org.apache.commons.statistics.inference.KolmogorovSmirnovDistribution Maven / Gradle / Ivy
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package org.apache.commons.statistics.inference;
import java.util.Arrays;
import org.apache.commons.numbers.combinatorics.Factorial;
import org.apache.commons.numbers.combinatorics.LogFactorial;
import org.apache.commons.numbers.core.DD;
import org.apache.commons.numbers.core.DDMath;
import org.apache.commons.numbers.core.Sum;
import org.apache.commons.statistics.inference.SquareMatrixSupport.RealSquareMatrix;
/**
* Computes the complementary probability for the one-sample Kolmogorov-Smirnov distribution.
*
* @since 1.1
*/
final class KolmogorovSmirnovDistribution {
/** pi^2. */
private static final double PI2 = 9.8696044010893586188344909;
/** sqrt(2*pi). */
private static final double ROOT_TWO_PI = 2.5066282746310005024157652;
/** Value of x when the KS sum is 0.5. */
private static final double X_KS_HALF = 0.8275735551899077;
/** Value of x when the KS sum is 1.0. */
private static final double X_KS_ONE = 0.1754243674345323;
/** Machine epsilon, 2^-52. */
private static final double EPS = 0x1.0p-52;
/** No instances. */
private KolmogorovSmirnovDistribution() {}
/**
* Computes the complementary probability {@code P[D_n >= x]}, or survival function (SF),
* for the two-sided one-sample Kolmogorov-Smirnov distribution.
*
*
* D_n = sup_x |F(x) - CDF_n(x)|
*
*
* where {@code n} is the sample size; {@code CDF_n(x)} is an empirical
* cumulative distribution function; and {@code F(x)} is the expected
* distribution.
*
*
* References:
*
* - Simard, R., & L’Ecuyer, P. (2011).
* Computing the Two-Sided Kolmogorov-Smirnov Distribution.
* Journal of Statistical Software, 39(11), 1–18.
*
-
* Marsaglia, G., Tsang, W. W., & Wang, J. (2003).
* Evaluating Kolmogorov's Distribution.
* Journal of Statistical Software, 8(18), 1–4.
*
*
* Note that [2] contains an error in computing h, refer to MATH-437 for details.
*
* @since 1.1
*/
static final class Two {
/** pi^2. */
private static final double PI2 = 9.8696044010893586188344909;
/** pi^4. */
private static final double PI4 = 97.409091034002437236440332;
/** pi^6. */
private static final double PI6 = 961.38919357530443703021944;
/** sqrt(2*pi). */
private static final double ROOT_TWO_PI = 2.5066282746310005024157652;
/** sqrt(pi/2). */
private static final double ROOT_HALF_PI = 1.2533141373155002512078826;
/** Threshold for Pelz-Good where the 1 - CDF == 1.
* Occurs when sqrt(2pi/z) exp(-pi^2 / (8 z^2)) is far below 2^-53.
* Threshold set at exp(-pi^2 / (8 z^2)) = 2^-80. */
private static final double LOG_PG_MIN = -55.451774444795625;
/** Factor 4a in the quadratic equation to solve max k: log(2^-52) * 8. */
private static final double FOUR_A = -288.3492271129372;
/** The scaling threshold in the MTW algorithm. Marsaglia used 1e-140. This uses 2^-400 ~ 3.87e-121. */
private static final double MTW_SCALE_THRESHOLD = 0x1.0p-400;
/** The up-scaling factor in the MTW algorithm. Marsaglia used 1e140. This uses 2^400 ~ 2.58e120. */
private static final double MTW_UP_SCALE = 0x1.0p400;
/** The power-of-2 of the up-scaling factor in the MTW algorithm, n if the up-scale factor is 2^n. */
private static final int MTW_UP_SCALE_POWER = 400;
/** The scaling threshold in the Pomeranz algorithm. */
private static final double P_DOWN_SCALE = 0x1.0p-128;
/** The up-scaling factor in the Pomeranz algorithm. */
private static final double P_UP_SCALE = 0x1.0p128;
/** The power-of-2 of the up-scaling factor in the Pomeranz algorithm, n if the up-scale factor is 2^n. */
private static final int P_SCALE_POWER = 128;
/** Maximum finite factorial. */
private static final int MAX_FACTORIAL = 170;
/** Approximate threshold for ln(MIN_NORMAL). */
private static final int LOG_MIN_NORMAL = -708;
/** 140, n threshold for small n for the sf computation.*/
private static final int N140 = 140;
/** 0.754693, nxx threshold for small n Durbin matrix sf computation. */
private static final double NXX_0_754693 = 0.754693;
/** 4, nxx threshold for small n Pomeranz sf computation. */
private static final int NXX_4 = 4;
/** 2.2, nxx threshold for large n Miller approximation sf computation. */
private static final double NXX_2_2 = 2.2;
/** 100000, n threshold for large n Durbin matrix sf computation. */
private static final int N_100000 = 100000;
/** 1.4, nx^(3/2) threshold for large n Durbin matrix sf computation. */
private static final double NX32_1_4 = 1.4;
/** 1/2. */
private static final double HALF = 0.5;
/** No instances. */
private Two() {}
/**
* Calculates complementary probability {@code P[D_n >= x]} for the two-sided
* one-sample Kolmogorov-Smirnov distribution.
*
* @param x Statistic.
* @param n Sample size (assumed to be positive).
* @return \(P(D_n ≥ x)\)
*/
static double sf(double x, int n) {
final double p = sfExact(x, n);
if (p >= 0) {
return p;
}
// The computation is divided based on the x-n plane.
final double nxx = n * x * x;
if (n <= N140) {
// 10 decimal digits of precision
// nx^2 < 4 use 1 - CDF(x).
if (nxx < NXX_0_754693) {
// Durbin matrix (MTW)
return 1 - durbinMTW(x, n);
}
if (nxx < NXX_4) {
// Pomeranz
return 1 - pomeranz(x, n);
}
// Miller approximation: 2 * one-sided D+ computation
return 2 * One.sf(x, n);
}
// n > 140
if (nxx >= NXX_2_2) {
// 6 decimal digits of precision
// Miller approximation: 2 * one-sided D+ computation
return 2 * One.sf(x, n);
}
// nx^2 < 2.2 use 1 - CDF(x).
// 5 decimal digits of precision (for n < 200000)
// nx^1.5 <= 1.4
if (n <= N_100000 && n * Math.pow(x, 1.5) < NX32_1_4) {
// Durbin matrix (MTW)
return 1 - durbinMTW(x, n);
}
// Pelz-Good, algorithm modified to sum negative terms from 1 for the SF.
// (precision increases with n)
return pelzGood(x, n);
}
/**
* Calculates exact cases for the complementary probability
* {@code P[D_n >= x]} the two-sided one-sample Kolmogorov-Smirnov distribution.
*
*
Exact cases handle x not in [0, 1]. It is assumed n is positive.
*
* @param x Statistic.
* @param n Sample size (assumed to be positive).
* @return \(P(D_n ≥ x)\)
*/
private static double sfExact(double x, int n) {
if (n * x * x >= 370 || x >= 1) {
// p would underflow, or x is out of the domain
return 0;
}
final double nx = x * n;
if (nx <= 1) {
// x <= 1/(2n)
if (nx <= HALF) {
// Also detects x <= 0 (iff n is positive)
return 1;
}
if (n == 1) {
// Simplification of:
// 1 - (n! (2x - 1/n)^n) == 1 - (2x - 1)
return 2.0 - 2.0 * x;
}
// 1/(2n) < x <= 1/n
// 1 - (n! (2x - 1/n)^n)
final double f = 2 * x - 1.0 / n;
// Switch threshold where (2x - 1/n)^n is sub-normal
// Max factorial threshold is n=170
final double logf = Math.log(f);
if (n <= MAX_FACTORIAL && n * logf > LOG_MIN_NORMAL) {
return 1 - Factorial.doubleValue(n) * Math.pow(f, n);
}
return -Math.expm1(LogFactorial.create().value(n) + n * logf);
}
// 1 - 1/n <= x < 1
if (n - 1 <= nx) {
// 2 * (1-x)^n
return 2 * Math.pow(1 - x, n);
}
return -1;
}
/**
* Computes the Durbin matrix approximation for {@code P(D_n < d)} using the method
* of Marsaglia, Tsang and Wang (2003).
*
* @param x Statistic.
* @param n Sample size (assumed to be positive).
* @return \(P(D_n < x)\)
*/
private static double durbinMTW(double x, int n) {
final int k = (int) Math.ceil(n * x);
final RealSquareMatrix h = createH(x, n).power(n);
// Use scaling as per Marsaglia's code to avoid underflow.
double pFrac = h.get(k - 1, k - 1);
int scale = h.scale();
// Omit i == n as this is a no-op
for (int i = 1; i < n; ++i) {
pFrac *= (double) i / n;
if (pFrac < MTW_SCALE_THRESHOLD) {
pFrac *= MTW_UP_SCALE;
scale -= MTW_UP_SCALE_POWER;
}
}
// Return the CDF
return clipProbability(Math.scalb(pFrac, scale));
}
/***
* Creates {@code H} of size {@code m x m} as described in [1].
*
* @param x Statistic.
* @param n Sample size (assumed to be positive).
* @return H matrix
*/
private static RealSquareMatrix createH(double x, int n) {
// MATH-437:
// This is *not* (int) (n * x) + 1.
// This is only ever called when 1/n < x < 1 - 1/n.
// => h cannot be >= 1 when using ceil. h can be 0 if nx is integral.
final int k = (int) Math.ceil(n * x);
final double h = k - n * x;
final int m = 2 * k - 1;
final double[] data = new double[m * m];
// Start by filling everything with either 0 or 1.
for (int i = 0; i < m; ++i) {
// h[i][j] = i - j + 1 < 0 ? 0 : 1
// => h[i][j<=i+1] = 1
final int jend = Math.min(m - 1, i + 1);
for (int j = i * m; j <= i * m + jend; j++) {
data[j] = 1;
}
}
// Setting up power-array to avoid calculating the same value twice:
// hp[0] = h^1, ..., hp[m-1] = h^m
final double[] hp = new double[m];
hp[0] = h;
for (int i = 1; i < m; ++i) {
// Avoid compound rounding errors using h * hp[i - 1]
// with Math.pow as it is within 1 ulp of the exact result
hp[i] = Math.pow(h, i + 1);
}
// First column and last row has special values (each other reversed).
for (int i = 0; i < m; ++i) {
data[i * m] -= hp[i];
data[(m - 1) * m + i] -= hp[m - i - 1];
}
// [1] states: "For 1/2 < h < 1 the bottom left element of the matrix should be
// (1 - 2*h^m + (2h - 1)^m )/m!"
if (2 * h - 1 > 0) {
data[(m - 1) * m] += Math.pow(2 * h - 1, m);
}
// Aside from the first column and last row, the (i, j)-th element is 1/(i - j + 1)! if i -
// j + 1 >= 0, else 0. 1's and 0's are already put, so only division with (i - j + 1)! is
// needed in the elements that have 1's. Note that i - j + 1 > 0 <=> i + 1 > j instead of
// j'ing all the way to m. Also note that we can use pre-computed factorials given
// the limits where this method is called.
for (int i = 0; i < m; ++i) {
final int im = i * m;
for (int j = 0; j < i + 1; ++j) {
// Here (i - j + 1 > 0)
// Divide by (i - j + 1)!
// Note: This method is used when:
// n <= 140; nxx < 0.754693
// n <= 100000; n x^1.5 < 1.4
// max m ~ 2nx ~ (1.4/1e5)^(2/3) * 2e5 = 116
// Use a tabulated factorial
data[im + j] /= Factorial.doubleValue(i - j + 1);
}
}
return SquareMatrixSupport.create(m, data);
}
/**
* Computes the Pomeranz approximation for {@code P(D_n < d)} using the method
* as described in Simard and L’Ecuyer (2011).
*
*
Modifications have been made to the scaling of the intermediate values.
*
* @param x Statistic.
* @param n Sample size (assumed to be positive).
* @return \(P(D_n < x)\)
*/
private static double pomeranz(double x, int n) {
final double t = n * x;
// Store floor(A-t) and ceil(A+t). This does not require computing A.
final int[] amt = new int[2 * n + 3];
final int[] apt = new int[2 * n + 3];
computeA(n, t, amt, apt);
// Precompute ((A[i] - A[i-1])/n)^(j-k) / (j-k)!
// A[i] - A[i-1] has 4 possible values (based on multiples of A2)
// A1 - A0 = 0 - 0 = 0
// A2 - A1 = A2 - 0 = A2
// A3 - A2 = (1 - A2) - A2 = 1 - 2 * A2
// A4 - A3 = (A2 + 1) - (1 - A2) = 2 * A2
// A5 - A4 = (1 - A2 + 1) - (A2 + 1) = 1 - 2 * A2
// A6 - A5 = (A2 + 1 + 1) - (1 - A2 + 1) = 2 * A2
// A7 - A6 = (1 - A2 + 1 + 1) - (A2 + 1 + 1) = 1 - 2 * A2
// A8 - A7 = (A2 + 1 + 1 + 1) - (1 - A2 + 1 + 1) = 2 * A2
// ...
// Ai - Ai-1 = ((i-1)/2 - A2) - (A2 + (i-2)/2) = 1 - 2 * A2 ; i = odd
// Ai - Ai-1 = (A2 + (i-1)/2) - ((i-2)/2 - A2) = 2 * A2 ; i = even
// ...
// A2n+2 - A2n+1 = n - (n - A2) = A2
// ap[][j - k] = ((A[i] - A[i-1])/n)^(j-k) / (j-k)!
// for each case: A[i] - A[i-1] in [A2, 1 - 2 * A2, 2 * A2]
// Ignore case 0 as this is not used. Factors are ap[0] = 1, else 0.
// If A2==0.5 then this is computed as a no-op due to multiplication by zero.
final int n2 = n + 2;
final double[][] ap = new double[3][n2];
final double a2 = Math.min(t - Math.floor(t), Math.ceil(t) - t);
computeAP(ap[0], a2 / n);
computeAP(ap[1], (1 - 2 * a2) / n);
computeAP(ap[2], (2 * a2) / n);
// Current and previous V
double[] vc = new double[n2];
double[] vp = new double[n2];
// Count of re-scaling
int scale = 0;
// V_1,1 = 1
vc[1] = 1;
for (int i = 2; i <= 2 * n + 2; i++) {
final double[] v = vc;
vc = vp;
vp = v;
// This is useful for following current values of vc
Arrays.fill(vc, 0);
// Select (A[i] - A[i-1]) factor
final double[] p;
if (i == 2 || i == 2 * n + 2) {
// First or last
p = ap[0];
} else {
// odd: [1] 1 - 2 * 2A
// even: [2] 2 * A2
p = ap[2 - (i & 1)];
}
// Set limits.
// j is the ultimate bound for k and should be in [1, n+1]
final int jmin = Math.max(1, amt[i] + 2);
final int jmax = Math.min(n + 1, apt[i]);
final int k1 = Math.max(1, amt[i - 1] + 2);
// All numbers will reduce in size.
// Maintain the largest close to 1.0.
// This is a change from Simard and L’Ecuyer which scaled based on the smallest.
double max = 0;
for (int j = jmin; j <= jmax; j++) {
final int k2 = Math.min(j, apt[i - 1]);
// Accurate sum.
// vp[high] is smaller
// p[high] is smaller
// Sum ascending has smaller products first.
double sum = 0;
for (int k = k1; k <= k2; k++) {
sum += vp[k] * p[j - k];
}
vc[j] = sum;
if (max < sum) {
// Note: max *may* always be the first sum: vc[jmin]
max = sum;
}
}
// Rescale if too small
if (max < P_DOWN_SCALE) {
// Only scale in current range from V
for (int j = jmin; j <= jmax; j++) {
vc[j] *= P_UP_SCALE;
}
scale -= P_SCALE_POWER;
}
}
// F_n(x) = n! V_{2n+2,n+1}
double v = vc[n + 1];
// This method is used when n < 140 where all n! are finite.
// v is below 1 so we can directly compute the result without using logs.
v *= Factorial.doubleValue(n);
// Return the CDF (rescaling as required)
return Math.scalb(v, scale);
}
/**
* Compute the power factors.
*
* factor[j] = z^j / j!
*
*
* @param p Power factors.
* @param z (A[i] - A[i-1]) / n
*/
private static void computeAP(double[] p, double z) {
// Note z^0 / 0! = 1 for any z
p[0] = 1;
p[1] = z;
for (int j = 2; j < p.length; j++) {
// Only used when n <= 140 and can use the tabulated values of n!
// This avoids using recursion: p[j] = z * p[j-1] / j.
// Direct computation more closely agrees with the recursion using BigDecimal
// with 200 digits of precision.
p[j] = Math.pow(z, j) / Factorial.doubleValue(j);
}
}
/**
* Compute the factors floor(A-t) and ceil(A+t).
* Arrays should have length 2n+3.
*
* @param n Sample size.
* @param t Statistic x multiplied by n.
* @param amt floor(A-t)
* @param apt ceil(A+t)
*/
// package-private for testing
static void computeA(int n, double t, int[] amt, int[] apt) {
final int l = (int) Math.floor(t);
final double f = t - l;
final int limit = 2 * n + 2;
// 3-cases
if (f > HALF) {
// Case (iii): 1/2 < f < 1
// for i = 1, 2, ...
for (int j = 2; j <= limit; j += 2) {
final int i = j >>> 1;
amt[j] = i - 2 - l;
apt[j] = i + l;
}
// for i = 0, 1, 2, ...
for (int j = 1; j <= limit; j += 2) {
final int i = j >>> 1;
amt[j] = i - 1 - l;
apt[j] = i + 1 + l;
}
} else if (f > 0) {
// Case (ii): 0 < f <= 1/2
amt[1] = -l - 1;
apt[1] = l + 1;
// for i = 1, 2, ...
for (int j = 2; j <= limit; j++) {
final int i = j >>> 1;
amt[j] = i - 1 - l;
apt[j] = i + l;
}
} else {
// Case (i): f = 0
// for i = 1, 2, ...
for (int j = 2; j <= limit; j += 2) {
final int i = j >>> 1;
amt[j] = i - 1 - l;
apt[j] = i - 1 + l;
}
// for i = 0, 1, 2, ...
for (int j = 1; j <= limit; j += 2) {
final int i = j >>> 1;
amt[j] = i - l;
apt[j] = i + l;
}
}
}
/**
* Computes the Pelz-Good approximation for {@code P(D_n >= d)} as described in
* Simard and L’Ecuyer (2011).
*
* This has been modified to compute the complementary CDF by subtracting the
* terms k0, k1, k2, k3 from 1. For use in computing the CDF the method should
* be updated to return the sum of k0 ... k3.
*
* @param x Statistic.
* @param n Sample size (assumed to be positive).
* @return \(P(D_n ≥ x)\)
* @throws ArithmeticException if the series does not converge
*/
// package-private for testing
static double pelzGood(double x, int n) {
// Change the variable to z since approximation is for the distribution evaluated at d / sqrt(n)
final double z2 = x * x * n;
double lne = -PI2 / (8 * z2);
// Final result is ~ (1 - K0) ~ 1 - sqrt(2pi/z) exp(-pi^2 / (8 z^2))
// Do not compute when the exp value is far below eps.
if (lne < LOG_PG_MIN) {
// z ~ sqrt(-pi^2/(8*min)) ~ 0.1491
return 1;
}
// Note that summing K1, ..., K3 over all k with factor
// (k + 1/2) is equivalent to summing over all k with
// 2 (k - 1/2) / 2 == (2k - 1) / 2
// This is the form for K0.
// Compute all together over odd integers and divide factors
// of (k + 1/2)^b by 2^b.
double k0 = 0;
double k1 = 0;
double k2 = 0;
double k3 = 0;
final double rootN = Math.sqrt(n);
final double z = x * rootN;
final double z3 = z * z2;
final double z4 = z2 * z2;
final double z6 = Math.pow(z2, 3);
final double z7 = Math.pow(z2, 3.5);
final double z8 = Math.pow(z2, 4);
final double z10 = Math.pow(z2, 5);
final double a1 = PI2 / 4;
final double a2 = 6 * z6 + 2 * z4;
final double b2 = (PI2 * (2 * z4 - 5 * z2)) / 4;
final double c2 = (PI4 * (1 - 2 * z2)) / 16;
final double a3 = (PI6 * (5 - 30 * z2)) / 64;
final double b3 = (PI4 * (-60 * z2 + 212 * z4)) / 16;
final double c3 = (PI2 * (135 * z4 - 96 * z6)) / 4;
final double d3 = -(30 * z6 + 90 * z8);
// Iterate j=(2k - 1) for k=1, 2, ...
// Terms reduce in size. Stop when:
// exp(-pi^2 / 8z^2) * eps = exp((2k-1)^2 * -pi^2 / 8z^2)
// (2k-1)^2 = 1 - log(eps) * 8z^2 / pi^2
// 0 = k^2 - k + log(eps) * 2z^2 / pi^2
// Solve using quadratic equation and eps = ulp(1.0): 4a ~ -288
final int max = (int) Math.ceil((1 + Math.sqrt(1 - FOUR_A * z2 / PI2)) / 2);
// Sum smallest terms first
for (int k = max; k > 0; k--) {
final int j = 2 * k - 1;
// Create (2k-1)^2; (2k-1)^4; (2k-1)^6
final double j2 = (double) j * j;
final double j4 = Math.pow(j, 4);
final double j6 = Math.pow(j, 6);
// exp(-pi^2 * (2k-1)^2 / 8z^2)
final double e = Math.exp(lne * j2);
k0 += e;
k1 += (a1 * j2 - z2) * e;
k2 += (a2 + b2 * j2 + c2 * j4) * e;
k3 += (a3 * j6 + b3 * j4 + c3 * j2 + d3) * e;
}
k0 *= ROOT_TWO_PI / z;
// Factors are halved as the sum is for k in -inf to +inf
k1 *= ROOT_HALF_PI / (3 * z4);
k2 *= ROOT_HALF_PI / (36 * z7);
k3 *= ROOT_HALF_PI / (3240 * z10);
// Compute additional K2,K3 terms
double k2b = 0;
double k3b = 0;
// -pi^2 / (2z^2)
lne *= 4;
final double a3b = 3 * PI2 * z2;
// Iterate for j=1, 2, ...
// Note: Here max = sqrt(1 - FOUR_A z^2 / (4 pi^2)).
// This is marginally smaller so we reuse the same value.
for (int j = max; j > 0; j--) {
final double j2 = (double) j * j;
final double j4 = Math.pow(j, 4);
// exp(-pi^2 * k^2 / 2z^2)
final double e = Math.exp(lne * j2);
k2b += PI2 * j2 * e;
k3b += (-PI4 * j4 + a3b * j2) * e;
}
// Factors are halved as the sum is for k in -inf to +inf
k2b *= ROOT_HALF_PI / (18 * z3);
k3b *= ROOT_HALF_PI / (108 * z6);
// Series: K0(z) + K1(z)/n^0.5 + K2(z)/n + K3(z)/n^1.5 + O(1/n^2)
k1 /= rootN;
k2 /= n;
k3 /= n * rootN;
k2b /= n;
k3b /= n * rootN;
// Return (1 - CDF) with an extended precision sum in order of descending magnitude
return clipProbability(Sum.of(1, -k0, -k1, -k2, -k3, +k2b, -k3b).getAsDouble());
}
}
/**
* Computes the complementary probability {@code P[D_n^+ >= x]} for the one-sided
* one-sample Kolmogorov-Smirnov distribution.
*
*
* D_n^+ = sup_x {CDF_n(x) - F(x)}
*
*
* where {@code n} is the sample size; {@code CDF_n(x)} is an empirical
* cumulative distribution function; and {@code F(x)} is the expected
* distribution. The computation uses Smirnov's stable formula:
*
*
* floor(n(1-x)) (n) ( j ) (j-1) ( j ) (n-j)
* P[D_n^+ >= x] = x Sum ( ) ( - + x ) ( 1 - x - - )
* j=0 (j) ( n ) ( n )
*
*
* Computing using logs is not as accurate as direct multiplication when n is large.
* However the terms are very large and small. Multiplication uses a scaled representation
* with a separate exponent term to support the extreme range. Extended precision
* representation of the numbers reduces the error in the power terms. Details in
* van Mulbregt (2018).
*
*
* References:
*
* -
* van Mulbregt, P. (2018).
* Computing the Cumulative Distribution Function and Quantiles of the One-sided Kolmogorov-Smirnov Statistic
* arxiv:1802.06966.
*
- Magg & Dicaire (1971).
* On Kolmogorov-Smirnov Type One-Sample Statistics
* Biometrika 58.3 pp. 653–656.
*
*
* @since 1.1
*/
static final class One {
/** "Very large" n to use a asymptotic limiting form.
* [1] suggests 1e12 but this is reduced to avoid excess
* computation time. */
private static final int VERY_LARGE_N = 1000000;
/** Maximum number of term for the Smirnov-Dwass algorithm. */
private static final int SD_MAX_TERMS = 3;
/** Minimum sample size for the Smirnov-Dwass algorithm. */
private static final int SD_MIN_N = 8;
/** Number of bits of precision in the sum of terms Aj.
* This does not have to be the full 106 bits of a double-double as the final result
* is used as a double. The terms are represented as fractions with an exponent:
*
* Aj = 2^b * f
* f of sum(A) in [0.5, 1)
* f of Aj in [0.25, 2]
*
* The terms can be added if their exponents overlap. The bits of precision must
* account for the extra range of the fractional part of Aj by 1 bit. Note that
* additional bits are added to this dynamically based on the number of terms. */
private static final int SUM_PRECISION_BITS = 53;
/** Number of bits of precision in the sum of terms Aj.
* For Smirnov-Dwass we use the full 106 bits of a double-double due to the summation
* of terms that cancel. Account for the extra range of the fractional part of Aj by 1 bit. */
private static final int SD_SUM_PRECISION_BITS = 107;
/** Proxy for the default choice of the scaled power function.
* The actual choice is based on the chosen algorithm. */
private static final ScaledPower POWER_DEFAULT = null;
/**
* Defines a scaled power function.
* Package-private to allow the main sf method to be called direct in testing.
*/
interface ScaledPower {
/**
* Compute the number {@code x} raised to the power {@code n}.
*
*
The value is returned as fractional {@code f} and integral
* {@code 2^exp} components.
*
* (x+xx)^n = (f+ff) * 2^exp
*
*
* @param x x.
* @param n Power.
* @param exp Result power of two scale factor (integral exponent).
* @return Fraction part.
* @see DD#frexp(int[])
* @see DD#pow(int, long[])
* @see DDMath#pow(DD, int, long[])
*/
DD pow(DD x, int n, long[] exp);
}
/** No instances. */
private One() {}
/**
* Calculates complementary probability {@code P[D_n^+ >= x]}, or survival
* function (SF), for the one-sided one-sample Kolmogorov-Smirnov distribution.
*
* @param x Statistic.
* @param n Sample size (assumed to be positive).
* @return \(P(D_n^+ ≥ x)\)
*/
static double sf(double x, int n) {
final double p = sfExact(x, n);
if (p >= 0) {
return p;
}
// Note: This is not referring to N = floor(n*x).
// Here n is the sample size and a suggested limit 10^12 is noted on pp.15 in [1].
// This uses a lower threshold where the full computation takes ~ 1 second.
if (n > VERY_LARGE_N) {
return sfAsymptotic(x, n);
}
return sf(x, n, POWER_DEFAULT);
}
/**
* Calculates exact cases for the complementary probability
* {@code P[D_n^+ >= x]} the one-sided one-sample Kolmogorov-Smirnov distribution.
*
* Exact cases handle x not in [0, 1]. It is assumed n is positive.
*
* @param x Statistic.
* @param n Sample size (assumed to be positive).
* @return \(P(D_n^+ ≥ x)\)
*/
private static double sfExact(double x, int n) {
if (n * x * x >= 372.5 || x >= 1) {
// p would underflow, or x is out of the domain
return 0;
}
if (x <= 0) {
// edge-of, or out-of, the domain
return 1;
}
if (n == 1) {
return x;
}
// x <= 1/n
// [1] Equation (33)
final double nx = n * x;
if (nx <= 1) {
// 1 - x (1+x)^(n-1): here x may be small so use log1p
return 1 - x * Math.exp((n - 1) * Math.log1p(x));
}
// 1 - 1/n <= x < 1
// [1] Equation (16)
if (n - 1 <= nx) {
// (1-x)^n: here x > 0.5 and 1-x is exact
return Math.pow(1 - x, n);
}
return -1;
}
/**
* Calculates complementary probability {@code P[D_n^+ >= x]}, or survival
* function (SF), for the one-sided one-sample Kolmogorov-Smirnov distribution.
*
*
Computes the result using the asymptotic formula Eq 5 in [1].
*
* @param x Statistic.
* @param n Sample size (assumed to be positive).
* @return \(P(D_n^+ ≥ x)\)
*/
private static double sfAsymptotic(double x, int n) {
// Magg & Dicaire (1971) limiting form
return Math.exp(-Math.pow(6.0 * n * x + 1, 2) / (18.0 * n));
}
/**
* Calculates complementary probability {@code P[D_n^+ >= x]}, or survival
* function (SF), for the one-sided one-sample Kolmogorov-Smirnov distribution.
*
*
Computes the result using double-double arithmetic. The power function
* can use a fast approximation or a full power computation.
*
*
This function is safe for {@code x > 1/n}. When {@code x} approaches
* sub-normal then division or multiplication by x can under/overflow. The
* case of {@code x < 1/n} can be computed in {@code sfExact}.
*
* @param x Statistic (typically in (1/n, 1 - 1/n)).
* @param n Sample size (assumed to be positive).
* @param power Function to compute the scaled power (can be null).
* @return \(P(D_n^+ ≥ x)\)
* @see DD#pow(int, long[])
* @see DDMath#pow(DD, int, long[])
*/
static double sf(double x, int n, ScaledPower power) {
// Compute only the SF using Algorithm 1 pp 12.
// Compute: k = floor(n*x), alpha = nx - k; x = (k+alpha)/n with 0 <= alpha < 1
final double[] alpha = {0};
final int k = splitX(n, x, alpha);
// Choose the algorithm:
// Eq (13) Smirnov/Birnbaum-Tingey; or Smirnov/Dwass Eq (31)
// Eq. 13 sums j = 0 : floor( n(1-x) ) = n - 1 - floor(nx) iff alpha != 0; else n - floor(nx)
// Eq. 31 sums j = ceil( n(1-x) ) : n = n - floor(nx)
// Drop a term term if x = (n-j)/n. Equates to shifting the floor* down and ceil* up:
// Eq. 13 N = floor*( n(1-x) ) = n - k - ((alpha!=0) ? 1 : 0) - ((alpha==0) ? 1 : 0)
// Eq. 31 N = n - ceil*( n(1-x) ) = k - ((alpha==0) ? 1 : 0)
// Where N is the number of terms - 1. This differs from Algorithm 1 by dropping
// a SD term when it should be zero (to working precision).
final int regN = n - k - 1;
final int sdN = k - ((alpha[0] == 0) ? 1 : 0);
// SD : Figure 3 (c) (pp. 6)
// Terms Aj (j = n -> 0) have alternating signs through the range and may involve
// numbers much bigger than 1 causing cancellation; magnitudes increase then decrease.
// Section 3.3: Extra digits of precision required
// grows like Order(sqrt(n)). E.g. sf=0.7 (x ~ 0.4/sqrt(n)) loses 8 digits.
//
// Regular : Figure 3 (a, b)
// Terms Aj can have similar magnitude through the range; when x >= 1/sqrt(n)
// the final few terms can be magnitudes smaller and could be ignored.
// Section 3.4: As x increases the magnitude of terms becomes more peaked,
// centred at j = (n-nx)/2, i.e. 50% of the terms.
//
// As n -> inf the sf for x = k/n agrees with the asymptote Eq 5 in log2(n) bits.
//
// Figure 4 has lines at x = 1/n and x = 3/sqrt(n).
// Point between is approximately x = 4/n, i.e. nx < 4 : k <= 3.
// If faster when x < 0.5 and requiring nx ~ 4 then requires n >= 8.
//
// Note: If SD accuracy scales with sqrt(n) then we could use 1 / sqrt(n).
// That threshold is always above 4 / n when n is 16 (4/n = 1/sqrt(n) : n = 4^2).
// So the current thresholds are conservative.
boolean sd = false;
if (sdN < regN) {
// Here x < 0.5 and SD has fewer terms
// Always choose when we only have one additional term (i.e x < 2/n)
sd = sdN <= 1;
// Otherwise when x < 4 / n
sd |= sdN <= SD_MAX_TERMS && n >= SD_MIN_N;
}
final int maxN = sd ? sdN : regN;
// Note: if N > "very large" use the asymptotic approximation.
// Currently this check is done on n (sample size) in the calling function.
// This provides a monotonic p-value for all x with the same n.
// Configure the algorithm.
// The error of double-double addition and multiplication is low (< 2^-102).
// The error in Aj is mainly from the power function.
// fastPow error is around 2^-52, pow error is ~ 2^-70 or lower.
// Smirnoff-Dwass has a sum of terms that cancel and requires higher precision.
// The power can optionally be specified.
final ScaledPower fpow;
if (power == POWER_DEFAULT) {
// SD has only a few terms. Use a high accuracy power.
fpow = sd ? DDMath::pow : DD::pow;
} else {
fpow = power;
}
// For the regular summation we must sum at least 50% of the terms. The number
// of required bits to sum remaining terms of the same magnitude is log2(N/2).
// These guards bits are conservative and > ~99% of terms are typically used.
final int sumBits = sd ? SD_SUM_PRECISION_BITS : SUM_PRECISION_BITS + log2(maxN >> 1);
// Working variable for the exponent of scaled values
final int[] ie = {0};
final long[] le = {0};
// The terms Aj may over/underflow.
// This is handled by maintaining the sum(Aj) using a fractional representation.
// sum(Aj) maintained as 2^e * f with f in [0.5, 1)
DD sum;
long esum;
// Compute A0
if (sd) {
// A0 = (1+x)^(n-1)
sum = fpow.pow(DD.ofSum(1, x), n - 1, le);
esum = le[0];
} else {
// A0 = (1-x)^n / x
sum = fpow.pow(DD.ofDifference(1, x), n, le);
esum = le[0];
// x in (1/n, 1 - 1/n) so the divide of the fraction is safe
sum = sum.divide(x).frexp(ie);
esum += ie[0];
}
// Binomial coefficient c(n, j) maintained as 2^e * f with f in [1, 2)
// This value is integral but maintained to limited precision
DD c = DD.ONE;
long ec = 0;
for (int i = 1; i <= maxN; i++) {
// c(n, j) = c(n, j-1) * (n-j+1) / j
c = c.multiply(DD.fromQuotient(n - i + 1, i));
// Here we maintain c in [1, 2) to restrict the scaled Aj term to [0.25, 2].
final int b = Math.getExponent(c.hi());
if (b != 0) {
c = c.scalb(-b);
ec += b;
}
// Compute Aj
final int j = sd ? n - i : i;
// Algorithm 4 pp. 27
// S = ((j/n) + x)^(j-1)
// T = ((n-j)/n - x)^(n-j)
final DD s = fpow.pow(DD.fromQuotient(j, n).add(x), j - 1, le);
final long es = le[0];
final DD t = fpow.pow(DD.fromQuotient(n - j, n).subtract(x), n - j, le);
final long et = le[0];
// Aj = C(n, j) * T * S
// = 2^e * [1, 2] * [0.5, 1] * [0.5, 1]
// = 2^e * [0.25, 2]
final long eaj = ec + es + et;
// Only compute and add to the sum when the exponents overlap by n-bits.
if (eaj > esum - sumBits) {
DD aj = c.multiply(t).multiply(s);
// Scaling must offset by the scale of the sum
aj = aj.scalb((int) (eaj - esum));
sum = sum.add(aj);
} else {
// Terms are expected to increase in magnitude then reduce.
// Here the terms are insignificant and we can stop.
// Effectively Aj -> eps * sum, and most of the computation is done.
break;
}
// Re-scale the sum
sum = sum.frexp(ie);
esum += ie[0];
}
// p = x * sum(Ai). Since the sum is normalised
// this is safe as long as x does not approach a sub-normal.
// Typically x in (1/n, 1 - 1/n).
sum = sum.multiply(x);
// Rescale the result
sum = sum.scalb((int) esum);
if (sd) {
// SF = 1 - CDF
sum = sum.negate().add(1);
}
return clipProbability(sum.doubleValue());
}
/**
* Compute exactly {@code x = (k + alpha) / n} with {@code k} an integer and
* {@code alpha in [0, 1)}. Note that {@code k ~ floor(nx)} but may be rounded up
* if {@code alpha -> 1} within working precision.
*
*
This computation is a significant source of increased error if performed in
* 64-bit arithmetic. Although the value alpha is only used for the PDF computation
* a value of {@code alpha == 0} indicates the final term of the SF summation can be
* dropped due to the cancellation of a power term {@code (x + j/n)} to zero with
* {@code x = (n-j)/n}. That is if {@code alpha == 0} then x is the fraction {@code k/n}
* and one Aj term is zero.
*
* @param n Sample size.
* @param x Statistic.
* @param alpha Output alpha.
* @return k
*/
static int splitX(int n, double x, double[] alpha) {
// Described on page 14 in van Mulbregt [1].
// nx = U+V (exact)
DD z = DD.ofProduct(n, x);
// Integer part of nx is *almost* the integer part of U.
// Compute k = floor((U,V)) (changed from the listing of floor(U)).
int k = (int) z.floor().hi();
// nx = k + ((U - k) + V) = k + (U1 + V1)
// alpha = (U1, V1) = z - k
z = z.subtract(k);
// alpha is in [0, 1) in double-double precision.
// Ensure the high part is in [0, 1) (i.e. in double precision).
if (z.hi() == 1) {
// Here alpha is ~ 1.0-eps.
// This occurs when x ~ j/n and n is large.
k += 1;
alpha[0] = 0;
} else {
alpha[0] = z.hi();
}
return k;
}
/**
* Returns {@code floor(log2(n))}.
*
* @param n Value.
* @return approximate log2(n)
*/
private static int log2(int n) {
return 31 - Integer.numberOfLeadingZeros(n);
}
}
/**
* Computes {@code P(sqrt(n) D_n > x)}, the limiting form for the distribution of
* Kolmogorov's D_n as described in Simard and L’Ecuyer (2011) (Eq. 5, or K0 Eq. 6).
*
*
Computes \( 2 \sum_{i=1}^\infty (-1)^(i-1) e^{-2 i^2 x^2} \), or
* \( 1 - (\sqrt{2 \pi} / x) * \sum_{i=1}^\infty { e^{-(2i-1)^2 \pi^2 / (8x^2) } } \)
* when x is small.
*
*
Note: This computes the upper Kolmogorov sum.
*
* @param x Argument x = sqrt(n) * d
* @return Upper Kolmogorov sum evaluated at x
*/
static double ksSum(double x) {
// Switch computation when p ~ 0.5
if (x < X_KS_HALF) {
// When x -> 0 the result is 1
if (x < X_KS_ONE) {
return 1;
}
// t = exp(-pi^2/8x^2)
// p = 1 - sqrt(2pi)/x * (t + t^9 + t^25 + t^49 + t^81 + ...)
// = 1 - sqrt(2pi)/x * t * (1 + t^8 + t^24 + t^48 + t^80 + ...)
final double logt = -PI2 / (8 * x * x);
final double t = Math.exp(logt);
final double s = ROOT_TWO_PI / x;
final double t8 = Math.pow(t, 8);
if (t8 < EPS) {
// Cannot compute 1 + t^8.
// 1 - sqrt(2pi)/x * exp(-pi^2/8x^2)
// 1 - exp(log(sqrt(2pi)/x) - pi^2/8x^2)
return -Math.expm1(Math.log(s) + logt);
}
// sum = t^((2i-1)^2 - 1), i=1, 2, 3, 4, 5, ...
// = 1 + t^8 + t^24 + t^48 + t^80 + ...
// With x = 0.82757... the smallest terms cannot be added when i==5
// i.e. t^48 + t^80 == t^48
// sum = 1 + (t^8 * (1 + t^16 * (1 + t^24)))
final double sum = 1 + (t8 * (1 + t8 * t8 * (1 + t8 * t8 * t8)));
return 1 - s * t * sum;
}
// t = exp(-2 x^2)
// p = 2 * (t - t^4 + t^9 - t^16 + ...)
// sum = -1^(i-1) t^(i^2), i=i, 2, 3, ...
// Sum of alternating terms of reducing magnitude:
// Will converge when exp(-2x^2) * eps >= exp(-2x^2)^(i^2)
// When x = 0.82757... this requires max i==5
// i.e. t * eps >= t^36 (i=6)
final double t = Math.exp(-2 * x * x);
// (t - t^4 + t^9 - t^16 + t^25)
// t * (1 - t^3 * (1 - t^5 * (1 - t^7 * (1 - t^9))))
final double t2 = t * t;
final double t3 = t * t * t;
final double t4 = t2 * t2;
final double sum = t * (1 - t3 * (1 - t2 * t3 * (1 - t3 * t4 * (1 - t2 * t3 * t4))));
return clipProbability(2 * sum);
}
/**
* Clip the probability to the range [0, 1].
*
* @param p Probability.
* @return p in [0, 1]
*/
static double clipProbability(double p) {
return Math.min(1, Math.max(0, p));
}
}