Added trigamma, javadoc fixes for digamma. JIRA: MATH-267. Patched by Ted Dunning.

git-svn-id: https://svn.apache.org/repos/asf/commons/proper/math/trunk@778416 13f79535-47bb-0310-9956-ffa450edef68
This commit is contained in:
Phil Steitz 2009-05-25 13:20:07 +00:00
parent 8897d15259
commit af79797eaa
2 changed files with 66 additions and 11 deletions

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@ -271,25 +271,28 @@ public class Gamma implements Serializable {
// limits for switching algorithm in digamma
/** C limit */
private static final double C_LIMIT = 49;
/** S limit */
private static final double S_LIMIT = 1e-5;
private static final double C_LIMIT = 49;
/** S limit */
private static final double S_LIMIT = 1e-5;
/**
* <p>Computes the <a href="http://en.wikipedia.org/wiki/Digamma_function">digamma function</a>
* using the algorithm defined in <br/>
* <p>Computes the digamma function of x.</p>
*
* <p>This is an independently written implementation of the algorithm described in
* Jose Bernardo, Algorithm AS 103: Psi (Digamma) Function, Applied Statistics, 1976.</p>
*
* <p>Some of the constants have been changed to increase accuracy at the moderate expense
* of run-time performance. The result should be accurate to within 10^-8 absolute tolerance for
* of run-time. The result should be accurate to within 10^-8 absolute tolerance for
* x >= 10^-5 and within 10^-8 relative tolerance for x > 0.</p>
*
* <p> Performance for large negative values of x will be quite expensive (proportional to
* <p>Performance for large negative values of x will be quite expensive (proportional to
* |x|). Accuracy for negative values of x should be about 10^-8 absolute for results
* less than 10^5 and 10^-8 relative for results larger than that.
* less than 10^5 and 10^-8 relative for results larger than that.</p>
*
* @param x argument
* @return value of the digamma function
* @param x the argument
* @return digamma(x) to within 10-8 relative or absolute error whichever is smaller
* @see <a href="http://en.wikipedia.org/wiki/Digamma_function"> Digamma at wikipedia </a>
* @see <a href="http://www.uv.es/~bernardo/1976AppStatist.pdf"> Bernardo's original article </a>
* @since 2.0
*/
public static double digamma(double x) {
@ -303,11 +306,38 @@ public class Gamma implements Serializable {
// use method 4 (accurate to O(1/x^8)
double inv = 1 / (x * x);
// 1 1 1 1
// log(x) - --- - ------ - ------- - -------
// log(x) - --- - ------ + ------- - -------
// 2 x 12 x^2 120 x^4 252 x^6
return Math.log(x) - 0.5 / x - inv * ((1.0 / 12) + inv * (1.0 / 120 - inv / 252));
}
return digamma(x + 1) - 1 / x;
}
/**
* <p>Computes the trigamma function of x. This function is derived by taking the derivative of
* the implementation of digamma.</p>
*
* @param x the argument
* @return trigamma(x) to within 10-8 relative or absolute error whichever is smaller
* @see <a href="http://en.wikipedia.org/wiki/Trigamma_function"> Trigamma at wikipedia </a>
* @see Gamma#digamma(double)
* @since 2.0
*/
public static double trigamma(double x) {
if (x > 0 && x <= S_LIMIT) {
return 1 / (x * x);
}
if (x >= C_LIMIT) {
double inv = 1 / (x * x);
// 1 1 1 1 1
// - + ---- + ---- - ----- + -----
// x 2 3 5 7
// 2 x 6 x 30 x 42 x
return 1 / x + inv / 2 + inv / x * (1.0 / 6 - inv * (1.0 / 30 + inv / 42));
}
return trigamma(x + 1) + 1 / (x * x);
}
}

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@ -119,6 +119,31 @@ public class GammaTest extends TestCase {
}
}
public void testTrigamma() {
double eps = 1e-8;
// computed using webMathematica. For example, to compute trigamma($i) = Polygamma(1, $i), use
//
// http://functions.wolfram.com/webMathematica/Evaluated.jsp?name=PolyGamma2&plottype=0&vars={%221%22,%22$i%22}&digits=20
double[] data = {
1e-4, 1.0000000164469368793e8,
1e-3, 1.0000016425331958690e6,
1e-2, 10001.621213528313220,
1e-1, 101.43329915079275882,
1, 1.6449340668482264365,
2, 0.64493406684822643647,
3, 0.39493406684822643647,
4, 0.28382295573711532536,
5, 0.22132295573711532536,
10, 0.10516633568168574612,
20, 0.051270822935203119832,
50, 0.020201333226697125806,
100, 0.010050166663333571395
};
for (int i = data.length - 2; i >= 0; i -= 2) {
assertEquals(String.format("trigamma %.0f", data[i]), data[i + 1], Gamma.trigamma(data[i]), eps);
}
}
private void checkRelativeError(String msg, double expected, double actual, double tolerance) {
assertEquals(msg, expected, actual, Math.abs(tolerance * actual));
}