public void testMinStep() {

    try {
      TestProblem5 pb = new TestProblem5();
      double minStep = 0.1 * Math.abs(pb.getFinalTime() - pb.getInitialTime());
      double maxStep = Math.abs(pb.getFinalTime() - pb.getInitialTime());
      double[] vecAbsoluteTolerance = {1.0e-20, 1.0e-21};
      double[] vecRelativeTolerance = {1.0e-20, 1.0e-21};

      FirstOrderIntegrator integ =
          new GraggBulirschStoerIntegrator(
              minStep, maxStep,
              vecAbsoluteTolerance, vecRelativeTolerance);
      TestProblemHandler handler = new TestProblemHandler(pb, integ);
      integ.addStepHandler(handler);
      integ.integrate(
          pb,
          pb.getInitialTime(),
          pb.getInitialState(),
          pb.getFinalTime(),
          new double[pb.getDimension()]);
      fail("an exception should have been thrown");
    } catch (DerivativeException de) {
      fail("wrong exception caught");
    } catch (IntegratorException ie) {
    }
  }
  /** Test of generateQAMSignal method, of class simulator.qam.FadingNoisyQAM. */
  public void testGenerateQAMSignal() {
    System.out.println("generateQAMSignal");

    FadingNoisyQAM instance = new FadingNoisyQAM();

    double[] xr = instance.getTransmittedRealQAMSignal();
    double[] xi = instance.getTransmittedImagQAMSignal();

    for (int i = 0; i < xr.length; i++) {
      assertEquals(true, Math.abs(xr[i]) <= 7);
      assertEquals(true, Math.abs(xr[i]) <= 7);
    }
  }
예제 #3
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 private double computeFitness(IGPProgram a_program, Variable vx) {
   double error = 0.0f;
   Object[] noargs = new Object[0];
   // Initialize local stores.
   // ------------------------
   a_program.getGPConfiguration().clearStack();
   a_program.getGPConfiguration().clearMemory();
   // Compute fitness for each program.
   // ---------------------------------
   for (int i = 2; i < 15; i++) {
     for (int j = 0; j < a_program.size(); j++) {
       vx.set(new Integer(i));
       try {
         try {
           // Only evaluate after whole GP program was run.
           // ---------------------------------------------
           if (j == a_program.size() - 1) {
             double result = a_program.execute_int(j, noargs);
             error += Math.abs(result - fib_iter(i));
           } else {
             a_program.execute_void(j, noargs);
           }
         } catch (IllegalStateException iex) {
           error = Double.MAX_VALUE / 2;
           break;
         }
       } catch (ArithmeticException ex) {
         System.out.println("Arithmetic Exception with x = " + i);
         System.out.println(a_program.getChromosome(j));
         throw ex;
       }
     }
   }
   return error;
 }
    public void handleStep(StepInterpolator interpolator, boolean isLast) {

      double step = Math.abs(interpolator.getCurrentTime() - interpolator.getPreviousTime());
      if (firstTime) {
        minStep = Math.abs(step);
        maxStep = minStep;
        firstTime = false;
      } else {
        if (step < minStep) {
          minStep = step;
        }
        if (step > maxStep) {
          maxStep = step;
        }
      }

      if (isLast) {
        assertTrue(minStep < 8.2e-3);
        assertTrue(maxStep > 1.7);
      }
    }
  // test offset only gets applied once
  public void testWrapOnce() throws IOException {
    String filename = TestAll.cdmUnitTestDir + "ncml/coords/testCoordScaling.ncml";
    System.out.printf("%s%n", filename);
    NetcdfDataset ncd = ucar.nc2.dataset.NetcdfDataset.openDataset(filename);
    Variable v = ncd.findCoordinateAxis("Longitude");
    assert v != null;
    assert v instanceof CoordinateAxis1D;

    // if offset is applied twice, the result is not in +-180 range
    Array data = v.read();
    NCdumpW.printArray(data);
    IndexIterator ii = data.getIndexIterator();
    while (ii.hasNext()) {
      assert Math.abs(ii.getDoubleNext()) < 180 : ii.getDoubleCurrent();
    }
  }
 public void checkValue(double value, double reference) {
   assertTrue(Math.abs(value - reference) < 1.0e-10);
 }
 boolean close(double d1, double d2) {
   return Math.abs(d1 - d2) < TOLERENCE;
 }
  public static double testCostAndGradientCurrentParameters(Minimizable.ByGradient minable) {
    Matrix parameters = minable.getParameters(minable.getNewMatrix());
    double cost = minable.getCost();
    // the gradient from the minimizable function
    Matrix analyticGradient = minable.getCostGradient(minable.getNewMatrix());
    // the gradient calculate from the slope of the cost
    Matrix empiricalGradient = (Matrix) analyticGradient.cloneMatrix();
    // This setting of epsilon should make the individual elements of
    // the analytical gradient and the empirical gradient equal.  This
    // simplifies the comparison of the individual dimensions of the
    // gradient and thus makes debugging easier.
    double epsilon = 0.1 / analyticGradient.twoNorm();
    double tolerance = epsilon * 5;
    System.out.println("epsilon = " + epsilon + " tolerance=" + tolerance);

    // Check each direction, perturb it, measure new cost,
    // and make sure it agrees with the gradient from minable.getCostGradient()
    for (int i = 0; i < parameters.singleSize(); i++) {
      double param = parameters.singleValue(i);
      parameters.setSingleValue(i, param + epsilon);
      // logger.fine ("Parameters:"); parameters.print();
      minable.setParameters(parameters);
      double epsCost = minable.getCost();
      double slope = (epsCost - cost) / epsilon;
      System.out.println(
          "cost="
              + cost
              + " epsCost="
              + epsCost
              + " slope["
              + i
              + "] = "
              + slope
              + " gradient[]="
              + analyticGradient.singleValue(i));
      assert (!Double.isNaN(slope));
      logger.fine(
          "TestMinimizable checking singleIndex "
              + i
              + ": gradient slope = "
              + analyticGradient.singleValue(i)
              + ", cost+epsilon slope = "
              + slope
              + ": slope difference = "
              + Math.abs(slope - analyticGradient.singleValue(i)));
      // No negative below because the gradient points in the direction
      // of maximizing the function.
      empiricalGradient.setSingleValue(i, slope);
      parameters.setSingleValue(i, param);
    }
    // Normalize the matrices to have the same L2 length
    System.out.println("empiricalGradient.twoNorm = " + empiricalGradient.twoNorm());
    analyticGradient.timesEquals(1.0 / analyticGradient.twoNorm());
    empiricalGradient.timesEquals(1.0 / empiricalGradient.twoNorm());
    // logger.info ("AnalyticGradient:"); analyticGradient.print();
    // logger.info ("EmpiricalGradient:"); empiricalGradient.print();
    // Return the angle between the two vectors, in radians
    double angle = Math.acos(analyticGradient.dotProduct(empiricalGradient));
    logger.info("TestMinimizable angle = " + angle);
    if (Math.abs(angle) > tolerance)
      throw new IllegalStateException("Gradient/Cost mismatch: angle=" + angle);
    if (Double.isNaN(angle)) throw new IllegalStateException("Gradient/Cost error: angle is NaN!");
    return angle;
  }
예제 #9
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 private void checkValue(double value, double expected) {
   assertTrue(Math.abs(value - expected) < 1.0e-10);
 }