public double execute(long in1, long in2) throws DMLRuntimeException {
switch (bFunc) {
case MAX:
case CUMMAX:
return (in1 >= in2 ? in1 : in2);
case MIN:
case CUMMIN:
return (in1 <= in2 ? in1 : in2);
case MAXINDEX:
return (in1 >= in2) ? 1 : 0;
case MININDEX:
return (in1 <= in2) ? 1 : 0;
case LOG:
// if ( in1 <= 0 )
// throw new DMLRuntimeException("Builtin.execute(): logarithm can be computed only for
// non-negative numbers.");
if (FASTMATH) return (FastMath.log(in1) / FastMath.log(in2));
else return (Math.log(in1) / Math.log(in2));
case LOG_NZ:
if (FASTMATH) return (in1 == 0) ? 0 : (FastMath.log(in1) / FastMath.log(in2));
else return (in1 == 0) ? 0 : (Math.log(in1) / Math.log(in2));
default:
throw new DMLRuntimeException("Builtin.execute(): Unknown operation: " + bFunc);
}
}
/**
* Compute the logarithm of the PMF for a binomial distribution using the saddle point expansion.
*
* @param x the value at which the probability is evaluated.
* @param n the number of trials.
* @param p the probability of success.
* @param q the probability of failure (1 - p).
* @return log(p(x)).
*/
static double logBinomialProbability(int x, int n, double p, double q) {
double ret;
if (x == 0) {
if (p < 0.1) {
ret = -getDeviancePart(n, n * q) - n * p;
} else {
ret = n * FastMath.log(q);
}
} else if (x == n) {
if (q < 0.1) {
ret = -getDeviancePart(n, n * p) - n * q;
} else {
ret = n * FastMath.log(p);
}
} else {
ret =
getStirlingError(n)
- getStirlingError(x)
- getStirlingError(n - x)
- getDeviancePart(x, n * p)
- getDeviancePart(n - x, n * q);
double f = (MathUtils.TWO_PI * x * (n - x)) / n;
ret = -0.5 * FastMath.log(f) + ret;
}
return ret;
}
/**
* Calculate the probability of a new category being used for the item being sold. See
* CategoryCountSpread.xlsx
*
* @param auctionNumber Count of auctions to submit, including this. Starts from 1.
* @param logParam
* @return
*/
public static boolean useNewAuctionCategory(int auctionNumber, double logParam, double rand) {
if (auctionNumber < 1 || logParam < 1) throw new IllegalArgumentException();
if (auctionNumber == 1) return true;
// log( (x + 1) / x), log-base to be given
double probNewCat = FastMath.log(1 + (double) 1 / auctionNumber) / FastMath.log(logParam);
return rand < probNewCat; // new category
}
public double execute(double in) throws DMLRuntimeException {
switch (bFunc) {
case SIN:
return FASTMATH ? FastMath.sin(in) : Math.sin(in);
case COS:
return FASTMATH ? FastMath.cos(in) : Math.cos(in);
case TAN:
return FASTMATH ? FastMath.tan(in) : Math.tan(in);
case ASIN:
return FASTMATH ? FastMath.asin(in) : Math.asin(in);
case ACOS:
return FASTMATH ? FastMath.acos(in) : Math.acos(in);
case ATAN:
return Math.atan(in); // faster in Math
case CEIL:
return FASTMATH ? FastMath.ceil(in) : Math.ceil(in);
case FLOOR:
return FASTMATH ? FastMath.floor(in) : Math.floor(in);
case LOG:
return FASTMATH ? FastMath.log(in) : Math.log(in);
case LOG_NZ:
return (in == 0) ? 0 : FASTMATH ? FastMath.log(in) : Math.log(in);
case ABS:
return Math.abs(in); // no need for FastMath
case SIGN:
return FASTMATH ? FastMath.signum(in) : Math.signum(in);
case SQRT:
return Math.sqrt(in); // faster in Math
case EXP:
return FASTMATH ? FastMath.exp(in) : Math.exp(in);
case ROUND:
return Math.round(in); // no need for FastMath
case PLOGP:
if (in == 0.0) return 0.0;
else if (in < 0) return Double.NaN;
else return (in * (FASTMATH ? FastMath.log(in) : Math.log(in)));
case SPROP:
// sample proportion: P*(1-P)
return in * (1 - in);
case SIGMOID:
// sigmoid: 1/(1+exp(-x))
return FASTMATH ? 1 / (1 + FastMath.exp(-in)) : 1 / (1 + Math.exp(-in));
case SELP:
// select positive: x*(x>0)
return (in > 0) ? in : 0;
default:
throw new DMLRuntimeException("Builtin.execute(): Unknown operation: " + bFunc);
}
}
/* (non-Javadoc)
* @see net.finmath.stochastic.RandomVariableInterface#log()
*/
public RandomVariable log() {
if (isDeterministic()) {
double newValueIfNonStochastic = FastMath.log(valueIfNonStochastic);
return new RandomVariable(time, newValueIfNonStochastic);
} else {
double[] newRealizations = new double[realizations.length];
for (int i = 0; i < newRealizations.length; i++)
newRealizations[i] = FastMath.log(realizations[i]);
return new RandomVariable(time, newRealizations);
}
}
/**
* Returns the regularized beta function I(x, a, b).
*
* <p>The implementation of this method is based on:
*
* <ul>
* <li><a href="http://mathworld.wolfram.com/RegularizedBetaFunction.html">Regularized Beta
* Function</a>.
* <li><a href="http://functions.wolfram.com/06.21.10.0001.01">Regularized Beta Function</a>.
* </ul>
*
* @param x the value.
* @param a Parameter {@code a}.
* @param b Parameter {@code b}.
* @param epsilon When the absolute value of the nth item in the series is less than epsilon the
* approximation ceases to calculate further elements in the series.
* @param maxIterations Maximum number of "iterations" to complete.
* @return the regularized beta function I(x, a, b)
* @throws org.apache.commons.math3.exception.MaxCountExceededException if the algorithm fails to
* converge.
*/
public static double regularizedBeta(
double x, final double a, final double b, double epsilon, int maxIterations) {
double ret;
if (Double.isNaN(x)
|| Double.isNaN(a)
|| Double.isNaN(b)
|| x < 0
|| x > 1
|| a <= 0.0
|| b <= 0.0) {
ret = Double.NaN;
} else if (x > (a + 1.0) / (a + b + 2.0)) {
ret = 1.0 - regularizedBeta(1.0 - x, b, a, epsilon, maxIterations);
} else {
ContinuedFraction fraction =
new ContinuedFraction() {
@Override
protected double getB(int n, double x) {
double ret;
double m;
if (n % 2 == 0) { // even
m = n / 2.0;
ret = (m * (b - m) * x) / ((a + (2 * m) - 1) * (a + (2 * m)));
} else {
m = (n - 1.0) / 2.0;
ret = -((a + m) * (a + b + m) * x) / ((a + (2 * m)) * (a + (2 * m) + 1.0));
}
return ret;
}
@Override
protected double getA(int n, double x) {
return 1.0;
}
};
ret =
FastMath.exp(
(a * FastMath.log(x))
+ (b * FastMath.log(1.0 - x))
- FastMath.log(a)
- logBeta(a, b))
* 1.0
/ fraction.evaluate(x, epsilon, maxIterations);
}
return ret;
}
/**
* Returns the value of log[Γ(b) / Γ(a + b)] for a ≥ 0 and b ≥ 10. Based on the <em>NSWC Library
* of Mathematics Subroutines</em> double precision implementation, {@code DLGDIV}. In {@code
* BetaTest.testLogGammaMinusLogGammaSum()}, this private method is accessed through reflection.
*
* @param a First argument.
* @param b Second argument.
* @return the value of {@code log(Gamma(b) / Gamma(a + b))}.
* @throws NumberIsTooSmallException if {@code a < 0.0} or {@code b < 10.0}.
*/
private static double logGammaMinusLogGammaSum(final double a, final double b)
throws NumberIsTooSmallException {
if (a < 0.0) {
throw new NumberIsTooSmallException(a, 0.0, true);
}
if (b < 10.0) {
throw new NumberIsTooSmallException(b, 10.0, true);
}
/*
* d = a + b - 0.5
*/
final double d;
final double w;
if (a <= b) {
d = b + (a - 0.5);
w = deltaMinusDeltaSum(a, b);
} else {
d = a + (b - 0.5);
w = deltaMinusDeltaSum(b, a);
}
final double u = d * FastMath.log1p(a / b);
final double v = a * (FastMath.log(b) - 1.0);
return u <= v ? (w - u) - v : (w - v) - u;
}
/**
* Compute the <a href="http://mathworld.wolfram.com/NaturalLogarithm.html" TARGET="_top"> natural
* logarithm</a> of this complex number. Implements the formula:
*
* <pre>
* <code>
* log(a + bi) = ln(|a + bi|) + arg(a + bi)i
* </code>
* </pre>
*
* where ln on the right hand side is {@link java.lang.Math#log}, {@code |a + bi|} is the modulus,
* {@link Complex#abs}, and {@code arg(a + bi) = }{@link java.lang.Math#atan2}(b, a). <br>
* Returns {@link Complex#NaN} if either real or imaginary part of the input argument is {@code
* NaN}. <br>
* Infinite (or critical) values in real or imaginary parts of the input may result in infinite or
* NaN values returned in parts of the result.
*
* <pre>
* Examples:
* <code>
* log(1 ± INFINITY i) = INFINITY ± (π/2)i
* log(INFINITY + i) = INFINITY + 0i
* log(-INFINITY + i) = INFINITY + πi
* log(INFINITY ± INFINITY i) = INFINITY ± (π/4)i
* log(-INFINITY ± INFINITY i) = INFINITY ± (3π/4)i
* log(0 + 0i) = -INFINITY + 0i
* </code>
* </pre>
*
* @return the value <code>ln this</code>, the natural logarithm of {@code this}.
* @since 1.2
*/
public Complex log() {
if (isNaN) {
return NaN;
}
return createComplex(FastMath.log(abs()), FastMath.atan2(imaginary, real));
}
private double onePoint(double x0, double x1, double[] point) {
final IMonteCarloModel<Double, Double> mc = factory.createModel(x0, x1, point);
mc.addSamples(samples);
final double value = mc.getStats().value();
return FastMath.log(value);
}
@Override
public IComplexNumber log() {
IComplexNumber result = dup();
float real = (float) result.realComponent();
float imaginary = (float) result.imaginaryComponent();
double modulus = FastMath.sqrt(real * real + imaginary * imaginary);
double arg = FastMath.atan2(imaginary, real);
return result.set(FastMath.log(modulus), arg);
}
public static ItemType chooseOldItemType(double logParam, List<ItemType> itemTypes, double rand) {
// sum of log values for each category in itemTypesUsed
// log( (x + 1) / x), log-base to be given
double totalWeight = FastMath.log(logParam, itemTypes.size() + 1);
// x = 2 * k ^ (y - 1), where k is the log base, y is a random number between 1 and totalWeight
// basically given a random number, decides which category to reuse.
double scaledRand = rand * totalWeight;
int index =
(int) (FastMath.pow(logParam, scaledRand))
- 1; // gives a value between 0 and itemTypesUsed.size() exclusive
return itemTypes.get(index);
}
/**
* {@inheritDoc}
*
* <p>Returns {@code 0} when {@code p= = 0} and {@code Double.POSITIVE_INFINITY} when {@code p ==
* 1}.
*/
@Override
public double inverseCumulativeProbability(double p) throws OutOfRangeException {
double ret;
if (p < 0.0 || p > 1.0) {
throw new OutOfRangeException(p, 0.0, 1.0);
} else if (p == 1.0) {
ret = Double.POSITIVE_INFINITY;
} else {
ret = -mean * FastMath.log(1.0 - p);
}
return ret;
}
/**
* Returns the value of log Γ(a + b) for 1 ≤ a, b ≤ 2. Based on the <em>NSWC Library of
* Mathematics Subroutines</em> double precision implementation, {@code DGSMLN}. In {@code
* BetaTest.testLogGammaSum()}, this private method is accessed through reflection.
*
* @param a First argument.
* @param b Second argument.
* @return the value of {@code log(Gamma(a + b))}.
* @throws OutOfRangeException if {@code a} or {@code b} is lower than {@code 1.0} or greater than
* {@code 2.0}.
*/
private static double logGammaSum(final double a, final double b) throws OutOfRangeException {
if ((a < 1.0) || (a > 2.0)) {
throw new OutOfRangeException(a, 1.0, 2.0);
}
if ((b < 1.0) || (b > 2.0)) {
throw new OutOfRangeException(b, 1.0, 2.0);
}
final double x = (a - 1.0) + (b - 1.0);
if (x <= 0.5) {
return Gamma.logGamma1p(1.0 + x);
} else if (x <= 1.5) {
return Gamma.logGamma1p(x) + FastMath.log1p(x);
} else {
return Gamma.logGamma1p(x - 1.0) + FastMath.log(x * (1.0 + x));
}
}
private void calculateCoherence() {
LOG.info("calculating coherence scores");
coherenceScores = new double[model.numberOfTopics()];
for (int k = 0; k < model.numberOfTopics(); k++) {
for (int m = 1; m < numberOfTerms; m++) {
for (int n = 0; n < m; n++) {
int i = term2id.get(model.topTermIds()[k][m]);
int j = term2id.get(model.topTermIds()[k][n]);
if (occurrenceCounts[j] > 0) {
coherenceScores[k] +=
FastMath.log(
(double) (cooccurrenceCounts[i][j] + 1d) / (double) occurrenceCounts[j]);
}
}
}
}
}
/**
* A part of the deviance portion of the saddle point approximation.
*
* <p>References:
*
* <ol>
* <li>Catherine Loader (2000). "Fast and Accurate Computation of Binomial Probabilities.". <a
* target="_blank" href="http://www.herine.net/stat/papers/dbinom.pdf">
* http://www.herine.net/stat/papers/dbinom.pdf</a>
* </ol>
*
* @param x the x value.
* @param mu the average.
* @return a part of the deviance.
*/
static double getDeviancePart(double x, double mu) {
double ret;
if (FastMath.abs(x - mu) < 0.1 * (x + mu)) {
double d = x - mu;
double v = d / (x + mu);
double s1 = v * d;
double s = Double.NaN;
double ej = 2.0 * x * v;
v *= v;
int j = 1;
while (s1 != s) {
s = s1;
ej *= v;
s1 = s + ej / ((j * 2) + 1);
++j;
}
ret = s1;
} else {
ret = x * FastMath.log(x / mu) + mu - x;
}
return ret;
}
/** {@inheritDoc} * */
@Override
public double logDensity(double x) {
recomputeZ();
if (x < 0 || x > 1) {
return Double.NEGATIVE_INFINITY;
} else if (x == 0) {
if (alpha < 1) {
throw new NumberIsTooSmallException(
LocalizedFormats.CANNOT_COMPUTE_BETA_DENSITY_AT_0_FOR_SOME_ALPHA, alpha, 1, false);
}
return Double.NEGATIVE_INFINITY;
} else if (x == 1) {
if (beta < 1) {
throw new NumberIsTooSmallException(
LocalizedFormats.CANNOT_COMPUTE_BETA_DENSITY_AT_1_FOR_SOME_BETA, beta, 1, false);
}
return Double.NEGATIVE_INFINITY;
} else {
double logX = FastMath.log(x);
double log1mX = FastMath.log1p(-x);
return (alpha - 1) * logX + (beta - 1) * log1mX - z;
}
}
/**
* Compute the error of Stirling's series at the given value.
*
* <p>References:
*
* <ol>
* <li>Eric W. Weisstein. "Stirling's Series." From MathWorld--A Wolfram Web Resource. <a
* target="_blank" href="http://mathworld.wolfram.com/StirlingsSeries.html">
* http://mathworld.wolfram.com/StirlingsSeries.html</a>
* </ol>
*
* @param z the value.
* @return the Striling's series error.
*/
static double getStirlingError(double z) {
double ret;
if (z < 15.0) {
double z2 = 2.0 * z;
if (FastMath.floor(z2) == z2) {
ret = EXACT_STIRLING_ERRORS[(int) z2];
} else {
ret = Gamma.logGamma(z + 1.0) - (z + 0.5) * FastMath.log(z) + z - HALF_LOG_2_PI;
}
} else {
double z2 = z * z;
ret =
(0.083333333333333333333
- (0.00277777777777777777778
- (0.00079365079365079365079365
- (0.000595238095238095238095238
- 0.0008417508417508417508417508 / z2)
/ z2)
/ z2)
/ z2)
/ z;
}
return ret;
}
/**
* Utility class used by various distributions to accurately compute their respective probability
* mass functions. The implementation for this class is based on the Catherine Loader's <a
* target="_blank" href="http://www.herine.net/stat/software/dbinom.html">dbinom</a> routines.
*
* <p>This class is not intended to be called directly.
*
* <p>References:
*
* <ol>
* <li>Catherine Loader (2000). "Fast and Accurate Computation of Binomial Probabilities.". <a
* target="_blank" href="http://www.herine.net/stat/papers/dbinom.pdf">
* http://www.herine.net/stat/papers/dbinom.pdf</a>
* </ol>
*
* @since 2.1
* @version $Id: SaddlePointExpansion.java 1538368 2013-11-03 13:57:37Z erans $
*/
final class SaddlePointExpansion {
/** 1/2 * log(2 π). */
private static final double HALF_LOG_2_PI = 0.5 * FastMath.log(MathUtils.TWO_PI);
/** exact Stirling expansion error for certain values. */
private static final double[] EXACT_STIRLING_ERRORS = {
0.0, /* 0.0 */
0.1534264097200273452913848, /* 0.5 */
0.0810614667953272582196702, /* 1.0 */
0.0548141210519176538961390, /* 1.5 */
0.0413406959554092940938221, /* 2.0 */
0.03316287351993628748511048, /* 2.5 */
0.02767792568499833914878929, /* 3.0 */
0.02374616365629749597132920, /* 3.5 */
0.02079067210376509311152277, /* 4.0 */
0.01848845053267318523077934, /* 4.5 */
0.01664469118982119216319487, /* 5.0 */
0.01513497322191737887351255, /* 5.5 */
0.01387612882307074799874573, /* 6.0 */
0.01281046524292022692424986, /* 6.5 */
0.01189670994589177009505572, /* 7.0 */
0.01110455975820691732662991, /* 7.5 */
0.010411265261972096497478567, /* 8.0 */
0.009799416126158803298389475, /* 8.5 */
0.009255462182712732917728637, /* 9.0 */
0.008768700134139385462952823, /* 9.5 */
0.008330563433362871256469318, /* 10.0 */
0.007934114564314020547248100, /* 10.5 */
0.007573675487951840794972024, /* 11.0 */
0.007244554301320383179543912, /* 11.5 */
0.006942840107209529865664152, /* 12.0 */
0.006665247032707682442354394, /* 12.5 */
0.006408994188004207068439631, /* 13.0 */
0.006171712263039457647532867, /* 13.5 */
0.005951370112758847735624416, /* 14.0 */
0.005746216513010115682023589, /* 14.5 */
0.005554733551962801371038690 /* 15.0 */
};
/** Default constructor. */
private SaddlePointExpansion() {
super();
}
/**
* Compute the error of Stirling's series at the given value.
*
* <p>References:
*
* <ol>
* <li>Eric W. Weisstein. "Stirling's Series." From MathWorld--A Wolfram Web Resource. <a
* target="_blank" href="http://mathworld.wolfram.com/StirlingsSeries.html">
* http://mathworld.wolfram.com/StirlingsSeries.html</a>
* </ol>
*
* @param z the value.
* @return the Striling's series error.
*/
static double getStirlingError(double z) {
double ret;
if (z < 15.0) {
double z2 = 2.0 * z;
if (FastMath.floor(z2) == z2) {
ret = EXACT_STIRLING_ERRORS[(int) z2];
} else {
ret = Gamma.logGamma(z + 1.0) - (z + 0.5) * FastMath.log(z) + z - HALF_LOG_2_PI;
}
} else {
double z2 = z * z;
ret =
(0.083333333333333333333
- (0.00277777777777777777778
- (0.00079365079365079365079365
- (0.000595238095238095238095238
- 0.0008417508417508417508417508 / z2)
/ z2)
/ z2)
/ z2)
/ z;
}
return ret;
}
/**
* A part of the deviance portion of the saddle point approximation.
*
* <p>References:
*
* <ol>
* <li>Catherine Loader (2000). "Fast and Accurate Computation of Binomial Probabilities.". <a
* target="_blank" href="http://www.herine.net/stat/papers/dbinom.pdf">
* http://www.herine.net/stat/papers/dbinom.pdf</a>
* </ol>
*
* @param x the x value.
* @param mu the average.
* @return a part of the deviance.
*/
static double getDeviancePart(double x, double mu) {
double ret;
if (FastMath.abs(x - mu) < 0.1 * (x + mu)) {
double d = x - mu;
double v = d / (x + mu);
double s1 = v * d;
double s = Double.NaN;
double ej = 2.0 * x * v;
v *= v;
int j = 1;
while (s1 != s) {
s = s1;
ej *= v;
s1 = s + ej / ((j * 2) + 1);
++j;
}
ret = s1;
} else {
ret = x * FastMath.log(x / mu) + mu - x;
}
return ret;
}
/**
* Compute the logarithm of the PMF for a binomial distribution using the saddle point expansion.
*
* @param x the value at which the probability is evaluated.
* @param n the number of trials.
* @param p the probability of success.
* @param q the probability of failure (1 - p).
* @return log(p(x)).
*/
static double logBinomialProbability(int x, int n, double p, double q) {
double ret;
if (x == 0) {
if (p < 0.1) {
ret = -getDeviancePart(n, n * q) - n * p;
} else {
ret = n * FastMath.log(q);
}
} else if (x == n) {
if (q < 0.1) {
ret = -getDeviancePart(n, n * p) - n * q;
} else {
ret = n * FastMath.log(p);
}
} else {
ret =
getStirlingError(n)
- getStirlingError(x)
- getStirlingError(n - x)
- getDeviancePart(x, n * p)
- getDeviancePart(n - x, n * q);
double f = (MathUtils.TWO_PI * x * (n - x)) / n;
ret = -0.5 * FastMath.log(f) + ret;
}
return ret;
}
}
/**
* Returns the value of log B(p, q) for 0 ≤ x ≤ 1 and p, q > 0. Based on the <em>NSWC Library of
* Mathematics Subroutines</em> implementation, {@code DBETLN}.
*
* @param p First argument.
* @param q Second argument.
* @return the value of {@code log(Beta(p, q))}, {@code NaN} if {@code p <= 0} or {@code q <= 0}.
*/
public static double logBeta(final double p, final double q) {
if (Double.isNaN(p) || Double.isNaN(q) || (p <= 0.0) || (q <= 0.0)) {
return Double.NaN;
}
final double a = FastMath.min(p, q);
final double b = FastMath.max(p, q);
if (a >= 10.0) {
final double w = sumDeltaMinusDeltaSum(a, b);
final double h = a / b;
final double c = h / (1.0 + h);
final double u = -(a - 0.5) * FastMath.log(c);
final double v = b * FastMath.log1p(h);
if (u <= v) {
return (((-0.5 * FastMath.log(b) + HALF_LOG_TWO_PI) + w) - u) - v;
} else {
return (((-0.5 * FastMath.log(b) + HALF_LOG_TWO_PI) + w) - v) - u;
}
} else if (a > 2.0) {
if (b > 1000.0) {
final int n = (int) FastMath.floor(a - 1.0);
double prod = 1.0;
double ared = a;
for (int i = 0; i < n; i++) {
ared -= 1.0;
prod *= ared / (1.0 + ared / b);
}
return (FastMath.log(prod) - n * FastMath.log(b))
+ (Gamma.logGamma(ared) + logGammaMinusLogGammaSum(ared, b));
} else {
double prod1 = 1.0;
double ared = a;
while (ared > 2.0) {
ared -= 1.0;
final double h = ared / b;
prod1 *= h / (1.0 + h);
}
if (b < 10.0) {
double prod2 = 1.0;
double bred = b;
while (bred > 2.0) {
bred -= 1.0;
prod2 *= bred / (ared + bred);
}
return FastMath.log(prod1)
+ FastMath.log(prod2)
+ (Gamma.logGamma(ared) + (Gamma.logGamma(bred) - logGammaSum(ared, bred)));
} else {
return FastMath.log(prod1) + Gamma.logGamma(ared) + logGammaMinusLogGammaSum(ared, b);
}
}
} else if (a >= 1.0) {
if (b > 2.0) {
if (b < 10.0) {
double prod = 1.0;
double bred = b;
while (bred > 2.0) {
bred -= 1.0;
prod *= bred / (a + bred);
}
return FastMath.log(prod)
+ (Gamma.logGamma(a) + (Gamma.logGamma(bred) - logGammaSum(a, bred)));
} else {
return Gamma.logGamma(a) + logGammaMinusLogGammaSum(a, b);
}
} else {
return Gamma.logGamma(a) + Gamma.logGamma(b) - logGammaSum(a, b);
}
} else {
if (b >= 10.0) {
return Gamma.logGamma(a) + logGammaMinusLogGammaSum(a, b);
} else {
// The following command is the original NSWC implementation.
// return Gamma.logGamma(a) +
// (Gamma.logGamma(b) - Gamma.logGamma(a + b));
// The following command turns out to be more accurate.
return FastMath.log(Gamma.gamma(a) * Gamma.gamma(b) / Gamma.gamma(a + b));
}
}
}
// Qf <- function(par)
private double qF(double[] par, ArrayRealVector y, ArrayRealVector w) {
// fv <- solve((exp(-par[1])*W) + (exp(-par[2])*G),
// (exp(-par[1])*W)%*%as.vector(y))
solver =
new QRDecomposition(
wM.scalarMultiply(FastMath.exp(-par[0]))
.add(gM.scalarMultiply(FastMath.exp(-par[1]))))
.getSolver();
fv = (ArrayRealVector) solver.solve(wM.scalarMultiply(FastMath.exp(-par[0])).operate(y));
// b <- diff(fv, differences=order)
b = MatrixFunctions.diffV(fv, Controls.RW_ORDER);
// DT <- determinant((exp(-par[1])*W) + (exp(-par[2])*G))$modulus
double dt =
new LUD(
wM.scalarMultiply(FastMath.exp(-par[0]))
.add(gM.scalarMultiply(FastMath.exp(-par[1]))))
.getDeterminant();
// f <- -(N/2)*log(2*pi*exp(par[1])) + # 1
tempV = y.subtract(fv);
double f =
-(n / 2) * FastMath.log(2 * FastMath.PI * FastMath.exp(par[0]))
// .5*sum(log(weights))- # 2
+ 0.5 * MatrixFunctions.sumV(MatrixFunctions.logVec(w))
// sum(weights*(y-fv)^2)/(2*exp(par[1])) + # 3
- MatrixFunctions.sumV(w.ebeMultiply(tempV).ebeMultiply(tempV))
/ (2 * FastMath.exp(par[0]))
// (N/2)*log(2*pi) - # 4
+ (n * FastMath.log(2 * FastMath.PI)) / 2
// ((N-order)/2)*log(2*pi*exp(par[2]))- # 5
- ((n - Controls.RW_ORDER) * FastMath.log(2 * FastMath.PI * FastMath.exp(par[1]))) / 2
// sum(b^2)/(2*exp(par[2])) - # 6
- MatrixFunctions.sumV(b.ebeMultiply(b)) / (2 * FastMath.exp(par[1]))
// .5*DT # 7
- 0.5 * dt;
// attributes(f) <- NULL
// Pen <- if (penalty==TRUE) delta[1]*(par[1]-shift[1])^2
// + delta[2]*(par[2]-shift[2])^2 else 0 -f+Pen
double pen = 0;
if (Controls.PENALTY) {
pen =
delta.getEntry(0) * (par[0] - Controls.RW_SHIFT[0]) * (par[0] - Controls.RW_SHIFT[0])
+ delta.getEntry(1)
* (par[1] - Controls.RW_SHIFT[1])
* (par[1] - Controls.RW_SHIFT[1]);
}
// -f+Pen # no penalty yet
return (-f + pen);
}
/**
* The main fitting method of Random Walk smoother.
*
* @param additiveFit - object of AdditiveFit class
* @return residuals
*/
public ArrayRealVector solve(AdditiveFit additiveFit) {
ArrayRealVector y = additiveFit.getZ();
ArrayRealVector w = additiveFit.getW();
int whichDistParameter = additiveFit.getWhichDistParameter();
// if (any(is.na(y)))
for (int i = 0; i < y.getDimension(); i++) {
if (Double.isNaN(y.getEntry(i))) {
// weights[is.na(y)] <- 0
w.setEntry(i, 0.0);
// y[is.na(y)] <- 0
y.setEntry(i, 0.0);
}
}
// N <- length(y)
n = y.getDimension();
// ------------------------------------------------------- BECOMES VERY SLOW HERE
// W <- diag.spam(x=weights) # weights
wM = (BlockRealMatrix) MatrixUtils.createRealDiagonalMatrix(w.getDataRef());
// E <- diag.spam(N)
eM = MatrixFunctions.buildIdentityMatrix(n);
// D <- diff(E, diff = order) # order
dM = MatrixFunctions.diff(eM, Controls.RW_ORDER);
// ------------------------------------------------------- FREEZES HERE
// G <- t(D)%*%D
gM = dM.transpose().multiply(dM);
// logsig2e <- log(sig2e) # getting logs
double logsig2e = FastMath.log(sigmasHash.get(whichDistParameter)[0]);
// logsig2b <- log(sig2b)
double logsig2b = FastMath.log(sigmasHash.get(whichDistParameter)[1]);
// fv <- solve(exp(-logsig2e)*W + exp(-logsig2b)*G,
// (exp(-logsig2e)*W)%*% as.vector(y))
solver =
new QRDecomposition(
wM.scalarMultiply(FastMath.exp(-logsig2e))
.add(gM.scalarMultiply(FastMath.exp(-logsig2b))))
.getSolver();
fv = (ArrayRealVector) solver.solve(wM.scalarMultiply(FastMath.exp(-logsig2e)).operate(y));
// if (sig2e.fix==FALSE && sig2b.fix==FALSE) # both estimated{
if (Controls.SIG2E_FIX) {
if (Controls.SIG2B_FIX) {
// out <- list()
// par <- c(logsig2e, logsig2b)
// out$par <- par
// value.of.Q <- -Qf(par)
valueOfQ = -qF(new double[] {logsig2e, logsig2b}, y, w);
// se <- NULL
se = null;
} else {
// par <- log(sum((D%*%fv)^2)/N)
tempV = (ArrayRealVector) dM.operate(fv);
logsig2b = FastMath.log(MatrixFunctions.sumV(tempV.ebeMultiply(tempV)) / n);
// Qf1 <- function(par) Qf(c(logsig2e, par))
// out<-nlminb(start = par,objective = Qf1,
// lower = c(-20), upper = c(20))
nonLinObj1.setResponse(y);
nonLinObj1.setWeights(w);
nonLinObj1.setLogsig2e(logsig2e);
maxiter = new MaxIter(Controls.BOBYQA_MAX_ITER);
maxeval = new MaxEval(Controls.BOBYQA_MAX_EVAL);
initguess = new InitialGuess(new double[] {logsig2b, logsig2b});
bounds =
new SimpleBounds(
new double[] {-Double.MAX_VALUE, -Double.MAX_VALUE},
new double[] {Double.MAX_VALUE, Double.MAX_VALUE});
obj = new ObjectiveFunction(nonLinObj1);
PointValuePair values =
optimizer.optimize(maxiter, maxeval, obj, initguess, bounds, GoalType.MINIMIZE);
logsig2b = values.getPoint()[0];
if (logsig2b > 20.0) {
logsig2b = 20.0;
valueOfQ = -qF(new double[] {logsig2e, logsig2b}, y, w);
} else if (logsig2b < -20.0) {
logsig2b = -20.0;
valueOfQ = -qF(new double[] {logsig2e, logsig2b}, y, w);
} else {
valueOfQ = -values.getValue();
}
// out$hessian <- optimHess(out$par, Qf1)
// value.of.Q <- -out$objective
// shes <- 1/out$hessian
// se1 <- ifelse(shes>0, sqrt(shes), NA)
// par <- c(logsig2e, out$par)
// names(par) <- c("logsig2e", "logsig2b")
/// out$par <- par
// se <- c(NA, se1)
// System.out.println(logsig2e+" "+logsig2b +" "+qF(new double[]{logsig2e, logsig2b}, y,
// w));
}
} else {
if (Controls.SIG2B_FIX) {
// par <- log(sum(weights*(y-fv)^2)/N)
tempV = y.subtract(fv);
logsig2e = FastMath.log(MatrixFunctions.sumV(tempV.ebeMultiply(tempV).ebeMultiply(w)) / n);
// Qf2 <- function(par) Qf(c(par, logsig2b))
// out <- nlminb(start = par, objective = Qf2,
// lower = c(-20), upper = c(20))
nonLinObj2.setResponse(y);
nonLinObj2.setWeights(w);
nonLinObj2.setLogsig2b(logsig2b);
maxiter = new MaxIter(Controls.BOBYQA_MAX_ITER);
maxeval = new MaxEval(Controls.BOBYQA_MAX_EVAL);
initguess = new InitialGuess(new double[] {logsig2e, logsig2e});
bounds =
new SimpleBounds(
new double[] {-Double.MAX_VALUE, -Double.MAX_VALUE},
new double[] {Double.MAX_VALUE, Double.MAX_VALUE});
obj = new ObjectiveFunction(nonLinObj2);
PointValuePair values =
optimizer.optimize(maxiter, maxeval, obj, initguess, bounds, GoalType.MINIMIZE);
logsig2e = values.getPoint()[0];
// out$hessian <- optimHess(out$par, Qf2)
// value.of.Q <- -out$objective
if (logsig2e > 20.0) {
logsig2e = 20.0;
valueOfQ = -qF(new double[] {logsig2e, logsig2b}, y, w);
} else if (logsig2e < -20.0) {
logsig2e = -20.0;
valueOfQ = -qF(new double[] {logsig2e, logsig2b}, y, w);
} else {
valueOfQ = -values.getValue();
}
// shes <- 1/out$hessian
// se1 <- ifelse(shes>0, sqrt(shes), NA)
// par <- c( out$par, logsig2b)
// names(par) <- c("logsig2e", "logsig2b")
// out$par <- par
// se <- c(se1, NA)
// System.out.println(logsig2e+" "+logsig2b +" "+qF(new double[]{logsig2e, logsig2b}, y,
// w));
} else {
// par <- c(logsig2e <- log(sum(weights*(y-fv)^2)/N),
// logsig2b <-log(sum((D%*%fv)^2)/N))
tempV = y.subtract(fv);
logsig2e = FastMath.log(MatrixFunctions.sumV(w.ebeMultiply(tempV).ebeMultiply(tempV)) / n);
tempV = (ArrayRealVector) dM.operate(fv);
logsig2b = FastMath.log(MatrixFunctions.sumV(tempV.ebeMultiply(tempV)) / n);
nonLinObj.setResponse(y);
nonLinObj.setWeights(w);
// out <- nlminb(start = par, objective = Qf,
// lower = c(-20, -20), upper = c(20, 20))
maxiter = new MaxIter(Controls.BOBYQA_MAX_ITER);
maxeval = new MaxEval(Controls.BOBYQA_MAX_EVAL);
initguess = new InitialGuess(new double[] {logsig2e, logsig2b});
bounds = new SimpleBounds(new double[] {-20.0, -20.0}, new double[] {20.0, 20.0});
obj = new ObjectiveFunction(nonLinObj);
PointValuePair values =
optimizer.optimize(maxiter, maxeval, obj, initguess, bounds, GoalType.MINIMIZE);
logsig2e = values.getPoint()[0];
logsig2b = values.getPoint()[1];
// System.out.println(logsig2e+" "+logsig2b +" "+values.getValue());
// out$hessian <- optimHess(out$par, Qf) !!! Missing in Java
// value.of.Q <- -out$objective
valueOfQ = -values.getValue();
// shes <- try(solve(out$hessian))
// se <- if (any(class(shes)%in%"try-error"))
// rep(NA_real_,2) else sqrt(diag(shes))
}
}
// fv <- solve(exp(-out$par[1])*W + exp(-out$par[2])*G,
// (exp(-out$par[1])*W)%*% as.vector(y))
solver =
new QRDecomposition(
wM.scalarMultiply(FastMath.exp(-logsig2e))
.add(gM.scalarMultiply(FastMath.exp(-logsig2b))))
.getSolver();
fv = (ArrayRealVector) solver.solve(wM.scalarMultiply(FastMath.exp(-logsig2e)).operate(y));
sigmasHash.put(
whichDistParameter, new Double[] {FastMath.exp(logsig2e), FastMath.exp(logsig2b)});
// b <- diff(fv, differences=order)
b = MatrixFunctions.diffV(fv, Controls.RW_ORDER);
// tr1 <- order + sum(b^2)/(exp(out$par[2])) # this always right
// attributes(tr1) <- NULL
double tr1 =
Controls.RW_ORDER + MatrixFunctions.sumV(b.ebeMultiply(b)) / (FastMath.exp(logsig2b));
tempV = null;
return y.subtract(fv);
}
/*
* Builtin functions with two inputs
*/
public double execute(double in1, double in2) throws DMLRuntimeException {
switch (bFunc) {
/*
* Arithmetic relational operators (==, !=, <=, >=) must be instead of
* <code>Double.compare()</code> due to the inconsistencies in the way
* NaN and -0.0 are handled. The behavior of methods in
* <code>Double</code> class are designed mainly to make Java
* collections work properly. For more details, see the help for
* <code>Double.equals()</code> and <code>Double.comapreTo()</code>.
*/
case MAX:
case CUMMAX:
// return (Double.compare(in1, in2) >= 0 ? in1 : in2);
return (in1 >= in2 ? in1 : in2);
case MIN:
case CUMMIN:
// return (Double.compare(in1, in2) <= 0 ? in1 : in2);
return (in1 <= in2 ? in1 : in2);
// *** HACK ALERT *** HACK ALERT *** HACK ALERT ***
// rowIndexMax() and its siblings require comparing four values, but
// the aggregation API only allows two values. So the execute()
// method receives as its argument the two cell values to be
// compared and performs just the value part of the comparison. We
// return an integer cast down to a double, since the aggregation
// API doesn't have any way to return anything but a double. The
// integer returned takes on three posssible values: //
// . 0 => keep the index associated with in1 //
// . 1 => use the index associated with in2 //
// . 2 => use whichever index is higher (tie in value) //
case MAXINDEX:
if (in1 == in2) {
return 2;
} else if (in1 > in2) {
return 1;
} else { // in1 < in2
return 0;
}
case MININDEX:
if (in1 == in2) {
return 2;
} else if (in1 < in2) {
return 1;
} else { // in1 > in2
return 0;
}
// *** END HACK ***
case LOG:
// if ( in1 <= 0 )
// throw new DMLRuntimeException("Builtin.execute(): logarithm can be computed only for
// non-negative numbers.");
if (FASTMATH) return (FastMath.log(in1) / FastMath.log(in2));
else return (Math.log(in1) / Math.log(in2));
case LOG_NZ:
if (FASTMATH) return (in1 == 0) ? 0 : (FastMath.log(in1) / FastMath.log(in2));
else return (in1 == 0) ? 0 : (Math.log(in1) / Math.log(in2));
default:
throw new DMLRuntimeException("Builtin.execute(): Unknown operation: " + bFunc);
}
}
