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finmath/finmath-lib
src/main/java/net/finmath/marketdata/model/curves/DiscountCurveInterpolation.java
DiscountCurveInterpolation.getZeroRates
public double[] getZeroRates(double[] maturities) { double[] values = new double[maturities.length]; for(int i=0; i<maturities.length; i++) { values[i] = getZeroRate(maturities[i]); } return values; }
java
public double[] getZeroRates(double[] maturities) { double[] values = new double[maturities.length]; for(int i=0; i<maturities.length; i++) { values[i] = getZeroRate(maturities[i]); } return values; }
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Returns the zero rates for a given vector maturities. @param maturities The given maturities. @return The zero rates.
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a3c067d52dd33feb97d851df6cab130e4116759f
https://github.com/finmath/finmath-lib/blob/a3c067d52dd33feb97d851df6cab130e4116759f/src/main/java/net/finmath/marketdata/model/curves/DiscountCurveInterpolation.java#L427-L436
train
finmath/finmath-lib
src/main/java/net/finmath/functions/LinearAlgebra.java
LinearAlgebra.invert
public static double[][] invert(double[][] matrix) { if(isSolverUseApacheCommonsMath) { // Use LU from common math LUDecomposition lu = new LUDecomposition(new Array2DRowRealMatrix(matrix)); double[][] matrixInverse = lu.getSolver().getInverse().getData(); return matrixInverse; } else { return org.jblas.Solve.pinv(new org.jblas.DoubleMatrix(matrix)).toArray2(); } }
java
public static double[][] invert(double[][] matrix) { if(isSolverUseApacheCommonsMath) { // Use LU from common math LUDecomposition lu = new LUDecomposition(new Array2DRowRealMatrix(matrix)); double[][] matrixInverse = lu.getSolver().getInverse().getData(); return matrixInverse; } else { return org.jblas.Solve.pinv(new org.jblas.DoubleMatrix(matrix)).toArray2(); } }
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Returns the inverse of a given matrix. @param matrix A matrix given as double[n][n]. @return The inverse of the given matrix.
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a3c067d52dd33feb97d851df6cab130e4116759f
https://github.com/finmath/finmath-lib/blob/a3c067d52dd33feb97d851df6cab130e4116759f/src/main/java/net/finmath/functions/LinearAlgebra.java#L267-L279
train
finmath/finmath-lib
src/main/java/net/finmath/functions/LinearAlgebra.java
LinearAlgebra.factorReductionUsingCommonsMath
public static double[][] factorReductionUsingCommonsMath(double[][] correlationMatrix, int numberOfFactors) { // Extract factors corresponding to the largest eigenvalues double[][] factorMatrix = getFactorMatrix(correlationMatrix, numberOfFactors); // Renormalize rows for (int row = 0; row < correlationMatrix.length; row++) { double sumSquared = 0; for (int factor = 0; factor < numberOfFactors; factor++) { sumSquared += factorMatrix[row][factor] * factorMatrix[row][factor]; } if(sumSquared != 0) { for (int factor = 0; factor < numberOfFactors; factor++) { factorMatrix[row][factor] = factorMatrix[row][factor] / Math.sqrt(sumSquared); } } else { // This is a rare case: The factor reduction of a completely decorrelated system to 1 factor for (int factor = 0; factor < numberOfFactors; factor++) { factorMatrix[row][factor] = 1.0; } } } // Orthogonalized again double[][] reducedCorrelationMatrix = (new Array2DRowRealMatrix(factorMatrix).multiply(new Array2DRowRealMatrix(factorMatrix).transpose())).getData(); return getFactorMatrix(reducedCorrelationMatrix, numberOfFactors); }
java
public static double[][] factorReductionUsingCommonsMath(double[][] correlationMatrix, int numberOfFactors) { // Extract factors corresponding to the largest eigenvalues double[][] factorMatrix = getFactorMatrix(correlationMatrix, numberOfFactors); // Renormalize rows for (int row = 0; row < correlationMatrix.length; row++) { double sumSquared = 0; for (int factor = 0; factor < numberOfFactors; factor++) { sumSquared += factorMatrix[row][factor] * factorMatrix[row][factor]; } if(sumSquared != 0) { for (int factor = 0; factor < numberOfFactors; factor++) { factorMatrix[row][factor] = factorMatrix[row][factor] / Math.sqrt(sumSquared); } } else { // This is a rare case: The factor reduction of a completely decorrelated system to 1 factor for (int factor = 0; factor < numberOfFactors; factor++) { factorMatrix[row][factor] = 1.0; } } } // Orthogonalized again double[][] reducedCorrelationMatrix = (new Array2DRowRealMatrix(factorMatrix).multiply(new Array2DRowRealMatrix(factorMatrix).transpose())).getData(); return getFactorMatrix(reducedCorrelationMatrix, numberOfFactors); }
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Returns a correlation matrix which has rank &lt; n and for which the first n factors agree with the factors of correlationMatrix. @param correlationMatrix The given correlation matrix. @param numberOfFactors The requested number of factors (Eigenvectors). @return Factor reduced correlation matrix.
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a3c067d52dd33feb97d851df6cab130e4116759f
https://github.com/finmath/finmath-lib/blob/a3c067d52dd33feb97d851df6cab130e4116759f/src/main/java/net/finmath/functions/LinearAlgebra.java#L438-L466
train
finmath/finmath-lib
src/main/java/net/finmath/functions/LinearAlgebra.java
LinearAlgebra.pseudoInverse
public static double[][] pseudoInverse(double[][] matrix){ if(isSolverUseApacheCommonsMath) { // Use LU from common math SingularValueDecomposition svd = new SingularValueDecomposition(new Array2DRowRealMatrix(matrix)); double[][] matrixInverse = svd.getSolver().getInverse().getData(); return matrixInverse; } else { return org.jblas.Solve.pinv(new org.jblas.DoubleMatrix(matrix)).toArray2(); } }
java
public static double[][] pseudoInverse(double[][] matrix){ if(isSolverUseApacheCommonsMath) { // Use LU from common math SingularValueDecomposition svd = new SingularValueDecomposition(new Array2DRowRealMatrix(matrix)); double[][] matrixInverse = svd.getSolver().getInverse().getData(); return matrixInverse; } else { return org.jblas.Solve.pinv(new org.jblas.DoubleMatrix(matrix)).toArray2(); } }
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Pseudo-Inverse of a matrix calculated in the least square sense. @param matrix The given matrix A. @return pseudoInverse The pseudo-inverse matrix P, such that A*P*A = A and P*A*P = P
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a3c067d52dd33feb97d851df6cab130e4116759f
https://github.com/finmath/finmath-lib/blob/a3c067d52dd33feb97d851df6cab130e4116759f/src/main/java/net/finmath/functions/LinearAlgebra.java#L524-L535
train
finmath/finmath-lib
src/main/java/net/finmath/functions/LinearAlgebra.java
LinearAlgebra.diag
public static double[][] diag(double[] vector){ // Note: According to the Java Language spec, an array is initialized with the default value, here 0. double[][] diagonalMatrix = new double[vector.length][vector.length]; for(int index = 0; index < vector.length; index++) { diagonalMatrix[index][index] = vector[index]; } return diagonalMatrix; }
java
public static double[][] diag(double[] vector){ // Note: According to the Java Language spec, an array is initialized with the default value, here 0. double[][] diagonalMatrix = new double[vector.length][vector.length]; for(int index = 0; index < vector.length; index++) { diagonalMatrix[index][index] = vector[index]; } return diagonalMatrix; }
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Generates a diagonal matrix with the input vector on its diagonal @param vector The given matrix A. @return diagonalMatrix The matrix with the vectors entries on its diagonal
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a3c067d52dd33feb97d851df6cab130e4116759f
https://github.com/finmath/finmath-lib/blob/a3c067d52dd33feb97d851df6cab130e4116759f/src/main/java/net/finmath/functions/LinearAlgebra.java#L543-L553
train
finmath/finmath-lib
src/main/java/net/finmath/fouriermethod/calibration/CalibratedModel.java
CalibratedModel.formatTargetValuesForOptimizer
private double[] formatTargetValuesForOptimizer() { //Put all values in an array for the optimizer. int numberOfMaturities = surface.getMaturities().length; double mats[] = surface.getMaturities(); ArrayList<Double> vals = new ArrayList<Double>(); for(int t = 0; t<numberOfMaturities; t++) { double mat = mats[t]; double[] myStrikes = surface.getSurface().get(mat).getStrikes(); OptionSmileData smileOfInterest = surface.getSurface().get(mat); for(int k = 0; k < myStrikes.length; k++) { vals.add(smileOfInterest.getSmile().get(myStrikes[k]).getValue()); } } Double[] targetVals = new Double[vals.size()]; return ArrayUtils.toPrimitive(vals.toArray(targetVals)); }
java
private double[] formatTargetValuesForOptimizer() { //Put all values in an array for the optimizer. int numberOfMaturities = surface.getMaturities().length; double mats[] = surface.getMaturities(); ArrayList<Double> vals = new ArrayList<Double>(); for(int t = 0; t<numberOfMaturities; t++) { double mat = mats[t]; double[] myStrikes = surface.getSurface().get(mat).getStrikes(); OptionSmileData smileOfInterest = surface.getSurface().get(mat); for(int k = 0; k < myStrikes.length; k++) { vals.add(smileOfInterest.getSmile().get(myStrikes[k]).getValue()); } } Double[] targetVals = new Double[vals.size()]; return ArrayUtils.toPrimitive(vals.toArray(targetVals)); }
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This is a service method that takes care of putting al the target values in a single array. @return
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a3c067d52dd33feb97d851df6cab130e4116759f
https://github.com/finmath/finmath-lib/blob/a3c067d52dd33feb97d851df6cab130e4116759f/src/main/java/net/finmath/fouriermethod/calibration/CalibratedModel.java#L157-L177
train
finmath/finmath-lib
src/main/java/net/finmath/marketdata2/model/curves/ForwardCurveInterpolation.java
ForwardCurveInterpolation.createForwardCurveFromDiscountFactors
public static ForwardCurveInterpolation createForwardCurveFromDiscountFactors(String name, double[] times, RandomVariable[] givenDiscountFactors, double paymentOffset) { ForwardCurveInterpolation forwardCurveInterpolation = new ForwardCurveInterpolation(name, paymentOffset, InterpolationEntityForward.FORWARD, null); if(times.length == 0) { throw new IllegalArgumentException("Vector of times must not be empty."); } if(times[0] > 0) { // Add first forward RandomVariable forward = givenDiscountFactors[0].sub(1.0).pow(-1.0).div(times[0]); forwardCurveInterpolation.addForward(null, 0.0, forward, true); } for(int timeIndex=0; timeIndex<times.length-1;timeIndex++) { RandomVariable forward = givenDiscountFactors[timeIndex].div(givenDiscountFactors[timeIndex+1].sub(1.0)).div(times[timeIndex+1] - times[timeIndex]); double fixingTime = times[timeIndex]; boolean isParameter = (fixingTime > 0); forwardCurveInterpolation.addForward(null, fixingTime, forward, isParameter); } return forwardCurveInterpolation; }
java
public static ForwardCurveInterpolation createForwardCurveFromDiscountFactors(String name, double[] times, RandomVariable[] givenDiscountFactors, double paymentOffset) { ForwardCurveInterpolation forwardCurveInterpolation = new ForwardCurveInterpolation(name, paymentOffset, InterpolationEntityForward.FORWARD, null); if(times.length == 0) { throw new IllegalArgumentException("Vector of times must not be empty."); } if(times[0] > 0) { // Add first forward RandomVariable forward = givenDiscountFactors[0].sub(1.0).pow(-1.0).div(times[0]); forwardCurveInterpolation.addForward(null, 0.0, forward, true); } for(int timeIndex=0; timeIndex<times.length-1;timeIndex++) { RandomVariable forward = givenDiscountFactors[timeIndex].div(givenDiscountFactors[timeIndex+1].sub(1.0)).div(times[timeIndex+1] - times[timeIndex]); double fixingTime = times[timeIndex]; boolean isParameter = (fixingTime > 0); forwardCurveInterpolation.addForward(null, fixingTime, forward, isParameter); } return forwardCurveInterpolation; }
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Create a forward curve from given times and discount factors. The forward curve will have times.length-1 fixing times from times[0] to times[times.length-2] <code> forward[timeIndex] = (givenDiscountFactors[timeIndex]/givenDiscountFactors[timeIndex+1]-1.0) / (times[timeIndex+1] - times[timeIndex]); </code> Note: If time[0] &gt; 0, then the discount factor 1.0 will inserted at time 0.0 @param name The name of this curve. @param times A vector of given time points. @param givenDiscountFactors A vector of given discount factors (corresponding to the given time points). @param paymentOffset The maturity of the underlying index modeled by this curve. @return A new ForwardCurve object.
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a3c067d52dd33feb97d851df6cab130e4116759f
https://github.com/finmath/finmath-lib/blob/a3c067d52dd33feb97d851df6cab130e4116759f/src/main/java/net/finmath/marketdata2/model/curves/ForwardCurveInterpolation.java#L261-L282
train
finmath/finmath-lib
src/main/java/net/finmath/marketdata2/model/curves/ForwardCurveInterpolation.java
ForwardCurveInterpolation.createForwardCurveFromMonteCarloLiborModel
public static ForwardCurveInterpolation createForwardCurveFromMonteCarloLiborModel(String name, LIBORModelMonteCarloSimulationModel model, double startTime) throws CalculationException{ int timeIndex = model.getTimeIndex(startTime); // Get all Libors at timeIndex which are not yet fixed (others null) and times for the timeDiscretizationFromArray of the curves ArrayList<RandomVariable> liborsAtTimeIndex = new ArrayList<>(); int firstLiborIndex = model.getLiborPeriodDiscretization().getTimeIndexNearestGreaterOrEqual(startTime); double firstLiborTime = model.getLiborPeriodDiscretization().getTime(firstLiborIndex); if(firstLiborTime>startTime) { liborsAtTimeIndex.add(model.getLIBOR(startTime, startTime, firstLiborTime)); } // Vector of times for the forward curve double[] times = new double[firstLiborTime==startTime ? (model.getNumberOfLibors()-firstLiborIndex) : (model.getNumberOfLibors()-firstLiborIndex+1)]; times[0]=0; int indexOffset = firstLiborTime==startTime ? 0 : 1; for(int i=firstLiborIndex;i<model.getNumberOfLibors();i++) { liborsAtTimeIndex.add(model.getLIBOR(timeIndex,i)); times[i-firstLiborIndex+indexOffset]=model.getLiborPeriodDiscretization().getTime(i)-startTime; } RandomVariable[] libors = liborsAtTimeIndex.toArray(new RandomVariable[liborsAtTimeIndex.size()]); return ForwardCurveInterpolation.createForwardCurveFromForwards(name, times, libors, model.getLiborPeriodDiscretization().getTimeStep(firstLiborIndex)); }
java
public static ForwardCurveInterpolation createForwardCurveFromMonteCarloLiborModel(String name, LIBORModelMonteCarloSimulationModel model, double startTime) throws CalculationException{ int timeIndex = model.getTimeIndex(startTime); // Get all Libors at timeIndex which are not yet fixed (others null) and times for the timeDiscretizationFromArray of the curves ArrayList<RandomVariable> liborsAtTimeIndex = new ArrayList<>(); int firstLiborIndex = model.getLiborPeriodDiscretization().getTimeIndexNearestGreaterOrEqual(startTime); double firstLiborTime = model.getLiborPeriodDiscretization().getTime(firstLiborIndex); if(firstLiborTime>startTime) { liborsAtTimeIndex.add(model.getLIBOR(startTime, startTime, firstLiborTime)); } // Vector of times for the forward curve double[] times = new double[firstLiborTime==startTime ? (model.getNumberOfLibors()-firstLiborIndex) : (model.getNumberOfLibors()-firstLiborIndex+1)]; times[0]=0; int indexOffset = firstLiborTime==startTime ? 0 : 1; for(int i=firstLiborIndex;i<model.getNumberOfLibors();i++) { liborsAtTimeIndex.add(model.getLIBOR(timeIndex,i)); times[i-firstLiborIndex+indexOffset]=model.getLiborPeriodDiscretization().getTime(i)-startTime; } RandomVariable[] libors = liborsAtTimeIndex.toArray(new RandomVariable[liborsAtTimeIndex.size()]); return ForwardCurveInterpolation.createForwardCurveFromForwards(name, times, libors, model.getLiborPeriodDiscretization().getTimeStep(firstLiborIndex)); }
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Create a forward curve from forwards given by a LIBORMonteCarloModel. @param name name of the forward curve. @param model Monte Carlo model providing the forwards. @param startTime time at which the curve starts, i.e. zero time for the curve @return a forward curve from forwards given by a LIBORMonteCarloModel. @throws CalculationException Thrown if the model failed to provide the forward rates.
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a3c067d52dd33feb97d851df6cab130e4116759f
https://github.com/finmath/finmath-lib/blob/a3c067d52dd33feb97d851df6cab130e4116759f/src/main/java/net/finmath/marketdata2/model/curves/ForwardCurveInterpolation.java#L334-L356
train
finmath/finmath-lib
src/main/java/net/finmath/marketdata2/model/curves/ForwardCurveInterpolation.java
ForwardCurveInterpolation.addForward
private void addForward(AnalyticModel model, double fixingTime, RandomVariable forward, boolean isParameter) { double interpolationEntitiyTime; RandomVariable interpolationEntityForwardValue; switch(interpolationEntityForward) { case FORWARD: default: interpolationEntitiyTime = fixingTime; interpolationEntityForwardValue = forward; break; case FORWARD_TIMES_DISCOUNTFACTOR: interpolationEntitiyTime = fixingTime; interpolationEntityForwardValue = forward.mult(model.getDiscountCurve(getDiscountCurveName()).getValue(model, fixingTime+getPaymentOffset(fixingTime))); break; case ZERO: { double paymentOffset = getPaymentOffset(fixingTime); interpolationEntitiyTime = fixingTime+paymentOffset; interpolationEntityForwardValue = forward.mult(paymentOffset).add(1.0).log().div(paymentOffset); break; } case DISCOUNTFACTOR: { double paymentOffset = getPaymentOffset(fixingTime); interpolationEntitiyTime = fixingTime+paymentOffset; interpolationEntityForwardValue = getValue(fixingTime).div(forward.mult(paymentOffset).add(1.0)); break; } } super.addPoint(interpolationEntitiyTime, interpolationEntityForwardValue, isParameter); }
java
private void addForward(AnalyticModel model, double fixingTime, RandomVariable forward, boolean isParameter) { double interpolationEntitiyTime; RandomVariable interpolationEntityForwardValue; switch(interpolationEntityForward) { case FORWARD: default: interpolationEntitiyTime = fixingTime; interpolationEntityForwardValue = forward; break; case FORWARD_TIMES_DISCOUNTFACTOR: interpolationEntitiyTime = fixingTime; interpolationEntityForwardValue = forward.mult(model.getDiscountCurve(getDiscountCurveName()).getValue(model, fixingTime+getPaymentOffset(fixingTime))); break; case ZERO: { double paymentOffset = getPaymentOffset(fixingTime); interpolationEntitiyTime = fixingTime+paymentOffset; interpolationEntityForwardValue = forward.mult(paymentOffset).add(1.0).log().div(paymentOffset); break; } case DISCOUNTFACTOR: { double paymentOffset = getPaymentOffset(fixingTime); interpolationEntitiyTime = fixingTime+paymentOffset; interpolationEntityForwardValue = getValue(fixingTime).div(forward.mult(paymentOffset).add(1.0)); break; } } super.addPoint(interpolationEntitiyTime, interpolationEntityForwardValue, isParameter); }
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Add a forward to this curve. @param model An analytic model providing a context. The discount curve (if needed) is obtained from this model. @param fixingTime The given fixing time. @param forward The given forward. @param isParameter If true, then this point is server via {@link #getParameter()} and changed via {@link #setParameter(RandomVariable[])} and {@link #getCloneForParameter(RandomVariable[])}, i.e., it can be calibrated.
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a3c067d52dd33feb97d851df6cab130e4116759f
https://github.com/finmath/finmath-lib/blob/a3c067d52dd33feb97d851df6cab130e4116759f/src/main/java/net/finmath/marketdata2/model/curves/ForwardCurveInterpolation.java#L416-L445
train
finmath/finmath-lib
src/main/java/net/finmath/montecarlo/process/LinearInterpolatedTimeDiscreteProcess.java
LinearInterpolatedTimeDiscreteProcess.add
public LinearInterpolatedTimeDiscreteProcess add(LinearInterpolatedTimeDiscreteProcess process) throws CalculationException { Map<Double, RandomVariable> sum = new HashMap<>(); for(double time: timeDiscretization) { sum.put(time, realizations.get(time).add(process.getProcessValue(time, 0))); } return new LinearInterpolatedTimeDiscreteProcess(timeDiscretization, sum); }
java
public LinearInterpolatedTimeDiscreteProcess add(LinearInterpolatedTimeDiscreteProcess process) throws CalculationException { Map<Double, RandomVariable> sum = new HashMap<>(); for(double time: timeDiscretization) { sum.put(time, realizations.get(time).add(process.getProcessValue(time, 0))); } return new LinearInterpolatedTimeDiscreteProcess(timeDiscretization, sum); }
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Create a new linear interpolated time discrete process by using the time discretization of this process and the sum of this process and the given one as its values. @param process A given process. @return A new process representing the of this and the given process. @throws CalculationException Thrown if the given process fails to evaluate at a certain time point.
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a3c067d52dd33feb97d851df6cab130e4116759f
https://github.com/finmath/finmath-lib/blob/a3c067d52dd33feb97d851df6cab130e4116759f/src/main/java/net/finmath/montecarlo/process/LinearInterpolatedTimeDiscreteProcess.java#L70-L78
train
finmath/finmath-lib
src/main/java/net/finmath/optimizer/StochasticPathwiseLevenbergMarquardt.java
StochasticPathwiseLevenbergMarquardt.getCloneWithModifiedTargetValues
public StochasticPathwiseLevenbergMarquardt getCloneWithModifiedTargetValues(List<RandomVariable> newTargetVaues, List<RandomVariable> newWeights, boolean isUseBestParametersAsInitialParameters) throws CloneNotSupportedException { StochasticPathwiseLevenbergMarquardt clonedOptimizer = clone(); clonedOptimizer.targetValues = numberListToDoubleArray(newTargetVaues); clonedOptimizer.weights = numberListToDoubleArray(newWeights); if(isUseBestParametersAsInitialParameters && this.done()) { clonedOptimizer.initialParameters = this.getBestFitParameters(); } return clonedOptimizer; }
java
public StochasticPathwiseLevenbergMarquardt getCloneWithModifiedTargetValues(List<RandomVariable> newTargetVaues, List<RandomVariable> newWeights, boolean isUseBestParametersAsInitialParameters) throws CloneNotSupportedException { StochasticPathwiseLevenbergMarquardt clonedOptimizer = clone(); clonedOptimizer.targetValues = numberListToDoubleArray(newTargetVaues); clonedOptimizer.weights = numberListToDoubleArray(newWeights); if(isUseBestParametersAsInitialParameters && this.done()) { clonedOptimizer.initialParameters = this.getBestFitParameters(); } return clonedOptimizer; }
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Create a clone of this LevenbergMarquardt optimizer with a new vector for the target values and weights. The clone will use the same objective function than this implementation, i.e., the implementation of {@link #setValues(RandomVariable[], RandomVariable[])} and that of {@link #setDerivatives(RandomVariable[], RandomVariable[][])} is reused. The initial values of the cloned optimizer will either be the original initial values of this object or the best parameters obtained by this optimizer, the latter is used only if this optimized signals a {@link #done()}. @param newTargetVaues New list of target values. @param newWeights New list of weights. @param isUseBestParametersAsInitialParameters If true and this optimizer is done(), then the clone will use this.{@link #getBestFitParameters()} as initial parameters. @return A new LevenbergMarquardt optimizer, cloning this one except modified target values and weights. @throws CloneNotSupportedException Thrown if this optimizer cannot be cloned.
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a3c067d52dd33feb97d851df6cab130e4116759f
https://github.com/finmath/finmath-lib/blob/a3c067d52dd33feb97d851df6cab130e4116759f/src/main/java/net/finmath/optimizer/StochasticPathwiseLevenbergMarquardt.java#L718-L728
train
finmath/finmath-lib
src/main/java6/net/finmath/montecarlo/interestrate/products/components/Option.java
Option.getBasisFunctions
public RandomVariableInterface[] getBasisFunctions(double exerciseDate, LIBORModelMonteCarloSimulationInterface model) throws CalculationException { ArrayList<RandomVariableInterface> basisFunctions = new ArrayList<RandomVariableInterface>(); RandomVariableInterface basisFunction; // Constant basisFunction = model.getRandomVariableForConstant(1.0); basisFunctions.add(basisFunction); // LIBORs int liborPeriodIndex, liborPeriodIndexEnd; RandomVariableInterface rate; // 1 Period basisFunction = model.getRandomVariableForConstant(1.0); liborPeriodIndex = model.getLiborPeriodIndex(exerciseDate); if(liborPeriodIndex < 0) { liborPeriodIndex = -liborPeriodIndex-1; } liborPeriodIndexEnd = liborPeriodIndex+1; double periodLength1 = model.getLiborPeriod(liborPeriodIndexEnd) - model.getLiborPeriod(liborPeriodIndex); rate = model.getLIBOR(exerciseDate, model.getLiborPeriod(liborPeriodIndex), model.getLiborPeriod(liborPeriodIndexEnd)); basisFunction = basisFunction.discount(rate, periodLength1); basisFunctions.add(basisFunction);//.div(Math.sqrt(basisFunction.mult(basisFunction).getAverage()))); basisFunction = basisFunction.discount(rate, periodLength1); basisFunctions.add(basisFunction);//.div(Math.sqrt(basisFunction.mult(basisFunction).getAverage()))); // n/2 Period basisFunction = model.getRandomVariableForConstant(1.0); liborPeriodIndex = model.getLiborPeriodIndex(exerciseDate); if(liborPeriodIndex < 0) { liborPeriodIndex = -liborPeriodIndex-1; } liborPeriodIndexEnd = (liborPeriodIndex + model.getNumberOfLibors())/2; double periodLength2 = model.getLiborPeriod(liborPeriodIndexEnd) - model.getLiborPeriod(liborPeriodIndex); if(periodLength2 != periodLength1) { rate = model.getLIBOR(exerciseDate, model.getLiborPeriod(liborPeriodIndex), model.getLiborPeriod(liborPeriodIndexEnd)); basisFunction = basisFunction.discount(rate, periodLength2); basisFunctions.add(basisFunction);//.div(Math.sqrt(basisFunction.mult(basisFunction).getAverage()))); basisFunction = basisFunction.discount(rate, periodLength2); // basisFunctions.add(basisFunction);//.div(Math.sqrt(basisFunction.mult(basisFunction).getAverage()))); basisFunction = basisFunction.discount(rate, periodLength2); // basisFunctions.add(basisFunction);//.div(Math.sqrt(basisFunction.mult(basisFunction).getAverage()))); } // n Period basisFunction = model.getRandomVariableForConstant(1.0); liborPeriodIndex = model.getLiborPeriodIndex(exerciseDate); if(liborPeriodIndex < 0) { liborPeriodIndex = -liborPeriodIndex-1; } liborPeriodIndexEnd = model.getNumberOfLibors(); double periodLength3 = model.getLiborPeriod(liborPeriodIndexEnd) - model.getLiborPeriod(liborPeriodIndex); if(periodLength3 != periodLength1 && periodLength3 != periodLength2) { rate = model.getLIBOR(exerciseDate, model.getLiborPeriod(liborPeriodIndex), model.getLiborPeriod(liborPeriodIndexEnd)); basisFunction = basisFunction.discount(rate, periodLength3); basisFunctions.add(basisFunction);//.div(Math.sqrt(basisFunction.mult(basisFunction).getAverage()))); basisFunction = basisFunction.discount(rate, periodLength3); // basisFunctions.add(basisFunction);//.div(Math.sqrt(basisFunction.mult(basisFunction).getAverage()))); } return basisFunctions.toArray(new RandomVariableInterface[0]); }
java
public RandomVariableInterface[] getBasisFunctions(double exerciseDate, LIBORModelMonteCarloSimulationInterface model) throws CalculationException { ArrayList<RandomVariableInterface> basisFunctions = new ArrayList<RandomVariableInterface>(); RandomVariableInterface basisFunction; // Constant basisFunction = model.getRandomVariableForConstant(1.0); basisFunctions.add(basisFunction); // LIBORs int liborPeriodIndex, liborPeriodIndexEnd; RandomVariableInterface rate; // 1 Period basisFunction = model.getRandomVariableForConstant(1.0); liborPeriodIndex = model.getLiborPeriodIndex(exerciseDate); if(liborPeriodIndex < 0) { liborPeriodIndex = -liborPeriodIndex-1; } liborPeriodIndexEnd = liborPeriodIndex+1; double periodLength1 = model.getLiborPeriod(liborPeriodIndexEnd) - model.getLiborPeriod(liborPeriodIndex); rate = model.getLIBOR(exerciseDate, model.getLiborPeriod(liborPeriodIndex), model.getLiborPeriod(liborPeriodIndexEnd)); basisFunction = basisFunction.discount(rate, periodLength1); basisFunctions.add(basisFunction);//.div(Math.sqrt(basisFunction.mult(basisFunction).getAverage()))); basisFunction = basisFunction.discount(rate, periodLength1); basisFunctions.add(basisFunction);//.div(Math.sqrt(basisFunction.mult(basisFunction).getAverage()))); // n/2 Period basisFunction = model.getRandomVariableForConstant(1.0); liborPeriodIndex = model.getLiborPeriodIndex(exerciseDate); if(liborPeriodIndex < 0) { liborPeriodIndex = -liborPeriodIndex-1; } liborPeriodIndexEnd = (liborPeriodIndex + model.getNumberOfLibors())/2; double periodLength2 = model.getLiborPeriod(liborPeriodIndexEnd) - model.getLiborPeriod(liborPeriodIndex); if(periodLength2 != periodLength1) { rate = model.getLIBOR(exerciseDate, model.getLiborPeriod(liborPeriodIndex), model.getLiborPeriod(liborPeriodIndexEnd)); basisFunction = basisFunction.discount(rate, periodLength2); basisFunctions.add(basisFunction);//.div(Math.sqrt(basisFunction.mult(basisFunction).getAverage()))); basisFunction = basisFunction.discount(rate, periodLength2); // basisFunctions.add(basisFunction);//.div(Math.sqrt(basisFunction.mult(basisFunction).getAverage()))); basisFunction = basisFunction.discount(rate, periodLength2); // basisFunctions.add(basisFunction);//.div(Math.sqrt(basisFunction.mult(basisFunction).getAverage()))); } // n Period basisFunction = model.getRandomVariableForConstant(1.0); liborPeriodIndex = model.getLiborPeriodIndex(exerciseDate); if(liborPeriodIndex < 0) { liborPeriodIndex = -liborPeriodIndex-1; } liborPeriodIndexEnd = model.getNumberOfLibors(); double periodLength3 = model.getLiborPeriod(liborPeriodIndexEnd) - model.getLiborPeriod(liborPeriodIndex); if(periodLength3 != periodLength1 && periodLength3 != periodLength2) { rate = model.getLIBOR(exerciseDate, model.getLiborPeriod(liborPeriodIndex), model.getLiborPeriod(liborPeriodIndexEnd)); basisFunction = basisFunction.discount(rate, periodLength3); basisFunctions.add(basisFunction);//.div(Math.sqrt(basisFunction.mult(basisFunction).getAverage()))); basisFunction = basisFunction.discount(rate, periodLength3); // basisFunctions.add(basisFunction);//.div(Math.sqrt(basisFunction.mult(basisFunction).getAverage()))); } return basisFunctions.toArray(new RandomVariableInterface[0]); }
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Return the regression basis functions. @param exerciseDate The date w.r.t. which the basis functions should be measurable. @param model The model. @return Array of random variables. @throws net.finmath.exception.CalculationException Thrown if the valuation fails, specific cause may be available via the <code>cause()</code> method.
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a3c067d52dd33feb97d851df6cab130e4116759f
https://github.com/finmath/finmath-lib/blob/a3c067d52dd33feb97d851df6cab130e4116759f/src/main/java6/net/finmath/montecarlo/interestrate/products/components/Option.java#L267-L339
train
finmath/finmath-lib
src/main/java/net/finmath/marketdata/calibration/CalibratedCurves.java
CalibratedCurves.createDiscountCurve
private DiscountCurve createDiscountCurve(String discountCurveName) { DiscountCurve discountCurve = model.getDiscountCurve(discountCurveName); if(discountCurve == null) { discountCurve = DiscountCurveInterpolation.createDiscountCurveFromDiscountFactors(discountCurveName, new double[] { 0.0 }, new double[] { 1.0 }); model = model.addCurves(discountCurve); } return discountCurve; }
java
private DiscountCurve createDiscountCurve(String discountCurveName) { DiscountCurve discountCurve = model.getDiscountCurve(discountCurveName); if(discountCurve == null) { discountCurve = DiscountCurveInterpolation.createDiscountCurveFromDiscountFactors(discountCurveName, new double[] { 0.0 }, new double[] { 1.0 }); model = model.addCurves(discountCurve); } return discountCurve; }
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Get a discount curve from the model, if not existing create a discount curve. @param discountCurveName The name of the discount curve to create. @return The discount factor curve associated with the given name.
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a3c067d52dd33feb97d851df6cab130e4116759f
https://github.com/finmath/finmath-lib/blob/a3c067d52dd33feb97d851df6cab130e4116759f/src/main/java/net/finmath/marketdata/calibration/CalibratedCurves.java#L760-L768
train
finmath/finmath-lib
src/main/java/net/finmath/marketdata/model/curves/locallinearregression/Partition.java
Partition.d
public double d(double x){ int intervalNumber =getIntervalNumber(x); if (intervalNumber==0 || intervalNumber==points.length) { return x; } return getIntervalReferencePoint(intervalNumber-1); }
java
public double d(double x){ int intervalNumber =getIntervalNumber(x); if (intervalNumber==0 || intervalNumber==points.length) { return x; } return getIntervalReferencePoint(intervalNumber-1); }
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If a given x is into an interval of the partition, this method returns the reference point of the corresponding interval. If the given x is not contained in any interval of the partition, this method returns x. @param x The point of interest. @return The discretized value.
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a3c067d52dd33feb97d851df6cab130e4116759f
https://github.com/finmath/finmath-lib/blob/a3c067d52dd33feb97d851df6cab130e4116759f/src/main/java/net/finmath/marketdata/model/curves/locallinearregression/Partition.java#L84-L90
train
finmath/finmath-lib
src/main/java6/net/finmath/montecarlo/interestrate/products/Swaption.java
Swaption.getValue
public double getValue(ForwardCurveInterface forwardCurve, double swaprateVolatility) { double swaprate = swaprates[0]; for (double swaprate1 : swaprates) { if (swaprate1 != swaprate) { throw new RuntimeException("Uneven swaprates not allows for analytical pricing."); } } double[] swapTenor = new double[fixingDates.length+1]; System.arraycopy(fixingDates, 0, swapTenor, 0, fixingDates.length); swapTenor[swapTenor.length-1] = paymentDates[paymentDates.length-1]; double forwardSwapRate = Swap.getForwardSwapRate(new TimeDiscretization(swapTenor), new TimeDiscretization(swapTenor), forwardCurve); double swapAnnuity = SwapAnnuity.getSwapAnnuity(new TimeDiscretization(swapTenor), forwardCurve); return AnalyticFormulas.blackModelSwaptionValue(forwardSwapRate, swaprateVolatility, exerciseDate, swaprate, swapAnnuity); }
java
public double getValue(ForwardCurveInterface forwardCurve, double swaprateVolatility) { double swaprate = swaprates[0]; for (double swaprate1 : swaprates) { if (swaprate1 != swaprate) { throw new RuntimeException("Uneven swaprates not allows for analytical pricing."); } } double[] swapTenor = new double[fixingDates.length+1]; System.arraycopy(fixingDates, 0, swapTenor, 0, fixingDates.length); swapTenor[swapTenor.length-1] = paymentDates[paymentDates.length-1]; double forwardSwapRate = Swap.getForwardSwapRate(new TimeDiscretization(swapTenor), new TimeDiscretization(swapTenor), forwardCurve); double swapAnnuity = SwapAnnuity.getSwapAnnuity(new TimeDiscretization(swapTenor), forwardCurve); return AnalyticFormulas.blackModelSwaptionValue(forwardSwapRate, swaprateVolatility, exerciseDate, swaprate, swapAnnuity); }
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This method returns the value of the product using a Black-Scholes model for the swap rate The model is determined by a discount factor curve and a swap rate volatility. @param forwardCurve The forward curve on which to value the swap. @param swaprateVolatility The Black volatility. @return Value of this product
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a3c067d52dd33feb97d851df6cab130e4116759f
https://github.com/finmath/finmath-lib/blob/a3c067d52dd33feb97d851df6cab130e4116759f/src/main/java6/net/finmath/montecarlo/interestrate/products/Swaption.java#L189-L205
train
finmath/finmath-lib
src/main/java/net/finmath/marketdata/products/ForwardRateAgreement.java
ForwardRateAgreement.getRate
public double getRate(AnalyticModel model) { if(model==null) { throw new IllegalArgumentException("model==null"); } ForwardCurve forwardCurve = model.getForwardCurve(forwardCurveName); if(forwardCurve==null) { throw new IllegalArgumentException("No forward curve of name '" + forwardCurveName + "' found in given model:\n" + model.toString()); } double fixingDate = schedule.getFixing(0); return forwardCurve.getForward(model,fixingDate); }
java
public double getRate(AnalyticModel model) { if(model==null) { throw new IllegalArgumentException("model==null"); } ForwardCurve forwardCurve = model.getForwardCurve(forwardCurveName); if(forwardCurve==null) { throw new IllegalArgumentException("No forward curve of name '" + forwardCurveName + "' found in given model:\n" + model.toString()); } double fixingDate = schedule.getFixing(0); return forwardCurve.getForward(model,fixingDate); }
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Return the par FRA rate for a given curve. @param model A given model. @return The par FRA rate.
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a3c067d52dd33feb97d851df6cab130e4116759f
https://github.com/finmath/finmath-lib/blob/a3c067d52dd33feb97d851df6cab130e4116759f/src/main/java/net/finmath/marketdata/products/ForwardRateAgreement.java#L103-L115
train
finmath/finmath-lib
src/main/java/net/finmath/montecarlo/interestrate/models/covariance/AbstractLIBORCovarianceModelParametric.java
AbstractLIBORCovarianceModelParametric.getParameter
public RandomVariable[] getParameter() { double[] parameterAsDouble = this.getParameterAsDouble(); RandomVariable[] parameter = new RandomVariable[parameterAsDouble.length]; for(int i=0; i<parameter.length; i++) { parameter[i] = new Scalar(parameterAsDouble[i]); } return parameter; }
java
public RandomVariable[] getParameter() { double[] parameterAsDouble = this.getParameterAsDouble(); RandomVariable[] parameter = new RandomVariable[parameterAsDouble.length]; for(int i=0; i<parameter.length; i++) { parameter[i] = new Scalar(parameterAsDouble[i]); } return parameter; }
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Get the parameters of determining this parametric covariance model. The parameters are usually free parameters which may be used in calibration. @return Parameter vector.
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a3c067d52dd33feb97d851df6cab130e4116759f
https://github.com/finmath/finmath-lib/blob/a3c067d52dd33feb97d851df6cab130e4116759f/src/main/java/net/finmath/montecarlo/interestrate/models/covariance/AbstractLIBORCovarianceModelParametric.java#L89-L96
train
finmath/finmath-lib
src/main/java/net/finmath/marketdata2/model/curves/DiscountCurveInterpolation.java
DiscountCurveInterpolation.createDiscountCurveFromMonteCarloLiborModel
public static DiscountCurveInterface createDiscountCurveFromMonteCarloLiborModel(String forwardCurveName, LIBORModelMonteCarloSimulationModel model, double startTime) throws CalculationException{ // Check if the LMM uses a discount curve which is created from a forward curve if(model.getModel().getDiscountCurve()==null || model.getModel().getDiscountCurve().getName().toLowerCase().contains("DiscountCurveFromForwardCurve".toLowerCase())){ return new DiscountCurveFromForwardCurve(ForwardCurveInterpolation.createForwardCurveFromMonteCarloLiborModel(forwardCurveName, model, startTime)); } else { // i.e. forward curve of Libor Model not OIS. In this case return the OIS curve. // Only at startTime 0! return (DiscountCurveInterface) model.getModel().getDiscountCurve(); } }
java
public static DiscountCurveInterface createDiscountCurveFromMonteCarloLiborModel(String forwardCurveName, LIBORModelMonteCarloSimulationModel model, double startTime) throws CalculationException{ // Check if the LMM uses a discount curve which is created from a forward curve if(model.getModel().getDiscountCurve()==null || model.getModel().getDiscountCurve().getName().toLowerCase().contains("DiscountCurveFromForwardCurve".toLowerCase())){ return new DiscountCurveFromForwardCurve(ForwardCurveInterpolation.createForwardCurveFromMonteCarloLiborModel(forwardCurveName, model, startTime)); } else { // i.e. forward curve of Libor Model not OIS. In this case return the OIS curve. // Only at startTime 0! return (DiscountCurveInterface) model.getModel().getDiscountCurve(); } }
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Create a discount curve from forwards given by a LIBORMonteCarloModel. If the model uses multiple curves, return its discount curve. @param forwardCurveName name of the forward curve. @param model Monte Carlo model providing the forwards. @param startTime time at which the curve starts, i.e. zero time for the curve @return a discount curve from forwards given by a LIBORMonteCarloModel. @throws CalculationException Thrown if the model failed to provide the forward rates.
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a3c067d52dd33feb97d851df6cab130e4116759f
https://github.com/finmath/finmath-lib/blob/a3c067d52dd33feb97d851df6cab130e4116759f/src/main/java/net/finmath/marketdata2/model/curves/DiscountCurveInterpolation.java#L401-L412
train
finmath/finmath-lib
src/main/java6/net/finmath/time/businessdaycalendar/BusinessdayCalendarExcludingTARGETHolidays.java
BusinessdayCalendarExcludingTARGETHolidays.isEasterSunday
public static boolean isEasterSunday(LocalDate date) { int y = date.getYear(); int a = y % 19; int b = y / 100; int c = y % 100; int d = b / 4; int e = b % 4; int f = (b + 8) / 25; int g = (b - f + 1) / 3; int h = (19 * a + b - d - g + 15) % 30; int i = c / 4; int k = c % 4; int l = (32 + 2 * e + 2 * i - h - k) % 7; int m = (a + 11 * h + 22 * l) / 451; int easterSundayMonth = (h + l - 7 * m + 114) / 31; int easterSundayDay = ((h + l - 7 * m + 114) % 31) + 1; int month = date.getMonthValue(); int day = date.getDayOfMonth(); return (easterSundayMonth == month) && (easterSundayDay == day); }
java
public static boolean isEasterSunday(LocalDate date) { int y = date.getYear(); int a = y % 19; int b = y / 100; int c = y % 100; int d = b / 4; int e = b % 4; int f = (b + 8) / 25; int g = (b - f + 1) / 3; int h = (19 * a + b - d - g + 15) % 30; int i = c / 4; int k = c % 4; int l = (32 + 2 * e + 2 * i - h - k) % 7; int m = (a + 11 * h + 22 * l) / 451; int easterSundayMonth = (h + l - 7 * m + 114) / 31; int easterSundayDay = ((h + l - 7 * m + 114) % 31) + 1; int month = date.getMonthValue(); int day = date.getDayOfMonth(); return (easterSundayMonth == month) && (easterSundayDay == day); }
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Test a given date for being easter sunday. The method uses the algorithms sometimes cited as Meeus,Jones, Butcher Gregorian algorithm. Taken from http://en.wikipedia.org/wiki/Computus @param date The date to check. @return True, if date is easter sunday.
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a3c067d52dd33feb97d851df6cab130e4116759f
https://github.com/finmath/finmath-lib/blob/a3c067d52dd33feb97d851df6cab130e4116759f/src/main/java6/net/finmath/time/businessdaycalendar/BusinessdayCalendarExcludingTARGETHolidays.java#L67-L88
train
finmath/finmath-lib
src/main/java/net/finmath/time/FloatingpointDate.java
FloatingpointDate.getDateFromFloatingPointDate
public static LocalDateTime getDateFromFloatingPointDate(LocalDateTime referenceDate, double floatingPointDate) { if(referenceDate == null) { return null; } Duration duration = Duration.ofSeconds(Math.round(floatingPointDate * SECONDS_PER_DAY)); return referenceDate.plus(duration); }
java
public static LocalDateTime getDateFromFloatingPointDate(LocalDateTime referenceDate, double floatingPointDate) { if(referenceDate == null) { return null; } Duration duration = Duration.ofSeconds(Math.round(floatingPointDate * SECONDS_PER_DAY)); return referenceDate.plus(duration); }
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Convert a floating point date to a LocalDateTime. Note: This method currently performs a rounding to the next second. If referenceDate is null, the method returns null. @param referenceDate The reference date associated with \( t=0 \). @param floatingPointDate The value to the time offset \( t \). @return The date resulting from adding Math.round(fixingTime*SECONDS_PER_DAY) seconds to referenceDate, where one day has SECONDS_PER_DAY seconds and SECONDS_PER_DAY is a constant 365*24*60*60
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a3c067d52dd33feb97d851df6cab130e4116759f
https://github.com/finmath/finmath-lib/blob/a3c067d52dd33feb97d851df6cab130e4116759f/src/main/java/net/finmath/time/FloatingpointDate.java#L67-L74
train
finmath/finmath-lib
src/main/java/net/finmath/time/FloatingpointDate.java
FloatingpointDate.getFloatingPointDateFromDate
public static double getFloatingPointDateFromDate(LocalDateTime referenceDate, LocalDateTime date) { Duration duration = Duration.between(referenceDate, date); return ((double)duration.getSeconds()) / SECONDS_PER_DAY; }
java
public static double getFloatingPointDateFromDate(LocalDateTime referenceDate, LocalDateTime date) { Duration duration = Duration.between(referenceDate, date); return ((double)duration.getSeconds()) / SECONDS_PER_DAY; }
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Convert a given date to a floating point date using a given reference date. @param referenceDate The reference date associated with \( t=0 \). @param date The given date to be associated with the return value \( T \). @return The value T measuring the distance of reference date and date by ACT/365 with SECONDS_PER_DAY seconds used as the smallest time unit and SECONDS_PER_DAY is a constant 365*24*60*60.
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a3c067d52dd33feb97d851df6cab130e4116759f
https://github.com/finmath/finmath-lib/blob/a3c067d52dd33feb97d851df6cab130e4116759f/src/main/java/net/finmath/time/FloatingpointDate.java#L83-L86
train
finmath/finmath-lib
src/main/java/net/finmath/time/FloatingpointDate.java
FloatingpointDate.getDateFromFloatingPointDate
public static LocalDate getDateFromFloatingPointDate(LocalDate referenceDate, double floatingPointDate) { if(referenceDate == null) { return null; } return referenceDate.plusDays((int)Math.round(floatingPointDate*365.0)); }
java
public static LocalDate getDateFromFloatingPointDate(LocalDate referenceDate, double floatingPointDate) { if(referenceDate == null) { return null; } return referenceDate.plusDays((int)Math.round(floatingPointDate*365.0)); }
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Convert a floating point date to a LocalDate. Note: This method currently performs a rounding to the next day. In a future extension intra-day time offsets may be considered. If referenceDate is null, the method returns null. @param referenceDate The reference date associated with \( t=0 \). @param floatingPointDate The value to the time offset \( t \). @return The date resulting from adding Math.round(fixingTime*365.0) days to referenceDate.
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a3c067d52dd33feb97d851df6cab130e4116759f
https://github.com/finmath/finmath-lib/blob/a3c067d52dd33feb97d851df6cab130e4116759f/src/main/java/net/finmath/time/FloatingpointDate.java#L100-L105
train
finmath/finmath-lib
src/main/java6/net/finmath/functions/PoissonDistribution.java
PoissonDistribution.inverseCumulativeDistribution
public double inverseCumulativeDistribution(double x) { double p = Math.exp(-lambda); double dp = p; int k = 0; while(x > p) { k++; dp *= lambda / k; p += dp; } return k; }
java
public double inverseCumulativeDistribution(double x) { double p = Math.exp(-lambda); double dp = p; int k = 0; while(x > p) { k++; dp *= lambda / k; p += dp; } return k; }
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Return the inverse cumulative distribution function at x. @param x Argument @return Inverse cumulative distribution function at x.
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a3c067d52dd33feb97d851df6cab130e4116759f
https://github.com/finmath/finmath-lib/blob/a3c067d52dd33feb97d851df6cab130e4116759f/src/main/java6/net/finmath/functions/PoissonDistribution.java#L27-L37
train
finmath/finmath-lib
src/main/java/net/finmath/marketdata2/model/curves/CurveInterpolation.java
CurveInterpolation.addPoint
protected void addPoint(double time, RandomVariable value, boolean isParameter) { synchronized (rationalFunctionInterpolationLazyInitLock) { if(interpolationEntity == InterpolationEntity.LOG_OF_VALUE_PER_TIME && time == 0) { boolean containsOne = false; int index=0; for(int i = 0; i< value.size(); i++){if(value.get(i)==1.0) {containsOne = true; index=i; break;}} if(containsOne && isParameter == false) { return; } else { throw new IllegalArgumentException("The interpolation method LOG_OF_VALUE_PER_TIME does not allow to add a value at time = 0 other than 1.0 (received 1 at index" + index + ")."); } } RandomVariable interpolationEntityValue = interpolationEntityFromValue(value, time); int index = getTimeIndex(time); if(index >= 0) { if(points.get(index).value == interpolationEntityValue) { return; // Already in list } else if(isParameter) { return; } else { throw new RuntimeException("Trying to add a value for a time for which another value already exists."); } } else { // Insert the new point, retain ordering. Point point = new Point(time, interpolationEntityValue, isParameter); points.add(-index-1, point); if(isParameter) { // Add this point also to the list of parameters int parameterIndex = getParameterIndex(time); if(parameterIndex >= 0) { new RuntimeException("CurveFromInterpolationPoints inconsistent."); } pointsBeingParameters.add(-parameterIndex-1, point); } } rationalFunctionInterpolation = null; curveCacheReference = null; } }
java
protected void addPoint(double time, RandomVariable value, boolean isParameter) { synchronized (rationalFunctionInterpolationLazyInitLock) { if(interpolationEntity == InterpolationEntity.LOG_OF_VALUE_PER_TIME && time == 0) { boolean containsOne = false; int index=0; for(int i = 0; i< value.size(); i++){if(value.get(i)==1.0) {containsOne = true; index=i; break;}} if(containsOne && isParameter == false) { return; } else { throw new IllegalArgumentException("The interpolation method LOG_OF_VALUE_PER_TIME does not allow to add a value at time = 0 other than 1.0 (received 1 at index" + index + ")."); } } RandomVariable interpolationEntityValue = interpolationEntityFromValue(value, time); int index = getTimeIndex(time); if(index >= 0) { if(points.get(index).value == interpolationEntityValue) { return; // Already in list } else if(isParameter) { return; } else { throw new RuntimeException("Trying to add a value for a time for which another value already exists."); } } else { // Insert the new point, retain ordering. Point point = new Point(time, interpolationEntityValue, isParameter); points.add(-index-1, point); if(isParameter) { // Add this point also to the list of parameters int parameterIndex = getParameterIndex(time); if(parameterIndex >= 0) { new RuntimeException("CurveFromInterpolationPoints inconsistent."); } pointsBeingParameters.add(-parameterIndex-1, point); } } rationalFunctionInterpolation = null; curveCacheReference = null; } }
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Add a point to this curveFromInterpolationPoints. The method will throw an exception if the point is already part of the curveFromInterpolationPoints. @param time The x<sub>i</sub> in <sub>i</sub> = f(x<sub>i</sub>). @param value The y<sub>i</sub> in <sub>i</sub> = f(x<sub>i</sub>). @param isParameter If true, then this point is served via {@link #getParameter()} and changed via {@link #getCloneForParameter(RandomVariable[])}, i.e., it can be calibrated.
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a3c067d52dd33feb97d851df6cab130e4116759f
https://github.com/finmath/finmath-lib/blob/a3c067d52dd33feb97d851df6cab130e4116759f/src/main/java/net/finmath/marketdata2/model/curves/CurveInterpolation.java#L372-L413
train
finmath/finmath-lib
src/main/java/net/finmath/time/SchedulePrototype.java
SchedulePrototype.getOffsetCodeFromSchedule
public static String getOffsetCodeFromSchedule(Schedule schedule) { double doubleLength = 0; for(int i = 0; i < schedule.getNumberOfPeriods(); i ++) { doubleLength += schedule.getPeriodLength(i); } doubleLength /= schedule.getNumberOfPeriods(); doubleLength *= 12; int periodLength = (int) Math.round(doubleLength); String offsetCode = periodLength + "M"; return offsetCode; }
java
public static String getOffsetCodeFromSchedule(Schedule schedule) { double doubleLength = 0; for(int i = 0; i < schedule.getNumberOfPeriods(); i ++) { doubleLength += schedule.getPeriodLength(i); } doubleLength /= schedule.getNumberOfPeriods(); doubleLength *= 12; int periodLength = (int) Math.round(doubleLength); String offsetCode = periodLength + "M"; return offsetCode; }
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Determines the offset code of a forward contract from a schedule. Rounds the average period length to full months. @param schedule The schedule. @return The offset code as String
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a3c067d52dd33feb97d851df6cab130e4116759f
https://github.com/finmath/finmath-lib/blob/a3c067d52dd33feb97d851df6cab130e4116759f/src/main/java/net/finmath/time/SchedulePrototype.java#L52-L66
train
finmath/finmath-lib
src/main/java/net/finmath/time/SchedulePrototype.java
SchedulePrototype.getOffsetCodeFromCurveName
public static String getOffsetCodeFromCurveName(String curveName) { if(curveName == null || curveName.length() == 0) { return null; } String[] splits = curveName.split("(?<=\\D)(?=\\d)"); String offsetCode = splits[splits.length-1]; if(!Character.isDigit(offsetCode.charAt(0))) { return null; } offsetCode = offsetCode.split("(?<=[A-Za-z])(?=.)", 2)[0]; offsetCode = offsetCode.replaceAll( "[\\W_]", "" ); return offsetCode; }
java
public static String getOffsetCodeFromCurveName(String curveName) { if(curveName == null || curveName.length() == 0) { return null; } String[] splits = curveName.split("(?<=\\D)(?=\\d)"); String offsetCode = splits[splits.length-1]; if(!Character.isDigit(offsetCode.charAt(0))) { return null; } offsetCode = offsetCode.split("(?<=[A-Za-z])(?=.)", 2)[0]; offsetCode = offsetCode.replaceAll( "[\\W_]", "" ); return offsetCode; }
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Determines the offset code of a forward contract from the name of a forward curve. This method will extract a group of one or more digits together with the first letter behind them, if any. If there are multiple groups of digits in the name, this method will extract the last. If there is no number in the string, this method will return null. @param curveName The name of the curve. @return The offset code as String
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a3c067d52dd33feb97d851df6cab130e4116759f
https://github.com/finmath/finmath-lib/blob/a3c067d52dd33feb97d851df6cab130e4116759f/src/main/java/net/finmath/time/SchedulePrototype.java#L77-L90
train
finmath/finmath-lib
src/main/java/net/finmath/time/SchedulePrototype.java
SchedulePrototype.generateScheduleDescriptor
public ScheduleDescriptor generateScheduleDescriptor(LocalDate startDate, LocalDate endDate) { return new ScheduleDescriptor(startDate, endDate, getFrequency(), getDaycountConvention(), getShortPeriodConvention(), getDateRollConvention(), getBusinessdayCalendar(), getFixingOffsetDays(), getPaymentOffsetDays(), isUseEndOfMonth()); }
java
public ScheduleDescriptor generateScheduleDescriptor(LocalDate startDate, LocalDate endDate) { return new ScheduleDescriptor(startDate, endDate, getFrequency(), getDaycountConvention(), getShortPeriodConvention(), getDateRollConvention(), getBusinessdayCalendar(), getFixingOffsetDays(), getPaymentOffsetDays(), isUseEndOfMonth()); }
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Generate a schedule descriptor for the given start and end date. @param startDate The start date. @param endDate The end date. @return The schedule descriptor
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a3c067d52dd33feb97d851df6cab130e4116759f
https://github.com/finmath/finmath-lib/blob/a3c067d52dd33feb97d851df6cab130e4116759f/src/main/java/net/finmath/time/SchedulePrototype.java#L126-L129
train
finmath/finmath-lib
src/main/java/net/finmath/time/SchedulePrototype.java
SchedulePrototype.generateSchedule
public Schedule generateSchedule(LocalDate referenceDate, LocalDate startDate, LocalDate endDate) { return ScheduleGenerator.createScheduleFromConventions(referenceDate, startDate, endDate, getFrequency(), getDaycountConvention(), getShortPeriodConvention(), getDateRollConvention(), getBusinessdayCalendar(), getFixingOffsetDays(), getPaymentOffsetDays(), isUseEndOfMonth()); }
java
public Schedule generateSchedule(LocalDate referenceDate, LocalDate startDate, LocalDate endDate) { return ScheduleGenerator.createScheduleFromConventions(referenceDate, startDate, endDate, getFrequency(), getDaycountConvention(), getShortPeriodConvention(), getDateRollConvention(), getBusinessdayCalendar(), getFixingOffsetDays(), getPaymentOffsetDays(), isUseEndOfMonth()); }
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Generate a schedule for the given start and end date. @param referenceDate The reference date (corresponds to \( t = 0 \). @param startDate The start date. @param endDate The end date. @return The schedule
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a3c067d52dd33feb97d851df6cab130e4116759f
https://github.com/finmath/finmath-lib/blob/a3c067d52dd33feb97d851df6cab130e4116759f/src/main/java/net/finmath/time/SchedulePrototype.java#L139-L142
train
finmath/finmath-lib
src/main/java6/net/finmath/time/ScheduleGenerator.java
ScheduleGenerator.createScheduleFromConventions
@Deprecated public static ScheduleInterface createScheduleFromConventions( LocalDate referenceDate, LocalDate startDate, String frequency, double maturity, String daycountConvention, String shortPeriodConvention ) { return createScheduleFromConventions( referenceDate, startDate, frequency, maturity, daycountConvention, shortPeriodConvention, "UNADJUSTED", new BusinessdayCalendarAny(), 0, 0); }
java
@Deprecated public static ScheduleInterface createScheduleFromConventions( LocalDate referenceDate, LocalDate startDate, String frequency, double maturity, String daycountConvention, String shortPeriodConvention ) { return createScheduleFromConventions( referenceDate, startDate, frequency, maturity, daycountConvention, shortPeriodConvention, "UNADJUSTED", new BusinessdayCalendarAny(), 0, 0); }
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Generates a schedule based on some meta data. The schedule generation considers short periods. Date rolling is ignored. @param referenceDate The date which is used in the schedule to internally convert dates to doubles, i.e., the date where t=0. @param startDate The start date of the first period. @param frequency The frequency. @param maturity The end date of the last period. @param daycountConvention The daycount convention. @param shortPeriodConvention If short period exists, have it first or last. @return The corresponding schedule @deprecated Will be removed in version 2.3
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a3c067d52dd33feb97d851df6cab130e4116759f
https://github.com/finmath/finmath-lib/blob/a3c067d52dd33feb97d851df6cab130e4116759f/src/main/java6/net/finmath/time/ScheduleGenerator.java#L790-L810
train
finmath/finmath-lib
src/main/java6/net/finmath/marketdata/calibration/CalibratedCurves.java
CalibratedCurves.getCalibrationProductForSymbol
public AnalyticProductInterface getCalibrationProductForSymbol(String symbol) { /* * The internal data structure is not optimal here (a map would make more sense here), * if the user does not require access to the products, we would allow non-unique symbols. * Hence we store both in two side by side vectors. */ for(int i=0; i<calibrationProductsSymbols.size(); i++) { String calibrationProductSymbol = calibrationProductsSymbols.get(i); if(calibrationProductSymbol.equals(symbol)) { return calibrationProducts.get(i); } } return null; }
java
public AnalyticProductInterface getCalibrationProductForSymbol(String symbol) { /* * The internal data structure is not optimal here (a map would make more sense here), * if the user does not require access to the products, we would allow non-unique symbols. * Hence we store both in two side by side vectors. */ for(int i=0; i<calibrationProductsSymbols.size(); i++) { String calibrationProductSymbol = calibrationProductsSymbols.get(i); if(calibrationProductSymbol.equals(symbol)) { return calibrationProducts.get(i); } } return null; }
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Returns the first product found in the vector of calibration products which matches the given symbol, where symbol is the String set in the calibrationSpecs. @param symbol A given symbol string. @return The product associated with that symbol.
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a3c067d52dd33feb97d851df6cab130e4116759f
https://github.com/finmath/finmath-lib/blob/a3c067d52dd33feb97d851df6cab130e4116759f/src/main/java6/net/finmath/marketdata/calibration/CalibratedCurves.java#L649-L664
train
finmath/finmath-lib
src/main/java/net/finmath/montecarlo/assetderivativevaluation/products/BermudanDigitalOption.java
BermudanDigitalOption.getValue
@Override public RandomVariable getValue(double evaluationTime, AssetModelMonteCarloSimulationModel model) throws CalculationException { if(exerciseMethod == ExerciseMethod.UPPER_BOUND_METHOD) { // Find optimal lambda GoldenSectionSearch optimizer = new GoldenSectionSearch(-1.0, 1.0); while(!optimizer.isDone()) { double lambda = optimizer.getNextPoint(); double value = this.getValues(evaluationTime, model, lambda).getAverage(); optimizer.setValue(value); } return getValues(evaluationTime, model, optimizer.getBestPoint()); } else { return getValues(evaluationTime, model, 0.0); } }
java
@Override public RandomVariable getValue(double evaluationTime, AssetModelMonteCarloSimulationModel model) throws CalculationException { if(exerciseMethod == ExerciseMethod.UPPER_BOUND_METHOD) { // Find optimal lambda GoldenSectionSearch optimizer = new GoldenSectionSearch(-1.0, 1.0); while(!optimizer.isDone()) { double lambda = optimizer.getNextPoint(); double value = this.getValues(evaluationTime, model, lambda).getAverage(); optimizer.setValue(value); } return getValues(evaluationTime, model, optimizer.getBestPoint()); } else { return getValues(evaluationTime, model, 0.0); } }
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This method returns the value random variable of the product within the specified model, evaluated at a given evalutationTime. Cash-flows prior evaluationTime are not considered. @param evaluationTime The time on which this products value should be observed. @param model The model used to price the product. @return The random variable representing the value of the product discounted to evaluation time. @throws net.finmath.exception.CalculationException Thrown if the valuation fails, specific cause may be available via the <code>cause()</code> method.
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a3c067d52dd33feb97d851df6cab130e4116759f
https://github.com/finmath/finmath-lib/blob/a3c067d52dd33feb97d851df6cab130e4116759f/src/main/java/net/finmath/montecarlo/assetderivativevaluation/products/BermudanDigitalOption.java#L96-L111
train
finmath/finmath-lib
src/main/java6/net/finmath/marketdata/model/curves/HazardCurve.java
HazardCurve.createHazardCurveFromSurvivalProbabilities
public static HazardCurve createHazardCurveFromSurvivalProbabilities(String name, double[] times, double[] givenSurvivalProbabilities){ HazardCurve survivalProbabilities = new HazardCurve(name); for(int timeIndex=0; timeIndex<times.length;timeIndex++) { survivalProbabilities.addSurvivalProbability(times[timeIndex], givenSurvivalProbabilities[timeIndex], times[timeIndex] > 0); } return survivalProbabilities; }
java
public static HazardCurve createHazardCurveFromSurvivalProbabilities(String name, double[] times, double[] givenSurvivalProbabilities){ HazardCurve survivalProbabilities = new HazardCurve(name); for(int timeIndex=0; timeIndex<times.length;timeIndex++) { survivalProbabilities.addSurvivalProbability(times[timeIndex], givenSurvivalProbabilities[timeIndex], times[timeIndex] > 0); } return survivalProbabilities; }
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Create a hazard curve from given times and given discount factors using default interpolation and extrapolation methods. @param name The name of this hazard curve. @param times Array of times as doubles. @param givenSurvivalProbabilities Array of corresponding survival probabilities. @return A new discount factor object.
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a3c067d52dd33feb97d851df6cab130e4116759f
https://github.com/finmath/finmath-lib/blob/a3c067d52dd33feb97d851df6cab130e4116759f/src/main/java6/net/finmath/marketdata/model/curves/HazardCurve.java#L152-L160
train
finmath/finmath-lib
src/main/java/net/finmath/marketdata/model/volatilities/SwaptionDataLattice.java
SwaptionDataLattice.convertLattice
public SwaptionDataLattice convertLattice(QuotingConvention targetConvention, double displacement, AnalyticModel model) { if(displacement != 0 && targetConvention != QuotingConvention.PAYERVOLATILITYLOGNORMAL) { throw new IllegalArgumentException("SwaptionDataLattice only supports displacement, when using QuotingCOnvention.PAYERVOLATILITYLOGNORMAL."); } //Reverse sign of moneyness, if switching between payer and receiver convention. int reverse = ((targetConvention == QuotingConvention.RECEIVERPRICE) ^ (quotingConvention == QuotingConvention.RECEIVERPRICE)) ? -1 : 1; List<Integer> maturities = new ArrayList<>(); List<Integer> tenors = new ArrayList<>(); List<Integer> moneynesss = new ArrayList<>(); List<Double> values = new ArrayList<>(); for(DataKey key : entryMap.keySet()) { maturities.add(key.maturity); tenors.add(key.tenor); moneynesss.add(key.moneyness * reverse); values.add(getValue(key.maturity, key.tenor, key.moneyness, targetConvention, displacement, model)); } return new SwaptionDataLattice(referenceDate, targetConvention, displacement, forwardCurveName, discountCurveName, floatMetaSchedule, fixMetaSchedule, maturities.stream().mapToInt(Integer::intValue).toArray(), tenors.stream().mapToInt(Integer::intValue).toArray(), moneynesss.stream().mapToInt(Integer::intValue).toArray(), values.stream().mapToDouble(Double::doubleValue).toArray()); }
java
public SwaptionDataLattice convertLattice(QuotingConvention targetConvention, double displacement, AnalyticModel model) { if(displacement != 0 && targetConvention != QuotingConvention.PAYERVOLATILITYLOGNORMAL) { throw new IllegalArgumentException("SwaptionDataLattice only supports displacement, when using QuotingCOnvention.PAYERVOLATILITYLOGNORMAL."); } //Reverse sign of moneyness, if switching between payer and receiver convention. int reverse = ((targetConvention == QuotingConvention.RECEIVERPRICE) ^ (quotingConvention == QuotingConvention.RECEIVERPRICE)) ? -1 : 1; List<Integer> maturities = new ArrayList<>(); List<Integer> tenors = new ArrayList<>(); List<Integer> moneynesss = new ArrayList<>(); List<Double> values = new ArrayList<>(); for(DataKey key : entryMap.keySet()) { maturities.add(key.maturity); tenors.add(key.tenor); moneynesss.add(key.moneyness * reverse); values.add(getValue(key.maturity, key.tenor, key.moneyness, targetConvention, displacement, model)); } return new SwaptionDataLattice(referenceDate, targetConvention, displacement, forwardCurveName, discountCurveName, floatMetaSchedule, fixMetaSchedule, maturities.stream().mapToInt(Integer::intValue).toArray(), tenors.stream().mapToInt(Integer::intValue).toArray(), moneynesss.stream().mapToInt(Integer::intValue).toArray(), values.stream().mapToDouble(Double::doubleValue).toArray()); }
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Convert this lattice to store data in the given convention. Conversion involving receiver premium assumes zero wide collar. @param targetConvention The convention to store the data in. @param displacement The displacement to use, if applicable. @param model The model for context. @return The converted lattice.
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a3c067d52dd33feb97d851df6cab130e4116759f
https://github.com/finmath/finmath-lib/blob/a3c067d52dd33feb97d851df6cab130e4116759f/src/main/java/net/finmath/marketdata/model/volatilities/SwaptionDataLattice.java#L260-L287
train
finmath/finmath-lib
src/main/java/net/finmath/marketdata/model/volatilities/SwaptionDataLattice.java
SwaptionDataLattice.append
public SwaptionDataLattice append(SwaptionDataLattice other, AnalyticModel model) { SwaptionDataLattice combined = new SwaptionDataLattice(referenceDate, quotingConvention, displacement, forwardCurveName, discountCurveName, floatMetaSchedule, fixMetaSchedule); combined.entryMap.putAll(entryMap); if(quotingConvention == other.quotingConvention && displacement == other.displacement) { combined.entryMap.putAll(other.entryMap); } else { SwaptionDataLattice converted = other.convertLattice(quotingConvention, displacement, model); combined.entryMap.putAll(converted.entryMap); } return combined; }
java
public SwaptionDataLattice append(SwaptionDataLattice other, AnalyticModel model) { SwaptionDataLattice combined = new SwaptionDataLattice(referenceDate, quotingConvention, displacement, forwardCurveName, discountCurveName, floatMetaSchedule, fixMetaSchedule); combined.entryMap.putAll(entryMap); if(quotingConvention == other.quotingConvention && displacement == other.displacement) { combined.entryMap.putAll(other.entryMap); } else { SwaptionDataLattice converted = other.convertLattice(quotingConvention, displacement, model); combined.entryMap.putAll(converted.entryMap); } return combined; }
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Append the data of another lattice to this lattice. If the other lattice follows a different quoting convention, it is automatically converted. However, this method does not check, whether the two lattices are aligned in terms of reference date, curve names and meta schedules. If the two lattices have shared data points, the data from this lattice will be overwritten. @param other The lattice containing the data to be appended. @param model The model to use for context, in case the other lattice follows a different convention. @return The lattice with the combined swaption entries.
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a3c067d52dd33feb97d851df6cab130e4116759f
https://github.com/finmath/finmath-lib/blob/a3c067d52dd33feb97d851df6cab130e4116759f/src/main/java/net/finmath/marketdata/model/volatilities/SwaptionDataLattice.java#L299-L313
train
finmath/finmath-lib
src/main/java/net/finmath/marketdata/model/volatilities/SwaptionDataLattice.java
SwaptionDataLattice.getMoneynessAsOffsets
public double[] getMoneynessAsOffsets() { DoubleStream moneyness = getGridNodesPerMoneyness().keySet().stream().mapToDouble(Integer::doubleValue); if(quotingConvention == QuotingConvention.PAYERVOLATILITYLOGNORMAL) { moneyness = moneyness.map(new DoubleUnaryOperator() { @Override public double applyAsDouble(double x) { return x * 0.01; } }); } else if(quotingConvention == QuotingConvention.RECEIVERPRICE) { moneyness = moneyness.map(new DoubleUnaryOperator() { @Override public double applyAsDouble(double x) { return - x * 0.0001; } }); } else { moneyness = moneyness.map(new DoubleUnaryOperator() { @Override public double applyAsDouble(double x) { return x * 0.0001; } }); } return moneyness.toArray(); }
java
public double[] getMoneynessAsOffsets() { DoubleStream moneyness = getGridNodesPerMoneyness().keySet().stream().mapToDouble(Integer::doubleValue); if(quotingConvention == QuotingConvention.PAYERVOLATILITYLOGNORMAL) { moneyness = moneyness.map(new DoubleUnaryOperator() { @Override public double applyAsDouble(double x) { return x * 0.01; } }); } else if(quotingConvention == QuotingConvention.RECEIVERPRICE) { moneyness = moneyness.map(new DoubleUnaryOperator() { @Override public double applyAsDouble(double x) { return - x * 0.0001; } }); } else { moneyness = moneyness.map(new DoubleUnaryOperator() { @Override public double applyAsDouble(double x) { return x * 0.0001; } }); } return moneyness.toArray(); }
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Return all levels of moneyness for which data exists. Moneyness is returned as actual difference strike - par swap rate. @return The levels of moneyness as difference of strike to par swap rate.
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a3c067d52dd33feb97d851df6cab130e4116759f
https://github.com/finmath/finmath-lib/blob/a3c067d52dd33feb97d851df6cab130e4116759f/src/main/java/net/finmath/marketdata/model/volatilities/SwaptionDataLattice.java#L372-L397
train
finmath/finmath-lib
src/main/java/net/finmath/marketdata/model/volatilities/SwaptionDataLattice.java
SwaptionDataLattice.getMaturities
public double[] getMaturities(double moneyness) { int[] maturitiesInMonths = getMaturities(convertMoneyness(moneyness)); double[] maturities = new double[maturitiesInMonths.length]; for(int index = 0; index < maturities.length; index++) { maturities[index] = convertMaturity(maturitiesInMonths[index]); } return maturities; }
java
public double[] getMaturities(double moneyness) { int[] maturitiesInMonths = getMaturities(convertMoneyness(moneyness)); double[] maturities = new double[maturitiesInMonths.length]; for(int index = 0; index < maturities.length; index++) { maturities[index] = convertMaturity(maturitiesInMonths[index]); } return maturities; }
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Return all valid maturities for a given moneyness. Uses the fixing times of the fix schedule to determine fractions. @param moneyness The moneyness as actual offset from par swap rate for which to get the maturities. @return The maturities as year fraction from reference date.
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a3c067d52dd33feb97d851df6cab130e4116759f
https://github.com/finmath/finmath-lib/blob/a3c067d52dd33feb97d851df6cab130e4116759f/src/main/java/net/finmath/marketdata/model/volatilities/SwaptionDataLattice.java#L434-L442
train
finmath/finmath-lib
src/main/java/net/finmath/marketdata/model/volatilities/SwaptionDataLattice.java
SwaptionDataLattice.getTenors
public int[] getTenors() { Set<Integer> setTenors = new HashSet<>(); for(int moneyness : getGridNodesPerMoneyness().keySet()) { setTenors.addAll(Arrays.asList((IntStream.of(keyMap.get(moneyness)[1]).boxed().toArray(Integer[]::new)))); } return setTenors.stream().sorted().mapToInt(Integer::intValue).toArray(); }
java
public int[] getTenors() { Set<Integer> setTenors = new HashSet<>(); for(int moneyness : getGridNodesPerMoneyness().keySet()) { setTenors.addAll(Arrays.asList((IntStream.of(keyMap.get(moneyness)[1]).boxed().toArray(Integer[]::new)))); } return setTenors.stream().sorted().mapToInt(Integer::intValue).toArray(); }
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Return all tenors for which data exists. @return The tenors in months.
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a3c067d52dd33feb97d851df6cab130e4116759f
https://github.com/finmath/finmath-lib/blob/a3c067d52dd33feb97d851df6cab130e4116759f/src/main/java/net/finmath/marketdata/model/volatilities/SwaptionDataLattice.java#L449-L456
train
finmath/finmath-lib
src/main/java/net/finmath/marketdata/model/volatilities/SwaptionDataLattice.java
SwaptionDataLattice.getTenors
public int[] getTenors(int moneynessBP, int maturityInMonths) { try { List<Integer> ret = new ArrayList<>(); for(int tenor : getGridNodesPerMoneyness().get(moneynessBP)[1]) { if(containsEntryFor(maturityInMonths, tenor, moneynessBP)) { ret.add(tenor); } } return ret.stream().mapToInt(Integer::intValue).toArray(); } catch (NullPointerException e) { return new int[0]; } }
java
public int[] getTenors(int moneynessBP, int maturityInMonths) { try { List<Integer> ret = new ArrayList<>(); for(int tenor : getGridNodesPerMoneyness().get(moneynessBP)[1]) { if(containsEntryFor(maturityInMonths, tenor, moneynessBP)) { ret.add(tenor); } } return ret.stream().mapToInt(Integer::intValue).toArray(); } catch (NullPointerException e) { return new int[0]; } }
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Return all valid tenors for a given moneyness and maturity. @param moneynessBP The moneyness in bp for which to get the tenors. @param maturityInMonths The maturities in months for which to get the tenors. @return The tenors in months.
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a3c067d52dd33feb97d851df6cab130e4116759f
https://github.com/finmath/finmath-lib/blob/a3c067d52dd33feb97d851df6cab130e4116759f/src/main/java/net/finmath/marketdata/model/volatilities/SwaptionDataLattice.java#L465-L478
train
finmath/finmath-lib
src/main/java/net/finmath/marketdata/model/volatilities/SwaptionDataLattice.java
SwaptionDataLattice.getTenors
public double[] getTenors(double moneyness, double maturity) { int maturityInMonths = (int) Math.round(maturity * 12); int[] tenorsInMonths = getTenors(convertMoneyness(moneyness), maturityInMonths); double[] tenors = new double[tenorsInMonths.length]; for(int index = 0; index < tenors.length; index++) { tenors[index] = convertTenor(maturityInMonths, tenorsInMonths[index]); } return tenors; }
java
public double[] getTenors(double moneyness, double maturity) { int maturityInMonths = (int) Math.round(maturity * 12); int[] tenorsInMonths = getTenors(convertMoneyness(moneyness), maturityInMonths); double[] tenors = new double[tenorsInMonths.length]; for(int index = 0; index < tenors.length; index++) { tenors[index] = convertTenor(maturityInMonths, tenorsInMonths[index]); } return tenors; }
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Return all valid tenors for a given moneyness and maturity. Uses the payment times of the fix schedule to determine fractions. @param moneyness The moneyness as actual offset from par swap rate for which to get the maturities. @param maturity The maturities as year fraction from the reference date. @return The tenors as year fraction from reference date.
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a3c067d52dd33feb97d851df6cab130e4116759f
https://github.com/finmath/finmath-lib/blob/a3c067d52dd33feb97d851df6cab130e4116759f/src/main/java/net/finmath/marketdata/model/volatilities/SwaptionDataLattice.java#L488-L497
train
finmath/finmath-lib
src/main/java/net/finmath/marketdata/model/volatilities/SwaptionDataLattice.java
SwaptionDataLattice.convertMoneyness
private int convertMoneyness(double moneyness) { if(quotingConvention == QuotingConvention.PAYERVOLATILITYLOGNORMAL) { return (int) Math.round(moneyness * 100); } else if(quotingConvention == QuotingConvention.RECEIVERPRICE) { return - (int) Math.round(moneyness * 10000); } else { return (int) Math.round(moneyness * 10000); } }
java
private int convertMoneyness(double moneyness) { if(quotingConvention == QuotingConvention.PAYERVOLATILITYLOGNORMAL) { return (int) Math.round(moneyness * 100); } else if(quotingConvention == QuotingConvention.RECEIVERPRICE) { return - (int) Math.round(moneyness * 10000); } else { return (int) Math.round(moneyness * 10000); } }
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Convert moneyness given as difference to par swap rate to moneyness in bp. Uses the fixing times of the fix schedule to determine fractions. @param moneyness as offset. @return Moneyness in bp.
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a3c067d52dd33feb97d851df6cab130e4116759f
https://github.com/finmath/finmath-lib/blob/a3c067d52dd33feb97d851df6cab130e4116759f/src/main/java/net/finmath/marketdata/model/volatilities/SwaptionDataLattice.java#L506-L514
train
finmath/finmath-lib
src/main/java/net/finmath/marketdata/model/volatilities/SwaptionDataLattice.java
SwaptionDataLattice.convertMaturity
private double convertMaturity(int maturityInMonths) { Schedule schedule = fixMetaSchedule.generateSchedule(referenceDate, maturityInMonths, 12); return schedule.getFixing(0); }
java
private double convertMaturity(int maturityInMonths) { Schedule schedule = fixMetaSchedule.generateSchedule(referenceDate, maturityInMonths, 12); return schedule.getFixing(0); }
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Convert maturity given as offset in months to year fraction. @param maturityInMonths The maturity as offset in months. @return The maturity as year fraction.
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a3c067d52dd33feb97d851df6cab130e4116759f
https://github.com/finmath/finmath-lib/blob/a3c067d52dd33feb97d851df6cab130e4116759f/src/main/java/net/finmath/marketdata/model/volatilities/SwaptionDataLattice.java#L522-L525
train
finmath/finmath-lib
src/main/java/net/finmath/marketdata/model/volatilities/SwaptionDataLattice.java
SwaptionDataLattice.convertTenor
private double convertTenor(int maturityInMonths, int tenorInMonths) { Schedule schedule = fixMetaSchedule.generateSchedule(referenceDate, maturityInMonths, tenorInMonths); return schedule.getPayment(schedule.getNumberOfPeriods()-1); }
java
private double convertTenor(int maturityInMonths, int tenorInMonths) { Schedule schedule = fixMetaSchedule.generateSchedule(referenceDate, maturityInMonths, tenorInMonths); return schedule.getPayment(schedule.getNumberOfPeriods()-1); }
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Convert tenor given as offset in months to year fraction. @param maturityInMonths The maturity as offset in months. @param tenorInMonths The tenor as offset in months. @return THe tenor as year fraction.
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a3c067d52dd33feb97d851df6cab130e4116759f
https://github.com/finmath/finmath-lib/blob/a3c067d52dd33feb97d851df6cab130e4116759f/src/main/java/net/finmath/marketdata/model/volatilities/SwaptionDataLattice.java#L534-L537
train
finmath/finmath-lib
src/main/java/net/finmath/marketdata/model/volatilities/SwaptionDataLattice.java
SwaptionDataLattice.containsEntryFor
public boolean containsEntryFor(int maturityInMonths, int tenorInMonths, int moneynessBP) { return entryMap.containsKey(new DataKey(maturityInMonths, tenorInMonths, moneynessBP)); }
java
public boolean containsEntryFor(int maturityInMonths, int tenorInMonths, int moneynessBP) { return entryMap.containsKey(new DataKey(maturityInMonths, tenorInMonths, moneynessBP)); }
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Returns true if the lattice contains an entry at the specified location. @param maturityInMonths The maturity in months to check. @param tenorInMonths The tenor in months to check. @param moneynessBP The moneyness in bp to check. @return True iff there is an entry at the specified location.
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a3c067d52dd33feb97d851df6cab130e4116759f
https://github.com/finmath/finmath-lib/blob/a3c067d52dd33feb97d851df6cab130e4116759f/src/main/java/net/finmath/marketdata/model/volatilities/SwaptionDataLattice.java#L547-L549
train
finmath/finmath-lib
src/main/java/net/finmath/marketdata/model/volatilities/SwaptionDataLattice.java
SwaptionDataLattice.convertToConvention
private double convertToConvention(double value, DataKey key, QuotingConvention toConvention, double toDisplacement, QuotingConvention fromConvention, double fromDisplacement, AnalyticModel model) { if(toConvention == fromConvention) { if(toConvention != QuotingConvention.PAYERVOLATILITYLOGNORMAL) { return value; } else { if(toDisplacement == fromDisplacement) { return value; } else { return convertToConvention(convertToConvention(value, key, QuotingConvention.PAYERPRICE, 0, fromConvention, fromDisplacement, model), key, toConvention, toDisplacement, QuotingConvention.PAYERPRICE, 0, model); } } } Schedule floatSchedule = floatMetaSchedule.generateSchedule(getReferenceDate(), key.maturity, key.tenor); Schedule fixSchedule = fixMetaSchedule.generateSchedule(getReferenceDate(), key.maturity, key.tenor); double forward = Swap.getForwardSwapRate(fixSchedule, floatSchedule, model.getForwardCurve(forwardCurveName), model); double optionMaturity = floatSchedule.getFixing(0); double offset = key.moneyness /10000.0; double optionStrike = forward + (quotingConvention == QuotingConvention.RECEIVERPRICE ? -offset : offset); double payoffUnit = SwapAnnuity.getSwapAnnuity(fixSchedule.getFixing(0), fixSchedule, model.getDiscountCurve(discountCurveName), model); if(toConvention.equals(QuotingConvention.PAYERPRICE) && fromConvention.equals(QuotingConvention.PAYERVOLATILITYLOGNORMAL)) { return AnalyticFormulas.blackScholesGeneralizedOptionValue(forward + fromDisplacement, value, optionMaturity, optionStrike + fromDisplacement, payoffUnit); } else if(toConvention.equals(QuotingConvention.PAYERPRICE) && fromConvention.equals(QuotingConvention.PAYERVOLATILITYNORMAL)) { return AnalyticFormulas.bachelierOptionValue(forward, value, optionMaturity, optionStrike, payoffUnit); } else if(toConvention.equals(QuotingConvention.PAYERPRICE) && fromConvention.equals(QuotingConvention.RECEIVERPRICE)) { return value + (forward - optionStrike) * payoffUnit; } else if(toConvention.equals(QuotingConvention.PAYERVOLATILITYLOGNORMAL) && fromConvention.equals(QuotingConvention.PAYERPRICE)) { return AnalyticFormulas.blackScholesOptionImpliedVolatility(forward + toDisplacement, optionMaturity, optionStrike + toDisplacement, payoffUnit, value); } else if(toConvention.equals(QuotingConvention.PAYERVOLATILITYNORMAL) && fromConvention.equals(QuotingConvention.PAYERPRICE)) { return AnalyticFormulas.bachelierOptionImpliedVolatility(forward, optionMaturity, optionStrike, payoffUnit, value); } else if(toConvention.equals(QuotingConvention.RECEIVERPRICE) && fromConvention.equals(QuotingConvention.PAYERPRICE)) { return value - (forward - optionStrike) * payoffUnit; } else { return convertToConvention(convertToConvention(value, key, QuotingConvention.PAYERPRICE, 0, fromConvention, fromDisplacement, model), key, toConvention, toDisplacement, QuotingConvention.PAYERPRICE, 0, model); } }
java
private double convertToConvention(double value, DataKey key, QuotingConvention toConvention, double toDisplacement, QuotingConvention fromConvention, double fromDisplacement, AnalyticModel model) { if(toConvention == fromConvention) { if(toConvention != QuotingConvention.PAYERVOLATILITYLOGNORMAL) { return value; } else { if(toDisplacement == fromDisplacement) { return value; } else { return convertToConvention(convertToConvention(value, key, QuotingConvention.PAYERPRICE, 0, fromConvention, fromDisplacement, model), key, toConvention, toDisplacement, QuotingConvention.PAYERPRICE, 0, model); } } } Schedule floatSchedule = floatMetaSchedule.generateSchedule(getReferenceDate(), key.maturity, key.tenor); Schedule fixSchedule = fixMetaSchedule.generateSchedule(getReferenceDate(), key.maturity, key.tenor); double forward = Swap.getForwardSwapRate(fixSchedule, floatSchedule, model.getForwardCurve(forwardCurveName), model); double optionMaturity = floatSchedule.getFixing(0); double offset = key.moneyness /10000.0; double optionStrike = forward + (quotingConvention == QuotingConvention.RECEIVERPRICE ? -offset : offset); double payoffUnit = SwapAnnuity.getSwapAnnuity(fixSchedule.getFixing(0), fixSchedule, model.getDiscountCurve(discountCurveName), model); if(toConvention.equals(QuotingConvention.PAYERPRICE) && fromConvention.equals(QuotingConvention.PAYERVOLATILITYLOGNORMAL)) { return AnalyticFormulas.blackScholesGeneralizedOptionValue(forward + fromDisplacement, value, optionMaturity, optionStrike + fromDisplacement, payoffUnit); } else if(toConvention.equals(QuotingConvention.PAYERPRICE) && fromConvention.equals(QuotingConvention.PAYERVOLATILITYNORMAL)) { return AnalyticFormulas.bachelierOptionValue(forward, value, optionMaturity, optionStrike, payoffUnit); } else if(toConvention.equals(QuotingConvention.PAYERPRICE) && fromConvention.equals(QuotingConvention.RECEIVERPRICE)) { return value + (forward - optionStrike) * payoffUnit; } else if(toConvention.equals(QuotingConvention.PAYERVOLATILITYLOGNORMAL) && fromConvention.equals(QuotingConvention.PAYERPRICE)) { return AnalyticFormulas.blackScholesOptionImpliedVolatility(forward + toDisplacement, optionMaturity, optionStrike + toDisplacement, payoffUnit, value); } else if(toConvention.equals(QuotingConvention.PAYERVOLATILITYNORMAL) && fromConvention.equals(QuotingConvention.PAYERPRICE)) { return AnalyticFormulas.bachelierOptionImpliedVolatility(forward, optionMaturity, optionStrike, payoffUnit, value); } else if(toConvention.equals(QuotingConvention.RECEIVERPRICE) && fromConvention.equals(QuotingConvention.PAYERPRICE)) { return value - (forward - optionStrike) * payoffUnit; } else { return convertToConvention(convertToConvention(value, key, QuotingConvention.PAYERPRICE, 0, fromConvention, fromDisplacement, model), key, toConvention, toDisplacement, QuotingConvention.PAYERPRICE, 0, model); } }
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Convert the value to requested quoting convention. Conversion involving receiver premium assumes zero wide collar. @param value The value to convert. @param key The key of the value. @param toConvention The convention to convert to. @param toDisplacement The displacement to be used, if converting to log normal implied volatility. @param fromConvention The current convention of the value. @param fromDisplacement The current displacement. @param model The model for context. @return The converted value.
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a3c067d52dd33feb97d851df6cab130e4116759f
https://github.com/finmath/finmath-lib/blob/a3c067d52dd33feb97d851df6cab130e4116759f/src/main/java/net/finmath/marketdata/model/volatilities/SwaptionDataLattice.java#L666-L713
train
finmath/finmath-lib
src/main/java/net/finmath/marketdata/model/bond/Bond.java
Bond.getCouponPayment
public double getCouponPayment(int periodIndex, AnalyticModel model) { ForwardCurve forwardCurve = model.getForwardCurve(forwardCurveName); if(forwardCurve == null && forwardCurveName != null && forwardCurveName.length() > 0) { throw new IllegalArgumentException("No forward curve with name '" + forwardCurveName + "' was found in the model:\n" + model.toString()); } double periodLength = schedule.getPeriodLength(periodIndex); double couponPayment=fixedCoupon ; if(forwardCurve != null ) { couponPayment = floatingSpread+forwardCurve.getForward(model, schedule.getFixing(periodIndex)); } return couponPayment*periodLength; }
java
public double getCouponPayment(int periodIndex, AnalyticModel model) { ForwardCurve forwardCurve = model.getForwardCurve(forwardCurveName); if(forwardCurve == null && forwardCurveName != null && forwardCurveName.length() > 0) { throw new IllegalArgumentException("No forward curve with name '" + forwardCurveName + "' was found in the model:\n" + model.toString()); } double periodLength = schedule.getPeriodLength(periodIndex); double couponPayment=fixedCoupon ; if(forwardCurve != null ) { couponPayment = floatingSpread+forwardCurve.getForward(model, schedule.getFixing(periodIndex)); } return couponPayment*periodLength; }
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Returns the coupon payment of the period with the given index. The analytic model is needed in case of floating bonds. @param periodIndex The index of the period of interest. @param model The model under which the product is valued. @return The value of the coupon payment in the given period.
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a3c067d52dd33feb97d851df6cab130e4116759f
https://github.com/finmath/finmath-lib/blob/a3c067d52dd33feb97d851df6cab130e4116759f/src/main/java/net/finmath/marketdata/model/bond/Bond.java#L209-L222
train
finmath/finmath-lib
src/main/java/net/finmath/marketdata/model/bond/Bond.java
Bond.getValueWithGivenSpreadOverCurve
public double getValueWithGivenSpreadOverCurve(double evaluationTime,Curve referenceCurve, double spread, AnalyticModel model) { double value=0; for(int periodIndex=0; periodIndex<schedule.getNumberOfPeriods();periodIndex++) { double paymentDate = schedule.getPayment(periodIndex); value+= paymentDate>evaluationTime ? getCouponPayment(periodIndex,model)*Math.exp(-spread*paymentDate)*referenceCurve.getValue(paymentDate): 0.0; } double paymentDate = schedule.getPayment(schedule.getNumberOfPeriods()-1); return paymentDate>evaluationTime ? value+Math.exp(-spread*paymentDate)*referenceCurve.getValue(paymentDate):0.0; }
java
public double getValueWithGivenSpreadOverCurve(double evaluationTime,Curve referenceCurve, double spread, AnalyticModel model) { double value=0; for(int periodIndex=0; periodIndex<schedule.getNumberOfPeriods();periodIndex++) { double paymentDate = schedule.getPayment(periodIndex); value+= paymentDate>evaluationTime ? getCouponPayment(periodIndex,model)*Math.exp(-spread*paymentDate)*referenceCurve.getValue(paymentDate): 0.0; } double paymentDate = schedule.getPayment(schedule.getNumberOfPeriods()-1); return paymentDate>evaluationTime ? value+Math.exp(-spread*paymentDate)*referenceCurve.getValue(paymentDate):0.0; }
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Returns the value of the sum of discounted cash flows of the bond where the discounting is done with the given reference curve and an additional spread. This method can be used for optimizer. @param evaluationTime The evaluation time as double. Cash flows prior and including this time are not considered. @param referenceCurve The reference curve used for discounting the coupon payments. @param spread The spread which should be added to the discount curve. @param model The model under which the product is valued. @return The value of the bond for the given curve and spread.
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a3c067d52dd33feb97d851df6cab130e4116759f
https://github.com/finmath/finmath-lib/blob/a3c067d52dd33feb97d851df6cab130e4116759f/src/main/java/net/finmath/marketdata/model/bond/Bond.java#L235-L244
train
finmath/finmath-lib
src/main/java/net/finmath/marketdata/model/bond/Bond.java
Bond.getValueWithGivenYield
public double getValueWithGivenYield(double evaluationTime, double rate, AnalyticModel model) { DiscountCurve referenceCurve = DiscountCurveInterpolation.createDiscountCurveFromDiscountFactors("referenceCurve", new double[] {0.0, 1.0}, new double[] {1.0, 1.0}); return getValueWithGivenSpreadOverCurve(evaluationTime, referenceCurve, rate, model); }
java
public double getValueWithGivenYield(double evaluationTime, double rate, AnalyticModel model) { DiscountCurve referenceCurve = DiscountCurveInterpolation.createDiscountCurveFromDiscountFactors("referenceCurve", new double[] {0.0, 1.0}, new double[] {1.0, 1.0}); return getValueWithGivenSpreadOverCurve(evaluationTime, referenceCurve, rate, model); }
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Returns the value of the sum of discounted cash flows of the bond where the discounting is done with the given yield curve. This method can be used for optimizer. @param evaluationTime The evaluation time as double. Cash flows prior and including this time are not considered. @param rate The yield which is used for discounted the coupon payments. @param model The model under which the product is valued. @return The value of the bond for the given yield.
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a3c067d52dd33feb97d851df6cab130e4116759f
https://github.com/finmath/finmath-lib/blob/a3c067d52dd33feb97d851df6cab130e4116759f/src/main/java/net/finmath/marketdata/model/bond/Bond.java#L256-L259
train
finmath/finmath-lib
src/main/java/net/finmath/marketdata/model/bond/Bond.java
Bond.getSpread
public double getSpread(double bondPrice, Curve referenceCurve, AnalyticModel model) { GoldenSectionSearch search = new GoldenSectionSearch(-2.0, 2.0); while(search.getAccuracy() > 1E-11 && !search.isDone()) { double x = search.getNextPoint(); double fx=getValueWithGivenSpreadOverCurve(0.0,referenceCurve,x,model); double y = (bondPrice-fx)*(bondPrice-fx); search.setValue(y); } return search.getBestPoint(); }
java
public double getSpread(double bondPrice, Curve referenceCurve, AnalyticModel model) { GoldenSectionSearch search = new GoldenSectionSearch(-2.0, 2.0); while(search.getAccuracy() > 1E-11 && !search.isDone()) { double x = search.getNextPoint(); double fx=getValueWithGivenSpreadOverCurve(0.0,referenceCurve,x,model); double y = (bondPrice-fx)*(bondPrice-fx); search.setValue(y); } return search.getBestPoint(); }
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Returns the spread value such that the sum of cash flows of the bond discounted with a given reference curve with the additional spread coincides with a given price. @param bondPrice The target price as double. @param referenceCurve The reference curve used for discounting the coupon payments. @param model The model under which the product is valued. @return The optimal spread value.
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a3c067d52dd33feb97d851df6cab130e4116759f
https://github.com/finmath/finmath-lib/blob/a3c067d52dd33feb97d851df6cab130e4116759f/src/main/java/net/finmath/marketdata/model/bond/Bond.java#L270-L280
train
finmath/finmath-lib
src/main/java/net/finmath/marketdata/model/bond/Bond.java
Bond.getYield
public double getYield(double bondPrice, AnalyticModel model) { GoldenSectionSearch search = new GoldenSectionSearch(-2.0, 2.0); while(search.getAccuracy() > 1E-11 && !search.isDone()) { double x = search.getNextPoint(); double fx=getValueWithGivenYield(0.0,x,model); double y = (bondPrice-fx)*(bondPrice-fx); search.setValue(y); } return search.getBestPoint(); }
java
public double getYield(double bondPrice, AnalyticModel model) { GoldenSectionSearch search = new GoldenSectionSearch(-2.0, 2.0); while(search.getAccuracy() > 1E-11 && !search.isDone()) { double x = search.getNextPoint(); double fx=getValueWithGivenYield(0.0,x,model); double y = (bondPrice-fx)*(bondPrice-fx); search.setValue(y); } return search.getBestPoint(); }
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Returns the yield value such that the sum of cash flows of the bond discounted with the yield curve coincides with a given price. @param bondPrice The target price as double. @param model The model under which the product is valued. @return The optimal yield value.
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a3c067d52dd33feb97d851df6cab130e4116759f
https://github.com/finmath/finmath-lib/blob/a3c067d52dd33feb97d851df6cab130e4116759f/src/main/java/net/finmath/marketdata/model/bond/Bond.java#L290-L300
train
finmath/finmath-lib
src/main/java/net/finmath/marketdata/model/bond/Bond.java
Bond.getAccruedInterest
public double getAccruedInterest(LocalDate date, AnalyticModel model) { int periodIndex=schedule.getPeriodIndex(date); Period period=schedule.getPeriod(periodIndex); DayCountConvention dcc= schedule.getDaycountconvention(); double accruedInterest=getCouponPayment(periodIndex,model)*(dcc.getDaycountFraction(period.getPeriodStart(), date))/schedule.getPeriodLength(periodIndex); return accruedInterest; }
java
public double getAccruedInterest(LocalDate date, AnalyticModel model) { int periodIndex=schedule.getPeriodIndex(date); Period period=schedule.getPeriod(periodIndex); DayCountConvention dcc= schedule.getDaycountconvention(); double accruedInterest=getCouponPayment(periodIndex,model)*(dcc.getDaycountFraction(period.getPeriodStart(), date))/schedule.getPeriodLength(periodIndex); return accruedInterest; }
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Returns the accrued interest of the bond for a given date. @param date The date of interest. @param model The model under which the product is valued. @return The accrued interest.
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a3c067d52dd33feb97d851df6cab130e4116759f
https://github.com/finmath/finmath-lib/blob/a3c067d52dd33feb97d851df6cab130e4116759f/src/main/java/net/finmath/marketdata/model/bond/Bond.java#L309-L315
train
finmath/finmath-lib
src/main/java/net/finmath/marketdata/model/bond/Bond.java
Bond.getAccruedInterest
public double getAccruedInterest(double time, AnalyticModel model) { LocalDate date= FloatingpointDate.getDateFromFloatingPointDate(schedule.getReferenceDate(), time); return getAccruedInterest(date, model); }
java
public double getAccruedInterest(double time, AnalyticModel model) { LocalDate date= FloatingpointDate.getDateFromFloatingPointDate(schedule.getReferenceDate(), time); return getAccruedInterest(date, model); }
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Returns the accrued interest of the bond for a given time. @param time The time of interest as double. @param model The model under which the product is valued. @return The accrued interest.
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a3c067d52dd33feb97d851df6cab130e4116759f
https://github.com/finmath/finmath-lib/blob/a3c067d52dd33feb97d851df6cab130e4116759f/src/main/java/net/finmath/marketdata/model/bond/Bond.java#L324-L327
train
finmath/finmath-lib
src/main/java/net/finmath/functions/AnalyticFormulas.java
AnalyticFormulas.blackScholesOptionTheta
public static double blackScholesOptionTheta( double initialStockValue, double riskFreeRate, double volatility, double optionMaturity, double optionStrike) { if(optionStrike <= 0.0 || optionMaturity <= 0.0) { // The Black-Scholes model does not consider it being an option return 0.0; } else { // Calculate theta double dPlus = (Math.log(initialStockValue / optionStrike) + (riskFreeRate + 0.5 * volatility * volatility) * optionMaturity) / (volatility * Math.sqrt(optionMaturity)); double dMinus = dPlus - volatility * Math.sqrt(optionMaturity); double theta = volatility * Math.exp(-0.5*dPlus*dPlus) / Math.sqrt(2.0 * Math.PI) / Math.sqrt(optionMaturity) / 2 * initialStockValue + riskFreeRate * optionStrike * Math.exp(-riskFreeRate * optionMaturity) * NormalDistribution.cumulativeDistribution(dMinus); return theta; } }
java
public static double blackScholesOptionTheta( double initialStockValue, double riskFreeRate, double volatility, double optionMaturity, double optionStrike) { if(optionStrike <= 0.0 || optionMaturity <= 0.0) { // The Black-Scholes model does not consider it being an option return 0.0; } else { // Calculate theta double dPlus = (Math.log(initialStockValue / optionStrike) + (riskFreeRate + 0.5 * volatility * volatility) * optionMaturity) / (volatility * Math.sqrt(optionMaturity)); double dMinus = dPlus - volatility * Math.sqrt(optionMaturity); double theta = volatility * Math.exp(-0.5*dPlus*dPlus) / Math.sqrt(2.0 * Math.PI) / Math.sqrt(optionMaturity) / 2 * initialStockValue + riskFreeRate * optionStrike * Math.exp(-riskFreeRate * optionMaturity) * NormalDistribution.cumulativeDistribution(dMinus); return theta; } }
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This static method calculated the vega of a call option under a Black-Scholes model @param initialStockValue The initial value of the underlying, i.e., the spot. @param riskFreeRate The risk free rate of the bank account numerarie. @param volatility The Black-Scholes volatility. @param optionMaturity The option maturity T. @param optionStrike The option strike. @return The vega of the option
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a3c067d52dd33feb97d851df6cab130e4116759f
https://github.com/finmath/finmath-lib/blob/a3c067d52dd33feb97d851df6cab130e4116759f/src/main/java/net/finmath/functions/AnalyticFormulas.java#L439-L461
train
finmath/finmath-lib
src/main/java6/net/finmath/montecarlo/AbstractMonteCarloProduct.java
AbstractMonteCarloProduct.getValues
public Map<String, Object> getValues(double evaluationTime, MonteCarloSimulationInterface model) throws CalculationException { RandomVariableInterface values = getValue(evaluationTime, model); if(values == null) { return null; } // Sum up values on path double value = values.getAverage(); double error = values.getStandardError(); Map<String, Object> results = new HashMap<String, Object>(); results.put("value", value); results.put("error", error); return results; }
java
public Map<String, Object> getValues(double evaluationTime, MonteCarloSimulationInterface model) throws CalculationException { RandomVariableInterface values = getValue(evaluationTime, model); if(values == null) { return null; } // Sum up values on path double value = values.getAverage(); double error = values.getStandardError(); Map<String, Object> results = new HashMap<String, Object>(); results.put("value", value); results.put("error", error); return results; }
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This method returns the value of the product under the specified model and other information in a key-value map. @param evaluationTime The time on which this products value should be observed. @param model A model used to evaluate the product. @return The values of the product. @throws net.finmath.exception.CalculationException Thrown if the valuation fails, specific cause may be available via the <code>cause()</code> method.
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a3c067d52dd33feb97d851df6cab130e4116759f
https://github.com/finmath/finmath-lib/blob/a3c067d52dd33feb97d851df6cab130e4116759f/src/main/java6/net/finmath/montecarlo/AbstractMonteCarloProduct.java#L113-L130
train
finmath/finmath-lib
src/main/java6/net/finmath/interpolation/RationalFunctionInterpolation.java
RationalFunctionInterpolation.getValue
public double getValue(double x) { synchronized(interpolatingRationalFunctionsLazyInitLock) { if(interpolatingRationalFunctions == null) { doCreateRationalFunctions(); } } // Get interpolating rational function for the given point x int pointIndex = java.util.Arrays.binarySearch(points, x); if(pointIndex >= 0) { return values[pointIndex]; } int intervallIndex = -pointIndex-2; // Check for extrapolation if(intervallIndex < 0) { // Extrapolation if(this.extrapolationMethod == ExtrapolationMethod.CONSTANT) { return values[0]; } else if(this.extrapolationMethod == ExtrapolationMethod.LINEAR) { return values[0]+(values[1]-values[0])/(points[1]-points[0])*(x-points[0]); } else { intervallIndex = 0; } } else if(intervallIndex > points.length-2) { // Extrapolation if(this.extrapolationMethod == ExtrapolationMethod.CONSTANT) { return values[points.length-1]; } else if(this.extrapolationMethod == ExtrapolationMethod.LINEAR) { return values[points.length-1]+(values[points.length-2]-values[points.length-1])/(points[points.length-2]-points[points.length-1])*(x-points[points.length-1]); } else { intervallIndex = points.length-2; } } RationalFunction rationalFunction = interpolatingRationalFunctions[intervallIndex]; // Calculate interpolating value return rationalFunction.getValue(x-points[intervallIndex]); }
java
public double getValue(double x) { synchronized(interpolatingRationalFunctionsLazyInitLock) { if(interpolatingRationalFunctions == null) { doCreateRationalFunctions(); } } // Get interpolating rational function for the given point x int pointIndex = java.util.Arrays.binarySearch(points, x); if(pointIndex >= 0) { return values[pointIndex]; } int intervallIndex = -pointIndex-2; // Check for extrapolation if(intervallIndex < 0) { // Extrapolation if(this.extrapolationMethod == ExtrapolationMethod.CONSTANT) { return values[0]; } else if(this.extrapolationMethod == ExtrapolationMethod.LINEAR) { return values[0]+(values[1]-values[0])/(points[1]-points[0])*(x-points[0]); } else { intervallIndex = 0; } } else if(intervallIndex > points.length-2) { // Extrapolation if(this.extrapolationMethod == ExtrapolationMethod.CONSTANT) { return values[points.length-1]; } else if(this.extrapolationMethod == ExtrapolationMethod.LINEAR) { return values[points.length-1]+(values[points.length-2]-values[points.length-1])/(points[points.length-2]-points[points.length-1])*(x-points[points.length-1]); } else { intervallIndex = points.length-2; } } RationalFunction rationalFunction = interpolatingRationalFunctions[intervallIndex]; // Calculate interpolating value return rationalFunction.getValue(x-points[intervallIndex]); }
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Get an interpolated value for a given argument x. @param x The abscissa at which the interpolation should be performed. @return The interpolated value (ordinate).
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a3c067d52dd33feb97d851df6cab130e4116759f
https://github.com/finmath/finmath-lib/blob/a3c067d52dd33feb97d851df6cab130e4116759f/src/main/java6/net/finmath/interpolation/RationalFunctionInterpolation.java#L186-L228
train
finmath/finmath-lib
src/main/java/net/finmath/montecarlo/automaticdifferentiation/backward/alternative/RandomVariableUniqueVariable.java
RandomVariableUniqueVariable.getGradient
public RandomVariable[] getGradient(){ // for now let us take the case for output-dimension equal to one! int numberOfVariables = getNumberOfVariablesInList(); int numberOfCalculationSteps = factory.getNumberOfEntriesInList(); RandomVariable[] omega_hat = new RandomVariable[numberOfCalculationSteps]; // first entry gets initialized omega_hat[numberOfCalculationSteps-1] = new RandomVariableFromDoubleArray(1.0); /* * TODO: Find way that calculations form here on are not 'recorded' by the factory * IDEA: Let the calculation below run on {@link RandomVariableFromDoubleArray}, ie cast everything down! * */ for(int functionIndex = numberOfCalculationSteps - 2; functionIndex > 0; functionIndex--){ // apply chain rule omega_hat[functionIndex] = new RandomVariableFromDoubleArray(0.0); /*TODO: save all D_{i,j}*\omega_j in vector and sum up later */ for(RandomVariableUniqueVariable parent:parentsVariables){ int variableIndex = parent.getVariableID(); omega_hat[functionIndex] = omega_hat[functionIndex].add(getPartialDerivative(functionIndex, variableIndex).mult(omega_hat[variableIndex])); } } /* Due to the fact that we can still introduce 'new' true variables on the fly they are NOT the last couple of indices! * Thus save the indices of the true variables and recover them after finalizing all the calculations * IDEA: quit calculation after minimal true variable index is reached */ RandomVariable[] gradient = new RandomVariable[numberOfVariables]; /* TODO: sort array in correct manner! */ int[] indicesOfVariables = getIDsOfVariablesInList(); for(int i = 0; i < numberOfVariables; i++){ gradient[i] = omega_hat[numberOfCalculationSteps - numberOfVariables + indicesOfVariables[i]]; } return gradient; }
java
public RandomVariable[] getGradient(){ // for now let us take the case for output-dimension equal to one! int numberOfVariables = getNumberOfVariablesInList(); int numberOfCalculationSteps = factory.getNumberOfEntriesInList(); RandomVariable[] omega_hat = new RandomVariable[numberOfCalculationSteps]; // first entry gets initialized omega_hat[numberOfCalculationSteps-1] = new RandomVariableFromDoubleArray(1.0); /* * TODO: Find way that calculations form here on are not 'recorded' by the factory * IDEA: Let the calculation below run on {@link RandomVariableFromDoubleArray}, ie cast everything down! * */ for(int functionIndex = numberOfCalculationSteps - 2; functionIndex > 0; functionIndex--){ // apply chain rule omega_hat[functionIndex] = new RandomVariableFromDoubleArray(0.0); /*TODO: save all D_{i,j}*\omega_j in vector and sum up later */ for(RandomVariableUniqueVariable parent:parentsVariables){ int variableIndex = parent.getVariableID(); omega_hat[functionIndex] = omega_hat[functionIndex].add(getPartialDerivative(functionIndex, variableIndex).mult(omega_hat[variableIndex])); } } /* Due to the fact that we can still introduce 'new' true variables on the fly they are NOT the last couple of indices! * Thus save the indices of the true variables and recover them after finalizing all the calculations * IDEA: quit calculation after minimal true variable index is reached */ RandomVariable[] gradient = new RandomVariable[numberOfVariables]; /* TODO: sort array in correct manner! */ int[] indicesOfVariables = getIDsOfVariablesInList(); for(int i = 0; i < numberOfVariables; i++){ gradient[i] = omega_hat[numberOfCalculationSteps - numberOfVariables + indicesOfVariables[i]]; } return gradient; }
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Apply the AAD algorithm to this very variable NOTE: in this case it is indeed correct to assume that the output dimension is "one" meaning that there is only one {@link RandomVariableUniqueVariable} as an output. @return gradient for the built up function
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a3c067d52dd33feb97d851df6cab130e4116759f
https://github.com/finmath/finmath-lib/blob/a3c067d52dd33feb97d851df6cab130e4116759f/src/main/java/net/finmath/montecarlo/automaticdifferentiation/backward/alternative/RandomVariableUniqueVariable.java#L712-L754
train
finmath/finmath-lib
src/main/java/net/finmath/marketdata/model/volatilities/AbstractVolatilitySurfaceParametric.java
AbstractVolatilitySurfaceParametric.getCloneCalibrated
public AbstractVolatilitySurfaceParametric getCloneCalibrated(final AnalyticModel calibrationModel, final Vector<AnalyticProduct> calibrationProducts, final List<Double> calibrationTargetValues, Map<String,Object> calibrationParameters, final ParameterTransformation parameterTransformation, OptimizerFactory optimizerFactory) throws SolverException { if(calibrationParameters == null) { calibrationParameters = new HashMap<>(); } Integer maxIterationsParameter = (Integer)calibrationParameters.get("maxIterations"); Double accuracyParameter = (Double)calibrationParameters.get("accuracy"); Double evaluationTimeParameter = (Double)calibrationParameters.get("evaluationTime"); // @TODO currently ignored, we use the setting form the OptimizerFactory int maxIterations = maxIterationsParameter != null ? maxIterationsParameter.intValue() : 600; double accuracy = accuracyParameter != null ? accuracyParameter.doubleValue() : 1E-8; double evaluationTime = evaluationTimeParameter != null ? evaluationTimeParameter.doubleValue() : 0.0; AnalyticModel model = calibrationModel.addVolatilitySurfaces(this); Solver solver = new Solver(model, calibrationProducts, calibrationTargetValues, parameterTransformation, evaluationTime, optimizerFactory); Set<ParameterObject> objectsToCalibrate = new HashSet<>(); objectsToCalibrate.add(this); AnalyticModel modelCalibrated = solver.getCalibratedModel(objectsToCalibrate); // Diagnostic output if (logger.isLoggable(Level.FINE)) { double lastAccuracy = solver.getAccuracy(); int lastIterations = solver.getIterations(); logger.fine("The solver achieved an accuracy of " + lastAccuracy + " in " + lastIterations + "."); } return (AbstractVolatilitySurfaceParametric)modelCalibrated.getVolatilitySurface(this.getName()); }
java
public AbstractVolatilitySurfaceParametric getCloneCalibrated(final AnalyticModel calibrationModel, final Vector<AnalyticProduct> calibrationProducts, final List<Double> calibrationTargetValues, Map<String,Object> calibrationParameters, final ParameterTransformation parameterTransformation, OptimizerFactory optimizerFactory) throws SolverException { if(calibrationParameters == null) { calibrationParameters = new HashMap<>(); } Integer maxIterationsParameter = (Integer)calibrationParameters.get("maxIterations"); Double accuracyParameter = (Double)calibrationParameters.get("accuracy"); Double evaluationTimeParameter = (Double)calibrationParameters.get("evaluationTime"); // @TODO currently ignored, we use the setting form the OptimizerFactory int maxIterations = maxIterationsParameter != null ? maxIterationsParameter.intValue() : 600; double accuracy = accuracyParameter != null ? accuracyParameter.doubleValue() : 1E-8; double evaluationTime = evaluationTimeParameter != null ? evaluationTimeParameter.doubleValue() : 0.0; AnalyticModel model = calibrationModel.addVolatilitySurfaces(this); Solver solver = new Solver(model, calibrationProducts, calibrationTargetValues, parameterTransformation, evaluationTime, optimizerFactory); Set<ParameterObject> objectsToCalibrate = new HashSet<>(); objectsToCalibrate.add(this); AnalyticModel modelCalibrated = solver.getCalibratedModel(objectsToCalibrate); // Diagnostic output if (logger.isLoggable(Level.FINE)) { double lastAccuracy = solver.getAccuracy(); int lastIterations = solver.getIterations(); logger.fine("The solver achieved an accuracy of " + lastAccuracy + " in " + lastIterations + "."); } return (AbstractVolatilitySurfaceParametric)modelCalibrated.getVolatilitySurface(this.getName()); }
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Create a clone of this volatility surface using a generic calibration of its parameters to given market data. @param calibrationModel The model used during calibration (contains additional objects required during valuation, e.g. curves). @param calibrationProducts The calibration products. @param calibrationTargetValues The target values of the calibration products. @param calibrationParameters A map containing additional settings like "evaluationTime" (Double). @param parameterTransformation An optional parameter transformation. @param optimizerFactory The factory providing the optimizer to be used during calibration. @return An object having the same type as this one, using (hopefully) calibrated parameters. @throws SolverException Exception thrown when solver fails.
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a3c067d52dd33feb97d851df6cab130e4116759f
https://github.com/finmath/finmath-lib/blob/a3c067d52dd33feb97d851df6cab130e4116759f/src/main/java/net/finmath/marketdata/model/volatilities/AbstractVolatilitySurfaceParametric.java#L81-L110
train
finmath/finmath-lib
src/main/java/net/finmath/marketdata/model/curves/SeasonalCurve.java
SeasonalCurve.computeSeasonalAdjustments
public static double[] computeSeasonalAdjustments(double[] realizedCPIValues, int lastMonth, int numberOfYearsToAverage) { /* * Cacluate average log returns */ double[] averageLogReturn = new double[12]; Arrays.fill(averageLogReturn, 0.0); for(int arrayIndex = 0; arrayIndex < 12*numberOfYearsToAverage; arrayIndex++){ int month = (((((lastMonth-1 - arrayIndex) % 12) + 12) % 12)); double logReturn = Math.log(realizedCPIValues[realizedCPIValues.length - 1 - arrayIndex] / realizedCPIValues[realizedCPIValues.length - 2 - arrayIndex]); averageLogReturn[month] += logReturn/numberOfYearsToAverage; } /* * Normalize */ double sum = 0.0; for(int index = 0; index < averageLogReturn.length; index++){ sum += averageLogReturn[index]; } double averageSeasonal = sum / averageLogReturn.length; double[] seasonalAdjustments = new double[averageLogReturn.length]; for(int index = 0; index < seasonalAdjustments.length; index++){ seasonalAdjustments[index] = averageLogReturn[index] - averageSeasonal; } // Annualize seasonal adjustments for(int index = 0; index < seasonalAdjustments.length; index++){ seasonalAdjustments[index] = seasonalAdjustments[index] * 12; } return seasonalAdjustments; }
java
public static double[] computeSeasonalAdjustments(double[] realizedCPIValues, int lastMonth, int numberOfYearsToAverage) { /* * Cacluate average log returns */ double[] averageLogReturn = new double[12]; Arrays.fill(averageLogReturn, 0.0); for(int arrayIndex = 0; arrayIndex < 12*numberOfYearsToAverage; arrayIndex++){ int month = (((((lastMonth-1 - arrayIndex) % 12) + 12) % 12)); double logReturn = Math.log(realizedCPIValues[realizedCPIValues.length - 1 - arrayIndex] / realizedCPIValues[realizedCPIValues.length - 2 - arrayIndex]); averageLogReturn[month] += logReturn/numberOfYearsToAverage; } /* * Normalize */ double sum = 0.0; for(int index = 0; index < averageLogReturn.length; index++){ sum += averageLogReturn[index]; } double averageSeasonal = sum / averageLogReturn.length; double[] seasonalAdjustments = new double[averageLogReturn.length]; for(int index = 0; index < seasonalAdjustments.length; index++){ seasonalAdjustments[index] = averageLogReturn[index] - averageSeasonal; } // Annualize seasonal adjustments for(int index = 0; index < seasonalAdjustments.length; index++){ seasonalAdjustments[index] = seasonalAdjustments[index] * 12; } return seasonalAdjustments; }
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Computes annualized seasonal adjustments from given monthly realized CPI values. @param realizedCPIValues An array of consecutive monthly CPI values (minimum size is 12*numberOfYearsToAverage)) @param lastMonth The index of the last month in the sequence of realizedCPIValues (corresponding to the enums in <code>{@link java.time.Month}</code>). @param numberOfYearsToAverage The number of years to go back in the array of realizedCPIValues. @return Array of annualized seasonal adjustments, where [0] corresponds to the adjustment for from December to January.
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a3c067d52dd33feb97d851df6cab130e4116759f
https://github.com/finmath/finmath-lib/blob/a3c067d52dd33feb97d851df6cab130e4116759f/src/main/java/net/finmath/marketdata/model/curves/SeasonalCurve.java#L176-L211
train
finmath/finmath-lib
src/main/java/net/finmath/modelling/productfactory/ProductFactoryCascade.java
ProductFactoryCascade.addFactoryBefore
public ProductFactoryCascade<T> addFactoryBefore(ProductFactory<? extends T> factory) { ArrayList<ProductFactory<? extends T>> factories = new ArrayList<ProductFactory<? extends T>>(this.factories.size()+1); factories.addAll(this.factories); factories.add(0, factory); return new ProductFactoryCascade<>(factories); }
java
public ProductFactoryCascade<T> addFactoryBefore(ProductFactory<? extends T> factory) { ArrayList<ProductFactory<? extends T>> factories = new ArrayList<ProductFactory<? extends T>>(this.factories.size()+1); factories.addAll(this.factories); factories.add(0, factory); return new ProductFactoryCascade<>(factories); }
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Add a given factory to the list of factories at the BEGINNING. @param factory The factory to be added. @return Cascade with amended factory list.
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a3c067d52dd33feb97d851df6cab130e4116759f
https://github.com/finmath/finmath-lib/blob/a3c067d52dd33feb97d851df6cab130e4116759f/src/main/java/net/finmath/modelling/productfactory/ProductFactoryCascade.java#L49-L54
train
finmath/finmath-lib
src/main/java/net/finmath/montecarlo/interestrate/products/ForwardRateVolatilitySurfaceCurvature.java
ForwardRateVolatilitySurfaceCurvature.getValues
public RandomVariable getValues(double evaluationTime, LIBORMarketModel model) { if(evaluationTime > 0) { throw new RuntimeException("Forward start evaluation currently not supported."); } // Fetch the covariance model of the model LIBORCovarianceModel covarianceModel = model.getCovarianceModel(); // We sum over all forward rates int numberOfComponents = covarianceModel.getLiborPeriodDiscretization().getNumberOfTimeSteps(); // Accumulator RandomVariable integratedLIBORCurvature = new RandomVariableFromDoubleArray(0.0); for(int componentIndex = 0; componentIndex < numberOfComponents; componentIndex++) { // Integrate from 0 up to the fixing of the rate double timeEnd = covarianceModel.getLiborPeriodDiscretization().getTime(componentIndex); int timeEndIndex = covarianceModel.getTimeDiscretization().getTimeIndex(timeEnd); // If timeEnd is not in the time discretization we get timeEndIndex = -insertionPoint-1. In that case, we use the index prior to the insertionPoint if(timeEndIndex < 0) { timeEndIndex = -timeEndIndex - 2; } // Sum squared second derivative of the variance for all components at this time step RandomVariable integratedLIBORCurvatureCurrentRate = new RandomVariableFromDoubleArray(0.0); for(int timeIndex = 0; timeIndex < timeEndIndex-2; timeIndex++) { double timeStep1 = covarianceModel.getTimeDiscretization().getTimeStep(timeIndex); double timeStep2 = covarianceModel.getTimeDiscretization().getTimeStep(timeIndex+1); RandomVariable covarianceLeft = covarianceModel.getCovariance(timeIndex+0, componentIndex, componentIndex, null); RandomVariable covarianceCenter = covarianceModel.getCovariance(timeIndex+1, componentIndex, componentIndex, null); RandomVariable covarianceRight = covarianceModel.getCovariance(timeIndex+2, componentIndex, componentIndex, null); // Calculate second derivative RandomVariable curvatureSquared = covarianceRight.sub(covarianceCenter.mult(2.0)).add(covarianceLeft); curvatureSquared = curvatureSquared.div(timeStep1 * timeStep2); // Take square curvatureSquared = curvatureSquared.squared(); // Integrate over time integratedLIBORCurvatureCurrentRate = integratedLIBORCurvatureCurrentRate.add(curvatureSquared.mult(timeStep1)); } // Empty intervall - skip if(timeEnd == 0) { continue; } // Average over time integratedLIBORCurvatureCurrentRate = integratedLIBORCurvatureCurrentRate.div(timeEnd); // Take square root integratedLIBORCurvatureCurrentRate = integratedLIBORCurvatureCurrentRate.sqrt(); // Take max over all forward rates integratedLIBORCurvature = integratedLIBORCurvature.add(integratedLIBORCurvatureCurrentRate); } integratedLIBORCurvature = integratedLIBORCurvature.div(numberOfComponents); return integratedLIBORCurvature.sub(tolerance).floor(0.0); }
java
public RandomVariable getValues(double evaluationTime, LIBORMarketModel model) { if(evaluationTime > 0) { throw new RuntimeException("Forward start evaluation currently not supported."); } // Fetch the covariance model of the model LIBORCovarianceModel covarianceModel = model.getCovarianceModel(); // We sum over all forward rates int numberOfComponents = covarianceModel.getLiborPeriodDiscretization().getNumberOfTimeSteps(); // Accumulator RandomVariable integratedLIBORCurvature = new RandomVariableFromDoubleArray(0.0); for(int componentIndex = 0; componentIndex < numberOfComponents; componentIndex++) { // Integrate from 0 up to the fixing of the rate double timeEnd = covarianceModel.getLiborPeriodDiscretization().getTime(componentIndex); int timeEndIndex = covarianceModel.getTimeDiscretization().getTimeIndex(timeEnd); // If timeEnd is not in the time discretization we get timeEndIndex = -insertionPoint-1. In that case, we use the index prior to the insertionPoint if(timeEndIndex < 0) { timeEndIndex = -timeEndIndex - 2; } // Sum squared second derivative of the variance for all components at this time step RandomVariable integratedLIBORCurvatureCurrentRate = new RandomVariableFromDoubleArray(0.0); for(int timeIndex = 0; timeIndex < timeEndIndex-2; timeIndex++) { double timeStep1 = covarianceModel.getTimeDiscretization().getTimeStep(timeIndex); double timeStep2 = covarianceModel.getTimeDiscretization().getTimeStep(timeIndex+1); RandomVariable covarianceLeft = covarianceModel.getCovariance(timeIndex+0, componentIndex, componentIndex, null); RandomVariable covarianceCenter = covarianceModel.getCovariance(timeIndex+1, componentIndex, componentIndex, null); RandomVariable covarianceRight = covarianceModel.getCovariance(timeIndex+2, componentIndex, componentIndex, null); // Calculate second derivative RandomVariable curvatureSquared = covarianceRight.sub(covarianceCenter.mult(2.0)).add(covarianceLeft); curvatureSquared = curvatureSquared.div(timeStep1 * timeStep2); // Take square curvatureSquared = curvatureSquared.squared(); // Integrate over time integratedLIBORCurvatureCurrentRate = integratedLIBORCurvatureCurrentRate.add(curvatureSquared.mult(timeStep1)); } // Empty intervall - skip if(timeEnd == 0) { continue; } // Average over time integratedLIBORCurvatureCurrentRate = integratedLIBORCurvatureCurrentRate.div(timeEnd); // Take square root integratedLIBORCurvatureCurrentRate = integratedLIBORCurvatureCurrentRate.sqrt(); // Take max over all forward rates integratedLIBORCurvature = integratedLIBORCurvature.add(integratedLIBORCurvatureCurrentRate); } integratedLIBORCurvature = integratedLIBORCurvature.div(numberOfComponents); return integratedLIBORCurvature.sub(tolerance).floor(0.0); }
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Calculates the squared curvature of the LIBOR instantaneous variance. @param evaluationTime Time at which the product is evaluated. @param model A model implementing the LIBORModelMonteCarloSimulationModel @return The squared curvature of the LIBOR instantaneous variance (reduced a possible tolerance). The return value is &ge; 0.
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a3c067d52dd33feb97d851df6cab130e4116759f
https://github.com/finmath/finmath-lib/blob/a3c067d52dd33feb97d851df6cab130e4116759f/src/main/java/net/finmath/montecarlo/interestrate/products/ForwardRateVolatilitySurfaceCurvature.java#L117-L179
train
finmath/finmath-lib
src/main/java6/net/finmath/montecarlo/interestrate/modelplugins/AbstractLIBORCovarianceModel.java
AbstractLIBORCovarianceModel.getFactorLoading
public RandomVariableInterface[] getFactorLoading(double time, double component, RandomVariableInterface[] realizationAtTimeIndex) { int componentIndex = liborPeriodDiscretization.getTimeIndex(component); if(componentIndex < 0) { componentIndex = -componentIndex - 2; } return getFactorLoading(time, componentIndex, realizationAtTimeIndex); }
java
public RandomVariableInterface[] getFactorLoading(double time, double component, RandomVariableInterface[] realizationAtTimeIndex) { int componentIndex = liborPeriodDiscretization.getTimeIndex(component); if(componentIndex < 0) { componentIndex = -componentIndex - 2; } return getFactorLoading(time, componentIndex, realizationAtTimeIndex); }
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Return the factor loading for a given time and a given component. The factor loading is the vector <i>f<sub>i</sub></i> such that the scalar product <br> <i>f<sub>j</sub>f<sub>k</sub> = f<sub>j,1</sub>f<sub>k,1</sub> + ... + f<sub>j,m</sub>f<sub>k,m</sub></i> <br> is the instantaneous covariance of the component <i>j</i> and <i>k</i>. With respect to simulation time <i>t</i>, this method uses a piece wise constant interpolation, i.e., it calculates <i>t_<sub>i</sub></i> such that <i>t_<sub>i</sub></i> is the largest point in <code>getTimeDiscretization</code> such that <i>t_<sub>i</sub> &le; t </i>. The component here, it given via a double <i>T</i> which may be associated with the LIBOR fixing date. With respect to component time <i>T</i>, this method uses a piece wise constant interpolation, i.e., it calculates <i>T_<sub>j</sub></i> such that <i>T_<sub>j</sub></i> is the largest point in <code>getTimeDiscretization</code> such that <i>T_<sub>j</sub> &le; T </i>. @param time The time <i>t</i> at which factor loading is requested. @param component The component time (as a double associated with the fixing of the forward rate) <i>T<sub>i</sub></i>. @param realizationAtTimeIndex The realization of the stochastic process (may be used to implement local volatility/covariance/correlation models). @return The factor loading <i>f<sub>i</sub>(t)</i>.
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a3c067d52dd33feb97d851df6cab130e4116759f
https://github.com/finmath/finmath-lib/blob/a3c067d52dd33feb97d851df6cab130e4116759f/src/main/java6/net/finmath/montecarlo/interestrate/modelplugins/AbstractLIBORCovarianceModel.java#L63-L69
train
finmath/finmath-lib
src/main/java/net/finmath/montecarlo/interestrate/products/BermudanSwaptionFromSwapSchedules.java
BermudanSwaptionFromSwapSchedules.getValueUnderlyingNumeraireRelative
private RandomVariable getValueUnderlyingNumeraireRelative(LIBORModelMonteCarloSimulationModel model, Schedule legSchedule, boolean paysFloat, double swaprate, double notional) throws CalculationException { RandomVariable value = model.getRandomVariableForConstant(0.0); for(int periodIndex = legSchedule.getNumberOfPeriods() - 1; periodIndex >= 0; periodIndex--) { double fixingTime = FloatingpointDate.getFloatingPointDateFromDate(model.getReferenceDate().toLocalDate(), legSchedule.getPeriod(periodIndex).getFixing()); double paymentTime = FloatingpointDate.getFloatingPointDateFromDate(model.getReferenceDate().toLocalDate(), legSchedule.getPeriod(periodIndex).getPayment()); double periodLength = legSchedule.getPeriodLength(periodIndex); RandomVariable numeraireAtPayment = model.getNumeraire(paymentTime); RandomVariable monteCarloProbabilitiesAtPayment = model.getMonteCarloWeights(paymentTime); if(swaprate != 0.0) { RandomVariable periodCashFlowFix = model.getRandomVariableForConstant(swaprate * periodLength * notional).div(numeraireAtPayment).mult(monteCarloProbabilitiesAtPayment); value = value.add(periodCashFlowFix); } if(paysFloat) { RandomVariable libor = model.getLIBOR(fixingTime, fixingTime, paymentTime); RandomVariable periodCashFlowFloat = libor.mult(periodLength).mult(notional).div(numeraireAtPayment).mult(monteCarloProbabilitiesAtPayment); value = value.add(periodCashFlowFloat); } } return value; }
java
private RandomVariable getValueUnderlyingNumeraireRelative(LIBORModelMonteCarloSimulationModel model, Schedule legSchedule, boolean paysFloat, double swaprate, double notional) throws CalculationException { RandomVariable value = model.getRandomVariableForConstant(0.0); for(int periodIndex = legSchedule.getNumberOfPeriods() - 1; periodIndex >= 0; periodIndex--) { double fixingTime = FloatingpointDate.getFloatingPointDateFromDate(model.getReferenceDate().toLocalDate(), legSchedule.getPeriod(periodIndex).getFixing()); double paymentTime = FloatingpointDate.getFloatingPointDateFromDate(model.getReferenceDate().toLocalDate(), legSchedule.getPeriod(periodIndex).getPayment()); double periodLength = legSchedule.getPeriodLength(periodIndex); RandomVariable numeraireAtPayment = model.getNumeraire(paymentTime); RandomVariable monteCarloProbabilitiesAtPayment = model.getMonteCarloWeights(paymentTime); if(swaprate != 0.0) { RandomVariable periodCashFlowFix = model.getRandomVariableForConstant(swaprate * periodLength * notional).div(numeraireAtPayment).mult(monteCarloProbabilitiesAtPayment); value = value.add(periodCashFlowFix); } if(paysFloat) { RandomVariable libor = model.getLIBOR(fixingTime, fixingTime, paymentTime); RandomVariable periodCashFlowFloat = libor.mult(periodLength).mult(notional).div(numeraireAtPayment).mult(monteCarloProbabilitiesAtPayment); value = value.add(periodCashFlowFloat); } } return value; }
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Calculated the numeraire relative value of an underlying swap leg. @param model The Monte Carlo model. @param legSchedule The schedule of the leg. @param paysFloat If true a floating rate is payed. @param swaprate The swaprate. May be 0.0 for pure floating leg. @param notional The notional. @return The sum of the numeraire relative cash flows. @throws CalculationException Thrown if underlying model failed to calculate stochastic process.
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a3c067d52dd33feb97d851df6cab130e4116759f
https://github.com/finmath/finmath-lib/blob/a3c067d52dd33feb97d851df6cab130e4116759f/src/main/java/net/finmath/montecarlo/interestrate/products/BermudanSwaptionFromSwapSchedules.java#L292-L314
train
finmath/finmath-lib
src/main/java/net/finmath/montecarlo/interestrate/products/BermudanSwaptionFromSwapSchedules.java
BermudanSwaptionFromSwapSchedules.getConditionalExpectationEstimator
public ConditionalExpectationEstimator getConditionalExpectationEstimator(double exerciseTime, LIBORModelMonteCarloSimulationModel model) throws CalculationException { RandomVariable[] regressionBasisFunctions = regressionBasisFunctionProvider.getBasisFunctions(exerciseTime, model); return conditionalExpectationRegressionFactory.getConditionalExpectationEstimator(regressionBasisFunctions, regressionBasisFunctions); }
java
public ConditionalExpectationEstimator getConditionalExpectationEstimator(double exerciseTime, LIBORModelMonteCarloSimulationModel model) throws CalculationException { RandomVariable[] regressionBasisFunctions = regressionBasisFunctionProvider.getBasisFunctions(exerciseTime, model); return conditionalExpectationRegressionFactory.getConditionalExpectationEstimator(regressionBasisFunctions, regressionBasisFunctions); }
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The conditional expectation is calculated using a Monte-Carlo regression technique. @param exerciseTime The exercise time @param model The valuation model @return The condition expectation estimator @throws CalculationException Thrown if underlying model failed to calculate stochastic process.
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a3c067d52dd33feb97d851df6cab130e4116759f
https://github.com/finmath/finmath-lib/blob/a3c067d52dd33feb97d851df6cab130e4116759f/src/main/java/net/finmath/montecarlo/interestrate/products/BermudanSwaptionFromSwapSchedules.java#L324-L327
train
finmath/finmath-lib
src/main/java/net/finmath/montecarlo/interestrate/models/LIBORMarketModelStandard.java
LIBORMarketModelStandard.getNumeraire
@Override public RandomVariable getNumeraire(double time) throws CalculationException { int timeIndex = getLiborPeriodIndex(time); if(timeIndex < 0) { // Interpolation of Numeraire: linear interpolation of the reciprocal. int lowerIndex = -timeIndex -1; int upperIndex = -timeIndex; double alpha = (time-getLiborPeriod(lowerIndex)) / (getLiborPeriod(upperIndex) - getLiborPeriod(lowerIndex)); return getNumeraire(getLiborPeriod(upperIndex)).invert().mult(alpha).add(getNumeraire(getLiborPeriod(lowerIndex)).invert().mult(1.0-alpha)).invert(); } // Calculate the numeraire, when time is part of liborPeriodDiscretization // Get the start of the product int firstLiborIndex = getLiborPeriodIndex(time); if(firstLiborIndex < 0) { throw new CalculationException("Simulation time discretization not part of forward rate tenor discretization."); } // Get the end of the product int lastLiborIndex = liborPeriodDiscretization.getNumberOfTimeSteps()-1; if(measure == Measure.SPOT) { // Spot measure firstLiborIndex = 0; lastLiborIndex = getLiborPeriodIndex(time)-1; } /* * Calculation of the numeraire */ // Initialize to 1.0 RandomVariable numeraire = new RandomVariableFromDoubleArray(time, 1.0); // The product for(int liborIndex = firstLiborIndex; liborIndex<=lastLiborIndex; liborIndex++) { RandomVariable libor = getLIBOR(getTimeIndex(Math.min(time,liborPeriodDiscretization.getTime(liborIndex))), liborIndex); double periodLength = liborPeriodDiscretization.getTimeStep(liborIndex); if(measure == Measure.SPOT) { numeraire = numeraire.accrue(libor, periodLength); } else { numeraire = numeraire.discount(libor, periodLength); } } /* * Adjust for discounting */ if(discountCurve != null) { DiscountCurve discountcountCurveFromForwardPerformance = new DiscountCurveFromForwardCurve(forwardRateCurve); double deterministicNumeraireAdjustment = discountcountCurveFromForwardPerformance.getDiscountFactor(time) / discountCurve.getDiscountFactor(time); numeraire = numeraire.mult(deterministicNumeraireAdjustment); } return numeraire; }
java
@Override public RandomVariable getNumeraire(double time) throws CalculationException { int timeIndex = getLiborPeriodIndex(time); if(timeIndex < 0) { // Interpolation of Numeraire: linear interpolation of the reciprocal. int lowerIndex = -timeIndex -1; int upperIndex = -timeIndex; double alpha = (time-getLiborPeriod(lowerIndex)) / (getLiborPeriod(upperIndex) - getLiborPeriod(lowerIndex)); return getNumeraire(getLiborPeriod(upperIndex)).invert().mult(alpha).add(getNumeraire(getLiborPeriod(lowerIndex)).invert().mult(1.0-alpha)).invert(); } // Calculate the numeraire, when time is part of liborPeriodDiscretization // Get the start of the product int firstLiborIndex = getLiborPeriodIndex(time); if(firstLiborIndex < 0) { throw new CalculationException("Simulation time discretization not part of forward rate tenor discretization."); } // Get the end of the product int lastLiborIndex = liborPeriodDiscretization.getNumberOfTimeSteps()-1; if(measure == Measure.SPOT) { // Spot measure firstLiborIndex = 0; lastLiborIndex = getLiborPeriodIndex(time)-1; } /* * Calculation of the numeraire */ // Initialize to 1.0 RandomVariable numeraire = new RandomVariableFromDoubleArray(time, 1.0); // The product for(int liborIndex = firstLiborIndex; liborIndex<=lastLiborIndex; liborIndex++) { RandomVariable libor = getLIBOR(getTimeIndex(Math.min(time,liborPeriodDiscretization.getTime(liborIndex))), liborIndex); double periodLength = liborPeriodDiscretization.getTimeStep(liborIndex); if(measure == Measure.SPOT) { numeraire = numeraire.accrue(libor, periodLength); } else { numeraire = numeraire.discount(libor, periodLength); } } /* * Adjust for discounting */ if(discountCurve != null) { DiscountCurve discountcountCurveFromForwardPerformance = new DiscountCurveFromForwardCurve(forwardRateCurve); double deterministicNumeraireAdjustment = discountcountCurveFromForwardPerformance.getDiscountFactor(time) / discountCurve.getDiscountFactor(time); numeraire = numeraire.mult(deterministicNumeraireAdjustment); } return numeraire; }
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Return the numeraire at a given time. The numeraire is provided for interpolated points. If requested on points which are not part of the tenor discretization, the numeraire uses a linear interpolation of the reciprocal value. See ISBN 0470047224 for details. @param time Time time <i>t</i> for which the numeraire should be returned <i>N(t)</i>. @return The numeraire at the specified time as <code>RandomVariableFromDoubleArray</code> @throws net.finmath.exception.CalculationException Thrown if the valuation fails, specific cause may be available via the <code>cause()</code> method.
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a3c067d52dd33feb97d851df6cab130e4116759f
https://github.com/finmath/finmath-lib/blob/a3c067d52dd33feb97d851df6cab130e4116759f/src/main/java/net/finmath/montecarlo/interestrate/models/LIBORMarketModelStandard.java#L282-L341
train
finmath/finmath-lib
src/main/java/net/finmath/finitedifference/solvers/FDMThetaMethod.java
FDMThetaMethod.u_neg_inf
private double u_neg_inf(double x, double tau) { return f(boundaryCondition.getValueAtLowerBoundary(model, f_t(tau), f_s(x)), x, tau); }
java
private double u_neg_inf(double x, double tau) { return f(boundaryCondition.getValueAtLowerBoundary(model, f_t(tau), f_s(x)), x, tau); }
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Heat Equation Boundary Conditions
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a3c067d52dd33feb97d851df6cab130e4116759f
https://github.com/finmath/finmath-lib/blob/a3c067d52dd33feb97d851df6cab130e4116759f/src/main/java/net/finmath/finitedifference/solvers/FDMThetaMethod.java#L150-L152
train
finmath/finmath-lib
src/main/java/net/finmath/montecarlo/conditionalexpectation/LinearRegression.java
LinearRegression.getRegressionCoefficients
public double[] getRegressionCoefficients(RandomVariable value) { if(basisFunctions.length == 0) { return new double[] { }; } else if(basisFunctions.length == 1) { /* * Regression with one basis function is just a projection on that vector. <b,x>/<b,b> */ return new double[] { value.mult(basisFunctions[0]).getAverage() / basisFunctions[0].squared().getAverage() }; } else if(basisFunctions.length == 2) { /* * Regression with two basis functions can be solved explicitly if determinant != 0 (otherwise we will fallback to SVD) */ double a = basisFunctions[0].squared().getAverage(); double b = basisFunctions[0].mult(basisFunctions[1]).average().squared().doubleValue(); double c = b; double d = basisFunctions[1].squared().getAverage(); double determinant = (a * d - b * c); if(determinant != 0) { double x = value.mult(basisFunctions[0]).getAverage(); double y = value.mult(basisFunctions[1]).getAverage(); double alpha0 = (d * x - b * y) / determinant; double alpha1 = (a * y - c * x) / determinant; return new double[] { alpha0, alpha1 }; } } /* * General case */ // Build regression matrix double[][] BTB = new double[basisFunctions.length][basisFunctions.length]; for(int i=0; i<basisFunctions.length; i++) { for(int j=0; j<=i; j++) { double covariance = basisFunctions[i].mult(basisFunctions[j]).getAverage(); BTB[i][j] = covariance; BTB[j][i] = covariance; } } double[] BTX = new double[basisFunctions.length]; for(int i=0; i<basisFunctions.length; i++) { double covariance = basisFunctions[i].mult(value).getAverage(); BTX[i] = covariance; } return LinearAlgebra.solveLinearEquationLeastSquare(BTB, BTX); }
java
public double[] getRegressionCoefficients(RandomVariable value) { if(basisFunctions.length == 0) { return new double[] { }; } else if(basisFunctions.length == 1) { /* * Regression with one basis function is just a projection on that vector. <b,x>/<b,b> */ return new double[] { value.mult(basisFunctions[0]).getAverage() / basisFunctions[0].squared().getAverage() }; } else if(basisFunctions.length == 2) { /* * Regression with two basis functions can be solved explicitly if determinant != 0 (otherwise we will fallback to SVD) */ double a = basisFunctions[0].squared().getAverage(); double b = basisFunctions[0].mult(basisFunctions[1]).average().squared().doubleValue(); double c = b; double d = basisFunctions[1].squared().getAverage(); double determinant = (a * d - b * c); if(determinant != 0) { double x = value.mult(basisFunctions[0]).getAverage(); double y = value.mult(basisFunctions[1]).getAverage(); double alpha0 = (d * x - b * y) / determinant; double alpha1 = (a * y - c * x) / determinant; return new double[] { alpha0, alpha1 }; } } /* * General case */ // Build regression matrix double[][] BTB = new double[basisFunctions.length][basisFunctions.length]; for(int i=0; i<basisFunctions.length; i++) { for(int j=0; j<=i; j++) { double covariance = basisFunctions[i].mult(basisFunctions[j]).getAverage(); BTB[i][j] = covariance; BTB[j][i] = covariance; } } double[] BTX = new double[basisFunctions.length]; for(int i=0; i<basisFunctions.length; i++) { double covariance = basisFunctions[i].mult(value).getAverage(); BTX[i] = covariance; } return LinearAlgebra.solveLinearEquationLeastSquare(BTB, BTX); }
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Get the vector of regression coefficients. @param value The random variable to regress. @return The vector of regression coefficients.
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a3c067d52dd33feb97d851df6cab130e4116759f
https://github.com/finmath/finmath-lib/blob/a3c067d52dd33feb97d851df6cab130e4116759f/src/main/java/net/finmath/montecarlo/conditionalexpectation/LinearRegression.java#L36-L88
train
finmath/finmath-lib
src/main/java/net/finmath/modelling/descriptor/xmlparser/FPMLParser.java
FPMLParser.getSwapProductDescriptor
private ProductDescriptor getSwapProductDescriptor(Element trade) { InterestRateSwapLegProductDescriptor legReceiver = null; InterestRateSwapLegProductDescriptor legPayer = null; NodeList legs = trade.getElementsByTagName("swapStream"); for(int legIndex = 0; legIndex < legs.getLength(); legIndex++) { Element leg = (Element) legs.item(legIndex); boolean isPayer = leg.getElementsByTagName("payerPartyReference").item(0).getAttributes().getNamedItem("href").getNodeValue().equals(homePartyId); if(isPayer) { legPayer = getSwapLegProductDescriptor(leg); } else { legReceiver = getSwapLegProductDescriptor(leg); } } return new InterestRateSwapProductDescriptor(legReceiver, legPayer); }
java
private ProductDescriptor getSwapProductDescriptor(Element trade) { InterestRateSwapLegProductDescriptor legReceiver = null; InterestRateSwapLegProductDescriptor legPayer = null; NodeList legs = trade.getElementsByTagName("swapStream"); for(int legIndex = 0; legIndex < legs.getLength(); legIndex++) { Element leg = (Element) legs.item(legIndex); boolean isPayer = leg.getElementsByTagName("payerPartyReference").item(0).getAttributes().getNamedItem("href").getNodeValue().equals(homePartyId); if(isPayer) { legPayer = getSwapLegProductDescriptor(leg); } else { legReceiver = getSwapLegProductDescriptor(leg); } } return new InterestRateSwapProductDescriptor(legReceiver, legPayer); }
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Construct an InterestRateSwapProductDescriptor from a node in a FpML file. @param trade The node containing the swap. @return Descriptor of the swap.
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a3c067d52dd33feb97d851df6cab130e4116759f
https://github.com/finmath/finmath-lib/blob/a3c067d52dd33feb97d851df6cab130e4116759f/src/main/java/net/finmath/modelling/descriptor/xmlparser/FPMLParser.java#L101-L120
train
finmath/finmath-lib
src/main/java/net/finmath/information/Library.java
Library.getVersionString
public static String getVersionString() { String versionString = "UNKNOWN"; Properties propeties = getProperites(); if(propeties != null) { versionString = propeties.getProperty("finmath-lib.version"); } return versionString; }
java
public static String getVersionString() { String versionString = "UNKNOWN"; Properties propeties = getProperites(); if(propeties != null) { versionString = propeties.getProperty("finmath-lib.version"); } return versionString; }
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Return the version string of this instance of finmath-lib. @return The version string of this instance of finmath-lib.
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a3c067d52dd33feb97d851df6cab130e4116759f
https://github.com/finmath/finmath-lib/blob/a3c067d52dd33feb97d851df6cab130e4116759f/src/main/java/net/finmath/information/Library.java#L40-L47
train
finmath/finmath-lib
src/main/java/net/finmath/information/Library.java
Library.getBuildString
public static String getBuildString() { String versionString = "UNKNOWN"; Properties propeties = getProperites(); if(propeties != null) { versionString = propeties.getProperty("finmath-lib.build"); } return versionString; }
java
public static String getBuildString() { String versionString = "UNKNOWN"; Properties propeties = getProperites(); if(propeties != null) { versionString = propeties.getProperty("finmath-lib.build"); } return versionString; }
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Return the build string of this instance of finmath-lib. Currently this is the Git commit hash. @return The build string of this instance of finmath-lib.
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a3c067d52dd33feb97d851df6cab130e4116759f
https://github.com/finmath/finmath-lib/blob/a3c067d52dd33feb97d851df6cab130e4116759f/src/main/java/net/finmath/information/Library.java#L55-L62
train
finmath/finmath-lib
src/main/java6/net/finmath/marketdata/model/curves/DiscountCurve.java
DiscountCurve.createDiscountCurveFromDiscountFactors
public static DiscountCurve createDiscountCurveFromDiscountFactors(String name, double[] times, double[] givenDiscountFactors) { DiscountCurve discountFactors = new DiscountCurve(name); for(int timeIndex=0; timeIndex<times.length;timeIndex++) { discountFactors.addDiscountFactor(times[timeIndex], givenDiscountFactors[timeIndex], times[timeIndex] > 0); } return discountFactors; }
java
public static DiscountCurve createDiscountCurveFromDiscountFactors(String name, double[] times, double[] givenDiscountFactors) { DiscountCurve discountFactors = new DiscountCurve(name); for(int timeIndex=0; timeIndex<times.length;timeIndex++) { discountFactors.addDiscountFactor(times[timeIndex], givenDiscountFactors[timeIndex], times[timeIndex] > 0); } return discountFactors; }
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Create a discount curve from given times and given discount factors using default interpolation and extrapolation methods. @param name The name of this discount curve. @param times Array of times as doubles. @param givenDiscountFactors Array of corresponding discount factors. @return A new discount factor object.
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a3c067d52dd33feb97d851df6cab130e4116759f
https://github.com/finmath/finmath-lib/blob/a3c067d52dd33feb97d851df6cab130e4116759f/src/main/java6/net/finmath/marketdata/model/curves/DiscountCurve.java#L158-L166
train
finmath/finmath-lib
src/main/java/net/finmath/fouriermethod/products/smile/EuropeanOptionSmile.java
EuropeanOptionSmile.getDescriptors
public Map<Double, SingleAssetEuropeanOptionProductDescriptor> getDescriptors(LocalDate referenceDate){ int numberOfStrikes = strikes.length; HashMap<Double, SingleAssetEuropeanOptionProductDescriptor> descriptors = new HashMap<Double, SingleAssetEuropeanOptionProductDescriptor>(); LocalDate maturityDate = FloatingpointDate.getDateFromFloatingPointDate(referenceDate, maturity); for(int i = 0; i< numberOfStrikes; i++) { descriptors.put(strikes[i], new SingleAssetEuropeanOptionProductDescriptor(underlyingName, maturityDate, strikes[i])); } return descriptors; }
java
public Map<Double, SingleAssetEuropeanOptionProductDescriptor> getDescriptors(LocalDate referenceDate){ int numberOfStrikes = strikes.length; HashMap<Double, SingleAssetEuropeanOptionProductDescriptor> descriptors = new HashMap<Double, SingleAssetEuropeanOptionProductDescriptor>(); LocalDate maturityDate = FloatingpointDate.getDateFromFloatingPointDate(referenceDate, maturity); for(int i = 0; i< numberOfStrikes; i++) { descriptors.put(strikes[i], new SingleAssetEuropeanOptionProductDescriptor(underlyingName, maturityDate, strikes[i])); } return descriptors; }
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Return a collection of product descriptors for each option in the smile. @param referenceDate The reference date (translating the maturity floating point date to dates. @return a collection of product descriptors for each option in the smile.
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a3c067d52dd33feb97d851df6cab130e4116759f
https://github.com/finmath/finmath-lib/blob/a3c067d52dd33feb97d851df6cab130e4116759f/src/main/java/net/finmath/fouriermethod/products/smile/EuropeanOptionSmile.java#L88-L99
train
finmath/finmath-lib
src/main/java/net/finmath/fouriermethod/products/smile/EuropeanOptionSmile.java
EuropeanOptionSmile.getDescriptor
public SingleAssetEuropeanOptionProductDescriptor getDescriptor(LocalDate referenceDate, int index) throws ArrayIndexOutOfBoundsException{ LocalDate maturityDate = FloatingpointDate.getDateFromFloatingPointDate(referenceDate, maturity); if(index >= strikes.length) { throw new ArrayIndexOutOfBoundsException("Strike index out of bounds"); }else { return new SingleAssetEuropeanOptionProductDescriptor(underlyingName, maturityDate, strikes[index]); } }
java
public SingleAssetEuropeanOptionProductDescriptor getDescriptor(LocalDate referenceDate, int index) throws ArrayIndexOutOfBoundsException{ LocalDate maturityDate = FloatingpointDate.getDateFromFloatingPointDate(referenceDate, maturity); if(index >= strikes.length) { throw new ArrayIndexOutOfBoundsException("Strike index out of bounds"); }else { return new SingleAssetEuropeanOptionProductDescriptor(underlyingName, maturityDate, strikes[index]); } }
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Return a product descriptor for a specific strike. @param referenceDate The reference date (translating the maturity floating point date to dates. @param index The index corresponding to the strike grid. @return a product descriptor for a specific strike. @throws ArrayIndexOutOfBoundsException Thrown if index is out of bound.
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a3c067d52dd33feb97d851df6cab130e4116759f
https://github.com/finmath/finmath-lib/blob/a3c067d52dd33feb97d851df6cab130e4116759f/src/main/java/net/finmath/fouriermethod/products/smile/EuropeanOptionSmile.java#L109-L116
train
finmath/finmath-lib
src/main/java/net/finmath/marketdata2/model/curves/AbstractCurve.java
AbstractCurve.getValues
public RandomVariable[] getValues(double[] times) { RandomVariable[] values = new RandomVariable[times.length]; for(int i=0; i<times.length; i++) { values[i] = getValue(null, times[i]); } return values; }
java
public RandomVariable[] getValues(double[] times) { RandomVariable[] values = new RandomVariable[times.length]; for(int i=0; i<times.length; i++) { values[i] = getValue(null, times[i]); } return values; }
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Return a vector of values corresponding to a given vector of times. @param times A given vector of times. @return A vector of values corresponding to the given vector of times.
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a3c067d52dd33feb97d851df6cab130e4116759f
https://github.com/finmath/finmath-lib/blob/a3c067d52dd33feb97d851df6cab130e4116759f/src/main/java/net/finmath/marketdata2/model/curves/AbstractCurve.java#L60-L68
train
finmath/finmath-lib
src/main/java6/net/finmath/time/daycount/DayCountConventionFactory.java
DayCountConventionFactory.getDaycount
public static double getDaycount(LocalDate startDate, LocalDate endDate, String convention) { DayCountConventionInterface daycountConvention = getDayCountConvention(convention); return daycountConvention.getDaycount(startDate, endDate); }
java
public static double getDaycount(LocalDate startDate, LocalDate endDate, String convention) { DayCountConventionInterface daycountConvention = getDayCountConvention(convention); return daycountConvention.getDaycount(startDate, endDate); }
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Return the number of days between startDate and endDate given the specific daycount convention. @param startDate The start date given as a {@link org.threeten.bp.LocalDate}. @param endDate The end date given as a {@link org.threeten.bp.LocalDate}. @param convention A convention string. @return The number of days within the given period.
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a3c067d52dd33feb97d851df6cab130e4116759f
https://github.com/finmath/finmath-lib/blob/a3c067d52dd33feb97d851df6cab130e4116759f/src/main/java6/net/finmath/time/daycount/DayCountConventionFactory.java#L76-L79
train
finmath/finmath-lib
src/main/java/net/finmath/montecarlo/interestrate/products/CMSOption.java
CMSOption.getValue
public double getValue(ForwardCurve forwardCurve, double swaprateVolatility) { double[] swapTenor = new double[fixingDates.length+1]; System.arraycopy(fixingDates, 0, swapTenor, 0, fixingDates.length); swapTenor[swapTenor.length-1] = paymentDates[paymentDates.length-1]; TimeDiscretization fixTenor = new TimeDiscretizationFromArray(swapTenor); TimeDiscretization floatTenor = new TimeDiscretizationFromArray(swapTenor); double forwardSwapRate = Swap.getForwardSwapRate(fixTenor, floatTenor, forwardCurve); double swapAnnuity = SwapAnnuity.getSwapAnnuity(fixTenor, forwardCurve); double payoffUnit = SwapAnnuity.getSwapAnnuity(new TimeDiscretizationFromArray(swapTenor[0], swapTenor[1]), forwardCurve) / (swapTenor[1] - swapTenor[0]); return AnalyticFormulas.huntKennedyCMSOptionValue(forwardSwapRate, swaprateVolatility, swapAnnuity, exerciseDate, swapTenor[swapTenor.length-1]-swapTenor[0], payoffUnit, strike) * (swapTenor[1] - swapTenor[0]); }
java
public double getValue(ForwardCurve forwardCurve, double swaprateVolatility) { double[] swapTenor = new double[fixingDates.length+1]; System.arraycopy(fixingDates, 0, swapTenor, 0, fixingDates.length); swapTenor[swapTenor.length-1] = paymentDates[paymentDates.length-1]; TimeDiscretization fixTenor = new TimeDiscretizationFromArray(swapTenor); TimeDiscretization floatTenor = new TimeDiscretizationFromArray(swapTenor); double forwardSwapRate = Swap.getForwardSwapRate(fixTenor, floatTenor, forwardCurve); double swapAnnuity = SwapAnnuity.getSwapAnnuity(fixTenor, forwardCurve); double payoffUnit = SwapAnnuity.getSwapAnnuity(new TimeDiscretizationFromArray(swapTenor[0], swapTenor[1]), forwardCurve) / (swapTenor[1] - swapTenor[0]); return AnalyticFormulas.huntKennedyCMSOptionValue(forwardSwapRate, swaprateVolatility, swapAnnuity, exerciseDate, swapTenor[swapTenor.length-1]-swapTenor[0], payoffUnit, strike) * (swapTenor[1] - swapTenor[0]); }
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This method returns the value of the product using a Black-Scholes model for the swap rate with the Hunt-Kennedy convexity adjustment. The model is determined by a discount factor curve and a swap rate volatility. @param forwardCurve The forward curve from which the swap rate is calculated. The discount curve, associated with this forward curve is used for discounting this option. @param swaprateVolatility The volatility of the log-swaprate. @return Value of this product
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a3c067d52dd33feb97d851df6cab130e4116759f
https://github.com/finmath/finmath-lib/blob/a3c067d52dd33feb97d851df6cab130e4116759f/src/main/java/net/finmath/montecarlo/interestrate/products/CMSOption.java#L132-L143
train
finmath/finmath-lib
src/main/java6/net/finmath/marketdata/model/curves/DiscountCurveNelsonSiegelSvensson.java
DiscountCurveNelsonSiegelSvensson.getDiscountFactor
@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); }
java
@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); }
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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)
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a3c067d52dd33feb97d851df6cab130e4116759f
https://github.com/finmath/finmath-lib/blob/a3c067d52dd33feb97d851df6cab130e4116759f/src/main/java6/net/finmath/marketdata/model/curves/DiscountCurveNelsonSiegelSvensson.java#L71-L93
train
finmath/finmath-lib
src/main/java/net/finmath/time/ScheduleGenerator.java
ScheduleGenerator.createScheduleFromConventions
@Deprecated public static Schedule createScheduleFromConventions( LocalDate referenceDate, LocalDate startDate, String frequency, double maturity, String daycountConvention, String shortPeriodConvention, String dateRollConvention, BusinessdayCalendar businessdayCalendar, int fixingOffsetDays, int paymentOffsetDays ) { LocalDate maturityDate = createDateFromDateAndOffset(startDate, maturity); return createScheduleFromConventions( referenceDate, startDate, maturityDate, Frequency.valueOf(frequency.toUpperCase()), DaycountConvention.getEnum(daycountConvention), ShortPeriodConvention.valueOf(shortPeriodConvention.toUpperCase()), DateRollConvention.getEnum(dateRollConvention), businessdayCalendar, fixingOffsetDays, paymentOffsetDays ); }
java
@Deprecated public static Schedule createScheduleFromConventions( LocalDate referenceDate, LocalDate startDate, String frequency, double maturity, String daycountConvention, String shortPeriodConvention, String dateRollConvention, BusinessdayCalendar businessdayCalendar, int fixingOffsetDays, int paymentOffsetDays ) { LocalDate maturityDate = createDateFromDateAndOffset(startDate, maturity); return createScheduleFromConventions( referenceDate, startDate, maturityDate, Frequency.valueOf(frequency.toUpperCase()), DaycountConvention.getEnum(daycountConvention), ShortPeriodConvention.valueOf(shortPeriodConvention.toUpperCase()), DateRollConvention.getEnum(dateRollConvention), businessdayCalendar, fixingOffsetDays, paymentOffsetDays ); }
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Generates a schedule based on some meta data. The schedule generation considers short periods. @param referenceDate The date which is used in the schedule to internally convert dates to doubles, i.e., the date where t=0. @param startDate The start date of the first period. @param frequency The frequency. @param maturity The end date of the last period. @param daycountConvention The daycount convention. @param shortPeriodConvention If short period exists, have it first or last. @param dateRollConvention Adjustment to be applied to the all dates. @param businessdayCalendar Businessday calendar (holiday calendar) to be used for date roll adjustment. @param fixingOffsetDays Number of business days to be added to period start to get the fixing date. @param paymentOffsetDays Number of business days to be added to period end to get the payment date. @return The corresponding schedule @deprecated Will be removed in version 2.3
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a3c067d52dd33feb97d851df6cab130e4116759f
https://github.com/finmath/finmath-lib/blob/a3c067d52dd33feb97d851df6cab130e4116759f/src/main/java/net/finmath/time/ScheduleGenerator.java#L745-L774
train
finmath/finmath-lib
src/main/java/net/finmath/marketdata/model/curves/locallinearregression/CurveEstimation.java
CurveEstimation.getRegressionCurve
public Curve getRegressionCurve(){ // @TODO Add threadsafe lazy init. if(regressionCurve !=null) { return regressionCurve; } DoubleMatrix a = solveEquationSystem(); double[] curvePoints=new double[partition.getLength()]; curvePoints[0]=a.get(0); for(int i=1;i<curvePoints.length;i++) { curvePoints[i]=curvePoints[i-1]+a.get(i)*(partition.getIntervalLength(i-1)); } return new CurveInterpolation( "RegressionCurve", referenceDate, CurveInterpolation.InterpolationMethod.LINEAR, CurveInterpolation.ExtrapolationMethod.CONSTANT, CurveInterpolation.InterpolationEntity.VALUE, partition.getPoints(), curvePoints); }
java
public Curve getRegressionCurve(){ // @TODO Add threadsafe lazy init. if(regressionCurve !=null) { return regressionCurve; } DoubleMatrix a = solveEquationSystem(); double[] curvePoints=new double[partition.getLength()]; curvePoints[0]=a.get(0); for(int i=1;i<curvePoints.length;i++) { curvePoints[i]=curvePoints[i-1]+a.get(i)*(partition.getIntervalLength(i-1)); } return new CurveInterpolation( "RegressionCurve", referenceDate, CurveInterpolation.InterpolationMethod.LINEAR, CurveInterpolation.ExtrapolationMethod.CONSTANT, CurveInterpolation.InterpolationEntity.VALUE, partition.getPoints(), curvePoints); }
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Returns the curve resulting from the local linear regression with discrete kernel. @return The regression curve.
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a3c067d52dd33feb97d851df6cab130e4116759f
https://github.com/finmath/finmath-lib/blob/a3c067d52dd33feb97d851df6cab130e4116759f/src/main/java/net/finmath/marketdata/model/curves/locallinearregression/CurveEstimation.java#L123-L142
train
finmath/finmath-lib
src/main/java/net/finmath/marketdata/products/Cap.java
Cap.getValueAsPrice
public double getValueAsPrice(double evaluationTime, AnalyticModel model) { ForwardCurve forwardCurve = model.getForwardCurve(forwardCurveName); DiscountCurve discountCurve = model.getDiscountCurve(discountCurveName); DiscountCurve discountCurveForForward = null; if(forwardCurve == null && forwardCurveName != null && forwardCurveName.length() > 0) { // User might like to get forward from discount curve. discountCurveForForward = model.getDiscountCurve(forwardCurveName); if(discountCurveForForward == null) { // User specified a name for the forward curve, but no curve was found. throw new IllegalArgumentException("No curve of the name " + forwardCurveName + " was found in the model."); } } double value = 0.0; for(int periodIndex=0; periodIndex<schedule.getNumberOfPeriods(); periodIndex++) { double fixingDate = schedule.getFixing(periodIndex); double paymentDate = schedule.getPayment(periodIndex); double periodLength = schedule.getPeriodLength(periodIndex); /* * We do not count empty periods. * Since empty periods are an indication for a ill-specified product, * it might be reasonable to throw an illegal argument exception instead. */ if(periodLength == 0) { continue; } double forward = 0.0; if(forwardCurve != null) { forward += forwardCurve.getForward(model, fixingDate, paymentDate-fixingDate); } else if(discountCurveForForward != null) { /* * Classical single curve case: using a discount curve as a forward curve. * This is only implemented for demonstration purposes (an exception would also be appropriate :-) */ if(fixingDate != paymentDate) { forward += (discountCurveForForward.getDiscountFactor(fixingDate) / discountCurveForForward.getDiscountFactor(paymentDate) - 1.0) / (paymentDate-fixingDate); } } double discountFactor = paymentDate > evaluationTime ? discountCurve.getDiscountFactor(model, paymentDate) : 0.0; double payoffUnit = discountFactor * periodLength; double effektiveStrike = strike; if(isStrikeMoneyness) { effektiveStrike += getATMForward(model, true); } VolatilitySurface volatilitySurface = model.getVolatilitySurface(volatiltiySufaceName); if(volatilitySurface == null) { throw new IllegalArgumentException("Volatility surface not found in model: " + volatiltiySufaceName); } if(volatilitySurface.getQuotingConvention() == QuotingConvention.VOLATILITYLOGNORMAL) { double volatility = volatilitySurface.getValue(model, fixingDate, effektiveStrike, VolatilitySurface.QuotingConvention.VOLATILITYLOGNORMAL); value += AnalyticFormulas.blackScholesGeneralizedOptionValue(forward, volatility, fixingDate, effektiveStrike, payoffUnit); } else { // Default to normal volatility as quoting convention double volatility = volatilitySurface.getValue(model, fixingDate, effektiveStrike, VolatilitySurface.QuotingConvention.VOLATILITYNORMAL); value += AnalyticFormulas.bachelierOptionValue(forward, volatility, fixingDate, effektiveStrike, payoffUnit); } } return value / discountCurve.getDiscountFactor(model, evaluationTime); }
java
public double getValueAsPrice(double evaluationTime, AnalyticModel model) { ForwardCurve forwardCurve = model.getForwardCurve(forwardCurveName); DiscountCurve discountCurve = model.getDiscountCurve(discountCurveName); DiscountCurve discountCurveForForward = null; if(forwardCurve == null && forwardCurveName != null && forwardCurveName.length() > 0) { // User might like to get forward from discount curve. discountCurveForForward = model.getDiscountCurve(forwardCurveName); if(discountCurveForForward == null) { // User specified a name for the forward curve, but no curve was found. throw new IllegalArgumentException("No curve of the name " + forwardCurveName + " was found in the model."); } } double value = 0.0; for(int periodIndex=0; periodIndex<schedule.getNumberOfPeriods(); periodIndex++) { double fixingDate = schedule.getFixing(periodIndex); double paymentDate = schedule.getPayment(periodIndex); double periodLength = schedule.getPeriodLength(periodIndex); /* * We do not count empty periods. * Since empty periods are an indication for a ill-specified product, * it might be reasonable to throw an illegal argument exception instead. */ if(periodLength == 0) { continue; } double forward = 0.0; if(forwardCurve != null) { forward += forwardCurve.getForward(model, fixingDate, paymentDate-fixingDate); } else if(discountCurveForForward != null) { /* * Classical single curve case: using a discount curve as a forward curve. * This is only implemented for demonstration purposes (an exception would also be appropriate :-) */ if(fixingDate != paymentDate) { forward += (discountCurveForForward.getDiscountFactor(fixingDate) / discountCurveForForward.getDiscountFactor(paymentDate) - 1.0) / (paymentDate-fixingDate); } } double discountFactor = paymentDate > evaluationTime ? discountCurve.getDiscountFactor(model, paymentDate) : 0.0; double payoffUnit = discountFactor * periodLength; double effektiveStrike = strike; if(isStrikeMoneyness) { effektiveStrike += getATMForward(model, true); } VolatilitySurface volatilitySurface = model.getVolatilitySurface(volatiltiySufaceName); if(volatilitySurface == null) { throw new IllegalArgumentException("Volatility surface not found in model: " + volatiltiySufaceName); } if(volatilitySurface.getQuotingConvention() == QuotingConvention.VOLATILITYLOGNORMAL) { double volatility = volatilitySurface.getValue(model, fixingDate, effektiveStrike, VolatilitySurface.QuotingConvention.VOLATILITYLOGNORMAL); value += AnalyticFormulas.blackScholesGeneralizedOptionValue(forward, volatility, fixingDate, effektiveStrike, payoffUnit); } else { // Default to normal volatility as quoting convention double volatility = volatilitySurface.getValue(model, fixingDate, effektiveStrike, VolatilitySurface.QuotingConvention.VOLATILITYNORMAL); value += AnalyticFormulas.bachelierOptionValue(forward, volatility, fixingDate, effektiveStrike, payoffUnit); } } return value / discountCurve.getDiscountFactor(model, evaluationTime); }
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Returns the value of this product under the given model. @param evaluationTime Evaluation time. @param model The model. @return Value of this product und the given model.
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a3c067d52dd33feb97d851df6cab130e4116759f
https://github.com/finmath/finmath-lib/blob/a3c067d52dd33feb97d851df6cab130e4116759f/src/main/java/net/finmath/marketdata/products/Cap.java#L115-L183
train
finmath/finmath-lib
src/main/java6/net/finmath/optimizer/LevenbergMarquardt.java
LevenbergMarquardt.setDerivatives
public void setDerivatives(double[] parameters, double[][] derivatives) throws SolverException { // Calculate new derivatives. Note that this method is called only with // parameters = parameterCurrent, so we may use valueCurrent. Vector<Future<double[]>> valueFutures = new Vector<Future<double[]>>(parameterCurrent.length); for (int parameterIndex = 0; parameterIndex < parameterCurrent.length; parameterIndex++) { final double[] parametersNew = parameters.clone(); final double[] derivative = derivatives[parameterIndex]; final int workerParameterIndex = parameterIndex; Callable<double[]> worker = new Callable<double[]>() { public double[] call() { double parameterFiniteDifference; if(parameterSteps != null) { parameterFiniteDifference = parameterSteps[workerParameterIndex]; } else { /* * Try to adaptively set a parameter shift. Note that in some * applications it may be important to set parameterSteps. * appropriately. */ parameterFiniteDifference = (Math.abs(parametersNew[workerParameterIndex]) + 1) * 1E-8; } // Shift parameter value parametersNew[workerParameterIndex] += parameterFiniteDifference; // Calculate derivative as (valueUpShift - valueCurrent) / parameterFiniteDifference try { setValues(parametersNew, derivative); } catch (Exception e) { // We signal an exception to calculate the derivative as NaN Arrays.fill(derivative, Double.NaN); } for (int valueIndex = 0; valueIndex < valueCurrent.length; valueIndex++) { derivative[valueIndex] -= valueCurrent[valueIndex]; derivative[valueIndex] /= parameterFiniteDifference; if(Double.isNaN(derivative[valueIndex])) { derivative[valueIndex] = 0.0; } } return derivative; } }; if(executor != null) { Future<double[]> valueFuture = executor.submit(worker); valueFutures.add(parameterIndex, valueFuture); } else { FutureTask<double[]> valueFutureTask = new FutureTask<double[]>(worker); valueFutureTask.run(); valueFutures.add(parameterIndex, valueFutureTask); } } for (int parameterIndex = 0; parameterIndex < parameterCurrent.length; parameterIndex++) { try { derivatives[parameterIndex] = valueFutures.get(parameterIndex).get(); } catch (InterruptedException e) { throw new SolverException(e); } catch (ExecutionException e) { throw new SolverException(e); } } }
java
public void setDerivatives(double[] parameters, double[][] derivatives) throws SolverException { // Calculate new derivatives. Note that this method is called only with // parameters = parameterCurrent, so we may use valueCurrent. Vector<Future<double[]>> valueFutures = new Vector<Future<double[]>>(parameterCurrent.length); for (int parameterIndex = 0; parameterIndex < parameterCurrent.length; parameterIndex++) { final double[] parametersNew = parameters.clone(); final double[] derivative = derivatives[parameterIndex]; final int workerParameterIndex = parameterIndex; Callable<double[]> worker = new Callable<double[]>() { public double[] call() { double parameterFiniteDifference; if(parameterSteps != null) { parameterFiniteDifference = parameterSteps[workerParameterIndex]; } else { /* * Try to adaptively set a parameter shift. Note that in some * applications it may be important to set parameterSteps. * appropriately. */ parameterFiniteDifference = (Math.abs(parametersNew[workerParameterIndex]) + 1) * 1E-8; } // Shift parameter value parametersNew[workerParameterIndex] += parameterFiniteDifference; // Calculate derivative as (valueUpShift - valueCurrent) / parameterFiniteDifference try { setValues(parametersNew, derivative); } catch (Exception e) { // We signal an exception to calculate the derivative as NaN Arrays.fill(derivative, Double.NaN); } for (int valueIndex = 0; valueIndex < valueCurrent.length; valueIndex++) { derivative[valueIndex] -= valueCurrent[valueIndex]; derivative[valueIndex] /= parameterFiniteDifference; if(Double.isNaN(derivative[valueIndex])) { derivative[valueIndex] = 0.0; } } return derivative; } }; if(executor != null) { Future<double[]> valueFuture = executor.submit(worker); valueFutures.add(parameterIndex, valueFuture); } else { FutureTask<double[]> valueFutureTask = new FutureTask<double[]>(worker); valueFutureTask.run(); valueFutures.add(parameterIndex, valueFutureTask); } } for (int parameterIndex = 0; parameterIndex < parameterCurrent.length; parameterIndex++) { try { derivatives[parameterIndex] = valueFutures.get(parameterIndex).get(); } catch (InterruptedException e) { throw new SolverException(e); } catch (ExecutionException e) { throw new SolverException(e); } } }
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The derivative of the objective function. You may override this method if you like to implement your own derivative. @param parameters Input value. The parameter vector. @param derivatives Output value, where derivatives[i][j] is d(value(j)) / d(parameters(i) @throws SolverException Thrown if the valuation fails, specific cause may be available via the <code>cause()</code> method.
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a3c067d52dd33feb97d851df6cab130e4116759f
https://github.com/finmath/finmath-lib/blob/a3c067d52dd33feb97d851df6cab130e4116759f/src/main/java6/net/finmath/optimizer/LevenbergMarquardt.java#L547-L613
train
spring-cloud/spring-cloud-stream-app-starters
processor/spring-cloud-starter-stream-processor-tasklaunchrequest-transform/src/main/java/org/springframework/cloud/stream/app/tasklaunchrequest/transform/processor/TasklaunchrequestTransformProcessorConfiguration.java
TasklaunchrequestTransformProcessorConfiguration.parseParams
private List<String> parseParams(String param) { Assert.hasText(param, "param must not be empty nor null"); List<String> paramsToUse = new ArrayList<>(); Matcher regexMatcher = DEPLOYMENT_PARAMS_PATTERN.matcher(param); int start = 0; while (regexMatcher.find()) { String p = removeQuoting(param.substring(start, regexMatcher.start()).trim()); if (StringUtils.hasText(p)) { paramsToUse.add(p); } start = regexMatcher.start(); } if (param != null && param.length() > 0) { String p = removeQuoting(param.substring(start, param.length()).trim()); if (StringUtils.hasText(p)) { paramsToUse.add(p); } } return paramsToUse; }
java
private List<String> parseParams(String param) { Assert.hasText(param, "param must not be empty nor null"); List<String> paramsToUse = new ArrayList<>(); Matcher regexMatcher = DEPLOYMENT_PARAMS_PATTERN.matcher(param); int start = 0; while (regexMatcher.find()) { String p = removeQuoting(param.substring(start, regexMatcher.start()).trim()); if (StringUtils.hasText(p)) { paramsToUse.add(p); } start = regexMatcher.start(); } if (param != null && param.length() > 0) { String p = removeQuoting(param.substring(start, param.length()).trim()); if (StringUtils.hasText(p)) { paramsToUse.add(p); } } return paramsToUse; }
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Parses a string of space delimited command line parameters and returns a list of parameters which doesn't contain any special quoting either for values or whole parameter. @param param string containing a list @return the list
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c82827a9b5a6eab78fe6a2d56a946b5f9fb0ead9
https://github.com/spring-cloud/spring-cloud-stream-app-starters/blob/c82827a9b5a6eab78fe6a2d56a946b5f9fb0ead9/processor/spring-cloud-starter-stream-processor-tasklaunchrequest-transform/src/main/java/org/springframework/cloud/stream/app/tasklaunchrequest/transform/processor/TasklaunchrequestTransformProcessorConfiguration.java#L136-L155
train
spring-cloud/spring-cloud-stream-app-starters
gpfdist/spring-cloud-starter-stream-sink-gpfdist/src/main/java/org/springframework/cloud/stream/app/gpfdist/sink/support/SqlUtils.java
SqlUtils.load
public static String load(LoadConfiguration config, String prefix) { if (config.getMode() == Mode.INSERT) { return loadInsert(config, prefix); } else if (config.getMode() == Mode.UPDATE) { return loadUpdate(config, prefix); } throw new IllegalArgumentException("Unsupported mode " + config.getMode()); }
java
public static String load(LoadConfiguration config, String prefix) { if (config.getMode() == Mode.INSERT) { return loadInsert(config, prefix); } else if (config.getMode() == Mode.UPDATE) { return loadUpdate(config, prefix); } throw new IllegalArgumentException("Unsupported mode " + config.getMode()); }
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Builds sql clause to load data into a database. @param config Load configuration. @param prefix Prefix for temporary resources. @return the load DDL
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c82827a9b5a6eab78fe6a2d56a946b5f9fb0ead9
https://github.com/spring-cloud/spring-cloud-stream-app-starters/blob/c82827a9b5a6eab78fe6a2d56a946b5f9fb0ead9/gpfdist/spring-cloud-starter-stream-sink-gpfdist/src/main/java/org/springframework/cloud/stream/app/gpfdist/sink/support/SqlUtils.java#L158-L166
train
spring-cloud/spring-cloud-stream-app-starters
triggertask/spring-cloud-starter-stream-source-triggertask/src/main/java/org/springframework/cloud/stream/app/triggertask/source/TriggertaskSourceConfiguration.java
TriggertaskSourceConfiguration.parseProperties
public static Map<String, String> parseProperties(String s) { Map<String, String> properties = new HashMap<String, String>(); if (!StringUtils.isEmpty(s)) { Matcher matcher = PROPERTIES_PATTERN.matcher(s); int start = 0; while (matcher.find()) { addKeyValuePairAsProperty(s.substring(start, matcher.start()), properties); start = matcher.start() + 1; } addKeyValuePairAsProperty(s.substring(start), properties); } return properties; }
java
public static Map<String, String> parseProperties(String s) { Map<String, String> properties = new HashMap<String, String>(); if (!StringUtils.isEmpty(s)) { Matcher matcher = PROPERTIES_PATTERN.matcher(s); int start = 0; while (matcher.find()) { addKeyValuePairAsProperty(s.substring(start, matcher.start()), properties); start = matcher.start() + 1; } addKeyValuePairAsProperty(s.substring(start), properties); } return properties; }
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Parses a String comprised of 0 or more comma-delimited key=value pairs. @param s the string to parse @return the Map of parsed key value pairs
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c82827a9b5a6eab78fe6a2d56a946b5f9fb0ead9
https://github.com/spring-cloud/spring-cloud-stream-app-starters/blob/c82827a9b5a6eab78fe6a2d56a946b5f9fb0ead9/triggertask/spring-cloud-starter-stream-source-triggertask/src/main/java/org/springframework/cloud/stream/app/triggertask/source/TriggertaskSourceConfiguration.java#L93-L105
train
spring-cloud/spring-cloud-stream-app-starters
gpfdist/spring-cloud-starter-stream-sink-gpfdist/src/main/java/org/springframework/cloud/stream/app/gpfdist/sink/GpfdistServer.java
GpfdistServer.start
public synchronized HttpServer<Buffer, Buffer> start() throws Exception { if (server == null) { server = createProtocolListener(); } return server; }
java
public synchronized HttpServer<Buffer, Buffer> start() throws Exception { if (server == null) { server = createProtocolListener(); } return server; }
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Start a server. @return the http server @throws Exception the exception
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c82827a9b5a6eab78fe6a2d56a946b5f9fb0ead9
https://github.com/spring-cloud/spring-cloud-stream-app-starters/blob/c82827a9b5a6eab78fe6a2d56a946b5f9fb0ead9/gpfdist/spring-cloud-starter-stream-sink-gpfdist/src/main/java/org/springframework/cloud/stream/app/gpfdist/sink/GpfdistServer.java#L82-L87
train
spring-cloud/spring-cloud-stream-app-starters
gpfdist/spring-cloud-starter-stream-sink-gpfdist/src/main/java/org/springframework/cloud/stream/app/gpfdist/sink/support/ReadableTableFactoryBean.java
ReadableTableFactoryBean.setSegmentReject
public void setSegmentReject(String reject) { if (!StringUtils.hasText(reject)) { return; } Integer parsedLimit = null; try { parsedLimit = Integer.parseInt(reject); segmentRejectType = SegmentRejectType.ROWS; } catch (NumberFormatException e) { } if (parsedLimit == null && reject.contains("%")) { try { parsedLimit = Integer.parseInt(reject.replace("%", "").trim()); segmentRejectType = SegmentRejectType.PERCENT; } catch (NumberFormatException e) { } } segmentRejectLimit = parsedLimit; }
java
public void setSegmentReject(String reject) { if (!StringUtils.hasText(reject)) { return; } Integer parsedLimit = null; try { parsedLimit = Integer.parseInt(reject); segmentRejectType = SegmentRejectType.ROWS; } catch (NumberFormatException e) { } if (parsedLimit == null && reject.contains("%")) { try { parsedLimit = Integer.parseInt(reject.replace("%", "").trim()); segmentRejectType = SegmentRejectType.PERCENT; } catch (NumberFormatException e) { } } segmentRejectLimit = parsedLimit; }
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Sets the segment reject as a string. This method is for convenience to be able to set percent reject type just by calling with '3%' and otherwise it uses rows. All this assuming that parsing finds '%' characher and is able to parse a raw reject number. @param reject the new segment reject
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c82827a9b5a6eab78fe6a2d56a946b5f9fb0ead9
https://github.com/spring-cloud/spring-cloud-stream-app-starters/blob/c82827a9b5a6eab78fe6a2d56a946b5f9fb0ead9/gpfdist/spring-cloud-starter-stream-sink-gpfdist/src/main/java/org/springframework/cloud/stream/app/gpfdist/sink/support/ReadableTableFactoryBean.java#L133-L151
train
spring-cloud/spring-cloud-stream-app-starters
twitter/spring-cloud-starter-stream-source-twitterstream/src/main/java/org/springframework/cloud/stream/app/twitterstream/source/AbstractTwitterInboundChannelAdapter.java
AbstractTwitterInboundChannelAdapter.setReadTimeout
public void setReadTimeout(int millis) { // Hack to get round Spring's dynamic loading of http client stuff ClientHttpRequestFactory f = getRequestFactory(); if (f instanceof SimpleClientHttpRequestFactory) { ((SimpleClientHttpRequestFactory) f).setReadTimeout(millis); } else { ((HttpComponentsClientHttpRequestFactory) f).setReadTimeout(millis); } }
java
public void setReadTimeout(int millis) { // Hack to get round Spring's dynamic loading of http client stuff ClientHttpRequestFactory f = getRequestFactory(); if (f instanceof SimpleClientHttpRequestFactory) { ((SimpleClientHttpRequestFactory) f).setReadTimeout(millis); } else { ((HttpComponentsClientHttpRequestFactory) f).setReadTimeout(millis); } }
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The read timeout for the underlying URLConnection to the twitter stream.
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c82827a9b5a6eab78fe6a2d56a946b5f9fb0ead9
https://github.com/spring-cloud/spring-cloud-stream-app-starters/blob/c82827a9b5a6eab78fe6a2d56a946b5f9fb0ead9/twitter/spring-cloud-starter-stream-source-twitterstream/src/main/java/org/springframework/cloud/stream/app/twitterstream/source/AbstractTwitterInboundChannelAdapter.java#L83-L92
train
spring-cloud/spring-cloud-stream-app-starters
twitter/spring-cloud-starter-stream-source-twitterstream/src/main/java/org/springframework/cloud/stream/app/twitterstream/source/AbstractTwitterInboundChannelAdapter.java
AbstractTwitterInboundChannelAdapter.setConnectTimeout
public void setConnectTimeout(int millis) { ClientHttpRequestFactory f = getRequestFactory(); if (f instanceof SimpleClientHttpRequestFactory) { ((SimpleClientHttpRequestFactory) f).setConnectTimeout(millis); } else { ((HttpComponentsClientHttpRequestFactory) f).setConnectTimeout(millis); } }
java
public void setConnectTimeout(int millis) { ClientHttpRequestFactory f = getRequestFactory(); if (f instanceof SimpleClientHttpRequestFactory) { ((SimpleClientHttpRequestFactory) f).setConnectTimeout(millis); } else { ((HttpComponentsClientHttpRequestFactory) f).setConnectTimeout(millis); } }
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The connection timeout for making a connection to Twitter.
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c82827a9b5a6eab78fe6a2d56a946b5f9fb0ead9
https://github.com/spring-cloud/spring-cloud-stream-app-starters/blob/c82827a9b5a6eab78fe6a2d56a946b5f9fb0ead9/twitter/spring-cloud-starter-stream-source-twitterstream/src/main/java/org/springframework/cloud/stream/app/twitterstream/source/AbstractTwitterInboundChannelAdapter.java#L97-L105
train
spring-cloud/spring-cloud-stream-app-starters
websocket/spring-cloud-starter-stream-sink-websocket/src/main/java/org/springframework/cloud/stream/app/websocket/sink/WebsocketSinkServerHandler.java
WebsocketSinkServerHandler.handleTextWebSocketFrameInternal
private void handleTextWebSocketFrameInternal(TextWebSocketFrame frame, ChannelHandlerContext ctx) { if (logger.isTraceEnabled()) { logger.trace(String.format("%s received %s", ctx.channel(), frame.text())); } addTraceForFrame(frame, "text"); ctx.channel().write(new TextWebSocketFrame("Echo: " + frame.text())); }
java
private void handleTextWebSocketFrameInternal(TextWebSocketFrame frame, ChannelHandlerContext ctx) { if (logger.isTraceEnabled()) { logger.trace(String.format("%s received %s", ctx.channel(), frame.text())); } addTraceForFrame(frame, "text"); ctx.channel().write(new TextWebSocketFrame("Echo: " + frame.text())); }
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simple echo implementation
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c82827a9b5a6eab78fe6a2d56a946b5f9fb0ead9
https://github.com/spring-cloud/spring-cloud-stream-app-starters/blob/c82827a9b5a6eab78fe6a2d56a946b5f9fb0ead9/websocket/spring-cloud-starter-stream-sink-websocket/src/main/java/org/springframework/cloud/stream/app/websocket/sink/WebsocketSinkServerHandler.java#L157-L164
train
spring-cloud/spring-cloud-stream-app-starters
websocket/spring-cloud-starter-stream-sink-websocket/src/main/java/org/springframework/cloud/stream/app/websocket/sink/WebsocketSinkServerHandler.java
WebsocketSinkServerHandler.addTraceForFrame
private void addTraceForFrame(WebSocketFrame frame, String type) { Map<String, Object> trace = new LinkedHashMap<>(); trace.put("type", type); trace.put("direction", "in"); if (frame instanceof TextWebSocketFrame) { trace.put("payload", ((TextWebSocketFrame) frame).text()); } if (traceEnabled) { websocketTraceRepository.add(trace); } }
java
private void addTraceForFrame(WebSocketFrame frame, String type) { Map<String, Object> trace = new LinkedHashMap<>(); trace.put("type", type); trace.put("direction", "in"); if (frame instanceof TextWebSocketFrame) { trace.put("payload", ((TextWebSocketFrame) frame).text()); } if (traceEnabled) { websocketTraceRepository.add(trace); } }
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add trace information for received frame
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c82827a9b5a6eab78fe6a2d56a946b5f9fb0ead9
https://github.com/spring-cloud/spring-cloud-stream-app-starters/blob/c82827a9b5a6eab78fe6a2d56a946b5f9fb0ead9/websocket/spring-cloud-starter-stream-sink-websocket/src/main/java/org/springframework/cloud/stream/app/websocket/sink/WebsocketSinkServerHandler.java#L167-L178
train
spring-cloud/spring-cloud-stream-app-starters
mail/spring-cloud-starter-stream-source-mail/src/main/java/org/springframework/cloud/stream/app/mail/source/MailSourceConfiguration.java
MailSourceConfiguration.getFlowBuilder
@SuppressWarnings({ "rawtypes", "unchecked" }) private IntegrationFlowBuilder getFlowBuilder() { IntegrationFlowBuilder flowBuilder; URLName urlName = this.properties.getUrl(); if (this.properties.isIdleImap()) { flowBuilder = getIdleImapFlow(urlName); } else { MailInboundChannelAdapterSpec adapterSpec; switch (urlName.getProtocol().toUpperCase()) { case "IMAP": case "IMAPS": adapterSpec = getImapFlowBuilder(urlName); break; case "POP3": case "POP3S": adapterSpec = getPop3FlowBuilder(urlName); break; default: throw new IllegalArgumentException( "Unsupported mail protocol: " + urlName.getProtocol()); } flowBuilder = IntegrationFlows.from( adapterSpec.javaMailProperties(getJavaMailProperties(urlName)) .selectorExpression(this.properties.getExpression()) .shouldDeleteMessages(this.properties.isDelete()), new Consumer<SourcePollingChannelAdapterSpec>() { @Override public void accept( SourcePollingChannelAdapterSpec sourcePollingChannelAdapterSpec) { sourcePollingChannelAdapterSpec.poller(MailSourceConfiguration.this.defaultPoller); } }); } return flowBuilder; }
java
@SuppressWarnings({ "rawtypes", "unchecked" }) private IntegrationFlowBuilder getFlowBuilder() { IntegrationFlowBuilder flowBuilder; URLName urlName = this.properties.getUrl(); if (this.properties.isIdleImap()) { flowBuilder = getIdleImapFlow(urlName); } else { MailInboundChannelAdapterSpec adapterSpec; switch (urlName.getProtocol().toUpperCase()) { case "IMAP": case "IMAPS": adapterSpec = getImapFlowBuilder(urlName); break; case "POP3": case "POP3S": adapterSpec = getPop3FlowBuilder(urlName); break; default: throw new IllegalArgumentException( "Unsupported mail protocol: " + urlName.getProtocol()); } flowBuilder = IntegrationFlows.from( adapterSpec.javaMailProperties(getJavaMailProperties(urlName)) .selectorExpression(this.properties.getExpression()) .shouldDeleteMessages(this.properties.isDelete()), new Consumer<SourcePollingChannelAdapterSpec>() { @Override public void accept( SourcePollingChannelAdapterSpec sourcePollingChannelAdapterSpec) { sourcePollingChannelAdapterSpec.poller(MailSourceConfiguration.this.defaultPoller); } }); } return flowBuilder; }
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Method to build Integration Flow for Mail. Suppress Warnings for MailInboundChannelAdapterSpec. @return Integration Flow object for Mail Source
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c82827a9b5a6eab78fe6a2d56a946b5f9fb0ead9
https://github.com/spring-cloud/spring-cloud-stream-app-starters/blob/c82827a9b5a6eab78fe6a2d56a946b5f9fb0ead9/mail/spring-cloud-starter-stream-source-mail/src/main/java/org/springframework/cloud/stream/app/mail/source/MailSourceConfiguration.java#L73-L114
train
spring-cloud/spring-cloud-stream-app-starters
mail/spring-cloud-starter-stream-source-mail/src/main/java/org/springframework/cloud/stream/app/mail/source/MailSourceConfiguration.java
MailSourceConfiguration.getIdleImapFlow
private IntegrationFlowBuilder getIdleImapFlow(URLName urlName) { return IntegrationFlows.from(Mail.imapIdleAdapter(urlName.toString()) .shouldDeleteMessages(this.properties.isDelete()) .javaMailProperties(getJavaMailProperties(urlName)) .selectorExpression(this.properties.getExpression()) .shouldMarkMessagesAsRead(this.properties.isMarkAsRead())); }
java
private IntegrationFlowBuilder getIdleImapFlow(URLName urlName) { return IntegrationFlows.from(Mail.imapIdleAdapter(urlName.toString()) .shouldDeleteMessages(this.properties.isDelete()) .javaMailProperties(getJavaMailProperties(urlName)) .selectorExpression(this.properties.getExpression()) .shouldMarkMessagesAsRead(this.properties.isMarkAsRead())); }
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Method to build Integration flow for IMAP Idle configuration. @param urlName Mail source URL. @return Integration Flow object IMAP IDLE.
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c82827a9b5a6eab78fe6a2d56a946b5f9fb0ead9
https://github.com/spring-cloud/spring-cloud-stream-app-starters/blob/c82827a9b5a6eab78fe6a2d56a946b5f9fb0ead9/mail/spring-cloud-starter-stream-source-mail/src/main/java/org/springframework/cloud/stream/app/mail/source/MailSourceConfiguration.java#L121-L127
train
spring-cloud/spring-cloud-stream-app-starters
mail/spring-cloud-starter-stream-source-mail/src/main/java/org/springframework/cloud/stream/app/mail/source/MailSourceConfiguration.java
MailSourceConfiguration.getImapFlowBuilder
@SuppressWarnings("rawtypes") private MailInboundChannelAdapterSpec getImapFlowBuilder(URLName urlName) { return Mail.imapInboundAdapter(urlName.toString()) .shouldMarkMessagesAsRead(this.properties.isMarkAsRead()); }
java
@SuppressWarnings("rawtypes") private MailInboundChannelAdapterSpec getImapFlowBuilder(URLName urlName) { return Mail.imapInboundAdapter(urlName.toString()) .shouldMarkMessagesAsRead(this.properties.isMarkAsRead()); }
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Method to build Mail Channel Adapter for IMAP. @param urlName Mail source URL. @return Mail Channel for IMAP
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c82827a9b5a6eab78fe6a2d56a946b5f9fb0ead9
https://github.com/spring-cloud/spring-cloud-stream-app-starters/blob/c82827a9b5a6eab78fe6a2d56a946b5f9fb0ead9/mail/spring-cloud-starter-stream-source-mail/src/main/java/org/springframework/cloud/stream/app/mail/source/MailSourceConfiguration.java#L144-L148
train
spring-projects/spring-social-facebook
spring-social-facebook-web/src/main/java/org/springframework/social/facebook/web/CanvasSignInController.java
CanvasSignInController.postDeclineView
protected View postDeclineView() { return new TopLevelWindowRedirect() { @Override protected String getRedirectUrl(Map<String, ?> model) { return postDeclineUrl; } }; }
java
protected View postDeclineView() { return new TopLevelWindowRedirect() { @Override protected String getRedirectUrl(Map<String, ?> model) { return postDeclineUrl; } }; }
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View that redirects the top level window to the URL defined in postDeclineUrl property after user declines to authorize application. May be overridden for custom views, particularly in the case where the post-decline view should be rendered in-canvas. @return a view to display after a user declines authoriation. Defaults as a redirect to postDeclineUrl
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ae2234d94367eaa3adbba251ec7790d5ba7ffa41
https://github.com/spring-projects/spring-social-facebook/blob/ae2234d94367eaa3adbba251ec7790d5ba7ffa41/spring-social-facebook-web/src/main/java/org/springframework/social/facebook/web/CanvasSignInController.java#L165-L172
train
spring-projects/spring-social-facebook
spring-social-facebook-web/src/main/java/org/springframework/social/facebook/web/SignedRequestDecoder.java
SignedRequestDecoder.decodeSignedRequest
@SuppressWarnings("unchecked") public Map<String, ?> decodeSignedRequest(String signedRequest) throws SignedRequestException { return decodeSignedRequest(signedRequest, Map.class); }
java
@SuppressWarnings("unchecked") public Map<String, ?> decodeSignedRequest(String signedRequest) throws SignedRequestException { return decodeSignedRequest(signedRequest, Map.class); }
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Decodes a signed request, returning the payload of the signed request as a Map @param signedRequest the value of the signed_request parameter sent by Facebook. @return the payload of the signed request as a Map @throws SignedRequestException if there is an error decoding the signed request
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ae2234d94367eaa3adbba251ec7790d5ba7ffa41
https://github.com/spring-projects/spring-social-facebook/blob/ae2234d94367eaa3adbba251ec7790d5ba7ffa41/spring-social-facebook-web/src/main/java/org/springframework/social/facebook/web/SignedRequestDecoder.java#L58-L61
train
spring-projects/spring-social-facebook
spring-social-facebook-web/src/main/java/org/springframework/social/facebook/web/SignedRequestDecoder.java
SignedRequestDecoder.decodeSignedRequest
public <T> T decodeSignedRequest(String signedRequest, Class<T> type) throws SignedRequestException { String[] split = signedRequest.split("\\."); String encodedSignature = split[0]; String payload = split[1]; String decoded = base64DecodeToString(payload); byte[] signature = base64DecodeToBytes(encodedSignature); try { T data = objectMapper.readValue(decoded, type); String algorithm = objectMapper.readTree(decoded).get("algorithm").textValue(); if (algorithm == null || !algorithm.equals("HMAC-SHA256")) { throw new SignedRequestException("Unknown encryption algorithm: " + algorithm); } byte[] expectedSignature = encrypt(payload, secret); if (!Arrays.equals(expectedSignature, signature)) { throw new SignedRequestException("Invalid signature."); } return data; } catch (IOException e) { throw new SignedRequestException("Error parsing payload.", e); } }
java
public <T> T decodeSignedRequest(String signedRequest, Class<T> type) throws SignedRequestException { String[] split = signedRequest.split("\\."); String encodedSignature = split[0]; String payload = split[1]; String decoded = base64DecodeToString(payload); byte[] signature = base64DecodeToBytes(encodedSignature); try { T data = objectMapper.readValue(decoded, type); String algorithm = objectMapper.readTree(decoded).get("algorithm").textValue(); if (algorithm == null || !algorithm.equals("HMAC-SHA256")) { throw new SignedRequestException("Unknown encryption algorithm: " + algorithm); } byte[] expectedSignature = encrypt(payload, secret); if (!Arrays.equals(expectedSignature, signature)) { throw new SignedRequestException("Invalid signature."); } return data; } catch (IOException e) { throw new SignedRequestException("Error parsing payload.", e); } }
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Decodes a signed request, returning the payload of the signed request as a specified type. @param signedRequest the value of the signed_request parameter sent by Facebook. @param type the type to bind the signed_request to. @param <T> the Java type to bind the signed_request to. @return the payload of the signed request as an object @throws SignedRequestException if there is an error decoding the signed request
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ae2234d94367eaa3adbba251ec7790d5ba7ffa41
https://github.com/spring-projects/spring-social-facebook/blob/ae2234d94367eaa3adbba251ec7790d5ba7ffa41/spring-social-facebook-web/src/main/java/org/springframework/social/facebook/web/SignedRequestDecoder.java#L71-L91
train
spring-projects/spring-social-facebook
spring-social-facebook/src/main/java/org/springframework/social/facebook/api/FqlResult.java
FqlResult.getString
public String getString(String fieldName) { return hasValue(fieldName) ? String.valueOf(resultMap.get(fieldName)) : null; }
java
public String getString(String fieldName) { return hasValue(fieldName) ? String.valueOf(resultMap.get(fieldName)) : null; }
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Returns the value of the identified field as a String. @param fieldName the name of the field @return the value of the field as a String
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ae2234d94367eaa3adbba251ec7790d5ba7ffa41
https://github.com/spring-projects/spring-social-facebook/blob/ae2234d94367eaa3adbba251ec7790d5ba7ffa41/spring-social-facebook/src/main/java/org/springframework/social/facebook/api/FqlResult.java#L45-L47
train
spring-projects/spring-social-facebook
spring-social-facebook/src/main/java/org/springframework/social/facebook/api/FqlResult.java
FqlResult.getInteger
public Integer getInteger(String fieldName) { try { return hasValue(fieldName) ? Integer.valueOf(String.valueOf(resultMap.get(fieldName))) : null; } catch (NumberFormatException e) { throw new FqlException("Field '" + fieldName +"' is not a number.", e); } }
java
public Integer getInteger(String fieldName) { try { return hasValue(fieldName) ? Integer.valueOf(String.valueOf(resultMap.get(fieldName))) : null; } catch (NumberFormatException e) { throw new FqlException("Field '" + fieldName +"' is not a number.", e); } }
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Returns the value of the identified field as an Integer. @param fieldName the name of the field @return the value of the field as an Integer @throws FqlException if the field cannot be expressed as an Integer
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ae2234d94367eaa3adbba251ec7790d5ba7ffa41
https://github.com/spring-projects/spring-social-facebook/blob/ae2234d94367eaa3adbba251ec7790d5ba7ffa41/spring-social-facebook/src/main/java/org/springframework/social/facebook/api/FqlResult.java#L55-L61
train
spring-projects/spring-social-facebook
spring-social-facebook/src/main/java/org/springframework/social/facebook/api/FqlResult.java
FqlResult.getLong
public Long getLong(String fieldName) { try { return hasValue(fieldName) ? Long.valueOf(String.valueOf(resultMap.get(fieldName))) : null; } catch (NumberFormatException e) { throw new FqlException("Field '" + fieldName +"' is not a number.", e); } }
java
public Long getLong(String fieldName) { try { return hasValue(fieldName) ? Long.valueOf(String.valueOf(resultMap.get(fieldName))) : null; } catch (NumberFormatException e) { throw new FqlException("Field '" + fieldName +"' is not a number.", e); } }
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Returns the value of the identified field as a Long. @param fieldName the name of the field @return the value of the field as a Long @throws FqlException if the field cannot be expressed as an Long
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ae2234d94367eaa3adbba251ec7790d5ba7ffa41
https://github.com/spring-projects/spring-social-facebook/blob/ae2234d94367eaa3adbba251ec7790d5ba7ffa41/spring-social-facebook/src/main/java/org/springframework/social/facebook/api/FqlResult.java#L69-L75
train
spring-projects/spring-social-facebook
spring-social-facebook/src/main/java/org/springframework/social/facebook/api/FqlResult.java
FqlResult.getFloat
public Float getFloat(String fieldName) { try { return hasValue(fieldName) ? Float.valueOf(String.valueOf(resultMap.get(fieldName))) : null; } catch (NumberFormatException e) { throw new FqlException("Field '" + fieldName +"' is not a number.", e); } }
java
public Float getFloat(String fieldName) { try { return hasValue(fieldName) ? Float.valueOf(String.valueOf(resultMap.get(fieldName))) : null; } catch (NumberFormatException e) { throw new FqlException("Field '" + fieldName +"' is not a number.", e); } }
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Returns the value of the identified field as a Float. @param fieldName the name of the field @return the value of the field as a Float @throws FqlException if the field cannot be expressed as an Float
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ae2234d94367eaa3adbba251ec7790d5ba7ffa41
https://github.com/spring-projects/spring-social-facebook/blob/ae2234d94367eaa3adbba251ec7790d5ba7ffa41/spring-social-facebook/src/main/java/org/springframework/social/facebook/api/FqlResult.java#L83-L89
train
spring-projects/spring-social-facebook
spring-social-facebook/src/main/java/org/springframework/social/facebook/api/FqlResult.java
FqlResult.getBoolean
public Boolean getBoolean(String fieldName) { return hasValue(fieldName) ? Boolean.valueOf(String.valueOf(resultMap.get(fieldName))) : null; }
java
public Boolean getBoolean(String fieldName) { return hasValue(fieldName) ? Boolean.valueOf(String.valueOf(resultMap.get(fieldName))) : null; }
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Returns the value of the identified field as a Boolean. @param fieldName the name of the field @return the value of the field as a Boolean
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ae2234d94367eaa3adbba251ec7790d5ba7ffa41
https://github.com/spring-projects/spring-social-facebook/blob/ae2234d94367eaa3adbba251ec7790d5ba7ffa41/spring-social-facebook/src/main/java/org/springframework/social/facebook/api/FqlResult.java#L96-L98
train
spring-projects/spring-social-facebook
spring-social-facebook/src/main/java/org/springframework/social/facebook/api/FqlResult.java
FqlResult.getTime
public Date getTime(String fieldName) { try { if (hasValue(fieldName)) { return new Date(Long.valueOf(String.valueOf(resultMap.get(fieldName))) * 1000); } else { return null; } } catch (NumberFormatException e) { throw new FqlException("Field '" + fieldName +"' is not a time.", e); } }
java
public Date getTime(String fieldName) { try { if (hasValue(fieldName)) { return new Date(Long.valueOf(String.valueOf(resultMap.get(fieldName))) * 1000); } else { return null; } } catch (NumberFormatException e) { throw new FqlException("Field '" + fieldName +"' is not a time.", e); } }
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Returns the value of the identified field as a Date. Time fields returned from an FQL query are expressed in terms of seconds since midnight, January 1, 1970 UTC. @param fieldName the name of the field @return the value of the field as a Date @throws FqlException if the field's value cannot be expressed as a long value from which a Date object can be constructed.
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ae2234d94367eaa3adbba251ec7790d5ba7ffa41
https://github.com/spring-projects/spring-social-facebook/blob/ae2234d94367eaa3adbba251ec7790d5ba7ffa41/spring-social-facebook/src/main/java/org/springframework/social/facebook/api/FqlResult.java#L107-L117
train