Examples with SharedStateContinuousSampler org.apache.commons.rng.sampling.distribution.SharedStateContinuousSampler used on opensource projects

Example 1 with SharedStateContinuousSampler

use of org.apache.commons.rng.sampling.distribution.SharedStateContinuousSampler in project GDSC-SMLM by aherbert.

the class FrcTest method canComputeMirrored.

@SeededTest
void canComputeMirrored(RandomSeed seed) {
    // Sample lines through an image to create a structure.
    final int size = 1024;
    final double[][] data = new double[size * 2][];
    final UniformRandomProvider r = RngUtils.create(seed.getSeed());
    final SharedStateContinuousSampler gs = SamplerUtils.createGaussianSampler(r, 0, 5);
    for (int x = 0, y = 0, y2 = size, i = 0; x < size; x++, y++, y2--) {
        data[i++] = new double[] { x + gs.sample(), y + gs.sample() };
        data[i++] = new double[] { x + gs.sample(), y2 + gs.sample() };
    }
    // Create 2 images
    final Rectangle bounds = new Rectangle(0, 0, size, size);
    ImageJImagePeakResults i1 = createImage(bounds);
    ImageJImagePeakResults i2 = createImage(bounds);
    final int[] indices = SimpleArrayUtils.natural(data.length);
    PermutationSampler.shuffle(r, indices);
    for (final int i : indices) {
        final ImageJImagePeakResults image = i1;
        i1 = i2;
        i2 = image;
        image.add((float) data[i][0], (float) data[i][1], 1);
    }
    i1.end();
    i2.end();
    final ImageProcessor ip1 = i1.getImagePlus().getProcessor();
    final ImageProcessor ip2 = i2.getImagePlus().getProcessor();
    // Test
    final Frc frc = new Frc();
    FloatProcessor[] fft1;
    FloatProcessor[] fft2;
    fft1 = frc.getComplexFft(ip1);
    fft2 = frc.getComplexFft(ip2);
    final float[] dataA1 = (float[]) fft1[0].getPixels();
    final float[] dataB1 = (float[]) fft1[1].getPixels();
    final float[] dataA2 = (float[]) fft2[0].getPixels();
    final float[] dataB2 = (float[]) fft2[1].getPixels();
    final float[] numeratorE = new float[dataA1.length];
    final float[] absFft1E = new float[dataA1.length];
    final float[] absFft2E = new float[dataA1.length];
    Frc.compute(numeratorE, absFft1E, absFft2E, dataA1, dataB1, dataA2, dataB2);
    Assertions.assertTrue(checkSymmetry(numeratorE, size), "numeratorE");
    Assertions.assertTrue(checkSymmetry(absFft1E, size), "absFft1E");
    Assertions.assertTrue(checkSymmetry(absFft2E, size), "absFft2E");
    final float[] numeratorA = new float[dataA1.length];
    final float[] absFft1A = new float[dataA1.length];
    final float[] absFft2A = new float[dataA1.length];
    Frc.computeMirrored(size, numeratorA, absFft1A, absFft2A, dataA1, dataB1, dataA2, dataB2);
    // for (int y=0, i=0; y<size; y++)
    // for (int x=0; x<size; x++, i++)
    // {
    // logger.fine(FunctionUtils.getSupplier("[%d,%d = %d] %f ?= %f", x, y, i, numeratorE[i],
    // numeratorA[i]);
    // }
    Assertions.assertArrayEquals(numeratorE, numeratorA, "numerator");
    Assertions.assertArrayEquals(absFft1E, absFft1A, "absFft1");
    Assertions.assertArrayEquals(absFft2E, absFft2A, "absFft2");
    Frc.computeMirroredFast(size, numeratorA, absFft1A, absFft2A, dataA1, dataB1, dataA2, dataB2);
    // Check this.
    for (int y = 1; y < size; y++) {
        for (int x = 1, i = y * size + 1; x < size; x++, i++) {
            Assertions.assertEquals(numeratorE[i], numeratorA[i], "numerator");
            Assertions.assertEquals(absFft1E[i], absFft1A[i], "absFft1");
            Assertions.assertEquals(absFft2E[i], absFft2A[i], "absFft2");
        }
    }
}

Example 2 with SharedStateContinuousSampler

use of org.apache.commons.rng.sampling.distribution.SharedStateContinuousSampler in project GDSC-SMLM by aherbert.

the class FrcTest method computeMirroredIsFaster.

@SeededTest
void computeMirroredIsFaster(RandomSeed seed) {
    Assumptions.assumeTrue(TestSettings.allow(TestComplexity.MEDIUM));
    // Sample lines through an image to create a structure.
    final int N = 2048;
    final double[][] data = new double[N * 2][];
    final UniformRandomProvider r = RngUtils.create(seed.getSeed());
    final SharedStateContinuousSampler gs = SamplerUtils.createGaussianSampler(r, 0, 5);
    for (int x = 0, y = 0, y2 = N, i = 0; x < N; x++, y++, y2--) {
        data[i++] = new double[] { x + gs.sample(), y + gs.sample() };
        data[i++] = new double[] { x + gs.sample(), y2 + gs.sample() };
    }
    // Create 2 images
    final Rectangle bounds = new Rectangle(0, 0, N, N);
    ImageJImagePeakResults i1 = createImage(bounds);
    ImageJImagePeakResults i2 = createImage(bounds);
    final int[] indices = SimpleArrayUtils.natural(data.length);
    PermutationSampler.shuffle(r, indices);
    for (final int i : indices) {
        final ImageJImagePeakResults image = i1;
        i1 = i2;
        i2 = image;
        image.add((float) data[i][0], (float) data[i][1], 1);
    }
    i1.end();
    i2.end();
    final ImageProcessor ip1 = i1.getImagePlus().getProcessor();
    final ImageProcessor ip2 = i2.getImagePlus().getProcessor();
    // Test
    final Frc frc = new Frc();
    FloatProcessor[] fft1;
    FloatProcessor[] fft2;
    fft1 = frc.getComplexFft(ip1);
    fft2 = frc.getComplexFft(ip2);
    final float[] dataA1 = (float[]) fft1[0].getPixels();
    final float[] dataB1 = (float[]) fft1[1].getPixels();
    final float[] dataA2 = (float[]) fft2[0].getPixels();
    final float[] dataB2 = (float[]) fft2[1].getPixels();
    final float[] numerator = new float[dataA1.length];
    final float[] absFft1 = new float[dataA1.length];
    final float[] absFft2 = new float[dataA1.length];
    final TimingService ts = new TimingService(10);
    ts.execute(new MyTimingTask("compute") {

        @Override
        public Object run(Object data) {
            Frc.compute(numerator, absFft1, absFft2, dataA1, dataB1, dataA2, dataB2);
            return null;
        }
    });
    ts.execute(new MyTimingTask("computeMirrored") {

        @Override
        public Object run(Object data) {
            Frc.computeMirrored(N, numerator, absFft1, absFft2, dataA1, dataB1, dataA2, dataB2);
            return null;
        }
    });
    ts.execute(new MyTimingTask("computeMirroredFast") {

        @Override
        public Object run(Object data) {
            Frc.computeMirroredFast(N, numerator, absFft1, absFft2, dataA1, dataB1, dataA2, dataB2);
            return null;
        }
    });
    final int size = ts.getSize();
    ts.repeat(size);
    if (logger.isLoggable(Level.INFO)) {
        logger.info(ts.getReport(size));
    }
    // The 'Fast' method may not always be faster so just log results
    final TimingResult slow = ts.get(-3);
    final TimingResult fast = ts.get(-2);
    final TimingResult fastest = ts.get(-1);
    logger.log(TestLogUtils.getTimingRecord(slow, fastest));
    logger.log(TestLogUtils.getTimingRecord(fast, fastest));
    // It should be faster than the non mirrored version
    Assertions.assertTrue(ts.get(-1).getMean() <= ts.get(-3).getMean());
}

Example 3 with SharedStateContinuousSampler

use of org.apache.commons.rng.sampling.distribution.SharedStateContinuousSampler 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 SharedStateContinuousSampler

use of org.apache.commons.rng.sampling.distribution.SharedStateContinuousSampler in project GDSC-SMLM by aherbert.

the class BaseFunctionSolverTest method createData.

private static double[][] createData(RandomSeed source) {
    // Per observation read noise.
    // This is generated once so create the randon generator here.
    final UniformRandomProvider rg = RngUtils.create(source.getSeed());
    final ContinuousSampler ed = SamplerUtils.createExponentialSampler(rg, variance);
    final SharedStateContinuousSampler gs = SamplerUtils.createGaussianSampler(rg, gain, gainSD);
    final double[] w = new double[size * size];
    final double[] n = new double[size * size];
    for (int i = 0; i < w.length; i++) {
        final double pixelVariance = ed.sample();
        final double pixelGain = Math.max(0.1, gs.sample());
        // weights = var / g^2
        w[i] = pixelVariance / (pixelGain * pixelGain);
        // Convert to standard deviation for simulation
        n[i] = Math.sqrt(w[i]);
    }
    return new double[][] { w, n };
}

Example 5 with SharedStateContinuousSampler

use of org.apache.commons.rng.sampling.distribution.SharedStateContinuousSampler in project GDSC-SMLM by aherbert.

the class WeightedKernelFilterTest method filterPerformsWeightedKernelFiltering.

@SeededTest
void filterPerformsWeightedKernelFiltering(RandomSeed seed) {
    final DataFilter filter = createDataFilter();
    final UniformRandomProvider rg = RngUtils.create(seed.getSeed());
    final SharedStateContinuousSampler gs = SamplerUtils.createGaussianSampler(rg, 2, 0.2);
    final float[] offsets = getOffsets(filter);
    final int[] boxSizes = getBoxSizes(filter);
    final TDoubleArrayList l1 = new TDoubleArrayList();
    final FloatFloatBiPredicate equality = TestHelper.floatsAreClose(1e-6, 0);
    for (final int width : primes) {
        for (final int height : new int[] { 29 }) {
            final float[] data = createData(width, height, rg);
            l1.reset();
            // Uniform weights
            final float[] w1 = new float[width * height];
            Arrays.fill(w1, 0.5f);
            // Weights simulating the variance of sCMOS pixels
            final float[] w2 = new float[width * height];
            for (int i = 0; i < w2.length; i++) {
                w2[i] = (float) (1.0 / Math.max(0.01, gs.sample()));
            }
            for (final int boxSize : boxSizes) {
                for (final float offset : offsets) {
                    for (final boolean internal : checkInternal) {
                        // For each pixel over the range around the pixel (vi).
                        // kernel filter: sum(vi * ki) / sum(ki)
                        // Weighted kernel filter: sum(vi * wi * ki) / sum(ki * wi)
                        // Note: The kernel filter is like a weighted filter
                        // (New kernel = wi * ki)
                        filter.setWeights(null, width, height);
                        // Uniform weights
                        testfilterPerformsWeightedFiltering(filter, width, height, data, w1, boxSize, offset, internal, equality);
                        // Random weights.
                        testfilterPerformsWeightedFiltering(filter, width, height, data, w2, boxSize, offset, internal, equality);
                    }
                }
            }
        }
    }
}