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#exp()
*/
public RandomVariable exp() {
if (isDeterministic()) {
double newValueIfNonStochastic = FastMath.exp(valueIfNonStochastic);
return new RandomVariable(time, newValueIfNonStochastic);
} else {
double[] newRealizations = new double[realizations.length];
for (int i = 0; i < newRealizations.length; i++)
newRealizations[i] = FastMath.exp(realizations[i]);
return new RandomVariable(time, newRealizations);
}
}
public double value(double[] x) {
double f = 0;
double res2 = 0;
double fac = 0;
for (int i = 0; i < x.length; ++i) {
fac = FastMath.pow(axisratio, (i - 1.) / (x.length - 1.));
f += fac * fac * x[i] * x[i];
res2 += FastMath.cos(2. * FastMath.PI * fac * x[i]);
}
f =
(20.
- 20. * FastMath.exp(-0.2 * FastMath.sqrt(f / x.length))
+ FastMath.exp(1.)
- FastMath.exp(res2 / x.length));
return f;
}
@Override
public IComplexNumber exp() {
IComplexNumber result = dup();
double realExp = FastMath.exp(realComponent());
return result.set(
realExp * FastMath.cos(imaginaryComponent()), realExp * FastMath.sin(imaginaryComponent()));
}
/**
* Computes the value of the gradient at {@code x}. The components of the gradient vector are
* the partial derivatives of the function with respect to each of the <em>parameters</em>
* (lower asymptote and higher asymptote).
*
* @param x Value at which the gradient must be computed.
* @param param Values for lower asymptote and higher asymptote.
* @return the gradient vector at {@code x}.
* @throws NullArgumentException if {@code param} is {@code null}.
* @throws DimensionMismatchException if the size of {@code param} is not 2.
*/
public double[] gradient(double x, double... param) {
validateParameters(param);
final double invExp1 = 1 / (1 + FastMath.exp(-x));
return new double[] {1 - invExp1, invExp1};
}
public double[] initExpTable() {
double[] expTable = new double[1000];
for (int i = 0; i < expTable.length; i++) {
double tmp = FastMath.exp((i / (double) expTable.length * 2 - 1) * MAX_EXP);
expTable[i] = tmp / (tmp + 1.0);
}
return expTable;
}
/**
* Compute the <a href="http://mathworld.wolfram.com/ExponentialFunction.html" TARGET="_top">
* exponential function</a> of this complex number. Implements the formula:
*
* <pre>
* <code>
* exp(a + bi) = exp(a)cos(b) + exp(a)sin(b)i
* </code>
* </pre>
*
* where the (real) functions on the right-hand side are {@link java.lang.Math#exp}, {@link
* java.lang.Math#cos}, and {@link java.lang.Math#sin}. <br>
* Returns {@link Complex#NaN} if either real or imaginary part of the input argument is {@code
* NaN}. <br>
* Infinite 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>
* exp(1 ± INFINITY i) = NaN + NaN i
* exp(INFINITY + i) = INFINITY + INFINITY i
* exp(-INFINITY + i) = 0 + 0i
* exp(±INFINITY ± INFINITY i) = NaN + NaN i
* </code>
* </pre>
*
* @return <code><i>e</i><sup>this</sup></code>.
* @since 1.2
*/
public Complex exp() {
if (isNaN) {
return NaN;
}
double expReal = FastMath.exp(real);
return createComplex(expReal * FastMath.cos(imaginary), expReal * FastMath.sin(imaginary));
}
private static IComplexNumber sigmoidDeriv(IComplexNumber number) {
double arg = number.complexArgument().doubleValue();
double sigArg = 1 / 1 + (FastMath.exp(-arg)) - 1 + .5f;
double ret = Math.exp(sigArg);
IComplexDouble sigmoid = Nd4j.createDouble(ret, 0);
IComplexNumber oneMinus = Nd4j.createComplexNumber(1, 1).subi(sigmoid);
return sigmoid.mul(oneMinus);
}
private static double sigmoidDeriv(double input) {
double sigmoid = 1 / (1 + FastMath.exp(-input));
double out = sigmoid * (1.0 - sigmoid);
if (Nd4j.ENFORCE_NUMERICAL_STABILITY && (Double.isNaN(out) || Double.isInfinite(out))) {
out = Nd4j.EPS_THRESHOLD;
}
return out;
}
/**
* {@inheritDoc}
*
* <p>The implementation of this method is based on:
*
* <ul>
* <li><a href="http://mathworld.wolfram.com/ExponentialDistribution.html">Exponential
* Distribution</a>, equation (1).
* </ul>
*/
public double cumulativeProbability(double x) {
double ret;
if (x <= 0.0) {
ret = 0.0;
} else {
ret = 1.0 - FastMath.exp(-x / mean);
}
return ret;
}
/**
* Return the discount factor within a given model context for a given maturity.
*
* @param model The model used as a context (not required for this class).
* @param maturity The maturity in terms of ACT/365 daycount form this curve reference date. Note
* that this parameter might get rescaled to a different time parameter.
* @see
* net.finmath.marketdata.model.curves.DiscountCurveInterface#getDiscountFactor(net.finmath.marketdata.model.AnalyticModelInterface,
* double)
*/
@Override
public double getDiscountFactor(AnalyticModelInterface model, double maturity) {
// Change time scale
maturity *= timeScaling;
double beta1 = parameter[0];
double beta2 = parameter[1];
double beta3 = parameter[2];
double beta4 = parameter[3];
double tau1 = parameter[4];
double tau2 = parameter[5];
double x1 = tau1 > 0 ? FastMath.exp(-maturity / tau1) : 0.0;
double x2 = tau2 > 0 ? FastMath.exp(-maturity / tau2) : 0.0;
double y1 = tau1 > 0 ? (maturity > 0.0 ? (1.0 - x1) / maturity * tau1 : 1.0) : 0.0;
double y2 = tau2 > 0 ? (maturity > 0.0 ? (1.0 - x2) / maturity * tau2 : 1.0) : 0.0;
double zeroRate = beta1 + beta2 * y1 + beta3 * (y1 - x1) + beta4 * (y2 - x2);
return Math.exp(-zeroRate * maturity);
}
/**
* Returns te probability of each of the key items in the pattern. It leverages Poisson
* distribution. See the paper for its description
*
* @param distribution
* @param pattern
* @return
*/
private double getProb(Distribution distribution, Map<Integer, Integer> pattern) {
return pattern
.entrySet()
.stream()
.mapToDouble(
e -> {
double l = distribution.getFreq(e.getKey()) / capwidth;
return l == 0
? 0
: (new PoissonDistribution(l).probability(e.getValue()) / FastMath.exp(-l));
})
.reduce((a, b) -> (a * b))
.getAsDouble();
}
/**
* 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;
}
/** {@inheritDoc} */
public double probability(int x) {
double ret;
int[] domain = getDomain(populationSize, numberOfSuccesses, sampleSize);
if (x < domain[0] || x > domain[1]) {
ret = 0.0;
} else {
double p = (double) sampleSize / (double) populationSize;
double q = (double) (populationSize - sampleSize) / (double) populationSize;
double p1 = SaddlePointExpansion.logBinomialProbability(x, numberOfSuccesses, p, q);
double p2 =
SaddlePointExpansion.logBinomialProbability(
sampleSize - x, populationSize - numberOfSuccesses, p, q);
double p3 = SaddlePointExpansion.logBinomialProbability(sampleSize, populationSize, p, q);
ret = FastMath.exp(p1 + p2 - p3);
}
return ret;
}
@GenerateMicroBenchmark
public void commonsmath(BlackHole hole) {
for (int i = 0; i < data.length - 1; i++) hole.consume(FastMath.exp(data[i]));
}
@Override
public float op(float origin) {
return (float) FastMath.exp(origin);
}
@Override
public double op(double origin) {
return FastMath.exp(origin);
}
/** {@inheritDoc} */
public double density(double x) {
final double logDensity = logDensity(x);
return logDensity == Double.NEGATIVE_INFINITY ? 0 : FastMath.exp(logDensity);
}
@Test
public void testMissedEndEvent()
throws DimensionMismatchException, NumberIsTooSmallException, MaxCountExceededException,
NoBracketingException {
final double t0 = 1878250320.0000029;
final double tEvent = 1878250379.9999986;
final double[] k = {1.0e-4, 1.0e-5, 1.0e-6};
FirstOrderDifferentialEquations ode =
new FirstOrderDifferentialEquations() {
public int getDimension() {
return k.length;
}
public void computeDerivatives(double t, double[] y, double[] yDot) {
for (int i = 0; i < y.length; ++i) {
yDot[i] = k[i] * y[i];
}
}
};
DormandPrince853Integrator integrator =
new DormandPrince853Integrator(
0.0, 100.0,
1.0e-10, 1.0e-10);
double[] y0 = new double[k.length];
for (int i = 0; i < y0.length; ++i) {
y0[i] = i + 1;
}
double[] y = new double[k.length];
integrator.setInitialStepSize(60.0);
double finalT = integrator.integrate(ode, t0, y0, tEvent, y);
Assert.assertEquals(tEvent, finalT, 5.0e-6);
for (int i = 0; i < y.length; ++i) {
Assert.assertEquals(y0[i] * FastMath.exp(k[i] * (finalT - t0)), y[i], 1.0e-9);
}
integrator.setInitialStepSize(60.0);
integrator.addEventHandler(
new EventHandler() {
public void init(double t0, double[] y0, double t) {}
public void resetState(double t, double[] y) {}
public double g(double t, double[] y) {
return t - tEvent;
}
public Action eventOccurred(double t, double[] y, boolean increasing) {
Assert.assertEquals(tEvent, t, 5.0e-6);
return Action.CONTINUE;
}
},
Double.POSITIVE_INFINITY,
1.0e-20,
100);
finalT = integrator.integrate(ode, t0, y0, tEvent + 120, y);
Assert.assertEquals(tEvent + 120, finalT, 5.0e-6);
for (int i = 0; i < y.length; ++i) {
Assert.assertEquals(y0[i] * FastMath.exp(k[i] * (finalT - t0)), y[i], 1.0e-9);
}
}
/**
* 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$par[2])) # this always right
// attributes(tr1) <- NULL
double tr1 =
Controls.RW_ORDER + MatrixFunctions.sumV(b.ebeMultiply(b)) / (FastMath.exp(logsig2b));
tempV = null;
return y.subtract(fv);
}
// Qf <- function(par)
private double qF(double[] par, ArrayRealVector y, ArrayRealVector w) {
// fv <- solve((exp(-par[1])*W) + (exp(-par[2])*G),
// (exp(-par[1])*W)%*%as.vector(y))
solver =
new QRDecomposition(
wM.scalarMultiply(FastMath.exp(-par[0]))
.add(gM.scalarMultiply(FastMath.exp(-par[1]))))
.getSolver();
fv = (ArrayRealVector) solver.solve(wM.scalarMultiply(FastMath.exp(-par[0])).operate(y));
// b <- diff(fv, differences=order)
b = MatrixFunctions.diffV(fv, Controls.RW_ORDER);
// DT <- determinant((exp(-par[1])*W) + (exp(-par[2])*G))$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);
}
/**
* Returns the exp of a complex number: Let r be the real component and i be the imaginary Let ret
* be the complex number returned ret -> exp(r) * cos(i), exp(r) * sin(i) where the first number
* is the real component and the second number is the imaginary component
*
* @param d the number to getFromOrigin the exp of
* @return the exponential of this complex number
*/
public static ComplexDouble exp(ComplexDouble d) {
return new ComplexDouble(
FastMath.exp(d.real()) * FastMath.cos(d.imag()),
FastMath.exp(d.real()) * FastMath.sin(d.imag()));
}
/**
* Returns the exp of a complex number: Let r be the real component and i be the imaginary Let ret
* be the complex number returned ret -> exp(r) * cos(i), exp(r) * sin(i) where the first number
* is the real component and the second number is the imaginary component
*
* @param d the number to getFromOrigin the exp of
* @return the exponential of this complex number
*/
public static ComplexFloat exp(ComplexFloat d) {
return new ComplexFloat(
(float) FastMath.exp(d.real()) * (float) FastMath.cos(d.imag()),
(float) FastMath.exp(d.real()) * (float) FastMath.sin(d.imag()));
}
/**
* For this distribution, {@code X}, defined by the given hypergeometric distribution parameters,
* this method returns {@code P(X = x)}.
*
* @param x Value at which the PMF is evaluated.
* @param n the population size.
* @param m number of successes in the population.
* @param k the sample size.
* @return PMF for the distribution.
*/
private double probability(int n, int m, int k, int x) {
return FastMath.exp(
ArithmeticUtils.binomialCoefficientLog(m, x)
+ ArithmeticUtils.binomialCoefficientLog(n - m, k - x)
- ArithmeticUtils.binomialCoefficientLog(n, k));
}
/** {@inheritDoc} */
public double density(double x) {
final double x0 = x - mean;
final double x1 = x0 / standardDeviation;
return FastMath.exp(-0.5 * x1 * x1) / (standardDeviation * SQRT2PI);
}
/**
* @param x Value at which to compute the sigmoid.
* @param lo Lower asymptote.
* @param hi Higher asymptote.
* @return the value of the sigmoid function at {@code x}.
*/
private static double value(double x, double lo, double hi) {
return lo + (hi - lo) / (1 + FastMath.exp(-x));
}
/** {@inheritDoc} */
public double density(double x) {
if (x < 0) {
return 0;
}
return FastMath.exp(-x / mean) / mean;
}
/** As described by Bernt Arne Ødegaard in Financial Numerical Recipes in C++. */
private static double f(double x, double y, double aprime, double bprime, double c) {
return FastMath.exp(
aprime * (2d * x - aprime)
+ bprime * (2d * y - bprime)
+ 2d * c * (x - aprime) * (y - bprime));
}
