List of usage examples for org.apache.commons.math3.stat.descriptive.rank Max Max
public Max()
From source file:br.unicamp.ic.recod.gpsi.measures.gpsiDualScore.java
@Override public double score(double[][][] input) { int least;//from www . ja va2s .com Min min = new Min(); Max max = new Max(); double dist[][] = new double[2][]; double limits[][] = new double[2][2]; for (int i = 0; i <= 1; i++) { dist[i] = MatrixUtils.createRealMatrix(input[i]).getColumn(0); limits[i] = new double[] { min.evaluate(dist[i]), max.evaluate(dist[i]) }; } least = limits[0][0] <= limits[1][0] ? 0 : 1; double sep = limits[1 - least][0] - limits[least][1]; if (sep >= 0) return sep / (max.evaluate(new double[] { limits[0][1], limits[1][1] }) - min.evaluate(new double[] { limits[0][0], limits[1][0] })); double n = 0; for (int j = 0; j <= 1; j++) for (int i = 0; i < dist[j].length; i++) { if (dist[j][i] >= limits[1 - least][0] && dist[j][i] <= limits[least][1]) n++; } return -n / (dist[0].length + dist[1].length); }
From source file:br.unicamp.ic.recod.gpsi.measures.gpsiHellingerDistanceScore.java
@Override public double score(double[][][] input) { double dist[][] = new double[2][]; int bins = 1000; dist[0] = MatrixUtils.createRealMatrix(input[0]).getColumn(0); dist[1] = MatrixUtils.createRealMatrix(input[1]).getColumn(0); gpsiHistogram hist = new gpsiHistogram(); double globalMin = (new Min()).evaluate(ArrayUtils.addAll(dist[0], dist[1])); double globalMax = (new Max()).evaluate(ArrayUtils.addAll(dist[0], dist[1])); double[] h0 = hist.distribution(dist[0], bins, globalMin, globalMax); double[] h1 = hist.distribution(dist[1], bins, globalMin, globalMax); double BC = 0.0; for (int i = 0; i < bins; i++) BC += Math.sqrt(h0[i] * h1[i]); return Math.sqrt(1 - BC); }
From source file:com.cloudera.oryx.common.stats.RunningStatistics.java
public RunningStatistics() { this.mean = new Mean(); this.min = new Min(); this.max = new Max(); }
From source file:com.itemanalysis.psychometrics.histogram.AbstractBinCalculation.java
/** * Creates the object and instantiates the min and max objects. */ public AbstractBinCalculation() { min = new Min(); max = new Max(); }
From source file:com.itemanalysis.psychometrics.kernel.LeastSquaresCrossValidation.java
private void computeBounds() throws Exception { StandardDeviation stdev = new StandardDeviation(); this.sd = stdev.evaluate(x); Min min = new Min(); double from = min.evaluate(x); Max max = new Max(); double to = max.evaluate(x); }
From source file:com.itemanalysis.jmetrik.stats.irt.linking.DbThetaDistribution.java
public DistributionApproximation getDistribution(Connection conn, DataTableName tableName, VariableName thetaName, VariableName weightName, boolean hasWeight) throws SQLException { points = new ArrayList<Double>(); Min min = new Min(); Max max = new Max(); Table sqlTable = new Table(tableName.getNameForDatabase()); SelectQuery query = new SelectQuery(); query.addColumn(sqlTable, thetaName.nameForDatabase()); if (hasWeight) { query.addColumn(sqlTable, weightName.nameForDatabase()); weights = new ArrayList<Double>(); }/*from w w w . j a va 2 s. co m*/ Statement stmt = conn.createStatement(ResultSet.TYPE_SCROLL_INSENSITIVE, ResultSet.CONCUR_READ_ONLY); ResultSet rs = stmt.executeQuery(query.toString()); double value = 0.0; double w = 1.0; while (rs.next()) { value = rs.getDouble(thetaName.nameForDatabase()); if (!rs.wasNull()) { if (hasWeight) { w = rs.getDouble(weightName.nameForDatabase()); if (rs.wasNull()) { w = 0.0; } points.add(value); weights.add(w); min.increment(value); max.increment(value); } else { points.add(value); min.increment(value); max.increment(value); } } } rs.close(); stmt.close(); ContinuousDistributionApproximation dist = new ContinuousDistributionApproximation(points.size(), min.getResult(), max.getResult()); if (hasWeight) { for (int i = 0; i < points.size(); i++) { dist.setPointAt(i, points.get(i)); dist.setDensityAt(i, weights.get(i)); } } else { for (int i = 0; i < points.size(); i++) { dist.setPointAt(i, points.get(i)); } } return dist; }
From source file:com.itemanalysis.psychometrics.measurement.DefaultItemScoring.java
public DefaultItemScoring(boolean isContinuous) { this.isContinuous = isContinuous; categoryMap = new TreeMap<Object, Category>(new ItemResponseComparator()); maximumPossibleScore = new Max(); minimumPossibleScore = new Min(); specialDataCodes = new SpecialDataCodes(); scoreLevels = new TreeSet<Double>(); }
From source file:gedi.util.math.stat.counting.RollingStatistics.java
public double getCovariateRange() { return cov.evaluate(new Max()) - cov.evaluate(new Min()); }
From source file:com.itemanalysis.psychometrics.rasch.ScoreTable.java
public void updateScoreTable(int globalMaxIter, double globalConvergence, double adjust) { double DELTA = globalConvergence + 1.0; //LCONV Max maxDelta = new Max(); int iter = 0; //initialize raw score and extreme score arrays double maxRaw = validMRS(); int n = (int) maxRaw; int np1 = n + 1; scoreTable = new double[np1][2]; extremeScore = new boolean[np1]; stdError = new double[np1]; for (int i = 0; i < np1; i++) { scoreTable[i][0] = i;// ww w . j a va2 s. co m scoreTable[i][1] = scoreTableProx(scoreTable[i][0], maxRaw, adjust); stdError[i] = 0.0; if (i == 0 || i == n) { extremeScore[i] = true; } else { extremeScore[i] = false; } } double previousTheta = 0.0; //update nonextreme persons while (DELTA >= globalConvergence && iter < globalMaxIter) { //Change in person parameter is not counted toward delta. for (int i = 0; i < np1; i++) { if (!extremeScore[i]) { previousTheta = scoreTable[i][1]; scoreTable[i][1] = personUpdate(scoreTable[i][1], scoreTable[i][0], maxRaw, 0.0, DELTA); maxDelta.increment(Math.abs(previousTheta - scoreTable[i][1])); } } DELTA = maxDelta.getResult(); maxDelta.clear(); iter++; } int iter2 = 0; double DELTA2 = 1.0; double adjustedRaw = 0.0; int index = 0; //update lowest and highest (extreme) scores - convergence = 0.01 and maximum of 25 iterations while (DELTA2 >= 0.01 && iter2 < 25) { index = 0; previousTheta = scoreTable[index][1]; adjustedRaw = adjust; scoreTable[index][1] = personUpdate(scoreTable[index][1], adjustedRaw, maxRaw, 0.0, DELTA); maxDelta.increment(Math.abs(previousTheta - scoreTable[index][1])); index = n; previousTheta = scoreTable[index][1]; adjustedRaw = scoreTable[index][0] - adjust; scoreTable[index][1] = personUpdate(scoreTable[index][1], adjustedRaw, maxRaw, 0.0, DELTA); maxDelta.increment(Math.abs(previousTheta - scoreTable[index][1])); DELTA2 = maxDelta.getResult(); maxDelta.clear(); iter2++; } }
From source file:com.sciaps.utils.Util.java
public static Spectrum createAverage(Collection<? extends Spectrum> shots, double sampleRate) { Min minWL = new Min(); Max maxWL = new Max(); for (Spectrum shot : shots) { minWL.increment(shot.getValidRange().getMinimumDouble()); maxWL.increment(shot.getValidRange().getMaximumDouble()); }/*from ww w.j a v a2 s . c o m*/ double range = maxWL.getResult() - minWL.getResult(); int numSamples = (int) Math.floor(range * sampleRate); double[][] data = new double[2][numSamples]; Mean avgy = new Mean(); for (int i = 0; i < numSamples; i++) { double x = minWL.getResult() + i * (1 / sampleRate); avgy.clear(); for (Spectrum shot : shots) { if (shot.getValidRange().containsDouble(x)) { UnivariateFunction iv = shot.getIntensityFunction(); double y = iv.value(x); avgy.increment(y); } } data[0][i] = x; data[1][i] = avgy.getResult(); } RawDataSpectrum newSpectrum = new RawDataSpectrum(data); return newSpectrum; }