use of uk.ac.sussex.gdsc.test.utils.TimingService in project GDSC-SMLM by aherbert.
the class RampedSelectionStrategyTest method speedTest.
private static void speedTest(final int size, boolean faster, int runs) {
final long[] sum = RampedSelectionStrategy.createRampedSum(size);
final TimingService ts = new TimingService(runs);
ts.execute(new BaseTimingTask("search" + size) {
@Override
public Object getData(int index) {
return sum;
}
@Override
public Object run(Object data) {
for (int key = (int) sum[sum.length - 1]; key-- > 0; ) {
RampedSelectionStrategy.search(sum, key);
}
return null;
}
@Override
public int getSize() {
return 1;
}
});
ts.execute(new BaseTimingTask("binarySearch" + size) {
@Override
public Object getData(int index) {
return sum[index];
}
@Override
public Object run(Object data) {
for (int key = (int) sum[sum.length - 1]; key-- > 0; ) {
RampedSelectionStrategy.binarySearch(sum, key);
}
return null;
}
@Override
public int getSize() {
return 1;
}
});
final int n = ts.repeat();
ts.repeat(n);
if (logger.isLoggable(Level.INFO)) {
logger.info(ts.getReport());
}
final TimingResult slow = ts.get((faster) ? ts.getSize() - 2 : ts.getSize() - 1);
final TimingResult fast = ts.get((faster) ? ts.getSize() - 1 : ts.getSize() - 2);
logger.log(TestLogUtils.getTimingRecord(slow, fast));
}
use of uk.ac.sussex.gdsc.test.utils.TimingService in project GDSC-SMLM by aherbert.
the class ErfGaussian2DFunctionTest method functionIsFasterUsingForEach.
// Speed test forEach verses equivalent eval() function calls
@SpeedTag
@Test
void functionIsFasterUsingForEach() {
Assumptions.assumeTrue(TestSettings.allow(TestComplexity.MEDIUM));
final ErfGaussian2DFunction f1 = (ErfGaussian2DFunction) this.f1;
final LocalList<double[]> params = new LocalList<>();
for (final double background : testbackground) {
// Peak 1
for (final double signal1 : testsignal1) {
for (final double cx1 : testcx1) {
for (final double cy1 : testcy1) {
for (final double cz1 : testcz1) {
for (final double[] w1 : testw1) {
for (final double angle1 : testangle1) {
final double[] a = createParameters(background, signal1, cx1, cy1, cz1, w1[0], w1[1], angle1);
params.add(a);
}
}
}
}
}
}
}
final double[][] x = params.toArray(new double[0][]);
final int runs = 10000 / x.length;
final TimingService ts = new TimingService(runs);
ts.execute(new FunctionTimingTask(f1, x, 2));
ts.execute(new FunctionTimingTask(f1, x, 1));
ts.execute(new FunctionTimingTask(f1, x, 0));
ts.execute(new ForEachTimingTask(f1, x, 2));
ts.execute(new ForEachTimingTask(f1, x, 1));
ts.execute(new ForEachTimingTask(f1, x, 0));
final int size = ts.getSize();
ts.repeat(size);
if (logger.isLoggable(Level.INFO)) {
logger.info(ts.getReport());
}
for (int i = 1; i <= 3; i++) {
final TimingResult slow = ts.get(-i - 3);
final TimingResult fast = ts.get(-i);
logger.log(TestLogUtils.getTimingRecord(slow, fast));
}
}
use of uk.ac.sussex.gdsc.test.utils.TimingService in project GDSC-SMLM by aherbert.
the class ErfGaussian2DFunctionTest method functionIsFasterThanEquivalentGaussian2DFunction.
// Speed test verses equivalent Gaussian2DFunction
@SpeedTag
@Test
void functionIsFasterThanEquivalentGaussian2DFunction() {
Assumptions.assumeTrue(TestSettings.allow(TestComplexity.MEDIUM));
final int flags = this.flags & ~GaussianFunctionFactory.FIT_ERF;
final Gaussian2DFunction gf = GaussianFunctionFactory.create2D(1, maxx, maxy, flags, zModel);
final boolean zDepth = (flags & GaussianFunctionFactory.FIT_Z) != 0;
final LocalList<double[]> params1 = new LocalList<>();
final LocalList<double[]> params2 = new LocalList<>();
for (final double background : testbackground) {
// Peak 1
for (final double signal1 : testsignal1) {
for (final double cx1 : testcx1) {
for (final double cy1 : testcy1) {
for (final double cz1 : testcz1) {
for (final double[] w1 : testw1) {
for (final double angle1 : testangle1) {
double[] params = createParameters(background, signal1, cx1, cy1, cz1, w1[0], w1[1], angle1);
params1.add(params);
if (zDepth) {
// Change to a standard free circular function
params = params.clone();
params[Gaussian2DFunction.X_SD] *= zModel.getSx(params[Gaussian2DFunction.Z_POSITION]);
params[Gaussian2DFunction.Y_SD] *= zModel.getSy(params[Gaussian2DFunction.Z_POSITION]);
params[Gaussian2DFunction.Z_POSITION] = 0;
params2.add(params);
}
}
}
}
}
}
}
}
final double[][] x = params1.toArray(new double[0][]);
final double[][] x2 = (zDepth) ? params2.toArray(new double[0][]) : x;
final int runs = 10000 / x.length;
final TimingService ts = new TimingService(runs);
ts.execute(new FunctionTimingTask(gf, x2, 1));
ts.execute(new FunctionTimingTask(gf, x2, 0));
ts.execute(new FunctionTimingTask(f1, x, 2));
ts.execute(new FunctionTimingTask(f1, x, 1));
ts.execute(new FunctionTimingTask(f1, x, 0));
final int size = ts.getSize();
ts.repeat(size);
if (logger.isLoggable(Level.INFO)) {
logger.info(ts.getReport());
}
for (int i = 1; i <= 2; i++) {
final TimingResult slow = ts.get(-i - 3);
final TimingResult fast = ts.get(-i);
logger.log(TestLogUtils.getTimingRecord(slow, fast));
}
}
use of uk.ac.sussex.gdsc.test.utils.TimingService in project GDSC-SMLM by aherbert.
the class EjmlLinearSolverTest method runSolverSpeedTest.
private void runSolverSpeedTest(RandomSeed seed, int flags) {
Assumptions.assumeTrue(TestSettings.allow(TestComplexity.MEDIUM));
final Gaussian2DFunction f0 = GaussianFunctionFactory.create2D(1, 10, 10, flags, null);
final int n = f0.size();
final double[] y = new double[n];
final LocalList<DenseMatrix64F> aList = new LocalList<>();
final LocalList<DenseMatrix64F> bList = new LocalList<>();
final double[] testbackground = new double[] { 0.2, 0.7 };
final double[] testsignal1 = new double[] { 30, 100, 300 };
final double[] testcx1 = new double[] { 4.9, 5.3 };
final double[] testcy1 = new double[] { 4.8, 5.2 };
final double[] testw1 = new double[] { 1.1, 1.2, 1.5 };
final int np = f0.getNumberOfGradients();
final GradientCalculator calc = GradientCalculatorUtils.newCalculator(np);
final UniformRandomProvider rng = RngUtils.create(seed.getSeed());
// double lambda = 10;
for (final double background : testbackground) {
// Peak 1
for (final double signal1 : testsignal1) {
for (final double cx1 : testcx1) {
for (final double cy1 : testcy1) {
for (final double w1 : testw1) {
final double[] p = new double[] { background, signal1, 0, cx1, cy1, w1, w1 };
f0.initialise(p);
f0.forEach(new ValueProcedure() {
int index = 0;
@Override
public void execute(double value) {
// Poisson data
y[index++] = GdscSmlmTestUtils.createPoissonSampler(rng, value).sample();
}
});
final double[][] alpha = new double[np][np];
final double[] beta = new double[np];
// double ss =
calc.findLinearised(n, y, p, alpha, beta, f0);
// TestLog.fine(logger,"SS = %f", ss);
// As per the LVM algorithm
// for (int i = 0; i < np; i++)
// alpha[i][i] *= lambda;
aList.add(EjmlLinearSolver.toA(alpha));
bList.add(EjmlLinearSolver.toB(beta));
}
}
}
}
}
final DenseMatrix64F[] a = aList.toArray(new DenseMatrix64F[0]);
final DenseMatrix64F[] b = bList.toArray(new DenseMatrix64F[0]);
final int runs = 100000 / a.length;
final TimingService ts = new TimingService(runs);
final LocalList<SolverTimingTask> tasks = new LocalList<>();
// Added in descending speed order
tasks.add(new PseudoInverseSolverTimingTask(a, b));
tasks.add(new LinearSolverTimingTask(a, b));
tasks.add(new CholeskySolverTimingTask(a, b));
tasks.add(new CholeskyLdltSolverTimingTask(a, b));
tasks.add(new DirectInversionSolverTimingTask(a, b));
for (final SolverTimingTask task : tasks) {
if (!task.badSolver) {
ts.execute(task);
}
}
final int size = ts.getSize();
ts.repeat();
if (logger.isLoggable(Level.INFO)) {
logger.info(ts.getReport(size));
}
// Just check the PseudoInverse is slowest
for (int i = 1; i < size; i++) {
logger.log(TestLogUtils.getTimingRecord(ts.get(-(size)), ts.get(-i)));
}
if (np > 2) {
// The Direct solver may not be faster at size=5
int i = (np == 5) ? 2 : 1;
final int size_1 = size - 1;
for (; i < size_1; i++) {
logger.log(TestLogUtils.getTimingRecord(ts.get(-(size_1)), ts.get(-i)));
}
}
}
use of uk.ac.sussex.gdsc.test.utils.TimingService in project GDSC-SMLM by aherbert.
the class EjmlLinearSolverTest method runInversionSpeedTest.
private void runInversionSpeedTest(RandomSeed seed, int flags) {
Assumptions.assumeTrue(TestSettings.allow(TestComplexity.MEDIUM));
final Gaussian2DFunction f0 = GaussianFunctionFactory.create2D(1, 10, 10, flags, null);
final int n = f0.size();
final double[] y = new double[n];
final LocalList<DenseMatrix64F> aList = new LocalList<>();
final double[] testbackground = new double[] { 0.2, 0.7 };
final double[] testsignal1 = new double[] { 30, 100, 300 };
final double[] testcx1 = new double[] { 4.9, 5.3 };
final double[] testcy1 = new double[] { 4.8, 5.2 };
final double[] testw1 = new double[] { 1.1, 1.2, 1.5 };
final int np = f0.getNumberOfGradients();
final GradientCalculator calc = GradientCalculatorUtils.newCalculator(np);
final UniformRandomProvider rng = RngUtils.create(seed.getSeed());
// double lambda = 10;
for (final double background : testbackground) {
// Peak 1
for (final double signal1 : testsignal1) {
for (final double cx1 : testcx1) {
for (final double cy1 : testcy1) {
for (final double w1 : testw1) {
final double[] p = new double[] { background, signal1, 0, cx1, cy1, w1, w1 };
f0.initialise(p);
f0.forEach(new ValueProcedure() {
int index = 0;
@Override
public void execute(double value) {
// Poisson data
y[index++] = GdscSmlmTestUtils.createPoissonSampler(rng, value).sample();
}
});
final double[][] alpha = new double[np][np];
final double[] beta = new double[np];
// double ss =
calc.findLinearised(n, y, p, alpha, beta, f0);
// TestLog.fine(logger,"SS = %f", ss);
// As per the LVM algorithm
// for (int i = 0; i < np; i++)
// alpha[i][i] *= lambda;
aList.add(EjmlLinearSolver.toA(alpha));
}
}
}
}
}
final DenseMatrix64F[] a = aList.toArray(new DenseMatrix64F[0]);
final boolean[] ignore = new boolean[a.length];
final double[][] answer = new double[a.length][];
final int runs = 100000 / a.length;
final TimingService ts = new TimingService(runs);
final LocalList<InversionTimingTask> tasks = new LocalList<>();
// Added in descending speed order
tasks.add(new PseudoInverseInversionTimingTask(a, ignore, answer));
tasks.add(new LinearInversionTimingTask(a, ignore, answer));
tasks.add(new CholeskyLdltInversionTimingTask(a, ignore, answer));
tasks.add(new CholeskyInversionTimingTask(a, ignore, answer));
tasks.add(new DirectInversionInversionTimingTask(a, ignore, answer));
tasks.add(new DiagonalDirectInversionInversionTimingTask(a, ignore, answer));
for (final InversionTimingTask task : tasks) {
if (!task.badSolver) {
ts.execute(task);
}
}
final int size = ts.getSize();
ts.repeat();
if (logger.isLoggable(Level.INFO)) {
logger.info(ts.getReport(size));
}
// When it is present the DiagonalDirect is fastest (n<=5)
if (np <= 5) {
for (int i = 2; i <= size; i++) {
logger.log(TestLogUtils.getTimingRecord(ts.get(-i), ts.get(-1)));
}
if (np < 5) {
// n < 5 Direct is fastest
for (int i = 3; i <= size; i++) {
logger.log(TestLogUtils.getTimingRecord(ts.get(-i), ts.get(-2)));
}
} else {
// and may not be faster than Direct at n=5 so that comparison is ignored.
for (int i = 4; i <= size; i++) {
logger.log(TestLogUtils.getTimingRecord(ts.get(-i), ts.get(-3)));
}
}
} else {
// Cholesky should be fastest.
for (int i = 2; i <= size; i++) {
logger.log(TestLogUtils.getTimingRecord(ts.get(-i), ts.get(-1)));
}
}
}
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