Examples with ValueProcedure gdsc.smlm.function.ValueProcedure used on opensource projects

Example 1 with ValueProcedure

use of gdsc.smlm.function.ValueProcedure in project GDSC-SMLM by aherbert.

the class ApacheLVMFitter method computeFit.

public FitStatus computeFit(double[] y, final double[] y_fit, double[] a, double[] a_dev) {
    int n = y.length;
    try {
        // Different convergence thresholds seem to have no effect on the resulting fit, only the number of
        // iterations for convergence
        final double initialStepBoundFactor = 100;
        final double costRelativeTolerance = 1e-10;
        final double parRelativeTolerance = 1e-10;
        final double orthoTolerance = 1e-10;
        final double threshold = Precision.SAFE_MIN;
        // Extract the parameters to be fitted
        final double[] initialSolution = getInitialSolution(a);
        // TODO - Pass in more advanced stopping criteria.
        // Create the target and weight arrays
        final double[] yd = new double[n];
        final double[] w = new double[n];
        for (int i = 0; i < n; i++) {
            yd[i] = y[i];
            w[i] = 1;
        }
        LevenbergMarquardtOptimizer optimizer = new LevenbergMarquardtOptimizer(initialStepBoundFactor, costRelativeTolerance, parRelativeTolerance, orthoTolerance, threshold);
        //@formatter:off
        LeastSquaresBuilder builder = new LeastSquaresBuilder().maxEvaluations(Integer.MAX_VALUE).maxIterations(getMaxEvaluations()).start(initialSolution).target(yd).weight(new DiagonalMatrix(w));
        if (f instanceof ExtendedNonLinearFunction && ((ExtendedNonLinearFunction) f).canComputeValuesAndJacobian()) {
            // Compute together, or each individually
            builder.model(new ValueAndJacobianFunction() {

                final ExtendedNonLinearFunction fun = (ExtendedNonLinearFunction) f;

                public Pair<RealVector, RealMatrix> value(RealVector point) {
                    final double[] p = point.toArray();
                    final Pair<double[], double[][]> result = fun.computeValuesAndJacobian(p);
                    return new Pair<RealVector, RealMatrix>(new ArrayRealVector(result.getFirst(), false), new Array2DRowRealMatrix(result.getSecond(), false));
                }

                public RealVector computeValue(double[] params) {
                    return new ArrayRealVector(fun.computeValues(params), false);
                }

                public RealMatrix computeJacobian(double[] params) {
                    return new Array2DRowRealMatrix(fun.computeJacobian(params), false);
                }
            });
        } else {
            // Compute separately
            builder.model(new MultivariateVectorFunctionWrapper((NonLinearFunction) f, a, n), new MultivariateMatrixFunctionWrapper((NonLinearFunction) f, a, n));
        }
        LeastSquaresProblem problem = builder.build();
        Optimum optimum = optimizer.optimize(problem);
        final double[] parameters = optimum.getPoint().toArray();
        setSolution(a, parameters);
        iterations = optimum.getIterations();
        evaluations = optimum.getEvaluations();
        if (a_dev != null) {
            try {
                double[][] covar = optimum.getCovariances(threshold).getData();
                setDeviationsFromMatrix(a_dev, covar);
            } catch (SingularMatrixException e) {
                // Matrix inversion failed. In order to return a solution 
                // return the reciprocal of the diagonal of the Fisher information 
                // for a loose bound on the limit 
                final int[] gradientIndices = f.gradientIndices();
                final int nparams = gradientIndices.length;
                GradientCalculator calculator = GradientCalculatorFactory.newCalculator(nparams);
                double[][] alpha = new double[nparams][nparams];
                double[] beta = new double[nparams];
                calculator.findLinearised(nparams, y, a, alpha, beta, (NonLinearFunction) f);
                FisherInformationMatrix m = new FisherInformationMatrix(alpha);
                setDeviations(a_dev, m.crlb(true));
            }
        }
        // Compute function value
        if (y_fit != null) {
            Gaussian2DFunction f = (Gaussian2DFunction) this.f;
            f.initialise0(a);
            f.forEach(new ValueProcedure() {

                int i = 0;

                public void execute(double value) {
                    y_fit[i] = value;
                }
            });
        }
        // As this is unweighted then we can do this to get the sum of squared residuals
        // This is the same as optimum.getCost() * optimum.getCost(); The getCost() function
        // just computes the dot product anyway.
        value = optimum.getResiduals().dotProduct(optimum.getResiduals());
    } catch (TooManyEvaluationsException e) {
        return FitStatus.TOO_MANY_EVALUATIONS;
    } catch (TooManyIterationsException e) {
        return FitStatus.TOO_MANY_ITERATIONS;
    } catch (ConvergenceException e) {
        // Occurs when QR decomposition fails - mark as a singular non-linear model (no solution)
        return FitStatus.SINGULAR_NON_LINEAR_MODEL;
    } catch (Exception e) {
        // TODO - Find out the other exceptions from the fitter and add return values to match. 
        return FitStatus.UNKNOWN;
    }
    return FitStatus.OK;
}

Example 2 with ValueProcedure

use of gdsc.smlm.function.ValueProcedure in project GDSC-SMLM by aherbert.

the class EJMLLinearSolverTest method runInversionSpeedTest.

private void runInversionSpeedTest(int flags) {
    final Gaussian2DFunction f0 = GaussianFunctionFactory.create2D(1, 10, 10, flags, null);
    int n = f0.size();
    final double[] y = new double[n];
    final TurboList<DenseMatrix64F> aList = new TurboList<DenseMatrix64F>();
    double[] testbackground = new double[] { 0.2, 0.7 };
    double[] testsignal1 = new double[] { 30, 100, 300 };
    double[] testcx1 = new double[] { 4.9, 5.3 };
    double[] testcy1 = new double[] { 4.8, 5.2 };
    double[] testw1 = new double[] { 1.1, 1.2, 1.5 };
    int np = f0.getNumberOfGradients();
    GradientCalculator calc = GradientCalculatorFactory.newCalculator(np);
    final RandomDataGenerator rdg = new RandomDataGenerator(new Well19937c(30051977));
    //double lambda = 10;
    for (double background : testbackground) // Peak 1
    for (double signal1 : testsignal1) for (double cx1 : testcx1) for (double cy1 : testcy1) for (double w1 : testw1) {
        double[] p = new double[] { background, signal1, 0, cx1, cy1, w1, w1 };
        f0.initialise(p);
        f0.forEach(new ValueProcedure() {

            int i = 0;

            public void execute(double value) {
                // Poisson data 
                y[i++] = rdg.nextPoisson(value);
            }
        });
        double[][] alpha = new double[np][np];
        double[] beta = new double[np];
        //double ss = 
        calc.findLinearised(n, y, p, alpha, beta, f0);
        //System.out.printf("SS = %f\n", ss);
        // As per the LVM algorithm
        //for (int i = 0; i < np; i++)
        //	alpha[i][i] *= lambda;
        aList.add(EJMLLinearSolver.toA(alpha));
    }
    DenseMatrix64F[] a = aList.toArray(new DenseMatrix64F[aList.size()]);
    boolean[] ignore = new boolean[a.length];
    double[][] answer = new double[a.length][];
    int runs = 100000 / a.length;
    TimingService ts = new TimingService(runs);
    TurboList<InversionTimingTask> tasks = new TurboList<InversionTimingTask>();
    tasks.add(new PseudoInverseInversionTimingTask(a, ignore, answer));
    tasks.add(new CholeskyInversionTimingTask(a, ignore, answer));
    tasks.add(new LinearInversionTimingTask(a, ignore, answer));
    tasks.add(new CholeskyLDLTInversionTimingTask(a, ignore, answer));
    tasks.add(new DirectInversionInversionTimingTask(a, ignore, answer));
    tasks.add(new DiagonalDirectInversionInversionTimingTask(a, ignore, answer));
    for (InversionTimingTask task : tasks) if (!task.badSolver)
        ts.execute(task);
    ts.repeat();
    ts.report();
}

Example 3 with ValueProcedure

use of gdsc.smlm.function.ValueProcedure in project GDSC-SMLM by aherbert.

the class LVMGradientProcedureTest method gradientProcedureSupportsPrecomputed.

private void gradientProcedureSupportsPrecomputed(final Type type) {
    int iter = 10;
    rdg = new RandomDataGenerator(new Well19937c(30051977));
    ArrayList<double[]> paramsList = new ArrayList<double[]>(iter);
    ArrayList<double[]> yList = new ArrayList<double[]>(iter);
    // 3 peaks
    createData(3, iter, paramsList, yList, true);
    for (int i = 0; i < paramsList.size(); i++) {
        final double[] y = yList.get(i);
        // Add Gaussian read noise so we have negatives
        double min = Maths.min(y);
        for (int j = 0; j < y.length; j++) y[j] = y[i] - min + rdg.nextGaussian(0, Noise);
    }
    // We want to know that:
    // y|peak1+peak2+peak3 == y|peak1+peak2+peak3(precomputed)
    // We want to know when:
    // y|peak1+peak2+peak3 != y-peak3|peak1+peak2
    // i.e. we cannot subtract a precomputed peak from the data, it must be included in the fit
    // E.G. LSQ - subtraction is OK, MLE/WLSQ - subtraction is not allowed
    Gaussian2DFunction f123 = GaussianFunctionFactory.create2D(3, blockWidth, blockWidth, GaussianFunctionFactory.FIT_ERF_FREE_CIRCLE, null);
    Gaussian2DFunction f12 = GaussianFunctionFactory.create2D(2, blockWidth, blockWidth, GaussianFunctionFactory.FIT_ERF_FREE_CIRCLE, null);
    Gaussian2DFunction f3 = GaussianFunctionFactory.create2D(1, blockWidth, blockWidth, GaussianFunctionFactory.FIT_ERF_FREE_CIRCLE, null);
    int nparams = f12.getNumberOfGradients();
    int[] indices = f12.gradientIndices();
    final double[] b = new double[f12.size()];
    double delta = 1e-3;
    DoubleEquality eq = new DoubleEquality(5e-3, 1e-6);
    double[] a1peaks = new double[7];
    final double[] y_b = new double[b.length];
    for (int i = 0; i < paramsList.size(); i++) {
        final double[] y = yList.get(i);
        double[] a3peaks = paramsList.get(i);
        double[] a2peaks = Arrays.copyOf(a3peaks, 1 + 2 * 6);
        double[] a2peaks2 = a2peaks.clone();
        for (int j = 1; j < 7; j++) a1peaks[j] = a3peaks[j + 2 * 6];
        // Evaluate peak 3 to get the background and subtract it from the data to get the new data
        f3.initialise0(a1peaks);
        f3.forEach(new ValueProcedure() {

            int k = 0;

            public void execute(double value) {
                b[k] = value;
                // Remove negatives for MLE
                if (type == Type.MLE) {
                    y[k] = Math.max(0, y[k]);
                    y_b[k] = Math.max(0, y[k] - value);
                } else {
                    y_b[k] = y[k] - value;
                }
                k++;
            }
        });
        // These should be the same
        LVMGradientProcedure p123 = LVMGradientProcedureFactory.create(y, f123, type);
        LVMGradientProcedure p12b3 = LVMGradientProcedureFactory.create(y, PrecomputedGradient1Function.wrapGradient1Function(f12, b), type);
        // This may be different
        LVMGradientProcedure p12m3 = LVMGradientProcedureFactory.create(y_b, f12, type);
        // Check they are the same
        p123.gradient(a3peaks);
        double[][] m123 = p123.getAlphaMatrix();
        p12b3.gradient(a2peaks);
        double s = p12b3.value;
        double[] beta = p12b3.beta.clone();
        double[][] alpha = p12b3.getAlphaMatrix();
        System.out.printf("MLE=%b [%d] p12b3  %f  %f\n", type, i, p123.value, s);
        Assert.assertTrue("p12b3 Not same value @ " + i, eq.almostEqualRelativeOrAbsolute(p123.value, s));
        Assert.assertTrue("p12b3 Not same gradient @ " + i, eq.almostEqualRelativeOrAbsolute(beta, p123.beta));
        for (int j = 0; j < alpha.length; j++) Assert.assertTrue("p12b3 Not same alpha @ " + j, eq.almostEqualRelativeOrAbsolute(alpha[j], m123[j]));
        // Check actual gradients are correct
        for (int j = 0; j < nparams; j++) {
            int k = indices[j];
            double d = Precision.representableDelta(a2peaks[k], (a2peaks[k] == 0) ? 1e-3 : a2peaks[k] * delta);
            a2peaks2[k] = a2peaks[k] + d;
            p12b3.value(a2peaks2);
            double s1 = p12b3.value;
            a2peaks2[k] = a2peaks[k] - d;
            p12b3.value(a2peaks2);
            double s2 = p12b3.value;
            a2peaks2[k] = a2peaks[k];
            // Apply a factor of -2 to compute the actual gradients:
            // See Numerical Recipes in C++, 2nd Ed. Equation 15.5.6 for Nonlinear Models
            beta[j] *= -2;
            double gradient = (s1 - s2) / (2 * d);
            System.out.printf("[%d,%d] %f  (%s %f+/-%f)  %f  ?=  %f  (%f)\n", i, k, s, f12.getName(k), a2peaks[k], d, beta[j], gradient, DoubleEquality.relativeError(gradient, beta[j]));
            Assert.assertTrue("Not same gradient @ " + j, eq.almostEqualRelativeOrAbsolute(beta[j], gradient));
        }
        // Check these may be different
        p12m3.gradient(a2peaks);
        s = p12m3.value;
        beta = p12m3.beta.clone();
        alpha = p12m3.getAlphaMatrix();
        System.out.printf("%s [%d] p12m3  %f  %f\n", type, i, p123.value, s);
        if (type != Type.LSQ) {
            Assert.assertFalse("p12b3 Same value @ " + i, eq.almostEqualRelativeOrAbsolute(p123.value, s));
            Assert.assertFalse("p12b3 Same gradient @ " + i, eq.almostEqualRelativeOrAbsolute(beta, p123.beta));
            for (int j = 0; j < alpha.length; j++) {
                //System.out.printf("%s != %s\n", Arrays.toString(alpha[j]), Arrays.toString(m123[j]));
                Assert.assertFalse("p12b3 Same alpha @ " + j, eq.almostEqualRelativeOrAbsolute(alpha[j], m123[j]));
            }
        } else {
            Assert.assertTrue("p12b3 Not same value @ " + i, eq.almostEqualRelativeOrAbsolute(p123.value, s));
            Assert.assertTrue("p12b3 Not same gradient @ " + i, eq.almostEqualRelativeOrAbsolute(beta, p123.beta));
            for (int j = 0; j < alpha.length; j++) Assert.assertTrue("p12b3 Not same alpha @ " + j, eq.almostEqualRelativeOrAbsolute(alpha[j], m123[j]));
        }
        // Check actual gradients are correct
        for (int j = 0; j < nparams; j++) {
            int k = indices[j];
            double d = Precision.representableDelta(a2peaks[k], (a2peaks[k] == 0) ? 1e-3 : a2peaks[k] * delta);
            a2peaks2[k] = a2peaks[k] + d;
            p12m3.value(a2peaks2);
            double s1 = p12m3.value;
            a2peaks2[k] = a2peaks[k] - d;
            p12m3.value(a2peaks2);
            double s2 = p12m3.value;
            a2peaks2[k] = a2peaks[k];
            // Apply a factor of -2 to compute the actual gradients:
            // See Numerical Recipes in C++, 2nd Ed. Equation 15.5.6 for Nonlinear Models
            beta[j] *= -2;
            double gradient = (s1 - s2) / (2 * d);
            System.out.printf("[%d,%d] %f  (%s %f+/-%f)  %f  ?=  %f  (%f)\n", i, k, s, f12.getName(k), a2peaks[k], d, beta[j], gradient, DoubleEquality.relativeError(gradient, beta[j]));
            Assert.assertTrue("Not same gradient @ " + j, eq.almostEqualRelativeOrAbsolute(beta[j], gradient));
        }
    }
}

Example 4 with ValueProcedure

use of gdsc.smlm.function.ValueProcedure in project GDSC-SMLM by aherbert.

the class Gaussian2DFunction method computeValues.

/*
	 * (non-Javadoc)
	 * 
	 * @see gdsc.smlm.function.ExtendedNonLinearFunction#computeValues(double[])
	 */
public double[] computeValues(double[] variables) {
    initialise0(variables);
    final double[] values = new double[size()];
    forEach(new ValueProcedure() {

        int i = 0;

        public void execute(double value) {
            values[i++] = value;
        }
    });
    return values;
}

Example 5 with ValueProcedure

use of gdsc.smlm.function.ValueProcedure in project GDSC-SMLM by aherbert.

the class FastMLEGradient2ProcedureTest method gradientProcedureComputesSameWithPrecomputed.

@Test
public void gradientProcedureComputesSameWithPrecomputed() {
    int iter = 10;
    rdg = new RandomDataGenerator(new Well19937c(30051977));
    ErfGaussian2DFunction f1 = (ErfGaussian2DFunction) GaussianFunctionFactory.create2D(1, 10, 10, GaussianFunctionFactory.FIT_ERF_FREE_CIRCLE, null);
    ErfGaussian2DFunction f2 = (ErfGaussian2DFunction) GaussianFunctionFactory.create2D(2, 10, 10, GaussianFunctionFactory.FIT_ERF_FREE_CIRCLE, null);
    double[] a1 = new double[7];
    double[] a2 = new double[13];
    final double[] x = new double[f1.size()];
    final double[] b = new double[f1.size()];
    for (int i = 0; i < iter; i++) {
        a2[Gaussian2DFunction.BACKGROUND] = rdg.nextUniform(0.1, 0.3);
        a2[Gaussian2DFunction.SIGNAL] = rdg.nextUniform(100, 300);
        a2[Gaussian2DFunction.X_POSITION] = rdg.nextUniform(3, 5);
        a2[Gaussian2DFunction.Y_POSITION] = rdg.nextUniform(3, 5);
        a2[Gaussian2DFunction.X_SD] = rdg.nextUniform(1, 1.3);
        a2[Gaussian2DFunction.Y_SD] = rdg.nextUniform(1, 1.3);
        a2[6 + Gaussian2DFunction.SIGNAL] = rdg.nextUniform(100, 300);
        a2[6 + Gaussian2DFunction.X_POSITION] = rdg.nextUniform(5, 7);
        a2[6 + Gaussian2DFunction.Y_POSITION] = rdg.nextUniform(5, 7);
        a2[6 + Gaussian2DFunction.X_SD] = rdg.nextUniform(1, 1.3);
        a2[6 + Gaussian2DFunction.Y_SD] = rdg.nextUniform(1, 1.3);
        // Simulate Poisson data
        f2.initialise0(a2);
        f1.forEach(new ValueProcedure() {

            int k = 0;

            public void execute(double value) {
                x[k++] = (value > 0) ? rdg.nextPoisson(value) : 0;
            }
        });
        // Precompute peak 2 (no background)
        a1[Gaussian2DFunction.BACKGROUND] = 0;
        for (int j = 1; j < 7; j++) a1[j] = a2[6 + j];
        f1.initialise0(a1);
        f1.forEach(new ValueProcedure() {

            int k = 0;

            public void execute(double value) {
                b[k++] = value;
            }
        });
        // Reset to peak 1
        for (int j = 0; j < 7; j++) a1[j] = a2[j];
        // Compute peak 1+2
        FastMLEGradient2Procedure p12 = FastMLEGradient2ProcedureFactory.create(x, f2);
        p12.computeSecondDerivative(a2);
        double[] d11 = Arrays.copyOf(p12.d1, f1.getNumberOfGradients());
        double[] d21 = Arrays.copyOf(p12.d2, f1.getNumberOfGradients());
        // Compute peak 1+(precomputed 2)
        FastMLEGradient2Procedure p1b2 = FastMLEGradient2ProcedureFactory.create(x, PrecomputedGradient2Function.wrapGradient2Function(f1, b));
        p1b2.computeSecondDerivative(a1);
        double[] d12 = p1b2.d1;
        double[] d22 = p1b2.d2;
        Assert.assertArrayEquals(" Result: Not same @ " + i, p12.u, p1b2.u, 1e-10);
        Assert.assertArrayEquals(" D1: Not same @ " + i, d11, d12, 1e-10);
        Assert.assertArrayEquals(" D2: Not same @ " + i, d21, d22, 1e-10);
        double[] v1 = p12.computeValue(a2);
        double[] v2 = p1b2.computeValue(a1);
        Assert.assertArrayEquals(" Value: Not same @ " + i, v1, v2, 1e-10);
        double[] d1 = Arrays.copyOf(p12.computeFirstDerivative(a2), f1.getNumberOfGradients());
        double[] d2 = p1b2.computeFirstDerivative(a1);
        Assert.assertArrayEquals(" 1st derivative: Not same @ " + i, d1, d2, 1e-10);
    }
}