Examples with Mean org.apache.commons.math3.stat.descriptive.moment.Mean used on opensource projects

Example 36 with Mean

use of org.apache.commons.math3.stat.descriptive.moment.Mean in project knime-core by knime.

the class LeveneTestStatistics method getLeveneTestTwoGroupsCells.

/**
 * Get the test result of the Levene test. This is an optimized version for
 * two groups.
 * @return the Levene test
 */
public List<List<DataCell>> getLeveneTestTwoGroupsCells() {
    SummaryStatistics statsX = m_denStats.get(0);
    SummaryStatistics statsY = m_denStats.get(1);
    // overall sample mean
    double m = m_lstats.getMean();
    // first sample mean
    double m1 = statsX.getMean();
    // second sample mean
    double m2 = statsY.getMean();
    // first sample variance
    double v1 = statsX.getVariance();
    // second sample variance
    double v2 = statsY.getVariance();
    // first sample count
    double n1 = statsX.getN();
    // second sample count
    double n2 = statsY.getN();
    // Levene's test
    double num = n1 * (m1 - m) * (m1 - m) + n2 * (m2 - m) * (m2 - m);
    double den = (n1 - 1) * v1 + (n2 - 1) * v2;
    double L = (n1 + n2 - 2) / den * num;
    long df1 = 1;
    long df2 = (long) n1 + (long) n2 - 2;
    FDistribution distribution = new FDistribution(df1, df2);
    double pValue = 1 - distribution.cumulativeProbability(L);
    List<DataCell> cells = new ArrayList<DataCell>();
    cells.add(new StringCell(m_column));
    cells.add(new DoubleCell(L));
    cells.add(new IntCell((int) df1));
    cells.add(new IntCell((int) df2));
    cells.add(new DoubleCell(pValue));
    return Collections.singletonList(cells);
}

Example 37 with Mean

use of org.apache.commons.math3.stat.descriptive.moment.Mean in project knime-core by knime.

the class BinaryNominalSplitsPCA method calculateMeanClassProbabilityVector.

/**
 * Calculates the mean class probability vector based on the class probability vectors of the
 * CombinedAttributeValues in attVals.
 *
 * @param attVals
 * @param totalWeight
 * @param numTargetVals
 * @return the mean class probability vector
 */
static RealVector calculateMeanClassProbabilityVector(final CombinedAttributeValues[] attVals, final double totalWeight, final int numTargetVals) {
    RealVector meanClassProbabilityVec = MatrixUtils.createRealVector(new double[numTargetVals]);
    for (CombinedAttributeValues attVal : attVals) {
        meanClassProbabilityVec = meanClassProbabilityVec.add(attVal.m_classFrequencyVector);
    }
    meanClassProbabilityVec = meanClassProbabilityVec.mapDivide(totalWeight);
    return meanClassProbabilityVec;
}

Example 38 with Mean

use of org.apache.commons.math3.stat.descriptive.moment.Mean in project knime-core by knime.

the class PolyRegLearnerNodeModel method execute.

/**
 * {@inheritDoc}
 */
@Override
protected PortObject[] execute(final PortObject[] inData, final ExecutionContext exec) throws Exception {
    BufferedDataTable inTable = (BufferedDataTable) inData[0];
    DataTableSpec inSpec = inTable.getDataTableSpec();
    final int colCount = inSpec.getNumColumns();
    String[] selectedCols = computeSelectedColumns(inSpec);
    Set<String> hash = new HashSet<String>(Arrays.asList(selectedCols));
    m_colSelected = new boolean[colCount];
    for (int i = 0; i < colCount; i++) {
        m_colSelected[i] = hash.contains(inTable.getDataTableSpec().getColumnSpec(i).getName());
    }
    final int rowCount = inTable.getRowCount();
    String[] temp = new String[m_columnNames.length + 1];
    System.arraycopy(m_columnNames, 0, temp, 0, m_columnNames.length);
    temp[temp.length - 1] = m_settings.getTargetColumn();
    FilterColumnTable filteredTable = new FilterColumnTable(inTable, temp);
    final DataArray rowContainer = new DefaultDataArray(filteredTable, 1, m_settings.getMaxRowsForView());
    // handle the optional PMML input
    PMMLPortObject inPMMLPort = m_pmmlInEnabled ? (PMMLPortObject) inData[1] : null;
    PortObjectSpec[] outputSpec = configure((inPMMLPort == null) ? new PortObjectSpec[] { inData[0].getSpec(), null } : new PortObjectSpec[] { inData[0].getSpec(), inPMMLPort.getSpec() });
    Learner learner = new Learner((PMMLPortObjectSpec) outputSpec[0], 0d, m_settings.getMissingValueHandling() == MissingValueHandling.fail, m_settings.getDegree());
    try {
        PolyRegContent polyRegContent = learner.perform(inTable, exec);
        m_betas = fillBeta(polyRegContent);
        m_meanValues = polyRegContent.getMeans();
        ColumnRearranger crea = new ColumnRearranger(inTable.getDataTableSpec());
        crea.append(getCellFactory(inTable.getDataTableSpec().findColumnIndex(m_settings.getTargetColumn())));
        PortObject[] bdt = new PortObject[] { createPMMLModel(inPMMLPort, inSpec), exec.createColumnRearrangeTable(inTable, crea, exec.createSilentSubExecutionContext(.2)), polyRegContent.createTablePortObject(exec.createSubExecutionContext(0.2)) };
        m_squaredError /= rowCount;
        if (polyRegContent.getWarningMessage() != null) {
            setWarningMessage(polyRegContent.getWarningMessage());
        }
        double[] stdErrors = PolyRegViewData.mapToArray(polyRegContent.getStandardErrors(), m_columnNames, m_settings.getDegree(), polyRegContent.getInterceptStdErr());
        double[] tValues = PolyRegViewData.mapToArray(polyRegContent.getTValues(), m_columnNames, m_settings.getDegree(), polyRegContent.getInterceptTValue());
        double[] pValues = PolyRegViewData.mapToArray(polyRegContent.getPValues(), m_columnNames, m_settings.getDegree(), polyRegContent.getInterceptPValue());
        m_viewData = new PolyRegViewData(m_meanValues, m_betas, stdErrors, tValues, pValues, m_squaredError, polyRegContent.getAdjustedRSquared(), m_columnNames, m_settings.getDegree(), m_settings.getTargetColumn(), rowContainer);
        return bdt;
    } catch (ModelSpecificationException e) {
        final String origWarning = getWarningMessage();
        final String warning = (origWarning != null && !origWarning.isEmpty()) ? (origWarning + "\n") : "" + e.getMessage();
        setWarningMessage(warning);
        final ExecutionContext subExec = exec.createSubExecutionContext(.1);
        final BufferedDataContainer empty = subExec.createDataContainer(STATS_SPEC);
        int rowIdx = 1;
        for (final String column : m_columnNames) {
            for (int d = 1; d <= m_settings.getDegree(); ++d) {
                empty.addRowToTable(new DefaultRow("Row" + rowIdx++, new StringCell(column), new IntCell(d), new DoubleCell(0.0d), DataType.getMissingCell(), DataType.getMissingCell(), DataType.getMissingCell()));
            }
        }
        empty.addRowToTable(new DefaultRow("Row" + rowIdx, new StringCell("Intercept"), new IntCell(0), new DoubleCell(0.0d), DataType.getMissingCell(), DataType.getMissingCell(), DataType.getMissingCell()));
        double[] nans = new double[m_columnNames.length * m_settings.getDegree() + 1];
        Arrays.fill(nans, Double.NaN);
        m_betas = new double[nans.length];
        // Mean only for the linear tags
        m_meanValues = new double[nans.length / m_settings.getDegree()];
        m_viewData = new PolyRegViewData(m_meanValues, m_betas, nans, nans, nans, m_squaredError, Double.NaN, m_columnNames, m_settings.getDegree(), m_settings.getTargetColumn(), rowContainer);
        empty.close();
        ColumnRearranger crea = new ColumnRearranger(inTable.getDataTableSpec());
        crea.append(getCellFactory(inTable.getDataTableSpec().findColumnIndex(m_settings.getTargetColumn())));
        BufferedDataTable rearrangerTable = exec.createColumnRearrangeTable(inTable, crea, exec.createSubProgress(0.6));
        PMMLPortObject model = createPMMLModel(inPMMLPort, inTable.getDataTableSpec());
        PortObject[] bdt = new PortObject[] { model, rearrangerTable, empty.getTable() };
        return bdt;
    }
}

Example 39 with Mean

use of org.apache.commons.math3.stat.descriptive.moment.Mean in project knime-core by knime.

the class MeanAbsoluteDeviationOperator method getResultInternal.

/**
 * {@inheritDoc}
 */
@Override
protected DataCell getResultInternal() {
    final double[] cells = super.getCells().getElements();
    if (cells.length == 0) {
        return DataType.getMissingCell();
    }
    final Mean mean = new Mean();
    double meanValue = mean.evaluate(cells);
    for (int i = 0; i < cells.length; i++) {
        cells[i] = Math.abs(meanValue - cells[i]);
    }
    meanValue = mean.evaluate(cells);
    return new DoubleCell(meanValue);
}

Example 40 with Mean

use of org.apache.commons.math3.stat.descriptive.moment.Mean in project vcell by virtualcell.

the class StochFileWriter method write.

/**
 * Write the model to a text file which serves as an input for Stochastic simulation engine.
 * Creation date: (6/22/2006 5:37:26 PM)
 */
public void write(String[] parameterNames) throws Exception, ExpressionException {
    Simulation simulation = simTask.getSimulation();
    SimulationSymbolTable simSymbolTable = simTask.getSimulationJob().getSimulationSymbolTable();
    initialize();
    if (bUseMessaging) {
        writeJMSParamters();
    }
    // Write control information
    printWriter.println("<control>");
    cbit.vcell.solver.SolverTaskDescription solverTaskDescription = simulation.getSolverTaskDescription();
    cbit.vcell.solver.TimeBounds timeBounds = solverTaskDescription.getTimeBounds();
    cbit.vcell.solver.OutputTimeSpec outputTimeSpec = solverTaskDescription.getOutputTimeSpec();
    ErrorTolerance errorTolerance = solverTaskDescription.getErrorTolerance();
    NonspatialStochSimOptions stochOpt = solverTaskDescription.getStochOpt();
    printWriter.println("STARTING_TIME" + "\t" + timeBounds.getStartingTime());
    printWriter.println("ENDING_TIME " + "\t" + timeBounds.getEndingTime());
    // pw.println("MAX_ITERATION"+"\t"+outputTimeSpec.getKeepAtMost());
    printWriter.println("TOLERANCE " + "\t" + errorTolerance.getAbsoluteErrorTolerance());
    if (outputTimeSpec.isDefault()) {
        printWriter.println("SAMPLE_INTERVAL" + "\t" + ((DefaultOutputTimeSpec) outputTimeSpec).getKeepEvery());
        printWriter.println("MAX_SAVE_POINTS" + "\t" + ((DefaultOutputTimeSpec) outputTimeSpec).getKeepAtMost());
    } else if (outputTimeSpec.isUniform()) {
        printWriter.println("SAVE_PERIOD" + "\t" + ((UniformOutputTimeSpec) outputTimeSpec).getOutputTimeStep());
    }
    printWriter.println("NUM_TRIAL" + "\t" + solverTaskDescription.getStochOpt().getNumOfTrials());
    if (stochOpt.isUseCustomSeed()) {
        printWriter.println("SEED" + "\t" + stochOpt.getCustomSeed());
    } else {
        // we generate our own random seed
        RandomDataGenerator rdg = new RandomDataGenerator();
        int randomSeed = rdg.nextInt(1, Integer.MAX_VALUE);
        printWriter.println("SEED" + "\t" + randomSeed);
    }
    printWriter.println("</control>");
    printWriter.println();
    // write model information
    // Model info. will be extracted from subDomain of mathDescription
    Enumeration<SubDomain> e = simulation.getMathDescription().getSubDomains();
    SubDomain subDomain = null;
    if (e.hasMoreElements()) {
        subDomain = e.nextElement();
    }
    if (subDomain != null) {
        printWriter.println("<model>");
        // variables
        printWriter.println("<discreteVariables>");
        // Species iniCondition (if in concentration) is sampled from a poisson distribution(which has a mean of the current iniExp value)
        // There is only one subDomain for compartmental model
        List<VarIniCondition> varInis = subDomain.getVarIniConditions();
        if ((varInis != null) && (varInis.size() > 0)) {
            RandomDataGenerator dist = new RandomDataGenerator();
            if (simulation.getSolverTaskDescription().getStochOpt().isUseCustomSeed()) {
                Integer randomSeed = simulation.getSolverTaskDescription().getStochOpt().getCustomSeed();
                if (randomSeed != null) {
                    dist.reSeed(randomSeed);
                }
            }
            printWriter.println("TotalVars" + "\t" + varInis.size());
            for (VarIniCondition varIniCondition : varInis) {
                try {
                    Expression iniExp = varIniCondition.getIniVal();
                    iniExp.bindExpression(simSymbolTable);
                    iniExp = simSymbolTable.substituteFunctions(iniExp).flatten();
                    double expectedCount = iniExp.evaluateConstant();
                    // 1000 mill
                    final Integer limit = 1000000000;
                    if (limit < expectedCount) {
                        String eMessage = "The Initial count for Species '" + varIniCondition.getVar().getName() + "' is " + BigDecimal.valueOf(expectedCount).toBigInteger() + "\n";
                        eMessage += "which is higher than the internal vCell limit of " + limit + ".\n";
                        eMessage += "Please reduce the Initial Condition value for this Species or reduce the compartment size.";
                        throw new MathFormatException(eMessage);
                    }
                    long varCount = 0;
                    if (varIniCondition instanceof VarIniCount) {
                        varCount = (long) expectedCount;
                    } else {
                        if (expectedCount > 0) {
                            varCount = dist.nextPoisson(expectedCount);
                        }
                    }
                    // System.out.println("expectedCount: " + expectedCount + ", varCount: " + varCount);
                    printWriter.println(varIniCondition.getVar().getName() + "\t" + varCount);
                } catch (ExpressionException ex) {
                    ex.printStackTrace();
                    throw new MathFormatException("variable " + varIniCondition.getVar().getName() + "'s initial condition is required to be a constant.");
                }
            }
        } else
            printWriter.println("TotalVars" + "\t" + "0");
        printWriter.println("</discreteVariables>");
        printWriter.println();
        // jump processes
        printWriter.println("<jumpProcesses>");
        List<JumpProcess> jumpProcesses = subDomain.getJumpProcesses();
        if ((jumpProcesses != null) && (jumpProcesses.size() > 0)) {
            printWriter.println("TotalProcesses" + "\t" + jumpProcesses.size());
            for (int i = 0; i < jumpProcesses.size(); i++) {
                printWriter.println(jumpProcesses.get(i).getName());
            }
        } else
            printWriter.println("TotalProcesses" + "\t" + "0");
        printWriter.println("</jumpProcesses>");
        printWriter.println();
        // process description
        printWriter.println("<processDesc>");
        if ((jumpProcesses != null) && (jumpProcesses.size() > 0)) {
            printWriter.println("TotalDescriptions" + "\t" + jumpProcesses.size());
            for (int i = 0; i < jumpProcesses.size(); i++) {
                JumpProcess temProc = (JumpProcess) jumpProcesses.get(i);
                // jump process name
                printWriter.println("JumpProcess" + "\t" + temProc.getName());
                Expression probExp = temProc.getProbabilityRate();
                try {
                    probExp.bindExpression(simSymbolTable);
                    probExp = simSymbolTable.substituteFunctions(probExp).flatten();
                    if (!isValidProbabilityExpression(probExp)) {
                        throw new MathFormatException("probability rate in jump process " + temProc.getName() + " has illegal symbols(should only contain variable names).");
                    }
                } catch (cbit.vcell.parser.ExpressionException ex) {
                    ex.printStackTrace();
                    throw new cbit.vcell.parser.ExpressionException("Binding math description error in probability rate in jump process " + temProc.getName() + ". Some symbols can not be resolved.");
                }
                // Expression temp = replaceVarIniInProbability(probExp);
                // Propensity
                printWriter.println("\t" + "Propensity" + "\t" + probExp.infix());
                // effects
                printWriter.println("\t" + "Effect" + "\t" + temProc.getActions().size());
                for (int j = 0; j < temProc.getActions().size(); j++) {
                    printWriter.print("\t\t" + ((Action) temProc.getActions().get(j)).getVar().getName() + "\t" + ((Action) temProc.getActions().get(j)).getOperation());
                    printWriter.println("\t" + ((Action) temProc.getActions().get(j)).evaluateOperand());
                    printWriter.println();
                }
                // dependencies
                Vector<String> dependencies = getDependencies(temProc, jumpProcesses);
                if ((dependencies != null) && (dependencies.size() > 0)) {
                    printWriter.println("\t" + "DependentProcesses" + "\t" + dependencies.size());
                    for (int j = 0; j < dependencies.size(); j++) printWriter.println("\t\t" + dependencies.elementAt(j));
                } else
                    printWriter.println("\t" + "DependentProcesses" + "\t" + "0");
                printWriter.println();
            }
        } else
            printWriter.println("TotalDescriptions" + "\t" + "0");
        printWriter.println("</processDesc>");
        printWriter.println("</model>");
    }
// if (subDomain != null)
}