/// <summary>
/// Runs the generated prediction algorithm for the specified features.
/// </summary>
/// <param name="featureValues">The feature values.</param>
/// <param name="featureIndexes">The feature indexes.</param>
/// <param name="iterationCount">The number of iterations to run the prediction algorithm for.</param>
/// <returns>The predictive distributions over labels.</returns>
public IEnumerable <TLabelDistribution> PredictDistribution(double[][] featureValues, int[][] featureIndexes, int iterationCount)
{
InferenceAlgorithmUtilities.CheckIterationCount(iterationCount);
InferenceAlgorithmUtilities.CheckFeatures(this.UseSparseFeatures, this.FeatureCount, featureValues, featureIndexes);
// Update prior weight distributions of the prediction algorithm to the posterior weight distributions of the training algorithm
this.PredictionAlgorithm.SetObservedValue(InferenceQueryVariableNames.WeightPriors, this.WeightMarginals);
// Infer posterior distribution over labels, one instance after the other
for (int i = 0; i < featureValues.Length; i++)
{
// Observe a single feature vector
this.PredictionAlgorithm.SetObservedValue(InferenceQueryVariableNames.InstanceCount, 1);
if (this.UseSparseFeatures)
{
this.PredictionAlgorithm.SetObservedValue(InferenceQueryVariableNames.InstanceFeatureCounts, new[] { featureValues[i].Length });
this.PredictionAlgorithm.SetObservedValue(InferenceQueryVariableNames.FeatureIndexes, new[] { featureIndexes[i] });
}
this.PredictionAlgorithm.SetObservedValue(InferenceQueryVariableNames.FeatureValues, new[] { featureValues[i] });
// Infer the posterior distribution over the label
yield return(this.CopyLabelDistribution(this.RunPredictionAlgorithm(iterationCount)));
}
}
/// <summary>
/// Runs the generated training algorithm for the specified features and labels.
/// </summary>
/// <param name="featureValues">The feature values.</param>
/// <param name="featureIndexes">The feature indexes.</param>
/// <param name="labels">The labels.</param>
/// <param name="iterationCount">The number of iterations to run the training algorithm for.</param>
/// <param name="batchNumber">
/// An optional batch number. Defaults to 0 and is used only if the training data is divided into batches.
/// </param>
protected virtual void TrainInternal(double[][] featureValues, int[][] featureIndexes, TLabel[] labels, int iterationCount, int batchNumber = 0)
{
InferenceAlgorithmUtilities.CheckIterationCount(iterationCount);
InferenceAlgorithmUtilities.CheckBatchNumber(batchNumber, this.BatchCount);
InferenceAlgorithmUtilities.CheckFeatures(this.UseSparseFeatures, this.FeatureCount, featureValues, featureIndexes);
Debug.Assert(featureValues.Length == labels.Length, "There must be the same number of feature values and labels.");
// Observe features and labels
this.TrainingAlgorithm.SetObservedValue(InferenceQueryVariableNames.InstanceCount, featureValues.Length);
if (this.UseSparseFeatures)
{
this.TrainingAlgorithm.SetObservedValue(
InferenceQueryVariableNames.InstanceFeatureCounts,
Util.ArrayInit(featureValues.Length, instance => featureValues[instance].Length));
this.TrainingAlgorithm.SetObservedValue(InferenceQueryVariableNames.FeatureIndexes, featureIndexes);
}
this.TrainingAlgorithm.SetObservedValue(InferenceQueryVariableNames.FeatureValues, featureValues);
this.Labels = labels;
if (this.BatchCount == 1)
{
// Required for incremental training
this.WeightConstraints = this.WeightMarginalsDividedByPriors;
// Run the training algorithm
this.RunTrainingAlgorithm(iterationCount);
}
else
{
// Print information about current batch to console
if (this.ShowProgress)
{
Console.WriteLine("Batch {0} [{1} instance{2}]", batchNumber + 1, featureValues.Length, featureValues.Length == 1 ? string.Empty : "s");
}
// Compute the constraint distributions for the weights for the given batch
this.WeightConstraints.SetToRatio(this.WeightMarginalsDividedByPriors, this.BatchWeightOutputMessages[batchNumber]);
// Run the training algorithm
this.RunTrainingAlgorithm(iterationCount);
// Update the output messages for the weights for the given batch
this.BatchWeightOutputMessages[batchNumber].SetToRatio(this.WeightMarginalsDividedByPriors, this.WeightConstraints);
}
}
