kscience/kmath
9
0
Fork 1
Code Issues Pull Requests 6 Packages Projects Releases 16 Wiki Activity

Fix orders in DerivativeStructures

Browse Source
This commit is contained in:
Alexander Nozik 2020-10-26 14:44:57 +03:00
parent 30132964dd
commit 4450c0fcc7
6 changed files with 60 additions and 22 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

4
kmath-commons/src/main/kotlin/kscience/kmath/commons/expressions/DerivativeStructureExpression.kt
View File

@ -38,13 +38,13 @@ public class DerivativeStructureField(
key.identity to DerivativeStructureSymbol(key, value)
}
override fun const(value: Double): DerivativeStructure = DerivativeStructure(order, bindings.size, value)
override fun const(value: Double): DerivativeStructure = DerivativeStructure(bindings.size, order, value)
public override fun bindOrNull(symbol: Symbol): DerivativeStructureSymbol? = variables[symbol.identity]
public fun bind(symbol: Symbol): DerivativeStructureSymbol = variables.getValue(symbol.identity)
public fun Number.const(): DerivativeStructure = const(toDouble())
//public fun Number.const(): DerivativeStructure = const(toDouble())
public fun DerivativeStructure.derivative(parameter: Symbol, order: Int = 1): Double {
return derivative(mapOf(parameter to order))

19
kmath-commons/src/main/kotlin/kscience/kmath/commons/optimization/CMFit.kt
View File

@ -17,9 +17,9 @@ public object CMFit {
/**
* Generate a chi squared expression from given x-y-sigma model represented by an expression. Does not provide derivatives
* TODO move to core/separate module
* TODO move to prob/stat
*/
public fun chiSquaredExpression(
public fun chiSquared(
x: Buffer<Double>,
y: Buffer<Double>,
yErr: Buffer<Double>,
@ -35,7 +35,7 @@ public object CMFit {
val yErrValue = yErr[it]
val modifiedArgs = arguments + (xSymbol to xValue)
val modelValue = model(modifiedArgs)
((yValue - modelValue) / yErrValue).pow(2) / 2
((yValue - modelValue) / yErrValue).pow(2)
}
}
}
@ -43,7 +43,7 @@ public object CMFit {
/**
* Generate a chi squared expression from given x-y-sigma data and inline model. Provides automatic differentiation
*/
public fun chiSquaredExpression(
public fun chiSquared(
x: Buffer<Double>,
y: Buffer<Double>,
yErr: Buffer<Double>,
@ -58,7 +58,7 @@ public object CMFit {
val yValue = y[it]
val yErrValue = yErr[it]
val modelValue = model(const(xValue))
sum += ((yValue - modelValue) / yErrValue).pow(2) / 2
sum += ((yValue - modelValue) / yErrValue).pow(2)
}
sum
}
@ -92,12 +92,13 @@ public fun DifferentiableExpression<Double>.optimize(
}
public fun DifferentiableExpression<Double>.minimize(
vararg symbols: Symbol,
configuration: CMOptimizationProblem.() -> Unit,
vararg startPoint: Pair<Symbol, Double>,
configuration: CMOptimizationProblem.() -> Unit = {},
): OptimizationResult<Double> {
require(symbols.isNotEmpty()) { "Must provide a list of symbols for optimization" }
val problem = CMOptimizationProblem(symbols.toList()).apply(configuration)
require(startPoint.isNotEmpty()) { "Must provide a list of symbols for optimization" }
val problem = CMOptimizationProblem(startPoint.map { it.first }).apply(configuration)
problem.diffExpression(this)
problem.initialGuess(startPoint.toMap())
problem.goal(GoalType.MINIMIZE)
return problem.optimize()
}

11
kmath-commons/src/main/kotlin/kscience/kmath/commons/optimization/CMOptimizationProblem.kt
View File

@ -17,14 +17,13 @@ public operator fun PointValuePair.component2(): Double = value
public class CMOptimizationProblem(
override val symbols: List<Symbol>,
) : OptimizationProblem<Double>, SymbolIndexer {
protected val optimizationData: HashMap<KClass<out OptimizationData>, OptimizationData> = HashMap()
) : 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,
DEFAULT_ABSOLUTE_TOLERANCE, DEFAULT_MAX_ITER)
private fun addOptimizationData(data: OptimizationData) {
public fun addOptimizationData(data: OptimizationData) {
optimizationData[data::class] = data
}
@ -32,6 +31,8 @@ public class CMOptimizationProblem(
addOptimizationData(MaxEval.unlimited())
}
public fun exportOptimizationData(): List<OptimizationData> = optimizationData.values.toList()
public fun initialGuess(map: Map<Symbol, Double>): Unit {
addOptimizationData(InitialGuess(map.toDoubleArray()))
}
@ -90,7 +91,7 @@ public class CMOptimizationProblem(
override fun optimize(): OptimizationResult<Double> {
val optimizer = optimizatorBuilder?.invoke() ?: error("Optimizer not defined")
val (point, value) = optimizer.optimize(*optimizationData.values.toTypedArray())
return OptimizationResult(point.toMap(), value)
return OptimizationResult(point.toMap(), value, setOf(this))
}
public companion object {

11
kmath-commons/src/main/kotlin/kscience/kmath/commons/optimization/OptimizationProblem.kt
View File

@ -4,14 +4,19 @@ import kscience.kmath.expressions.DifferentiableExpression
import kscience.kmath.expressions.Expression
import kscience.kmath.expressions.Symbol
public interface OptimizationResultFeature
public interface OptimizationFeature
//TODO move to prob/stat
public class OptimizationResult<T>(
public val point: Map<Symbol, T>,
public val value: T,
public val features: Set<OptimizationResultFeature> = emptySet(),
)
public val features: Set<OptimizationFeature> = emptySet(),
){
override fun toString(): String {
return "OptimizationResult(point=$point, value=$value)"
}
}
/**
* A configuration builder for optimization problem

5
kmath-commons/src/main/kotlin/kscience/kmath/commons/random/CMRandomGeneratorWrapper.kt
View File

@ -2,8 +2,9 @@ package kscience.kmath.commons.random
import kscience.kmath.prob.RandomGenerator
public class CMRandomGeneratorWrapper(public val factory: (IntArray) -> RandomGenerator) :
org.apache.commons.math3.random.RandomGenerator {
public class CMRandomGeneratorWrapper(
public val factory: (IntArray) -> RandomGenerator,
) : org.apache.commons.math3.random.RandomGenerator {
private var generator: RandomGenerator = factory(intArrayOf())
public override fun nextBoolean(): Boolean = generator.nextBoolean()

32
kmath-commons/src/test/kotlin/kscience/kmath/commons/optimization/OptimizeTest.kt
View File

@ -2,14 +2,19 @@ package kscience.kmath.commons.optimization
import kscience.kmath.commons.expressions.DerivativeStructureExpression
import kscience.kmath.expressions.symbol
import kscience.kmath.prob.Distribution
import kscience.kmath.prob.RandomGenerator
import kscience.kmath.prob.normal
import kscience.kmath.structures.asBuffer
import org.junit.jupiter.api.Test
import kotlin.math.pow
internal class OptimizeTest {
val x by symbol
val y by symbol
val normal = DerivativeStructureExpression {
exp(-bind(x).pow(2) / 2) + exp(- bind(y).pow(2) / 2)
exp(-bind(x).pow(2) / 2) + exp(-bind(y).pow(2) / 2)
}
@Test
@ -32,4 +37,29 @@ internal class OptimizeTest {
println(result.point)
println(result.value)
}
@Test
fun testFit() {
val a by symbol
val b by symbol
val c by symbol
val sigma = 1.0
val generator = Distribution.normal(0.0, sigma)
val chain = generator.sample(RandomGenerator.default(1126))
val x = (1..100).map { it.toDouble() }
val y = x.map { it ->
it.pow(2) + it + 1 + chain.nextDouble()
}
val yErr = x.map { sigma }
with(CMFit) {
val chi2 = chiSquared(x.asBuffer(), y.asBuffer(), yErr.asBuffer()) { x ->
bind(a) * x.pow(2) + bind(b) * x + bind(c)
}
val result = chi2.minimize(a to 1.5, b to 0.9, c to 1.0)
println(result)
println("Chi2/dof = ${result.value / (x.size - 3)}")
}
}
}