List of usage examples for org.apache.commons.math3.stat.descriptive SummaryStatistics getSum
public double getSum()
From source file:org.eclipse.dataset.ComplexDoubleDataset.java
@Override public Object sum() { final String n = storeName(false, STORE_STATS_ITEM_NAME); if (storedValues == null || storedValues.isEmpty()) { calculateSummaryStats(false, false, n); }/*from w w w . j ava 2s. c o m*/ final SummaryStatistics rstats = (SummaryStatistics) storedValues.get(n + "0"); final SummaryStatistics istats = (SummaryStatistics) storedValues.get(n + "1"); return new Complex(rstats.getSum(), istats.getSum()); }
From source file:org.eclipse.january.dataset.AbstractDataset.java
/** * Calculate summary statistics for a dataset along an axis * @param ignoreNaNs if true, ignore NaNs * @param ignoreInfs if true, ignore infinities * @param axis/*from ww w. jav a 2s . com*/ */ protected void calculateSummaryStats(final boolean ignoreNaNs, final boolean ignoreInfs, final int axis) { int rank = getRank(); int[] oshape = getShape(); int alen = oshape[axis]; oshape[axis] = 1; int[] nshape = new int[rank - 1]; for (int i = 0; i < axis; i++) { nshape[i] = oshape[i]; } for (int i = axis + 1; i < rank; i++) { nshape[i - 1] = oshape[i]; } final int dtype = getDType(); IntegerDataset count = new IntegerDataset(nshape); Dataset max = DatasetFactory.zeros(nshape, dtype); Dataset min = DatasetFactory.zeros(nshape, dtype); IntegerDataset maxIndex = new IntegerDataset(nshape); IntegerDataset minIndex = new IntegerDataset(nshape); Dataset sum = DatasetFactory.zeros(nshape, DTypeUtils.getLargestDType(dtype)); DoubleDataset mean = new DoubleDataset(nshape); DoubleDataset var = new DoubleDataset(nshape); IndexIterator qiter = max.getIterator(true); int[] qpos = qiter.getPos(); int[] spos = oshape.clone(); while (qiter.hasNext()) { int i = 0; for (; i < axis; i++) { spos[i] = qpos[i]; } spos[i++] = 0; for (; i < rank; i++) { spos[i] = qpos[i - 1]; } final SummaryStatistics stats = new SummaryStatistics(); //sum of logs is slow and we dont use it, so blocking its calculation here stats.setSumLogImpl(new NullStorelessUnivariateStatistic()); double amax = Double.NEGATIVE_INFINITY; double amin = Double.POSITIVE_INFINITY; boolean hasNaNs = false; if (ignoreNaNs) { for (int j = 0; j < alen; j++) { spos[axis] = j; final double val = getDouble(spos); if (Double.isNaN(val)) { hasNaNs = true; continue; } else if (ignoreInfs && Double.isInfinite(val)) { continue; } if (val > amax) { amax = val; } if (val < amin) { amin = val; } stats.addValue(val); } } else { for (int j = 0; j < alen; j++) { spos[axis] = j; final double val = getDouble(spos); if (hasNaNs) { if (!Double.isNaN(val)) stats.addValue(0); continue; } if (Double.isNaN(val)) { amax = Double.NaN; amin = Double.NaN; hasNaNs = true; } else if (ignoreInfs && Double.isInfinite(val)) { continue; } else { if (val > amax) { amax = val; } if (val < amin) { amin = val; } } stats.addValue(val); } } count.setAbs(qiter.index, (int) stats.getN()); max.setObjectAbs(qiter.index, amax); min.setObjectAbs(qiter.index, amin); boolean fmax = false; boolean fmin = false; if (hasNaNs) { if (ignoreNaNs) { for (int j = 0; j < alen; j++) { spos[axis] = j; final double val = getDouble(spos); if (Double.isNaN(val)) continue; if (!fmax && val == amax) { maxIndex.setAbs(qiter.index, j); fmax = true; if (fmin) break; } if (!fmin && val == amin) { minIndex.setAbs(qiter.index, j); fmin = true; if (fmax) break; } } } else { for (int j = 0; j < alen; j++) { spos[axis] = j; final double val = getDouble(spos); if (Double.isNaN(val)) { maxIndex.setAbs(qiter.index, j); minIndex.setAbs(qiter.index, j); break; } } } } else { for (int j = 0; j < alen; j++) { spos[axis] = j; final double val = getDouble(spos); if (!fmax && val == amax) { maxIndex.setAbs(qiter.index, j); fmax = true; if (fmin) break; } if (!fmin && val == amin) { minIndex.setAbs(qiter.index, j); fmin = true; if (fmax) break; } } } sum.setObjectAbs(qiter.index, stats.getSum()); mean.setAbs(qiter.index, stats.getMean()); var.setAbs(qiter.index, stats.getVariance()); } setStoredValue(storeName(ignoreNaNs, ignoreInfs, STORE_COUNT + "-" + axis), count); storedValues.put(storeName(ignoreNaNs, ignoreInfs, STORE_MAX + "-" + axis), max); storedValues.put(storeName(ignoreNaNs, ignoreInfs, STORE_MIN + "-" + axis), min); storedValues.put(storeName(ignoreNaNs, ignoreInfs, STORE_SUM + "-" + axis), sum); storedValues.put(storeName(ignoreNaNs, ignoreInfs, STORE_MEAN + "-" + axis), mean); storedValues.put(storeName(ignoreNaNs, ignoreInfs, STORE_VAR + "-" + axis), var); storedValues.put(storeName(ignoreNaNs, ignoreInfs, STORE_MAX + STORE_INDEX + "-" + axis), maxIndex); storedValues.put(storeName(ignoreNaNs, ignoreInfs, STORE_MIN + STORE_INDEX + "-" + axis), minIndex); }
From source file:org.eclipse.january.metadata.internal.StatisticsMetadataImpl.java
/** * Calculate summary statistics for a dataset along an axis * @param ignoreNaNs if true, ignore NaNs * @param ignoreInfs if true, ignore infinities * @param axis//from ww w. j a va 2 s. c om */ @SuppressWarnings("deprecation") private Dataset[] createAxisStats(final int axis, final boolean ignoreNaNs, final boolean ignoreInfs) { int rank = dataset.getRank(); int[] oshape = dataset.getShape(); int alen = oshape[axis]; oshape[axis] = 1; int[] nshape = new int[rank - 1]; for (int i = 0; i < axis; i++) { nshape[i] = oshape[i]; } for (int i = axis + 1; i < rank; i++) { nshape[i - 1] = oshape[i]; } Dataset max; Dataset min; IntegerDataset maxIndex; IntegerDataset minIndex; LongDataset count = DatasetFactory.zeros(LongDataset.class, nshape); Dataset sum; Dataset mean; Dataset var; if (isize == 1) { max = DatasetFactory.zeros(nshape, dtype); min = DatasetFactory.zeros(nshape, dtype); maxIndex = DatasetFactory.zeros(IntegerDataset.class, nshape); minIndex = DatasetFactory.zeros(IntegerDataset.class, nshape); sum = DatasetFactory.zeros(nshape, DTypeUtils.getLargestDType(dtype)); mean = DatasetFactory.zeros(DoubleDataset.class, nshape); var = DatasetFactory.zeros(DoubleDataset.class, nshape); } else { max = null; min = null; maxIndex = null; minIndex = null; sum = DatasetFactory.zeros(isize, nshape, DTypeUtils.getLargestDType(dtype)); mean = DatasetFactory.zeros(isize, CompoundDoubleDataset.class, nshape); var = DatasetFactory.zeros(isize, CompoundDoubleDataset.class, nshape); } IndexIterator qiter = count.getIterator(true); int[] qpos = qiter.getPos(); int[] spos = oshape.clone(); if (isize == 1) { DoubleDataset lmean = (DoubleDataset) mean; DoubleDataset lvar = (DoubleDataset) var; final SummaryStatistics stats = new SummaryStatistics(); while (qiter.hasNext()) { int i = 0; for (; i < axis; i++) { spos[i] = qpos[i]; } spos[i++] = 0; for (; i < rank; i++) { spos[i] = qpos[i - 1]; } stats.clear(); //sum of logs is slow and we dont use it, so blocking its calculation here stats.setSumLogImpl(new NullStorelessUnivariateStatistic()); double amax = Double.NEGATIVE_INFINITY; double amin = Double.POSITIVE_INFINITY; boolean hasNaNs = false; if (ignoreNaNs) { for (int j = 0; j < alen; j++) { spos[axis] = j; final double val = dataset.getDouble(spos); if (Double.isNaN(val)) { hasNaNs = true; continue; } else if (ignoreInfs && Double.isInfinite(val)) { continue; } if (val > amax) { amax = val; } if (val < amin) { amin = val; } stats.addValue(val); } } else { for (int j = 0; j < alen; j++) { spos[axis] = j; final double val = dataset.getDouble(spos); if (hasNaNs) { if (!Double.isNaN(val)) stats.addValue(0); continue; } if (Double.isNaN(val)) { amax = Double.NaN; amin = Double.NaN; hasNaNs = true; } else if (ignoreInfs && Double.isInfinite(val)) { continue; } else { if (val > amax) { amax = val; } if (val < amin) { amin = val; } } stats.addValue(val); } } count.setAbs(qiter.index, stats.getN()); max.set(amax, qpos); min.set(amin, qpos); boolean fmax = false; boolean fmin = false; if (hasNaNs) { if (ignoreNaNs) { for (int j = 0; j < alen; j++) { spos[axis] = j; final double val = dataset.getDouble(spos); if (Double.isNaN(val)) continue; if (!fmax && val == amax) { // FIXME qiter.index is wrong!!! maxIndex.setAbs(qiter.index, j); fmax = true; if (fmin) break; } if (!fmin && val == amin) { minIndex.setAbs(qiter.index, j); fmin = true; if (fmax) break; } } } else { for (int j = 0; j < alen; j++) { spos[axis] = j; final double val = dataset.getDouble(spos); if (Double.isNaN(val)) { maxIndex.setAbs(qiter.index, j); minIndex.setAbs(qiter.index, j); break; } } } } else { for (int j = 0; j < alen; j++) { spos[axis] = j; final double val = dataset.getDouble(spos); if (!fmax && val == amax) { maxIndex.setAbs(qiter.index, j); fmax = true; if (fmin) break; } if (!fmin && val == amin) { minIndex.setAbs(qiter.index, j); fmin = true; if (fmax) break; } } } sum.setObjectAbs(qiter.index, stats.getSum()); lmean.setAbs(qiter.index, stats.getMean()); lvar.setAbs(qiter.index, stats.getVariance()); } } else { CompoundDataset ldataset = (CompoundDataset) dataset; CompoundDoubleDataset lmean = (CompoundDoubleDataset) mean; CompoundDoubleDataset lvar = (CompoundDoubleDataset) var; double[] darray = new double[isize]; while (qiter.hasNext()) { int i = 0; for (; i < axis; i++) { spos[i] = qpos[i]; } spos[i++] = 0; for (; i < rank; i++) { spos[i] = qpos[i - 1]; } final SummaryStatistics[] stats = new SummaryStatistics[isize]; for (int k = 0; k < isize; k++) { stats[k] = new SummaryStatistics(); } for (int j = 0; j < alen; j++) { spos[axis] = j; ldataset.getDoubleArray(darray, spos); boolean skip = false; for (int k = 0; k < isize; k++) { double v = darray[k]; if (ignoreNaNs && Double.isNaN(v)) { skip = true; break; } if (ignoreInfs && Double.isInfinite(v)) { skip = true; break; } } if (!skip) for (int k = 0; k < isize; k++) { stats[k].addValue(darray[k]); } } count.setAbs(qiter.index, (int) stats[0].getN()); for (int k = 0; k < isize; k++) { darray[k] = stats[k].getSum(); } sum.set(darray, qpos); for (int k = 0; k < isize; k++) { darray[k] = stats[k].getMean(); } lmean.setItem(darray, qpos); for (int k = 0; k < isize; k++) { darray[k] = stats[k].getVariance(); } lvar.setItem(darray, qpos); } } return new Dataset[] { max, min, maxIndex, minIndex, count, mean, sum, var }; }
From source file:org.lightjason.agentspeak.action.buildin.math.statistic.EStatisticValue.java
/** * returns a statistic value// w ww . j ava 2 s. co m * * @param p_statistic statistic object * @return statistic value */ public final double value(final SummaryStatistics p_statistic) { switch (this) { case GEOMETRICMEAN: return p_statistic.getGeometricMean(); case MAX: return p_statistic.getMax(); case MIN: return p_statistic.getMin(); case COUNT: return p_statistic.getN(); case POPULATIONVARIANCE: return p_statistic.getPopulationVariance(); case QUADRATICMEAN: return p_statistic.getQuadraticMean(); case SECONDMOMENT: return p_statistic.getSecondMoment(); case STANDARDDEVIATION: return p_statistic.getStandardDeviation(); case SUM: return p_statistic.getSum(); case SUMLOG: return p_statistic.getSumOfLogs(); case SUMSQUARE: return p_statistic.getSumsq(); case VARIANCE: return p_statistic.getVariance(); case MEAN: return p_statistic.getMean(); default: throw new CIllegalStateException( org.lightjason.agentspeak.common.CCommon.languagestring(this, "unknown", this)); } }
From source file:org.lightjason.agentspeak.action.builtin.math.statistic.EStatisticValue.java
/** * returns a statistic value/*from w w w. j a v a 2 s .c om*/ * * @param p_statistic statistic object * @return statistic value */ public final double value(@Nonnull final SummaryStatistics p_statistic) { switch (this) { case GEOMETRICMEAN: return p_statistic.getGeometricMean(); case MAX: return p_statistic.getMax(); case MIN: return p_statistic.getMin(); case COUNT: return p_statistic.getN(); case POPULATIONVARIANCE: return p_statistic.getPopulationVariance(); case QUADRATICMEAN: return p_statistic.getQuadraticMean(); case SECONDMOMENT: return p_statistic.getSecondMoment(); case STANDARDDEVIATION: return p_statistic.getStandardDeviation(); case SUM: return p_statistic.getSum(); case SUMLOG: return p_statistic.getSumOfLogs(); case SUMSQUARE: return p_statistic.getSumsq(); case VARIANCE: return p_statistic.getVariance(); case MEAN: return p_statistic.getMean(); default: throw new CIllegalStateException( org.lightjason.agentspeak.common.CCommon.languagestring(this, "unknown", this)); } }
From source file:org.orbisgis.corejdbc.ReadTable.java
/** * Compute numeric stats of the specified table column using a limited input rows. Stats are not done in the sql side. * @param connection Available connection * @param tableName Table name/*from w ww. j a v a2s.c o m*/ * @param columnName Column name * @param rowNum Row id * @param pm Progress monitor * @return An array of attributes {@link STATS} * @throws SQLException */ public static String[] computeStatsLocal(Connection connection, String tableName, String columnName, SortedSet<Integer> rowNum, ProgressMonitor pm) throws SQLException { String[] res = new String[STATS.values().length]; SummaryStatistics stats = new SummaryStatistics(); try (Statement st = connection.createStatement()) { // Cancel select PropertyChangeListener listener = EventHandler.create(PropertyChangeListener.class, st, "cancel"); pm.addPropertyChangeListener(ProgressMonitor.PROP_CANCEL, listener); try (ResultSet rs = st.executeQuery(String.format("SELECT %s FROM %s", columnName, tableName))) { ProgressMonitor fetchProgress = pm.startTask(rowNum.size()); while (rs.next() && !pm.isCancelled()) { if (rowNum.contains(rs.getRow())) { stats.addValue(rs.getDouble(columnName)); fetchProgress.endTask(); } } } finally { pm.removePropertyChangeListener(listener); } } res[STATS.SUM.ordinal()] = Double.toString(stats.getSum()); res[STATS.AVG.ordinal()] = Double.toString(stats.getMean()); res[STATS.COUNT.ordinal()] = Long.toString(stats.getN()); res[STATS.MIN.ordinal()] = Double.toString(stats.getMin()); res[STATS.MAX.ordinal()] = Double.toString(stats.getMax()); res[STATS.STDDEV_SAMP.ordinal()] = Double.toString(stats.getStandardDeviation()); return res; }
From source file:tech.tablesaw.columns.numbers.Stats.java
private static Stats getStats(NumericColumn<?> values, SummaryStatistics summaryStatistics) { Stats stats = new Stats("Column: " + values.name()); stats.min = summaryStatistics.getMin(); stats.max = summaryStatistics.getMax(); stats.n = summaryStatistics.getN();//from w ww .ja va 2s.co m stats.sum = summaryStatistics.getSum(); stats.variance = summaryStatistics.getVariance(); stats.populationVariance = summaryStatistics.getPopulationVariance(); stats.quadraticMean = summaryStatistics.getQuadraticMean(); stats.geometricMean = summaryStatistics.getGeometricMean(); stats.mean = summaryStatistics.getMean(); stats.standardDeviation = summaryStatistics.getStandardDeviation(); stats.sumOfLogs = summaryStatistics.getSumOfLogs(); stats.sumOfSquares = summaryStatistics.getSumsq(); stats.secondMoment = summaryStatistics.getSecondMoment(); return stats; }