private double multiLL(DoubleMatrix2D coeffs, Node dep, List<Node> indep) {
DoubleMatrix2D indepData =
factory2D.make(internalData.subsetColumns(indep).getDoubleData().toArray());
List<Node> depList = new ArrayList<>();
depList.add(dep);
DoubleMatrix2D depData =
factory2D.make(internalData.subsetColumns(depList).getDoubleData().toArray());
int N = indepData.rows();
DoubleMatrix2D probs =
Algebra.DEFAULT.mult(factory2D.appendColumns(factory2D.make(N, 1, 1.0), indepData), coeffs);
probs =
factory2D
.appendColumns(factory2D.make(indepData.rows(), 1, 1.0), probs)
.assign(Functions.exp);
double ll = 0;
for (int i = 0; i < N; i++) {
DoubleMatrix1D curRow = probs.viewRow(i);
curRow.assign(Functions.div(curRow.zSum()));
ll += Math.log(curRow.get((int) depData.get(i, 0)));
}
return ll;
}
/** Find the dataModel model. (If it's a list, take the one that's selected.) */
private DataModel getSelectedDataModel(DataWrapper dataWrapper) {
DataModelList dataModelList = dataWrapper.getDataModelList();
if (dataModelList.size() > 1) {
return dataModelList;
}
DataModel dataModel = dataWrapper.getSelectedDataModel();
if (dataModel instanceof DataSet) {
DataSet dataSet = (DataSet) dataModel;
if (dataSet.isDiscrete()) {
return dataSet;
} else if (dataSet.isContinuous()) {
return dataSet;
} else if (dataSet.isMixed()) {
return dataSet;
}
throw new IllegalArgumentException(
"<html>"
+ "This data set contains a mixture of discrete and continuous "
+ "<br>columns; there are no algorithm in Tetrad currently to "
+ "<br>search over such data sets."
+ "</html>");
} else if (dataModel instanceof ICovarianceMatrix) {
return dataModel;
} else if (dataModel instanceof TimeSeriesData) {
return dataModel;
}
throw new IllegalArgumentException("Unexpected dataModel source: " + dataModel);
}
// Causes a package cycle.
public void testManualDiscretize2() {
Graph graph = new Dag(GraphUtils.randomGraph(5, 0, 5, 3, 3, 3, false));
SemPm pm = new SemPm(graph);
SemIm im = new SemIm(pm);
DataSet data = im.simulateData(100, false);
List<Node> nodes = data.getVariables();
Discretizer discretizer = new Discretizer(data);
// discretizer.setVariablesCopied(true);
discretizer.equalCounts(nodes.get(0), 3);
discretizer.equalIntervals(nodes.get(1), 2);
discretizer.equalCounts(nodes.get(2), 5);
discretizer.equalIntervals(nodes.get(3), 8);
discretizer.equalCounts(nodes.get(4), 4);
DataSet discretized = discretizer.discretize();
System.out.println(discretized);
assertEquals(2, maxInColumn(discretized, 0));
assertEquals(1, maxInColumn(discretized, 1));
assertEquals(4, maxInColumn(discretized, 2));
assertEquals(7, maxInColumn(discretized, 3));
assertEquals(3, maxInColumn(discretized, 4));
}
/**
* Executes the algorithm, producing (at least) a result workbench. Must be implemented in the
* extending class.
*/
public void execute() {
DataModel source = getDataModel();
if (!(source instanceof DataSet)) {
throw new IllegalArgumentException("Expecting a rectangular data set.");
}
DataSet data = (DataSet) source;
if (!data.isContinuous()) {
throw new IllegalArgumentException("Expecting a continuous data set.");
}
// Lingam_old lingam = new Lingam_old();
// lingam.setPruningDone(true);
// lingam.setAlpha(getParams().getIndTestParams().getAlpha());
// GraphWithParameters result = lingam.lingam(data);
// Graph graph = result.getGraph();
Lingam lingam = new Lingam();
LingamParams params = (LingamParams) getParams();
lingam.setPruneFactor(params.getPruneFactor());
Graph graph = lingam.search(data);
setResultGraph(graph);
if (getSourceGraph() != null) {
GraphUtils.arrangeBySourceGraph(graph, getSourceGraph());
} else {
GraphUtils.circleLayout(graph, 200, 200, 150);
}
}
/** Constructs the score using a covariance matrix. */
public BdeuScoreImages(List<DataModel> dataModels) {
if (dataModels == null) {
throw new NullPointerException();
}
List<BDeuScore> scores = new ArrayList<>();
for (DataModel model : dataModels) {
if (model instanceof DataSet) {
DataSet dataSet = (DataSet) model;
if (!dataSet.isDiscrete()) {
throw new IllegalArgumentException("Datasets must be continuous.");
}
scores.add(new BDeuScore(dataSet));
} else {
throw new IllegalArgumentException(
"Only continuous data sets and covariance matrices may be used as input.");
}
}
List<Node> variables = scores.get(0).getVariables();
for (int i = 2; i < scores.size(); i++) {
scores.get(i).setVariables(variables);
}
this.scores = scores;
this.variables = variables;
}
public FindOneFactorClustersWithCausalIndicators(
DataSet dataSet, TestType testType, double alpha) {
if (dataSet.isContinuous()) {
this.variables = dataSet.getVariables();
this.test = new ContinuousTetradTest(dataSet, testType, alpha);
this.indTest = new IndTestFisherZ(dataSet, alpha);
this.alpha = alpha;
this.testType = testType;
this.dataModel = dataSet;
if (testType == TestType.TETRAD_DELTA) {
deltaTest = new DeltaTetradTest(dataSet);
deltaTest.setCacheFourthMoments(false);
}
this.cov = new CovarianceMatrix(dataSet);
} else if (dataSet.isDiscrete()) {
this.variables = dataSet.getVariables();
this.test = new DiscreteTetradTest(dataSet, alpha);
this.indTest = new IndTestChiSquare(dataSet, alpha);
this.alpha = alpha;
this.testType = testType;
this.dataModel = dataSet;
if (testType == TestType.TETRAD_DELTA) {
deltaTest = new DeltaTetradTest(dataSet);
deltaTest.setCacheFourthMoments(false);
}
}
}
public IndTestFisherZPercentIndependent(List<DataSet> dataSets, double alpha) {
this.dataSets = dataSets;
this.variables = dataSets.get(0).getVariables();
data = new ArrayList<TetradMatrix>();
for (DataSet dataSet : dataSets) {
dataSet = DataUtils.center(dataSet);
TetradMatrix _data = dataSet.getDoubleData();
data.add(_data);
}
ncov = new ArrayList<TetradMatrix>();
for (TetradMatrix d : this.data) ncov.add(d.transpose().times(d).scalarMult(1.0 / d.rows()));
setAlpha(alpha);
rows = new int[dataSets.get(0).getNumRows()];
for (int i = 0; i < getRows().length; i++) getRows()[i] = i;
variablesMap = new HashMap<Node, Integer>();
for (int i = 0; i < variables.size(); i++) {
variablesMap.put(variables.get(i), i);
}
this.recursivePartialCorrelation = new ArrayList<RecursivePartialCorrelation>();
for (TetradMatrix covMatrix : ncov) {
recursivePartialCorrelation.add(new RecursivePartialCorrelation(getVariables(), covMatrix));
}
}
/**
* Factory to return the correct param editor for independence test params. This will go in a
* little box in the search editor.
*/
private JComponent getIndTestParamBox(IndTestParams indTestParams) {
if (indTestParams == null) {
throw new NullPointerException();
}
if (indTestParams instanceof GesIndTestParams) {
if (getAlgorithmRunner() instanceof IGesRunner) {
IGesRunner gesRunner = ((IGesRunner) getAlgorithmRunner());
GesIndTestParams params = (GesIndTestParams) indTestParams;
DataModel dataModel = gesRunner.getDataModel();
boolean discreteData = dataModel instanceof DataSet && ((DataSet) dataModel).isDiscrete();
return new GesIndTestParamsEditor(params, discreteData);
}
if (getAlgorithmRunner() instanceof ImagesRunner) {
ImagesRunner gesRunner = ((ImagesRunner) getAlgorithmRunner());
GesIndTestParams params = (GesIndTestParams) indTestParams;
DataSet dataSet = (DataSet) gesRunner.getDataModel();
boolean discreteData = dataSet.isDiscrete();
return new GesIndTestParamsEditor(params, discreteData);
}
}
return new IndTestParamsEditor(indTestParams);
}
/**
* Constructs a test using a given data set. If a data set is provided (that is, a tabular data
* set), fourth moment statistics can be calculated (p. 160); otherwise, it must be assumed that
* the data are multivariate Gaussian.
*/
public DeltaSextadTest(DataSet dataSet) {
if (dataSet == null) {
throw new NullPointerException();
}
if (!dataSet.isContinuous()) {
throw new IllegalArgumentException();
}
this.cov = new CovarianceMatrix(dataSet);
List<DataSet> data1 = new ArrayList<DataSet>();
data1.add(dataSet);
List<DataSet> data2 = DataUtils.center(data1);
this.dataSet = data2.get(0);
this.data = this.dataSet.getDoubleData().transpose().toArray();
this.N = dataSet.getNumRows();
this.variables = dataSet.getVariables();
this.numVars = dataSet.getNumColumns();
this.variablesHash = new HashMap<Node, Integer>();
for (int i = 0; i < variables.size(); i++) {
variablesHash.put(variables.get(i), i);
}
this.means = new double[numVars];
for (int i = 0; i < numVars; i++) {
means[i] = mean(data[i], N);
}
}
/**
* Constructs a new Fisher Z independence test with the listed arguments.
*
* @param data A 2D continuous data set with no missing values.
* @param variables A list of variables, a subset of the variables of <code>data</code>.
* @param alpha The significance cutoff level. p values less than alpha will be reported as
* dependent.
*/
public IndTestFisherZShortTriangular(TetradMatrix data, List<Node> variables, double alpha) {
DataSet dataSet = ColtDataSet.makeContinuousData(variables, data);
this.covMatrix = new ShortTriangularMatrix(dataSet.getNumColumns());
this.covMatrix.becomeCorrelationMatrix(dataSet);
this.variables = dataSet.getVariables();
setAlpha(alpha);
this.deterministicTest = new IndTestFisherZGeneralizedInverse(dataSet, alpha);
}
@Test
public void test2() {
RandomUtil.getInstance().setSeed(2999983L);
int sampleSize = 1000;
List<Node> variableNodes = new ArrayList<>();
ContinuousVariable x1 = new ContinuousVariable("X1");
ContinuousVariable x2 = new ContinuousVariable("X2");
ContinuousVariable x3 = new ContinuousVariable("X3");
ContinuousVariable x4 = new ContinuousVariable("X4");
ContinuousVariable x5 = new ContinuousVariable("X5");
variableNodes.add(x1);
variableNodes.add(x2);
variableNodes.add(x3);
variableNodes.add(x4);
variableNodes.add(x5);
Graph _graph = new EdgeListGraph(variableNodes);
SemGraph graph = new SemGraph(_graph);
graph.addDirectedEdge(x1, x3);
graph.addDirectedEdge(x2, x3);
graph.addDirectedEdge(x3, x4);
graph.addDirectedEdge(x2, x4);
graph.addDirectedEdge(x4, x5);
graph.addDirectedEdge(x2, x5);
SemPm semPm = new SemPm(graph);
SemIm semIm = new SemIm(semPm);
DataSet dataSet = semIm.simulateData(sampleSize, false);
print(semPm);
GeneralizedSemPm _semPm = new GeneralizedSemPm(semPm);
GeneralizedSemIm _semIm = new GeneralizedSemIm(_semPm, semIm);
DataSet _dataSet = _semIm.simulateDataMinimizeSurface(sampleSize, false);
print(_semPm);
// System.out.println(_dataSet);
for (int j = 0; j < dataSet.getNumColumns(); j++) {
double[] col = dataSet.getDoubleData().getColumn(j).toArray();
double[] _col = _dataSet.getDoubleData().getColumn(j).toArray();
double mean = StatUtils.mean(col);
double _mean = StatUtils.mean(_col);
double variance = StatUtils.variance(col);
double _variance = StatUtils.variance(_col);
assertEquals(mean, _mean, 0.3);
assertEquals(1.0, variance / _variance, .2);
}
}
/*
* @param dataSet A discrete data set.
* @param column the column in question.
* @return the max value in that column.
*/
private int maxInColumn(DataSet dataSet, int column) {
int max = -1;
for (int i = 0; i < dataSet.getNumRows(); i++) {
int value = dataSet.getInt(i, column);
if (value > max) max = value;
}
return max;
}
private void calcStats() {
// Graph resultGraph = getAlgorithmRunner().getResultGraph();
IGesRunner runner = (IGesRunner) getAlgorithmRunner();
if (runner.getTopGraphs().isEmpty()) {
throw new IllegalArgumentException(
"No patterns were recorded. Please adjust the number of " + "patterns to store.");
}
Graph resultGraph = runner.getTopGraphs().get(runner.getIndex()).getGraph();
if (getAlgorithmRunner().getDataModel() instanceof DataSet) {
// resultGraph may be the output of a PC search.
// Such graphs sometimes contain doubly directed edges.
// /We converte such edges to directed edges here.
// For the time being an orientation is arbitrarily selected.
Set<Edge> allEdges = resultGraph.getEdges();
for (Edge edge : allEdges) {
if (edge.getEndpoint1() == Endpoint.ARROW && edge.getEndpoint2() == Endpoint.ARROW) {
// Option 1 orient it from node1 to node2
resultGraph.setEndpoint(edge.getNode1(), edge.getNode2(), Endpoint.ARROW);
// Option 2 remove such edges:
resultGraph.removeEdge(edge);
}
}
Pattern pattern = new Pattern(resultGraph);
PatternToDag ptd = new PatternToDag(pattern);
Graph dag = ptd.patternToDagMeekRules();
DataSet dataSet = (DataSet) getAlgorithmRunner().getDataModel();
String report;
if (dataSet.isContinuous()) {
report = reportIfContinuous(dag, dataSet);
} else if (dataSet.isDiscrete()) {
report = reportIfDiscrete(dag, dataSet);
} else {
throw new IllegalArgumentException("");
}
JScrollPane dagWorkbenchScroll = dagWorkbenchScroll(dag);
modelStatsText.setLineWrap(true);
modelStatsText.setWrapStyleWord(true);
modelStatsText.setText(report);
removeStatsTabs();
tabbedPane.addTab("DAG in pattern", dagWorkbenchScroll);
tabbedPane.addTab("DAG Model Statistics", modelStatsText);
}
}
private void setDataSet(DataSet dataSet) {
List<String> _varNames = dataSet.getVariableNames();
this.variables = dataSet.getVariables();
this.dataSet = dataSet;
this.discrete = dataSet.isDiscrete();
if (!isDiscrete()) {
this.covariances = new CovarianceMatrix(dataSet);
}
this.sampleSize = dataSet.getNumRows();
}
/**
* Constructs a new Independence test which checks independence facts based on the correlation
* matrix implied by the given data set (must be continuous). The given significance level is
* used.
*
* @param dataSet A data set containing only continuous columns.
* @param alpha The alpha level of the test.
*/
public IndTestFisherZShortTriangular(DataSet dataSet, double alpha) {
if (!(dataSet.isContinuous())) {
throw new IllegalArgumentException("Data set must be continuous.");
}
this.covMatrix = new ShortTriangularMatrix(dataSet.getNumColumns());
this.covMatrix.becomeCorrelationMatrix(dataSet);
this.variables = dataSet.getVariables();
setAlpha(alpha);
this.deterministicTest = new IndTestFisherZGeneralizedInverse(dataSet, alpha);
this.dataSet = dataSet;
}
/** Creates a cell count table for the given data set. */
public DataSetProbs(DataSet dataSet) {
if (dataSet == null) {
throw new NullPointerException();
}
this.dataSet = dataSet;
dims = new int[dataSet.getNumColumns()];
for (int i = 0; i < dims.length; i++) {
DiscreteVariable variable = (DiscreteVariable) dataSet.getVariable(i);
dims[i] = variable.getNumCategories();
}
numRows = dataSet.getNumRows();
}
// This takes an inordinate amount of time. -jdramsey 20150929
private int[] getNonMissingRows(Node x, Node y, List<Node> z) {
// List<Integer> rows = new ArrayList<Integer>();
//
// I:
// for (int i = 0; i < internalData.getNumRows(); i++) {
// for (Node node : variablesPerNode.get(x)) {
// if (isMissing(node, i)) continue I;
// }
//
// for (Node node : variablesPerNode.get(y)) {
// if (isMissing(node, i)) continue I;
// }
//
// for (Node _z : z) {
// for (Node node : variablesPerNode.get(_z)) {
// if (isMissing(node, i)) continue I;
// }
// }
//
// rows.add(i);
// }
// int[] _rows = new int[rows.size()];
// for (int k = 0; k < rows.size(); k++) _rows[k] = rows.get(k);
if (_rows == null) {
_rows = new int[internalData.getNumRows()];
for (int k = 0; k < _rows.length; k++) _rows[k] = k;
}
return _rows;
}
/**
* Constructs a new on-the-fly BayesIM that will calculate conditional probabilities on the fly
* from the given discrete data set, for the given Bayes PM.
*
* @param bayesPm the given Bayes PM, which specifies a directed acyclic graph for a Bayes net and
* parametrization for the Bayes net, but not actual values for the parameters.
* @param dataSet the discrete data set from which conditional probabilities should be estimated
* on the fly.
*/
public OnTheFlyMarginalCalculator(BayesPm bayesPm, DataSet dataSet)
throws IllegalArgumentException {
if (bayesPm == null) {
throw new NullPointerException();
}
if (dataSet == null) {
throw new NullPointerException();
}
// Make sure all of the variables in the PM are in the data set;
// otherwise, estimation is impossible.
BayesUtils.ensureVarsInData(bayesPm.getVariables(), dataSet);
// DataUtils.ensureVariablesExist(bayesPm, dataSet);
this.bayesPm = new BayesPm(bayesPm);
// Get the nodes from the BayesPm. This fixes the order of the nodes
// in the BayesIm, independently of any change to the BayesPm.
// (This order must be maintained.)
Graph graph = bayesPm.getDag();
this.nodes = graph.getNodes().toArray(new Node[0]);
// Initialize.
initialize();
// Create a subset of the data set with the variables of the IM, in
// the order of the IM.
List<Node> variables = getVariables();
this.dataSet = dataSet.subsetColumns(variables);
// Create a tautologous proposition for evidence.
this.evidence = new Evidence(Proposition.tautology(this));
}
/**
* @return the estimated conditional probability for the given assertion conditional on the given
* condition.
*/
public double getConditionalProb(Proposition assertion, Proposition condition) {
if (assertion.getVariableSource() != condition.getVariableSource()) {
throw new IllegalArgumentException(
"Assertion and condition must be " + "for the same Bayes IM.");
}
List<Node> assertionVars = assertion.getVariableSource().getVariables();
List<Node> dataVars = dataSet.getVariables();
assertionVars = GraphUtils.replaceNodes(assertionVars, dataVars);
if (!new HashSet<Node>(assertionVars).equals(new HashSet<Node>(dataVars))) {
throw new IllegalArgumentException(
"Assertion variable and data variables"
+ " are either different or in a different order: "
+ "\n\tAssertion vars: "
+ assertionVars
+ "\n\tData vars: "
+ dataVars);
}
int[] point = new int[dims.length];
int count1 = 0;
int count2 = 0;
this.missingValueCaseFound = false;
point:
for (int i = 0; i < numRows; i++) {
for (int j = 0; j < dims.length; j++) {
point[j] = dataSet.getInt(i, j);
if (point[j] == DiscreteVariable.MISSING_VALUE) {
continue point;
}
}
if (condition.isPermissibleCombination(point)) {
count1++;
if (assertion.isPermissibleCombination(point)) {
count2++;
}
}
}
return count2 / (double) count1;
}
public IndTestMixedMultipleTTest(DataSet data, double alpha) {
this.searchVariables = data.getVariables();
this.originalData = data.copy();
DataSet internalData = data.copy();
this.alpha = alpha;
List<Node> variables = internalData.getVariables();
for (Node node : variables) {
List<Node> nodes = expandVariable(internalData, node);
variablesPerNode.put(node, nodes);
}
this.internalData = internalData;
this.logisticRegression = new LogisticRegression(internalData);
this.regression = new RegressionDataset(internalData);
}
public void actionPerformed(ActionEvent e) {
DataSet dataSet = (DataSet) dataEditor.getSelectedDataModel();
if (dataSet == null || dataSet.getNumColumns() == 0) {
JOptionPane.showMessageDialog(
findOwner(), "Cannot display a scatter plot for an empty data set.");
return;
}
JPanel panel = new ScatterPlotView(dataSet);
EditorWindow editorWindow = new EditorWindow(panel, "Scatter Plots", "Save", true, dataEditor);
// JPanel dialog = createScatterPlotDialog(null, null);
// EditorWindow editorWindow = new EditorWindow(dialog, "Scatter Plots", "Save", true,
// dataEditor);
DesktopController.getInstance().addEditorWindow(editorWindow, JLayeredPane.PALETTE_LAYER);
editorWindow.pack();
editorWindow.setVisible(true);
}
public GesConcurrent(DataSet dataSet) {
setDataSet(dataSet);
if (dataSet.isDiscrete()) {
BDeuScore score = new BDeuScore(dataSet);
score.setSamplePrior(10);
score.setStructurePrior(0.001);
}
setStructurePrior(0.001);
setSamplePrior(10.);
}
private String reportIfDiscrete(Graph dag, DataSet dataSet) {
List vars = dataSet.getVariables();
Map<String, DiscreteVariable> nodesToVars = new HashMap<String, DiscreteVariable>();
for (int i = 0; i < dataSet.getNumColumns(); i++) {
DiscreteVariable var = (DiscreteVariable) vars.get(i);
String name = var.getName();
Node node = new GraphNode(name);
nodesToVars.put(node.getName(), var);
}
BayesPm bayesPm = new BayesPm(new Dag(dag));
List<Node> nodes = bayesPm.getDag().getNodes();
for (Node node : nodes) {
Node var = nodesToVars.get(node.getName());
if (var instanceof DiscreteVariable) {
DiscreteVariable var2 = nodesToVars.get(node.getName());
int numCategories = var2.getNumCategories();
List<String> categories = new ArrayList<String>();
for (int j = 0; j < numCategories; j++) {
categories.add(var2.getCategory(j));
}
bayesPm.setCategories(node, categories);
}
}
BayesProperties properties = new BayesProperties(dataSet, dag);
properties.setGraph(dag);
NumberFormat nf = NumberFormat.getInstance();
nf.setMaximumFractionDigits(4);
StringBuilder buf = new StringBuilder();
buf.append("\nP-value = ").append(properties.getLikelihoodRatioP());
buf.append("\nDf = ").append(properties.getPValueDf());
buf.append("\nChi square = ").append(nf.format(properties.getPValueChisq()));
buf.append("\nBIC score = ").append(nf.format(properties.getBic()));
buf.append("\n\nH0: Completely disconnected graph.");
return buf.toString();
}
@Override
public Graph search(DataSet dataSet, Parameters parameters) {
edu.cmu.tetrad.search.Mbfs search =
new edu.cmu.tetrad.search.Mbfs(
test.getTest(dataSet, parameters), parameters.getInt("depth"));
search.setKnowledge(knowledge);
this.targetName = parameters.getString("targetName");
Node target = dataSet.getVariable(targetName);
return search.search(target);
}
/**
* Executes the algorithm, producing (at least) a result workbench. Must be implemented in the
* extending class.
*/
public void execute() {
DataModel source = getDataModel();
if (!(source instanceof DataSet)) {
throw new IllegalArgumentException("Expecting a rectangular data set.");
}
DataSet data = (DataSet) source;
if (!data.isContinuous()) {
throw new IllegalArgumentException("Expecting a continuous data set.");
}
Lingam lingam = new Lingam();
lingam.setAlpha(getParams().getIndTestParams().getAlpha());
lingam.setPruningDone(true);
lingam.setAlpha(getParams().getIndTestParams().getAlpha());
GraphWithParameters result = lingam.lingam(data);
setResultGraph(result.getGraph());
GraphUtils.arrangeBySourceGraph(getResultGraph(), getSourceGraph());
}
private boolean isMissing(Node x, int i) {
int j = internalData.getColumn(x);
if (x instanceof DiscreteVariable) {
int v = internalData.getInt(i, j);
if (v == -99) {
return true;
}
}
if (x instanceof ContinuousVariable) {
double v = internalData.getDouble(i, j);
if (Double.isNaN(v)) {
return true;
}
}
return false;
}
public void actionPerformed(ActionEvent e) {
DataSet dataSet = (DataSet) dataEditor.getSelectedDataModel();
if (dataSet == null || dataSet.getNumColumns() == 0) {
JOptionPane.showMessageDialog(
findOwner(), "Cannot run normality tests on an empty data set.");
return;
}
// if there are missing values warn and don't display q-q plot.
// if(DataUtils.containsMissingValue(dataSet)){
// JOptionPane.showMessageDialog(findOwner(), new JLabel("<html>Data has missing
// values, " +
// "remove all missing values before<br>" +
// "running normality tests.</html>"));
// return;
// }
JPanel panel = createNormalityTestDialog(null);
EditorWindow window = new EditorWindow(panel, "Normality Tests", "Close", false, dataEditor);
DesktopController.getInstance().addEditorWindow(window, JLayeredPane.PALETTE_LAYER);
window.setVisible(true);
}
private List<Node> expandVariable(DataSet dataSet, Node node) {
if (node instanceof ContinuousVariable) {
return Collections.singletonList(node);
}
if (node instanceof DiscreteVariable && ((DiscreteVariable) node).getNumCategories() < 3) {
return Collections.singletonList(node);
}
if (!(node instanceof DiscreteVariable)) {
throw new IllegalArgumentException();
}
List<String> varCats = new ArrayList<String>(((DiscreteVariable) node).getCategories());
// first category is reference
varCats.remove(0);
List<Node> variables = new ArrayList<Node>();
for (String cat : varCats) {
Node newVar;
do {
String newVarName = node.getName() + "MULTINOM" + "." + cat;
newVar = new DiscreteVariable(newVarName, 2);
} while (dataSet.getVariable(newVar.getName()) != null);
variables.add(newVar);
dataSet.addVariable(newVar);
int newVarIndex = dataSet.getColumn(newVar);
int numCases = dataSet.getNumRows();
for (int l = 0; l < numCases; l++) {
Object dataCell = dataSet.getObject(l, dataSet.getColumn(node));
int dataCellIndex = ((DiscreteVariable) node).getIndex(dataCell.toString());
if (dataCellIndex == ((DiscreteVariable) node).getIndex(cat))
dataSet.setInt(l, newVarIndex, 1);
else dataSet.setInt(l, newVarIndex, 0);
}
}
return variables;
}
/** @return the splitNames selected by the editor. */
public static DataModel createSplits(DataSet dataSet, SplitCasesParams params) {
List<Integer> indices = new ArrayList<Integer>(dataSet.getNumRows());
for (int i = 0; i < dataSet.getNumRows(); i++) {
indices.add(i);
}
if (params.isDataShuffled()) {
Collections.shuffle(indices);
}
SplitCasesSpec spec = params.getSpec();
int numSplits = params.getNumSplits();
int sampleSize = spec.getSampleSize();
int[] breakpoints = spec.getBreakpoints();
List<String> splitNames = spec.getSplitNames();
int[] _breakpoints = new int[breakpoints.length + 2];
_breakpoints[0] = 0;
_breakpoints[_breakpoints.length - 1] = sampleSize;
System.arraycopy(breakpoints, 0, _breakpoints, 1, breakpoints.length);
DataModelList list = new DataModelList();
int ncols = dataSet.getNumColumns();
for (int n = 0; n < numSplits; n++) {
int _sampleSize = _breakpoints[n + 1] - _breakpoints[n];
DataSet _data = new ColtDataSet(_sampleSize, dataSet.getVariables());
_data.setName(splitNames.get(n));
for (int i = 0; i < _sampleSize; i++) {
int oldCase = indices.get(i + _breakpoints[n]);
for (int j = 0; j < ncols; j++) {
_data.setObject(i, j, dataSet.getObject(oldCase, j));
}
}
list.add(_data);
}
return list;
}
private double getForthMoment(int x, int y, int z, int w) {
if (cacheFourthMoments) {
if (fourthMoment == null) {
initializeForthMomentMatrix(dataSet.getVariables());
}
double sxyzw = fourthMoment[x][y][z][w];
if (sxyzw == 0.0) {
sxyzw = sxyzw(x, y, z, w);
setForthMoment(x, y, z, w, sxyzw);
}
return sxyzw;
} else {
return sxyzw(x, y, z, w);
}
}
