Example usage for org.apache.commons.math3.analysis.interpolation LinearInterpolator LinearInterpolator

Introduction

In this page you can find the example usage for org.apache.commons.math3.analysis.interpolation LinearInterpolator LinearInterpolator.

Prototype

LinearInterpolator

Source Link

Usage

From source file:be.ugent.maf.cellmissy.analysis.singlecell.processing.impl.interpolation.TrackLinearInterpolator.java

@Override
public InterpolatedTrack interpolateTrack(double[] time, double[] x, double[] y) {
    // create a new linear interpolator
    LinearInterpolator linearInterpolator = new LinearInterpolator();
    int interpolationPoints = PropertiesConfigurationHolder.getInstance().getInt("numberOfInterpolationPoints");

    // create arrays to hold the interpolant time, the interpolated X and the interpolated Y
    double[] interpolantTime = new double[interpolationPoints];
    double[] interpolatedX = new double[interpolationPoints];
    double[] interpolatedY = new double[interpolationPoints];
    // the step used for the interpolation in both direction
    double interpolationStep = (time[time.length - 1] - time[0]) / interpolationPoints;

    // check for monotonicity
    boolean monotonic = MathArrays.isMonotonic(time, MathArrays.OrderDirection.INCREASING, false);
    // in case time is not monotonic, sort in place time, x and y coordinates
    if (!monotonic) {
        MathArrays.sortInPlace(time, x, y);
    }//from   w  w  w . ja v a  2 s . com

    // call the interpolator, and actually do the interpolation
    try {
        PolynomialSplineFunction functionX = linearInterpolator.interpolate(time, x);
        PolynomialSplineFunction functionY = linearInterpolator.interpolate(time, y);

        // get the polynomial functions in both directions
        PolynomialFunction polynomialFunctionX = functionX.getPolynomials()[0];
        PolynomialFunction polynomialFunctionY = functionY.getPolynomials()[0];

        for (int i = 0; i < interpolationPoints; i++) {
            interpolantTime[i] = time[0] + (i * interpolationStep);
            interpolatedX[i] = functionX.value(interpolantTime[i]);
            interpolatedY[i] = functionY.value(interpolantTime[i]);
        }
        for (int k = 0; k < interpolationPoints; k++) {
            if (Double.isNaN(interpolatedX[k]) | Double.isNaN(interpolatedY[k])) {
                return null;
            }
        }

        return new InterpolatedTrack(interpolantTime, interpolatedX, interpolatedY, polynomialFunctionX,
                polynomialFunctionY);
    } catch (NumberIsTooSmallException e) {
        LOG.error(e.getMessage());
        return null;
    }
}

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From source file:com.gordoni.opal.UniInterpolator.java

public UniInterpolator(MapPeriod mp, int what) {
    this.mp = mp;

    Scenario scenario = mp.scenario;//from  w w w. j  a  v  a  2s .c  om
    Config config = scenario.config;

    xval = new double[mp.length[0]];
    for (int i = 0; i < xval.length; i++)
        xval[(xval.length - 1) - i] = scenario.scale[0].bucket_to_pf(mp.bottom[0] + i);

    fval = new double[mp.length[0]];
    MapPeriodIterator<MapElement> mpitr = mp.iterator();
    while (mpitr.hasNext()) {
        int[] bucket = mpitr.nextIndex().clone();
        MapElement me = mpitr.next();
        int xindex = (fval.length - 1) - (bucket[0] - mp.bottom[0]);
        fval[xindex] = getWhat(me, what);
    }

    UnivariateInterpolator interpolator;
    if (config.interpolation1.equals("linear"))
        interpolator = new LinearInterpolator();
    else if (config.interpolation1.equals("spline"))
        interpolator = new SplineInterpolator();
    else {
        assert (false);
        return;
    }

    this.f = interpolator.interpolate(xval, fval);
}

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From source file:com.itemanalysis.psychometrics.scaling.PercentileRank.java

/**
 * Creates a TreeMap<Integer, Double> lookup table of percentile ranks.
 * The key is a score level and the rho is a percentile rank. This
 * method is useful when finding percentile ranks that correspond to an examinee's
 * raw score. After calling this method, individual elements in the
 * TreeMap can be accessed with getPercentileRankAt(int rho) or
 * valueIterator().//from   w  w  w  .  j  a v a2s .  c o m
 * 
 */
public void createLookupTable() {
    prankTable = new TreeMap<Integer, Double>();
    Iterator<Comparable<?>> iter = freqTable.valuesIterator();

    int index = 0;
    int length = freqTable.getUniqueCount();
    double[] xval = new double[length + 2];
    double[] yval = new double[length + 2];

    int x = 0;
    int xstar = 0;

    //create boundaries below the minimum possible test score
    //and above teh maximum possible test score.
    //This change allows for the interpolation of percentile
    //ranks at the min and max possible test score.
    xval[0] = min - .5;
    yval[0] = 0;
    xval[length + 1] = (double) max + 0.5;
    yval[length + 1] = 100;

    index = 1;

    //compute percentile ranks fro observed scores
    while (iter.hasNext()) {
        x = ((Long) iter.next()).intValue();
        xstar = Double.valueOf(Math.floor(x + 0.5)).intValue();

        xval[index] = Double.valueOf(xstar);
        yval[index] = Double.valueOf(percentileRank(x, xstar));
        index++;
    }

    //interpolate values
    LinearInterpolator interpolator = new LinearInterpolator();
    PolynomialSplineFunction splineFunction = interpolator.interpolate(xval, yval);

    //create lookup table with interpolated values
    x = min;
    double y = 0.0;
    while (x <= max) {
        y = splineFunction.value(x);
        prankTable.put(x, y);
        x += 1;
    }

}

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From source file:com.itemanalysis.psychometrics.kernel.KernelDensity.java

public UnivariateFunction getInterpolater() {
    UnivariateInterpolator interpolator = new LinearInterpolator();
    UnivariateFunction func = interpolator.interpolate(points, evaluate());
    return func;// w  ww  .ja  va 2s.com
}

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From source file:com.itemanalysis.psychometrics.kernel.KernelDensity.java

/**
 * Returns interpolator for squared evaluate
 *
 * @return/*from w  w w  .j  av a  2s .  c  o m*/
 */
public UnivariateFunction getInterpolater2() {
    UnivariateInterpolator interpolator = new LinearInterpolator();
    double[] d = evaluate();
    for (int i = 0; i < d.length; i++) {
        d[i] = d[i] * d[i];
    }
    UnivariateFunction func = interpolator.interpolate(points, d);
    return func;
}

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From source file:malware_classification.Malware_Classification.java

private ArrayList<double[]> interpolate_data(ArrayList<double[]> data_orig, ArrayList<double[]> timestamp_orig,
        int bin_size, int num_bins) {
    ArrayList<double[]> interp_data = new ArrayList<>();
    int num_points_orig = data_orig.size();
    // TODO: change some of the num_points_orig to num_bins
    double[] x = new double[num_points_orig];
    double[] y = new double[num_points_orig];
    int num_cols = data_orig.get(0).length;
    for (int i = 0; i < num_bins; i++) {
        interp_data.add(new double[num_cols]);
    }/* w w w .  j  ava  2s . c o  m*/

    for (int col = 0; col < num_cols; col++) {
        // To use LinearInterpolator, first need arrays
        for (int j = 0; j < num_points_orig; j++) {
            x[j] = timestamp_orig.get(j)[col];
            y[j] = data_orig.get(j)[col];
        }
        LinearInterpolator lin_interp = new LinearInterpolator();
        PolynomialSplineFunction interp_func = lin_interp.interpolate(x, y);
        for (int j = 0; j < num_bins; j++) {
            double curr_bin = bin_size * j;
            double[] knots = interp_func.getKnots();

            //                logger.info()
            if (interp_func.isValidPoint(curr_bin)) {
                interp_data.get(j)[col] = interp_func.value(curr_bin);
            } else if (knots[0] > curr_bin) //bin is too small
            {
                interp_data.get(j)[col] = y[0];
            } else if (knots[knots.length - 1] < curr_bin) // bin is larger than data
            {
                interp_data.get(j)[col] = y[y.length - 1];
            } else {
                logger.warning("Cannot interpolate at bin starting at " + curr_bin);
            }
        }
    }

    return interp_data;

}

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From source file:gdsc.smlm.ij.plugins.DriftCalculator.java

private void interpolate(double[] dx, double[] dy, double[] originalDriftTimePoints) {
    // Interpolator can only create missing values within the range provided by the input values.
    // The two ends have to be extrapolated.
    // TODO: Perform extrapolation. Currently the end values are used.

    // Find end points
    int startT = 0;
    while (originalDriftTimePoints[startT] == 0)
        startT++;/*from   w  w w.j a  va2 s  .c om*/
    int endT = originalDriftTimePoints.length - 1;
    while (originalDriftTimePoints[endT] == 0)
        endT--;

    // Extrapolate using a constant value
    for (int t = startT; t-- > 0;) {
        dx[t] = dx[startT];
        dy[t] = dy[startT];
    }
    for (int t = endT; ++t < dx.length;) {
        dx[t] = dx[endT];
        dy[t] = dy[endT];
    }

    double[][] values = extractValues(originalDriftTimePoints, startT, endT, dx, dy);

    PolynomialSplineFunction fx, fy;
    if (values[0].length < 3) {
        fx = new LinearInterpolator().interpolate(values[0], values[1]);
        fy = new LinearInterpolator().interpolate(values[0], values[2]);
    } else {
        fx = new SplineInterpolator().interpolate(values[0], values[1]);
        fy = new SplineInterpolator().interpolate(values[0], values[2]);
    }

    for (int t = startT; t <= endT; t++) {
        if (originalDriftTimePoints[t] == 0) {
            dx[t] = fx.value(t);
            dy[t] = fy.value(t);
        }
    }

    this.interpolationStart = startT;
    this.interpolationEnd = endT;
}

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From source file:org.apache.solr.client.solrj.io.eval.LerpEvaluator.java

@Override
public Object doWork(Object... objects) throws IOException {

    Object first = objects[0];/* w ww  .  j av  a2  s.co m*/

    double[] x = null;
    double[] y = null;

    if (objects.length == 1) {
        //Only the y values passed
        y = ((List) first).stream().mapToDouble(value -> ((Number) value).doubleValue()).toArray();
        x = new double[y.length];
        for (int i = 0; i < y.length; i++) {
            x[i] = i;
        }
    } else if (objects.length == 2) {
        Object second = objects[1];
        x = ((List) first).stream().mapToDouble(value -> ((Number) value).doubleValue()).toArray();
        y = ((List) second).stream().mapToDouble(value -> ((Number) value).doubleValue()).toArray();
    }

    LinearInterpolator interpolator = new LinearInterpolator();
    PolynomialSplineFunction spline = interpolator.interpolate(x, y);

    List<Number> list = new ArrayList();
    for (double xvalue : x) {
        list.add(spline.value(xvalue));
    }

    VectorFunction vec = new VectorFunction(spline, list);
    vec.addToContext("x", x);
    vec.addToContext("y", y);

    return vec;
}

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From source file:org.esa.s3tbx.olci.radiometry.rayleigh.RayleighAux.java

public static void initDefaultAuxiliary() {
    try {//from  w w  w  . j  ava2  s .  co m
        ElevationModelDescriptor getasse30 = ElevationModelRegistry.getInstance().getDescriptor(GETASSE_30);
        elevationModel = getasse30.createDem(Resampling.NEAREST_NEIGHBOUR);
        Path coeffMatrix = installAuxdata().resolve(COEFF_MATRIX_TXT);
        JSONParser jsonObject = new JSONParser();
        JSONObject parse = (JSONObject) jsonObject.parse(new FileReader(coeffMatrix.toString()));

        tau_ray = parseJSON1DimArray(parse, TAU_RAY);
        thetas = parseJSON1DimArray(parse, THETA);

        ArrayList<double[][][]> ray_coeff_matrix = parseJSON3DimArray(parse, RAY_COEFF_MATRIX);
        rayCooefMatrixA = ray_coeff_matrix.get(0);
        rayCooefMatrixB = ray_coeff_matrix.get(1);
        rayCooefMatrixC = ray_coeff_matrix.get(2);
        rayCooefMatrixD = ray_coeff_matrix.get(3);

        double[] lineSpace = getLineSpace(0, 1, 17);
        double[] rayAlbedoLuts = parseJSON1DimArray(parse, RAY_ALBEDO_LUT);
        linearInterpolate = new LinearInterpolator().interpolate(lineSpace, rayAlbedoLuts);

    } catch (IOException | ParseException e) {
        e.printStackTrace();
    }
}

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From source file:org.lightjason.agentspeak.action.builtin.TestCActionMathInterpolate.java

/**
 * test single interpolate//from   www .  j ava2s .co m
 */
@Test
public final void singleinterpolate() {
    final List<ITerm> l_return = new ArrayList<>();

    new CSingleInterpolate().execute(false, IContext.EMPTYPLAN, Stream
            .of(new LinearInterpolator().interpolate(new double[] { 3, 6 }, new double[] { 11, 13 }), 3, 4)
            .map(CRawTerm::from).collect(Collectors.toList()), l_return);
    Assert.assertEquals(l_return.size(), 2);
    Assert.assertEquals(l_return.get(0).<Number>raw(), 11.0);
    Assert.assertEquals(l_return.get(1).<Number>raw(), 11.666666666666666);
}

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