMOptimizationProblem.kt
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@ -19,9 +19,8 @@ import kotlin.reflect.KClass
public operator fun PointValuePair.component1(): DoubleArray = point
public operator fun PointValuePair.component2(): Double = value
public class CMOptimizationProblem(
override val symbols: List<Symbol>,
) : OptimizationProblem<Double>, SymbolIndexer, OptimizationFeature {
public class CMOptimizationProblem(override val symbols: List<Symbol>, ) :
OptimizationProblem<Double>, SymbolIndexer, OptimizationFeature {
private val optimizationData: HashMap<KClass<out OptimizationData>, OptimizationData> = HashMap()
private var optimizatorBuilder: (() -> MultivariateOptimizer)? = null
public var convergenceChecker: ConvergenceChecker<PointValuePair> = SimpleValueChecker(DEFAULT_RELATIVE_TOLERANCE,
@ -49,7 +48,7 @@ public class CMOptimizationProblem(
addOptimizationData(objectiveFunction)
}
public override fun diffExpression(expression: DifferentiableExpression<Double>): Unit {
public override fun diffExpression(expression: DifferentiableExpression<Double, Expression<Double>>) {
expression(expression)
val gradientFunction = ObjectiveFunctionGradient {
val args = it.toMap()

11
kmath-commons/src/main/kotlin/kscience/kmath/commons/optimization/cmFit.kt
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@ -12,7 +12,6 @@ import kscience.kmath.structures.asBuffer
import org.apache.commons.math3.analysis.differentiation.DerivativeStructure
import org.apache.commons.math3.optim.nonlinear.scalar.GoalType
/**
* Generate a chi squared expression from given x-y-sigma data and inline model. Provides automatic differentiation
*/
@ -21,7 +20,7 @@ public fun Fitting.chiSquared(
y: Buffer<Double>,
yErr: Buffer<Double>,
model: DerivativeStructureField.(x: DerivativeStructure) -> DerivativeStructure,
): DifferentiableExpression<Double> = chiSquared(DerivativeStructureField, x, y, yErr, model)
): DifferentiableExpression<Double, Expression<Double>> = chiSquared(DerivativeStructureField, x, y, yErr, model)
/**
* Generate a chi squared expression from given x-y-sigma data and inline model. Provides automatic differentiation
@ -31,7 +30,7 @@ public fun Fitting.chiSquared(
y: Iterable<Double>,
yErr: Iterable<Double>,
model: DerivativeStructureField.(x: DerivativeStructure) -> DerivativeStructure,
): DifferentiableExpression<Double> = chiSquared(
): DifferentiableExpression<Double, Expression<Double>> = chiSquared(
DerivativeStructureField,
x.toList().asBuffer(),
y.toList().asBuffer(),
@ -39,7 +38,6 @@ public fun Fitting.chiSquared(
model
)
/**
* Optimize expression without derivatives
*/
@ -48,16 +46,15 @@ public fun Expression<Double>.optimize(
configuration: CMOptimizationProblem.() -> Unit,
): OptimizationResult<Double> = optimizeWith(CMOptimizationProblem, symbols = symbols, configuration)
/**
* Optimize differentiable expression
*/
public fun DifferentiableExpression<Double>.optimize(
public fun DifferentiableExpression<Double, Expression<Double>>.optimize(
vararg symbols: Symbol,
configuration: CMOptimizationProblem.() -> Unit,
): OptimizationResult<Double> = optimizeWith(CMOptimizationProblem, symbols = symbols, configuration)
public fun DifferentiableExpression<Double>.minimize(
public fun DifferentiableExpression<Double, Expression<Double>>.minimize(
vararg startPoint: Pair<Symbol, Double>,
configuration: CMOptimizationProblem.() -> Unit = {},
): OptimizationResult<Double> {

13
kmath-commons/src/test/kotlin/kscience/kmath/commons/optimization/OptimizeTest.kt
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@ -47,14 +47,17 @@ internal class OptimizeTest {
val sigma = 1.0
val generator = Distribution.normal(0.0, sigma)
val chain = generator.sample(RandomGenerator.default(112667))
val x = (1..100).map { it.toDouble() }
val y = x.map { it ->
val x = (1..100).map(Int::toDouble)
val y = x.map {
it.pow(2) + it + 1 + chain.nextDouble()
}
val yErr = x.map { sigma }
val chi2 = Fitting.chiSquared(x, y, yErr) { x ->
val yErr = List(x.size) { sigma }
val chi2 = Fitting.chiSquared(x, y, yErr) { x1 ->
val cWithDefault = bindOrNull(c) ?: one
bind(a) * x.pow(2) + bind(b) * x + cWithDefault
bind(a) * x1.pow(2) + bind(b) * x1 + cWithDefault
}
val result = chi2.minimize(a to 1.5, b to 0.9, c to 1.0)

4
kmath-core/src/commonMain/kotlin/kscience/kmath/expressions/DifferentiableExpression.kt
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@ -6,7 +6,7 @@ package kscience.kmath.expressions
* @param T the type this expression takes as argument and returns.
* @param R the type of expression this expression can be differentiated to.
*/
public interface DifferentiableExpression<T, R : Expression<T>> : Expression<T> {
public interface DifferentiableExpression<T, out R : Expression<T>> : Expression<T> {
/**
* Differentiates this expression by ordered collection of [symbols].
*
@ -43,6 +43,6 @@ public abstract class FirstDerivativeExpression<T, R : Expression<T>> : Differen
/**
* A factory that converts an expression in autodiff variables to a [DifferentiableExpression]
*/
public fun interface AutoDiffProcessor<T : Any, I : Any, A : ExpressionAlgebra<T, I>, R : Expression<T>> {
public fun interface AutoDiffProcessor<T : Any, I : Any, A : ExpressionAlgebra<T, I>, out R : Expression<T>> {
public fun process(function: A.() -> I): DifferentiableExpression<T, R>
}

20
kmath-kotlingrad/src/test/kotlin/kscience/kmath/kotlingrad/AdaptingTests.kt
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@ -13,13 +13,11 @@ import kotlin.test.assertTrue
import kotlin.test.fail
internal class AdaptingTests {
private val proto: DReal = DoublePrecision.prototype
@Test
fun symbol() {
val c1 = MstAlgebra.symbol("x")
assertTrue(c1.toSVar(proto).name == "x")
val c2 = "kitten".parseMath().toSFun(proto)
assertTrue(c1.toSVar<KMathNumber<Double, RealField>>().name == "x")
val c2 = "kitten".parseMath().toSFun<KMathNumber<Double, RealField>>()
if (c2 is SVar) assertTrue(c2.name == "kitten") else fail()
}
@ -27,15 +25,15 @@ internal class AdaptingTests {
fun number() {
val c1 = MstAlgebra.number(12354324)
assertTrue(c1.toSConst<DReal>().doubleValue == 12354324.0)
val c2 = "0.234".parseMath().toSFun(proto)
val c2 = "0.234".parseMath().toSFun<KMathNumber<Double, RealField>>()
if (c2 is SConst) assertTrue(c2.doubleValue == 0.234) else fail()
val c3 = "1e-3".parseMath().toSFun(proto)
val c3 = "1e-3".parseMath().toSFun<KMathNumber<Double, RealField>>()
if (c3 is SConst) assertEquals(0.001, c3.value) else fail()
}
@Test
fun simpleFunctionShape() {
val linear = "2*x+16".parseMath().toSFun(proto)
val linear = "2*x+16".parseMath().toSFun<KMathNumber<Double, RealField>>()
if (linear !is Sum) fail()
if (linear.left !is Prod) fail()
if (linear.right !is SConst) fail()
@ -43,8 +41,8 @@ internal class AdaptingTests {
@Test
fun simpleFunctionDerivative() {
val x = MstAlgebra.symbol("x").toSVar(proto)
val quadratic = "x^2-4*x-44".parseMath().toSFun(proto)
val x = MstAlgebra.symbol("x").toSVar<KMathNumber<Double, RealField>>()
val quadratic = "x^2-4*x-44".parseMath().toSFun<KMathNumber<Double, RealField>>()
val actualDerivative = MstExpression(RealField, quadratic.d(x).toMst()).compile()
val expectedDerivative = MstExpression(RealField, "2*x-4".parseMath()).compile()
assertEquals(actualDerivative("x" to 123.0), expectedDerivative("x" to 123.0))
@ -52,8 +50,8 @@ internal class AdaptingTests {
@Test
fun moreComplexDerivative() {
val x = MstAlgebra.symbol("x").toSVar(proto)
val composition = "-sqrt(sin(x^2)-cos(x)^2-16*x)".parseMath().toSFun(proto)
val x = MstAlgebra.symbol("x").toSVar<KMathNumber<Double, RealField>>()
val composition = "-sqrt(sin(x^2)-cos(x)^2-16*x)".parseMath().toSFun<KMathNumber<Double, RealField>>()
val actualDerivative = MstExpression(RealField, composition.d(x).toMst()).compile()
val expectedDerivative = MstExpression(

8
kmath-stat/src/commonMain/kotlin/kscience/kmath/stat/Fitting.kt
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@ -12,16 +12,18 @@ public object Fitting {
* Generate a chi squared expression from given x-y-sigma data and inline model. Provides automatic differentiation
*/
public fun <T : Any, I : Any, A> chiSquared(
autoDiff: AutoDiffProcessor<T, I, A>,
autoDiff: AutoDiffProcessor<T, I, A, Expression<T>>,
x: Buffer<T>,
y: Buffer<T>,
yErr: Buffer<T>,
model: A.(I) -> I,
): DifferentiableExpression<T> where A : ExtendedField<I>, A : ExpressionAlgebra<T, I> {
): DifferentiableExpression<T, Expression<T>> where A : ExtendedField<I>, A : ExpressionAlgebra<T, I> {
require(x.size == y.size) { "X and y buffers should be of the same size" }
require(y.size == yErr.size) { "Y and yErr buffer should of the same size" }
return autoDiff.process {
var sum = zero
x.indices.forEach {
val xValue = const(x[it])
val yValue = const(y[it])
@ -29,6 +31,7 @@ public object Fitting {
val modelValue = model(xValue)
sum += ((yValue - modelValue) / yErrValue).pow(2)
}
sum
}
}
@ -45,6 +48,7 @@ public object Fitting {
): Expression<Double> {
require(x.size == y.size) { "X and y buffers should be of the same size" }
require(y.size == yErr.size) { "Y and yErr buffer should of the same size" }
return Expression { arguments ->
x.indices.sumByDouble {
val xValue = x[it]

13
kmath-stat/src/commonMain/kotlin/kscience/kmath/stat/OptimizationProblem.kt
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@ -27,17 +27,17 @@ public interface OptimizationProblem<T : Any> {
/**
* Define the initial guess for the optimization problem
*/
public fun initialGuess(map: Map<Symbol, T>): Unit
public fun initialGuess(map: Map<Symbol, T>)
/**
* Set an objective function expression
*/
public fun expression(expression: Expression<T>): Unit
public fun expression(expression: Expression<T>)
/**
* Set a differentiable expression as objective function as function and gradient provider
*/
public fun diffExpression(expression: DifferentiableExpression<T>): Unit
public fun diffExpression(expression: DifferentiableExpression<T, Expression<T>>)
/**
* Update the problem from previous optimization run
@ -50,9 +50,8 @@ public interface OptimizationProblem<T : Any> {
public fun optimize(): OptimizationResult<T>
}
public interface OptimizationProblemFactory<T : Any, out P : OptimizationProblem<T>> {
public fun interface OptimizationProblemFactory<T : Any, out P : OptimizationProblem<T>> {
public fun build(symbols: List<Symbol>): P
}
public operator fun <T : Any, P : OptimizationProblem<T>> OptimizationProblemFactory<T, P>.invoke(
@ -60,7 +59,6 @@ public operator fun <T : Any, P : OptimizationProblem<T>> OptimizationProblemFac
block: P.() -> Unit,
): P = build(symbols).apply(block)
/**
* Optimize expression without derivatives using specific [OptimizationProblemFactory]
*/
@ -78,7 +76,7 @@ public fun <T : Any, F : OptimizationProblem<T>> Expression<T>.optimizeWith(
/**
* Optimize differentiable expression using specific [OptimizationProblemFactory]
*/
public fun <T : Any, F : OptimizationProblem<T>> DifferentiableExpression<T>.optimizeWith(
public fun <T : Any, F : OptimizationProblem<T>> DifferentiableExpression<T, Expression<T>>.optimizeWith(
factory: OptimizationProblemFactory<T, F>,
vararg symbols: Symbol,
configuration: F.() -> Unit,
@ -88,4 +86,3 @@ public fun <T : Any, F : OptimizationProblem<T>> DifferentiableExpression<T>.op
problem.diffExpression(this)
return problem.optimize()
}

3
settings.gradle.kts
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@ -1,8 +1,7 @@
pluginManagement {
repositories {
mavenLocal()
jcenter()
gradlePluginPortal()
jcenter()
maven("https://dl.bintray.com/kotlin/kotlin-eap")
maven("https://dl.bintray.com/mipt-npm/kscience")
maven("https://dl.bintray.com/mipt-npm/dev")