List of usage examples for org.apache.commons.math.stat.regression SimpleRegression SimpleRegression
public SimpleRegression()
From source file:guineu.modules.filter.Alignment.RANSAC.RansacAlignerTask.java
private List<RTs> smooth(List<RTs> list, Range RTrange) { // Add one point at the begining and another at the end of the list to // ampliate the RT limits to cover the RT range completly try {//from www . j a va 2 s. c om Collections.sort(list, new RTs()); RTs firstPoint = list.get(0); RTs lastPoint = list.get(list.size() - 1); double min = Math.abs(firstPoint.RT - RTrange.getMin()); double RTx = firstPoint.RT - min; double RTy = firstPoint.RT2 - min; RTs newPoint = new RTs(RTx, RTy); list.add(newPoint); double max = Math.abs(RTrange.getMin() - lastPoint.RT); RTx = lastPoint.RT + max; RTy = lastPoint.RT2 + max; newPoint = new RTs(RTx, RTy); list.add(newPoint); } catch (Exception exception) { } // Add points to the model in between of the real points to smooth the regression model Collections.sort(list, new RTs()); for (int i = 0; i < list.size() - 1; i++) { RTs point1 = list.get(i); RTs point2 = list.get(i + 1); if (point1.RT < point2.RT - 2) { SimpleRegression regression = new SimpleRegression(); regression.addData(point1.RT, point1.RT2); regression.addData(point2.RT, point2.RT2); double rt = point1.RT + 1; while (rt < point2.RT) { RTs newPoint = new RTs(rt, regression.predict(rt)); list.add(newPoint); rt++; } } } return list; }
From source file:guineu.modules.filter.Alignment.RANSACGCGC.RansacGCGCAlignerTask.java
private List<GCGCRTs> smooth(List<GCGCRTs> list, Range RTrange) { // Add one point at the begining and another at the end of the list to // ampliate the RT limits to cover the RT range completly try {/*from ww w .j ava 2 s .c om*/ Collections.sort(list, new GCGCRTs()); GCGCRTs firstPoint = list.get(0); GCGCRTs lastPoint = list.get(list.size() - 1); double min = Math.abs(firstPoint.RT - RTrange.getMin()); double RTx = firstPoint.RT - min; double RTy = firstPoint.RT2 - min; GCGCRTs newPoint = new GCGCRTs(RTx, RTy); list.add(newPoint); double max = Math.abs(RTrange.getMin() - lastPoint.RT); RTx = lastPoint.RT + max; RTy = lastPoint.RT2 + max; newPoint = new GCGCRTs(RTx, RTy); list.add(newPoint); } catch (Exception exception) { } // Add points to the model in between of the real points to smooth the regression model Collections.sort(list, new GCGCRTs()); for (int i = 0; i < list.size() - 1; i++) { GCGCRTs point1 = list.get(i); GCGCRTs point2 = list.get(i + 1); if (point1.RT < point2.RT - 2) { SimpleRegression regression = new SimpleRegression(); regression.addData(point1.RT, point1.RT2); regression.addData(point2.RT, point2.RT2); double rt = point1.RT + 1; while (rt < point2.RT) { GCGCRTs newPoint = new GCGCRTs(rt, regression.predict(rt)); list.add(newPoint); rt++; } } } return list; }
From source file:io.github.msdk.features.ransacaligner.RansacAlignerMethod.java
private List<RTs> smooth(List<RTs> list) { // Add points to the model in between of the real points to smooth the // regression model Collections.sort(list, new RTs()); for (int i = 0; i < list.size() - 1; i++) { RTs point1 = list.get(i);// ww w . j a v a 2 s . c o m RTs point2 = list.get(i + 1); if (point1.RT < point2.RT - 2) { SimpleRegression regression = new SimpleRegression(); regression.addData(point1.RT, point1.RT2); regression.addData(point2.RT, point2.RT2); double rt = point1.RT + 1; while (rt < point2.RT) { RTs newPoint = new RTs(rt, regression.predict(rt)); list.add(newPoint); rt++; } } } return list; }
From source file:com.joliciel.jochre.graphics.SourceImageImpl.java
/** * Returns the slope of the current image's horizontal inclination. * Assumes an initial stab has already been made at group shapes into rows, * and that rows are grouped from top to bottom. * @return//w w w . j a va 2 s. c o m */ public double getInclination() { LOG.debug("#### getInclination ####"); // It may well be that rows have been grouped together // wrongly if the image has several columns. // The only rows that are fairly reliable are very long horizontal bars // and the first long row, in which all letters are aligned to the same baseline, // regardless of their size. // let's get the medium width first Mean widthMean = new Mean(); for (RowOfShapes row : this.getRows()) { widthMean.increment(row.getRight() - row.getLeft()); } double meanWidth = widthMean.getResult(); LOG.debug("meanWidth: " + meanWidth); double minWidth = meanWidth * 0.75; // find the first row with a pretty wide width RowOfShapes theRow = null; for (RowOfShapes row : this.getRows()) { int width = row.getRight() - row.getLeft(); if (width > minWidth) { theRow = row; break; } } // calculate a regression for average shapes on this row double minHeight = theRow.getAverageShapeHeight() - theRow.getAverageShapeHeightMargin(); double maxHeight = theRow.getAverageShapeHeight() + theRow.getAverageShapeHeightMargin(); SimpleRegression regression = new SimpleRegression(); for (Shape shape : theRow.getShapes()) { if (shape.getHeight() >= minHeight && shape.getHeight() <= maxHeight) { for (int x = 0; x < shape.getWidth(); x++) { for (int y = 0; y < shape.getHeight(); y++) { if (shape.isPixelBlack(x, y, this.getBlackThreshold())) { regression.addData(shape.getLeft() + x, shape.getTop() + y); } } } } } return regression.getSlope(); }
From source file:com.joliciel.jochre.graphics.RowOfShapesImpl.java
/** * The regression passes through the bottom of average shapes on this line. * It gives the line's slope, and a starting point for finding the baseline and meanline. *//*from w ww . j a v a2 s. co m*/ public SimpleRegression getRegression() { if (this.regression == null) { // begin by calculating some sort of average line crossing the whole row, so that we can see if the row is // rising or falling to start with? // Calculate the line crossing the mid-point of all "average" shapes on this row // get the "smoothed" linear approximation of the mid-points regression = new SimpleRegression(); int numShapes = 0; int minShapes = 10; DescriptiveStatistics shapeWidthStats = new DescriptiveStatistics(); DescriptiveStatistics shapeHeightStats = new DescriptiveStatistics(); for (Shape shape : this.getShapes()) { shapeWidthStats.addValue(shape.getWidth()); shapeHeightStats.addValue(shape.getHeight()); } double minWidth = shapeWidthStats.getPercentile(25); double maxWidth = shapeWidthStats.getPercentile(75); double minHeight = shapeHeightStats.getPercentile(25); double maxHeight = shapeHeightStats.getPercentile(75); for (Shape shape : this.getShapes()) { // only add points whose shape is of "average" width and height (to leave out commas, etc.) if (shape.getWidth() >= minWidth && shape.getWidth() <= maxWidth && shape.getHeight() >= minHeight && shape.getHeight() <= maxHeight) { // using bottom only, since rows with different font sizes tend to align bottom regression.addData((((double) shape.getLeft() + (double) shape.getRight()) / 2.0), ((double) shape.getBottom())); numShapes++; } } // special case where row contains very few shapes (generally letter or number + period) boolean horizontalLine = false; if (numShapes < minShapes) { LOG.debug("Too few shapes: " + numShapes + ", assuming straight horizontal line"); horizontalLine = true; } else if ((this.getRight() - this.getLeft()) < (this.getContainer().getWidth() / 6.0)) { LOG.debug("Too narrow: " + (this.getRight() - this.getLeft()) + ", assuming straight horizontal line"); horizontalLine = true; } if (horizontalLine) { // assume a straight horizontal line Mean midPointMean = new Mean(); for (Shape shape : this.getShapes()) { // only add points whose shape is of "average" height (to leave out commas, etc.) if (shape.getWidth() >= minWidth && shape.getWidth() <= maxWidth && shape.getHeight() >= minHeight && shape.getHeight() <= maxHeight) { midPointMean.increment((double) shape.getBottom()); } } if (midPointMean.getN() == 0) { for (Shape shape : this.getShapes()) { midPointMean.increment((double) shape.getBottom()); } } double meanMidPoint = midPointMean.getResult(); regression = new SimpleRegression(); regression.addData(this.getLeft(), meanMidPoint); regression.addData(this.getRight(), meanMidPoint); } // displays intercept of regression line LOG.debug("intercept: " + regression.getIntercept()); // displays slope of regression line LOG.debug("slope: " + regression.getSlope()); // displays slope standard error LOG.debug("std err: " + regression.getSlopeStdErr()); LOG.debug("x = 0, y = " + regression.predict(0)); LOG.debug("x = " + this.getContainer().getWidth() + ", y = " + regression.predict(this.getContainer().getWidth())); } return regression; }
From source file:net.sf.mzmine.modules.peaklistmethods.identification.metamsecorrelate.MetaMSEcorrelateTask.java
/** * correlates one row to another/*from www. j a va2 s . c o m*/ * peak shape correlation (Pearson) (if negative or zero direct escape) * otherwise avg(corr)>= minCorrPeakShape * intensity profile correlation (Pearson) >= minCorrIProfile * TODO: maybe impute low values instead of 0 for not detected! * @param row * @param g * @return average correlation over both factors ranging from minR to 1 (or 0 if minimum correlation was not met) * @throws Exception */ private double corrRowToRow(final PeakList peakList, final RawDataFile raw[], PeakListRow row, PeakListRow row2) throws Exception { double corr = 0; SimpleRegression reg = new SimpleRegression(); // count int c = 0; // go through all raw files for (int r = 0; r < raw.length; r++) { Feature f1 = row.getPeak(raw[r]); Feature f2 = row2.getPeak(raw[r]); if (f1 != null && f2 != null) { // peak shape correlation FeatureShapeCorrelationData cFS = corrFeatureShape(f1, f2, true); if (cFS != null) { double tmpcorr = cFS.getR(); // escape if peak shapes are showing a negative correlation if (tmpcorr <= 0) return tmpcorr; corr += tmpcorr; c++; } else { // correlation was not possible // maybe due to a small peak in this raw file // escape if features would be high enough for a correlation // this means the features are not intercepting if (countDPHigherThanNoise(f1) >= minCorrelatedDataPoints && countDPHigherThanNoise(f2) >= minCorrelatedDataPoints) return 0; } } // I profile correlation // TODO: low value imputation? double I1 = f1 != null ? f1.getHeight() : 0; double I2 = f2 != null ? f2.getHeight() : 0; reg.addData(I1, I2); } // First search for isotopes TODO later fill in isotopes from raw int absCharge = AlignedIsotopeGrouperTask.find13CIsotope(peakList, row, row2, maxCharge, mzTolerance); boolean isIsotope = absCharge != -1; // TODO search for adducts and add correlation: IProfile doesnt have to be the same for adducts boolean isAdduct = false; if (!isIsotope) findAdducts(peakList, row, row2, row.getRowCharge(), row2.getRowCharge()); double adductBonus = (isIsotope || isAdduct) && useAdductBonusR ? adductBonusR : 0; // TODO weighting of intensity corr and feature shape corr // there was no correlation possible due to small peaks if (c == 0) { // return isAdduct || isIsotope ? 1 : 0; } else { corr = (corr / c); double corrIprofile = reg.getR(); if (corr + adductBonus < minShapeCorrR) return 0; else if (corrIprofile < minIntensityProfileR) return 0; else return (corr + corrIprofile) / 2; } }
From source file:net.sf.mzmine.modules.peaklistmethods.identification.metamsecorrelate.MetaMSEcorrelateTask.java
/** * correlates the height profile of one row to another * NO escape routine/*w w w . j a v a 2s . c o m*/ * @param raw * @param row * @param g * @return Pearson r of height correlation */ public static double corrRowToRowIProfile(final RawDataFile raw[], PeakListRow row, PeakListRow g) { SimpleRegression reg = new SimpleRegression(); // go through all raw files for (int r = 0; r < raw.length; r++) { Feature f1 = row.getPeak(raw[r]); Feature f2 = g.getPeak(raw[r]); // I profile correlation // TODO: low value imputation? double I1 = f1 != null ? f1.getHeight() : 0; double I2 = f2 != null ? f2.getHeight() : 0; reg.addData(I1, I2); } // TODO weighting of intensity corr double corrIprofile = reg.getR(); return corrIprofile; }
From source file:net.sf.mzmine.modules.peaklistmethods.identification.metamsecorrelate.MetaMSEcorrelateTask.java
/** * feature shape correlation/*from w w w. j ava2 s .c om*/ * @param f1 * @param f2 * @return feature shape correlation * or null if not possible * not enough data points for a correlation * @throws Exception */ public static FeatureShapeCorrelationData corrFeatureShape(Feature f1, Feature f2, boolean sameRawFile) throws Exception { //Range<Double> rt1 = f1.getRawDataPointsRTRange(); //Range<Double> rt2 = f2.getRawDataPointsRTRange(); if (sameRawFile) { // scan numbers (not necessary 1,2,3...) int[] sn1 = f1.getScanNumbers(); int[] sn2 = f2.getScanNumbers(); int offsetI1 = 0; int offsetI2 = 0; // find corresponding value if (sn2[0] > sn1[0]) { for (int i = 1; i < sn1.length; i++) { if (sn1[i] == sn2[0]) { offsetI1 = i; break; } } // peaks are not overlapping if (offsetI1 == 0) return null; } if (sn2[0] < sn1[0]) { for (int i = 1; i < sn2.length; i++) { if (sn1[0] == sn2[i]) { offsetI2 = i; break; } } // peaks are not overlapping if (offsetI2 == 0) return null; } // only correlate intercepting areas 0-max int max = 0; if (sn1.length - offsetI1 <= sn2.length - offsetI2) max = sn1.length - offsetI1; if (sn1.length - offsetI1 > sn2.length - offsetI2) max = sn2.length - offsetI2; if (max - offsetI1 > minCorrelatedDataPoints && max - offsetI2 > minCorrelatedDataPoints) { RawDataFile raw = f1.getDataFile(); SimpleRegression reg = new SimpleRegression(); // save max and min of intensity of val1(x) double maxX = 0; double minX = Double.POSITIVE_INFINITY; Vector<Double> I1 = new Vector<Double>(); Vector<Double> I2 = new Vector<Double>(); // add all data points over a given threshold // raw data (not smoothed) for (int i = 0; i < max; i++) { if (sn1[i + offsetI1] != sn2[i + offsetI2]) throw new Exception("Scans are not the same for peak shape corr"); double val1 = f1.getDataPoint(sn1[i + offsetI1]).getIntensity(); double val2 = f2.getDataPoint(sn2[i + offsetI2]).getIntensity(); if (val1 >= noiseLevelShapeCorr && val2 >= noiseLevelShapeCorr) { reg.addData(val1, val2); if (val1 < minX) minX = val1; if (val1 > maxX) maxX = val1; I1.add(val1); I2.add(val2); } } // return pearson r if (reg.getN() >= minCorrelatedDataPoints) { Double[][] data = new Double[][] { I1.toArray(new Double[I1.size()]), I2.toArray(new Double[I2.size()]) }; return new FeatureShapeCorrelationData(reg, data, minX, maxX); } } } else { // TODO if different raw file search for same rt // impute rt/I values if between 2 data points } return null; }
From source file:org.noisemap.core.ProgressionOrbisGisManager.java
@Override public void run() { while (enabled) { double progression = (getMainProgression() * 100); if (monitor != null) { monitor.progressTo((long) progression); if (monitor.isCancelled()) { break; }//www . ja v a 2 s .com } // Evaluate computation time and print it in console. if (progressionHistoryTime.isEmpty()) { if (progression > 0) { progressionHistoryTime.push(System.currentTimeMillis()); progressionHistoryValue.push(progression); } } else { if (lastPushedProgress < System.currentTimeMillis() - historyTimeStep) { //reg.addData(progression,System.currentTimeMillis()); lastPushedProgress = System.currentTimeMillis(); if ((int) (progression - progressionHistoryValue.lastElement()) >= 1) { progressionHistoryTime.push(System.currentTimeMillis()); progressionHistoryValue.push(progression); } //Estimate end of computation SimpleRegression reg = new SimpleRegression(); double prog[][] = new double[progressionHistoryTime.size() + 1][2]; for (int t = 0; t < progressionHistoryTime.size(); t++) { prog[t][0] = progressionHistoryValue.get(t); prog[t][1] = progressionHistoryTime.get(t); } prog[progressionHistoryTime.size()][0] = progression; prog[progressionHistoryTime.size()][1] = System.currentTimeMillis(); reg.addData(prog); lastEstimation = reg.predict(100); //If estimation fails, use simple estimation if (lastEstimation < System.currentTimeMillis() && progressionHistoryTime.size() > 1) { lastEstimation = System.currentTimeMillis() + (System.currentTimeMillis() - progressionHistoryTime.get(1)) * 100 / progression; } } } //Round progression = (((int) (progression * 100000)) / 100000.); if (lastEstimation > 0) { System.out.println(progression + " % remaining " + getHumanTime((long) lastEstimation - System.currentTimeMillis())); } else { System.out.println(progression + " %"); } try { Thread.sleep(updateInterval); } catch (InterruptedException e) { break; } } }
From source file:org.rascalmpl.library.analysis.statistics.SimpleRegressions.java
SimpleRegression make(IList dataValues) {
if (dataValues.length() <= 2)
throw RuntimeExceptionFactory.illegalArgument(dataValues, null, null,
"SimpleRegression data should have more than 2 elements");
SimpleRegression simple = new SimpleRegression();
for (IValue v : dataValues) {
ITuple t = (ITuple) v;//from w w w . jav a 2 s . c om
INumber x = (INumber) t.get(0);
INumber y = (INumber) t.get(1);
simple.addData(x.toReal().doubleValue(), y.toReal().doubleValue());
}
return simple;
}