Add Apache Javadocs references

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

@@ -7,6 +7,11 @@ import scientifik.kmath.prob.chain
 import kotlin.math.ln
 import kotlin.math.pow
 
+/**
+ * Based on commons-rng implementation.
+ *
+ * See https://commons.apache.org/proper/commons-rng/commons-rng-sampling/apidocs/org/apache/commons/rng/sampling/distribution/AhrensDieterExponentialSampler.html
+ */
 class AhrensDieterExponentialSampler private constructor(val mean: Double) : Sampler<Double> {
     override fun sample(generator: RandomGenerator): Chain<Double> = generator.chain {
         // Step 1:

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

@@ -6,6 +6,11 @@ import scientifik.kmath.prob.Sampler
 import scientifik.kmath.prob.chain
 import kotlin.math.*
 
+/**
+ * Based on commons-rng implementation.
+ *
+ * See https://commons.apache.org/proper/commons-rng/commons-rng-sampling/apidocs/org/apache/commons/rng/sampling/distribution/BoxMullerNormalizedGaussianSampler.html
+ */
 class BoxMullerNormalizedGaussianSampler private constructor() : NormalizedGaussianSampler, Sampler<Double> {
     private var nextGaussian: Double = Double.NaN
 

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

@@ -5,6 +5,11 @@ import scientifik.kmath.chains.map
 import scientifik.kmath.prob.RandomGenerator
 import scientifik.kmath.prob.Sampler
 
+/**
+ * Based on commons-rng implementation.
+ *
+ * See https://commons.apache.org/proper/commons-rng/commons-rng-sampling/apidocs/org/apache/commons/rng/sampling/distribution/GaussianSampler.html
+ */
 class GaussianSampler private constructor(
     private val mean: Double,
     private val standardDeviation: Double,

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

@@ -6,6 +6,11 @@ import scientifik.kmath.prob.Sampler
 import scientifik.kmath.prob.chain
 import kotlin.math.exp
 
+/**
+ * Based on commons-rng implementation.
+ *
+ * See https://commons.apache.org/proper/commons-rng/commons-rng-sampling/apidocs/org/apache/commons/rng/sampling/distribution/KempSmallMeanPoissonSampler.html
+ */
 class KempSmallMeanPoissonSampler private constructor(
     private val p0: Double,
     private val mean: Double

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

@@ -8,9 +8,14 @@ import scientifik.kmath.prob.Sampler
 import scientifik.kmath.prob.next
 import kotlin.math.*
 
+/**
+ * Based on commons-rng implementation.
+ *
+ * See https://commons.apache.org/proper/commons-rng/commons-rng-sampling/apidocs/org/apache/commons/rng/sampling/distribution/LargeMeanPoissonSampler.html
+ */
 class LargeMeanPoissonSampler private constructor(val mean: Double) : Sampler<Int> {
     private val exponential: Sampler<Double> = AhrensDieterExponentialSampler.of(1.0)
-    private val gaussian: Sampler<Double> = ZigguratNormalizedGaussianSampler()
+    private val gaussian: Sampler<Double> = ZigguratNormalizedGaussianSampler.of()
     private val factorialLog: InternalUtils.FactorialLog = NO_CACHE_FACTORIAL_LOG
     private val lambda: Double = floor(mean)
     private val logLambda: Double = ln(lambda)

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

@@ -7,6 +7,11 @@ import scientifik.kmath.prob.chain
 import kotlin.math.ln
 import kotlin.math.sqrt
 
+/**
+ * Based on commons-rng implementation.
+ *
+ * See https://commons.apache.org/proper/commons-rng/commons-rng-sampling/apidocs/org/apache/commons/rng/sampling/distribution/MarsagliaNormalizedGaussianSampler.html
+ */
 class MarsagliaNormalizedGaussianSampler private constructor(): NormalizedGaussianSampler, Sampler<Double> {
     private var nextGaussian = Double.NaN
 

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

@@ -4,7 +4,11 @@ import scientifik.kmath.chains.Chain
 import scientifik.kmath.prob.RandomGenerator
 import scientifik.kmath.prob.Sampler
 
-
+/**
+ * Based on commons-rng implementation.
+ *
+ * https://commons.apache.org/proper/commons-rng/commons-rng-sampling/apidocs/org/apache/commons/rng/sampling/distribution/PoissonSampler.html
+ */
 class PoissonSampler private constructor(
     mean: Double
 ) : Sampler<Int> {

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

@@ -8,6 +8,11 @@ import scientifik.kmath.prob.chain
 import kotlin.math.ceil
 import kotlin.math.exp
 
+/**
+ * Based on commons-rng implementation.
+ *
+ * See https://commons.apache.org/proper/commons-rng/commons-rng-sampling/apidocs/org/apache/commons/rng/sampling/distribution/SmallMeanPoissonSampler.html
+ */
 class SmallMeanPoissonSampler private constructor(mean: Double) : Sampler<Int> {
     private val p0: Double = exp(-mean)
 

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

@@ -7,6 +7,11 @@ import scientifik.kmath.prob.Sampler
 import scientifik.kmath.prob.chain
 import kotlin.math.*
 
+/**
+ * Based on commons-rng implementation.
+ *
+ * See https://commons.apache.org/proper/commons-rng/commons-rng-sampling/apidocs/org/apache/commons/rng/sampling/distribution/ZigguratNormalizedGaussianSampler.html
+ */
 class ZigguratNormalizedGaussianSampler private constructor() :
     NormalizedGaussianSampler, Sampler<Double> {