/**
* Computes the specified quantile elements over the values previously added.
*
* @param phis the quantiles for which elements are to be computed. Each phi must be in the
* interval (0.0,1.0]. <tt>phis</tt> must be sorted ascending.
* @return the approximate quantile elements.
*/
public DoubleArrayList quantileElements(DoubleArrayList phis) {
if (precomputeEpsilon <= 0.0) return super.quantileElements(phis);
int quantilesToPrecompute = (int) Utils.epsilonCeiling(1.0 / precomputeEpsilon);
/*
* if (phis.size() > quantilesToPrecompute) { // illegal use case! // we
* compute results, but loose explicit approximation guarantees. return
* super.quantileElements(phis); }
*/
// select that quantile from the precomputed set that corresponds to a
// position closest to phi.
phis = phis.copy();
double e = precomputeEpsilon;
for (int index = phis.size(); --index >= 0; ) {
double phi = phis.get(index);
int i = (int) Math.round(((2.0 * phi / e) - 1.0) / 2.0); // finds
// closest
i = Math.min(quantilesToPrecompute - 1, Math.max(0, i));
double augmentedPhi = (e / 2.0) * (1 + 2 * i);
phis.set(index, augmentedPhi);
}
return super.quantileElements(phis);
}
private double pValue(Node node, List<Node> parents) {
List<Double> _residuals = new ArrayList<Double>();
Node _target = node;
List<Node> _regressors = parents;
Node target = getVariable(variables, _target.getName());
List<Node> regressors = new ArrayList<Node>();
for (Node _regressor : _regressors) {
Node variable = getVariable(variables, _regressor.getName());
regressors.add(variable);
}
DATASET:
for (int m = 0; m < dataSets.size(); m++) {
RegressionResult result = regressions.get(m).regress(target, regressors);
TetradVector residualsSingleDataset = result.getResiduals();
for (int h = 0; h < residualsSingleDataset.size(); h++) {
if (Double.isNaN(residualsSingleDataset.get(h))) {
continue DATASET;
}
}
DoubleArrayList _residualsSingleDataset =
new DoubleArrayList(residualsSingleDataset.toArray());
double mean = Descriptive.mean(_residualsSingleDataset);
double std =
Descriptive.standardDeviation(
Descriptive.variance(
_residualsSingleDataset.size(),
Descriptive.sum(_residualsSingleDataset),
Descriptive.sumOfSquares(_residualsSingleDataset)));
for (int i2 = 0; i2 < _residualsSingleDataset.size(); i2++) {
// _residualsSingleDataset.set(i2, (_residualsSingleDataset.get(i2) - mean) /
// std);
if (isMeanCenterResiduals()) {
_residualsSingleDataset.set(i2, (_residualsSingleDataset.get(i2) - mean));
}
// _residualsSingleDataset.set(i2, (_residualsSingleDataset.get(i2)));
}
for (int k = 0; k < _residualsSingleDataset.size(); k++) {
_residuals.add(_residualsSingleDataset.get(k));
}
}
double[] _f = new double[_residuals.size()];
for (int k = 0; k < _residuals.size(); k++) {
_f[k] = _residuals.get(k);
}
return new AndersonDarlingTest(_f).getP();
}
private double andersonDarlingPASquareStarB(Node node, List<Node> parents) {
List<Double> _residuals = new ArrayList<Double>();
Node _target = node;
List<Node> _regressors = parents;
Node target = getVariable(variables, _target.getName());
List<Node> regressors = new ArrayList<Node>();
for (Node _regressor : _regressors) {
Node variable = getVariable(variables, _regressor.getName());
regressors.add(variable);
}
double sum = 0.0;
DATASET:
for (int m = 0; m < dataSets.size(); m++) {
RegressionResult result = regressions.get(m).regress(target, regressors);
TetradVector residualsSingleDataset = result.getResiduals();
for (int h = 0; h < residualsSingleDataset.size(); h++) {
if (Double.isNaN(residualsSingleDataset.get(h))) {
continue DATASET;
}
}
DoubleArrayList _residualsSingleDataset =
new DoubleArrayList(residualsSingleDataset.toArray());
double mean = Descriptive.mean(_residualsSingleDataset);
double std =
Descriptive.standardDeviation(
Descriptive.variance(
_residualsSingleDataset.size(),
Descriptive.sum(_residualsSingleDataset),
Descriptive.sumOfSquares(_residualsSingleDataset)));
// By centering the individual residual columns, all moments of the mixture become weighted
// averages of the moments
// of the individual columns.
// http://en.wikipedia.org/wiki/Mixture_distribution#Finite_and_countable_mixtures
for (int i2 = 0; i2 < _residualsSingleDataset.size(); i2++) {
// _residualsSingleDataset.set(i2, (_residualsSingleDataset.get(i2) - mean) /
// std);
// _residualsSingleDataset.set(i2, (_residualsSingleDataset.get(i2)) / std);
if (isMeanCenterResiduals()) {
_residualsSingleDataset.set(i2, (_residualsSingleDataset.get(i2) - mean));
}
}
double[] _f = new double[_residuals.size()];
for (int k = 0; k < _residuals.size(); k++) {
_f[k] = _residuals.get(k);
}
sum += new AndersonDarlingTest(_f).getASquaredStar();
}
return sum / dataSets.size();
}
private double localScoreB(Node node, List<Node> parents) {
double score = 0.0;
double maxScore = Double.NEGATIVE_INFINITY;
Node _target = node;
List<Node> _regressors = parents;
Node target = getVariable(variables, _target.getName());
List<Node> regressors = new ArrayList<Node>();
for (Node _regressor : _regressors) {
Node variable = getVariable(variables, _regressor.getName());
regressors.add(variable);
}
DATASET:
for (int m = 0; m < dataSets.size(); m++) {
RegressionResult result = regressions.get(m).regress(target, regressors);
TetradVector residualsSingleDataset = result.getResiduals();
DoubleArrayList _residualsSingleDataset =
new DoubleArrayList(residualsSingleDataset.toArray());
for (int h = 0; h < residualsSingleDataset.size(); h++) {
if (Double.isNaN(residualsSingleDataset.get(h))) {
continue DATASET;
}
}
double mean = Descriptive.mean(_residualsSingleDataset);
double std =
Descriptive.standardDeviation(
Descriptive.variance(
_residualsSingleDataset.size(),
Descriptive.sum(_residualsSingleDataset),
Descriptive.sumOfSquares(_residualsSingleDataset)));
for (int i2 = 0; i2 < _residualsSingleDataset.size(); i2++) {
_residualsSingleDataset.set(i2, (_residualsSingleDataset.get(i2) - mean) / std);
}
double[] _f = new double[_residualsSingleDataset.size()];
for (int k = 0; k < _residualsSingleDataset.size(); k++) {
_f[k] = _residualsSingleDataset.get(k);
}
DoubleArrayList f = new DoubleArrayList(_f);
for (int k = 0; k < f.size(); k++) {
f.set(k, Math.abs(f.get(k)));
}
double _mean = Descriptive.mean(f);
double diff = _mean - Math.sqrt(2.0 / Math.PI);
score += diff * diff;
if (score > maxScore) {
maxScore = score;
}
}
double avg = score / dataSets.size();
return avg;
}