forked from kscience/kmath
Add additional constructor
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@ -36,7 +36,7 @@ internal class ViktorBenchmark {
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@Benchmark
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fun rawViktor() {
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val one = F64Array.full(init = 1.0, shape = *intArrayOf(dim, dim))
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val one = F64Array.full(init = 1.0, shape = intArrayOf(dim, dim))
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var res = one
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repeat(n) { res = res + one }
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}
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@ -5,9 +5,17 @@ import kscience.kmath.prob.RandomGenerator
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import kscience.kmath.prob.UnivariateDistribution
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import kscience.kmath.prob.internal.InternalErf
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import kscience.kmath.prob.samplers.GaussianSampler
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import kscience.kmath.prob.samplers.NormalizedGaussianSampler
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import kscience.kmath.prob.samplers.ZigguratNormalizedGaussianSampler
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import kotlin.math.*
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public inline class NormalDistribution(public val sampler: GaussianSampler) : UnivariateDistribution<Double> {
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public constructor(
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mean: Double,
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standardDeviation: Double,
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normalized: NormalizedGaussianSampler = ZigguratNormalizedGaussianSampler.of(),
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) : this(GaussianSampler.of(mean, standardDeviation, normalized))
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public override fun probability(arg: Double): Double {
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val x1 = (arg - sampler.mean) / sampler.standardDeviation
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return exp(-0.5 * x1 * x1 - (ln(sampler.standardDeviation) + 0.5 * ln(2 * PI)))
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