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);
}
}
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;
}
private void checkValue(double value, double expected) {
assertTrue(Math.abs(value - expected) < 1.0e-10);
}