Пример #1
0
  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();
  }
Пример #2
0
  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);
 }
Пример #5
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);
    }
  }
Пример #8
0
  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());
  }
Пример #11
0
  @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);
 }