public void execute(Session bot, String channel, User user, String message)
throws SessionException {
SimpleStringTokenizer tokens = new SimpleStringTokenizer(message);
List<SessionCommand> commandList = new LinkedList<SessionCommand>();
SessionCommandManager manager = bot.getCommandManager();
while (tokens.hasMoreTokens()) {
SessionCommand command = manager.getCommand(tokens.nextToken());
if (command != null) {
commandList.add(command);
}
}
if (commandList.size() < 1) {
commandList = manager.getCommands();
}
String target = channel;
if (commandList.size() > 1) {
target = user.getNick();
}
for (SessionCommand t : commandList) {
String msg;
if (t.getUsage() == null) {
msg = manager.getPrefix() + t.getName();
} else {
msg = manager.getPrefix() + t.getName() + " " + t.getUsage();
}
bot.msg(target, msg);
}
}
public List<TrainAndTestReportCrisp> computeAvgCTSperM(List<TrainAndTestReportCrisp> reportsCTS) {
weightsCrisp.clear();
weightsInterval.clear();
Map<Model, List<TrainAndTestReportCrisp>> mapForAvg = new HashMap<>();
for (TrainAndTestReportCrisp r : reportsCTS) {
if (mapForAvg.containsKey(r.getModel())) {
mapForAvg.get(r.getModel()).add(r);
} else {
List<TrainAndTestReportCrisp> l = new ArrayList<>();
l.add(r);
mapForAvg.put(r.getModel(), l);
}
}
List<TrainAndTestReportCrisp> avgReports = new ArrayList<>();
for (Model model : mapForAvg.keySet()) {
List<TrainAndTestReportCrisp> l = mapForAvg.get(model);
if (l.size() == 1) { // does not make sense to compute average over one series
// do not compute anything
} else {
TrainAndTestReportCrisp thisAvgReport = computeAvgCTS(l, model);
if (thisAvgReport != null) {
avgReports.add(thisAvgReport);
} else { // should never happen for the same method
System.err.println("nerovnake percenttrain v ramci 1 modelu pri avg CTS per method :/");
}
}
}
return avgReports;
}
public List<TrainAndTestReportInterval> computeAvgIntTSperM(
List<TrainAndTestReportInterval> reportsIntTS) {
Map<Model, List<TrainAndTestReportInterval>> mapForAvg = new HashMap<>();
// first go through all the reports once to determine how many of each kind there are:
for (TrainAndTestReportInterval r : reportsIntTS) {
if (mapForAvg.containsKey(r.getModel())) {
mapForAvg.get(r.getModel()).add(r);
List<TrainAndTestReportInterval> l = new ArrayList<>();
l.add(r);
mapForAvg.put(r.getModel(), l);
}
}
List<TrainAndTestReportInterval> avgReports = new ArrayList<>();
for (Model model : mapForAvg.keySet()) {
List<TrainAndTestReportInterval> l = mapForAvg.get(model);
if (l.size() > 1) { // does not make sense to compute average over one series
TrainAndTestReportInterval reportAvgMethod = computeAvgIntTS(l, model);
if (reportAvgMethod != null) {
avgReports.add(reportAvgMethod);
} else { // should never happen for the same method
System.err.println("not equal percenttrain for 1 model (avg ITS per method)");
}
}
}
return avgReports;
}
private static void recomputeTrainTestSets(TrainAndTestReport r) { // TODO a nicer way? :)
if (r instanceof TrainAndTestReportCrisp) {
TrainAndTestReportCrisp rep = (TrainAndTestReportCrisp) r;
double[] newFittedValues =
Arrays.copyOfRange(rep.getFittedValues(), 0, rep.getNumTrainingEntries());
double[] newForecastValsTest =
Arrays.copyOfRange(
rep.getFittedValues(), rep.getNumTrainingEntries(), rep.getFittedValues().length);
rep.setFittedValues(newFittedValues);
rep.setForecastValuesTest(newForecastValsTest);
double[] newRealTrain =
Arrays.copyOfRange(rep.getRealOutputsTrain(), 0, rep.getNumTrainingEntries());
double[] newRealTest =
Arrays.copyOfRange(
rep.getRealOutputsTrain(),
rep.getNumTrainingEntries(),
rep.getRealOutputsTrain().length);
rep.setRealOutputsTrain(newRealTrain);
rep.setRealOutputsTest(newRealTest);
rep.setErrorMeasures(
ErrorMeasuresUtils.computeAllErrorMeasuresCrisp(
Utils.arrayToList(newRealTrain),
Utils.arrayToList(newRealTest),
Utils.arrayToList(newFittedValues),
Utils.arrayToList(newForecastValsTest),
0)); // TODO I hope the 0 is not a problem
} else if (r instanceof TrainAndTestReportInterval) {
TrainAndTestReportInterval rep = (TrainAndTestReportInterval) r;
List<Interval> newFittedValues =
new ArrayList<>(rep.getFittedValues().subList(0, rep.getNumTrainingEntries()));
List<Interval> newForecastValsTest =
new ArrayList<>(
rep.getFittedValues()
.subList(rep.getNumTrainingEntries(), rep.getFittedValues().size()));
rep.setFittedValues(newFittedValues);
rep.setForecastValuesTest(newForecastValsTest);
List<Interval> realVals =
Utils.zipLowerUpperToIntervals(rep.getRealValuesLowers(), rep.getRealValuesUppers());
List<Interval> realValsTrain =
new ArrayList<>(realVals.subList(0, rep.getNumTrainingEntries()));
List<Interval> realValsTest =
new ArrayList<>(realVals.subList(rep.getNumTrainingEntries(), realVals.size()));
// TODO somehow add the actual distance and seasonality from params
rep.setErrorMeasures(
ErrorMeasuresUtils.computeAllErrorMeasuresInterval(
realValsTrain,
realValsTest,
newFittedValues,
newForecastValsTest,
new WeightedEuclideanDistance(0.5),
0));
}
}
public TrainAndTestReportInterval computeAvgIntTS(
List<TrainAndTestReportInterval> reportsIntTS, Model model) {
if (!allTheSamePercentTrain(reportsIntTS)) { // throw an error, we cannot compute it like this
return null;
} else {
MyRengine rengine = MyRengine.getRengine();
if (reportsIntTS.size() == 1) { // does not make sense to compute average over one series
return reportsIntTS.get(0);
} else {
StringBuilder avgAllLowersTrain = new StringBuilder("(");
StringBuilder avgAllLowersTest = new StringBuilder("(");
StringBuilder avgAllLowersFuture = new StringBuilder("(");
StringBuilder avgAllUppersTrain = new StringBuilder("(");
StringBuilder avgAllUppersTest = new StringBuilder("(");
StringBuilder avgAllUppersFuture = new StringBuilder("(");
StringBuilder sumWeightsTrain = new StringBuilder("(");
StringBuilder sumWeightsTest = new StringBuilder("(");
StringBuilder sumWeightsFuture = new StringBuilder("(");
boolean next = false;
for (TrainAndTestReportInterval r : reportsIntTS) {
if (next) {
avgAllLowersTrain.append(" + ");
avgAllLowersTest.append(" + ");
avgAllLowersFuture.append(" + ");
avgAllUppersTrain.append(" + ");
avgAllUppersTest.append(" + ");
avgAllUppersFuture.append(" + ");
sumWeightsTrain.append(" + ");
sumWeightsTest.append(" + ");
sumWeightsFuture.append(" + ");
} else {
next = true;
}
double weightTrain = getWeightForModelTrain(r);
double weightTest = getWeightForModelTest(r);
double weightFuture = getWeightForModelFuture(r);
weightsInterval.put(r.toString(), weightFuture);
sumWeightsTrain.append(weightTrain);
sumWeightsTest.append(weightTest);
avgAllLowersTrain
.append(weightTrain)
.append("*")
.append(Utils.arrayToRVectorString(r.getFittedValuesLowers()));
avgAllLowersTest
.append(weightTest)
.append("*")
.append(Utils.arrayToRVectorString(r.getForecastValuesTestLowers()));
avgAllUppersTrain
.append(weightTrain)
.append("*")
.append(Utils.arrayToRVectorString(r.getFittedValuesUppers()));
avgAllUppersTest
.append(weightTest)
.append("*")
.append(Utils.arrayToRVectorString(r.getForecastValuesTestUppers()));
avgAllLowersFuture.append(weightFuture).append("*");
avgAllUppersFuture.append(weightFuture).append("*");
if (r.getForecastValuesFuture().size() > 0) {
avgAllLowersFuture.append(
Utils.arrayToRVectorString(r.getForecastValuesFutureLowers()));
avgAllUppersFuture.append(
Utils.arrayToRVectorString(r.getForecastValuesFutureUppers()));
sumWeightsFuture.append(weightFuture);
} else {
avgAllLowersFuture.append("0");
avgAllUppersFuture.append("0");
sumWeightsFuture.append("0");
}
}
sumWeightsTrain.append(")");
sumWeightsTest.append(")");
sumWeightsFuture.append(")");
avgAllLowersTrain.append(")/").append(sumWeightsTrain);
avgAllLowersTest.append(")/").append(sumWeightsTest);
avgAllLowersFuture.append(")/").append(sumWeightsFuture);
avgAllUppersTrain.append(")/").append(sumWeightsTrain);
avgAllUppersTest.append(")/").append(sumWeightsTest);
avgAllUppersFuture.append(")/").append(sumWeightsFuture);
rengine.eval("lowerTrain <- " + avgAllLowersTrain.toString());
rengine.eval("lowerTest <- " + avgAllLowersTest.toString());
rengine.eval("lowerFuture <- " + avgAllLowersFuture.toString());
rengine.eval("upperTrain <- " + avgAllUppersTrain.toString());
rengine.eval("upperTest <- " + avgAllUppersTest.toString());
rengine.eval("upperFuture <- " + avgAllUppersFuture.toString());
// add report:
List<Double> allLowersTrainList = rengine.evalAndReturnList("lowerTrain");
List<Double> allLowersTestList = rengine.evalAndReturnList("lowerTest");
List<Double> allUppersTrainList = rengine.evalAndReturnList("upperTrain");
List<Double> allUppersTestList = rengine.evalAndReturnList("upperTest");
List<Interval> allIntervalsTrain =
Utils.zipLowerUpperToIntervals(allLowersTrainList, allUppersTrainList);
List<Interval> allIntervalsTest =
Utils.zipLowerUpperToIntervals(allLowersTestList, allUppersTestList);
List<Double> realValuesLowers = reportsIntTS.get(0).getRealValuesLowers();
List<Double> realValuesUppers = reportsIntTS.get(0).getRealValuesUppers();
List<Double> realValuesLowersTrain =
realValuesLowers.subList(0, reportsIntTS.get(0).getNumTrainingEntries());
List<Double> realValuesUppersTrain =
realValuesUppers.subList(0, reportsIntTS.get(0).getNumTrainingEntries());
List<Double> realValuesLowersTest =
realValuesLowers.subList(
reportsIntTS.get(0).getNumTrainingEntries(), realValuesLowers.size());
List<Double> realValuesUppersTest =
realValuesUppers.subList(
reportsIntTS.get(0).getNumTrainingEntries(), realValuesUppers.size());
List<Interval> realValuesTrain =
Utils.zipLowerUpperToIntervals(realValuesLowersTrain, realValuesUppersTrain);
List<Interval> realValuesTest =
Utils.zipLowerUpperToIntervals(realValuesLowersTest, realValuesUppersTest);
ErrorMeasuresInterval errorMeasures =
ErrorMeasuresUtils.computeAllErrorMeasuresInterval(
realValuesTrain,
realValuesTest,
allIntervalsTrain,
allIntervalsTest,
new WeightedEuclideanDistance(0.5),
0);
// TODO chg; for now takes WeightedEuclid, but allow any distance
TrainAndTestReportInterval reportAvgAllITS =
new TrainAndTestReportInterval(model, "_int(" + getName() + ")", true);
reportAvgAllITS.setErrorMeasures(errorMeasures);
reportAvgAllITS.setFittedValues(allIntervalsTrain);
reportAvgAllITS.setForecastValuesTest(allIntervalsTest);
List<Double> allLowersFutureList = rengine.evalAndReturnList("lowerFuture");
List<Double> allUppersFutureList = rengine.evalAndReturnList("upperFuture");
List<Interval> allIntervalsFuture =
Utils.zipLowerUpperToIntervals(allLowersFutureList, allUppersFutureList);
reportAvgAllITS.setForecastValuesFuture(allIntervalsFuture);
reportAvgAllITS.setNumTrainingEntries(reportsIntTS.get(0).getNumTrainingEntries());
realValuesTrain.addAll(realValuesTest);
reportAvgAllITS.setRealValues(realValuesTrain);
rengine.rm(
"lowerTrain", "lowerTest", "lowerFuture", "upperTrain", "upperTest", "upperFuture");
return reportAvgAllITS;
}
}
}
public TrainAndTestReportCrisp computeAvgCTS(
List<TrainAndTestReportCrisp> reportsCTS, Model model) {
if (reportsCTS.size() == 1) { // does not make sense to compute average over one series
return reportsCTS.get(
0); // TODO do not return anything (but take care of it on the receiving end), because
// otherwise it draws twice. but then problems with "drawOnlyAVG"
} else {
if (!allTheSamePercentTrain(reportsCTS)) {
return null;
} else {
StringBuilder fittedValsAvgAll = new StringBuilder("(");
StringBuilder forecastValsTestAvgAll = new StringBuilder("(");
StringBuilder forecastValsFutureAvgAll = new StringBuilder("(");
StringBuilder sumWeightsTrain = new StringBuilder("(");
StringBuilder sumWeightsTest = new StringBuilder("(");
StringBuilder sumWeightsFuture = new StringBuilder("(");
boolean next = false;
for (TrainAndTestReportCrisp r : reportsCTS) {
if (next) {
fittedValsAvgAll.append(" + ");
forecastValsTestAvgAll.append(" + ");
forecastValsFutureAvgAll.append(" + ");
sumWeightsTrain.append(" + ");
sumWeightsTest.append(" + ");
sumWeightsFuture.append(" + ");
} else {
next = true;
}
double weightTrain = getWeightForModelTrain(r);
double weightTest = getWeightForModelTest(r);
double weightFuture = getWeightForModelFuture(r);
weightsCrisp.put(r.toString(), weightFuture);
sumWeightsTrain.append(weightTrain);
sumWeightsTest.append(weightTest);
fittedValsAvgAll
.append(weightTrain)
.append("*")
.append(Utils.arrayToRVectorString(r.getFittedValues()));
forecastValsTestAvgAll
.append(weightTest)
.append("*")
.append(Utils.arrayToRVectorString(r.getForecastValuesTest()));
forecastValsFutureAvgAll.append(weightFuture).append("*");
if (r.getForecastValuesFuture().length > 0) {
forecastValsFutureAvgAll.append(
Utils.arrayToRVectorString(r.getForecastValuesFuture()));
sumWeightsFuture.append(weightFuture);
} else {
forecastValsFutureAvgAll.append("0");
sumWeightsFuture.append("0");
}
}
sumWeightsTrain.append(")");
sumWeightsTest.append(")");
sumWeightsFuture.append(")");
fittedValsAvgAll.append(")/").append(sumWeightsTrain);
forecastValsTestAvgAll.append(")/").append(sumWeightsTest);
forecastValsFutureAvgAll.append(")/").append(sumWeightsFuture);
String avgAll =
"c("
+ fittedValsAvgAll
+ ","
+ forecastValsTestAvgAll
+ ","
+ forecastValsFutureAvgAll
+ ")";
MyRengine rengine = MyRengine.getRengine();
// and create a new report for this avg and add it to reportsCTS:
TrainAndTestReportCrisp thisAvgReport =
new TrainAndTestReportCrisp(model, "(" + getName() + ")", true);
double[] fittedValsAvg = rengine.evalAndReturnArray(fittedValsAvgAll.toString());
double[] forecastValsTestAvg =
rengine.evalAndReturnArray(forecastValsTestAvgAll.toString());
ErrorMeasuresCrisp errorMeasures =
ErrorMeasuresUtils.computeAllErrorMeasuresCrisp(
Utils.arrayToList(reportsCTS.get(0).getRealOutputsTrain()),
Utils.arrayToList(reportsCTS.get(0).getRealOutputsTest()),
Utils.arrayToList(fittedValsAvg),
Utils.arrayToList(forecastValsTestAvg),
0);
thisAvgReport.setErrorMeasures(errorMeasures);
double[] forecastValsFutureAvg =
rengine.evalAndReturnArray(forecastValsFutureAvgAll.toString());
thisAvgReport.setForecastValuesFuture(forecastValsFutureAvg);
thisAvgReport.setPlotCode("plot.ts(" + avgAll + ", lty=2)");
thisAvgReport.setFittedValues(fittedValsAvg);
thisAvgReport.setForecastValuesTest(forecastValsTestAvg);
thisAvgReport.setNumTrainingEntries(fittedValsAvg.length);
thisAvgReport.setRealOutputsTrain(reportsCTS.get(0).getRealOutputsTrain());
thisAvgReport.setRealOutputsTest(reportsCTS.get(0).getRealOutputsTest());
return thisAvgReport;
}
}
}