private String generateWorkingHours(final String locale) {
Calendar calendar = Calendar.getInstance();
calendar.set(Calendar.HOUR_OF_DAY, 8);
calendar.set(Calendar.MINUTE, 0);
calendar.set(Calendar.SECOND, 0);
long minHours = calendar.getTimeInMillis();
calendar.set(Calendar.HOUR_OF_DAY, 20);
calendar.set(Calendar.MINUTE, 0);
calendar.set(Calendar.SECOND, 0);
long maxHours = calendar.getTimeInMillis();
long workBeginHours = (long) (RANDOM.nextDouble() * (maxHours / 2 - minHours) + minHours);
long workEndHours = (long) (RANDOM.nextDouble() * (maxHours - workBeginHours) + workBeginHours);
Date workBeginDate = new Date(workBeginHours);
Date workEndDate = new Date(workEndHours);
StringBuilder workingHours = new StringBuilder();
SimpleDateFormat hourFormat = new SimpleDateFormat("HH:mm", LocaleUtils.toLocale(locale));
workingHours
.append(hourFormat.format(workBeginDate))
.append("-")
.append(hourFormat.format(workEndDate));
return workingHours.toString();
}
private void generateAndAddOperation() {
Entity operation =
dataDefinitionService.get(TECHNOLOGIES_PLUGIN, L_TECHNOLOGY_MODEL_OPERATION).create();
String number = generateString(CHARS_ONLY, RANDOM.nextInt(40) + 5);
operation.setField(L_NUMBER, number);
operation.setField("name", getNameFromNumberAndPrefix("Operation-", number));
operation.setField(L_BASIC_MODEL_STAFF, getRandomStaff());
operation.setField(L_BASIC_MODEL_WORKSTATION_TYPE, getRandomMachine());
operation.setField(L_TPZ, RANDOM.nextInt(1000));
operation.setField(L_TJ, RANDOM.nextInt(1000));
operation.setField("productionInOneCycle", RANDOM.nextInt(20));
operation.setField(L_NEXT_OPERATION_AFTER_PRODUCED_TYPE, RANDOM.nextInt(10));
operation.setField(
"machineUtilization", numberService.setScale(new BigDecimal(RANDOM.nextDouble()).abs()));
operation.setField(
"laborUtilization", numberService.setScale(new BigDecimal(RANDOM.nextDouble()).abs()));
operation.setField("nextOperationAfterProducedQuantity", RANDOM.nextInt(15));
operation.setField(L_NEXT_OPERATION_AFTER_PRODUCED_TYPE, "01all");
operation.setField("timeNextOperation", RANDOM.nextInt(30));
operation.setField("nextOperationAfterProducedQuantity", "0");
if (isEnabledOrEnabling("costNormsForOperation")) {
operation.setField("pieceworkCost", RANDOM.nextInt(100));
operation.setField("machineHourlyCost", RANDOM.nextInt(100));
operation.setField("laborHourlyCost", RANDOM.nextInt(100));
operation.setField("numberOfOperations", RANDOM.nextInt(10) + 1);
}
dataDefinitionService.get(TECHNOLOGIES_PLUGIN, L_TECHNOLOGY_MODEL_OPERATION).save(operation);
}
@Override
public MultivariatePolyaDistribution createInstance() {
final double r = 10.0;
int N = 6;
Vector a =
VectorFactory.getDefault()
.copyValues(r * RANDOM.nextDouble(), r * RANDOM.nextDouble(), r * RANDOM.nextDouble());
return new MultivariatePolyaDistribution(a, N);
}
@Test
// TODO if this interpolator cannot get the answer right then an exception should be thrown
public void testFlat() {
final double x1 = 10 * RANDOM.nextDouble();
final double x2 = 10 * RANDOM.nextDouble();
final double x3 = 10 * RANDOM.nextDouble();
// Fails utterly for flat surface since the variogram function will be zero for all r
final InterpolatorND interpolator = new KrigingInterpolatorND(1.99);
final InterpolatorNDDataBundle dataBundle = interpolator.getDataBundle(FLAT_DATA);
assertEquals(INTERPOLATOR.interpolate(dataBundle, new double[] {x1, x2, x3}), 0, 0);
}
@Override
public void spawnServerSide(
EntityPlayer player, NBTTagCompound dataFromClient, NBTTagCompound rewardData) {
for (int i = 0; i < dataFromClient.getInteger(AMOUNTOFORBS_KEY); i++) {
double X = player.posX, Y = player.posY, Z = player.posZ;
X += (0.5 - RANDOM.nextDouble());
Z += (0.5 - RANDOM.nextDouble());
player.worldObj.spawnEntityInWorld(
new EntityXPOrb(player.worldObj, X, Y, Z, RANDOM.nextInt(5) + 1));
}
}
@Override
public void testPDFKnownValues() {
System.out.println("PDF.knownValues");
for (int i = 0; i < 100; i++) {
UniformDistribution.PDF instance = this.createInstance().getProbabilityFunction();
double a = instance.getMinSupport();
double b = instance.getMaxSupport();
double x = (RANDOM.nextDouble() * (b - a + 2)) + a - 1;
double h = 1 / (b - a);
double y;
if (x < a) {
y = 0;
} else if (x > b) {
y = 0;
} else {
y = h;
}
double yhat = instance.evaluate(x);
assertEquals(y, yhat);
assertEquals(h, instance.evaluate(a));
assertEquals(h, instance.evaluate(b));
}
}
@Test
public void test() {
for (int i = 0; i < 10; i++) {
final double x = A + (B - A) * RANDOM.nextDouble();
final double y = 5 * NORMAL.nextRandom();
assertRoundTrip(RANGE_LIMITS, x);
assertReverseRoundTrip(RANGE_LIMITS, y);
assertGradient(RANGE_LIMITS, x);
assertInverseGradient(RANGE_LIMITS, y);
assertGradientRoundTrip(RANGE_LIMITS, x);
}
}
private void addSubstituteToProduct(final Entity product) {
Entity substitute = dataDefinitionService.get(L_BASIC_PLUGIN_IDENTIFIER, "substitute").create();
String number = generateString(DIGITS_ONLY, RANDOM.nextInt(34) + 5);
substitute.setField(L_NUMBER, number);
substitute.setField(L_NAME, getNameFromNumberAndPrefix("ProductSubstitute-", number));
substitute.setField(L_BASIC_MODEL_PRODUCT, product);
substitute.setField("priority", RANDOM.nextInt(7));
substitute =
dataDefinitionService.get(L_BASIC_PLUGIN_IDENTIFIER, "substitute").save(substitute);
addSubstituteComponent(
substitute, getRandomProduct(), RANDOM.nextInt(997) * RANDOM.nextDouble());
}
@Override
public void onNext(Word word) {
Timber.d("Got word " + word + " and this is the subscriber " + this);
if (RANDOM.nextDouble() <= 0.25 || currentAvailableWords.isEmpty()) {
word = targetWord;
}
if (targetWordLanguage.equals(ENGLISH)) {
view.showOptionalWord(word.getTextSpa());
currentDisplayedWord = word.getTextSpa();
} else {
view.showOptionalWord(word.getTextEng());
currentDisplayedWord = word.getTextEng();
}
}
@Override
public void testCDFConstructors() {
System.out.println("CDF Constructor");
UniformDistribution.CDF u = new UniformDistribution.CDF();
assertEquals(UniformDistribution.DEFAULT_MIN, u.getMinSupport());
assertEquals(UniformDistribution.DEFAULT_MAX, u.getMaxSupport());
double a = RANDOM.nextGaussian();
double b = RANDOM.nextDouble() + a;
u = new UniformDistribution.CDF(a, b);
assertEquals(a, u.getMinSupport());
assertEquals(b, u.getMaxSupport());
UniformDistribution.CDF u2 = new UniformDistribution.CDF(u);
assertEquals(u.getMinSupport(), u2.getMinSupport());
assertEquals(u.getMaxSupport(), u2.getMaxSupport());
}
@Override
protected DoubleMatrix1D getGlobalStart(
final double forward,
final double[] strikes,
final double expiry,
final double[] impliedVols) {
final DoubleMatrix1D fitP = getPolynomialFit(forward, strikes, impliedVols);
final double a = fitP.getEntry(0);
final double b = fitP.getEntry(1);
final double c = fitP.getEntry(2);
double alpha, beta, rho, nu;
// TODO make better use of the polynomial fit information
if (_externalBeta) {
beta = _beta;
} else {
beta = RANDOM.nextDouble();
}
if (a <= 0.0) { // negative ATM vol - can get this if fit points are far from ATM
double sum = 0;
final int n = strikes.length;
for (int i = 0; i < n; i++) {
sum += impliedVols[i];
}
final double approxAlpha = sum / n * Math.pow(forward, 1 - beta);
alpha = (RANDOM.nextDouble() + 0.5) * approxAlpha;
rho = RANDOM.nextDouble() - 0.5;
nu = 0.5 * RANDOM.nextDouble() + 0.1;
return new DoubleMatrix1D(alpha, beta, rho, nu);
}
if (Math.abs(b) < 1e-3 && Math.abs(c) < 1e-3) { // almost flat smile
if (_externalBeta && _beta != 1.0) {
s_logger.warn(
"Smile almost flat. Cannot use beta = ",
+_beta + " so extenal value ignored, and beta = 1.0 used");
}
return new DoubleMatrix1D(a, 1.0, 0.0, Math.max(0.0, 4 * c));
}
final double approxAlpha = a * Math.pow(forward, 1 - beta);
alpha = (RANDOM.nextDouble() + 0.5) * approxAlpha;
rho = RANDOM.nextDouble() - 0.5;
nu = (RANDOM.nextDouble() + 0.5) * Math.max(0.0, 4 * c);
return new DoubleMatrix1D(alpha, beta, rho, nu);
}
@Override
public UniformDistribution createInstance() {
double a = RANDOM.nextGaussian();
double b = a + RANDOM.nextDouble() * 2.0;
return new UniformDistribution(a, b);
}
