/// <summary>
/// Build the feature package including NTB likelihoods for the specified query. Doesn't include the target likelihood.
/// </summary>
/// <param name="query"></param>
/// <param name="partialQUery"></param>
/// <returns></returns>
private FeaturePackage BuildFeaturePackage(BaseIndexEntry prefixEntry, string partialQuery, bool includeQueryCountsFeature = false)
{
FeaturePackage tpq = new FeaturePackage();
tpq.Query = prefixEntry.Query;
tpq.NtbFeatures = new double[_ntbs.Length];
// Set the NTB counts
for (int i = 0; i < _ntbs.Length; i++)
{
double queryFrequency = _ntbs[i][partialQuery].GetQueryFrequency(prefixEntry.Query);
// Calculate probability and apply
tpq.NtbFeatures[i] = queryFrequency; // TODO: don't normalise for now / Convert.ToDouble(ntbN);
// Include query counts feature if necessary
if (includeQueryCountsFeature)
{
// IGNORE THIS FOR NOW
//tpq.QueriesSinceLastTrain = _currentQueryCount - _queryCountsForPrefix[partialQuery].OldestItem();
}
}
return tpq;
}
/// <summary>
/// Train the model with a FeaturePackage for a query
/// </summary>
/// <param name="features"></param>
/// <param name="target"></param>
public void TrainModel(FeaturePackage fp)
{
double[] instance = fp.NtbFeatures; //new double[fp.NtbFeatures.Length + 1];
// Prepend the 1.0 intercept parameter to the features
//instance[0] = 1.0;
//for (int i = 0; i < fp.NtbFeatures.Length; i++)
// instance[i + 1] = fp.NtbFeatures[i]; // Set the rest of the features
// Set scale factors
for (int i = 0; i < instance.Length; i++)
instance[i] = instance[i] / _maxNtbSize;
double target = fp.TargetLikelihood / _predictionHorizon; // Predict in next 100 - so use 100 scaling factor
double prediction = Predict(instance);
double squaredError = Math.Pow(target - prediction, 2);
_totalSqrdError += squaredError;
// Iterate using SGD for each parameter
for (int j = 0; j < _modelWeights.Length; j++)
{
double error = (prediction - target);
_modelWeights[j] = _modelWeights[j] - (
_learningRateAlpha * error * instance[j]
);
}
_trainingInstances++;
if (_trainingInstances % 2000 == 0)
{
string modelStr = "";
foreach (double weight in _modelWeights)
modelStr += weight.ToString("F8") + ", ";
// Output
Console.WriteLine("SGD LR Training instances at " + _trainingInstances.ToString() + ", avg sqrd error: " + (_totalSqrdError / Convert.ToDouble(_trainingInstances)).ToString() + ", model: " + modelStr);
}
}