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Merge branch 'MATH_3_X' of https://git-wip-us.apache.org/repos/asf/commons-math into MATH_3_X

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Gilles 2015-09-18 00:09:23 +02:00
parent 7929e10a8f 1c9c43c1d4
commit 88cee6d7b3
2 changed files with 170 additions and 28 deletions
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3
src/changes/changes.xml
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@ -51,6 +51,9 @@ If the output is not quite correct, check for invisible trailing spaces!
</properties>
<body>
<release version="3.6" date="XXXX-XX-XX" description="">
<action dev="oertl" type="update" issue="MATH-1274">
Representation of Kolmogorov-Smirnov statistic as integral value.
</action>
<action dev="luc" type="add" issue="MATH-1273" due-to="Qualtagh">
Added negative zero support in FastMath.pow.
</action>

195
src/main/java/org/apache/commons/math3/stat/inference/KolmogorovSmirnovTest.java
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@ -21,7 +21,6 @@ import java.math.BigDecimal;
import java.util.Arrays;
import java.util.Iterator;
import org.apache.commons.math3.util.Precision;
import org.apache.commons.math3.distribution.RealDistribution;
import org.apache.commons.math3.exception.InsufficientDataException;
import org.apache.commons.math3.exception.MathArithmeticException;
@ -220,7 +219,10 @@ public class KolmogorovSmirnovTest {
* {@value #SMALL_SAMPLE_PRODUCT}), the exact distribution is used to compute the p-value. This
* is accomplished by enumerating all partitions of the combined sample into two subsamples of
* the respective sample sizes, computing \(D_{n,m}\) for each partition and returning the
* proportion of partitions that give \(D\) values exceeding the observed value.</li>
* proportion of partitions that give \(D\) values exceeding the observed value. In the very
* small sample case, if there are ties in the data, the actual sample values (including ties)
* are used in generating the partitions (which are basically multi-set partitions in this
* case).</li>
* <li>For mid-size samples (product of sample sizes greater than or equal to
* {@value #SMALL_SAMPLE_PRODUCT} but less than {@value #LARGE_SAMPLE_PRODUCT}), Monte Carlo
* simulation is used to compute the p-value. The simulation randomly generates partitions and
@ -243,10 +245,10 @@ public class KolmogorovSmirnovTest {
public double kolmogorovSmirnovTest(double[] x, double[] y, boolean strict) {
final long lengthProduct = (long) x.length * y.length;
if (lengthProduct < SMALL_SAMPLE_PRODUCT) {
return exactP(kolmogorovSmirnovStatistic(x, y), x.length, y.length, strict);
return integralExactP(integralKolmogorovSmirnovStatistic(x, y) + ((strict)?1l:0l), x.length, y.length);
}
if (lengthProduct < LARGE_SAMPLE_PRODUCT) {
return monteCarloP(kolmogorovSmirnovStatistic(x, y), x.length, y.length, strict, MONTE_CARLO_ITERATIONS);
return integralMonteCarloP(integralKolmogorovSmirnovStatistic(x, y) + ((strict)?1l:0l), x.length, y.length, MONTE_CARLO_ITERATIONS);
}
return approximateP(kolmogorovSmirnovStatistic(x, y), x.length, y.length);
}
@ -285,6 +287,25 @@ public class KolmogorovSmirnovTest {
* @throws NullArgumentException if either {@code x} or {@code y} is null
*/
public double kolmogorovSmirnovStatistic(double[] x, double[] y) {
return integralKolmogorovSmirnovStatistic(x, y)/((double)(x.length * (long)y.length));
}
/**
* Computes the two-sample Kolmogorov-Smirnov test statistic, \(D_{n,m}=\sup_x |F_n(x)-F_m(x)|\)
* where \(n\) is the length of {@code x}, \(m\) is the length of {@code y}, \(F_n\) is the
* empirical distribution that puts mass \(1/n\) at each of the values in {@code x} and \(F_m\)
* is the empirical distribution of the {@code y} values. Finally \(n m D_{n,m}\) is returned
* as long value.
*
* @param x first sample
* @param y second sample
* @return test statistic \(n m D_{n,m}\) used to evaluate the null hypothesis that {@code x} and
* {@code y} represent samples from the same underlying distribution
* @throws InsufficientDataException if either {@code x} or {@code y} does not have length at
* least 2
* @throws NullArgumentException if either {@code x} or {@code y} is null
*/
private long integralKolmogorovSmirnovStatistic(double[] x, double[] y) {
checkArray(x);
checkArray(y);
// Copy and sort the sample arrays
@ -297,23 +318,26 @@ public class KolmogorovSmirnovTest {
int rankX = 0;
int rankY = 0;
long curD = 0l;
// Find the max difference between cdf_x and cdf_y
double supD = 0d;
long supD = 0l;
do {
double z = Double.compare(sx[rankX], sy[rankY]) <= 0 ? sx[rankX] : sy[rankY];
while(rankX < n && Double.compare(sx[rankX], z) == 0) {
rankX += 1;
curD += m;
}
while(rankY < m && Double.compare(sy[rankY], z) == 0) {
rankY += 1;
curD -= n;
}
final double cdf_x = rankX / (double) n;
final double cdf_y = rankY / (double) m;
final double curD = FastMath.abs(cdf_x - cdf_y);
if (curD > supD) {
supD = curD;
}
else if (-curD > supD) {
supD = -curD;
}
} while(rankX < n && rankY < m);
return supD;
}
@ -857,6 +881,32 @@ public class KolmogorovSmirnovTest {
return partialSum * 2;
}
/**
* Given a d-statistic in the range [0, 1] and the two sample sizes n and m,
* an integral d-statistic in the range [0, n*m] is calculated, that can be used for
* comparison with other integral d-statistics. Depending whether {@code strict} is
* {@code true} or not, the returned value divided by (n*m) is greater than
* (resp greater than or equal to) the given d value (allowing some tolerance).
*
* @param d a d-statistic in the range [0, 1]
* @param n first sample size
* @param m second sample size
* @param strict whether the returned value divided by (n*m) is allowed to be equal to d
* @return the integral d-statistic in the range [0, n*m]
*/
private static long calculateIntegralD(double d, int n, int m, boolean strict) {
final double tol = 1e-12; // d-values within tol of one another are considered equal
long nm = n * (long)m;
long upperBound = (long)FastMath.ceil((d - tol) * nm);
long lowerBound = (long)FastMath.floor((d + tol) * nm);
if (strict && lowerBound == upperBound) {
return upperBound + 1l;
}
else {
return upperBound;
}
}
/**
* Computes \(P(D_{n,m} > d)\) if {@code strict} is {@code true}; otherwise \(P(D_{n,m} \ge
* d)\), where \(D_{n,m}\) is the 2-sample Kolmogorov-Smirnov statistic. See
@ -882,11 +932,28 @@ public class KolmogorovSmirnovTest {
* greater than (resp. greater than or equal to) {@code d}
*/
public double exactP(double d, int n, int m, boolean strict) {
return integralExactP(calculateIntegralD(d, n, m, strict), n, m);
}
/**
* Computes \(P(D_{n,m} >= d/(n*m))\), where \(D_{n,m}\) is the
* 2-sample Kolmogorov-Smirnov statistic.
* <p>
* Here d is the D-statistic represented as long value.
* The real D-statistic is obtained by dividing d by n*m.
* See also {@link #exactP(double, int, int, boolean)}.
*
* @param d integral D-statistic
* @param n first sample size
* @param m second sample size
* @return probability that a randomly selected m-n partition of m + n generates \(D_{n,m}\)
* greater than or equal to {@code d/(n*m)}
*/
private double integralExactP(long d, int n, int m) {
Iterator<int[]> combinationsIterator = CombinatoricsUtils.combinationsIterator(n + m, n);
long tail = 0;
final double[] nSet = new double[n];
final double[] mSet = new double[m];
final double tol = 1e-12; // d-values within tol of one another are considered equal
while (combinationsIterator.hasNext()) {
// Generate an n-set
final int[] nSetI = combinationsIterator.next();
@ -900,9 +967,67 @@ public class KolmogorovSmirnovTest {
mSet[k++] = i;
}
}
final double curD = kolmogorovSmirnovStatistic(nSet, mSet);
final int order = Precision.compareTo(curD, d, tol);
if (order > 0 || (order == 0 && !strict)) {
final long curD = integralKolmogorovSmirnovStatistic(nSet, mSet);
if (curD >= d) {
tail++;
}
}
return (double) tail / (double) CombinatoricsUtils.binomialCoefficient(n + m, n);
}
/**
* Computes the exact p value for a two-sample Kolmogorov-Smirnov test with
* {@code x} and {@code y} as samples, possibly containing ties. This method
* uses the same implementation as {@link #exactP(double, int, int, boolean)}
* with the exception that it examines partitions of the combined sample,
* preserving ties in the data. What is returned is the exact probability
* that a random partition of the combined dataset into a subset of size
* {@code x.length} and another of size {@code y.length} yields a \(D\)
* value greater than (resp greater than or equal to) \(D(x,y)\).
* <p>
* This method should not be used on large samples (a good rule of thumb is
* to keep the product of the sample sizes less than
* {@link #SMALL_SAMPLE_PRODUCT} when using this method). If the data do
* not contain ties, {@link #exactP(double[], double[], boolean)} should be
* used instead of this method.</p>
*
* @param x first sample
* @param y second sample
* @param strict whether or not the inequality in the null hypothesis is strict
* @return p-value
*/
public double exactP(double[] x, double[] y, boolean strict) {
final long d = integralKolmogorovSmirnovStatistic(x, y);
final int n = x.length;
final int m = y.length;
// Concatenate x and y into universe, preserving ties in the data
final double[] universe = new double[n + m];
System.arraycopy(x, 0, universe, 0, n);
System.arraycopy(y, 0, universe, n, m);
// Iterate over all n, m partitions of the n + m elements in the universe,
// Computing D for each one
Iterator<int[]> combinationsIterator = CombinatoricsUtils.combinationsIterator(n + m, n);
long tail = 0;
final double[] nSet = new double[n];
final double[] mSet = new double[m];
while (combinationsIterator.hasNext()) {
// Generate an n-set
final int[] nSetI = combinationsIterator.next();
// Copy the elements of the universe in the n-set to nSet
// and the others to mSet
int j = 0;
int k = 0;
for (int i = 0; i < n + m; i++) {
if (j < n && nSetI[j] == i) {
nSet[j++] = universe[i];
} else {
mSet[k++] = universe[i];
}
}
final long curD = integralKolmogorovSmirnovStatistic(nSet, mSet);
if (curD > d || (curD == d && !strict)) {
tail++;
}
}
@ -974,35 +1099,49 @@ public class KolmogorovSmirnovTest {
*/
public double monteCarloP(final double d, final int n, final int m, final boolean strict,
final int iterations) {
return integralMonteCarloP(calculateIntegralD(d, n, m, strict), n, m, iterations);
}
/**
* Uses Monte Carlo simulation to approximate \(P(D_{n,m} >= d/(n*m))\) where \(D_{n,m}\) is the
* 2-sample Kolmogorov-Smirnov statistic.
* <p>
* Here d is the D-statistic represented as long value.
* The real D-statistic is obtained by dividing d by n*m.
* See also {@link #monteCarloP(double, int, int, boolean, int)}.
*
* @param d integral D-statistic
* @param n first sample size
* @param m second sample size
* @param iterations number of random partitions to generate
* @return proportion of randomly generated m-n partitions of m + n that result in \(D_{n,m}\)
* greater than or equal to {@code d/(n*m))}
*/
private double integralMonteCarloP(final long d, final int n, final int m, final int iterations) {
// ensure that nn is always the max of (n, m) to require fewer random numbers
final int nn = FastMath.max(n, m);
final int mm = FastMath.min(n, m);
final int sum = nn + mm;
final double tol = 1e-12; // d-values within tol of one another are considered equal
int tail = 0;
final boolean b[] = new boolean[sum];
for (int i = 0; i < iterations; i++) {
fillBooleanArrayRandomlyWithFixedNumberTrueValues(b, nn, rng);
int rankN = b[0] ? 1 : 0;
int rankM = b[0] ? 0 : 1;
boolean previous = b[0];
for(int j = 1; j < b.length; ++j) {
if (b[j] != previous) {
final double cdf_n = rankN / (double) nn;
final double cdf_m = rankM / (double) mm;
final double curD = FastMath.abs(cdf_n - cdf_m);
final int order = Precision.compareTo(curD, d, tol);
if (order > 0 || (order == 0 && !strict)) {
long curD = 0l;
for(int j = 0; j < b.length; ++j) {
if (b[j]) {
curD += mm;
if (curD >= d) {
tail++;
break;
}
}
previous = b[j];
if (b[j]) {
rankN++;
} else {
rankM++;
curD -= nn;
if (curD <= -d) {
tail++;
break;
}
}
}
}