diff --git a/kmath-commons-rng-part/build.gradle.kts b/kmath-commons-rng-part/build.gradle.kts new file mode 100644 index 000000000..9d3cd0e7d --- /dev/null +++ b/kmath-commons-rng-part/build.gradle.kts @@ -0,0 +1,2 @@ +plugins { id("scientifik.mpp") } +kotlin.sourceSets { commonMain.get().dependencies { api(project(":kmath-coroutines")) } } diff --git a/kmath-commons-rng-part/src/commonMain/kotlin/scientifik/commons/rng/UniformRandomProvider.kt b/kmath-commons-rng-part/src/commonMain/kotlin/scientifik/commons/rng/UniformRandomProvider.kt new file mode 100644 index 000000000..2fbf7a0a2 --- /dev/null +++ b/kmath-commons-rng-part/src/commonMain/kotlin/scientifik/commons/rng/UniformRandomProvider.kt @@ -0,0 +1,19 @@ +package scientifik.commons.rng + +interface UniformRandomProvider { + fun nextBytes(bytes: ByteArray) + + fun nextBytes( + bytes: ByteArray, + start: Int, + len: Int + ) + + fun nextInt(): Int + fun nextInt(n: Int): Int + fun nextLong(): Long + fun nextLong(n: Long): Long + fun nextBoolean(): Boolean + fun nextFloat(): Float + fun nextDouble(): Double +} \ No newline at end of file diff --git a/kmath-commons-rng-part/src/commonMain/kotlin/scientifik/commons/rng/sampling/SharedStateSampler.kt b/kmath-commons-rng-part/src/commonMain/kotlin/scientifik/commons/rng/sampling/SharedStateSampler.kt new file mode 100644 index 000000000..d32a646c2 --- /dev/null +++ b/kmath-commons-rng-part/src/commonMain/kotlin/scientifik/commons/rng/sampling/SharedStateSampler.kt @@ -0,0 +1,7 @@ +package scientifik.commons.rng.sampling + +import scientifik.commons.rng.UniformRandomProvider + +interface SharedStateSampler { + fun withUniformRandomProvider(rng: UniformRandomProvider): R +} diff --git a/kmath-commons-rng-part/src/commonMain/kotlin/scientifik/commons/rng/sampling/distribution/AhrensDieterExponentialSampler.kt b/kmath-commons-rng-part/src/commonMain/kotlin/scientifik/commons/rng/sampling/distribution/AhrensDieterExponentialSampler.kt new file mode 100644 index 000000000..7aa061951 --- /dev/null +++ b/kmath-commons-rng-part/src/commonMain/kotlin/scientifik/commons/rng/sampling/distribution/AhrensDieterExponentialSampler.kt @@ -0,0 +1,95 @@ +package scientifik.commons.rng.sampling.distribution + +import scientifik.commons.rng.UniformRandomProvider +import kotlin.math.ln +import kotlin.math.pow + +class AhrensDieterExponentialSampler : SamplerBase, + SharedStateContinuousSampler { + private val mean: Double + private val rng: UniformRandomProvider + + constructor( + rng: UniformRandomProvider, + mean: Double + ) : super(null) { + require(mean > 0) { "mean is not strictly positive: $mean" } + this.rng = rng + this.mean = mean + } + + private constructor( + rng: UniformRandomProvider, + source: AhrensDieterExponentialSampler + ) : super(null) { + this.rng = rng + mean = source.mean + } + + override fun sample(): Double { + // Step 1: + var a = 0.0 + var u: Double = rng.nextDouble() + + // Step 2 and 3: + while (u < 0.5) { + a += EXPONENTIAL_SA_QI.get( + 0 + ) + u *= 2.0 + } + + // Step 4 (now u >= 0.5): + u += u - 1 + + // Step 5: + if (u <= EXPONENTIAL_SA_QI.get( + 0 + ) + ) { + return mean * (a + u) + } + + // Step 6: + var i = 0 // Should be 1, be we iterate before it in while using 0. + var u2: Double = rng.nextDouble() + var umin = u2 + + // Step 7 and 8: + do { + ++i + u2 = rng.nextDouble() + if (u2 < umin) umin = u2 + // Step 8: + } while (u > EXPONENTIAL_SA_QI[i]) // Ensured to exit since EXPONENTIAL_SA_QI[MAX] = 1. + return mean * (a + umin * EXPONENTIAL_SA_QI[0]) + } + + override fun toString(): String = "Ahrens-Dieter Exponential deviate [$rng]" + + override fun withUniformRandomProvider(rng: UniformRandomProvider): SharedStateContinuousSampler = + AhrensDieterExponentialSampler(rng, this) + + companion object { + private val EXPONENTIAL_SA_QI = DoubleArray(16) + + fun of( + rng: UniformRandomProvider, + mean: Double + ): SharedStateContinuousSampler = AhrensDieterExponentialSampler(rng, mean) + + init { + /** + * Filling EXPONENTIAL_SA_QI table. + * Note that we don't want qi = 0 in the table. + */ + val ln2 = ln(2.0) + var qi = 0.0 + + EXPONENTIAL_SA_QI.indices.forEach { i -> + qi += ln2.pow(i + 1.0) / InternalUtils.factorial(i + 1) + EXPONENTIAL_SA_QI[i] = qi + } + } + } +} diff --git a/kmath-commons-rng-part/src/commonMain/kotlin/scientifik/commons/rng/sampling/distribution/BoxMullerNormalizedGaussianSampler.kt b/kmath-commons-rng-part/src/commonMain/kotlin/scientifik/commons/rng/sampling/distribution/BoxMullerNormalizedGaussianSampler.kt new file mode 100644 index 000000000..91b3314b5 --- /dev/null +++ b/kmath-commons-rng-part/src/commonMain/kotlin/scientifik/commons/rng/sampling/distribution/BoxMullerNormalizedGaussianSampler.kt @@ -0,0 +1,50 @@ +package scientifik.commons.rng.sampling.distribution + +import scientifik.commons.rng.UniformRandomProvider +import kotlin.math.* + +class BoxMullerNormalizedGaussianSampler( + private val rng: UniformRandomProvider +) : + NormalizedGaussianSampler, + SharedStateContinuousSampler { + private var nextGaussian: Double = Double.NaN + + override fun sample(): Double { + val random: Double + + if (nextGaussian.isNaN()) { + // Generate a pair of Gaussian numbers. + val x = rng.nextDouble() + val y = rng.nextDouble() + val alpha = 2 * PI * x + val r = sqrt(-2 * ln(y)) + + // Return the first element of the generated pair. + random = r * cos(alpha) + + // Keep second element of the pair for next invocation. + nextGaussian = r * sin(alpha) + } else { + // Use the second element of the pair (generated at the + // previous invocation). + random = nextGaussian + + // Both elements of the pair have been used. + nextGaussian = Double.NaN + } + + return random + } + + override fun toString(): String = "Box-Muller normalized Gaussian deviate [$rng]" + + override fun withUniformRandomProvider(rng: UniformRandomProvider): SharedStateContinuousSampler = + BoxMullerNormalizedGaussianSampler(rng) + + companion object { + @Suppress("UNCHECKED_CAST") + fun of(rng: UniformRandomProvider): S where S : NormalizedGaussianSampler?, S : SharedStateContinuousSampler? = + BoxMullerNormalizedGaussianSampler(rng) as S + } +} diff --git a/kmath-commons-rng-part/src/commonMain/kotlin/scientifik/commons/rng/sampling/distribution/ContinuousSampler.kt b/kmath-commons-rng-part/src/commonMain/kotlin/scientifik/commons/rng/sampling/distribution/ContinuousSampler.kt new file mode 100644 index 000000000..4841672a2 --- /dev/null +++ b/kmath-commons-rng-part/src/commonMain/kotlin/scientifik/commons/rng/sampling/distribution/ContinuousSampler.kt @@ -0,0 +1,5 @@ +package scientifik.commons.rng.sampling.distribution + +interface ContinuousSampler { + fun sample(): Double +} diff --git a/kmath-commons-rng-part/src/commonMain/kotlin/scientifik/commons/rng/sampling/distribution/DiscreteSampler.kt b/kmath-commons-rng-part/src/commonMain/kotlin/scientifik/commons/rng/sampling/distribution/DiscreteSampler.kt new file mode 100644 index 000000000..8e8bccd18 --- /dev/null +++ b/kmath-commons-rng-part/src/commonMain/kotlin/scientifik/commons/rng/sampling/distribution/DiscreteSampler.kt @@ -0,0 +1,5 @@ +package scientifik.commons.rng.sampling.distribution + +interface DiscreteSampler { + fun sample(): Int +} diff --git a/kmath-commons-rng-part/src/commonMain/kotlin/scientifik/commons/rng/sampling/distribution/GaussianSampler.kt b/kmath-commons-rng-part/src/commonMain/kotlin/scientifik/commons/rng/sampling/distribution/GaussianSampler.kt new file mode 100644 index 000000000..424ea90fe --- /dev/null +++ b/kmath-commons-rng-part/src/commonMain/kotlin/scientifik/commons/rng/sampling/distribution/GaussianSampler.kt @@ -0,0 +1,45 @@ +package scientifik.commons.rng.sampling.distribution + +import scientifik.commons.rng.UniformRandomProvider + +class GaussianSampler : SharedStateContinuousSampler { + private val mean: Double + private val standardDeviation: Double + private val normalized: NormalizedGaussianSampler + + constructor( + normalized: NormalizedGaussianSampler, + mean: Double, + standardDeviation: Double + ) { + require(standardDeviation > 0) { "standard deviation is not strictly positive: $standardDeviation" } + this.normalized = normalized + this.mean = mean + this.standardDeviation = standardDeviation + } + + private constructor( + rng: UniformRandomProvider, + source: GaussianSampler + ) { + mean = source.mean + standardDeviation = source.standardDeviation + normalized = InternalUtils.newNormalizedGaussianSampler(source.normalized, rng) + } + + override fun sample(): Double = standardDeviation * normalized.sample() + mean + + override fun toString(): String = "Gaussian deviate [$normalized]" + + override fun withUniformRandomProvider(rng: UniformRandomProvider): SharedStateContinuousSampler { + return GaussianSampler(rng, this) + } + + companion object { + fun of( + normalized: NormalizedGaussianSampler, + mean: Double, + standardDeviation: Double + ): SharedStateContinuousSampler = GaussianSampler(normalized, mean, standardDeviation) + } +} diff --git a/kmath-commons-rng-part/src/commonMain/kotlin/scientifik/commons/rng/sampling/distribution/InternalGamma.kt b/kmath-commons-rng-part/src/commonMain/kotlin/scientifik/commons/rng/sampling/distribution/InternalGamma.kt new file mode 100644 index 000000000..a75ba1608 --- /dev/null +++ b/kmath-commons-rng-part/src/commonMain/kotlin/scientifik/commons/rng/sampling/distribution/InternalGamma.kt @@ -0,0 +1,43 @@ +package scientifik.commons.rng.sampling.distribution + +import kotlin.math.PI +import kotlin.math.ln + +internal object InternalGamma { + 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] + } +} diff --git a/kmath-commons-rng-part/src/commonMain/kotlin/scientifik/commons/rng/sampling/distribution/InternalUtils.kt b/kmath-commons-rng-part/src/commonMain/kotlin/scientifik/commons/rng/sampling/distribution/InternalUtils.kt new file mode 100644 index 000000000..8134252aa --- /dev/null +++ b/kmath-commons-rng-part/src/commonMain/kotlin/scientifik/commons/rng/sampling/distribution/InternalUtils.kt @@ -0,0 +1,92 @@ +package scientifik.commons.rng.sampling.distribution + +import scientifik.commons.rng.UniformRandomProvider +import scientifik.commons.rng.sampling.SharedStateSampler +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 + } + + fun validateProbability(probability: Double): Unit = + require(!(probability < 0 || probability.isInfinite() || probability.isNaN())) { "Invalid probability: $probability" } + + fun newNormalizedGaussianSampler( + sampler: NormalizedGaussianSampler, + rng: UniformRandomProvider + ): NormalizedGaussianSampler { + if (sampler !is SharedStateSampler<*>) throw UnsupportedOperationException("The underlying sampler cannot share state") + + val newSampler: Any = + (sampler as SharedStateSampler<*>).withUniformRandomProvider(rng) as? NormalizedGaussianSampler + ?: throw UnsupportedOperationException( + "The underlying sampler did not create a normalized Gaussian sampler" + ) + + return newSampler as NormalizedGaussianSampler + } + + 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 withCache(cacheSize: Int): FactorialLog = FactorialLog(cacheSize, logFactorials) + + 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) + } + } +} diff --git a/kmath-commons-rng-part/src/commonMain/kotlin/scientifik/commons/rng/sampling/distribution/KempSmallMeanPoissonSampler.kt b/kmath-commons-rng-part/src/commonMain/kotlin/scientifik/commons/rng/sampling/distribution/KempSmallMeanPoissonSampler.kt new file mode 100644 index 000000000..6501089b2 --- /dev/null +++ b/kmath-commons-rng-part/src/commonMain/kotlin/scientifik/commons/rng/sampling/distribution/KempSmallMeanPoissonSampler.kt @@ -0,0 +1,56 @@ +package scientifik.commons.rng.sampling.distribution + +import scientifik.commons.rng.UniformRandomProvider +import kotlin.math.exp + +class KempSmallMeanPoissonSampler private constructor( + private val rng: UniformRandomProvider, + private val p0: Double, + private val mean: Double +) : SharedStateDiscreteSampler { + override fun sample(): Int { + // Note on the algorithm: + // - X is the unknown sample deviate (the output of the algorithm) + // - x is the current value from the distribution + // - p is the probability of the current value x, p(X=x) + // - u is effectively the cumulative probability that the sample X + // is equal or above the current value x, p(X>=x) + // So if p(X>=x) > p(X=x) the sample must be above x, otherwise it is x + var u = rng.nextDouble() + var x = 0 + var p = p0 + + while (u > p) { + u -= p + // Compute the next probability using a recurrence relation. + // p(x+1) = p(x) * mean / (x+1) + p *= mean / ++x + // The algorithm listed in Kemp (1981) does not check that the rolling probability + // is positive. This check is added to ensure no errors when the limit of the summation + // 1 - sum(p(x)) is above 0 due to cumulative error in floating point arithmetic. + if (p == 0.0) return x + } + + return x + } + + override fun toString(): String = "Kemp Small Mean Poisson deviate [$rng]" + + override fun withUniformRandomProvider(rng: UniformRandomProvider): SharedStateDiscreteSampler = + KempSmallMeanPoissonSampler(rng, p0, mean) + + companion object { + fun of( + rng: UniformRandomProvider, + mean: Double + ): SharedStateDiscreteSampler { + require(mean > 0) { "Mean is not strictly positive: $mean" } + val p0: Double = exp(-mean) + + // Probability must be positive. As mean increases then p(0) decreases. + if (p0 > 0) return KempSmallMeanPoissonSampler(rng, p0, mean) + throw IllegalArgumentException("No probability for mean: $mean") + } + } +} + diff --git a/kmath-commons-rng-part/src/commonMain/kotlin/scientifik/commons/rng/sampling/distribution/LargenMeanPoissonSampler.kt b/kmath-commons-rng-part/src/commonMain/kotlin/scientifik/commons/rng/sampling/distribution/LargenMeanPoissonSampler.kt new file mode 100644 index 000000000..ce1e1e3b0 --- /dev/null +++ b/kmath-commons-rng-part/src/commonMain/kotlin/scientifik/commons/rng/sampling/distribution/LargenMeanPoissonSampler.kt @@ -0,0 +1,233 @@ +package scientifik.commons.rng.sampling.distribution + +import scientifik.commons.rng.UniformRandomProvider +import kotlin.math.* + +class LargeMeanPoissonSampler : SharedStateDiscreteSampler { + private val rng: UniformRandomProvider + private val exponential: SharedStateContinuousSampler + private val gaussian: SharedStateContinuousSampler + private val factorialLog: InternalUtils.FactorialLog + private val lambda: Double + private val logLambda: Double + private val logLambdaFactorial: Double + private val delta: Double + private val halfDelta: Double + private val twolpd: Double + private val p1: Double + private val p2: Double + private val c1: Double + private val smallMeanPoissonSampler: SharedStateDiscreteSampler + + constructor( + rng: UniformRandomProvider, + mean: Double + ) { + require(mean >= 1) { "mean is not >= 1: $mean" } + // The algorithm is not valid if Math.floor(mean) is not an integer. + require(mean <= MAX_MEAN) { "mean $mean > $MAX_MEAN" } + this.rng = rng + gaussian = ZigguratNormalizedGaussianSampler(rng) + exponential = AhrensDieterExponentialSampler.of(rng, 1.0) + // Plain constructor uses the uncached function. + factorialLog = NO_CACHE_FACTORIAL_LOG!! + // Cache values used in the algorithm + lambda = floor(mean) + logLambda = ln(lambda) + logLambdaFactorial = getFactorialLog(lambda.toInt()) + delta = sqrt(lambda * ln(32 * lambda / PI + 1)) + halfDelta = delta / 2 + twolpd = 2 * lambda + delta + c1 = 1 / (8 * lambda) + val a1: Double = sqrt(PI * twolpd) * exp(c1) + val a2: Double = twolpd / delta * exp(-delta * (1 + delta) / twolpd) + val aSum = a1 + a2 + 1 + p1 = a1 / aSum + p2 = a2 / aSum + + // The algorithm requires a Poisson sample from the remaining lambda fraction. + val lambdaFractional = mean - lambda + smallMeanPoissonSampler = + if (lambdaFractional < Double.MIN_VALUE) NO_SMALL_MEAN_POISSON_SAMPLER else // Not used. + KempSmallMeanPoissonSampler.of(rng, lambdaFractional) + } + + internal constructor( + rng: UniformRandomProvider, + state: LargeMeanPoissonSamplerState, + lambdaFractional: Double + ) { + require(!(lambdaFractional < 0 || lambdaFractional >= 1)) { "lambdaFractional must be in the range 0 (inclusive) to 1 (exclusive): $lambdaFractional" } + this.rng = rng + gaussian = ZigguratNormalizedGaussianSampler(rng) + exponential = AhrensDieterExponentialSampler.of(rng, 1.0) + // Plain constructor uses the uncached function. + factorialLog = NO_CACHE_FACTORIAL_LOG!! + // Use the state to initialise the algorithm + lambda = state.lambdaRaw + logLambda = state.logLambda + logLambdaFactorial = state.logLambdaFactorial + delta = state.delta + halfDelta = state.halfDelta + twolpd = state.twolpd + p1 = state.p1 + p2 = state.p2 + c1 = state.c1 + + // The algorithm requires a Poisson sample from the remaining lambda fraction. + smallMeanPoissonSampler = + if (lambdaFractional < Double.MIN_VALUE) + NO_SMALL_MEAN_POISSON_SAMPLER + else // Not used. + KempSmallMeanPoissonSampler.of(rng, lambdaFractional) + } + + /** + * @param rng Generator of uniformly distributed random numbers. + * @param source Source to copy. + */ + private constructor( + rng: UniformRandomProvider, + source: LargeMeanPoissonSampler + ) { + this.rng = rng + gaussian = source.gaussian.withUniformRandomProvider(rng)!! + exponential = source.exponential.withUniformRandomProvider(rng)!! + // Reuse the cache + factorialLog = source.factorialLog + lambda = source.lambda + logLambda = source.logLambda + logLambdaFactorial = source.logLambdaFactorial + delta = source.delta + halfDelta = source.halfDelta + twolpd = source.twolpd + p1 = source.p1 + p2 = source.p2 + c1 = source.c1 + + // Share the state of the small sampler + smallMeanPoissonSampler = source.smallMeanPoissonSampler.withUniformRandomProvider(rng)!! + } + + /** {@inheritDoc} */ + override fun sample(): Int { + // This will never be null. It may be a no-op delegate that returns zero. + val y2: Int = smallMeanPoissonSampler.sample() + var x: Double + var y: Double + var v: Double + var a: Int + var t: Double + var qr: Double + var qa: Double + while (true) { + // Step 1: + val u = rng.nextDouble() + + if (u <= p1) { + // Step 2: + val n = gaussian.sample() + x = n * sqrt(lambda + halfDelta) - 0.5 + if (x > delta || x < -lambda) continue + y = if (x < 0) floor(x) else ceil(x) + val e = exponential.sample() + v = -e - 0.5 * n * n + c1 + } else { + // Step 3: + if (u > p1 + p2) { + y = lambda + break + } + + x = delta + twolpd / delta * exponential.sample() + y = ceil(x) + v = -exponential.sample() - delta * (x + 1) / twolpd + } + // The Squeeze Principle + // Step 4.1: + a = if (x < 0) 1 else 0 + t = y * (y + 1) / (2 * lambda) + + // Step 4.2 + if (v < -t && a == 0) { + y += lambda + break + } + + // Step 4.3: + qr = t * ((2 * y + 1) / (6 * lambda) - 1) + qa = qr - t * t / (3 * (lambda + a * (y + 1))) + + // Step 4.4: + if (v < qa) { + y += lambda + break + } + + // Step 4.5: + if (v > qr) continue + + // Step 4.6: + if (v < y * logLambda - getFactorialLog((y + lambda).toInt()) + logLambdaFactorial) { + y += lambda + break + } + } + + return min(y2 + y.toLong(), Int.MAX_VALUE.toLong()).toInt() + } + + + private fun getFactorialLog(n: Int): Double = factorialLog.value(n) + + override fun toString(): String = "Large Mean Poisson deviate [$rng]" + + override fun withUniformRandomProvider(rng: UniformRandomProvider): SharedStateDiscreteSampler = + LargeMeanPoissonSampler(rng, this) + + val state: LargeMeanPoissonSamplerState + get() = LargeMeanPoissonSamplerState( + lambda, logLambda, logLambdaFactorial, + delta, halfDelta, twolpd, p1, p2, c1 + ) + + class LargeMeanPoissonSamplerState( + val lambdaRaw: Double, + val logLambda: Double, + val logLambdaFactorial: Double, + val delta: Double, + val halfDelta: Double, + val twolpd: Double, + val p1: Double, + val p2: Double, + val c1: Double + ) { + fun getLambda(): Int = lambdaRaw.toInt() + } + + companion object { + private const val MAX_MEAN = 0.5 * Int.MAX_VALUE + private var NO_CACHE_FACTORIAL_LOG: InternalUtils.FactorialLog? = null + + private val NO_SMALL_MEAN_POISSON_SAMPLER: SharedStateDiscreteSampler = + object : SharedStateDiscreteSampler { + override fun withUniformRandomProvider(rng: UniformRandomProvider): SharedStateDiscreteSampler = +// No requirement for RNG + this + + override fun sample(): Int =// No Poisson sample + 0 + } + + fun of( + rng: UniformRandomProvider, + mean: Double + ): SharedStateDiscreteSampler = LargeMeanPoissonSampler(rng, mean) + + init { + // Create without a cache. + NO_CACHE_FACTORIAL_LOG = + InternalUtils.FactorialLog.create() + } + } +} \ No newline at end of file diff --git a/kmath-commons-rng-part/src/commonMain/kotlin/scientifik/commons/rng/sampling/distribution/MarsagliaNormalizedGaussianSampler.kt b/kmath-commons-rng-part/src/commonMain/kotlin/scientifik/commons/rng/sampling/distribution/MarsagliaNormalizedGaussianSampler.kt new file mode 100644 index 000000000..26ee9ece9 --- /dev/null +++ b/kmath-commons-rng-part/src/commonMain/kotlin/scientifik/commons/rng/sampling/distribution/MarsagliaNormalizedGaussianSampler.kt @@ -0,0 +1,54 @@ +package scientifik.commons.rng.sampling.distribution + +import scientifik.commons.rng.UniformRandomProvider +import kotlin.math.ln +import kotlin.math.sqrt + +class MarsagliaNormalizedGaussianSampler(private val rng: UniformRandomProvider) : + NormalizedGaussianSampler, + SharedStateContinuousSampler { + private var nextGaussian = Double.NaN + + override fun sample(): Double { + if (nextGaussian.isNaN()) { + // Rejection scheme for selecting a pair that lies within the unit circle. + while (true) { + // Generate a pair of numbers within [-1 , 1). + val x = 2.0 * rng.nextDouble() - 1.0 + val y = 2.0 * rng.nextDouble() - 1.0 + val r2 = x * x + y * y + if (r2 < 1 && r2 > 0) { + // Pair (x, y) is within unit circle. + val 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. + return alpha * x + } + + // Pair is not within the unit circle: Generate another one. + } + } else { + // 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 + return r + } + } + + override fun toString(): String = "Box-Muller (with rejection) normalized Gaussian deviate [$rng]" + + override fun withUniformRandomProvider(rng: UniformRandomProvider): SharedStateContinuousSampler = + MarsagliaNormalizedGaussianSampler(rng) + + companion object { + @Suppress("UNCHECKED_CAST") + fun of(rng: UniformRandomProvider): S where S : NormalizedGaussianSampler?, S : SharedStateContinuousSampler? = + MarsagliaNormalizedGaussianSampler(rng) as S + } +} diff --git a/kmath-commons-rng-part/src/commonMain/kotlin/scientifik/commons/rng/sampling/distribution/NormalizedGaussianSampler.kt b/kmath-commons-rng-part/src/commonMain/kotlin/scientifik/commons/rng/sampling/distribution/NormalizedGaussianSampler.kt new file mode 100644 index 000000000..feff4d954 --- /dev/null +++ b/kmath-commons-rng-part/src/commonMain/kotlin/scientifik/commons/rng/sampling/distribution/NormalizedGaussianSampler.kt @@ -0,0 +1,3 @@ +package scientifik.commons.rng.sampling.distribution + +interface NormalizedGaussianSampler : ContinuousSampler diff --git a/kmath-commons-rng-part/src/commonMain/kotlin/scientifik/commons/rng/sampling/distribution/PoissonSampler.kt b/kmath-commons-rng-part/src/commonMain/kotlin/scientifik/commons/rng/sampling/distribution/PoissonSampler.kt new file mode 100644 index 000000000..4a7f56f60 --- /dev/null +++ b/kmath-commons-rng-part/src/commonMain/kotlin/scientifik/commons/rng/sampling/distribution/PoissonSampler.kt @@ -0,0 +1,32 @@ +package scientifik.commons.rng.sampling.distribution + +import scientifik.commons.rng.UniformRandomProvider + +class PoissonSampler( + rng: UniformRandomProvider, + mean: Double +) : SamplerBase(null), SharedStateDiscreteSampler { + private val poissonSamplerDelegate: SharedStateDiscreteSampler + + override fun sample(): Int = poissonSamplerDelegate.sample() + override fun toString(): String = poissonSamplerDelegate.toString() + + override fun withUniformRandomProvider(rng: UniformRandomProvider): SharedStateDiscreteSampler? = +// Direct return of the optimised sampler + poissonSamplerDelegate.withUniformRandomProvider(rng) + + companion object { + const val PIVOT = 40.0 + + fun of( + rng: UniformRandomProvider, + mean: Double + ): SharedStateDiscreteSampler =// Each sampler should check the input arguments. + if (mean < PIVOT) SmallMeanPoissonSampler.of(rng, mean) else LargeMeanPoissonSampler.of(rng, mean) + } + + init { + // Delegate all work to specialised samplers. + poissonSamplerDelegate = of(rng, mean) + } +} \ No newline at end of file diff --git a/kmath-commons-rng-part/src/commonMain/kotlin/scientifik/commons/rng/sampling/distribution/SamplerBase.kt b/kmath-commons-rng-part/src/commonMain/kotlin/scientifik/commons/rng/sampling/distribution/SamplerBase.kt new file mode 100644 index 000000000..38df9eb4e --- /dev/null +++ b/kmath-commons-rng-part/src/commonMain/kotlin/scientifik/commons/rng/sampling/distribution/SamplerBase.kt @@ -0,0 +1,12 @@ +package scientifik.commons.rng.sampling.distribution + +import scientifik.commons.rng.UniformRandomProvider + +@Deprecated("Since version 1.1. Class intended for internal use only.") +open class SamplerBase protected constructor(private val rng: UniformRandomProvider?) { + protected fun nextDouble(): Double = rng!!.nextDouble() + protected fun nextInt(): Int = rng!!.nextInt() + protected fun nextInt(max: Int): Int = rng!!.nextInt(max) + protected fun nextLong(): Long = rng!!.nextLong() + override fun toString(): String = "rng=$rng" +} diff --git a/kmath-commons-rng-part/src/commonMain/kotlin/scientifik/commons/rng/sampling/distribution/SharedStateContinuousSampler.kt b/kmath-commons-rng-part/src/commonMain/kotlin/scientifik/commons/rng/sampling/distribution/SharedStateContinuousSampler.kt new file mode 100644 index 000000000..ddac4b7a7 --- /dev/null +++ b/kmath-commons-rng-part/src/commonMain/kotlin/scientifik/commons/rng/sampling/distribution/SharedStateContinuousSampler.kt @@ -0,0 +1,7 @@ +package scientifik.commons.rng.sampling.distribution + +import scientifik.commons.rng.sampling.SharedStateSampler + +interface SharedStateContinuousSampler : ContinuousSampler, + SharedStateSampler { +} diff --git a/kmath-commons-rng-part/src/commonMain/kotlin/scientifik/commons/rng/sampling/distribution/SharedStateDiscreteSampler.kt b/kmath-commons-rng-part/src/commonMain/kotlin/scientifik/commons/rng/sampling/distribution/SharedStateDiscreteSampler.kt new file mode 100644 index 000000000..2d8adada6 --- /dev/null +++ b/kmath-commons-rng-part/src/commonMain/kotlin/scientifik/commons/rng/sampling/distribution/SharedStateDiscreteSampler.kt @@ -0,0 +1,7 @@ +package scientifik.commons.rng.sampling.distribution + +import scientifik.commons.rng.sampling.SharedStateSampler + +interface SharedStateDiscreteSampler : DiscreteSampler, + SharedStateSampler { // Composite interface +} diff --git a/kmath-commons-rng-part/src/commonMain/kotlin/scientifik/commons/rng/sampling/distribution/SmallMeanPoissonSampler.kt b/kmath-commons-rng-part/src/commonMain/kotlin/scientifik/commons/rng/sampling/distribution/SmallMeanPoissonSampler.kt new file mode 100644 index 000000000..a3188620f --- /dev/null +++ b/kmath-commons-rng-part/src/commonMain/kotlin/scientifik/commons/rng/sampling/distribution/SmallMeanPoissonSampler.kt @@ -0,0 +1,58 @@ +package scientifik.commons.rng.sampling.distribution + +import scientifik.commons.rng.UniformRandomProvider +import kotlin.math.ceil +import kotlin.math.exp + +class SmallMeanPoissonSampler : SharedStateDiscreteSampler { + private val p0: Double + private val limit: Int + private val rng: UniformRandomProvider + + constructor( + rng: UniformRandomProvider, + mean: Double + ) { + this.rng = rng + require(mean > 0) { "mean is not strictly positive: $mean" } + p0 = exp(-mean) + + limit = (if (p0 > 0) ceil(1000 * mean) else throw IllegalArgumentException("No p(x=0) probability for mean: $mean")).toInt() + // This excludes NaN values for the mean + // else + // The returned sample is bounded by 1000 * mean + } + + private constructor( + rng: UniformRandomProvider, + source: SmallMeanPoissonSampler + ) { + this.rng = rng + p0 = source.p0 + limit = source.limit + } + + override fun sample(): Int { + var n = 0 + var r = 1.0 + + while (n < limit) { + r *= rng.nextDouble() + if (r >= p0) n++ else break + } + + return n + } + + override fun toString(): String = "Small Mean Poisson deviate [$rng]" + + override fun withUniformRandomProvider(rng: UniformRandomProvider): SharedStateDiscreteSampler = + SmallMeanPoissonSampler(rng, this) + + companion object { + fun of( + rng: UniformRandomProvider, + mean: Double + ): SharedStateDiscreteSampler = SmallMeanPoissonSampler(rng, mean) + } +} \ No newline at end of file diff --git a/kmath-commons-rng-part/src/commonMain/kotlin/scientifik/commons/rng/sampling/distribution/ZigguratNormalizedGaussianSampler.kt b/kmath-commons-rng-part/src/commonMain/kotlin/scientifik/commons/rng/sampling/distribution/ZigguratNormalizedGaussianSampler.kt new file mode 100644 index 000000000..a916121db --- /dev/null +++ b/kmath-commons-rng-part/src/commonMain/kotlin/scientifik/commons/rng/sampling/distribution/ZigguratNormalizedGaussianSampler.kt @@ -0,0 +1,89 @@ +package scientifik.commons.rng.sampling.distribution + +import scientifik.commons.rng.UniformRandomProvider +import kotlin.math.* + +class ZigguratNormalizedGaussianSampler(private val rng: UniformRandomProvider) : + NormalizedGaussianSampler, + SharedStateContinuousSampler { + + companion object { + private const val R = 3.442619855899 + private const val ONE_OVER_R: Double = 1 / R + private const val V = 9.91256303526217e-3 + private val MAX: Double = 2.0.pow(63.0) + private val ONE_OVER_MAX: Double = 1.0 / MAX + private const val LEN = 128 + private const val LAST: Int = LEN - 1 + private val K = LongArray(LEN) + private val W = DoubleArray(LEN) + private val F = DoubleArray(LEN) + private fun gauss(x: Double): Double = exp(-0.5 * x * x) + + @Suppress("UNCHECKED_CAST") + fun of(rng: UniformRandomProvider): S where S : NormalizedGaussianSampler?, S : SharedStateContinuousSampler? = + ZigguratNormalizedGaussianSampler(rng) as S + + init { + // Filling the tables. + var d = R + var t = d + var fd = gauss(d) + val q = V / fd + K[0] = (d / q * MAX).toLong() + K[1] = 0 + W[0] = q * ONE_OVER_MAX + W[LAST] = d * ONE_OVER_MAX + F[0] = 1.0 + F[LAST] = fd + + (LAST - 1 downTo 1).forEach { i -> + d = sqrt(-2 * ln(V / d + fd)) + fd = gauss(d) + K[i + 1] = (d / t * MAX).toLong() + t = d + F[i] = fd + W[i] = d * ONE_OVER_MAX + } + } + } + + override fun sample(): Double { + val j = rng.nextLong() + val i = (j and LAST.toLong()).toInt() + return if (abs(j) < K[i]) j * W[i] else fix(j, i) + } + + override fun toString(): String = "Ziggurat normalized Gaussian deviate [$rng]" + + private fun fix( + hz: Long, + iz: Int + ): Double { + var x: Double + var y: Double + x = hz * W[iz] + + return if (iz == 0) { + // Base strip. + // This branch is called about 5.7624515E-4 times per sample. + do { + y = -ln(rng.nextDouble()) + x = -ln(rng.nextDouble()) * ONE_OVER_R + } while (y + y < x * x) + + val out = R + x + if (hz > 0) out else -out + } else { + // Wedge of other strips. + // This branch is called about 0.027323 times per sample. + // else + // Try again. + // This branch is called about 0.012362 times per sample. + if (F[iz] + rng.nextDouble() * (F[iz - 1] - F[iz]) < gauss(x)) x else sample() + } + } + + override fun withUniformRandomProvider(rng: UniformRandomProvider): SharedStateContinuousSampler = + ZigguratNormalizedGaussianSampler(rng) +} diff --git a/kmath-prob/build.gradle.kts b/kmath-prob/build.gradle.kts index a69d61b73..4ec254e82 100644 --- a/kmath-prob/build.gradle.kts +++ b/kmath-prob/build.gradle.kts @@ -6,10 +6,12 @@ kotlin.sourceSets { commonMain { dependencies { api(project(":kmath-coroutines")) + api(project(":kmath-commons-rng-part")) } } - jvmMain{ - dependencies{ + + jvmMain { + dependencies { api("org.apache.commons:commons-rng-sampling:1.3") api("org.apache.commons:commons-rng-simple:1.3") } diff --git a/kmath-prob/src/commonMain/kotlin/scientifik/kmath/prob/Distributions.kt b/kmath-prob/src/commonMain/kotlin/scientifik/kmath/prob/Distributions.kt new file mode 100644 index 000000000..fd8cc4116 --- /dev/null +++ b/kmath-prob/src/commonMain/kotlin/scientifik/kmath/prob/Distributions.kt @@ -0,0 +1,53 @@ +package scientifik.kmath.prob + +import scientifik.commons.rng.UniformRandomProvider +import scientifik.commons.rng.sampling.distribution.* +import scientifik.kmath.chains.BlockingIntChain +import scientifik.kmath.chains.BlockingRealChain +import scientifik.kmath.chains.Chain + +abstract class ContinuousSamplerDistribution : Distribution { + + private inner class ContinuousSamplerChain(val generator: RandomGenerator) : BlockingRealChain() { + private val sampler = buildCMSampler(generator) + + override fun nextDouble(): Double = sampler.sample() + + override fun fork(): Chain = ContinuousSamplerChain(generator.fork()) + } + + protected abstract fun buildCMSampler(generator: RandomGenerator): ContinuousSampler + + override fun sample(generator: RandomGenerator): BlockingRealChain = ContinuousSamplerChain(generator) +} + +abstract class DiscreteSamplerDistribution : Distribution { + + private inner class ContinuousSamplerChain(val generator: RandomGenerator) : BlockingIntChain() { + private val sampler = buildSampler(generator) + + override fun nextInt(): Int = sampler.sample() + + override fun fork(): Chain = ContinuousSamplerChain(generator.fork()) + } + + protected abstract fun buildSampler(generator: RandomGenerator): DiscreteSampler + + override fun sample(generator: RandomGenerator): BlockingIntChain = ContinuousSamplerChain(generator) +} + +enum class NormalSamplerMethod { + BoxMuller, + Marsaglia, + Ziggurat +} + +fun normalSampler(method: NormalSamplerMethod, provider: UniformRandomProvider): NormalizedGaussianSampler = + when (method) { + NormalSamplerMethod.BoxMuller -> BoxMullerNormalizedGaussianSampler( + provider + ) + NormalSamplerMethod.Marsaglia -> MarsagliaNormalizedGaussianSampler(provider) + NormalSamplerMethod.Ziggurat -> ZigguratNormalizedGaussianSampler(provider) + } + diff --git a/kmath-prob/src/commonMain/kotlin/scientifik/kmath/prob/RandomSourceGenerator.kt b/kmath-prob/src/commonMain/kotlin/scientifik/kmath/prob/RandomSourceGenerator.kt new file mode 100644 index 000000000..7be8276f3 --- /dev/null +++ b/kmath-prob/src/commonMain/kotlin/scientifik/kmath/prob/RandomSourceGenerator.kt @@ -0,0 +1,28 @@ +package scientifik.kmath.prob + +import scientifik.commons.rng.UniformRandomProvider + + +inline class RandomGeneratorProvider(val generator: RandomGenerator) : UniformRandomProvider { + override fun nextBoolean(): Boolean = generator.nextBoolean() + + override fun nextFloat(): Float = generator.nextDouble().toFloat() + + override fun nextBytes(bytes: ByteArray) { + generator.fillBytes(bytes) + } + + override fun nextBytes(bytes: ByteArray, start: Int, len: Int) { + generator.fillBytes(bytes, start, start + len) + } + + override fun nextInt(): Int = generator.nextInt() + + override fun nextInt(n: Int): Int = generator.nextInt(n) + + override fun nextDouble(): Double = generator.nextDouble() + + override fun nextLong(): Long = generator.nextLong() + + override fun nextLong(n: Long): Long = generator.nextLong(n) +} diff --git a/kmath-prob/src/jvmMain/kotlin/scientifik/kmath/prob/Distributions.kt b/kmath-prob/src/jvmMain/kotlin/scientifik/kmath/prob/Distributions.kt new file mode 100644 index 000000000..aaeeb1b26 --- /dev/null +++ b/kmath-prob/src/jvmMain/kotlin/scientifik/kmath/prob/Distributions.kt @@ -0,0 +1,62 @@ +package scientifik.kmath.prob + +import scientifik.commons.rng.sampling.distribution.ContinuousSampler +import scientifik.commons.rng.sampling.distribution.DiscreteSampler +import scientifik.commons.rng.sampling.distribution.GaussianSampler +import scientifik.commons.rng.sampling.distribution.PoissonSampler +import kotlin.math.PI +import kotlin.math.exp +import kotlin.math.pow +import kotlin.math.sqrt + +fun Distribution.Companion.normal( + method: NormalSamplerMethod = NormalSamplerMethod.Ziggurat +): Distribution = object : ContinuousSamplerDistribution() { + override fun buildCMSampler(generator: RandomGenerator): ContinuousSampler { + val provider = generator.asUniformRandomProvider() + return normalSampler(method, provider) + } + + override fun probability(arg: Double): Double { + return exp(-arg.pow(2) / 2) / sqrt(PI * 2) + } +} + +fun Distribution.Companion.normal( + mean: Double, + sigma: Double, + method: NormalSamplerMethod = NormalSamplerMethod.Ziggurat +): ContinuousSamplerDistribution = object : ContinuousSamplerDistribution() { + private val sigma2 = sigma.pow(2) + private val norm = sigma * sqrt(PI * 2) + + override fun buildCMSampler(generator: RandomGenerator): ContinuousSampler { + val provider = generator.asUniformRandomProvider() + val normalizedSampler = normalSampler(method, provider) + return GaussianSampler(normalizedSampler, mean, sigma) + } + + override fun probability(arg: Double): Double { + return exp(-(arg - mean).pow(2) / 2 / sigma2) / norm + } +} + +fun Distribution.Companion.poisson( + lambda: Double +): DiscreteSamplerDistribution = object : DiscreteSamplerDistribution() { + + override fun buildSampler(generator: RandomGenerator): DiscreteSampler { + return PoissonSampler.of(generator.asUniformRandomProvider(), lambda) + } + + private val computedProb: HashMap = hashMapOf(0 to exp(-lambda)) + + override fun probability(arg: Int): Double { + require(arg >= 0) { "The argument must be >= 0" } + + return if (arg > 40) + exp(-(arg - lambda).pow(2) / 2 / lambda) / sqrt(2 * PI * lambda) + else + computedProb.getOrPut(arg) { probability(arg - 1) * lambda / arg } + } +} diff --git a/kmath-prob/src/jvmMain/kotlin/scientifik/kmath/prob/RandomSourceGenerator.kt b/kmath-prob/src/jvmMain/kotlin/scientifik/kmath/prob/RandomSourceGenerator.kt index f5a73a08b..8c4b59abb 100644 --- a/kmath-prob/src/jvmMain/kotlin/scientifik/kmath/prob/RandomSourceGenerator.kt +++ b/kmath-prob/src/jvmMain/kotlin/scientifik/kmath/prob/RandomSourceGenerator.kt @@ -1,20 +1,18 @@ package scientifik.kmath.prob -import org.apache.commons.rng.UniformRandomProvider import org.apache.commons.rng.simple.RandomSource +import scientifik.commons.rng.UniformRandomProvider -class RandomSourceGenerator(val source: RandomSource, seed: Long?) : RandomGenerator { - internal val random: UniformRandomProvider = seed?.let { +class RandomSourceGenerator(val source: RandomSource, seed: Long?) : + RandomGenerator { + internal val random = seed?.let { RandomSource.create(source, seed) } ?: RandomSource.create(source) override fun nextBoolean(): Boolean = random.nextBoolean() - override fun nextDouble(): Double = random.nextDouble() - override fun nextInt(): Int = random.nextInt() override fun nextInt(until: Int): Int = random.nextInt(until) - override fun nextLong(): Long = random.nextLong() override fun nextLong(until: Long): Long = random.nextLong(until) @@ -26,39 +24,23 @@ class RandomSourceGenerator(val source: RandomSource, seed: Long?) : RandomGener override fun fork(): RandomGenerator = RandomSourceGenerator(source, nextLong()) } -inline class RandomGeneratorProvider(val generator: RandomGenerator) : UniformRandomProvider { - override fun nextBoolean(): Boolean = generator.nextBoolean() - - override fun nextFloat(): Float = generator.nextDouble().toFloat() - - override fun nextBytes(bytes: ByteArray) { - generator.fillBytes(bytes) - } - - override fun nextBytes(bytes: ByteArray, start: Int, len: Int) { - generator.fillBytes(bytes, start, start + len) - } - - override fun nextInt(): Int = generator.nextInt() - - override fun nextInt(n: Int): Int = generator.nextInt(n) - - override fun nextDouble(): Double = generator.nextDouble() - - override fun nextLong(): Long = generator.nextLong() - - override fun nextLong(n: Long): Long = generator.nextLong(n) -} - /** * Represent this [RandomGenerator] as commons-rng [UniformRandomProvider] preserving and mirroring its current state. * Getting new value from one of those changes the state of another. */ fun RandomGenerator.asUniformRandomProvider(): UniformRandomProvider = if (this is RandomSourceGenerator) { - random -} else { - RandomGeneratorProvider(this) -} + object : UniformRandomProvider { + override fun nextBytes(bytes: ByteArray) = random.nextBytes(bytes) + override fun nextBytes(bytes: ByteArray, start: Int, len: Int) = random.nextBytes(bytes, start, len) + override fun nextInt(): Int = random.nextInt() + override fun nextInt(n: Int): Int = random.nextInt(n) + override fun nextLong(): Long = random.nextLong() + override fun nextLong(n: Long): Long = random.nextLong(n) + override fun nextBoolean(): Boolean = random.nextBoolean() + override fun nextFloat(): Float = random.nextFloat() + override fun nextDouble(): Double = random.nextDouble() + } +} else RandomGeneratorProvider(this) fun RandomGenerator.Companion.fromSource(source: RandomSource, seed: Long? = null): RandomSourceGenerator = RandomSourceGenerator(source, seed) diff --git a/kmath-prob/src/jvmMain/kotlin/scientifik/kmath/prob/distributions.kt b/kmath-prob/src/jvmMain/kotlin/scientifik/kmath/prob/distributions.kt deleted file mode 100644 index 412454994..000000000 --- a/kmath-prob/src/jvmMain/kotlin/scientifik/kmath/prob/distributions.kt +++ /dev/null @@ -1,109 +0,0 @@ -package scientifik.kmath.prob - -import org.apache.commons.rng.UniformRandomProvider -import org.apache.commons.rng.sampling.distribution.* -import scientifik.kmath.chains.BlockingIntChain -import scientifik.kmath.chains.BlockingRealChain -import scientifik.kmath.chains.Chain -import java.util.* -import kotlin.math.PI -import kotlin.math.exp -import kotlin.math.pow -import kotlin.math.sqrt - -abstract class ContinuousSamplerDistribution : Distribution { - - private inner class ContinuousSamplerChain(val generator: RandomGenerator) : BlockingRealChain() { - private val sampler = buildCMSampler(generator) - - override fun nextDouble(): Double = sampler.sample() - - override fun fork(): Chain = ContinuousSamplerChain(generator.fork()) - } - - protected abstract fun buildCMSampler(generator: RandomGenerator): ContinuousSampler - - override fun sample(generator: RandomGenerator): BlockingRealChain = ContinuousSamplerChain(generator) -} - -abstract class DiscreteSamplerDistribution : Distribution { - - private inner class ContinuousSamplerChain(val generator: RandomGenerator) : BlockingIntChain() { - private val sampler = buildSampler(generator) - - override fun nextInt(): Int = sampler.sample() - - override fun fork(): Chain = ContinuousSamplerChain(generator.fork()) - } - - protected abstract fun buildSampler(generator: RandomGenerator): DiscreteSampler - - override fun sample(generator: RandomGenerator): BlockingIntChain = ContinuousSamplerChain(generator) -} - -enum class NormalSamplerMethod { - BoxMuller, - Marsaglia, - Ziggurat -} - -private fun normalSampler(method: NormalSamplerMethod, provider: UniformRandomProvider): NormalizedGaussianSampler = - when (method) { - NormalSamplerMethod.BoxMuller -> BoxMullerNormalizedGaussianSampler(provider) - NormalSamplerMethod.Marsaglia -> MarsagliaNormalizedGaussianSampler(provider) - NormalSamplerMethod.Ziggurat -> ZigguratNormalizedGaussianSampler(provider) - } - -fun Distribution.Companion.normal( - method: NormalSamplerMethod = NormalSamplerMethod.Ziggurat -): Distribution = object : ContinuousSamplerDistribution() { - override fun buildCMSampler(generator: RandomGenerator): ContinuousSampler { - val provider: UniformRandomProvider = generator.asUniformRandomProvider() - return normalSampler(method, provider) - } - - override fun probability(arg: Double): Double { - return exp(-arg.pow(2) / 2) / sqrt(PI * 2) - } -} - -fun Distribution.Companion.normal( - mean: Double, - sigma: Double, - method: NormalSamplerMethod = NormalSamplerMethod.Ziggurat -): ContinuousSamplerDistribution = object : ContinuousSamplerDistribution() { - private val sigma2 = sigma.pow(2) - private val norm = sigma * sqrt(PI * 2) - - override fun buildCMSampler(generator: RandomGenerator): ContinuousSampler { - val provider: UniformRandomProvider = generator.asUniformRandomProvider() - val normalizedSampler = normalSampler(method, provider) - return GaussianSampler(normalizedSampler, mean, sigma) - } - - override fun probability(arg: Double): Double { - return exp(-(arg - mean).pow(2) / 2 / sigma2) / norm - } -} - -fun Distribution.Companion.poisson( - lambda: Double -): DiscreteSamplerDistribution = object : DiscreteSamplerDistribution() { - - override fun buildSampler(generator: RandomGenerator): DiscreteSampler { - return PoissonSampler.of(generator.asUniformRandomProvider(), lambda) - } - - private val computedProb: HashMap = hashMapOf(0 to exp(-lambda)) - - override fun probability(arg: Int): Double { - require(arg >= 0) { "The argument must be >= 0" } - return if (arg > 40) { - exp(-(arg - lambda).pow(2) / 2 / lambda) / sqrt(2 * PI * lambda) - } else { - computedProb.getOrPut(arg) { - probability(arg - 1) * lambda / arg - } - } - } -} diff --git a/settings.gradle.kts b/settings.gradle.kts index 57173250b..f73a80994 100644 --- a/settings.gradle.kts +++ b/settings.gradle.kts @@ -35,6 +35,7 @@ include( ":kmath-functions", // ":kmath-io", ":kmath-coroutines", + "kmath-commons-rng-part", ":kmath-histograms", ":kmath-commons", ":kmath-viktor",