public SequenceStreamEventStream(ISequenceStream sequenceStream)
{
if (sequenceStream == null)
{
throw new ArgumentNullException("sequenceStream");
}
this.sequenceStream = sequenceStream;
}
public IMaxentModel Train(ISequenceStream events) {
if (!IsValid()) {
throw new InvalidOperationException("trainParams are not valid!");
}
var model = DoTrain(events);
AddToReport(Parameters.TrainerType, "EventModelSequence"); // EventModelSequenceTrainer.SEQUENCE_VALUE
return model;
}
public IMaxentModel Train(ISequenceStream events)
{
if (!IsValid())
{
throw new InvalidOperationException("trainParams are not valid!");
}
var model = DoTrain(events);
AddToReport(Parameters.TrainerType, "EventModelSequence"); // EventModelSequenceTrainer.SEQUENCE_VALUE
return(model);
}
protected override IMaxentModel DoTrain(ISequenceStream events) {
var useAvg = GetBoolParam("UseAverage", true);
return TrainModel(Iterations, events, Cutoff, useAvg);
}
public AbstractModel TrainModel(int trainIterations, ISequenceStream trainStream, int cutoff,
bool trainUseAverage) {
iterations = trainIterations;
useAverage = trainUseAverage;
sequenceStream = trainStream;
info.Append("Trained using Perceptron Sequence algorithm.\n\n");
var di = new OnePassDataIndexer(new SequenceStreamEventStream(trainStream), cutoff, false);
trainStream.Reset();
numSequences = 0;
while (trainStream.Read() != null) {
numSequences++;
}
outcomeList = di.GetOutcomeList();
predLabels = di.GetPredLabels();
pMap = new IndexHashTable<string>(predLabels, 0.7d);
// Incorporation indexed data for training...
numEvents = di.GetNumEvents();
outcomeLabels = di.GetOutcomeLabels();
oMap = new Dictionary<string, int>();
for (var i = 0; i < outcomeLabels.Length; i++) {
oMap.Add(outcomeLabels[i], i);
}
outcomeList = di.GetOutcomeList();
numPreds = predLabels.Length;
numOutcomes = outcomeLabels.Length;
if (trainUseAverage) {
updates = new int[numPreds][][];
for (var i = 0; i < numPreds; i++) {
updates[i] = new int[numOutcomes][];
for (var j = 0; j < numOutcomes; j++) {
updates[i][j] = new int[3];
}
}
}
// done.
Display("done.\n");
info.Append("Number of Event Tokens: {0}\n" +
" Number of Outcomes: {1}\n" +
" Number of Predicates: {2}\n", numEvents, numOutcomes, numPreds);
Display("\tNumber of Event Tokens: " + numEvents);
Display("\t Number of Outcomes: " + numOutcomes);
Display("\t Number of Predicates: " + numPreds);
param = new MutableContext[numPreds];
if (trainUseAverage)
averageParams = new MutableContext[numPreds];
allOutcomesPattern = new int[numOutcomes];
for (var i = 0; i < numOutcomes; i++) {
allOutcomesPattern[i] = i;
}
for (var pi = 0; pi < numPreds; pi++) {
param[pi] = new MutableContext(allOutcomesPattern, new double[numOutcomes]);
if (trainUseAverage)
averageParams[pi] = new MutableContext(allOutcomesPattern, new double[numOutcomes]);
for (var aoi = 0; aoi < numOutcomes; aoi++) {
param[pi].SetParameter(aoi, 0.0d);
if (trainUseAverage)
averageParams[pi].SetParameter(aoi, 0.0d);
}
}
Display("Computing model parameters...");
FindParameters();
Display("...done.");
/*************** Create and return the model ******************/
// ReSharper disable CoVariantArrayConversion
if (trainUseAverage)
return new PerceptronModel(averageParams, predLabels, outcomeLabels) {
info = info
};
return new PerceptronModel(param, predLabels, outcomeLabels) {
info = info
};
// ReSharper restore CoVariantArrayConversion
}
/// <summary>
/// Initializes a new instance of the <see cref="SequenceStreamEventStream"/> class.
/// </summary>
/// <param name="sequenceStream">The sequence stream.</param>
public SequenceStreamEventStream(ISequenceStream<Event> sequenceStream)
{
_enumerator = sequenceStream.GetEnumerator();
EventIndex = -1;
}
protected override IMaxentModel DoTrain(ISequenceStream events)
{
var useAvg = GetBoolParam("UseAverage", true);
return(TrainModel(Iterations, events, Cutoff, useAvg));
}
public AbstractModel TrainModel(int trainIterations, ISequenceStream trainStream, int cutoff,
bool trainUseAverage)
{
iterations = trainIterations;
useAverage = trainUseAverage;
sequenceStream = trainStream;
info.Append("Trained using Perceptron Sequence algorithm.\n\n");
var di = new OnePassDataIndexer(new SequenceStreamEventStream(trainStream), cutoff, false);
trainStream.Reset();
numSequences = 0;
while (trainStream.Read() != null)
{
numSequences++;
}
outcomeList = di.GetOutcomeList();
predLabels = di.GetPredLabels();
pMap = new IndexHashTable <string>(predLabels, 0.7d);
// Incorporation indexed data for training...
numEvents = di.GetNumEvents();
outcomeLabels = di.GetOutcomeLabels();
oMap = new Dictionary <string, int>();
for (var i = 0; i < outcomeLabels.Length; i++)
{
oMap.Add(outcomeLabels[i], i);
}
outcomeList = di.GetOutcomeList();
numPreds = predLabels.Length;
numOutcomes = outcomeLabels.Length;
if (trainUseAverage)
{
updates = new int[numPreds][][];
for (var i = 0; i < numPreds; i++)
{
updates[i] = new int[numOutcomes][];
for (var j = 0; j < numOutcomes; j++)
{
updates[i][j] = new int[3];
}
}
}
// done.
Display("done.\n");
info.Append("Number of Event Tokens: {0}\n" +
" Number of Outcomes: {1}\n" +
" Number of Predicates: {2}\n", numEvents, numOutcomes, numPreds);
Display("\tNumber of Event Tokens: " + numEvents);
Display("\t Number of Outcomes: " + numOutcomes);
Display("\t Number of Predicates: " + numPreds);
param = new MutableContext[numPreds];
if (trainUseAverage)
{
averageParams = new MutableContext[numPreds];
}
allOutcomesPattern = new int[numOutcomes];
for (var i = 0; i < numOutcomes; i++)
{
allOutcomesPattern[i] = i;
}
for (var pi = 0; pi < numPreds; pi++)
{
param[pi] = new MutableContext(allOutcomesPattern, new double[numOutcomes]);
if (trainUseAverage)
{
averageParams[pi] = new MutableContext(allOutcomesPattern, new double[numOutcomes]);
}
for (var aoi = 0; aoi < numOutcomes; aoi++)
{
param[pi].SetParameter(aoi, 0.0d);
if (trainUseAverage)
{
averageParams[pi].SetParameter(aoi, 0.0d);
}
}
}
Display("Computing model parameters...");
FindParameters();
Display("...done.");
/*************** Create and return the model ******************/
// ReSharper disable CoVariantArrayConversion
if (trainUseAverage)
{
return new PerceptronModel(averageParams, predLabels, outcomeLabels)
{
info = info
}
}
;
return(new PerceptronModel(param, predLabels, outcomeLabels)
{
info = info
});
// ReSharper restore CoVariantArrayConversion
}
/// <summary>
/// Initializes a new instance of the <see cref="SequenceStreamEventStream"/> class.
/// </summary>
/// <param name="sequenceStream">The sequence stream.</param>
public SequenceStreamEventStream(ISequenceStream <Event> sequenceStream)
{
_enumerator = sequenceStream.GetEnumerator();
EventIndex = -1;
}
protected abstract IMaxentModel DoTrain(ISequenceStream events);
protected abstract IMaxentModel DoTrain(ISequenceStream events);
