examples/src/main/kotlin/scientifik/kmath/linear/LinearAlgebraBenchmark.kt

@@ -1,6 +1,9 @@
 package scientifik.kmath.linear
 
 import koma.matrix.ejml.EJMLMatrixFactory
+import scientifik.kmath.commons.linear.CMMatrixContext
+import scientifik.kmath.commons.linear.inverse
+import scientifik.kmath.commons.linear.toCM
 import scientifik.kmath.operations.RealField
 import scientifik.kmath.structures.Matrix
 import kotlin.contracts.ExperimentalContracts

examples/src/main/kotlin/scientifik/kmath/linear/MultiplicationBenchmark.kt

@@ -1,6 +1,8 @@
 package scientifik.kmath.linear
 
 import koma.matrix.ejml.EJMLMatrixFactory
+import scientifik.kmath.commons.linear.CMMatrixContext
+import scientifik.kmath.commons.linear.toCM
 import scientifik.kmath.operations.RealField
 import scientifik.kmath.structures.Matrix
 import kotlin.random.Random

kmath-commons/build.gradle.kts

@@ -8,6 +8,7 @@ description = "Commons math binding for kmath"
 dependencies {
     api(project(":kmath-core"))
     api(project(":kmath-coroutines"))
+    api(project(":kmath-prob"))
     api("org.apache.commons:commons-math3:3.6.1")
     testImplementation("org.jetbrains.kotlin:kotlin-test")
     testImplementation("org.jetbrains.kotlin:kotlin-test-junit")

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

@@ -1,6 +1,8 @@
-package scientifik.kmath.expressions
+package scientifik.kmath.commons.expressions
 
 import org.apache.commons.math3.analysis.differentiation.DerivativeStructure
+import scientifik.kmath.expressions.Expression
+import scientifik.kmath.expressions.ExpressionContext
 import scientifik.kmath.operations.ExtendedField
 import scientifik.kmath.operations.Field
 import kotlin.properties.ReadOnlyProperty
@@ -81,8 +83,12 @@ class DerivativeStructureField(
 /**
  * A constructs that creates a derivative structure with required order on-demand
  */
-class DiffExpression(val function: DerivativeStructureField.() -> DerivativeStructure) : Expression<Double> {
+class DiffExpression(val function: DerivativeStructureField.() -> DerivativeStructure) :
-    override fun invoke(arguments: Map<String, Double>): Double = DerivativeStructureField(0, arguments)
+    Expression<Double> {
+    override fun invoke(arguments: Map<String, Double>): Double = DerivativeStructureField(
+        0,
+        arguments
+    )
         .run(function).value
 
     /**
@@ -109,21 +115,27 @@ fun DiffExpression.derivative(name: String) = derivative(name to 1)
  * A context for [DiffExpression] (not to be confused with [DerivativeStructure])
  */
 object DiffExpressionContext : ExpressionContext<Double>, Field<DiffExpression> {
-    override fun variable(name: String, default: Double?) = DiffExpression { variable(name, default?.const()) }
+    override fun variable(name: String, default: Double?) =
+        DiffExpression { variable(name, default?.const()) }
 
-    override fun const(value: Double): DiffExpression = DiffExpression { value.const() }
+    override fun const(value: Double): DiffExpression =
+        DiffExpression { value.const() }
 
-    override fun add(a: DiffExpression, b: DiffExpression) = DiffExpression { a.function(this) + b.function(this) }
+    override fun add(a: DiffExpression, b: DiffExpression) =
+        DiffExpression { a.function(this) + b.function(this) }
 
     override val zero = DiffExpression { 0.0.const() }
 
-    override fun multiply(a: DiffExpression, k: Number) = DiffExpression { a.function(this) * k }
+    override fun multiply(a: DiffExpression, k: Number) =
+        DiffExpression { a.function(this) * k }
 
     override val one = DiffExpression { 1.0.const() }
 
-    override fun multiply(a: DiffExpression, b: DiffExpression) = DiffExpression { a.function(this) * b.function(this) }
+    override fun multiply(a: DiffExpression, b: DiffExpression) =
+        DiffExpression { a.function(this) * b.function(this) }
 
-    override fun divide(a: DiffExpression, b: DiffExpression) = DiffExpression { a.function(this) / b.function(this) }
+    override fun divide(a: DiffExpression, b: DiffExpression) =
+        DiffExpression { a.function(this) / b.function(this) }
 }
 
 

kmath-commons/src/main/kotlin/scientifik/kmath/commons/linear/CMMatrix.kt

@@ -1,11 +1,13 @@
-package scientifik.kmath.linear
+package scientifik.kmath.commons.linear
 
 import org.apache.commons.math3.linear.*
 import org.apache.commons.math3.linear.RealMatrix
 import org.apache.commons.math3.linear.RealVector
+import scientifik.kmath.linear.*
 import scientifik.kmath.structures.Matrix
 
-class CMMatrix(val origin: RealMatrix, features: Set<MatrixFeature>? = null) : FeaturedMatrix<Double> {
+class CMMatrix(val origin: RealMatrix, features: Set<MatrixFeature>? = null) :
+    FeaturedMatrix<Double> {
     override val rowNum: Int get() = origin.rowDimension
     override val colNum: Int get() = origin.columnDimension
 
@@ -70,10 +72,14 @@ object CMMatrixContext : MatrixContext<Double> {
     override fun multiply(a: Matrix<Double>, k: Number) =
         CMMatrix(a.toCM().origin.scalarMultiply(k.toDouble()))
 
-    override fun Matrix<Double>.times(value: Double): Matrix<Double>  = produce(rowNum,colNum){i,j-> get(i,j)*value}
+    override fun Matrix<Double>.times(value: Double): Matrix<Double>  =
+        produce(rowNum, colNum) { i, j -> get(i, j) * value }
 }
 
-operator fun CMMatrix.plus(other: CMMatrix): CMMatrix = CMMatrix(this.origin.add(other.origin))
+operator fun CMMatrix.plus(other: CMMatrix): CMMatrix =
-operator fun CMMatrix.minus(other: CMMatrix): CMMatrix = CMMatrix(this.origin.subtract(other.origin))
+    CMMatrix(this.origin.add(other.origin))
+operator fun CMMatrix.minus(other: CMMatrix): CMMatrix =
+    CMMatrix(this.origin.subtract(other.origin))
 
-infix fun CMMatrix.dot(other: CMMatrix): CMMatrix = CMMatrix(this.origin.multiply(other.origin))
+infix fun CMMatrix.dot(other: CMMatrix): CMMatrix =
+    CMMatrix(this.origin.multiply(other.origin))

kmath-commons/src/main/kotlin/scientifik/kmath/commons/linear/CMSolver.kt

@@ -1,6 +1,7 @@
-package scientifik.kmath.linear
+package scientifik.kmath.commons.linear
 
 import org.apache.commons.math3.linear.*
+import scientifik.kmath.linear.Point
 import scientifik.kmath.structures.Matrix
 
 enum class CMDecomposition {

kmath-commons/src/main/kotlin/scientifik/kmath/commons/prob/CMRandomGeneratorWrapper.kt

@@ -0,0 +1,18 @@
+package scientifik.kmath.commons.prob
+
+import org.apache.commons.math3.random.RandomGenerator
+
+inline class CMRandomGeneratorWrapper(val generator: RandomGenerator) : scientifik.kmath.prob.RandomGenerator {
+    override fun nextDouble(): Double = generator.nextDouble()
+
+    override fun nextInt(): Int = generator.nextInt()
+
+    override fun nextLong(): Long = generator.nextLong()
+
+    override fun nextBlock(size: Int): ByteArray = ByteArray(size).apply { generator.nextBytes(this) }
+}
+
+fun RandomGenerator.asKmathGenerator() = CMRandomGeneratorWrapper(this)
+
+fun scientifik.kmath.prob.RandomGenerator.asCMGenerator() =
+    (this as? CMRandomGeneratorWrapper)?.generator ?: TODO("Implement reverse CM wrapper")

kmath-commons/src/main/kotlin/scientifik/kmath/commons/prob/CommonsDistribution.kt

@@ -0,0 +1,84 @@
+package scientifik.kmath.commons.prob
+
+import org.apache.commons.math3.distribution.*
+import scientifik.kmath.chains.Chain
+import scientifik.kmath.chains.SimpleChain
+import scientifik.kmath.prob.Distribution
+import scientifik.kmath.prob.RandomChain
+import scientifik.kmath.prob.RandomGenerator
+import scientifik.kmath.prob.UnivariateDistribution
+import org.apache.commons.math3.random.RandomGenerator as CMRandom
+
+class CMRealDistributionWrapper(val builder: (CMRandom?) -> RealDistribution) : UnivariateDistribution<Double> {
+
+    private val defaultDistribution by lazy { builder(null) }
+
+    override fun probability(arg: Double): Double = defaultDistribution.probability(arg)
+
+    override fun cumulative(arg: Double): Double = defaultDistribution.cumulativeProbability(arg)
+
+    override fun sample(generator: RandomGenerator): RandomChain<Double> {
+        val distribution = builder(generator.asCMGenerator())
+        return RandomChain(generator) { distribution.sample() }
+    }
+}
+
+class CMIntDistributionWrapper(val builder: (CMRandom?) -> IntegerDistribution) : UnivariateDistribution<Int> {
+
+    private val defaultDistribution by lazy { builder(null) }
+
+    override fun probability(arg: Int): Double = defaultDistribution.probability(arg)
+
+    override fun cumulative(arg: Int): Double = defaultDistribution.cumulativeProbability(arg)
+
+    override fun sample(generator: RandomGenerator): RandomChain<Int> {
+        val distribution = builder(generator.asCMGenerator())
+        return RandomChain(generator) { distribution.sample() }
+    }
+}
+
+
+fun Distribution.Companion.normal(mean: Double, sigma: Double): UnivariateDistribution<Double> =
+    CMRealDistributionWrapper { generator -> NormalDistribution(generator, mean, sigma) }
+
+fun Distribution.Companion.poisson(mean: Double): UnivariateDistribution<Int> = CMIntDistributionWrapper { generator ->
+    PoissonDistribution(
+        generator,
+        mean,
+        PoissonDistribution.DEFAULT_EPSILON,
+        PoissonDistribution.DEFAULT_MAX_ITERATIONS
+    )
+}
+
+fun Distribution.Companion.binomial(trials: Int, p: Double): UnivariateDistribution<Int> =
+    CMIntDistributionWrapper { generator ->
+        BinomialDistribution(generator, trials, p)
+    }
+
+fun Distribution.Companion.student(degreesOfFreedom: Double): UnivariateDistribution<Double> =
+    CMRealDistributionWrapper { generator ->
+        TDistribution(generator, degreesOfFreedom, TDistribution.DEFAULT_INVERSE_ABSOLUTE_ACCURACY)
+    }
+
+fun Distribution.Companion.chi2(degreesOfFreedom: Double): UnivariateDistribution<Double> =
+    CMRealDistributionWrapper { generator ->
+        ChiSquaredDistribution(generator, degreesOfFreedom)
+    }
+
+fun Distribution.Companion.fisher(
+    numeratorDegreesOfFreedom: Double,
+    denominatorDegreesOfFreedom: Double
+): UnivariateDistribution<Double> =
+    CMRealDistributionWrapper { generator ->
+        FDistribution(generator, numeratorDegreesOfFreedom, denominatorDegreesOfFreedom)
+    }
+
+fun Distribution.Companion.exponential(mean: Double): UnivariateDistribution<Double> =
+    CMRealDistributionWrapper { generator ->
+        ExponentialDistribution(generator, mean)
+    }
+
+fun Distribution.Companion.uniform(a: Double, b: Double): UnivariateDistribution<Double> =
+    CMRealDistributionWrapper { generator ->
+        UniformRealDistribution(generator, a, b)
+    }

kmath-commons/src/main/kotlin/scientifik/kmath/commons/transform/Transformations.kt

@@ -1,4 +1,4 @@
-package scientifik.kmath.transform
+package scientifik.kmath.commons.transform
 
 import kotlinx.coroutines.FlowPreview
 import kotlinx.coroutines.flow.Flow

kmath-commons/src/test/kotlin/scientifik/kmath/commons/expressions/AutoDiffTest.kt

@@ -1,6 +1,9 @@
-package scientifik.kmath.expressions
+package scientifik.kmath.commons.expressions
 
 import org.junit.Test
+import scientifik.kmath.commons.expressions.DerivativeStructureField
+import scientifik.kmath.commons.expressions.DiffExpression
+import scientifik.kmath.commons.expressions.derivative
 import kotlin.test.assertEquals
 
 inline fun <R> diff(order: Int, vararg parameters: Pair<String, Double>, block: DerivativeStructureField.() -> R) =

kmath-prob/build.gradle.kts

@@ -13,7 +13,7 @@ kotlin.sourceSets {
     jvmMain {
         dependencies {
             // https://mvnrepository.com/artifact/org.apache.commons/commons-rng-simple
-            api("org.apache.commons:commons-rng-sampling:1.2")
+            //api("org.apache.commons:commons-rng-sampling:1.2")
             compileOnly("org.jetbrains.kotlinx:atomicfu:${Versions.atomicfuVersion}")
         }
     }

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

@@ -16,8 +16,13 @@ interface Distribution<T : Any> {
      * Create a chain of samples from this distribution.
      * The chain is not guaranteed to be stateless.
      */
-    fun sample(generator: RandomGenerator): Chain<T>
+    fun sample(generator: RandomGenerator): RandomChain<T>
     //TODO add sample bunch generator
+
+    /**
+     * An empty companion. Distribution factories should be written as its extensions
+     */
+    companion object
 }
 
 interface UnivariateDistribution<T : Comparable<T>> : Distribution<T> {

kmath-prob/src/commonMain/kotlin/scientifik/kmath/prob/RandomChain.kt

@@ -0,0 +1,17 @@
+package scientifik.kmath.prob
+
+import kotlinx.atomicfu.atomic
+import scientifik.kmath.chains.Chain
+
+/**
+ * A possibly stateful chain producing random values.
+ * TODO make random chain properly fork generator
+ */
+class RandomChain<out R>(val generator: RandomGenerator, private val gen: suspend RandomGenerator.() -> R) : Chain<R> {
+    private val atomicValue = atomic<R?>(null)
+    override fun peek(): R? = atomicValue.value
+
+    override suspend fun next(): R = generator.gen().also { atomicValue.lazySet(it) }
+
+    override fun fork(): Chain<R> = RandomChain(generator, gen)
+}

kmath-prob/src/jvmMain/kotlin/scientifik/kmath/prob/CommonDistributions.kt

@@ -1,53 +0,0 @@
-package scientifik.kmath.prob
-
-import org.apache.commons.rng.sampling.distribution.*
-import scientifik.kmath.chains.Chain
-import scientifik.kmath.chains.SimpleChain
-import kotlin.math.PI
-import kotlin.math.exp
-import kotlin.math.sqrt
-
-class NormalDistribution(val mean: Double, val sigma: Double) : UnivariateDistribution<Double> {
-    enum class Sampler {
-        BoxMuller,
-        Marsaglia,
-        Ziggurat
-    }
-
-    override fun probability(arg: Double): Double {
-        val d = (arg - mean) / sigma
-        return 1.0 / sqrt(2.0 * PI * sigma) * exp(-d * d / 2)
-    }
-
-    override fun cumulative(arg: Double): Double {
-        TODO("not implemented") //To change body of created functions use File | Settings | File Templates.
-    }
-
-    fun sample(generator: RandomGenerator, sampler: Sampler): Chain<Double> {
-        val normalized = when (sampler) {
-            Sampler.BoxMuller -> BoxMullerNormalizedGaussianSampler(generator.asProvider())
-            Sampler.Marsaglia -> MarsagliaNormalizedGaussianSampler(generator.asProvider())
-            Sampler.Ziggurat -> ZigguratNormalizedGaussianSampler(generator.asProvider())
-        }
-        val gauss = GaussianSampler(normalized, mean, sigma)
-        //TODO add generator to chain state to allow stateful forks
-        return SimpleChain { gauss.sample() }
-    }
-
-    override fun sample(generator: RandomGenerator): Chain<Double> = sample(generator, Sampler.BoxMuller)
-}
-
-class PoissonDistribution(val mean: Double): UnivariateDistribution<Int>{
-    override fun probability(arg: Int): Double {
-        TODO("not implemented") //To change body of created functions use File | Settings | File Templates.
-    }
-
-    override fun cumulative(arg: Int): Double {
-        TODO("not implemented") //To change body of created functions use File | Settings | File Templates.
-    }
-
-    override fun sample(generator: RandomGenerator): Chain<Int> {
-        val sampler = PoissonSampler(generator.asProvider(), mean)
-        return SimpleChain{sampler.sample()}
-    }
-}

kmath-prob/src/jvmMain/kotlin/scientifik/kmath/prob/CommonsRandomProviderWrapper.kt

@@ -1,18 +0,0 @@
-package scientifik.kmath.prob
-
-import org.apache.commons.rng.UniformRandomProvider
-
-inline class CommonsRandomProviderWrapper(val provider: UniformRandomProvider) : RandomGenerator {
-    override fun nextDouble(): Double = provider.nextDouble()
-
-    override fun nextInt(): Int = provider.nextInt()
-
-    override fun nextLong(): Long = provider.nextLong()
-
-    override fun nextBlock(size: Int): ByteArray = ByteArray(size).also { provider.nextBytes(it) }
-}
-
-fun UniformRandomProvider.asGenerator(): RandomGenerator = CommonsRandomProviderWrapper(this)
-
-fun RandomGenerator.asProvider(): UniformRandomProvider =
-    (this as? CommonsRandomProviderWrapper)?.provider ?: TODO("implement reverse wrapper")

settings.gradle.kts

@@ -28,5 +28,5 @@ include(
     ":kmath-commons",
     ":kmath-koma",
     ":kmath-prob",
-    ":benchmarks"
+    ":examples"
 )