Examples with FastLog uk.ac.sussex.gdsc.smlm.function.FastLog used on opensource projects

Example 1 with FastLog

use of uk.ac.sussex.gdsc.smlm.function.FastLog in project GDSC-SMLM by aherbert.

the class LvmGradientProcedureTest method gradientProcedureIsNotSlowerThanGradientCalculator.

private void gradientProcedureIsNotSlowerThanGradientCalculator(RandomSeed seed, final int nparams, final Type type) {
    Assumptions.assumeTrue(TestSettings.allow(TestComplexity.MEDIUM));
    final int iter = 1000;
    final double[][] alpha = new double[nparams][nparams];
    final double[] beta = new double[nparams];
    final ArrayList<double[]> paramsList = new ArrayList<>(iter);
    final ArrayList<double[]> yList = new ArrayList<>(iter);
    final int[] x = createFakeData(RngUtils.create(seed.getSeed()), nparams, iter, paramsList, yList);
    final int n = x.length;
    final FakeGradientFunction func = new FakeGradientFunction(blockWidth, nparams);
    final boolean mle = type != Type.LSQ;
    final FastLog fastLog = (type == Type.FAST_LOG_MLE) ? getFastLog() : null;
    final GradientCalculator calc = GradientCalculatorUtils.newCalculator(nparams, mle);
    for (int i = 0; i < paramsList.size(); i++) {
        calc.findLinearised(n, yList.get(i), paramsList.get(i), alpha, beta, func);
    }
    for (int i = 0; i < paramsList.size(); i++) {
        final LvmGradientProcedure p = LvmGradientProcedureUtils.create(yList.get(i), func, type, fastLog);
        p.gradient(paramsList.get(i));
    }
    // Realistic loops for an optimisation
    final int loops = 15;
    // Run till stable timing
    final Timer t1 = new Timer() {

        @Override
        void run() {
            for (int i = 0, k = 0; i < iter; i++) {
                final GradientCalculator calc = GradientCalculatorUtils.newCalculator(nparams, mle);
                for (int j = loops; j-- > 0; ) {
                    calc.findLinearised(n, yList.get(i), paramsList.get(k++ % iter), alpha, beta, func);
                }
            }
        }
    };
    final long time1 = t1.getTime();
    final Timer t2 = new Timer(t1.loops) {

        @Override
        void run() {
            for (int i = 0, k = 0; i < iter; i++) {
                final LvmGradientProcedure p = LvmGradientProcedureUtils.create(yList.get(i), func, type, fastLog);
                for (int j = loops; j-- > 0; ) {
                    p.gradient(paramsList.get(k++ % iter));
                }
            }
        }
    };
    final long time2 = t2.getTime();
    logger.log(TestLogUtils.getTimingRecord(new TimingResult("GradientCalculator", time1), new TimingResult(() -> String.format("LVMGradientProcedure %d %s", nparams, type), time2)));
}

Example 2 with FastLog

use of uk.ac.sussex.gdsc.smlm.function.FastLog in project GDSC-SMLM by aherbert.

the class LvmGradientProcedureTest method gradientProcedureComputesSameAsGradientCalculator.

private void gradientProcedureComputesSameAsGradientCalculator(RandomSeed seed, int nparams, Type type, double error) {
    final int iter = 10;
    final double[][] alpha = new double[nparams][nparams];
    final double[] beta = new double[nparams];
    final ArrayList<double[]> paramsList = new ArrayList<>(iter);
    final ArrayList<double[]> yList = new ArrayList<>(iter);
    final int[] x = createFakeData(RngUtils.create(seed.getSeed()), nparams, iter, paramsList, yList);
    final int n = x.length;
    final FakeGradientFunction func = new FakeGradientFunction(blockWidth, nparams);
    final boolean mle = type != Type.LSQ;
    final FastLog fastLog = (type == Type.FAST_LOG_MLE) ? getFastLog() : null;
    final GradientCalculator calc = GradientCalculatorUtils.newCalculator(nparams, mle);
    final String name = String.format("[%d] %b", nparams, mle);
    // Create messages
    final IndexSupplier msgR = new IndexSupplier(1, name + "Result: Not same ", null);
    final IndexSupplier msgOb = new IndexSupplier(1, name + "Observations: Not same beta ", null);
    final IndexSupplier msgOal = new IndexSupplier(1, name + "Observations: Not same alpha linear ", null);
    final IndexSupplier msgOam = new IndexSupplier(1, name + "Observations: Not same alpha matrix ", null);
    final DoubleDoubleBiPredicate predicate = (error == 0) ? TestHelper.doublesEqual() : TestHelper.doublesAreClose(error, 0);
    for (int i = 0; i < paramsList.size(); i++) {
        // Reference implementation
        final double s = calc.findLinearised(n, yList.get(i), paramsList.get(i), alpha, beta, func);
        // Procedure
        final LvmGradientProcedure p = LvmGradientProcedureUtils.create(yList.get(i), func, type, fastLog);
        p.gradient(paramsList.get(i));
        final double s2 = p.value;
        // Value may be different depending on log implementation
        msgR.set(0, i);
        TestAssertions.assertTest(s, s2, predicate, msgR);
        // Exactly the same ...
        Assertions.assertArrayEquals(p.beta, beta, msgOb.set(0, i));
        final double[] al = p.getAlphaLinear();
        Assertions.assertArrayEquals(al, new DenseMatrix64F(alpha).data, msgOal.set(0, i));
        final double[][] am = p.getAlphaMatrix();
        Assertions.assertArrayEquals(am, alpha, msgOam.set(0, i));
    }
}

Example 3 with FastLog

use of uk.ac.sussex.gdsc.smlm.function.FastLog in project GDSC-SMLM by aherbert.

the class LvmGradientProcedureTest method gradientProcedureSupportsPrecomputed.

private void gradientProcedureSupportsPrecomputed(RandomSeed seed, final Type type, boolean checkGradients) {
    final int iter = 10;
    final UniformRandomProvider rng = RngUtils.create(seed.getSeed());
    final SharedStateContinuousSampler gs = SamplerUtils.createGaussianSampler(rng, 0, noise);
    final ArrayList<double[]> paramsList = new ArrayList<>(iter);
    final ArrayList<double[]> yList = new ArrayList<>(iter);
    // 3 peaks
    createData(rng, 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
        final double min = MathUtils.min(y);
        for (int j = 0; j < y.length; j++) {
            y[j] = y[i] - min + gs.sample();
        }
    }
    // 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
    final Gaussian2DFunction f123 = GaussianFunctionFactory.create2D(3, blockWidth, blockWidth, GaussianFunctionFactory.FIT_ERF_FREE_CIRCLE, null);
    final Gaussian2DFunction f12 = GaussianFunctionFactory.create2D(2, blockWidth, blockWidth, GaussianFunctionFactory.FIT_ERF_FREE_CIRCLE, null);
    final Gaussian2DFunction f3 = GaussianFunctionFactory.create2D(1, blockWidth, blockWidth, GaussianFunctionFactory.FIT_ERF_FREE_CIRCLE, null);
    final FastLog fastLog = type == Type.FAST_LOG_MLE ? getFastLog() : null;
    final int nparams = f12.getNumberOfGradients();
    final int[] indices = f12.gradientIndices();
    final double[] b = new double[f12.size()];
    // for checking strict equivalence
    final DoubleEquality eq = new DoubleEquality(1e-8, 1e-16);
    // for the gradients
    final double delta = 1e-4;
    final DoubleEquality eq2 = new DoubleEquality(5e-2, 1e-16);
    final double[] a1peaks = new double[1 + Gaussian2DFunction.PARAMETERS_PER_PEAK];
    final double[] y_b = new double[b.length];
    // Count the number of failures for each gradient
    final int failureLimit = TestCounter.computeFailureLimit(iter, 0.1);
    final TestCounter failCounter = new TestCounter(failureLimit, nparams * 2);
    for (int i = 0; i < paramsList.size(); i++) {
        final int ii = i;
        final double[] y = yList.get(i);
        final double[] a3peaks = paramsList.get(i);
        // logger.fine(FunctionUtils.getSupplier("[%d] a=%s", i, Arrays.toString(a3peaks));
        final double[] a2peaks = Arrays.copyOf(a3peaks, 1 + 2 * Gaussian2DFunction.PARAMETERS_PER_PEAK);
        final double[] a2peaks2 = a2peaks.clone();
        for (int j = 1; j < a1peaks.length; j++) {
            a1peaks[j] = a3peaks[j + 2 * Gaussian2DFunction.PARAMETERS_PER_PEAK];
        }
        // 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 index = 0;

            @Override
            public void execute(double value) {
                b[index] = value;
                // Remove negatives for MLE
                if (type.isMle()) {
                    y[index] = Math.max(0, y[index]);
                    y_b[index] = Math.max(0, y[index] - value);
                } else {
                    y_b[index] = y[index] - value;
                }
                index++;
            }
        });
        final LvmGradientProcedure p123 = LvmGradientProcedureUtils.create(y, f123, type, fastLog);
        // ///////////////////////////////////
        // These should be the same
        // ///////////////////////////////////
        final LvmGradientProcedure p12b3 = LvmGradientProcedureUtils.create(y, OffsetGradient1Function.wrapGradient1Function(f12, b), type, fastLog);
        // Check they are the same
        p123.gradient(a3peaks);
        final double[][] m123 = p123.getAlphaMatrix();
        p12b3.gradient(a2peaks);
        double value = p12b3.value;
        final double[] beta = p12b3.beta.clone();
        double[][] alpha = p12b3.getAlphaMatrix();
        if (!eq.almostEqualRelativeOrAbsolute(p123.value, value)) {
            Assertions.fail(FunctionUtils.getSupplier("p12b3 Not same value @ %d (error=%s) : %s == %s", i, DoubleEquality.relativeError(p123.value, value), p123.value, value));
        }
        if (!almostEqualRelativeOrAbsolute(eq, beta, p123.beta)) {
            Assertions.fail(FunctionUtils.getSupplier("p12b3 Not same gradient @ %d (error=%s) : %s vs %s", i, relativeError(beta, p123.beta), Arrays.toString(beta), Arrays.toString(p123.beta)));
        }
        for (int j = 0; j < alpha.length; j++) {
            // Arrays.toString(m123[j]));
            if (!almostEqualRelativeOrAbsolute(eq, alpha[j], m123[j])) {
                Assertions.fail(FunctionUtils.getSupplier("p12b3 Not same alpha @ %d,%d (error=%s) : %s vs %s", i, j, relativeError(alpha[j], m123[j]), Arrays.toString(alpha[j]), Arrays.toString(m123[j])));
            }
        }
        // Check actual gradients are correct
        if (checkGradients) {
            for (int j = 0; j < nparams; j++) {
                final int jj = j;
                final int k = indices[j];
                // double d = Precision.representableDelta(a2peaks[k], (a2peaks[k] == 0) ? 1e-3 :
                // a2peaks[k] * delta);
                final double d = Precision.representableDelta(a2peaks[k], delta);
                a2peaks2[k] = a2peaks[k] + d;
                p12b3.value(a2peaks2);
                final double s1 = p12b3.value;
                a2peaks2[k] = a2peaks[k] - d;
                p12b3.value(a2peaks2);
                final 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;
                final double gradient = (s1 - s2) / (2 * d);
                // logger.fine(FunctionUtils.getSupplier("[%d,%d] %f (%s %f+/-%f) %f ?= %f (%f)", i, k, s,
                // Gaussian2DFunction.getName(k), a2peaks[k], d, beta[j], gradient,
                // DoubleEquality.relativeError(gradient, beta[j]));
                failCounter.run(j, () -> eq2.almostEqualRelativeOrAbsolute(beta[jj], gradient), () -> {
                    Assertions.fail(() -> String.format("Not same gradient @ %d,%d: %s != %s (error=%s)", ii, jj, beta[jj], gradient, DoubleEquality.relativeError(beta[jj], gradient)));
                });
            }
        }
        // ///////////////////////////////////
        // This may be different
        // ///////////////////////////////////
        final LvmGradientProcedure p12m3 = LvmGradientProcedureUtils.create(y_b, f12, type, fastLog);
        // Check these may be different.
        // Sometimes they are not different.
        p12m3.gradient(a2peaks);
        value = p12m3.value;
        System.arraycopy(p12m3.beta, 0, beta, 0, p12m3.beta.length);
        alpha = p12m3.getAlphaMatrix();
        if (type != Type.LSQ) {
            if (eq.almostEqualRelativeOrAbsolute(p123.value, value)) {
                logger.log(TestLogUtils.getFailRecord("p12b3 Same value @ %d (error=%s) : %s == %s", i, DoubleEquality.relativeError(p123.value, value), p123.value, value));
            }
            if (almostEqualRelativeOrAbsolute(eq, beta, p123.beta)) {
                logger.log(TestLogUtils.getFailRecord("p12b3 Same gradient @ %d (error=%s) : %s vs %s", i, relativeError(beta, p123.beta), Arrays.toString(beta), Arrays.toString(p123.beta)));
            }
            // Note: Test the matrix is different by finding 1 different column
            int dj = -1;
            for (int j = 0; j < alpha.length; j++) {
                // Arrays.toString(m123[j]));
                if (!almostEqualRelativeOrAbsolute(eq, alpha[j], m123[j])) {
                    // Different column
                    dj = j;
                    break;
                }
            }
            if (dj == -1) {
                // Find biggest error for reporting. This helps set the test tolerance.
                double error = 0;
                dj = -1;
                for (int j = 0; j < alpha.length; j++) {
                    final double e = relativeError(alpha[j], m123[j]);
                    if (error <= e) {
                        error = e;
                        dj = j;
                    }
                }
                logger.log(TestLogUtils.getFailRecord("p12b3 Same alpha @ %d,%d (error=%s) : %s vs %s", i, dj, error, Arrays.toString(alpha[dj]), Arrays.toString(m123[dj])));
            }
        } else {
            if (!eq.almostEqualRelativeOrAbsolute(p123.value, value)) {
                logger.log(TestLogUtils.getFailRecord("p12b3 Not same value @ %d (error=%s) : %s == %s", i, DoubleEquality.relativeError(p123.value, value), p123.value, value));
            }
            if (!almostEqualRelativeOrAbsolute(eq, beta, p123.beta)) {
                logger.log(TestLogUtils.getFailRecord("p12b3 Not same gradient @ %d (error=%s) : %s vs %s", i, relativeError(beta, p123.beta), Arrays.toString(beta), Arrays.toString(p123.beta)));
            }
            for (int j = 0; j < alpha.length; j++) {
                // Arrays.toString(m123[j]));
                if (!almostEqualRelativeOrAbsolute(eq, alpha[j], m123[j])) {
                    logger.log(TestLogUtils.getFailRecord("p12b3 Not same alpha @ %d,%d (error=%s) : %s vs %s", i, j, relativeError(alpha[j], m123[j]), Arrays.toString(alpha[j]), Arrays.toString(m123[j])));
                }
            }
        }
        // Check actual gradients are correct
        if (!checkGradients) {
            continue;
        }
        for (int j = 0; j < nparams; j++) {
            final int jj = j;
            final int k = indices[j];
            // double d = Precision.representableDelta(a2peaks[k], (a2peaks[k] == 0) ? 1e-3 : a2peaks[k]
            // * delta);
            final double d = Precision.representableDelta(a2peaks[k], delta);
            a2peaks2[k] = a2peaks[k] + d;
            p12m3.value(a2peaks2);
            final double s1 = p12m3.value;
            a2peaks2[k] = a2peaks[k] - d;
            p12m3.value(a2peaks2);
            final 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;
            final double gradient = (s1 - s2) / (2 * d);
            // logger.fine(FunctionUtils.getSupplier("[%d,%d] %f (%s %f+/-%f) %f ?= %f (%f)", i, k, s,
            // Gaussian2DFunction.getName(k), a2peaks[k], d, beta[j], gradient,
            // DoubleEquality.relativeError(gradient, beta[j]));
            failCounter.run(nparams + j, () -> eq2.almostEqualRelativeOrAbsolute(beta[jj], gradient), () -> {
                Assertions.fail(() -> String.format("Not same gradient @ %d,%d: %s != %s (error=%s)", ii, jj, beta[jj], gradient, DoubleEquality.relativeError(beta[jj], gradient)));
            });
        }
    }
}

Example 4 with FastLog

use of uk.ac.sussex.gdsc.smlm.function.FastLog in project GDSC-SMLM by aherbert.

the class LvmGradientProcedureTest method gradientProcedureComputesGradient.

@SuppressWarnings("null")
private void gradientProcedureComputesGradient(RandomSeed seed, ErfGaussian2DFunction func, Type type, boolean precomputed) {
    final int nparams = func.getNumberOfGradients();
    final int[] indices = func.gradientIndices();
    final int iter = 100;
    final ArrayList<double[]> paramsList = new ArrayList<>(iter);
    final ArrayList<double[]> yList = new ArrayList<>(iter);
    createData(RngUtils.create(seed.getSeed()), 1, iter, paramsList, yList, true);
    // for the gradients
    final double delta = 1e-4;
    final DoubleEquality eq = new DoubleEquality(5e-2, 1e-16);
    final double[] b = (precomputed) ? new double[func.size()] : null;
    final FastLog fastLog = type == Type.FAST_LOG_MLE ? getFastLog() : null;
    // Must compute most of the time
    final int failureLimit = TestCounter.computeFailureLimit(iter, 0.1);
    final TestCounter failCounter = new TestCounter(failureLimit, nparams);
    for (int i = 0; i < paramsList.size(); i++) {
        final int ii = i;
        final double[] y = yList.get(i);
        final double[] a = paramsList.get(i);
        final double[] a2 = a.clone();
        LvmGradientProcedure gp;
        if (precomputed) {
            // Mock fitting part of the function already
            for (int j = 0; j < b.length; j++) {
                b[j] = y[j] * 0.5;
            }
            gp = LvmGradientProcedureUtils.create(y, OffsetGradient1Function.wrapGradient1Function(func, b), type, fastLog);
        } else {
            gp = LvmGradientProcedureUtils.create(y, func, type, fastLog);
        }
        gp.gradient(a);
        // double s = p.value;
        final double[] beta = gp.beta.clone();
        for (int j = 0; j < nparams; j++) {
            final int jj = j;
            final int k = indices[j];
            // double d = Precision.representableDelta(a[k], (a[k] == 0) ? 1e-3 : a[k] * delta);
            final double d = Precision.representableDelta(a[k], delta);
            a2[k] = a[k] + d;
            gp.value(a2);
            final double s1 = gp.value;
            a2[k] = a[k] - d;
            gp.value(a2);
            final double s2 = gp.value;
            a2[k] = a[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;
            final double gradient = (s1 - s2) / (2 * d);
            // logger.fine(FunctionUtils.getSupplier("[%d,%d] %f (%s %f+/-%f) %f ?= %f", i, k, s,
            // Gaussian2DFunction.getName(k),
            // a[k], d, beta[j], gradient);
            failCounter.run(j, () -> eq.almostEqualRelativeOrAbsolute(beta[jj], gradient), () -> {
                Assertions.fail(() -> String.format("Not same gradient @ %d,%d: %s != %s (error=%s)", ii, jj, beta[jj], gradient, DoubleEquality.relativeError(beta[jj], gradient)));
            });
        }
    }
}

Example 5 with FastLog

use of uk.ac.sussex.gdsc.smlm.function.FastLog in project GDSC-SMLM by aherbert.

the class LvmGradientProcedureTest method gradientProcedureIsFasterUnrolledThanGradientProcedure.

private void gradientProcedureIsFasterUnrolledThanGradientProcedure(RandomSeed seed, final int nparams, final Type type, final boolean precomputed) {
    Assumptions.assumeTrue(TestSettings.allow(TestComplexity.MEDIUM));
    final int iter = 100;
    final ArrayList<double[]> paramsList = new ArrayList<>(iter);
    final ArrayList<double[]> yList = new ArrayList<>(iter);
    createData(RngUtils.create(seed.getSeed()), 1, iter, paramsList, yList);
    // Remove the timing of the function call by creating a dummy function
    final FakeGradientFunction fgf = new FakeGradientFunction(blockWidth, nparams);
    final Gradient1Function func;
    if (precomputed) {
        final double[] b = SimpleArrayUtils.newArray(fgf.size(), 0.1, 1.3);
        func = OffsetGradient1Function.wrapGradient1Function(fgf, b);
    } else {
        func = fgf;
    }
    final FastLog fastLog = type == Type.FAST_LOG_MLE ? getFastLog() : null;
    final IntArrayFormatSupplier msgA = new IntArrayFormatSupplier("A [%d]", 1);
    final IntArrayFormatSupplier msgB = new IntArrayFormatSupplier("B [%d]", 1);
    for (int i = 0; i < paramsList.size(); i++) {
        final LvmGradientProcedure p1 = createProcedure(type, yList.get(i), func, fastLog);
        p1.gradient(paramsList.get(i));
        p1.gradient(paramsList.get(i));
        final LvmGradientProcedure p2 = LvmGradientProcedureUtils.create(yList.get(i), func, type, fastLog);
        p2.gradient(paramsList.get(i));
        p2.gradient(paramsList.get(i));
        // Check they are the same
        Assertions.assertArrayEquals(p1.getAlphaLinear(), p2.getAlphaLinear(), msgA.set(0, i));
        Assertions.assertArrayEquals(p1.beta, p2.beta, msgB.set(0, i));
    }
    // Realistic loops for an optimisation
    final int loops = 15;
    // Run till stable timing
    final Timer t1 = new Timer() {

        @Override
        void run() {
            for (int i = 0, k = 0; i < paramsList.size(); i++) {
                final LvmGradientProcedure p1 = createProcedure(type, yList.get(i), func, fastLog);
                for (int j = loops; j-- > 0; ) {
                    p1.gradient(paramsList.get(k++ % iter));
                }
            }
        }
    };
    final long time1 = t1.getTime();
    final Timer t2 = new Timer(t1.loops) {

        @Override
        void run() {
            for (int i = 0, k = 0; i < paramsList.size(); i++) {
                final LvmGradientProcedure p2 = LvmGradientProcedureUtils.create(yList.get(i), func, type, fastLog);
                for (int j = loops; j-- > 0; ) {
                    p2.gradient(paramsList.get(k++ % iter));
                }
            }
        }
    };
    final long time2 = t2.getTime();
    logger.log(TestLogUtils.getTimingRecord(new TimingResult(() -> String.format("%s, Precomputed=%b : Standard", type, precomputed), time1), new TimingResult(() -> String.format("Unrolled %d", nparams), time2)));
}