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

@@ -15,8 +15,9 @@ class GaussianSampler private constructor(
     private val standardDeviation: Double,
     private val normalized: NormalizedGaussianSampler
 ) : Sampler<Double> {
-    override fun sample(generator: RandomGenerator): Chain<Double> =
-        normalized.sample(generator).map { standardDeviation * it + mean }
+    override fun sample(generator: RandomGenerator): Chain<Double> = normalized
+        .sample(generator)
+        .map { standardDeviation * it + mean }
 
     override fun toString(): String = "Gaussian deviate [$normalized]"
 

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

@@ -30,10 +30,8 @@ class MarsagliaNormalizedGaussianSampler private constructor(): NormalizedGaussi
                 if (r2 < 1 && r2 > 0) {
                     // Pair (x, y) is within unit circle.
                     alpha = sqrt(-2 * ln(r2) / r2)
-
                     // Keep second element of the pair for next invocation.
                     nextGaussian = alpha * y
-
                     // Return the first element of the generated pair.
                     break
                 }
@@ -46,7 +44,6 @@ class MarsagliaNormalizedGaussianSampler private constructor(): NormalizedGaussi
             // Use the second element of the pair (generated at the
             // previous invocation).
             val r = nextGaussian
-
             // Both elements of the pair have been used.
             nextGaussian = Double.NaN
             r

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

@@ -73,8 +73,7 @@ class ZigguratNormalizedGaussianSampler private constructor() :
             // Filling the tables.
             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
@@ -85,10 +84,7 @@ class ZigguratNormalizedGaussianSampler private constructor() :
 
             (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