@Override
protected double getValue(final int x, final int y, final Coordinate crd)
throws GeoGridException {
final double wspHeight = crd.z;
if (Double.isNaN(wspHeight)) return Double.NaN;
// possible that waterdepth input grid contains water depth less than zero!
if (wspHeight <= 0.0) return Double.NaN;
if (m_polygonCollection.size() == 0) return Double.NaN;
final double cx = crd.x;
final double cy = crd.y;
try {
final GM_Position position =
m_geoTransformer.transform(GeometryFactory.createGM_Position(cx, cy), getSourceCRS());
final double damageValue = calculateDamageValue(position, wspHeight);
final Coordinate resultCrd = new Coordinate(position.getX(), position.getY(), damageValue);
if (Doubles.isFinite(damageValue))
m_statistics.addSpecificDamage(m_returnPeriod, resultCrd, m_cellSize);
return damageValue;
} catch (final Exception e) {
e.printStackTrace();
throw new GeoGridException(e.getLocalizedMessage(), e);
}
}
/**
* Calculates the price of the bond future product with z-spread.
*
* <p>The price of the product is the price on the valuation date.
*
* <p>The z-spread is a parallel shift applied to continuously compounded rates or periodic
* compounded rates of the issuer discounting curve.
*
* @param future the future to price
* @param provider the rates provider
* @param zSpread the z-spread
* @param compoundedRateType the compounded rate type
* @param periodPerYear the number of periods per year
* @return the price of the product, in decimal form
*/
public double priceWithZSpread(
BondFuture future,
LegalEntityDiscountingProvider provider,
double zSpread,
CompoundedRateType compoundedRateType,
int periodPerYear) {
ImmutableList<Security<FixedCouponBond>> bondSecurity = future.getBondSecurityBasket();
int size = bondSecurity.size();
double[] priceBonds = new double[size];
for (int i = 0; i < size; ++i) {
Security<FixedCouponBond> bond = bondSecurity.get(i);
double dirtyPrice =
bondPricer.dirtyPriceFromCurvesWithZSpread(
bond,
provider,
zSpread,
compoundedRateType,
periodPerYear,
future.getLastDeliveryDate());
priceBonds[i] =
bondPricer.cleanPriceFromDirtyPrice(
bond.getProduct(), future.getLastDeliveryDate(), dirtyPrice)
/ future.getConversionFactor().get(i);
}
return Doubles.min(priceBonds);
}
/**
* Attempts to convert the provided string value to a numeric type, trying Integer, Long and
* Double in order until successful.
*/
@Override
public Object convert(String value) {
if (value == null || value.isEmpty()) {
return value;
}
Object result = Ints.tryParse(value);
if (result != null) {
return result;
}
result = Longs.tryParse(value);
if (result != null) {
return result;
}
result = Doubles.tryParse(value);
if (result != null) {
return result;
}
return value;
}
private double[] getSparkModelInfoFromHDFS(Path location, Configuration conf) throws Exception {
FileSystem fileSystem = FileSystem.get(location.toUri(), conf);
FileStatus[] files = fileSystem.listStatus(location);
if (files == null) throw new Exception("Couldn't find Spark Truck ML weights at: " + location);
ArrayList<Double> modelInfo = new ArrayList<Double>();
for (FileStatus file : files) {
if (file.getPath().getName().startsWith("_")) {
continue;
}
InputStream stream = fileSystem.open(file.getPath());
StringWriter writer = new StringWriter();
IOUtils.copy(stream, writer, "UTF-8");
String raw = writer.toString();
for (String str : raw.split("\n")) {
modelInfo.add(Double.valueOf(str));
}
}
return Doubles.toArray(modelInfo);
}
@Override
public boolean apply(@Nullable Scenario scenario) {
final List<Double> loads =
newArrayList(
relative ? Metrics.measureRelativeLoad(scenario) : Metrics.measureLoad(scenario));
final int toAdd = desiredLoadList.size() - loads.size();
for (int j = 0; j < toAdd; j++) {
loads.add(0d);
}
final double[] deviations =
abs(subtract(Doubles.toArray(desiredLoadList), Doubles.toArray(loads)));
final double mean = StatUtils.mean(deviations);
final double max = Doubles.max(deviations);
return max <= maxMax && mean <= maxMean;
}
private void extremaOp(TiffMeta surface, GridCoverage2D gridCoverage2D) {
double min = Double.MAX_VALUE;
double max = Double.MIN_VALUE;
RenderedImage img = gridCoverage2D.getRenderedImage();
RenderedOp extremaOp = ExtremaDescriptor.create(img, null, 10, 10, false, 1, null);
double[] allMins = (double[]) extremaOp.getProperty("minimum");
min = Doubles.min(allMins);
double[] allMaxs = (double[]) extremaOp.getProperty("maximum");
max = Doubles.max(allMaxs);
surface.setMaxVal(max);
surface.setMinVal(min);
}
public void testFuzzyEqualsZeroTolerance() {
// make sure we test -0 tolerance
for (double zero : Doubles.asList(0.0, -0.0)) {
for (double a : ALL_DOUBLE_CANDIDATES) {
for (double b : ALL_DOUBLE_CANDIDATES) {
assertEquals(
a == b || (Double.isNaN(a) && Double.isNaN(b)), DoubleMath.fuzzyEquals(a, b, zero));
}
}
}
}
@SuppressWarnings("synthetic-access")
@Override
public CurrencyLabelledMatrix1D buildObject(
final FudgeDeserializer deserializer, final FudgeMsg message) {
final FudgeMsg msg = message.getMessage(MATRIX_FIELD_NAME);
final Queue<String> labelTypes = new LinkedList<String>();
final Queue<FudgeField> labelValues = new LinkedList<FudgeField>();
final List<Currency> keys = new LinkedList<Currency>();
final List<Object> labels = new LinkedList<Object>();
final List<Double> values = new LinkedList<Double>();
for (final FudgeField field : msg) {
switch (field.getOrdinal()) {
case LABEL_TYPE_ORDINAL:
labelTypes.add((String) field.getValue());
break;
case KEY_ORDINAL:
keys.add(Currency.of((String) field.getValue()));
break;
case LABEL_ORDINAL:
labelValues.add(field);
break;
case VALUE_ORDINAL:
values.add((Double) field.getValue());
break;
}
if (!labelTypes.isEmpty() && !labelValues.isEmpty()) {
// Have a type and a value, which can be consumed
final String labelType = labelTypes.remove();
Class<?> labelClass;
try {
labelClass = LabelledMatrix1DBuilder.getLabelClass(labelType, _loadedClasses);
} catch (final ClassNotFoundException ex) {
throw new OpenGammaRuntimeException(
"Could not deserialize label of type " + labelType, ex);
}
final FudgeField labelValue = labelValues.remove();
final Object label = deserializer.fieldValueToObject(labelClass, labelValue);
// labels.add(Currency.of((String) label));
labels.add(label);
}
}
final int matrixSize = keys.size();
final Currency[] keysArray = new Currency[matrixSize];
keys.toArray(keysArray);
final Object[] labelsArray = new Object[matrixSize];
labels.toArray(labelsArray);
final double[] valuesArray = Doubles.toArray(values);
return new CurrencyLabelledMatrix1D(keysArray, labelsArray, valuesArray);
}
static Map<Imt, Map<Gmm, List<Double>>> initValueMaps(Set<Imt> imts, Set<Gmm> gmms, int size) {
Map<Imt, Map<Gmm, List<Double>>> imtMap = Maps.newEnumMap(Imt.class);
for (Imt imt : imts) {
Map<Gmm, List<Double>> gmmMap = Maps.newEnumMap(Gmm.class);
for (Gmm gmm : gmms) {
gmmMap.put(gmm, Doubles.asList(new double[size]));
}
imtMap.put(imt, gmmMap);
}
return imtMap;
}
private double addScores(final IDType target, final Iterable<Integer> ids) {
Collection<Double> scores = new ArrayList<>();
for (Integer id : ids) {
Optional<Integer> my = mapping.getUnchecked(Pair.make(target, id));
if (!my.isPresent()) continue;
Double s = this.scores.get(my.get());
if (s == null) continue;
scores.add(s);
}
if (scores.isEmpty()) return Double.NaN;
if (scores.size() == 1) return scores.iterator().next().doubleValue();
return operator.combine(Doubles.toArray(scores));
}
static {
ImmutableSet.Builder<Double> integralBuilder = ImmutableSet.builder();
ImmutableSet.Builder<Double> fractionalBuilder = ImmutableSet.builder();
integralBuilder.addAll(Doubles.asList(0.0, -0.0, Double.MAX_VALUE, -Double.MAX_VALUE));
// Add small multiples of MIN_VALUE and MIN_NORMAL
for (int scale = 1; scale <= 4; scale++) {
for (double d : Doubles.asList(Double.MIN_VALUE, Double.MIN_NORMAL)) {
fractionalBuilder.add(d * scale).add(-d * scale);
}
}
for (double d :
Doubles.asList(
0,
1,
2,
7,
51,
102,
Math.scalb(1.0, 53),
Integer.MIN_VALUE,
Integer.MAX_VALUE,
Long.MIN_VALUE,
Long.MAX_VALUE)) {
for (double delta : Doubles.asList(0.0, 1.0, 2.0)) {
integralBuilder.addAll(Doubles.asList(d + delta, d - delta, -d - delta, -d + delta));
}
for (double delta : Doubles.asList(0.01, 0.1, 0.25, 0.499, 0.5, 0.501, 0.7, 0.8)) {
double x = d + delta;
if (x != Math.round(x)) {
fractionalBuilder.add(x);
}
}
}
INTEGRAL_DOUBLE_CANDIDATES = integralBuilder.build();
fractionalBuilder.add(1.414).add(1.415).add(Math.sqrt(2));
fractionalBuilder.add(5.656).add(5.657).add(4 * Math.sqrt(2));
for (double d : INTEGRAL_DOUBLE_CANDIDATES) {
double x = 1 / d;
if (x != Math.rint(x)) {
fractionalBuilder.add(x);
}
}
FRACTIONAL_DOUBLE_CANDIDATES = fractionalBuilder.build();
FINITE_DOUBLE_CANDIDATES =
Iterables.concat(FRACTIONAL_DOUBLE_CANDIDATES, INTEGRAL_DOUBLE_CANDIDATES);
POSITIVE_FINITE_DOUBLE_CANDIDATES =
Iterables.filter(
FINITE_DOUBLE_CANDIDATES,
new Predicate<Double>() {
@Override
public boolean apply(Double input) {
return input.doubleValue() > 0.0;
}
});
DOUBLE_CANDIDATES_EXCEPT_NAN = Iterables.concat(FINITE_DOUBLE_CANDIDATES, INFINITIES);
ALL_DOUBLE_CANDIDATES = Iterables.concat(DOUBLE_CANDIDATES_EXCEPT_NAN, asList(Double.NaN));
}
@Override
public int compare(TraitProxy o1, TraitProxy o2) {
Double value1 = Double.MIN_VALUE;
Double value2 = Double.MAX_VALUE;
try {
if (o1.getValue() != null) value1 = Double.parseDouble(o1.getValue());
} catch (NumberFormatException e) {
}
try {
if (o2.getValue() != null) value2 = Double.parseDouble(o2.getValue());
} catch (NumberFormatException e) {
}
return Doubles.compare(value1, value2);
}
@Override
public LocalDateLabelledMatrix1D buildObject(
final FudgeDeserializer deserializer, final FudgeMsg message) {
final FudgeMsg msg = message.getMessage(MATRIX_FIELD_NAME);
final Queue<String> labelTypes = new LinkedList<String>();
final Queue<FudgeField> labelValues = new LinkedList<FudgeField>();
final List<LocalDate> keys = new LinkedList<LocalDate>();
final List<Object> labels = new LinkedList<Object>();
final List<Double> values = new LinkedList<Double>();
for (final FudgeField field : msg) {
switch (field.getOrdinal()) {
case LABEL_TYPE_ORDINAL:
labelTypes.add((String) field.getValue());
break;
case KEY_ORDINAL:
keys.add(((FudgeDate) field.getValue()).toLocalDate());
break;
case LABEL_ORDINAL:
labelValues.add(field);
break;
case VALUE_ORDINAL:
values.add((Double) field.getValue());
break;
}
if (!labelTypes.isEmpty() && !labelValues.isEmpty()) {
// Have a type and a value, which can be consumed
final String labelType = labelTypes.remove();
Class<?> labelClass = getClass(labelType);
final FudgeField labelValue = labelValues.remove();
final Object label = deserializer.fieldValueToObject(labelClass, labelValue);
labels.add(label);
}
}
final int matrixSize = keys.size();
final LocalDate[] keysArray = new LocalDate[matrixSize];
keys.toArray(keysArray);
final Object[] labelsArray = new Object[matrixSize];
labels.toArray(labelsArray);
final double[] valuesArray = Doubles.toArray(values);
return new LocalDateLabelledMatrix1D(keysArray, labelsArray, valuesArray);
}
@Override
public SurfaceCurrencyParameterSensitivity surfaceCurrencyParameterSensitivity(
IborCapletFloorletSensitivity point) {
ArgChecker.isTrue(
point.getIndex().equals(index),
"Ibor index of provider must be the same as Ibor index of point sensitivity");
double expiry = relativeTime(point.getExpiry());
double strike = point.getStrike();
// copy to ImmutableMap to lock order (keySet and values used separately but must match)
Map<DoublesPair, Double> result =
ImmutableMap.copyOf(surface.zValueParameterSensitivity(expiry, strike));
SurfaceCurrencyParameterSensitivity parameterSensi =
SurfaceCurrencyParameterSensitivity.of(
updateSurfaceMetadata(result.keySet()),
point.getCurrency(),
DoubleArray.copyOf(Doubles.toArray(result.values())));
return parameterSensi.multipliedBy(point.getSensitivity());
}
protected final int compare(int index1, int index2) {
final boolean n1 = nulls.get(index1);
final boolean n2 = nulls.get(index2);
if (n1 && n2) {
return 0;
}
if (n1 ^ n2) {
return nullsFirst ? (n1 ? -1 : 1) : (n1 ? 1 : -1);
}
int compare;
switch (type) {
case 0:
compare = Booleans.compare(booleans[index1], booleans[index2]);
break;
case 1:
compare = bits[index1] - bits[index2];
break;
case 2:
compare = Shorts.compare(shorts[index1], shorts[index2]);
break;
case 3:
compare = Ints.compare(ints[index1], ints[index2]);
break;
case 4:
compare = Longs.compare(longs[index1], longs[index2]);
break;
case 5:
compare = Floats.compare(floats[index1], floats[index2]);
break;
case 6:
compare = Doubles.compare(doubles[index1], doubles[index2]);
break;
case 7:
compare = TextDatum.COMPARATOR.compare(bytes[index1], bytes[index2]);
break;
case 8:
compare = UnsignedInts.compare(ints[index1], ints[index2]);
break;
default:
throw new IllegalArgumentException();
}
return ascending ? compare : -compare;
}
private static Double evaluate(Collection<?> values, int quantile) {
List<Double> doubleValues = new ArrayList<>();
for (Object value : values) {
Double doubleValue = (Double) TypeUtil.parseOrCast(DataType.DOUBLE, value);
doubleValues.add(doubleValue);
}
double[] data = Doubles.toArray(doubleValues);
// The data must be (at least partially) ordered
Arrays.sort(data);
Percentile percentile = new Percentile();
percentile.setData(data);
return percentile.evaluate(quantile);
}
@Override
public StringLabelledMatrix1D buildObject(
final FudgeDeserializer deserializer, final FudgeMsg message) {
final FudgeMsg msg = message.getMessage(MATRIX_FIELD_NAME);
final List<String> keys = new LinkedList<String>();
final List<Double> values = new LinkedList<Double>();
for (final FudgeField field : msg) {
switch (field.getOrdinal()) {
case KEY_ORDINAL:
keys.add((String) field.getValue());
break;
case VALUE_ORDINAL:
values.add((Double) field.getValue());
break;
}
}
String[] keysArray = keys.toArray(ArrayUtils.EMPTY_STRING_ARRAY);
final double[] valuesArray = Doubles.toArray(values);
return new StringLabelledMatrix1D(keysArray, valuesArray);
}
private static Map<String, double[]> parseVectorDictionary(
SupportVectorMachineModel supportVectorMachineModel) {
VectorDictionary vectorDictionary = supportVectorMachineModel.getVectorDictionary();
VectorFields vectorFields = vectorDictionary.getVectorFields();
List<FieldRef> fieldRefs = vectorFields.getFieldRefs();
Map<String, double[]> result = new LinkedHashMap<>();
List<VectorInstance> vectorInstances = vectorDictionary.getVectorInstances();
for (VectorInstance vectorInstance : vectorInstances) {
String id = vectorInstance.getId();
if (id == null) {
throw new InvalidFeatureException(vectorInstance);
}
Array array = vectorInstance.getArray();
RealSparseArray sparseArray = vectorInstance.getREALSparseArray();
List<? extends Number> values;
if (array != null && sparseArray == null) {
values = ArrayUtil.asNumberList(array);
} else if (array == null && sparseArray != null) {
values = SparseArrayUtil.asNumberList(sparseArray);
} else {
throw new InvalidFeatureException(vectorInstance);
} // End if
if (fieldRefs.size() != values.size()) {
throw new InvalidFeatureException(vectorInstance);
}
double[] vector = Doubles.toArray(values);
result.put(id, vector);
}
return result;
}
@Override
public void execute(Tuple input) {
String line = input.getString(0);
List<Double> list = new ArrayList<Double>();
int firstIndex = line.indexOf("[");
int lastIndex = line.indexOf("]");
String subLine = line.substring(firstIndex + 1, lastIndex);
// System.out.println(subLine);
String[] subStrings = subLine.split(",");
String author = subStrings[0];
for (int i = 1; i < subStrings.length; i++) {
list.add(Double.valueOf(subStrings[i]));
}
vector = Doubles.toArray(list);
// System.out.println(author+" "+list+" "+vector);
collector.emit(input, new Values(author, list, vector));
collector.ack(input);
}
/**
* Computes the first and second order derivatives of the Black implied volatility in the SABR
* model.
*
* <p>The first derivative values will be stored in the input array {@code volatilityD} The array
* contains, [0] Derivative w.r.t the forward, [1] the derivative w.r.t the strike, [2] the
* derivative w.r.t. to alpha, [3] the derivative w.r.t. to beta, [4] the derivative w.r.t. to
* rho, and [5] the derivative w.r.t. to nu. Thus the length of the array should be 6.
*
* <p>The second derivative values will be stored in the input array {@code volatilityD2}. Only
* the second order derivative with respect to the forward and strike are implemented. The array
* contains [0][0] forward-forward; [0][1] forward-strike; [1][1] strike-strike. Thus the size
* should be 2 x 2.
*
* <p>Around ATM, a first order expansion is used to due to some 0/0-type indetermination. The
* second order derivative produced is poor around ATM.
*
* @param forward the forward value of the underlying
* @param strike the strike value of the option
* @param timeToExpiry the time to expiry of the option
* @param data the SABR data.
* @param volatilityD the array used to return the first order derivative
* @param volatilityD2 the array of array used to return the second order derivative
* @return the Black implied volatility
*/
@Override
public double volatilityAdjoint2(
double forward,
double strike,
double timeToExpiry,
SabrFormulaData data,
double[] volatilityD,
double[][] volatilityD2) {
double k = Math.max(strike, 0.000001);
double alpha = data.getAlpha();
double beta = data.getBeta();
double rho = data.getRho();
double nu = data.getNu();
// Forward
double h0 = (1 - beta) / 2;
double h1 = forward * k;
double h1h0 = Math.pow(h1, h0);
double h12 = h1h0 * h1h0;
double h2 = Math.log(forward / k);
double h22 = h2 * h2;
double h23 = h22 * h2;
double h24 = h23 * h2;
double f1 = h1h0 * (1 + h0 * h0 / 6.0 * (h22 + h0 * h0 / 20.0 * h24));
double f2 = nu / alpha * h1h0 * h2;
double f3 =
h0 * h0 / 6.0 * alpha * alpha / h12
+ rho * beta * nu * alpha / 4.0 / h1h0
+ (2 - 3 * rho * rho) / 24.0 * nu * nu;
double sqrtf2 = Math.sqrt(1 - 2 * rho * f2 + f2 * f2);
double f2x = 0.0;
double x = 0.0, xp = 0, xpp = 0;
if (DoubleMath.fuzzyEquals(f2, 0.0, SMALL_Z)) {
f2x = 1.0 - 0.5 * f2 * rho; // small f2 expansion to f2^2 terms
} else {
if (DoubleMath.fuzzyEquals(rho, 1.0, RHO_EPS)) {
x =
f2 < 1.0
? -Math.log(1.0 - f2) - 0.5 * Math.pow(f2 / (f2 - 1.0), 2) * (1.0 - rho)
: Math.log(2.0 * f2 - 2.0) - Math.log(1.0 - rho);
} else {
x = Math.log((sqrtf2 + f2 - rho) / (1 - rho));
}
xp = 1. / sqrtf2;
xpp = (rho - f2) / Math.pow(sqrtf2, 3.0);
f2x = f2 / x;
}
double sigma = Math.max(MIN_VOL, alpha / f1 * f2x * (1 + f3 * timeToExpiry));
// First level
double h0Dbeta = -0.5;
double sigmaDf1 = -sigma / f1;
double sigmaDf2 = 0;
if (DoubleMath.fuzzyEquals(f2, 0.0, SMALL_Z)) {
sigmaDf2 = alpha / f1 * (1 + f3 * timeToExpiry) * -0.5 * rho;
} else {
sigmaDf2 = alpha / f1 * (1 + f3 * timeToExpiry) * (1.0 / x - f2 * xp / (x * x));
}
double sigmaDf3 = alpha / f1 * f2x * timeToExpiry;
double sigmaDf4 = f2x / f1 * (1 + f3 * timeToExpiry);
double sigmaDx = -alpha / f1 * f2 / (x * x) * (1 + f3 * timeToExpiry);
double[][] sigmaD2ff = new double[3][3];
sigmaD2ff[0][0] = -sigmaDf1 / f1 + sigma / (f1 * f1); // OK
sigmaD2ff[0][1] = -sigmaDf2 / f1;
sigmaD2ff[0][2] = -sigmaDf3 / f1;
if (DoubleMath.fuzzyEquals(f2, 0.0, SMALL_Z)) {
sigmaD2ff[1][2] = alpha / f1 * -0.5 * rho * timeToExpiry;
} else {
sigmaD2ff[1][1] =
alpha
/ f1
* (1 + f3 * timeToExpiry)
* (-2 * xp / (x * x) - f2 * xpp / (x * x) + 2 * f2 * xp * xp / (x * x * x));
sigmaD2ff[1][2] = alpha / f1 * timeToExpiry * (1.0 / x - f2 * xp / (x * x));
}
sigmaD2ff[2][2] = 0.0;
// double sigma = alpha / f1 * f2x * (1 + f3 * theta);
// Second level
double[] f1Dh = new double[3];
double[] f2Dh = new double[3];
double[] f3Dh = new double[3];
f1Dh[0] = h1h0 * (h0 * (h22 / 3.0 + h0 * h0 / 40.0 * h24)) + Math.log(h1) * f1;
f1Dh[1] = h0 * f1 / h1;
f1Dh[2] = h1h0 * (h0 * h0 / 6.0 * (2.0 * h2 + h0 * h0 / 5.0 * h23));
f2Dh[0] = Math.log(h1) * f2;
f2Dh[1] = h0 * f2 / h1;
f2Dh[2] = nu / alpha * h1h0;
f3Dh[0] =
h0 / 3.0 * alpha * alpha / h12
- 2 * h0 * h0 / 6.0 * alpha * alpha / h12 * Math.log(h1)
- rho * beta * nu * alpha / 4.0 / h1h0 * Math.log(h1);
f3Dh[1] =
-2 * h0 * h0 / 6.0 * alpha * alpha / h12 * h0 / h1
- rho * beta * nu * alpha / 4.0 / h1h0 * h0 / h1;
f3Dh[2] = 0.0;
double[] f1Dp = new double[4]; // Derivative to sabr parameters
double[] f2Dp = new double[4];
double[] f3Dp = new double[4];
double[] f4Dp = new double[4];
f1Dp[0] = 0.0;
f1Dp[1] = f1Dh[0] * h0Dbeta;
f1Dp[2] = 0.0;
f1Dp[3] = 0.0;
f2Dp[0] = -f2 / alpha;
f2Dp[1] = f2Dh[0] * h0Dbeta;
f2Dp[2] = 0.0;
f2Dp[3] = h1h0 * h2 / alpha;
f3Dp[0] = h0 * h0 / 3.0 * alpha / h12 + rho * beta * nu / 4.0 / h1h0;
f3Dp[1] = rho * nu * alpha / 4.0 / h1h0 + f3Dh[0] * h0Dbeta;
f3Dp[2] = beta * nu * alpha / 4.0 / h1h0 - rho / 4.0 * nu * nu;
f3Dp[3] = rho * beta * alpha / 4.0 / h1h0 + (2 - 3 * rho * rho) / 12.0 * nu;
f4Dp[0] = 1.0;
f4Dp[1] = 0.0;
f4Dp[2] = 0.0;
f4Dp[3] = 0.0;
double sigmaDh1 = sigmaDf1 * f1Dh[1] + sigmaDf2 * f2Dh[1] + sigmaDf3 * f3Dh[1];
double sigmaDh2 = sigmaDf1 * f1Dh[2] + sigmaDf2 * f2Dh[2] + sigmaDf3 * f3Dh[2];
double[][] f1D2hh = new double[2][2]; // No h0
double[][] f2D2hh = new double[2][2];
double[][] f3D2hh = new double[2][2];
f1D2hh[0][0] = h0 * (h0 - 1) * f1 / (h1 * h1);
f1D2hh[0][1] = h0 * h1h0 / h1 * h0 * h0 / 6.0 * (2.0 * h2 + 4.0 * h0 * h0 / 20.0 * h23);
f1D2hh[1][1] = h1h0 * (h0 * h0 / 6.0 * (2.0 + 12.0 * h0 * h0 / 20.0 * h2));
f2D2hh[0][0] = h0 * (h0 - 1) * f2 / (h1 * h1);
f2D2hh[0][1] = nu / alpha * h0 * h1h0 / h1;
f2D2hh[1][1] = 0.0;
f3D2hh[0][0] =
2 * h0 * (2 * h0 + 1) * h0 * h0 / 6.0 * alpha * alpha / (h12 * h1 * h1)
+ h0 * (h0 + 1) * rho * beta * nu * alpha / 4.0 / (h1h0 * h1 * h1);
f3D2hh[0][1] = 0.0;
f3D2hh[1][1] = 0.0;
double[][] sigmaD2hh = new double[2][2]; // No h0
for (int loopx = 0; loopx < 2; loopx++) {
for (int loopy = loopx; loopy < 2; loopy++) {
sigmaD2hh[loopx][loopy] =
(sigmaD2ff[0][0] * f1Dh[loopy + 1]
+ sigmaD2ff[0][1] * f2Dh[loopy + 1]
+ sigmaD2ff[0][2] * f3Dh[loopy + 1])
* f1Dh[loopx + 1]
+ sigmaDf1 * f1D2hh[loopx][loopy]
+ (sigmaD2ff[0][1] * f1Dh[loopy + 1]
+ sigmaD2ff[1][1] * f2Dh[loopy + 1]
+ sigmaD2ff[1][2] * f3Dh[loopy + 1])
* f2Dh[loopx + 1]
+ sigmaDf2 * f2D2hh[loopx][loopy]
+ (sigmaD2ff[0][2] * f1Dh[loopy + 1]
+ sigmaD2ff[1][2] * f2Dh[loopy + 1]
+ sigmaD2ff[2][2] * f3Dh[loopy + 1])
* f3Dh[loopx + 1]
+ sigmaDf3 * f3D2hh[loopx][loopy];
}
}
// Third level
double h1Df = k;
double h1Dk = forward;
double h1D2ff = 0.0;
double h1D2kf = 1.0;
double h1D2kk = 0.0;
double h2Df = 1.0 / forward;
double h2Dk = -1.0 / k;
double h2D2ff = -1 / (forward * forward);
double h2D2fk = 0.0;
double h2D2kk = 1.0 / (k * k);
volatilityD[0] = sigmaDh1 * h1Df + sigmaDh2 * h2Df;
volatilityD[1] = sigmaDh1 * h1Dk + sigmaDh2 * h2Dk;
volatilityD[2] =
sigmaDf1 * f1Dp[0] + sigmaDf2 * f2Dp[0] + sigmaDf3 * f3Dp[0] + sigmaDf4 * f4Dp[0];
volatilityD[3] =
sigmaDf1 * f1Dp[1] + sigmaDf2 * f2Dp[1] + sigmaDf3 * f3Dp[1] + sigmaDf4 * f4Dp[1];
if (DoubleMath.fuzzyEquals(f2, 0.0, SMALL_Z)) {
volatilityD[4] = -0.5 * f2 + sigmaDf3 * f3Dp[2];
} else {
double xDr;
if (DoubleMath.fuzzyEquals(rho, 1.0, RHO_EPS)) {
xDr =
f2 > 1.0
? 1.0 / (1.0 - rho) + (0.5 - f2) / (f2 - 1.0) / (f2 - 1.0)
: 0.5 * Math.pow(f2 / (1.0 - f2), 2.0)
+ 0.25 * (f2 - 4.0) * Math.pow(f2 / (f2 - 1.0), 3) / (f2 - 1.0) * (1.0 - rho);
if (Doubles.isFinite(xDr)) {
volatilityD[4] =
sigmaDf1 * f1Dp[2] + sigmaDx * xDr + sigmaDf3 * f3Dp[2] + sigmaDf4 * f4Dp[2];
} else {
volatilityD[4] = Double.NEGATIVE_INFINITY;
}
} else {
xDr = (-f2 / sqrtf2 - 1 + (sqrtf2 + f2 - rho) / (1 - rho)) / (sqrtf2 + f2 - rho);
volatilityD[4] =
sigmaDf1 * f1Dp[2] + sigmaDx * xDr + sigmaDf3 * f3Dp[2] + sigmaDf4 * f4Dp[2];
}
}
volatilityD[5] =
sigmaDf1 * f1Dp[3] + sigmaDf2 * f2Dp[3] + sigmaDf3 * f3Dp[3] + sigmaDf4 * f4Dp[3];
volatilityD2[0][0] =
(sigmaD2hh[0][0] * h1Df + sigmaD2hh[0][1] * h2Df) * h1Df
+ sigmaDh1 * h1D2ff
+ (sigmaD2hh[0][1] * h1Df + sigmaD2hh[1][1] * h2Df) * h2Df
+ sigmaDh2 * h2D2ff;
volatilityD2[0][1] =
(sigmaD2hh[0][0] * h1Dk + sigmaD2hh[0][1] * h2Dk) * h1Df
+ sigmaDh1 * h1D2kf
+ (sigmaD2hh[0][1] * h1Dk + sigmaD2hh[1][1] * h2Dk) * h2Df
+ sigmaDh2 * h2D2fk;
volatilityD2[1][0] = volatilityD2[0][1];
volatilityD2[1][1] =
(sigmaD2hh[0][0] * h1Dk + sigmaD2hh[0][1] * h2Dk) * h1Dk
+ sigmaDh1 * h1D2kk
+ (sigmaD2hh[0][1] * h1Dk + sigmaD2hh[1][1] * h2Dk) * h2Dk
+ sigmaDh2 * h2D2kk;
return sigma;
}
/**
* Exhaustive input sets for every integral type.
*
* @author Louis Wasserman
*/
@GwtCompatible
public class MathTesting {
static final ImmutableSet<RoundingMode> ALL_ROUNDING_MODES =
ImmutableSet.copyOf(RoundingMode.values());
static final ImmutableList<RoundingMode> ALL_SAFE_ROUNDING_MODES =
ImmutableList.of(DOWN, UP, FLOOR, CEILING, HALF_EVEN, HALF_UP, HALF_DOWN);
// Exponents to test for the pow() function.
static final ImmutableList<Integer> EXPONENTS =
ImmutableList.of(0, 1, 2, 3, 4, 7, 10, 15, 20, 25, 40, 70);
/* Helper function to make a Long value from an Integer. */
private static final Function<Integer, Long> TO_LONG =
new Function<Integer, Long>() {
@Override
public Long apply(Integer n) {
return Long.valueOf(n);
}
};
/* Helper function to make a BigInteger value from a Long. */
private static final Function<Long, BigInteger> TO_BIGINTEGER =
new Function<Long, BigInteger>() {
@Override
public BigInteger apply(Long n) {
return BigInteger.valueOf(n);
}
};
private static final Function<Integer, Integer> NEGATE_INT =
new Function<Integer, Integer>() {
@Override
public Integer apply(Integer x) {
return -x;
}
};
private static final Function<Long, Long> NEGATE_LONG =
new Function<Long, Long>() {
@Override
public Long apply(Long x) {
return -x;
}
};
private static final Function<BigInteger, BigInteger> NEGATE_BIGINT =
new Function<BigInteger, BigInteger>() {
@Override
public BigInteger apply(BigInteger x) {
return x.negate();
}
};
/*
* This list contains values that attempt to provoke overflow in integer operations. It contains
* positive values on or near 2^N for N near multiples of 8 (near byte boundaries).
*/
static final ImmutableSet<Integer> POSITIVE_INTEGER_CANDIDATES;
static final Iterable<Integer> NEGATIVE_INTEGER_CANDIDATES;
static final Iterable<Integer> NONZERO_INTEGER_CANDIDATES;
static final Iterable<Integer> ALL_INTEGER_CANDIDATES;
static {
ImmutableSet.Builder<Integer> intValues = ImmutableSet.builder();
// Add boundary values manually to avoid over/under flow (this covers 2^N for 0 and 31).
intValues.add(Integer.MAX_VALUE - 1, Integer.MAX_VALUE);
// Add values up to 40. This covers cases like "square of a prime" and such.
for (int i = 1; i <= 40; i++) {
intValues.add(i);
}
// Now add values near 2^N for lots of values of N.
for (int exponent : asList(2, 3, 4, 9, 15, 16, 17, 24, 25, 30)) {
int x = 1 << exponent;
intValues.add(x, x + 1, x - 1);
}
intValues.add(9999).add(10000).add(10001).add(1000000); // near powers of 10
intValues.add(5792).add(5793); // sqrt(2^25) rounded up and down
POSITIVE_INTEGER_CANDIDATES = intValues.build();
NEGATIVE_INTEGER_CANDIDATES =
ImmutableList.copyOf(
Iterables.concat(
Iterables.transform(POSITIVE_INTEGER_CANDIDATES, NEGATE_INT),
ImmutableList.of(Integer.MIN_VALUE)));
NONZERO_INTEGER_CANDIDATES =
ImmutableList.copyOf(
Iterables.concat(POSITIVE_INTEGER_CANDIDATES, NEGATIVE_INTEGER_CANDIDATES));
ALL_INTEGER_CANDIDATES = Iterables.concat(NONZERO_INTEGER_CANDIDATES, ImmutableList.of(0));
}
/*
* This list contains values that attempt to provoke overflow in long operations. It contains
* positive values on or near 2^N for N near multiples of 8 (near byte boundaries). This list is
* a superset of POSITIVE_INTEGER_CANDIDATES.
*/
static final ImmutableSet<Long> POSITIVE_LONG_CANDIDATES;
static final Iterable<Long> NEGATIVE_LONG_CANDIDATES;
static final Iterable<Long> NONZERO_LONG_CANDIDATES;
static final Iterable<Long> ALL_LONG_CANDIDATES;
static {
ImmutableSet.Builder<Long> longValues = ImmutableSet.builder();
// First of all add all the integer candidate values.
longValues.addAll(Iterables.transform(POSITIVE_INTEGER_CANDIDATES, TO_LONG));
// Add boundary values manually to avoid over/under flow (this covers 2^N for 31 and 63).
longValues.add(Integer.MAX_VALUE + 1L, Long.MAX_VALUE - 1L, Long.MAX_VALUE);
// Now add values near 2^N for lots of values of N.
for (int exponent : asList(32, 33, 39, 40, 41, 47, 48, 49, 55, 56, 57)) {
long x = 1L << exponent;
longValues.add(x, x + 1, x - 1);
}
longValues.add(194368031998L).add(194368031999L); // sqrt(2^75) rounded up and down
POSITIVE_LONG_CANDIDATES = longValues.build();
NEGATIVE_LONG_CANDIDATES =
Iterables.concat(
Iterables.transform(POSITIVE_LONG_CANDIDATES, NEGATE_LONG),
ImmutableList.of(Long.MIN_VALUE));
NONZERO_LONG_CANDIDATES = Iterables.concat(POSITIVE_LONG_CANDIDATES, NEGATIVE_LONG_CANDIDATES);
ALL_LONG_CANDIDATES = Iterables.concat(NONZERO_LONG_CANDIDATES, ImmutableList.of(0L));
}
/*
* This list contains values that attempt to provoke overflow in big integer operations. It
* contains positive values on or near 2^N for N near multiples of 8 (near byte boundaries). This
* list is a superset of POSITIVE_LONG_CANDIDATES.
*/
static final ImmutableSet<BigInteger> POSITIVE_BIGINTEGER_CANDIDATES;
static final Iterable<BigInteger> NEGATIVE_BIGINTEGER_CANDIDATES;
static final Iterable<BigInteger> NONZERO_BIGINTEGER_CANDIDATES;
static final Iterable<BigInteger> ALL_BIGINTEGER_CANDIDATES;
static {
ImmutableSet.Builder<BigInteger> bigValues = ImmutableSet.builder();
// First of all add all the long candidate values.
bigValues.addAll(Iterables.transform(POSITIVE_LONG_CANDIDATES, TO_BIGINTEGER));
// Add boundary values manually to avoid over/under flow.
bigValues.add(BigInteger.valueOf(Long.MAX_VALUE).add(ONE));
// Now add values near 2^N for lots of values of N.
for (int exponent :
asList(
64,
65,
71,
72,
73,
79,
80,
81,
255,
256,
257,
511,
512,
513,
Double.MAX_EXPONENT - 1,
Double.MAX_EXPONENT,
Double.MAX_EXPONENT + 1)) {
BigInteger x = ONE.shiftLeft(exponent);
bigValues.add(x, x.add(ONE), x.subtract(ONE));
}
bigValues.add(new BigInteger("218838949120258359057546633")); // sqrt(2^175) rounded up and
// down
bigValues.add(new BigInteger("218838949120258359057546634"));
POSITIVE_BIGINTEGER_CANDIDATES = bigValues.build();
NEGATIVE_BIGINTEGER_CANDIDATES =
Iterables.transform(POSITIVE_BIGINTEGER_CANDIDATES, NEGATE_BIGINT);
NONZERO_BIGINTEGER_CANDIDATES =
Iterables.concat(POSITIVE_BIGINTEGER_CANDIDATES, NEGATIVE_BIGINTEGER_CANDIDATES);
ALL_BIGINTEGER_CANDIDATES =
Iterables.concat(NONZERO_BIGINTEGER_CANDIDATES, ImmutableList.of(ZERO));
}
static final ImmutableSet<Double> INTEGRAL_DOUBLE_CANDIDATES;
static final ImmutableSet<Double> FRACTIONAL_DOUBLE_CANDIDATES;
static final Iterable<Double> INFINITIES =
Doubles.asList(Double.POSITIVE_INFINITY, Double.NEGATIVE_INFINITY);
static final Iterable<Double> FINITE_DOUBLE_CANDIDATES;
static final Iterable<Double> POSITIVE_FINITE_DOUBLE_CANDIDATES;
static final Iterable<Double> ALL_DOUBLE_CANDIDATES;
static final Iterable<Double> DOUBLE_CANDIDATES_EXCEPT_NAN;
static {
ImmutableSet.Builder<Double> integralBuilder = ImmutableSet.builder();
ImmutableSet.Builder<Double> fractionalBuilder = ImmutableSet.builder();
integralBuilder.addAll(Doubles.asList(0.0, -0.0, Double.MAX_VALUE, -Double.MAX_VALUE));
// Add small multiples of MIN_VALUE and MIN_NORMAL
for (int scale = 1; scale <= 4; scale++) {
for (double d : Doubles.asList(Double.MIN_VALUE, Double.MIN_NORMAL)) {
fractionalBuilder.add(d * scale).add(-d * scale);
}
}
for (double d :
Doubles.asList(
0,
1,
2,
7,
51,
102,
Math.scalb(1.0, 53),
Integer.MIN_VALUE,
Integer.MAX_VALUE,
Long.MIN_VALUE,
Long.MAX_VALUE)) {
for (double delta : Doubles.asList(0.0, 1.0, 2.0)) {
integralBuilder.addAll(Doubles.asList(d + delta, d - delta, -d - delta, -d + delta));
}
for (double delta : Doubles.asList(0.01, 0.1, 0.25, 0.499, 0.5, 0.501, 0.7, 0.8)) {
double x = d + delta;
if (x != Math.round(x)) {
fractionalBuilder.add(x);
}
}
}
INTEGRAL_DOUBLE_CANDIDATES = integralBuilder.build();
fractionalBuilder.add(1.414).add(1.415).add(Math.sqrt(2));
fractionalBuilder.add(5.656).add(5.657).add(4 * Math.sqrt(2));
for (double d : INTEGRAL_DOUBLE_CANDIDATES) {
double x = 1 / d;
if (x != Math.rint(x)) {
fractionalBuilder.add(x);
}
}
FRACTIONAL_DOUBLE_CANDIDATES = fractionalBuilder.build();
FINITE_DOUBLE_CANDIDATES =
Iterables.concat(FRACTIONAL_DOUBLE_CANDIDATES, INTEGRAL_DOUBLE_CANDIDATES);
POSITIVE_FINITE_DOUBLE_CANDIDATES =
Iterables.filter(
FINITE_DOUBLE_CANDIDATES,
new Predicate<Double>() {
@Override
public boolean apply(Double input) {
return input.doubleValue() > 0.0;
}
});
DOUBLE_CANDIDATES_EXCEPT_NAN = Iterables.concat(FINITE_DOUBLE_CANDIDATES, INFINITIES);
ALL_DOUBLE_CANDIDATES = Iterables.concat(DOUBLE_CANDIDATES_EXCEPT_NAN, asList(Double.NaN));
}
}
/**
* Tests for {@code DoubleMath}.
*
* @author Louis Wasserman
*/
public class DoubleMathTest extends TestCase {
private static final BigDecimal MAX_INT_AS_BIG_DECIMAL = BigDecimal.valueOf(Integer.MAX_VALUE);
private static final BigDecimal MIN_INT_AS_BIG_DECIMAL = BigDecimal.valueOf(Integer.MIN_VALUE);
private static final BigDecimal MAX_LONG_AS_BIG_DECIMAL = BigDecimal.valueOf(Long.MAX_VALUE);
private static final BigDecimal MIN_LONG_AS_BIG_DECIMAL = BigDecimal.valueOf(Long.MIN_VALUE);
public void testConstantsMaxFactorial() {
BigInteger MAX_DOUBLE_VALUE = BigDecimal.valueOf(Double.MAX_VALUE).toBigInteger();
assertTrue(BigIntegerMath.factorial(DoubleMath.MAX_FACTORIAL).compareTo(MAX_DOUBLE_VALUE) <= 0);
assertTrue(
BigIntegerMath.factorial(DoubleMath.MAX_FACTORIAL + 1).compareTo(MAX_DOUBLE_VALUE) > 0);
}
public void testConstantsEverySixteenthFactorial() {
for (int i = 0, n = 0; n <= DoubleMath.MAX_FACTORIAL; i++, n += 16) {
assertEquals(
BigIntegerMath.factorial(n).doubleValue(), DoubleMath.EVERY_SIXTEENTH_FACTORIAL[i]);
}
}
public void testRoundIntegralDoubleToInt() {
for (double d : INTEGRAL_DOUBLE_CANDIDATES) {
for (RoundingMode mode : ALL_SAFE_ROUNDING_MODES) {
BigDecimal expected = new BigDecimal(d).setScale(0, mode);
boolean isInBounds =
expected.compareTo(MAX_INT_AS_BIG_DECIMAL) <= 0
& expected.compareTo(MIN_INT_AS_BIG_DECIMAL) >= 0;
try {
assertEquals(expected.intValue(), DoubleMath.roundToInt(d, mode));
assertTrue(isInBounds);
} catch (ArithmeticException e) {
assertFalse(isInBounds);
}
}
}
}
public void testRoundFractionalDoubleToInt() {
for (double d : FRACTIONAL_DOUBLE_CANDIDATES) {
for (RoundingMode mode : ALL_SAFE_ROUNDING_MODES) {
BigDecimal expected = new BigDecimal(d).setScale(0, mode);
boolean isInBounds =
expected.compareTo(MAX_INT_AS_BIG_DECIMAL) <= 0
& expected.compareTo(MIN_INT_AS_BIG_DECIMAL) >= 0;
try {
assertEquals(expected.intValue(), DoubleMath.roundToInt(d, mode));
assertTrue(isInBounds);
} catch (ArithmeticException e) {
assertFalse(isInBounds);
}
}
}
}
public void testRoundExactIntegralDoubleToInt() {
for (double d : INTEGRAL_DOUBLE_CANDIDATES) {
BigDecimal expected = new BigDecimal(d).setScale(0, UNNECESSARY);
boolean isInBounds =
expected.compareTo(MAX_INT_AS_BIG_DECIMAL) <= 0
& expected.compareTo(MIN_INT_AS_BIG_DECIMAL) >= 0;
try {
assertEquals(expected.intValue(), DoubleMath.roundToInt(d, UNNECESSARY));
assertTrue(isInBounds);
} catch (ArithmeticException e) {
assertFalse(isInBounds);
}
}
}
public void testRoundExactFractionalDoubleToIntFails() {
for (double d : FRACTIONAL_DOUBLE_CANDIDATES) {
try {
DoubleMath.roundToInt(d, UNNECESSARY);
fail("Expected ArithmeticException");
} catch (ArithmeticException expected) {
}
}
}
public void testRoundNaNToIntAlwaysFails() {
for (RoundingMode mode : ALL_ROUNDING_MODES) {
try {
DoubleMath.roundToInt(Double.NaN, mode);
fail("Expected ArithmeticException");
} catch (ArithmeticException expected) {
}
}
}
public void testRoundInfiniteToIntAlwaysFails() {
for (RoundingMode mode : ALL_ROUNDING_MODES) {
try {
DoubleMath.roundToInt(Double.POSITIVE_INFINITY, mode);
fail("Expected ArithmeticException");
} catch (ArithmeticException expected) {
}
try {
DoubleMath.roundToInt(Double.NEGATIVE_INFINITY, mode);
fail("Expected ArithmeticException");
} catch (ArithmeticException expected) {
}
}
}
public void testRoundIntegralDoubleToLong() {
for (double d : INTEGRAL_DOUBLE_CANDIDATES) {
for (RoundingMode mode : ALL_SAFE_ROUNDING_MODES) {
BigDecimal expected = new BigDecimal(d).setScale(0, mode);
boolean isInBounds =
expected.compareTo(MAX_LONG_AS_BIG_DECIMAL) <= 0
& expected.compareTo(MIN_LONG_AS_BIG_DECIMAL) >= 0;
try {
assertEquals(expected.longValue(), DoubleMath.roundToLong(d, mode));
assertTrue(isInBounds);
} catch (ArithmeticException e) {
assertFalse(isInBounds);
}
}
}
}
public void testRoundFractionalDoubleToLong() {
for (double d : FRACTIONAL_DOUBLE_CANDIDATES) {
for (RoundingMode mode : ALL_SAFE_ROUNDING_MODES) {
BigDecimal expected = new BigDecimal(d).setScale(0, mode);
boolean isInBounds =
expected.compareTo(MAX_LONG_AS_BIG_DECIMAL) <= 0
& expected.compareTo(MIN_LONG_AS_BIG_DECIMAL) >= 0;
try {
assertEquals(expected.longValue(), DoubleMath.roundToLong(d, mode));
assertTrue(isInBounds);
} catch (ArithmeticException e) {
assertFalse(isInBounds);
}
}
}
}
public void testRoundExactIntegralDoubleToLong() {
for (double d : INTEGRAL_DOUBLE_CANDIDATES) {
// every mode except UNNECESSARY
BigDecimal expected = new BigDecimal(d).setScale(0, UNNECESSARY);
boolean isInBounds =
expected.compareTo(MAX_LONG_AS_BIG_DECIMAL) <= 0
& expected.compareTo(MIN_LONG_AS_BIG_DECIMAL) >= 0;
try {
assertEquals(expected.longValue(), DoubleMath.roundToLong(d, UNNECESSARY));
assertTrue(isInBounds);
} catch (ArithmeticException e) {
assertFalse(isInBounds);
}
}
}
public void testRoundExactFractionalDoubleToLongFails() {
for (double d : FRACTIONAL_DOUBLE_CANDIDATES) {
try {
DoubleMath.roundToLong(d, UNNECESSARY);
fail("Expected ArithmeticException");
} catch (ArithmeticException expected) {
}
}
}
public void testRoundNaNToLongAlwaysFails() {
for (RoundingMode mode : ALL_ROUNDING_MODES) {
try {
DoubleMath.roundToLong(Double.NaN, mode);
fail("Expected ArithmeticException");
} catch (ArithmeticException expected) {
}
}
}
public void testRoundInfiniteToLongAlwaysFails() {
for (RoundingMode mode : ALL_ROUNDING_MODES) {
try {
DoubleMath.roundToLong(Double.POSITIVE_INFINITY, mode);
fail("Expected ArithmeticException");
} catch (ArithmeticException expected) {
}
try {
DoubleMath.roundToLong(Double.NEGATIVE_INFINITY, mode);
fail("Expected ArithmeticException");
} catch (ArithmeticException expected) {
}
}
}
public void testRoundIntegralDoubleToBigInteger() {
for (double d : INTEGRAL_DOUBLE_CANDIDATES) {
for (RoundingMode mode : ALL_SAFE_ROUNDING_MODES) {
BigDecimal expected = new BigDecimal(d).setScale(0, mode);
assertEquals(expected.toBigInteger(), DoubleMath.roundToBigInteger(d, mode));
}
}
}
public void testRoundFractionalDoubleToBigInteger() {
for (double d : FRACTIONAL_DOUBLE_CANDIDATES) {
for (RoundingMode mode : ALL_SAFE_ROUNDING_MODES) {
BigDecimal expected = new BigDecimal(d).setScale(0, mode);
assertEquals(expected.toBigInteger(), DoubleMath.roundToBigInteger(d, mode));
}
}
}
public void testRoundExactIntegralDoubleToBigInteger() {
for (double d : INTEGRAL_DOUBLE_CANDIDATES) {
BigDecimal expected = new BigDecimal(d).setScale(0, UNNECESSARY);
assertEquals(expected.toBigInteger(), DoubleMath.roundToBigInteger(d, UNNECESSARY));
}
}
public void testRoundExactFractionalDoubleToBigIntegerFails() {
for (double d : FRACTIONAL_DOUBLE_CANDIDATES) {
try {
DoubleMath.roundToBigInteger(d, UNNECESSARY);
fail("Expected ArithmeticException");
} catch (ArithmeticException expected) {
}
}
}
public void testRoundNaNToBigIntegerAlwaysFails() {
for (RoundingMode mode : ALL_ROUNDING_MODES) {
try {
DoubleMath.roundToBigInteger(Double.NaN, mode);
fail("Expected ArithmeticException");
} catch (ArithmeticException expected) {
}
}
}
public void testRoundInfiniteToBigIntegerAlwaysFails() {
for (RoundingMode mode : ALL_ROUNDING_MODES) {
try {
DoubleMath.roundToBigInteger(Double.POSITIVE_INFINITY, mode);
fail("Expected ArithmeticException");
} catch (ArithmeticException expected) {
}
try {
DoubleMath.roundToBigInteger(Double.NEGATIVE_INFINITY, mode);
fail("Expected ArithmeticException");
} catch (ArithmeticException expected) {
}
}
}
public void testRoundLog2Floor() {
for (double d : POSITIVE_FINITE_DOUBLE_CANDIDATES) {
int log2 = DoubleMath.log2(d, FLOOR);
assertTrue(StrictMath.pow(2.0, log2) <= d);
assertTrue(StrictMath.pow(2.0, log2 + 1) > d);
}
}
public void testRoundLog2Ceiling() {
for (double d : POSITIVE_FINITE_DOUBLE_CANDIDATES) {
int log2 = DoubleMath.log2(d, CEILING);
assertTrue(StrictMath.pow(2.0, log2) >= d);
double z = StrictMath.pow(2.0, log2 - 1);
assertTrue(z < d);
}
}
public void testRoundLog2Down() {
for (double d : POSITIVE_FINITE_DOUBLE_CANDIDATES) {
int log2 = DoubleMath.log2(d, DOWN);
if (d >= 1.0) {
assertTrue(log2 >= 0);
assertTrue(StrictMath.pow(2.0, log2) <= d);
assertTrue(StrictMath.pow(2.0, log2 + 1) > d);
} else {
assertTrue(log2 <= 0);
assertTrue(StrictMath.pow(2.0, log2) >= d);
assertTrue(StrictMath.pow(2.0, log2 - 1) < d);
}
}
}
public void testRoundLog2Up() {
for (double d : POSITIVE_FINITE_DOUBLE_CANDIDATES) {
int log2 = DoubleMath.log2(d, UP);
if (d >= 1.0) {
assertTrue(log2 >= 0);
assertTrue(StrictMath.pow(2.0, log2) >= d);
assertTrue(StrictMath.pow(2.0, log2 - 1) < d);
} else {
assertTrue(log2 <= 0);
assertTrue(StrictMath.pow(2.0, log2) <= d);
assertTrue(StrictMath.pow(2.0, log2 + 1) > d);
}
}
}
public void testRoundLog2Half() {
// We don't expect perfect rounding accuracy.
for (int exp : asList(-1022, -50, -1, 0, 1, 2, 3, 4, 100, 1022, 1023)) {
for (RoundingMode mode : asList(HALF_EVEN, HALF_UP, HALF_DOWN)) {
double x = Math.scalb(Math.sqrt(2) + 0.001, exp);
double y = Math.scalb(Math.sqrt(2) - 0.001, exp);
if (exp < 0) {
assertEquals(exp + 1, DoubleMath.log2(x, mode));
assertEquals(exp, DoubleMath.log2(y, mode));
} else {
assertEquals(exp + 1, DoubleMath.log2(x, mode));
assertEquals(exp, DoubleMath.log2(y, mode));
}
}
}
}
public void testRoundLog2Exact() {
for (double x : POSITIVE_FINITE_DOUBLE_CANDIDATES) {
boolean isPowerOfTwo = StrictMath.pow(2.0, DoubleMath.log2(x, FLOOR)) == x;
try {
int log2 = DoubleMath.log2(x, UNNECESSARY);
assertEquals(x, Math.scalb(1.0, log2));
assertTrue(isPowerOfTwo);
} catch (ArithmeticException e) {
assertFalse(isPowerOfTwo);
}
}
}
public void testRoundLog2ThrowsOnZerosInfinitiesAndNaN() {
for (RoundingMode mode : ALL_ROUNDING_MODES) {
for (double d :
asList(0.0, -0.0, Double.POSITIVE_INFINITY, Double.NEGATIVE_INFINITY, Double.NaN)) {
try {
DoubleMath.log2(d, mode);
fail("Expected IllegalArgumentException");
} catch (IllegalArgumentException e) {
}
}
}
}
public void testRoundLog2ThrowsOnNegative() {
for (RoundingMode mode : ALL_ROUNDING_MODES) {
for (double d : POSITIVE_FINITE_DOUBLE_CANDIDATES) {
try {
DoubleMath.log2(-d, mode);
fail("Expected IllegalArgumentException");
} catch (IllegalArgumentException e) {
}
}
}
}
public void testIsPowerOfTwoYes() {
for (int i = -1074; i <= 1023; i++) {
assertTrue(DoubleMath.isPowerOfTwo(StrictMath.pow(2.0, i)));
}
}
public void testIsPowerOfTwo() {
for (double x : ALL_DOUBLE_CANDIDATES) {
boolean expected =
x > 0
&& !Double.isInfinite(x)
&& !Double.isNaN(x)
&& StrictMath.pow(2.0, DoubleMath.log2(x, FLOOR)) == x;
assertEquals(expected, DoubleMath.isPowerOfTwo(x));
}
}
public void testLog2Accuracy() {
for (double d : POSITIVE_FINITE_DOUBLE_CANDIDATES) {
double dmLog2 = DoubleMath.log2(d);
double trueLog2 = trueLog2(d);
assertTrue(Math.abs(dmLog2 - trueLog2) <= Math.ulp(trueLog2));
}
}
public void testLog2SemiMonotonic() {
for (double d : POSITIVE_FINITE_DOUBLE_CANDIDATES) {
assertTrue(DoubleMath.log2(d + 0.01) >= DoubleMath.log2(d));
}
}
public void testLog2Negative() {
for (double d : POSITIVE_FINITE_DOUBLE_CANDIDATES) {
assertTrue(Double.isNaN(DoubleMath.log2(-d)));
}
}
public void testLog2Zero() {
assertEquals(Double.NEGATIVE_INFINITY, DoubleMath.log2(0.0));
assertEquals(Double.NEGATIVE_INFINITY, DoubleMath.log2(-0.0));
}
public void testLog2NaNInfinity() {
assertEquals(Double.POSITIVE_INFINITY, DoubleMath.log2(Double.POSITIVE_INFINITY));
assertTrue(Double.isNaN(DoubleMath.log2(Double.NEGATIVE_INFINITY)));
assertTrue(Double.isNaN(DoubleMath.log2(Double.NaN)));
}
private strictfp double trueLog2(double d) {
double trueLog2 = StrictMath.log(d) / StrictMath.log(2);
// increment until it's >= the true value
while (StrictMath.pow(2.0, trueLog2) < d) {
trueLog2 = StrictMath.nextUp(trueLog2);
}
// decrement until it's <= the true value
while (StrictMath.pow(2.0, trueLog2) > d) {
trueLog2 = StrictMath.nextAfter(trueLog2, Double.NEGATIVE_INFINITY);
}
if (StrictMath.abs(StrictMath.pow(2.0, trueLog2) - d)
> StrictMath.abs(StrictMath.pow(2.0, StrictMath.nextUp(trueLog2)) - d)) {
trueLog2 = StrictMath.nextUp(trueLog2);
}
return trueLog2;
}
public void testIsMathematicalIntegerIntegral() {
for (double d : INTEGRAL_DOUBLE_CANDIDATES) {
assertTrue(DoubleMath.isMathematicalInteger(d));
}
}
public void testIsMathematicalIntegerFractional() {
for (double d : FRACTIONAL_DOUBLE_CANDIDATES) {
assertFalse(DoubleMath.isMathematicalInteger(d));
}
}
public void testIsMathematicalIntegerNotFinite() {
for (double d : Arrays.asList(Double.POSITIVE_INFINITY, Double.NEGATIVE_INFINITY, Double.NaN)) {
assertFalse(DoubleMath.isMathematicalInteger(d));
}
}
public void testFactorial() {
for (int i = 0; i <= DoubleMath.MAX_FACTORIAL; i++) {
double actual = BigIntegerMath.factorial(i).doubleValue();
double result = DoubleMath.factorial(i);
assertEquals(actual, result, Math.ulp(actual));
}
}
public void testFactorialTooHigh() {
assertEquals(Double.POSITIVE_INFINITY, DoubleMath.factorial(DoubleMath.MAX_FACTORIAL + 1));
assertEquals(Double.POSITIVE_INFINITY, DoubleMath.factorial(DoubleMath.MAX_FACTORIAL + 20));
}
public void testFactorialNegative() {
for (int n : NEGATIVE_INTEGER_CANDIDATES) {
try {
DoubleMath.factorial(n);
fail("Expected IllegalArgumentException");
} catch (IllegalArgumentException expected) {
}
}
}
private static final ImmutableList<Double> FINITE_TOLERANCE_CANDIDATES =
ImmutableList.of(-0.0, 0.0, 1.0, 100.0, 10000.0, Double.MAX_VALUE);
private static final Iterable<Double> TOLERANCE_CANDIDATES =
Iterables.concat(FINITE_TOLERANCE_CANDIDATES, ImmutableList.of(Double.POSITIVE_INFINITY));
private static final List<Double> BAD_TOLERANCE_CANDIDATES =
Doubles.asList(
-Double.MIN_VALUE,
-Double.MIN_NORMAL,
-1,
-20,
Double.NaN,
Double.NEGATIVE_INFINITY,
-0.001);
public void testFuzzyEqualsFinite() {
for (double a : FINITE_DOUBLE_CANDIDATES) {
for (double b : FINITE_DOUBLE_CANDIDATES) {
for (double tolerance : FINITE_TOLERANCE_CANDIDATES) {
assertEquals(Math.abs(a - b) <= tolerance, DoubleMath.fuzzyEquals(a, b, tolerance));
}
}
}
}
public void testFuzzyInfiniteVersusFiniteWithFiniteTolerance() {
for (double inf : INFINITIES) {
for (double a : FINITE_DOUBLE_CANDIDATES) {
for (double tolerance : FINITE_TOLERANCE_CANDIDATES) {
assertFalse(DoubleMath.fuzzyEquals(a, inf, tolerance));
assertFalse(DoubleMath.fuzzyEquals(inf, a, tolerance));
}
}
}
}
public void testFuzzyInfiniteVersusInfiniteWithFiniteTolerance() {
for (double inf : INFINITIES) {
for (double tolerance : FINITE_TOLERANCE_CANDIDATES) {
assertTrue(DoubleMath.fuzzyEquals(inf, inf, tolerance));
assertFalse(DoubleMath.fuzzyEquals(inf, -inf, tolerance));
}
}
}
public void testFuzzyEqualsInfiniteTolerance() {
for (double a : DOUBLE_CANDIDATES_EXCEPT_NAN) {
for (double b : DOUBLE_CANDIDATES_EXCEPT_NAN) {
assertTrue(DoubleMath.fuzzyEquals(a, b, Double.POSITIVE_INFINITY));
}
}
}
public void testFuzzyEqualsOneNaN() {
for (double a : DOUBLE_CANDIDATES_EXCEPT_NAN) {
for (double tolerance : TOLERANCE_CANDIDATES) {
assertFalse(DoubleMath.fuzzyEquals(a, Double.NaN, tolerance));
assertFalse(DoubleMath.fuzzyEquals(Double.NaN, a, tolerance));
}
}
}
public void testFuzzyEqualsTwoNaNs() {
for (double tolerance : TOLERANCE_CANDIDATES) {
assertTrue(DoubleMath.fuzzyEquals(Double.NaN, Double.NaN, tolerance));
}
}
public void testFuzzyEqualsZeroTolerance() {
// make sure we test -0 tolerance
for (double zero : Doubles.asList(0.0, -0.0)) {
for (double a : ALL_DOUBLE_CANDIDATES) {
for (double b : ALL_DOUBLE_CANDIDATES) {
assertEquals(
a == b || (Double.isNaN(a) && Double.isNaN(b)), DoubleMath.fuzzyEquals(a, b, zero));
}
}
}
}
public void testFuzzyEqualsBadTolerance() {
for (double tolerance : BAD_TOLERANCE_CANDIDATES) {
try {
DoubleMath.fuzzyEquals(1, 2, tolerance);
fail("Expected IllegalArgumentException");
} catch (IllegalArgumentException expected) {
// success
}
}
}
public void testFuzzyCompare() {
for (double a : ALL_DOUBLE_CANDIDATES) {
for (double b : ALL_DOUBLE_CANDIDATES) {
for (double tolerance : TOLERANCE_CANDIDATES) {
int expected = DoubleMath.fuzzyEquals(a, b, tolerance) ? 0 : Double.compare(a, b);
int actual = DoubleMath.fuzzyCompare(a, b, tolerance);
assertEquals(Integer.signum(expected), Integer.signum(actual));
}
}
}
}
public void testFuzzyCompareBadTolerance() {
for (double tolerance : BAD_TOLERANCE_CANDIDATES) {
try {
DoubleMath.fuzzyCompare(1, 2, tolerance);
fail("Expected IllegalArgumentException");
} catch (IllegalArgumentException expected) {
// success
}
}
}
public void testNullPointers() {
NullPointerTester tester = new NullPointerTester();
tester.setDefault(RoundingMode.class, FLOOR);
tester.setDefault(double.class, 3.0);
tester.testAllPublicStaticMethods(DoubleMath.class);
}
}
@Override
public PersistedData serializeCollection(Collection<Double> value, SerializationContext context) {
return context.create(Doubles.toArray(value));
}
/**
* Tangent normalize a coverage profile.
*
* <p>Notes about the Spark tangent normalization can be found in docs/PoN/
*
* @param pon Not {@code null}
* @param targetFactorNormalizedCounts ReadCountCollection of counts that have already been
* normalized fully (typically, including the target factor normalization). I.e. a coverage
* profile The column names should be intact. Not {@code null} See {@link
* TangentNormalizer::createCoverageProfile}
* @return never {@code null}
*/
private static TangentNormalizationResult tangentNormalize(
final PoN pon, final ReadCountCollection targetFactorNormalizedCounts, JavaSparkContext ctx) {
Utils.nonNull(pon, "PoN cannot be null.");
Utils.nonNull(targetFactorNormalizedCounts, "targetFactorNormalizedCounts cannot be null.");
Utils.nonNull(
targetFactorNormalizedCounts.columnNames(),
"targetFactorNormalizedCounts column names cannot be null.");
ParamUtils.isPositive(
targetFactorNormalizedCounts.columnNames().size(),
"targetFactorNormalizedCounts column names cannot be an empty list.");
final Case2PoNTargetMapper targetMapper =
new Case2PoNTargetMapper(targetFactorNormalizedCounts.targets(), pon.getPanelTargetNames());
// The input counts with rows (targets) sorted so that they match the PoN's order.
final RealMatrix tangentNormalizationRawInputCounts =
targetMapper.fromCaseToPoNCounts(targetFactorNormalizedCounts.counts());
// We prepare the counts for tangent normalization.
final RealMatrix tangentNormalizationInputCounts =
composeTangentNormalizationInputMatrix(tangentNormalizationRawInputCounts);
if (ctx == null) {
// Calculate the beta-hats for the input read count columns (samples).
logger.info("Calculating beta hats...");
final RealMatrix tangentBetaHats =
pon.betaHats(tangentNormalizationInputCounts, true, EPSILON);
// Actual tangent normalization step.
logger.info(
"Performing actual tangent normalization ("
+ tangentNormalizationInputCounts.getColumnDimension()
+ " columns)...");
final RealMatrix tangentNormalizedCounts =
pon.tangentNormalization(tangentNormalizationInputCounts, tangentBetaHats, true);
// Output the tangent normalized counts.
logger.info("Post-processing tangent normalization results...");
final ReadCountCollection tangentNormalized =
targetMapper.fromPoNtoCaseCountCollection(
tangentNormalizedCounts, targetFactorNormalizedCounts.columnNames());
final ReadCountCollection preTangentNormalized =
targetMapper.fromPoNtoCaseCountCollection(
tangentNormalizationInputCounts, targetFactorNormalizedCounts.columnNames());
return new TangentNormalizationResult(
tangentNormalized, preTangentNormalized, tangentBetaHats, targetFactorNormalizedCounts);
} else {
/*
Using Spark: the code here is a little more complex for optimization purposes.
Please see notes in docs/PoN ...
Ahat^T = (C^T P^T) A^T
Therefore, C^T is the RowMatrix
pinv: P
panel: A
projection: Ahat
cases: C
betahat: C^T P^T
tangentNormalizedCounts: C - Ahat
*/
final RealMatrix pinv = pon.getReducedPanelPInverseCounts();
final RealMatrix panel = pon.getReducedPanelCounts();
// Make the C^T a distributed matrix (RowMatrix)
final RowMatrix caseTDistMat =
SparkConverter.convertRealMatrixToSparkRowMatrix(
ctx, tangentNormalizationInputCounts.transpose(), TN_NUM_SLICES_SPARK);
// Spark local matrices (transposed)
final Matrix pinvTLocalMat =
new DenseMatrix(
pinv.getRowDimension(),
pinv.getColumnDimension(),
Doubles.concat(pinv.getData()),
true)
.transpose();
final Matrix panelTLocalMat =
new DenseMatrix(
panel.getRowDimension(),
panel.getColumnDimension(),
Doubles.concat(panel.getData()),
true)
.transpose();
// Calculate the projection transpose in a distributed matrix, then convert to Apache Commons
// matrix (not transposed)
final RowMatrix betahatDistMat = caseTDistMat.multiply(pinvTLocalMat);
final RowMatrix projectionTDistMat = betahatDistMat.multiply(panelTLocalMat);
final RealMatrix projection =
SparkConverter.convertSparkRowMatrixToRealMatrix(
projectionTDistMat, tangentNormalizationInputCounts.transpose().getRowDimension())
.transpose();
// Subtract the cases from the projection
final RealMatrix tangentNormalizedCounts =
tangentNormalizationInputCounts.subtract(projection);
// Construct the result object and return it with the correct targets.
final ReadCountCollection tangentNormalized =
targetMapper.fromPoNtoCaseCountCollection(
tangentNormalizedCounts, targetFactorNormalizedCounts.columnNames());
final ReadCountCollection preTangentNormalized =
targetMapper.fromPoNtoCaseCountCollection(
tangentNormalizationInputCounts, targetFactorNormalizedCounts.columnNames());
final RealMatrix tangentBetaHats =
SparkConverter.convertSparkRowMatrixToRealMatrix(
betahatDistMat, tangentNormalizedCounts.getColumnDimension());
return new TangentNormalizationResult(
tangentNormalized,
preTangentNormalized,
tangentBetaHats.transpose(),
targetFactorNormalizedCounts);
}
}
@Description("test if value is finite")
@ScalarFunction
@SqlType(BooleanType.NAME)
public static boolean isFinite(@SqlType(DoubleType.NAME) double num) {
return Doubles.isFinite(num);
}
@Override
protected Integer visitFloatNode(FloatNode node) {
return Doubles.hashCode(node.getValue());
}
private static ImmutableList<Double> values(double... values) {
return ImmutableList.copyOf(Doubles.asList(values));
}
@Override
public int compareTo(Path that) {
return Doubles.compare(this.score, that.score);
}
@Override
public PiecewisePolynomialResultsWithSensitivity interpolateWithSensitivity(
final double[] xValues, final double[] yValues) {
ArgumentChecker.notNull(xValues, "xValues");
ArgumentChecker.notNull(yValues, "yValues");
ArgumentChecker.isTrue(
xValues.length == yValues.length | xValues.length + 2 == yValues.length,
"(xValues length = yValues length) or (xValues length + 2 = yValues length)");
ArgumentChecker.isTrue(xValues.length > 2, "Data points should be more than 2");
final int nDataPts = xValues.length;
final int yValuesLen = yValues.length;
for (int i = 0; i < nDataPts; ++i) {
ArgumentChecker.isFalse(Double.isNaN(xValues[i]), "xValues containing NaN");
ArgumentChecker.isFalse(Double.isInfinite(xValues[i]), "xValues containing Infinity");
}
for (int i = 0; i < yValuesLen; ++i) {
ArgumentChecker.isFalse(Double.isNaN(yValues[i]), "yValues containing NaN");
ArgumentChecker.isFalse(Double.isInfinite(yValues[i]), "yValues containing Infinity");
}
for (int i = 0; i < nDataPts - 1; ++i) {
for (int j = i + 1; j < nDataPts; ++j) {
ArgumentChecker.isFalse(xValues[i] == xValues[j], "xValues should be distinct");
}
}
double[] yValuesSrt = new double[nDataPts];
if (nDataPts == yValuesLen) {
yValuesSrt = Arrays.copyOf(yValues, nDataPts);
} else {
yValuesSrt = Arrays.copyOfRange(yValues, 1, nDataPts + 1);
}
final double[] intervals = _solver.intervalsCalculator(xValues);
final double[] slopes = _solver.slopesCalculator(yValuesSrt, intervals);
double[][] slopesSensitivity = _solver.slopeSensitivityCalculator(intervals);
final DoubleMatrix1D[] firstWithSensitivity = new DoubleMatrix1D[nDataPts + 1];
final DoubleMatrix1D[] secondWithSensitivity = new DoubleMatrix1D[nDataPts + 1];
final PiecewisePolynomialResult result = _method.interpolate(xValues, yValues);
ArgumentChecker.isTrue(result.getOrder() >= 3, "Primary interpolant should be degree >= 2");
final double[] initialFirst = _function.differentiate(result, xValues).getData()[0];
final double[] initialSecond = _function.differentiateTwice(result, xValues).getData()[0];
double[] first = firstDerivativeCalculator(yValuesSrt, intervals, slopes, initialFirst);
boolean modFirst = false;
int k;
double[] aValues = aValuesCalculator(slopes, first);
double[] bValues = bValuesCalculator(slopes, first);
double[][] intervalsA = getIntervalsA(intervals, slopes, first, bValues);
double[][] intervalsB = getIntervalsB(intervals, slopes, first, aValues);
while (modFirst == false) {
k = 0;
for (int i = 0; i < nDataPts - 2; ++i) {
if (first[i + 1] > 0.) {
if (intervalsA[i + 1][1] + Math.abs(intervalsA[i + 1][1]) * ERROR
< intervalsB[i][0] - Math.abs(intervalsB[i][0]) * ERROR
| intervalsA[i + 1][0] - Math.abs(intervalsA[i + 1][0]) * ERROR
> intervalsB[i][1] + Math.abs(intervalsB[i][1]) * ERROR) {
++k;
first[i + 1] = firstDerivativesRecalculator(intervals, slopes, aValues, bValues, i + 1);
}
}
}
if (k == 0) {
modFirst = true;
}
aValues = aValuesCalculator(slopes, first);
bValues = bValuesCalculator(slopes, first);
intervalsA = getIntervalsA(intervals, slopes, first, bValues);
intervalsB = getIntervalsB(intervals, slopes, first, aValues);
}
final double[] second = secondDerivativeCalculator(initialSecond, intervalsA, intervalsB);
firstWithSensitivity[0] = new DoubleMatrix1D(first);
secondWithSensitivity[0] = new DoubleMatrix1D(second);
/*
* Centered finite difference method is used for computing node sensitivity
*/
int nExtra = (nDataPts == yValuesLen) ? 0 : 1;
final double[] yValuesUp = Arrays.copyOf(yValues, nDataPts + 2 * nExtra);
final double[] yValuesDw = Arrays.copyOf(yValues, nDataPts + 2 * nExtra);
final double[][] tmpFirst = new double[nDataPts][nDataPts];
final double[][] tmpSecond = new double[nDataPts][nDataPts];
for (int l = nExtra; l < nDataPts + nExtra; ++l) {
final double den = Math.abs(yValues[l]) < SMALL ? EPS : yValues[l] * EPS;
yValuesUp[l] = Math.abs(yValues[l]) < SMALL ? EPS : yValues[l] * (1. + EPS);
yValuesDw[l] = Math.abs(yValues[l]) < SMALL ? -EPS : yValues[l] * (1. - EPS);
final double[] yValuesSrtUp = Arrays.copyOfRange(yValuesUp, nExtra, nDataPts + nExtra);
final double[] yValuesSrtDw = Arrays.copyOfRange(yValuesDw, nExtra, nDataPts + nExtra);
final DoubleMatrix1D[] yValuesUpDw =
new DoubleMatrix1D[] {new DoubleMatrix1D(yValuesUp), new DoubleMatrix1D(yValuesDw)};
final DoubleMatrix1D[] yValuesSrtUpDw =
new DoubleMatrix1D[] {new DoubleMatrix1D(yValuesSrtUp), new DoubleMatrix1D(yValuesSrtDw)};
final DoubleMatrix1D[] firstSecondUpDw = new DoubleMatrix1D[4];
for (int ii = 0; ii < 2; ++ii) {
final double[] slopesUpDw =
_solver.slopesCalculator(yValuesSrtUpDw[ii].getData(), intervals);
final PiecewisePolynomialResult resultUpDw =
_method.interpolate(xValues, yValuesUpDw[ii].getData());
final double[] initialFirstUpDw = _function.differentiate(resultUpDw, xValues).getData()[0];
final double[] initialSecondUpDw =
_function.differentiateTwice(resultUpDw, xValues).getData()[0];
double[] firstUpDw =
firstDerivativeCalculator(
yValuesSrtUpDw[ii].getData(), intervals, slopesUpDw, initialFirstUpDw);
boolean modFirstUpDw = false;
double[] aValuesUpDw = aValuesCalculator(slopesUpDw, firstUpDw);
double[] bValuesUpDw = bValuesCalculator(slopesUpDw, firstUpDw);
double[][] intervalsAUpDw = getIntervalsA(intervals, slopesUpDw, firstUpDw, bValuesUpDw);
double[][] intervalsBUpDw = getIntervalsB(intervals, slopesUpDw, firstUpDw, aValuesUpDw);
while (modFirstUpDw == false) {
k = 0;
for (int i = 0; i < nDataPts - 2; ++i) {
if (firstUpDw[i + 1] > 0.) {
if (intervalsAUpDw[i + 1][1] + Math.abs(intervalsAUpDw[i + 1][1]) * ERROR
< intervalsBUpDw[i][0] - Math.abs(intervalsBUpDw[i][0]) * ERROR
| intervalsAUpDw[i + 1][0] - Math.abs(intervalsAUpDw[i + 1][0]) * ERROR
> intervalsBUpDw[i][1] + Math.abs(intervalsBUpDw[i][1]) * ERROR) {
++k;
firstUpDw[i + 1] =
firstDerivativesRecalculator(
intervals, slopesUpDw, aValuesUpDw, bValuesUpDw, i + 1);
}
}
}
if (k == 0) {
modFirstUpDw = true;
}
aValuesUpDw = aValuesCalculator(slopesUpDw, firstUpDw);
bValuesUpDw = bValuesCalculator(slopesUpDw, firstUpDw);
intervalsAUpDw = getIntervalsA(intervals, slopesUpDw, firstUpDw, bValuesUpDw);
intervalsBUpDw = getIntervalsB(intervals, slopesUpDw, firstUpDw, aValuesUpDw);
}
final double[] secondUpDw =
secondDerivativeCalculator(initialSecondUpDw, intervalsAUpDw, intervalsBUpDw);
firstSecondUpDw[ii] = new DoubleMatrix1D(firstUpDw);
firstSecondUpDw[2 + ii] = new DoubleMatrix1D(secondUpDw);
}
for (int j = 0; j < nDataPts; ++j) {
tmpFirst[j][l - nExtra] =
0.5 * (firstSecondUpDw[0].getData()[j] - firstSecondUpDw[1].getData()[j]) / den;
tmpSecond[j][l - nExtra] =
0.5 * (firstSecondUpDw[2].getData()[j] - firstSecondUpDw[3].getData()[j]) / den;
}
yValuesUp[l] = yValues[l];
yValuesDw[l] = yValues[l];
}
for (int i = 0; i < nDataPts; ++i) {
firstWithSensitivity[i + 1] = new DoubleMatrix1D(tmpFirst[i]);
secondWithSensitivity[i + 1] = new DoubleMatrix1D(tmpSecond[i]);
}
final DoubleMatrix2D[] resMatrix =
_solver.solveWithSensitivity(
yValuesSrt,
intervals,
slopes,
slopesSensitivity,
firstWithSensitivity,
secondWithSensitivity);
for (int l = 0; l < nDataPts; ++l) {
DoubleMatrix2D m = resMatrix[l];
final int rows = m.getNumberOfRows();
final int cols = m.getNumberOfColumns();
for (int i = 0; i < rows; ++i) {
for (int j = 0; j < cols; ++j) {
ArgumentChecker.isTrue(
Doubles.isFinite(m.getEntry(i, j)), "Matrix contains a NaN or infinite");
}
}
}
final DoubleMatrix2D coefMatrix = resMatrix[0];
final DoubleMatrix2D[] coefSenseMatrix = new DoubleMatrix2D[nDataPts - 1];
System.arraycopy(resMatrix, 1, coefSenseMatrix, 0, nDataPts - 1);
final int nCoefs = coefMatrix.getNumberOfColumns();
return new PiecewisePolynomialResultsWithSensitivity(
new DoubleMatrix1D(xValues), coefMatrix, nCoefs, 1, coefSenseMatrix);
}
