List of usage examples for org.apache.commons.math3.fitting WeightedObservedPoints WeightedObservedPoints
WeightedObservedPoints
From source file:eu.tango.energymodeller.energypredictor.CpuAndAcceleratorEnergyPredictor.java
/** * This calculates the mathematical function that predicts the power * consumption given the cpu utilisation. * * @param host The host to get the function for * @return The mathematical function that predicts the power consumption * given the cpu utilisation.//from ww w .j a va 2s .com */ private PredictorFunction<PolynomialFunction> retrieveCpuModel(Host host) { PredictorFunction<PolynomialFunction> answer; if (modelCache.containsKey(host)) { /** * A small cache avoids recalculating the regression so often. */ return modelCache.get(host); } WeightedObservedPoints points = new WeightedObservedPoints(); for (HostEnergyCalibrationData data : host.getCalibrationData()) { points.add(data.getCpuUsage(), data.getWattsUsed()); } PolynomialCurveFitter fitter = PolynomialCurveFitter.create(2); final double[] best = fitter.fit(points.toList()); PolynomialFunction function = new PolynomialFunction(best); double sse = getSumOfSquareError(function, points.toList()); double rmse = getRootMeanSquareError(sse, points.toList().size()); answer = new PredictorFunction<>(function, sse, rmse); modelCache.put(host, answer); return answer; }
From source file:eu.tango.energymodeller.energypredictor.CpuAndBiModalAcceleratorEnergyPredictor.java
/** * This calculates the mathematical function that predicts the power * consumption given the cpu utilisation. * * @param host The host to get the function for * @return The mathematical function that predicts the power consumption * given the cpu utilisation./*w w w . j av a 2 s . com*/ */ private PredictorFunction<GroupingFunction> retrieveAcceleratorModel(Host host, String accelerator) { PredictorFunction<GroupingFunction> answer; if (modelAcceleratorCache.containsKey(host)) { /** * A small cache avoids recalculating the regression so often. */ return modelAcceleratorCache.get(host); } WeightedObservedPoints points = new WeightedObservedPoints(); for (Accelerator acc : host.getAccelerators()) { for (HostAcceleratorCalibrationData data : acc.getAcceleratorCalibrationData()) { if (data.getIdentifier().equals(accelerator)) { points.add(data.getParameter("clocks.current.sm [MHz]"), data.getPower()); } } } GroupingFunction function = new GroupingFunction(); function.fit(points); double sse = getSumOfSquareError(function, points.toList()); double rmse = getRootMeanSquareError(sse, points.toList().size()); answer = new PredictorFunction<>(function, sse, rmse); modelAcceleratorCache.put(host, answer); return answer; }
From source file:org.akvo.caddisfly.sensor.colorimetry.strip.calibration.CalibrationCard.java
@NonNull private static Mat do1D_3DCorrection(@NonNull Mat imgMat, @Nullable CalibrationData calData) throws CalibrationException { if (calData == null) { throw new CalibrationException("no calibration data."); }/*ww w . j a v a2 s . c o m*/ final WeightedObservedPoints obsL = new WeightedObservedPoints(); final WeightedObservedPoints obsA = new WeightedObservedPoints(); final WeightedObservedPoints obsB = new WeightedObservedPoints(); Map<String, double[]> calResultIllumination = new HashMap<>(); // iterate over all patches try { for (String label : calData.getCalValues().keySet()) { CalibrationData.CalValue cal = calData.getCalValues().get(label); CalibrationData.Location loc = calData.getLocations().get(label); float[] LAB_color = measurePatch(imgMat, loc.x, loc.y, calData); // measure patch color obsL.add(LAB_color[0], cal.getL()); obsA.add(LAB_color[1], cal.getA()); obsB.add(LAB_color[2], cal.getB()); calResultIllumination.put(label, new double[] { LAB_color[0], LAB_color[1], LAB_color[2] }); } } catch (Exception e) { throw new CalibrationException("1D calibration: error iterating over all patches.", e); } // Instantiate a second-degree polynomial fitter. final PolynomialCurveFitter fitter = PolynomialCurveFitter.create(2); // Retrieve fitted parameters (coefficients of the polynomial function). // order of coefficients is (c + bx + ax^2), so [c,b,a] try { final double[] coefficientL = fitter.fit(obsL.toList()); final double[] coefficientA = fitter.fit(obsA.toList()); final double[] coefficientB = fitter.fit(obsB.toList()); double[] valIllumination; double L_orig, A_orig, B_orig, L_new, A_new, B_new; // transform patch values using the 1d calibration results Map<String, double[]> calResult1D = new HashMap<>(); for (String label : calData.getCalValues().keySet()) { valIllumination = calResultIllumination.get(label); L_orig = valIllumination[0]; A_orig = valIllumination[1]; B_orig = valIllumination[2]; L_new = coefficientL[2] * L_orig * L_orig + coefficientL[1] * L_orig + coefficientL[0]; A_new = coefficientA[2] * A_orig * A_orig + coefficientA[1] * A_orig + coefficientA[0]; B_new = coefficientB[2] * B_orig * B_orig + coefficientB[1] * B_orig + coefficientB[0]; calResult1D.put(label, new double[] { L_new, A_new, B_new }); } // use the 1D calibration result for the second calibration step // Following http://docs.scipy.org/doc/scipy/reference/tutorial/linalg.html#solving-linear-least-squares-problems-and-pseudo-inverses // we will solve P = M x int total = calData.getLocations().keySet().size(); RealMatrix coefficient = new Array2DRowRealMatrix(total, 3); RealMatrix cal = new Array2DRowRealMatrix(total, 3); int index = 0; // create coefficient and calibration vectors for (String label : calData.getCalValues().keySet()) { CalibrationData.CalValue calv = calData.getCalValues().get(label); double[] cal1dResult = calResult1D.get(label); coefficient.setEntry(index, 0, cal1dResult[0]); coefficient.setEntry(index, 1, cal1dResult[1]); coefficient.setEntry(index, 2, cal1dResult[2]); cal.setEntry(index, 0, calv.getL()); cal.setEntry(index, 1, calv.getA()); cal.setEntry(index, 2, calv.getB()); index++; } DecompositionSolver solver = new SingularValueDecomposition(coefficient).getSolver(); RealMatrix sol = solver.solve(cal); float a_L, b_L, c_L, a_A, b_A, c_A, a_B, b_B, c_B; a_L = (float) sol.getEntry(0, 0); b_L = (float) sol.getEntry(1, 0); c_L = (float) sol.getEntry(2, 0); a_A = (float) sol.getEntry(0, 1); b_A = (float) sol.getEntry(1, 1); c_A = (float) sol.getEntry(2, 1); a_B = (float) sol.getEntry(0, 2); b_B = (float) sol.getEntry(1, 2); c_B = (float) sol.getEntry(2, 2); //use the solution to correct the image double L_temp, A_temp, B_temp, L_mid, A_mid, B_mid; int L_fin, A_fin, B_fin; int ii3; byte[] temp = new byte[imgMat.cols() * imgMat.channels()]; for (int i = 0; i < imgMat.rows(); i++) { // y imgMat.get(i, 0, temp); ii3 = 0; for (int ii = 0; ii < imgMat.cols(); ii++) { //x L_temp = temp[ii3] & 0xFF; A_temp = temp[ii3 + 1] & 0xFF; B_temp = temp[ii3 + 2] & 0xFF; L_mid = coefficientL[2] * L_temp * L_temp + coefficientL[1] * L_temp + coefficientL[0]; A_mid = coefficientA[2] * A_temp * A_temp + coefficientA[1] * A_temp + coefficientA[0]; B_mid = coefficientB[2] * B_temp * B_temp + coefficientB[1] * B_temp + coefficientB[0]; L_fin = (int) Math.round(a_L * L_mid + b_L * A_mid + c_L * B_mid); A_fin = (int) Math.round(a_A * L_mid + b_A * A_mid + c_A * B_mid); B_fin = (int) Math.round(a_B * L_mid + b_B * A_mid + c_B * B_mid); // cap values L_fin = capValue(L_fin, 0, 255); A_fin = capValue(A_fin, 0, 255); B_fin = capValue(B_fin, 0, 255); temp[ii3] = (byte) L_fin; temp[ii3 + 1] = (byte) A_fin; temp[ii3 + 2] = (byte) B_fin; ii3 += 3; } imgMat.put(i, 0, temp); } return imgMat; } catch (Exception e) { throw new CalibrationException("error while performing calibration: ", e); } }
From source file:org.akvo.caddisfly.sensor.colorimetry.strip.util.OpenCVUtil.java
@SuppressWarnings("UnusedParameters") public static Mat detectStrip(Mat stripArea, StripTest.Brand brand, double ratioW, double ratioH) { List<Mat> channels = new ArrayList<>(); Mat sArea = stripArea.clone();//from ww w . ja v a2s.c o m // Gaussian blur Imgproc.medianBlur(sArea, sArea, 3); Core.split(sArea, channels); // create binary image Mat binary = new Mat(); // determine min and max NOT USED Imgproc.threshold(channels.get(0), binary, 128, MAX_RGB_INT_VALUE, Imgproc.THRESH_BINARY); // compute first approximation of line through length of the strip final WeightedObservedPoints points = new WeightedObservedPoints(); final WeightedObservedPoints corrPoints = new WeightedObservedPoints(); double tot, yTot; for (int i = 0; i < binary.cols(); i++) { // iterate over cols tot = 0; yTot = 0; for (int j = 0; j < binary.rows(); j++) { // iterate over rows if (binary.get(j, i)[0] > 128) { yTot += j; tot++; } } if (tot > 0) { points.add((double) i, yTot / tot); } } // order of coefficients is (b + ax), so [b, a] final PolynomialCurveFitter fitter = PolynomialCurveFitter.create(1); List<WeightedObservedPoint> pointsList = points.toList(); final double[] coefficient = fitter.fit(pointsList); // second pass, remove outliers double estimate, actual; for (int i = 0; i < pointsList.size(); i++) { estimate = coefficient[1] * pointsList.get(i).getX() + coefficient[0]; actual = pointsList.get(i).getY(); if (actual > LOWER_PERCENTAGE_BOUND * estimate && actual < UPPER_PERCENTAGE_BOUND * estimate) { //if the point differs less than +/- 10%, keep the point corrPoints.add(pointsList.get(i).getX(), pointsList.get(i).getY()); } } final double[] coefficientCorr = fitter.fit(corrPoints.toList()); double slope = coefficientCorr[1]; double offset = coefficientCorr[0]; // compute rotation angle double rotAngleDeg = Math.atan(slope) * 180 / Math.PI; //determine a point on the line, in the middle of strip, in the horizontal middle of the whole image int midPointX = binary.cols() / 2; int midPointY = (int) Math.round(midPointX * slope + offset); // rotate around the midpoint, to straighten the binary strip Mat dstBinary = new Mat(binary.rows(), binary.cols(), binary.type()); Point center = new Point(midPointX, midPointY); Mat rotMat = Imgproc.getRotationMatrix2D(center, rotAngleDeg, 1.0); Imgproc.warpAffine(binary, dstBinary, rotMat, binary.size(), Imgproc.INTER_CUBIC + Imgproc.WARP_FILL_OUTLIERS); // also apply rotation to colored strip Mat dstStrip = new Mat(stripArea.rows(), stripArea.cols(), stripArea.type()); Imgproc.warpAffine(stripArea, dstStrip, rotMat, binary.size(), Imgproc.INTER_CUBIC + Imgproc.WARP_FILL_OUTLIERS); // Compute white points in each row double[] rowCount = new double[dstBinary.rows()]; int rowTot; for (int i = 0; i < dstBinary.rows(); i++) { // iterate over rows rowTot = 0; for (int j = 0; j < dstBinary.cols(); j++) { // iterate over cols if (dstBinary.get(i, j)[0] > 128) { rowTot++; } } rowCount[i] = rowTot; } // find width by finding rising and dropping edges // rising edge = largest positive difference // falling edge = largest negative difference int risePos = 0; int fallPos = 0; double riseVal = 0; double fallVal = 0; for (int i = 0; i < dstBinary.rows() - 1; i++) { if (rowCount[i + 1] - rowCount[i] > riseVal) { riseVal = rowCount[i + 1] - rowCount[i]; risePos = i + 1; } if (rowCount[i + 1] - rowCount[i] < fallVal) { fallVal = rowCount[i + 1] - rowCount[i]; fallPos = i; } } // cut out binary strip Point stripTopLeft = new Point(0, risePos); Point stripBottomRight = new Point(dstBinary.cols(), fallPos); org.opencv.core.Rect stripAreaRect = new org.opencv.core.Rect(stripTopLeft, stripBottomRight); Mat binaryStrip = dstBinary.submat(stripAreaRect); // also cut out colored strip Mat colorStrip = dstStrip.submat(stripAreaRect); // now right end of strip // method: first rising edge double[] colCount = new double[binaryStrip.cols()]; int colTotal; for (int i = 0; i < binaryStrip.cols(); i++) { // iterate over cols colTotal = 0; for (int j = 0; j < binaryStrip.rows(); j++) { // iterate over rows if (binaryStrip.get(j, i)[0] > 128) { colTotal++; } } //Log.d("Caddisfly", String.valueOf(colTotal)); colCount[i] = colTotal; } stripAreaRect = getStripRectangle(binaryStrip, colCount, brand.getStripLength(), ratioW); Mat resultStrip = colorStrip.submat(stripAreaRect).clone(); // release Mat objects stripArea.release(); sArea.release(); binary.release(); dstBinary.release(); dstStrip.release(); binaryStrip.release(); colorStrip.release(); return resultStrip; }
From source file:org.apache.solr.client.solrj.io.eval.HarmonicFitEvaluator.java
@Override public Object doWork(Object... objects) throws IOException { if (objects.length > 3) { throw new IOException("harmonicFit function takes a maximum of 2 arguments."); }/* w w w . j a v a 2 s . com*/ Object first = objects[0]; double[] x = null; double[] y = null; if (objects.length == 1) { //Only the y values passed y = ((List) first).stream().mapToDouble(value -> ((BigDecimal) value).doubleValue()).toArray(); x = new double[y.length]; for (int i = 0; i < y.length; i++) { x[i] = i; } } else if (objects.length == 2) { // x and y passed Object second = objects[1]; x = ((List) first).stream().mapToDouble(value -> ((BigDecimal) value).doubleValue()).toArray(); y = ((List) second).stream().mapToDouble(value -> ((BigDecimal) value).doubleValue()).toArray(); } HarmonicCurveFitter curveFitter = HarmonicCurveFitter.create(); WeightedObservedPoints points = new WeightedObservedPoints(); for (int i = 0; i < x.length; i++) { points.add(x[i], y[i]); } double[] coef = curveFitter.fit(points.toList()); HarmonicOscillator pf = new HarmonicOscillator(coef[0], coef[1], coef[2]); List list = new ArrayList(); for (double xvalue : x) { double yvalue = pf.value(xvalue); list.add(yvalue); } return list; }
From source file:org.apache.solr.client.solrj.io.eval.PolyFitDerivativeEvaluator.java
@Override public Object doWork(Object... objects) throws IOException { if (objects.length > 3) { throw new IOException("polyfitDerivative function takes a maximum of 3 arguments."); }//from w w w. j av a2s . c om Object first = objects[0]; double[] x = null; double[] y = null; int degree = 3; if (objects.length == 1) { //Only the y values passed y = ((List) first).stream().mapToDouble(value -> ((BigDecimal) value).doubleValue()).toArray(); x = new double[y.length]; for (int i = 0; i < y.length; i++) { x[i] = i; } } else if (objects.length == 3) { // x, y and degree passed Object second = objects[1]; x = ((List) first).stream().mapToDouble(value -> ((BigDecimal) value).doubleValue()).toArray(); y = ((List) second).stream().mapToDouble(value -> ((BigDecimal) value).doubleValue()).toArray(); degree = ((Number) objects[2]).intValue(); } else if (objects.length == 2) { if (objects[1] instanceof List) { // x and y passed Object second = objects[1]; x = ((List) first).stream().mapToDouble(value -> ((BigDecimal) value).doubleValue()).toArray(); y = ((List) second).stream().mapToDouble(value -> ((BigDecimal) value).doubleValue()).toArray(); } else { // y and degree passed y = ((List) first).stream().mapToDouble(value -> ((BigDecimal) value).doubleValue()).toArray(); x = new double[y.length]; for (int i = 0; i < y.length; i++) { x[i] = i; } degree = ((Number) objects[1]).intValue(); } } PolynomialCurveFitter curveFitter = PolynomialCurveFitter.create(degree); WeightedObservedPoints points = new WeightedObservedPoints(); for (int i = 0; i < x.length; i++) { points.add(x[i], y[i]); } double[] coef = curveFitter.fit(points.toList()); PolynomialFunction pf = new PolynomialFunction(coef); UnivariateFunction univariateFunction = pf.derivative(); List list = new ArrayList(); for (double xvalue : x) { double yvalue = univariateFunction.value(xvalue); list.add(yvalue); } return list; }
From source file:org.apache.solr.client.solrj.io.eval.PolyFitEvaluator.java
@Override public Object doWork(Object... objects) throws IOException { if (objects.length > 3) { throw new IOException("polyfit function takes a maximum of 3 arguments."); }// w w w . ja v a2s.c o m Object first = objects[0]; double[] x = null; double[] y = null; int degree = 3; if (objects.length == 1) { //Only the y values passed y = ((List) first).stream().mapToDouble(value -> ((BigDecimal) value).doubleValue()).toArray(); x = new double[y.length]; for (int i = 0; i < y.length; i++) { x[i] = i; } } else if (objects.length == 3) { // x, y and degree passed Object second = objects[1]; x = ((List) first).stream().mapToDouble(value -> ((BigDecimal) value).doubleValue()).toArray(); y = ((List) second).stream().mapToDouble(value -> ((BigDecimal) value).doubleValue()).toArray(); degree = ((Number) objects[2]).intValue(); } else if (objects.length == 2) { if (objects[1] instanceof List) { // x and y passed Object second = objects[1]; x = ((List) first).stream().mapToDouble(value -> ((BigDecimal) value).doubleValue()).toArray(); y = ((List) second).stream().mapToDouble(value -> ((BigDecimal) value).doubleValue()).toArray(); } else { // y and degree passed y = ((List) first).stream().mapToDouble(value -> ((BigDecimal) value).doubleValue()).toArray(); x = new double[y.length]; for (int i = 0; i < y.length; i++) { x[i] = i; } degree = ((Number) objects[1]).intValue(); } } PolynomialCurveFitter curveFitter = PolynomialCurveFitter.create(degree); WeightedObservedPoints points = new WeightedObservedPoints(); for (int i = 0; i < x.length; i++) { points.add(x[i], y[i]); } double[] coef = curveFitter.fit(points.toList()); PolynomialFunction pf = new PolynomialFunction(coef); List list = new ArrayList(); for (double xvalue : x) { double yvalue = pf.value(xvalue); list.add(yvalue); } return list; }
From source file:org.autobet.ai.TeamRatersStatsApproximation.java
private Polynomial approximate(TeamRaterStats teamRaterStats, GameResult gameResult) { Map<Integer, RateStats> stats = teamRaterStats.getHomeStats(); List<Integer> rates = teamRaterStats.getRates(); final WeightedObservedPoints obs = new WeightedObservedPoints(); for (int rate : rates) { RateStats rateStats = stats.get(rate); int stat = rateStats.get(gameResult); int count = rateStats.getCount(); obs.add(count, rate, (double) stat / count); }/*from w ww. j av a 2 s . co m*/ return new Polynomial(fitter.fit(obs.toList())); }
From source file:org.bonej.wrapperPlugins.FractalDimensionWrapper.java
private WeightedObservedPoints toWeightedObservedPoints( final Iterable<ValuePair<DoubleType, DoubleType>> pairs) { final WeightedObservedPoints points = new WeightedObservedPoints(); pairs.forEach(pair -> points.add(pair.a.get(), pair.b.get())); return points; }
From source file:org.micromanager.asidispim.fit.Fitter.java
/** * Utility to facilitate fitting data plotted in JFreeChart * Provide data in JFReeChart format (XYSeries), and retrieve univariate * function parameters that best fit (using least squares) the data. All data * points will be weighted equally.//from w w w . j av a2 s.c o m * * TODO: investigate whether weighting (possibly automatic weighting) can * improve accuracy * * @param data xy series in JFReeChart format * @param type one of the Fitter.FunctionType predefined functions * @param guess initial guess for the fit. The number and meaning of these parameters depends on the FunctionType. Implemented: Gaussian: 0: Normalization, 1: Mean 2: Sigma * @return array with parameters, whose meaning depends on the FunctionType. * Use the function getXYSeries to retrieve the XYDataset predicted * by this fit */ public static double[] fit(XYSeries data, FunctionType type, double[] guess) { if (type == FunctionType.NoFit) { return null; } // create the commons math data object from the JFreeChart data object final WeightedObservedPoints obs = new WeightedObservedPoints(); for (int i = 0; i < data.getItemCount(); i++) { obs.add(1.0, data.getX(i).doubleValue(), data.getY(i).doubleValue()); } double[] result = null; switch (type) { case Pol1: final PolynomialCurveFitter fitter1 = PolynomialCurveFitter.create(1); result = fitter1.fit(obs.toList()); break; case Pol2: final PolynomialCurveFitter fitter2 = PolynomialCurveFitter.create(2); result = fitter2.fit(obs.toList()); break; case Pol3: final PolynomialCurveFitter fitter3 = PolynomialCurveFitter.create(3); result = fitter3.fit(obs.toList()); break; case Gaussian: final GaussianWithOffsetCurveFitter gf = GaussianWithOffsetCurveFitter.create(); if (guess != null) { gf.withStartPoint(guess); } result = gf.fit(obs.toList()); default: break; } return result; }