kmath-commons-rng-part/build.gradle.kts

@@ -1,2 +0,0 @@
-plugins { id("scientifik.mpp") }
-kotlin.sourceSets { commonMain.get().dependencies { api(project(":kmath-coroutines")) } }

kmath-commons-rng-part/src/commonMain/kotlin/scientifik/commons/rng/sampling/SharedStateSampler.kt

@@ -1,7 +0,0 @@
-package scientifik.commons.rng.sampling
-
-import scientifik.commons.rng.UniformRandomProvider
-
-interface SharedStateSampler<R> {
-    fun withUniformRandomProvider(rng: UniformRandomProvider): R
-}

kmath-commons-rng-part/src/commonMain/kotlin/scientifik/commons/rng/sampling/distribution/ContinuousSampler.kt

@@ -1,5 +0,0 @@
-package scientifik.commons.rng.sampling.distribution
-
-interface ContinuousSampler {
-    fun sample(): Double
-}

kmath-commons-rng-part/src/commonMain/kotlin/scientifik/commons/rng/sampling/distribution/DiscreteSampler.kt

@@ -1,5 +0,0 @@
-package scientifik.commons.rng.sampling.distribution
-
-interface DiscreteSampler {
-    fun sample(): Int
-}

kmath-commons-rng-part/src/commonMain/kotlin/scientifik/commons/rng/sampling/distribution/NormalizedGaussianSampler.kt

@@ -1,3 +0,0 @@
-package scientifik.commons.rng.sampling.distribution
-
-interface NormalizedGaussianSampler : ContinuousSampler

kmath-commons-rng-part/src/commonMain/kotlin/scientifik/commons/rng/sampling/distribution/SharedStateContinuousSampler.kt

@@ -1,7 +0,0 @@
-package scientifik.commons.rng.sampling.distribution
-
-import scientifik.commons.rng.sampling.SharedStateSampler
-
-interface SharedStateContinuousSampler : ContinuousSampler,
-    SharedStateSampler<SharedStateContinuousSampler?> {
-}

kmath-prob/build.gradle.kts

@@ -3,17 +3,10 @@ plugins {
 }
 
 kotlin.sourceSets {
-    commonMain {
+    commonMain.get().dependencies { api(project(":kmath-coroutines")) }
-        dependencies {
-            api(project(":kmath-coroutines"))
-            api(project(":kmath-commons-rng-part"))
-        }
-    }
 
-    jvmMain {
+    jvmMain.get().dependencies {
-        dependencies {
         api("org.apache.commons:commons-rng-sampling:1.3")
         api("org.apache.commons:commons-rng-simple:1.3")
     }
-    }
 }

kmath-prob/src/commonMain/kotlin/scientifik/kmath/commons/rng/UniformRandomProvider.kt

@@ -1,4 +1,4 @@
-package scientifik.commons.rng
+package scientifik.kmath.commons.rng
 
 interface UniformRandomProvider {
     fun nextBytes(bytes: ByteArray)

kmath-prob/src/commonMain/kotlin/scientifik/kmath/commons/rng/sampling/SharedStateSampler.kt

@@ -0,0 +1,7 @@
+package scientifik.kmath.commons.rng.sampling
+
+import scientifik.kmath.commons.rng.UniformRandomProvider
+
+interface SharedStateSampler<R> {
+    fun withUniformRandomProvider(rng: UniformRandomProvider): R
+}

kmath-prob/src/commonMain/kotlin/scientifik/kmath/commons/rng/sampling/distribution/AhrensDieterExponentialSampler.kt

@@ -1,6 +1,6 @@
-package scientifik.commons.rng.sampling.distribution
+package scientifik.kmath.commons.rng.sampling.distribution
 
-import scientifik.commons.rng.UniformRandomProvider
+import scientifik.kmath.commons.rng.UniformRandomProvider
 import kotlin.math.ln
 import kotlin.math.pow
 
@@ -76,7 +76,11 @@ class AhrensDieterExponentialSampler : SamplerBase,
         fun of(
             rng: UniformRandomProvider,
             mean: Double
-        ): SharedStateContinuousSampler = AhrensDieterExponentialSampler(rng, mean)
+        ): SharedStateContinuousSampler =
+            AhrensDieterExponentialSampler(
+                rng,
+                mean
+            )
 
         init {
             /**
@@ -87,7 +91,9 @@ class AhrensDieterExponentialSampler : SamplerBase,
             var qi = 0.0
 
             EXPONENTIAL_SA_QI.indices.forEach { i ->
-                qi += ln2.pow(i + 1.0) / InternalUtils.factorial(i + 1)
+                qi += ln2.pow(i + 1.0) / InternalUtils.factorial(
+                    i + 1
+                )
                 EXPONENTIAL_SA_QI[i] = qi
             }
         }

kmath-prob/src/commonMain/kotlin/scientifik/kmath/commons/rng/sampling/distribution/BoxMullerNormalizedGaussianSampler.kt

@@ -1,6 +1,6 @@
-package scientifik.commons.rng.sampling.distribution
+package scientifik.kmath.commons.rng.sampling.distribution
 
-import scientifik.commons.rng.UniformRandomProvider
+import scientifik.kmath.commons.rng.UniformRandomProvider
 import kotlin.math.*
 
 class BoxMullerNormalizedGaussianSampler(

kmath-prob/src/commonMain/kotlin/scientifik/kmath/commons/rng/sampling/distribution/ContinuousSampler.kt

@@ -0,0 +1,5 @@
+package scientifik.kmath.commons.rng.sampling.distribution
+
+interface ContinuousSampler {
+    fun sample(): Double
+}

kmath-prob/src/commonMain/kotlin/scientifik/kmath/commons/rng/sampling/distribution/DiscreteSampler.kt

@@ -0,0 +1,5 @@
+package scientifik.kmath.commons.rng.sampling.distribution
+
+interface DiscreteSampler {
+    fun sample(): Int
+}

kmath-prob/src/commonMain/kotlin/scientifik/kmath/commons/rng/sampling/distribution/GaussianSampler.kt

@@ -1,8 +1,9 @@
-package scientifik.commons.rng.sampling.distribution
+package scientifik.kmath.commons.rng.sampling.distribution
 
-import scientifik.commons.rng.UniformRandomProvider
+import scientifik.kmath.commons.rng.UniformRandomProvider
 
-class GaussianSampler : SharedStateContinuousSampler {
+class GaussianSampler :
+    SharedStateContinuousSampler {
     private val mean: Double
     private val standardDeviation: Double
     private val normalized: NormalizedGaussianSampler
@@ -24,7 +25,11 @@ class GaussianSampler : SharedStateContinuousSampler {
     ) {
         mean = source.mean
         standardDeviation = source.standardDeviation
-        normalized = InternalUtils.newNormalizedGaussianSampler(source.normalized, rng)
+        normalized =
+            InternalUtils.newNormalizedGaussianSampler(
+                source.normalized,
+                rng
+            )
     }
 
     override fun sample(): Double = standardDeviation * normalized.sample() + mean
@@ -40,6 +45,11 @@ class GaussianSampler : SharedStateContinuousSampler {
             normalized: NormalizedGaussianSampler,
             mean: Double,
             standardDeviation: Double
-        ): SharedStateContinuousSampler = GaussianSampler(normalized, mean, standardDeviation)
+        ): SharedStateContinuousSampler =
+            GaussianSampler(
+                normalized,
+                mean,
+                standardDeviation
+            )
     }
 }

kmath-prob/src/commonMain/kotlin/scientifik/kmath/commons/rng/sampling/distribution/InternalGamma.kt

@@ -1,4 +1,4 @@
-package scientifik.commons.rng.sampling.distribution
+package scientifik.kmath.commons.rng.sampling.distribution
 
 import kotlin.math.PI
 import kotlin.math.ln

kmath-prob/src/commonMain/kotlin/scientifik/kmath/commons/rng/sampling/distribution/InternalUtils.kt

@@ -1,7 +1,7 @@
-package scientifik.commons.rng.sampling.distribution
+package scientifik.kmath.commons.rng.sampling.distribution
 
-import scientifik.commons.rng.UniformRandomProvider
+import scientifik.kmath.commons.rng.UniformRandomProvider
-import scientifik.commons.rng.sampling.SharedStateSampler
+import scientifik.kmath.commons.rng.sampling.SharedStateSampler
 import kotlin.math.ln
 import kotlin.math.min
 
@@ -63,30 +63,45 @@ internal object InternalUtils {
             if (cache != null && cache.size > BEGIN_LOG_FACTORIALS) {
                 // Copy available values.
                 endCopy = min(cache.size, numValues)
-                cache.copyInto(logFactorials, BEGIN_LOG_FACTORIALS, BEGIN_LOG_FACTORIALS, endCopy)
+                cache.copyInto(logFactorials,
+                    BEGIN_LOG_FACTORIALS,
+                    BEGIN_LOG_FACTORIALS, endCopy)
             }
             // All values to be computed
             else
-                endCopy = BEGIN_LOG_FACTORIALS
+                endCopy =
+                    BEGIN_LOG_FACTORIALS
 
             // Compute remaining values.
             (endCopy until numValues).forEach { i ->
-                if (i < FACTORIALS.size) logFactorials[i] = ln(FACTORIALS[i].toDouble()) else logFactorials[i] =
+                if (i < FACTORIALS.size) logFactorials[i] = ln(
+                    FACTORIALS[i].toDouble()) else logFactorials[i] =
                     logFactorials[i - 1] + ln(i.toDouble())
             }
         }
 
-        fun withCache(cacheSize: Int): FactorialLog = FactorialLog(cacheSize, logFactorials)
+        fun withCache(cacheSize: Int): FactorialLog =
+            FactorialLog(
+                cacheSize,
+                logFactorials
+            )
 
         fun value(n: Int): Double {
             if (n < logFactorials.size)
                 return logFactorials[n]
 
-            return if (n < FACTORIALS.size) ln(FACTORIALS[n].toDouble()) else InternalGamma.logGamma(n + 1.0)
+            return if (n < FACTORIALS.size) ln(
+                FACTORIALS[n].toDouble()) else InternalGamma.logGamma(
+                n + 1.0
+            )
         }
 
         companion object {
-            fun create(): FactorialLog = FactorialLog(0, null)
+            fun create(): FactorialLog =
+                FactorialLog(
+                    0,
+                    null
+                )
         }
     }
 }

kmath-prob/src/commonMain/kotlin/scientifik/kmath/commons/rng/sampling/distribution/KempSmallMeanPoissonSampler.kt

@@ -1,6 +1,6 @@
-package scientifik.commons.rng.sampling.distribution
+package scientifik.kmath.commons.rng.sampling.distribution
 
-import scientifik.commons.rng.UniformRandomProvider
+import scientifik.kmath.commons.rng.UniformRandomProvider
 import kotlin.math.exp
 
 class KempSmallMeanPoissonSampler private constructor(
@@ -48,7 +48,11 @@ class KempSmallMeanPoissonSampler private constructor(
             val p0: Double = exp(-mean)
 
             // Probability must be positive. As mean increases then p(0) decreases.
-            if (p0 > 0) return KempSmallMeanPoissonSampler(rng, p0, mean)
+            if (p0 > 0) return KempSmallMeanPoissonSampler(
+                rng,
+                p0,
+                mean
+            )
             throw IllegalArgumentException("No probability for mean: $mean")
         }
     }

kmath-prob/src/commonMain/kotlin/scientifik/kmath/commons/rng/sampling/distribution/LargenMeanPoissonSampler.kt

@@ -1,9 +1,10 @@
-package scientifik.commons.rng.sampling.distribution
+package scientifik.kmath.commons.rng.sampling.distribution
 
-import scientifik.commons.rng.UniformRandomProvider
+import scientifik.kmath.commons.rng.UniformRandomProvider
 import kotlin.math.*
 
-class LargeMeanPoissonSampler : SharedStateDiscreteSampler {
+class LargeMeanPoissonSampler :
+    SharedStateDiscreteSampler {
     private val rng: UniformRandomProvider
     private val exponential: SharedStateContinuousSampler
     private val gaussian: SharedStateContinuousSampler
@@ -27,8 +28,13 @@ class LargeMeanPoissonSampler : SharedStateDiscreteSampler {
         // The algorithm is not valid if Math.floor(mean) is not an integer.
         require(mean <= MAX_MEAN) { "mean $mean > $MAX_MEAN" }
         this.rng = rng
-        gaussian = ZigguratNormalizedGaussianSampler(rng)
+        gaussian =
-        exponential = AhrensDieterExponentialSampler.of(rng, 1.0)
+            ZigguratNormalizedGaussianSampler(rng)
+        exponential =
+            AhrensDieterExponentialSampler.of(
+                rng,
+                1.0
+            )
         // Plain constructor uses the uncached function.
         factorialLog = NO_CACHE_FACTORIAL_LOG!!
         // Cache values used in the algorithm
@@ -49,7 +55,10 @@ class LargeMeanPoissonSampler : SharedStateDiscreteSampler {
         val lambdaFractional = mean - lambda
         smallMeanPoissonSampler =
             if (lambdaFractional < Double.MIN_VALUE) NO_SMALL_MEAN_POISSON_SAMPLER else  // Not used.
-                KempSmallMeanPoissonSampler.of(rng, lambdaFractional)
+                KempSmallMeanPoissonSampler.of(
+                    rng,
+                    lambdaFractional
+                )
     }
 
     internal constructor(
@@ -59,8 +68,13 @@ class LargeMeanPoissonSampler : SharedStateDiscreteSampler {
     ) {
         require(!(lambdaFractional < 0 || lambdaFractional >= 1)) { "lambdaFractional must be in the range 0 (inclusive) to 1 (exclusive): $lambdaFractional" }
         this.rng = rng
-        gaussian = ZigguratNormalizedGaussianSampler(rng)
+        gaussian =
-        exponential = AhrensDieterExponentialSampler.of(rng, 1.0)
+            ZigguratNormalizedGaussianSampler(rng)
+        exponential =
+            AhrensDieterExponentialSampler.of(
+                rng,
+                1.0
+            )
         // Plain constructor uses the uncached function.
         factorialLog = NO_CACHE_FACTORIAL_LOG!!
         // Use the state to initialise the algorithm
@@ -79,7 +93,10 @@ class LargeMeanPoissonSampler : SharedStateDiscreteSampler {
             if (lambdaFractional < Double.MIN_VALUE)
                 NO_SMALL_MEAN_POISSON_SAMPLER
             else  // Not used.
-                KempSmallMeanPoissonSampler.of(rng, lambdaFractional)
+                KempSmallMeanPoissonSampler.of(
+                    rng,
+                    lambdaFractional
+                )
     }
 
     /**
@@ -222,7 +239,8 @@ class LargeMeanPoissonSampler : SharedStateDiscreteSampler {
         fun of(
             rng: UniformRandomProvider,
             mean: Double
-        ): SharedStateDiscreteSampler = LargeMeanPoissonSampler(rng, mean)
+        ): SharedStateDiscreteSampler =
+            LargeMeanPoissonSampler(rng, mean)
 
         init {
             // Create without a cache.

kmath-prob/src/commonMain/kotlin/scientifik/kmath/commons/rng/sampling/distribution/MarsagliaNormalizedGaussianSampler.kt

@@ -1,6 +1,6 @@
-package scientifik.commons.rng.sampling.distribution
+package scientifik.kmath.commons.rng.sampling.distribution
 
-import scientifik.commons.rng.UniformRandomProvider
+import scientifik.kmath.commons.rng.UniformRandomProvider
 import kotlin.math.ln
 import kotlin.math.sqrt
 

kmath-prob/src/commonMain/kotlin/scientifik/kmath/commons/rng/sampling/distribution/NormalizedGaussianSampler.kt

@@ -0,0 +1,4 @@
+package scientifik.kmath.commons.rng.sampling.distribution
+
+interface NormalizedGaussianSampler :
+    ContinuousSampler

kmath-prob/src/commonMain/kotlin/scientifik/kmath/commons/rng/sampling/distribution/PoissonSampler.kt

@@ -1,11 +1,12 @@
-package scientifik.commons.rng.sampling.distribution
+package scientifik.kmath.commons.rng.sampling.distribution
 
-import scientifik.commons.rng.UniformRandomProvider
+import scientifik.kmath.commons.rng.UniformRandomProvider
 
 class PoissonSampler(
     rng: UniformRandomProvider,
     mean: Double
-) : SamplerBase(null), SharedStateDiscreteSampler {
+) : SamplerBase(null),
+    SharedStateDiscreteSampler {
     private val poissonSamplerDelegate: SharedStateDiscreteSampler
 
     override fun sample(): Int = poissonSamplerDelegate.sample()
@@ -22,11 +23,18 @@ class PoissonSampler(
             rng: UniformRandomProvider,
             mean: Double
         ): SharedStateDiscreteSampler =// Each sampler should check the input arguments.
-            if (mean < PIVOT) SmallMeanPoissonSampler.of(rng, mean) else LargeMeanPoissonSampler.of(rng, mean)
+            if (mean < PIVOT) SmallMeanPoissonSampler.of(
+                rng,
+                mean
+            ) else LargeMeanPoissonSampler.of(
+                rng,
+                mean
+            )
     }
 
     init {
         // Delegate all work to specialised samplers.
-        poissonSamplerDelegate = of(rng, mean)
+        poissonSamplerDelegate =
+            of(rng, mean)
     }
 }

kmath-prob/src/commonMain/kotlin/scientifik/kmath/commons/rng/sampling/distribution/SamplerBase.kt

@@ -1,6 +1,6 @@
-package scientifik.commons.rng.sampling.distribution
+package scientifik.kmath.commons.rng.sampling.distribution
 
-import scientifik.commons.rng.UniformRandomProvider
+import scientifik.kmath.commons.rng.UniformRandomProvider
 
 @Deprecated("Since version 1.1. Class intended for internal use only.")
 open class SamplerBase protected constructor(private val rng: UniformRandomProvider?) {

kmath-prob/src/commonMain/kotlin/scientifik/kmath/commons/rng/sampling/distribution/SharedStateContinuousSampler.kt

@@ -0,0 +1,7 @@
+package scientifik.kmath.commons.rng.sampling.distribution
+
+import scientifik.kmath.commons.rng.sampling.SharedStateSampler
+
+interface SharedStateContinuousSampler : ContinuousSampler,
+    SharedStateSampler<SharedStateContinuousSampler?> {
+}

kmath-prob/src/commonMain/kotlin/scientifik/kmath/commons/rng/sampling/distribution/SharedStateDiscreteSampler.kt

@@ -1,6 +1,6 @@
-package scientifik.commons.rng.sampling.distribution
+package scientifik.kmath.commons.rng.sampling.distribution
 
-import scientifik.commons.rng.sampling.SharedStateSampler
+import scientifik.kmath.commons.rng.sampling.SharedStateSampler
 
 interface SharedStateDiscreteSampler : DiscreteSampler,
     SharedStateSampler<SharedStateDiscreteSampler?> { // Composite interface

kmath-prob/src/commonMain/kotlin/scientifik/kmath/commons/rng/sampling/distribution/SmallMeanPoissonSampler.kt

@@ -1,10 +1,11 @@
-package scientifik.commons.rng.sampling.distribution
+package scientifik.kmath.commons.rng.sampling.distribution
 
-import scientifik.commons.rng.UniformRandomProvider
+import scientifik.kmath.commons.rng.UniformRandomProvider
 import kotlin.math.ceil
 import kotlin.math.exp
 
-class SmallMeanPoissonSampler : SharedStateDiscreteSampler {
+class SmallMeanPoissonSampler :
+    SharedStateDiscreteSampler {
     private val p0: Double
     private val limit: Int
     private val rng: UniformRandomProvider
@@ -53,6 +54,7 @@ class SmallMeanPoissonSampler : SharedStateDiscreteSampler {
         fun of(
             rng: UniformRandomProvider,
             mean: Double
-        ): SharedStateDiscreteSampler = SmallMeanPoissonSampler(rng, mean)
+        ): SharedStateDiscreteSampler =
+            SmallMeanPoissonSampler(rng, mean)
     }
 }

kmath-prob/src/commonMain/kotlin/scientifik/kmath/commons/rng/sampling/distribution/ZigguratNormalizedGaussianSampler.kt

@@ -1,6 +1,6 @@
-package scientifik.commons.rng.sampling.distribution
+package scientifik.kmath.commons.rng.sampling.distribution
 
-import scientifik.commons.rng.UniformRandomProvider
+import scientifik.kmath.commons.rng.UniformRandomProvider
 import kotlin.math.*
 
 class ZigguratNormalizedGaussianSampler(private val rng: UniformRandomProvider) :
@@ -26,9 +26,13 @@ class ZigguratNormalizedGaussianSampler(private val rng: UniformRandomProvider)
 
         init {
             // Filling the tables.
-            var d = R
+            var d =
+                R
             var t = d
-            var fd = gauss(d)
+            var fd =
+                gauss(
+                    d
+                )
             val q = V / fd
             K[0] = (d / q * MAX).toLong()
             K[1] = 0
@@ -39,7 +43,10 @@ class ZigguratNormalizedGaussianSampler(private val rng: UniformRandomProvider)
 
             (LAST - 1 downTo 1).forEach { i ->
                 d = sqrt(-2 * ln(V / d + fd))
-                fd = gauss(d)
+                fd =
+                    gauss(
+                        d
+                    )
                 K[i + 1] = (d / t * MAX).toLong()
                 t = d
                 F[i] = fd
@@ -80,7 +87,10 @@ class ZigguratNormalizedGaussianSampler(private val rng: UniformRandomProvider)
             // else
             // Try again.
             // This branch is called about 0.012362 times per sample.
-            if (F[iz] + rng.nextDouble() * (F[iz - 1] - F[iz]) < gauss(x)) x else sample()
+            if (F[iz] + rng.nextDouble() * (F[iz - 1] - F[iz]) < gauss(
+                    x
+                )
+            ) x else sample()
         }
     }
 

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

@@ -1,53 +0,0 @@
-package scientifik.kmath.prob
-
-import scientifik.commons.rng.UniformRandomProvider
-import scientifik.commons.rng.sampling.distribution.*
-import scientifik.kmath.chains.BlockingIntChain
-import scientifik.kmath.chains.BlockingRealChain
-import scientifik.kmath.chains.Chain
-
-abstract class ContinuousSamplerDistribution : Distribution<Double> {
-
-    private inner class ContinuousSamplerChain(val generator: RandomGenerator) : BlockingRealChain() {
-        private val sampler = buildCMSampler(generator)
-
-        override fun nextDouble(): Double = sampler.sample()
-
-        override fun fork(): Chain<Double> = ContinuousSamplerChain(generator.fork())
-    }
-
-    protected abstract fun buildCMSampler(generator: RandomGenerator): ContinuousSampler
-
-    override fun sample(generator: RandomGenerator): BlockingRealChain = ContinuousSamplerChain(generator)
-}
-
-abstract class DiscreteSamplerDistribution : Distribution<Int> {
-
-    private inner class ContinuousSamplerChain(val generator: RandomGenerator) : BlockingIntChain() {
-        private val sampler = buildSampler(generator)
-
-        override fun nextInt(): Int = sampler.sample()
-
-        override fun fork(): Chain<Int> = ContinuousSamplerChain(generator.fork())
-    }
-
-    protected abstract fun buildSampler(generator: RandomGenerator): DiscreteSampler
-
-    override fun sample(generator: RandomGenerator): BlockingIntChain = ContinuousSamplerChain(generator)
-}
-
-enum class NormalSamplerMethod {
-    BoxMuller,
-    Marsaglia,
-    Ziggurat
-}
-
-fun normalSampler(method: NormalSamplerMethod, provider: UniformRandomProvider): NormalizedGaussianSampler =
-    when (method) {
-        NormalSamplerMethod.BoxMuller -> BoxMullerNormalizedGaussianSampler(
-            provider
-        )
-        NormalSamplerMethod.Marsaglia -> MarsagliaNormalizedGaussianSampler(provider)
-        NormalSamplerMethod.Ziggurat -> ZigguratNormalizedGaussianSampler(provider)
-    }
-

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

@@ -1,9 +1,10 @@
 package scientifik.kmath.prob
 
-import scientifik.commons.rng.UniformRandomProvider
+import scientifik.kmath.commons.rng.UniformRandomProvider
 
 
-inline class RandomGeneratorProvider(val generator: RandomGenerator) : UniformRandomProvider {
+inline class RandomGeneratorProvider(val generator: RandomGenerator) :
+    UniformRandomProvider {
     override fun nextBoolean(): Boolean = generator.nextBoolean()
 
     override fun nextFloat(): Float = generator.nextDouble().toFloat()

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

@@ -1,9 +1,9 @@
 package scientifik.kmath.prob
 
-import scientifik.commons.rng.sampling.distribution.ContinuousSampler
+import scientifik.kmath.commons.rng.sampling.distribution.ContinuousSampler
-import scientifik.commons.rng.sampling.distribution.DiscreteSampler
+import scientifik.kmath.commons.rng.sampling.distribution.DiscreteSampler
-import scientifik.commons.rng.sampling.distribution.GaussianSampler
+import scientifik.kmath.commons.rng.sampling.distribution.GaussianSampler
-import scientifik.commons.rng.sampling.distribution.PoissonSampler
+import scientifik.kmath.commons.rng.sampling.distribution.PoissonSampler
 import kotlin.math.PI
 import kotlin.math.exp
 import kotlin.math.pow
@@ -33,7 +33,11 @@ fun Distribution.Companion.normal(
     override fun buildCMSampler(generator: RandomGenerator): ContinuousSampler {
         val provider = generator.asUniformRandomProvider()
         val normalizedSampler = normalSampler(method, provider)
-        return GaussianSampler(normalizedSampler, mean, sigma)
+        return GaussianSampler(
+            normalizedSampler,
+            mean,
+            sigma
+        )
     }
 
     override fun probability(arg: Double): Double {

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

@@ -1,7 +1,7 @@
 package scientifik.kmath.prob
 
 import org.apache.commons.rng.simple.RandomSource
-import scientifik.commons.rng.UniformRandomProvider
+import scientifik.kmath.commons.rng.UniformRandomProvider
 
 class RandomSourceGenerator(val source: RandomSource, seed: Long?) :
     RandomGenerator {

settings.gradle.kts

@@ -29,13 +29,13 @@ pluginManagement {
 }
 
 rootProject.name = "kmath"
+
 include(
     ":kmath-memory",
     ":kmath-core",
     ":kmath-functions",
 //    ":kmath-io",
     ":kmath-coroutines",
-    "kmath-commons-rng-part",
     ":kmath-histograms",
     ":kmath-commons",
     ":kmath-viktor",