kscience/kmath
9
0
Fork 1
Code Issues Pull Requests 6 Packages Projects Releases 16 Wiki Activity

Add additional constructor

Browse Source
This commit is contained in:
Iaroslav Postovalov 2020-10-28 18:36:00 +07:00
parent 59b120e086
commit 55909aee0d
No known key found for this signature in database
GPG Key ID: 46E15E4A31B3BCD7
2 changed files with 9 additions and 1 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

2
examples/src/benchmarks/kotlin/kscience/kmath/structures/ViktorBenchmark.kt
View File

@ -36,7 +36,7 @@ internal class ViktorBenchmark {
@Benchmark @Benchmark
fun rawViktor() { fun rawViktor() {
val one = F64Array.full(init = 1.0, shape = *intArrayOf(dim, dim)) val one = F64Array.full(init = 1.0, shape = intArrayOf(dim, dim))
var res = one var res = one
repeat(n) { res = res + one } repeat(n) { res = res + one }
} }

8
kmath-prob/src/commonMain/kotlin/kscience/kmath/prob/distributions/NormalDistribution.kt
View File

@ -5,9 +5,17 @@ import kscience.kmath.prob.RandomGenerator
import kscience.kmath.prob.UnivariateDistribution import kscience.kmath.prob.UnivariateDistribution
import kscience.kmath.prob.internal.InternalErf import kscience.kmath.prob.internal.InternalErf
import kscience.kmath.prob.samplers.GaussianSampler import kscience.kmath.prob.samplers.GaussianSampler
import kscience.kmath.prob.samplers.NormalizedGaussianSampler
import kscience.kmath.prob.samplers.ZigguratNormalizedGaussianSampler
import kotlin.math.* import kotlin.math.*
public inline class NormalDistribution(public val sampler: GaussianSampler) : UnivariateDistribution<Double> { public inline class NormalDistribution(public val sampler: GaussianSampler) : UnivariateDistribution<Double> {
public constructor(
mean: Double,
standardDeviation: Double,
normalized: NormalizedGaussianSampler = ZigguratNormalizedGaussianSampler.of(),
) : this(GaussianSampler.of(mean, standardDeviation, normalized))
public override fun probability(arg: Double): Double { public override fun probability(arg: Double): Double {
val x1 = (arg - sampler.mean) / sampler.standardDeviation val x1 = (arg - sampler.mean) / sampler.standardDeviation
return exp(-0.5 * x1 * x1 - (ln(sampler.standardDeviation) + 0.5 * ln(2 * PI))) return exp(-0.5 * x1 * x1 - (ln(sampler.standardDeviation) + 0.5 * ln(2 * PI)))