List of usage examples for org.apache.commons.math3.stat.descriptive StatisticalSummary getMin
double getMin();
From source file:joinery.impl.Aggregation.java
@SuppressWarnings("unchecked") public static <V> DataFrame<V> describe(final DataFrame<V> df) { final DataFrame<V> desc = new DataFrame<>(); for (final Object col : df.columns()) { for (final Object row : df.index()) { final V value = df.get(row, col); if (value instanceof StatisticalSummary) { if (!desc.columns().contains(col)) { desc.add(col);/* w w w. ja v a2s .c o m*/ if (desc.isEmpty()) { for (final Object r : df.index()) { for (final Object stat : Arrays.asList("count", "mean", "std", "var", "max", "min")) { final Object name = name(df, r, stat); desc.append(name, Collections.<V>emptyList()); } } } } final StatisticalSummary summary = StatisticalSummary.class.cast(value); desc.set(name(df, row, "count"), col, (V) new Double(summary.getN())); desc.set(name(df, row, "mean"), col, (V) new Double(summary.getMean())); desc.set(name(df, row, "std"), col, (V) new Double(summary.getStandardDeviation())); desc.set(name(df, row, "var"), col, (V) new Double(summary.getVariance())); desc.set(name(df, row, "max"), col, (V) new Double(summary.getMax())); desc.set(name(df, row, "min"), col, (V) new Double(summary.getMin())); } } } return desc; }
From source file:be.ugent.maf.cellmissy.gui.view.table.model.SingleCellStatSummaryTableModel.java
/** * Initialize table/* w w w . j a v a 2s . com*/ */ private void initTable() { // list of summaries from the analysis group: number of rows List<StatisticalSummary> statisticalSummaries = singleCellAnalysisGroup.getStatisticalSummaries(); int size = statisticalSummaries.size(); // columns: 1 + 6 for statistical numbers columnNames = new String[7]; columnNames[0] = ""; columnNames[1] = "Max"; columnNames[2] = "Min"; columnNames[3] = "Mean"; columnNames[4] = "N"; columnNames[5] = "SD"; columnNames[6] = "Variance"; singleCellAnalysisGroup.getConditionDataHolders(); data = new Object[size][columnNames.length]; // fill in data for (int rowIndex = 0; rowIndex < data.length; rowIndex++) { data[rowIndex][0] = "Cond " + (singleCellAnalysisGroup.getConditionDataHolders().get(rowIndex).getPlateCondition()); // summary for a row StatisticalSummary statisticalSummary = statisticalSummaries.get(rowIndex); // distribute statistical objects per columns data[rowIndex][1] = statisticalSummary.getMax(); data[rowIndex][2] = statisticalSummary.getMin(); data[rowIndex][3] = statisticalSummary.getMean(); data[rowIndex][4] = statisticalSummary.getN(); data[rowIndex][5] = statisticalSummary.getStandardDeviation(); data[rowIndex][6] = statisticalSummary.getVariance(); } }
From source file:ijfx.core.stats.DefaultImageStatisticsService.java
@Override public Map<String, Double> summaryStatisticsToMap(StatisticalSummary summaryStats) { Map<String, Double> statistics = new HashMap<>(); statistics.put(LBL_MEAN, summaryStats.getMean()); statistics.put(LBL_MIN, summaryStats.getMin()); statistics.put(LBL_MAX, summaryStats.getMax()); statistics.put(LBL_SD, summaryStats.getStandardDeviation()); statistics.put(LBL_VARIANCE, summaryStats.getVariance()); statistics.put(LBL_PIXEL_COUNT, (double) summaryStats.getN()); return statistics; }
From source file:org.briljantframework.data.dataframe.DataFrames.java
/** * Presents a summary of the given data frame. For each column of {@code df} the returned summary * contains one row. Each row is described by four values, the {@code min}, {@code max}, * {@code mean} and {@code mode}. The first three are presented for numerical columns and the * fourth for categorical.//from ww w . j av a 2 s .co m * * <pre> * {@code * > DataFrame df = MixedDataFrame.of( * "a", Vector.of(1, 2, 3, 4, 5, 6), * "b", Vector.of("a", "b", "b", "b", "e", "f"), * "c", Vector.of(1.1, 1.2, 1.3, 1.4, 1.5, 1.6) * ); * * > DataFrames.summary(df) * mean var std min max mode * a 3.500 3.500 1.871 1.000 6.000 6 * b NA NA NA NA NA f * c 1.350 0.035 0.187 1.100 1.600 1.1 * * [3 rows x 6 columns] * } * </pre> * * @param df the data frame * @return a data frame summarizing {@code df} */ public static DataFrame summary(DataFrame df) { DataFrame.Builder builder = new MixedDataFrame.Builder(); builder.set("mean", VectorType.DOUBLE).set("var", VectorType.DOUBLE).set("std", VectorType.DOUBLE) .set("min", VectorType.DOUBLE).set("max", VectorType.DOUBLE).set("mode", VectorType.OBJECT); for (Object columnKey : df.getColumnIndex().keySet()) { Vector column = df.get(columnKey); if (Is.numeric(column)) { StatisticalSummary summary = column.collect(Number.class, Collectors.statisticalSummary()); builder.set(columnKey, "mean", summary.getMean()).set(columnKey, "var", summary.getVariance()) .set(columnKey, "std", summary.getStandardDeviation()) .set(columnKey, "min", summary.getMin()).set(columnKey, "max", summary.getMax()); } builder.set(columnKey, "mode", column.collect(Collectors.mode())); } return builder.build(); }
From source file:org.briljantframework.data.dataframe.transform.MinMaxNormalizer.java
@Override public Transformer fit(DataFrame df) { Vector.Builder min = Vector.Builder.of(Double.class); Vector.Builder max = Vector.Builder.of(Double.class); for (Object columnKey : df) { // // TODO: 11/14/15 Only consider numerical vectors StatisticalSummary summary = df.get(columnKey).statisticalSummary(); min.set(columnKey, summary.getMin()); max.set(columnKey, summary.getMax()); }/*from w ww . ja va 2s. c o m*/ return new MinMaxNormalizeTransformer(max.build(), min.build()); }
From source file:org.italiangrid.voms.aa.x509.stats.ExecutionTimeStats.java
public static ExecutionTimeStats fromSummaryStats(StatisticalSummary stats) { ExecutionTimeStats v = new ExecutionTimeStats(); v.setMax(stats.getMax());/*w ww . j a v a 2 s . co m*/ v.setMin(stats.getMin()); v.setMean(stats.getMean()); v.setCount(stats.getN()); return v; }