kmath-commons/src/main/kotlin/kscience/kmath/commons/expressions/DiffExpression.kt

@@ -33,10 +33,7 @@ public class DerivativeStructureField(
         variables[name] ?: default ?: error("A variable with name $name does not exist")
 
     public fun Number.const(): DerivativeStructure = DerivativeStructure(order, parameters.size, toDouble())
-
+    public fun DerivativeStructure.deriv(parName: String, order: Int = 1): Double = deriv(mapOf(parName to order))
-    public fun DerivativeStructure.deriv(parName: String, order: Int = 1): Double {
-        return deriv(mapOf(parName to order))
-    }
 
     public fun DerivativeStructure.deriv(orders: Map<String, Int>): Double {
         return getPartialDerivative(*parameters.keys.map { orders[it] ?: 0 }.toIntArray())
@@ -75,7 +72,6 @@ public class DerivativeStructureField(
     public fun power(arg: DerivativeStructure, pow: DerivativeStructure): DerivativeStructure = arg.pow(pow)
     public override fun exp(arg: DerivativeStructure): DerivativeStructure = arg.exp()
     public override fun ln(arg: DerivativeStructure): DerivativeStructure = arg.log()
-
     public override operator fun DerivativeStructure.plus(b: Number): DerivativeStructure = add(b.toDouble())
     public override operator fun DerivativeStructure.minus(b: Number): DerivativeStructure = subtract(b.toDouble())
     public override operator fun Number.plus(b: DerivativeStructure): DerivativeStructure = b + this

kmath-prob/src/commonMain/kotlin/kscience/kmath/prob/Distribution.kt

@@ -6,7 +6,7 @@ import kscience.kmath.chains.collect
 import kscience.kmath.structures.Buffer
 import kscience.kmath.structures.BufferFactory
 
-public interface Sampler<T : Any> {
+public fun interface Sampler<T : Any> {
     public fun sample(generator: RandomGenerator): Chain<T>
 }
 
@@ -20,11 +20,7 @@ public interface Distribution<T : Any> : Sampler<T> {
      */
     public fun probability(arg: T): Double
 
-    /**
+    public override fun sample(generator: RandomGenerator): Chain<T>
-     * Create a chain of samples from this distribution.
-     * The chain is not guaranteed to be stateless, but different sample chains should be independent.
-     */
-    override fun sample(generator: RandomGenerator): Chain<T>
 
     /**
      * An empty companion. Distribution factories should be written as its extensions
@@ -63,9 +59,7 @@ public fun <T : Any> Sampler<T>.sampleBuffer(
         //clear list from previous run
         tmp.clear()
         //Fill list
-        repeat(size) {
+        repeat(size) { tmp.add(chain.next()) }
-            tmp.add(chain.next())
-        }
         //return new buffer with elements from tmp
         bufferFactory(size) { tmp[it] }
     }

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

@@ -0,0 +1,30 @@
+package kscience.kmath.prob.distributions
+
+import kscience.kmath.chains.Chain
+import kscience.kmath.prob.RandomGenerator
+import kscience.kmath.prob.UnivariateDistribution
+import kscience.kmath.prob.internal.InternalErf
+import kscience.kmath.prob.samplers.GaussianSampler
+import kotlin.math.*
+
+public inline class NormalDistribution(public val sampler: GaussianSampler) : UnivariateDistribution<Double> {
+    public override fun probability(arg: Double): Double {
+        val x1 = (arg - sampler.mean) / sampler.standardDeviation
+        return exp(-0.5 * x1 * x1 - (ln(sampler.standardDeviation) + 0.5 * ln(2 * PI)))
+    }
+
+    public override fun sample(generator: RandomGenerator): Chain<Double> = sampler.sample(generator)
+
+    public override fun cumulative(arg: Double): Double {
+        val dev = arg - sampler.mean
+
+        return when {
+            abs(dev) > 40 * sampler.standardDeviation -> if (dev < 0) 0.0 else 1.0
+            else -> 0.5 * InternalErf.erfc(-dev / (sampler.standardDeviation * SQRT2))
+        }
+    }
+
+    private companion object {
+        private val SQRT2 = sqrt(2.0)
+    }
+}

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

@@ -4,6 +4,7 @@ import kscience.kmath.chains.Chain
 import kscience.kmath.prob.RandomGenerator
 import kscience.kmath.prob.Sampler
 import kscience.kmath.prob.chain
+import kscience.kmath.prob.internal.InternalUtils
 import kotlin.math.ln
 import kotlin.math.pow
 

kmath-prob/src/commonMain/kotlin/kscience/kmath/prob/samplers/AliasMethodDiscreteSampler.kt

@@ -4,6 +4,7 @@ import kscience.kmath.chains.Chain
 import kscience.kmath.prob.RandomGenerator
 import kscience.kmath.prob.Sampler
 import kscience.kmath.prob.chain
+import kscience.kmath.prob.internal.InternalUtils
 import kotlin.math.ceil
 import kotlin.math.max
 import kotlin.math.min

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

@@ -10,10 +10,13 @@ import kscience.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
+ *
+ * @property mean the mean of the distribution.
+ * @property standardDeviation the variance of the distribution.
  */
 public class GaussianSampler private constructor(
-    private val mean: Double,
+    public val mean: Double,
-    private val standardDeviation: Double,
+    public val standardDeviation: Double,
     private val normalized: NormalizedGaussianSampler
 ) : Sampler<Double> {
     public override fun sample(generator: RandomGenerator): Chain<Double> = normalized

kmath-prob/src/commonMain/kotlin/kscience/kmath/prob/samplers/InternalGamma.kt

@@ -1,43 +1,2 @@
 package kscience.kmath.prob.samplers
 
-import kotlin.math.PI
-import kotlin.math.ln
-
-internal object InternalGamma {
-    private const val LANCZOS_G = 607.0 / 128.0
-
-    private val LANCZOS_COEFFICIENTS = doubleArrayOf(
-        0.99999999999999709182,
-        57.156235665862923517,
-        -59.597960355475491248,
-        14.136097974741747174,
-        -0.49191381609762019978,
-        .33994649984811888699e-4,
-        .46523628927048575665e-4,
-        -.98374475304879564677e-4,
-        .15808870322491248884e-3,
-        -.21026444172410488319e-3,
-        .21743961811521264320e-3,
-        -.16431810653676389022e-3,
-        .84418223983852743293e-4,
-        -.26190838401581408670e-4,
-        .36899182659531622704e-5
-    )
-
-    private val HALF_LOG_2_PI: Double = 0.5 * ln(2.0 * PI)
-
-    fun logGamma(x: Double): Double {
-        // Stripped-down version of the same method defined in "Commons Math":
-        // Unused "if" branches (for when x < 8) have been removed here since
-        // this method is only used (by class "InternalUtils") in order to
-        // compute log(n!) for x > 20.
-        val sum = lanczos(x)
-        val tmp = x + LANCZOS_G + 0.5
-        return (x + 0.5) * ln(tmp) - tmp + HALF_LOG_2_PI + ln(sum / x)
-    }
-
-    private fun lanczos(x: Double): Double {
-        val sum = (LANCZOS_COEFFICIENTS.size - 1 downTo 1).sumByDouble { LANCZOS_COEFFICIENTS[it] / (x + it) }
-        return sum + LANCZOS_COEFFICIENTS[0]
-    }
-}

kmath-prob/src/commonMain/kotlin/kscience/kmath/prob/samplers/InternalUtils.kt

@@ -1,78 +1,2 @@
 package kscience.kmath.prob.samplers
 
-import kotlin.math.ln
-import kotlin.math.min
-
-internal object InternalUtils {
-    private val FACTORIALS = longArrayOf(
-        1L, 1L, 2L,
-        6L, 24L, 120L,
-        720L, 5040L, 40320L,
-        362880L, 3628800L, 39916800L,
-        479001600L, 6227020800L, 87178291200L,
-        1307674368000L, 20922789888000L, 355687428096000L,
-        6402373705728000L, 121645100408832000L, 2432902008176640000L
-    )
-
-    private const val BEGIN_LOG_FACTORIALS = 2
-
-    fun factorial(n: Int): Long = FACTORIALS[n]
-
-    fun validateProbabilities(probabilities: DoubleArray?): Double {
-        require(!(probabilities == null || probabilities.isEmpty())) { "Probabilities must not be empty." }
-        var sumProb = 0.0
-
-        probabilities.forEach { prob ->
-            validateProbability(prob)
-            sumProb += prob
-        }
-
-        require(!(sumProb.isInfinite() || sumProb <= 0)) { "Invalid sum of probabilities: $sumProb" }
-        return sumProb
-    }
-
-    private fun validateProbability(probability: Double): Unit =
-        require(!(probability < 0 || probability.isInfinite() || probability.isNaN())) { "Invalid probability: $probability" }
-
-    class FactorialLog private constructor(
-        numValues: Int,
-        cache: DoubleArray?
-    ) {
-        private val logFactorials: DoubleArray = DoubleArray(numValues)
-
-        init {
-            val endCopy: Int
-
-            if (cache != null && cache.size > BEGIN_LOG_FACTORIALS) {
-                // Copy available values.
-                endCopy = min(cache.size, numValues)
-                cache.copyInto(
-                    logFactorials,
-                    BEGIN_LOG_FACTORIALS,
-                    BEGIN_LOG_FACTORIALS, endCopy
-                )
-            }
-            // All values to be computed
-            else endCopy = BEGIN_LOG_FACTORIALS
-
-            // Compute remaining values.
-            (endCopy until numValues).forEach { i ->
-                if (i < FACTORIALS.size)
-                    logFactorials[i] = ln(FACTORIALS[i].toDouble())
-                else
-                    logFactorials[i] = logFactorials[i - 1] + ln(i.toDouble())
-            }
-        }
-
-        fun value(n: Int): Double {
-            if (n < logFactorials.size)
-                return logFactorials[n]
-
-            return if (n < FACTORIALS.size) ln(FACTORIALS[n].toDouble()) else InternalGamma.logGamma(n + 1.0)
-        }
-
-        companion object {
-            fun create(): FactorialLog = FactorialLog(0, null)
-        }
-    }
-}

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

@@ -5,6 +5,7 @@ import kscience.kmath.chains.ConstantChain
 import kscience.kmath.prob.RandomGenerator
 import kscience.kmath.prob.Sampler
 import kscience.kmath.prob.chain
+import kscience.kmath.prob.internal.InternalUtils
 import kscience.kmath.prob.next
 import kotlin.math.*
 
@@ -111,16 +112,13 @@ public class LargeMeanPoissonSampler private constructor(public val mean: Double
     }
 
     private fun getFactorialLog(n: Int): Double = factorialLog.value(n)
-
+    public override fun toString(): String = "Large Mean Poisson deviate"
-    override fun toString(): String = "Large Mean Poisson deviate"
 
     public companion object {
         private const val MAX_MEAN: Double = 0.5 * Int.MAX_VALUE
         private val NO_CACHE_FACTORIAL_LOG: InternalUtils.FactorialLog = InternalUtils.FactorialLog.create()
 
-        private val NO_SMALL_MEAN_POISSON_SAMPLER: Sampler<Int> = object : Sampler<Int> {
+        private val NO_SMALL_MEAN_POISSON_SAMPLER: Sampler<Int> = Sampler { ConstantChain(0) }
-            override fun sample(generator: RandomGenerator): Chain<Int> = ConstantChain(0)
-        }
 
         public fun of(mean: Double): LargeMeanPoissonSampler {
             require(mean >= 1) { "mean is not >= 1: $mean" }

kmath-prob/src/jvmMain/kotlin/kscience/kmath/prob/RandomSourceGenerator.kt

@@ -26,10 +26,7 @@ public class RandomSourceGenerator(public val source: RandomSource, seed: Long?)
 public inline class RandomGeneratorProvider(public val generator: RandomGenerator) : UniformRandomProvider {
     public override fun nextBoolean(): Boolean = generator.nextBoolean()
     public override fun nextFloat(): Float = generator.nextDouble().toFloat()
-
+    public override fun nextBytes(bytes: ByteArray): Unit = generator.fillBytes(bytes)
-    public override fun nextBytes(bytes: ByteArray) {
-        generator.fillBytes(bytes)
-    }
 
     public override fun nextBytes(bytes: ByteArray, start: Int, len: Int) {
         generator.fillBytes(bytes, start, start + len)