public DataSet simulateDataCholesky(
int sampleSize, TetradMatrix covar, List<Node> variableNodes) {
List<Node> variables = new LinkedList<Node>();
for (Node node : variableNodes) {
variables.add(node);
}
List<Node> newVariables = new ArrayList<Node>();
for (Node node : variables) {
ContinuousVariable continuousVariable = new ContinuousVariable(node.getName());
continuousVariable.setNodeType(node.getNodeType());
newVariables.add(continuousVariable);
}
TetradMatrix impliedCovar = covar;
DataSet fullDataSet = new ColtDataSet(sampleSize, newVariables);
TetradMatrix cholesky = MatrixUtils.choleskyC(impliedCovar);
// Simulate the data by repeatedly calling the Cholesky.exogenousData
// method. Store only the data for the measured variables.
ROW:
for (int row = 0; row < sampleSize; row++) {
// Step 1. Generate normal samples.
double exoData[] = new double[cholesky.rows()];
for (int i = 0; i < exoData.length; i++) {
exoData[i] = RandomUtil.getInstance().nextNormal(0, 1);
// exoData[i] = randomUtil.nextUniform(-1, 1);
}
// Step 2. Multiply by cholesky to get correct covariance.
double point[] = new double[exoData.length];
for (int i = 0; i < exoData.length; i++) {
double sum = 0.0;
for (int j = 0; j <= i; j++) {
sum += cholesky.get(i, j) * exoData[j];
}
point[i] = sum;
}
double rowData[] = point;
for (int col = 0; col < variables.size(); col++) {
int index = variableNodes.indexOf(variables.get(col));
double value = rowData[index];
if (Double.isNaN(value) || Double.isInfinite(value)) {
throw new IllegalArgumentException("Value out of range: " + value);
}
fullDataSet.setDouble(row, col, value);
}
}
return DataUtils.restrictToMeasured(fullDataSet);
}
