From 31460df721fccef597a62a48d0bbac92e4f1ae05 Mon Sep 17 00:00:00 2001
From: darksnake <altavir@gmail.com>
Date: Wed, 7 Apr 2021 14:14:22 +0300
Subject: [PATCH] GaussianSampler inherits Blocking Sampler

---
 .../kotlin/space/kscience/kmath/samplers/GaussianSampler.kt | 3 +--
 .../space/kscience/kmath/stat/CommonsDistributionsTest.kt   | 6 ++++--
 2 files changed, 5 insertions(+), 4 deletions(-)

diff --git a/kmath-stat/src/commonMain/kotlin/space/kscience/kmath/samplers/GaussianSampler.kt b/kmath-stat/src/commonMain/kotlin/space/kscience/kmath/samplers/GaussianSampler.kt
index 26047830c..237da920c 100644
--- a/kmath-stat/src/commonMain/kotlin/space/kscience/kmath/samplers/GaussianSampler.kt
+++ b/kmath-stat/src/commonMain/kotlin/space/kscience/kmath/samplers/GaussianSampler.kt
@@ -3,7 +3,6 @@ package space.kscience.kmath.samplers
 import space.kscience.kmath.chains.BlockingDoubleChain
 import space.kscience.kmath.chains.map
 import space.kscience.kmath.stat.RandomGenerator
-import space.kscience.kmath.stat.Sampler
 
 /**
  * Sampling from a Gaussian distribution with given mean and standard deviation.
@@ -18,7 +17,7 @@ public class GaussianSampler(
     public val mean: Double,
     public val standardDeviation: Double,
     private val normalized: NormalizedGaussianSampler = BoxMullerSampler
-) : Sampler<Double> {
+) : BlockingDoubleSampler {
 
     init {
         require(standardDeviation > 0.0) { "standard deviation is not strictly positive: $standardDeviation" }
diff --git a/kmath-stat/src/jvmTest/kotlin/space/kscience/kmath/stat/CommonsDistributionsTest.kt b/kmath-stat/src/jvmTest/kotlin/space/kscience/kmath/stat/CommonsDistributionsTest.kt
index c6b9cb17a..976020f08 100644
--- a/kmath-stat/src/jvmTest/kotlin/space/kscience/kmath/stat/CommonsDistributionsTest.kt
+++ b/kmath-stat/src/jvmTest/kotlin/space/kscience/kmath/stat/CommonsDistributionsTest.kt
@@ -18,10 +18,12 @@ internal class CommonsDistributionsTest {
     }
 
     @Test
-    fun testNormalDistributionBlocking() = runBlocking {
+    fun testNormalDistributionBlocking()  {
         val distribution = GaussianSampler(7.0, 2.0)
         val generator = RandomGenerator.default(1)
         val sample = distribution.sample(generator).nextBufferBlocking(1000)
-        Assertions.assertEquals(7.0, Mean.double(sample), 0.2)
+        runBlocking {
+            Assertions.assertEquals(7.0, Mean.double(sample), 0.2)
+        }
     }
 }