kmath-prob/src/commonMain/kotlin/scientifik/kmath/prob/samplers/AhrensDieterExponentialSampler.kt

@@ -7,11 +7,7 @@ import scientifik.kmath.prob.chain
 import kotlin.math.ln
 import kotlin.math.pow
 
-class AhrensDieterExponentialSampler(val mean: Double) : Sampler<Double> {
+class AhrensDieterExponentialSampler private constructor(val mean: Double) : Sampler<Double> {
-    init {
-        require(mean > 0) { "mean is not strictly positive: $mean" }
-    }
-
     override fun sample(generator: RandomGenerator): Chain<Double> = generator.chain {
         // Step 1:
         var a = 0.0
@@ -46,10 +42,7 @@ class AhrensDieterExponentialSampler(val mean: Double) : Sampler<Double> {
     override fun toString(): String = "Ahrens-Dieter Exponential deviate"
 
     companion object {
-        private val EXPONENTIAL_SA_QI = DoubleArray(16)
+        private val EXPONENTIAL_SA_QI by lazy { DoubleArray(16) }
-
-        fun of(mean: Double): Sampler<Double> =
-            AhrensDieterExponentialSampler(mean)
 
         init {
             /**
@@ -64,5 +57,10 @@ class AhrensDieterExponentialSampler(val mean: Double) : Sampler<Double> {
                 EXPONENTIAL_SA_QI[i] = qi
             }
         }
+
+        fun of(mean: Double): AhrensDieterExponentialSampler {
+            require(mean > 0) { "mean is not strictly positive: $mean" }
+            return AhrensDieterExponentialSampler(mean)
+        }
     }
 }

kmath-prob/src/commonMain/kotlin/scientifik/kmath/prob/samplers/BoxMullerNormalizedGaussianSampler.kt

@@ -6,7 +6,7 @@ import scientifik.kmath.prob.Sampler
 import scientifik.kmath.prob.chain
 import kotlin.math.*
 
-class BoxMullerNormalizedGaussianSampler : NormalizedGaussianSampler, Sampler<Double> {
+class BoxMullerNormalizedGaussianSampler private constructor() : NormalizedGaussianSampler, Sampler<Double> {
     private var nextGaussian: Double = Double.NaN
 
     override fun sample(generator: RandomGenerator): Chain<Double> = generator.chain {
@@ -34,4 +34,8 @@ class BoxMullerNormalizedGaussianSampler : NormalizedGaussianSampler, Sampler<Do
     }
 
     override fun toString(): String = "Box-Muller normalized Gaussian deviate"
+
+    companion object {
+        fun of(): BoxMullerNormalizedGaussianSampler = BoxMullerNormalizedGaussianSampler()
+    }
 }

kmath-prob/src/commonMain/kotlin/scientifik/kmath/prob/samplers/GaussianSampler.kt

@@ -5,16 +5,11 @@ import scientifik.kmath.chains.map
 import scientifik.kmath.prob.RandomGenerator
 import scientifik.kmath.prob.Sampler
 
-class GaussianSampler(
+class GaussianSampler private constructor(
     private val mean: Double,
     private val standardDeviation: Double,
     private val normalized: NormalizedGaussianSampler
 ) : Sampler<Double> {
-
-    init {
-        require(standardDeviation > 0) { "standard deviation is not strictly positive: $standardDeviation" }
-    }
-
     override fun sample(generator: RandomGenerator): Chain<Double> =
         normalized.sample(generator).map { standardDeviation * it + mean }
 
@@ -22,13 +17,17 @@ class GaussianSampler(
 
     companion object {
         fun of(
-            normalized: NormalizedGaussianSampler,
             mean: Double,
-            standardDeviation: Double
+            standardDeviation: Double,
-        ) = GaussianSampler(
+            normalized: NormalizedGaussianSampler
+        ): GaussianSampler {
+            require(standardDeviation > 0) { "standard deviation is not strictly positive: $standardDeviation" }
+
+            return GaussianSampler(
                 mean,
                 standardDeviation,
                 normalized
             )
         }
     }
+}

kmath-prob/src/commonMain/kotlin/scientifik/kmath/prob/samplers/KempSmallMeanPoissonSampler.kt

@@ -41,10 +41,10 @@ class KempSmallMeanPoissonSampler private constructor(
     companion object {
         fun of(mean: Double): KempSmallMeanPoissonSampler {
             require(mean > 0) { "Mean is not strictly positive: $mean" }
-            val p0: Double = exp(-mean)
+            val p0 = exp(-mean)
             // Probability must be positive. As mean increases then p(0) decreases.
-            if (p0 > 0) return KempSmallMeanPoissonSampler(p0, mean)
+            require(p0 > 0) { "No probability for mean: $mean" }
-            throw IllegalArgumentException("No probability for mean: $mean")
+            return KempSmallMeanPoissonSampler(p0, mean)
         }
     }
 }

kmath-prob/src/commonMain/kotlin/scientifik/kmath/prob/samplers/LargeMeanPoissonSampler.kt

@@ -1,6 +1,5 @@
 package scientifik.kmath.prob.samplers
 
-import kotlinx.coroutines.flow.first
 import scientifik.kmath.chains.Chain
 import scientifik.kmath.chains.ConstantChain
 import scientifik.kmath.chains.SimpleChain
@@ -9,12 +8,10 @@ import scientifik.kmath.prob.Sampler
 import scientifik.kmath.prob.next
 import kotlin.math.*
 
-class LargeMeanPoissonSampler(val mean: Double) : Sampler<Int> {
+class LargeMeanPoissonSampler private constructor(val mean: Double) : Sampler<Int> {
-    private val exponential: Sampler<Double> =
+    private val exponential: Sampler<Double> = AhrensDieterExponentialSampler.of(1.0)
-        AhrensDieterExponentialSampler.of(1.0)
+    private val gaussian: Sampler<Double> = ZigguratNormalizedGaussianSampler()
-    private val gaussian: Sampler<Double> =
+    private val factorialLog: InternalUtils.FactorialLog = NO_CACHE_FACTORIAL_LOG
-        ZigguratNormalizedGaussianSampler()
-    private val factorialLog: InternalUtils.FactorialLog = NO_CACHE_FACTORIAL_LOG!!
     private val lambda: Double = floor(mean)
     private val logLambda: Double = ln(lambda)
     private val logLambdaFactorial: Double = getFactorialLog(lambda.toInt())
@@ -33,12 +30,6 @@ class LargeMeanPoissonSampler(val mean: Double) : Sampler<Int> {
     else  // Not used.
         KempSmallMeanPoissonSampler.of(mean - lambda)
 
-    init {
-        require(mean >= 1) { "mean is not >= 1: $mean" }
-        // The algorithm is not valid if Math.floor(mean) is not an integer.
-        require(mean <= MAX_MEAN) { "mean $mean > $MAX_MEAN" }
-    }
-
     override fun sample(generator: RandomGenerator): Chain<Int> = SimpleChain {
         // This will never be null. It may be a no-op delegate that returns zero.
         val y2 = smallMeanPoissonSampler.next(generator)
@@ -113,20 +104,18 @@ class LargeMeanPoissonSampler(val mean: Double) : Sampler<Int> {
     override fun toString(): String = "Large Mean Poisson deviate"
 
     companion object {
-        private const val MAX_MEAN = 0.5 * Int.MAX_VALUE
+        private const val MAX_MEAN: Double = 0.5 * Int.MAX_VALUE
-        private var NO_CACHE_FACTORIAL_LOG: InternalUtils.FactorialLog? = null
+        private val NO_CACHE_FACTORIAL_LOG: InternalUtils.FactorialLog = InternalUtils.FactorialLog.create()
 
         private val NO_SMALL_MEAN_POISSON_SAMPLER = object : Sampler<Int> {
             override fun sample(generator: RandomGenerator): Chain<Int> = ConstantChain(0)
         }
 
-        fun of(mean: Double): LargeMeanPoissonSampler =
+        fun of(mean: Double): LargeMeanPoissonSampler {
-            LargeMeanPoissonSampler(mean)
+            require(mean >= 1) { "mean is not >= 1: $mean" }
-
+            // The algorithm is not valid if Math.floor(mean) is not an integer.
-        init {
+            require(mean <= MAX_MEAN) { "mean $mean > $MAX_MEAN" }
-            // Create without a cache.
+            return LargeMeanPoissonSampler(mean)
-            NO_CACHE_FACTORIAL_LOG =
-                InternalUtils.FactorialLog.create()
         }
     }
 }

kmath-prob/src/commonMain/kotlin/scientifik/kmath/prob/samplers/MarsagliaNormalizedGaussianSampler.kt

@@ -7,7 +7,7 @@ import scientifik.kmath.prob.chain
 import kotlin.math.ln
 import kotlin.math.sqrt
 
-class MarsagliaNormalizedGaussianSampler : NormalizedGaussianSampler, Sampler<Double> {
+class MarsagliaNormalizedGaussianSampler private constructor(): NormalizedGaussianSampler, Sampler<Double> {
     private var nextGaussian = Double.NaN
 
     override fun sample(generator: RandomGenerator): Chain<Double> = generator.chain {
@@ -49,4 +49,8 @@ class MarsagliaNormalizedGaussianSampler : NormalizedGaussianSampler, Sampler<Do
     }
 
     override fun toString(): String = "Box-Muller (with rejection) normalized Gaussian deviate"
+
+    companion object {
+        fun of(): MarsagliaNormalizedGaussianSampler = MarsagliaNormalizedGaussianSampler()
+    }
 }

kmath-prob/src/commonMain/kotlin/scientifik/kmath/prob/samplers/PoissonSampler.kt

@@ -5,16 +5,10 @@ import scientifik.kmath.prob.RandomGenerator
 import scientifik.kmath.prob.Sampler
 
 
-class PoissonSampler(
+class PoissonSampler private constructor(
     mean: Double
 ) : Sampler<Int> {
-    private val poissonSamplerDelegate: Sampler<Int>
+    private val poissonSamplerDelegate: Sampler<Int> = of(mean)
-
-    init {
-        // Delegate all work to specialised samplers.
-        poissonSamplerDelegate = of(mean)
-    }
-
     override fun sample(generator: RandomGenerator): Chain<Int> = poissonSamplerDelegate.sample(generator)
     override fun toString(): String = poissonSamplerDelegate.toString()
 
@@ -22,8 +16,6 @@ class PoissonSampler(
         private const val PIVOT = 40.0
 
         fun of(mean: Double) =// Each sampler should check the input arguments.
-            if (mean < PIVOT) SmallMeanPoissonSampler.of(
+            if (mean < PIVOT) SmallMeanPoissonSampler.of(mean) else LargeMeanPoissonSampler.of(mean)
-                mean
-            ) else LargeMeanPoissonSampler.of(mean)
     }
 }

kmath-prob/src/commonMain/kotlin/scientifik/kmath/prob/samplers/SmallMeanPoissonSampler.kt

@@ -8,19 +8,13 @@ import scientifik.kmath.prob.chain
 import kotlin.math.ceil
 import kotlin.math.exp
 
-class SmallMeanPoissonSampler(mean: Double) : Sampler<Int> {
+class SmallMeanPoissonSampler private constructor(mean: Double) : Sampler<Int> {
-    private val p0: Double
+    private val p0: Double = exp(-mean)
-    private val limit: Int
 
-    init {
+    private val limit: Int = (if (p0 > 0)
-        require(mean > 0) { "mean is not strictly positive: $mean" }
-        p0 = exp(-mean)
-
-        limit = (if (p0 > 0)
         ceil(1000 * mean)
     else
         throw IllegalArgumentException("No p(x=0) probability for mean: $mean")).toInt()
-    }
 
     override fun sample(generator: RandomGenerator): Chain<Int> = generator.chain {
         var n = 0
@@ -37,7 +31,9 @@ class SmallMeanPoissonSampler(mean: Double) : Sampler<Int> {
     override fun toString(): String = "Small Mean Poisson deviate"
 
     companion object {
-        fun of(mean: Double): SmallMeanPoissonSampler =
+        fun of(mean: Double): SmallMeanPoissonSampler {
-            SmallMeanPoissonSampler(mean)
+            require(mean > 0) { "mean is not strictly positive: $mean" }
+            return SmallMeanPoissonSampler(mean)
+        }
     }
 }

kmath-prob/src/commonMain/kotlin/scientifik/kmath/prob/samplers/ZigguratNormalizedGaussianSampler.kt

@@ -7,7 +7,7 @@ import scientifik.kmath.prob.Sampler
 import scientifik.kmath.prob.chain
 import kotlin.math.*
 
-class ZigguratNormalizedGaussianSampler() :
+class ZigguratNormalizedGaussianSampler private constructor() :
     NormalizedGaussianSampler, Sampler<Double> {
 
     private fun sampleOne(generator: RandomGenerator): Double {
@@ -63,7 +63,6 @@ class ZigguratNormalizedGaussianSampler() :
         private val K: LongArray = LongArray(LEN)
         private val W: DoubleArray = DoubleArray(LEN)
         private val F: DoubleArray = DoubleArray(LEN)
-        private fun gauss(x: Double): Double = exp(-0.5 * x * x)
 
         init {
             // Filling the tables.
@@ -91,5 +90,8 @@ class ZigguratNormalizedGaussianSampler() :
                 W[i] = d * ONE_OVER_MAX
             }
         }
+
+        fun of(): ZigguratNormalizedGaussianSampler = ZigguratNormalizedGaussianSampler()
+        private fun gauss(x: Double): Double = exp(-0.5 * x * x)
     }
 }