@Override
public void dispose() {
synchronized (undisposed) {
super.dispose();
undisposed.remove(this);
}
}
@Override
public void project(double scaleThis, double scaleOther, VectConst other) {
TestVect tv = (TestVect) other;
if (!identity.equals(tv.identity)) {
projectWasTested = true;
}
super.add(scaleThis, scaleOther, other);
}
@Override
public void add(double scaleThis, double scaleOther, VectConst other) {
assertSameType(other);
super.add(scaleThis, scaleOther, other);
}
/**
* test code
*
* @param args command line
* @throws Exception all errors
*/
public static void main(String[] args) throws Exception {
GaussNewtonSolver.setExpensiveDebug(true);
/* fit straight line to points (0,0) (1,8) (3,8) (4,20) */
final double[] coord = new double[] {0., 1., 3., 4.};
TestVect data = new TestVect(new double[] {0., 8., 8., 20.}, 0.0001, "data");
// model will be intercept and gradient
LinearTransform linearTransform =
new LinearTransform() {
public void forward(Vect data1, VectConst model) {
VectUtil.zero(data1);
double[] d = ((ArrayVect1) data1).getData();
double[] m = ((ArrayVect1) model).getData();
for (int i = 0; i < coord.length; ++i) {
d[i] += m[0];
d[i] += coord[i] * m[1];
}
}
public void addTranspose(VectConst data1, Vect model) {
double[] d = ((ArrayVect1) data1).getData();
double[] m = ((ArrayVect1) model).getData();
for (int i = 0; i < coord.length; ++i) {
m[0] += d[i];
m[1] += coord[i] * d[i];
}
}
public void inverseHessian(Vect model) {}
public void adjustRobustErrors(Vect dataError) {}
};
{ // bad starting model, damp full model
TestVect model = new TestVect(new double[] {-1., -1.}, 1., "model");
boolean dampOnlyPerturbation = false;
int conjugateGradIterations = 2;
ArrayVect1 result =
(ArrayVect1)
GaussNewtonSolver.solve(
data,
model,
linearTransform,
dampOnlyPerturbation,
conjugateGradIterations,
null);
LOG.fine("data = " + data);
LOG.fine("model = " + model);
LOG.fine("result = " + result);
assert (new Almost(4)).equal(1., result.getData()[0]) : "result=" + result;
assert (new Almost(5)).equal(4., result.getData()[1]) : "result=" + result;
model.dispose();
result.dispose();
}
double[] dampPerturb = null;
{ // good starting model, damp perturbations only
TestVect model = new TestVect(new double[] {0.9, 3.9}, 1., "model");
boolean dampOnlyPerturbation = true;
int conjugateGradIterations = 2;
ArrayVect1 result =
(ArrayVect1)
GaussNewtonSolver.solve(
data,
model,
linearTransform,
dampOnlyPerturbation,
conjugateGradIterations,
null);
LOG.fine("data = " + data);
LOG.fine("model = " + model);
LOG.fine("result = " + result);
dampPerturb = result.getData();
assert (new Almost(4)).equal(1., result.getData()[0]) : "result=" + result;
assert (new Almost(5)).equal(4., result.getData()[1]) : "result=" + result;
model.dispose();
result.dispose();
}
{ // good starting model, damp whole model, and compare to previous
TestVect model = new TestVect(new double[] {0.9, 3.9}, 1., "model");
boolean dampOnlyPerturbation = false;
int conjugateGradIterations = 2;
ArrayVect1 result =
(ArrayVect1)
GaussNewtonSolver.solve(
data,
model,
linearTransform,
dampOnlyPerturbation,
conjugateGradIterations,
null);
LOG.fine("data = " + data);
LOG.fine("model = " + model);
LOG.fine("result = " + result);
double[] dampAll = result.getData();
assert (new Almost(4)).equal(1., result.getData()[0]) : "result=" + result;
assert (new Almost(5)).equal(4., result.getData()[1]) : "result=" + result;
assert dampAll[0] > dampPerturb[0];
assert dampAll[1] < dampPerturb[1];
{ //
double dampAll2 = 0.;
double dampPerturb2 = 0.;
for (int i = 0; i < 2; ++i) {
dampAll2 += dampAll[i] * dampAll[i];
dampPerturb2 += dampPerturb[i] * dampPerturb[i];
}
LOG.fine("dampAll2=" + dampAll2 + " dampPerturb2=" + dampPerturb2);
assert dampAll2 < dampPerturb2;
}
model.dispose();
result.dispose();
}
assert TestVect.max < 10 : "max=" + TestVect.max;
// use full interface
for (int twice = 0; twice < 2; ++twice) {
boolean project = (twice == 1);
TestVect perturb = new TestVect(new double[2], 1., "perturb");
{ // Steepest descent: One conjugate gradient iteration and a line search
TestVect model = new TestVect(new double[] {0.9, 3.9}, 1., "model");
boolean dampOnlyPerturbation = false;
int linearizationIterations = 3;
int lineSearchIterations = 20;
double lineSearchError = 0.000001;
int conjugateGradIterations = 1;
Transform transform = new LinearTransformWrapper(linearTransform);
ArrayVect1 result =
(ArrayVect1)
GaussNewtonSolver.solve(
data,
model,
(project) ? perturb : null,
transform,
dampOnlyPerturbation,
conjugateGradIterations,
lineSearchIterations,
linearizationIterations,
lineSearchError,
null);
LOG.fine("data = " + data);
LOG.fine("model = " + model);
LOG.fine("result = " + result);
assert (new Almost(3)).equal(1., result.getData()[0]) : "result=" + result;
assert (new Almost(4)).equal(4., result.getData()[1]) : "result=" + result;
model.dispose();
result.dispose();
}
{ // Make sure unnecessary iterations are not a problem
TestVect model = new TestVect(new double[] {0.9, 3.9}, 1., "model");
boolean dampOnlyPerturbation = true;
int linearizationIterations = 3;
int lineSearchIterations = 30;
double lineSearchError = 0.000001;
int conjugateGradIterations = 2;
Transform transform = new LinearTransformWrapper(linearTransform);
ArrayVect1 result =
(ArrayVect1)
GaussNewtonSolver.solve(
data,
model,
project ? perturb : null,
transform,
dampOnlyPerturbation,
conjugateGradIterations,
lineSearchIterations,
linearizationIterations,
lineSearchError,
null); // new LogMonitor("Test inversion",LOG)
LOG.fine("data = " + data);
LOG.fine("model = " + model);
LOG.fine("result = " + result);
assert (new Almost(4)).equal(1., result.getData()[0]) : "result=" + result;
assert (new Almost(5)).equal(4., result.getData()[1]) : "result=" + result;
model.dispose();
result.dispose();
}
perturb.dispose();
}
data.dispose();
if (TestVect.undisposed.size() > 0) {
throw new IllegalStateException(TestVect.getTraces());
}
assert TestVect.max <= 10 : "max=" + TestVect.max;
assert projectWasTested;
GaussNewtonSolver.setExpensiveDebug(false);
}
