List of usage examples for org.apache.commons.math.distribution NormalDistributionImpl cumulativeProbability
public double cumulativeProbability(double x) throws MathException
x). From source file:com.dagobert_engine.statistics.service.MtGoxStatisticsService.java
private static double calcPropability(double avg, double dev, PropabilityType type, double value) { NormalDistributionImpl normDistr = new NormalDistributionImpl(); if (dev == 0.0) return 0.0; double stdValue = (value - avg) / dev; try {//w ww. j av a2 s . c o m switch (type) { case GREATER_THAN: return 1 - normDistr.cumulativeProbability(stdValue); case LESS_THAN: return normDistr.cumulativeProbability(stdValue); } return 0.0; } catch (MathException exc) { throw new RuntimeException(exc); } }
From source file:de.tud.kom.p2psim.impl.util.stat.distributions.LimitedNormalDistribution.java
/** * Returns a random value that is distributed as a Normal Distribution with * an upper and lower limit.//from w ww. j av a 2 s . c o m * * @param _mu * average * @param _sigma * standard deviation * @param _min * lower limit, set to "null", if no limit * @param _max * upper limit, set to "null", if no limit * @return as double */ public static double returnValue(double _mu, double _sigma, Double _min, Double _max) { int llimitType; double lmax; double lmin; double lpmax = 1; double lpmin = 0; double lpfactor; NormalDistributionImpl llimitedNormal = new NormalDistributionImpl(_mu, _sigma); if (_min == null) { if (_max == null) { llimitType = LIMIT_NORMAL_DIST_NONE; } else { // only max is limted llimitType = LIMIT_NORMAL_DIST_MAX; lmax = _max.doubleValue(); try { lpmax = llimitedNormal.cumulativeProbability(lmax); } catch (MathException e) { e.printStackTrace(); } } } else { if (_max == null) { // only min is limited. llimitType = LIMIT_NORMAL_DIST_MIN; lmin = _min.doubleValue(); try { lpmin = llimitedNormal.cumulativeProbability(lmin); } catch (MathException e) { // TODO Auto-generated catch block e.printStackTrace(); } } else { // both sides limited. llimitType = LIMIT_NORMAL_DIST_BOTH; // make sure min is really smaller than max. if (_max.doubleValue() > _min.doubleValue()) { lmin = _min.doubleValue(); lmax = _max.doubleValue(); } else { lmax = _min.doubleValue(); lmin = _max.doubleValue(); } // get min and max probabilites that are possible try { lpmin = llimitedNormal.cumulativeProbability(lmin); lpmax = llimitedNormal.cumulativeProbability(lmax); lpfactor = lpmax - lpmin; } catch (MathException e) { e.printStackTrace(); } } } lpfactor = lpmax - lpmin; double lrandom = lpmin + Simulator.getRandom().nextDouble() * lpfactor; double lresult; try { lresult = llimitedNormal.inverseCumulativeProbability(lrandom); } catch (MathException e) { // TODO Auto-generated catch block e.printStackTrace(); lresult = 0; } return lresult; }
From source file:geogebra.common.kernel.statistics.AlgoZProportionTest.java
@Override public final void compute() { String testType;// w w w. ja v a 2 s .c om if (tail.getTextString().equals("<")) { testType = "left"; } else if (tail.getTextString().equals(">")) { testType = "right"; } else if (StringUtil.isNotEqual(tail.getTextString())) { testType = "two"; } else { result.setUndefined(); return; } double n1 = n.getDouble(); double hyp = hypPropertion.getDouble(); double phat = proportion.getDouble(); se = Math.sqrt(hyp * (1 - hyp) / n1); double testStatistic = (phat - hyp) / se; NormalDistributionImpl normalDist = new NormalDistributionImpl(0, 1); double P = 0; try { P = normalDist.cumulativeProbability(testStatistic); } catch (Exception e) { result.setUndefined(); return; } if ("right".equals(testType)) { P = 1 - P; } else if ("two".equals(testType)) { if (testStatistic < 0) { P = 2 * P; } else { P = 2 * (1 - P); } } // put these results into the output list result.clear(); result.add(new GeoNumeric(cons, P)); result.add(new GeoNumeric(cons, testStatistic)); }
From source file:geogebra.common.kernel.statistics.AlgoZProportion2Test.java
@Override public final void compute() { String testType;//www .ja v a 2 s . c om if (tail.getTextString().equals("<")) { testType = "left"; } else if (tail.getTextString().equals(">")) { testType = "right"; } else if (StringUtil.isNotEqual(tail.getTextString())) { testType = "two"; } else { result.setUndefined(); return; } double n1 = n.getDouble(); double phat1 = proportion.getDouble(); double n2 = n_2.getDouble(); double phat2 = proportion2.getDouble(); double x1 = phat1 * n1; double x2 = phat2 * n2; double phatTotal = (x1 + x2) / (n1 + n2); se = Math.sqrt(phatTotal * (1 - phatTotal) * (1 / n1 + 1 / n2)); double testStatistic = (phat1 - phat2) / se; NormalDistributionImpl normalDist = new NormalDistributionImpl(0, 1); double P = 0; try { P = normalDist.cumulativeProbability(testStatistic); } catch (Exception e) { result.setUndefined(); return; } if ("right".equals(testType)) { P = 1 - P; } else if ("two".equals(testType)) { if (testStatistic < 0) { P = 2 * P; } else { P = 2 * (1 - P); } } // put these results into the output list result.clear(); result.add(new GeoNumeric(cons, P)); result.add(new GeoNumeric(cons, testStatistic)); }
From source file:geogebra.common.kernel.statistics.AlgoZMeanTest.java
@Override public final void compute() { String testType;/* w ww. j av a 2s .com*/ if (tail.getTextString().equals("<")) { testType = "left"; } else if (tail.getTextString().equals(">")) { testType = "right"; } else if (StringUtil.isNotEqual(tail.getTextString())) { testType = "two"; } else { result.setUndefined(); return; } double mean1; double n1; if (list == null) { mean1 = mean.getDouble(); n1 = n.getDouble(); } else { mean1 = list.mean(); n1 = list.size(); } double hyp = hypMean.getDouble(); double sd1 = sd.getDouble(); double se = sd1 / Math.sqrt(n1); double testStatistic = (mean1 - hyp) / se; NormalDistributionImpl normalDist = new NormalDistributionImpl(0, 1); double P = 0; try { P = normalDist.cumulativeProbability(testStatistic); } catch (Exception e) { result.setUndefined(); return; } if ("right".equals(testType)) { P = 1 - P; } else if ("two".equals(testType)) { if (testStatistic < 0) { P = 2 * P; } else { P = 2 * (1 - P); } } // put these results into the output list result.clear(); result.add(new GeoNumeric(cons, P)); result.add(new GeoNumeric(cons, testStatistic)); }
From source file:geogebra.common.kernel.statistics.AlgoZMean2Test.java
@Override public final void compute() { String testType;/* w ww. java 2s . c o m*/ if (tail.getTextString().equals("<")) { testType = "left"; } else if (tail.getTextString().equals(">")) { testType = "right"; } else if (StringUtil.isNotEqual(tail.getTextString())) { testType = "two"; } else { result.setUndefined(); return; } double mean1, mean2; double n1, n2; double sd1 = sd.getDouble(); double sd2 = sd_2.getDouble(); if (list == null) { mean1 = mean.getDouble(); mean2 = mean_2.getDouble(); n1 = n.getDouble(); n2 = n_2.getDouble(); } else { mean1 = list.mean(); n1 = list.size(); mean2 = list2.mean(); n2 = list2.size(); } se = Math.sqrt(sd1 * sd1 / n1 + sd2 * sd2 / n2); double testStatistic = (mean1 - mean2) / se; NormalDistributionImpl normalDist = new NormalDistributionImpl(0, 1); double P = 0; try { P = normalDist.cumulativeProbability(testStatistic); } catch (Exception e) { result.setUndefined(); return; } if ("right".equals(testType)) { P = 1 - P; } else if ("two".equals(testType)) { if (testStatistic < 0) { P = 2 * P; } else { P = 2 * (1 - P); } } // put these results into the output list result.clear(); result.add(new GeoNumeric(cons, P)); result.add(new GeoNumeric(cons, testStatistic)); }
From source file:cn.edu.tsinghua.gui.HistogramDemo.java
/** * Adjust to a normal distribution CDF, mean=128, SD=64. * @return the stretched image./*from ww w. j a v a 2s . c om*/ */ public PlanarImage normalize() { int numBands = source.getNumBands(); int binCount = 256; float[][] CDFnorm = new float[numBands][binCount]; NormalDistributionImpl norm = new NormalDistributionImpl(128.0, 64.0); for (int b = 0; b < numBands; b++) { for (int i = 0; i < binCount - 1; i++) { try { CDFnorm[b][i] = (float) norm.cumulativeProbability(i); } catch (MathException e) { e.printStackTrace(); } } // the cumulative probability must equal one CDFnorm[b][binCount - 1] = 1; } int[] bins = { 256 }; double[] low = { 0.0D }; double[] high = { 256.0D }; ParameterBlock pb = new ParameterBlock(); pb.addSource(source); pb.add(null); pb.add(1); pb.add(1); pb.add(bins); pb.add(low); pb.add(high); RenderedOp fmt = JAI.create("histogram", pb, null); return JAI.create("matchcdf", fmt, CDFnorm); }
From source file:org.peerfact.impl.util.stats.distributions.LimitedNormalDistribution.java
/** * Returns a random value that is distributed as a Normal Distribution with * an upper and lower limit.//from www . ja v a 2 s . com * * @param _mu * average * @param _sigma * standard deviation * @param _min * lower limit, set to "null", if no limit * @param _max * upper limit, set to "null", if no limit * @return as double */ public static double returnValue(double _mu, double _sigma, Double _min, Double _max) { double lmax; double lmin; double lpmax = 1; double lpmin = 0; double lpfactor; NormalDistributionImpl llimitedNormal = new NormalDistributionImpl(_mu, _sigma); if (_min == null) { if (_max != null) { // only max is limted lmax = _max.doubleValue(); try { lpmax = llimitedNormal.cumulativeProbability(lmax); } catch (MathException e) { e.printStackTrace(); } } } else { if (_max == null) { // only min is limited. lmin = _min.doubleValue(); try { lpmin = llimitedNormal.cumulativeProbability(lmin); } catch (MathException e) { // TODO Auto-generated catch block e.printStackTrace(); } } else { // both sides limited. // make sure min is really smaller than max. if (_max.doubleValue() > _min.doubleValue()) { lmin = _min.doubleValue(); lmax = _max.doubleValue(); } else { lmax = _min.doubleValue(); lmin = _max.doubleValue(); } // get min and max probabilites that are possible try { lpmin = llimitedNormal.cumulativeProbability(lmin); lpmax = llimitedNormal.cumulativeProbability(lmax); lpfactor = lpmax - lpmin; } catch (MathException e) { e.printStackTrace(); } } } lpfactor = lpmax - lpmin; double lrandom = lpmin + Simulator.getRandom().nextDouble() * lpfactor; double lresult; try { lresult = llimitedNormal.inverseCumulativeProbability(lrandom); } catch (MathException e) { // TODO Auto-generated catch block e.printStackTrace(); lresult = 0; } return lresult; }
From source file:org.xenmaster.monitoring.data.Record.java
protected final void applyStatistics(Collection<Double> values) { // Let's get statistical DescriptiveStatistics ds = new DescriptiveStatistics(); for (double util : values) { ds.addValue(util);/* w w w . j a v a 2s .c o m*/ } double a = ds.getMean(); double stdDev = ds.getStandardDeviation(); // TODO: actually test this and generate warning // Check if all vCPUs have a fair load, e.g. [45, 60, 50] would be fair, [90, 4, 2] indicates you should learn threading if (stdDev > 0.8) { Logger.getLogger(getClass()) .info((vm ? "VM" : "Host") + " " + reference + " has an unfair load distribution"); } if (stdDev > 0) { try { NormalDistributionImpl ndi = new NormalDistributionImpl(ds.getMean(), stdDev); double cp = ndi.cumulativeProbability(90); if (cp > 0.8) { // 80% of the CPUs have a >90% load // TODO warning Logger.getLogger(getClass()).info((vm ? "VM" : "Host") + " " + reference + " has a load >=90% on 80% of the available CPUs"); } } catch (MathException ex) { Logger.getLogger(getClass()).error("Flawed maths", ex); } } }