/// <summary>
/// Applies the specified inputs.
/// </summary>
/// <param name="inputs">The inputs.</param>
/// <param name="featureSet">The feature set.</param>
/// <param name="priors">The priors.</param>
/// <param name="results">The results.</param>
/// <param name="mode">The mode.</param>
/// <param name="showFactorGraph">if set to <c>true</c> [show factor graph].</param>
public void Apply(Inputs inputs, FeatureSet featureSet, CommunityPriors priors, ref Results results, InputMode mode, bool showFactorGraph = false)
{
this.Engine.ShowFactorGraph = showFactorGraph;
if (mode.HasFlag(InputMode.Validation))
{
this.SetObservedValues(new[] { inputs.Validation.Instances }, featureSet, InputMode.Validation, priors);
results.Validation = new Results.ResultsSet {
IsRepliedTo = this.Engine.Infer <Bernoulli[][]>(this.RepliedTo)[0]
};
}
if (mode.HasFlag(InputMode.Testing))
{
this.SetObservedValues(new[] { inputs.Test.Instances }, featureSet, InputMode.Testing, priors);
results.Test = new Results.ResultsSet {
IsRepliedTo = this.Engine.Infer <Bernoulli[][]>(this.RepliedTo)[0]
};
}
this.Engine.ShowFactorGraph = this.ShowFactorGraph;
}
/// <summary> /// Sets the observed values. /// </summary> /// <param name="instances">The instances.</param> /// <param name="featureSet">The feature set.</param> /// <param name="mode">The mode.</param> /// <param name="communityPriors">The community priors.</param> public abstract void SetObservedValues(IList <IList <Inputs.Instance> > instances, FeatureSet featureSet, InputMode mode, CommunityPriors communityPriors);
/// <summary>
/// Sets the observed values.
/// </summary>
/// <param name="instances">The instances.</param>
/// <param name="featureSet">The feature set.</param>
/// <param name="mode">The mode.</param>
/// <param name="communityPriors">The community priors.</param>
/// <exception cref="System.InvalidOperationException">Data is null</exception>
public override void SetObservedValues(IList <IList <Inputs.Instance> > instances, FeatureSet featureSet, InputMode mode, CommunityPriors communityPriors)
{
// This is for personalisation training and testing
if (instances == null)
{
throw new InvalidOperationException("Data is null");
}
this.NumberOfPeople.ObservedValue = instances.Count;
this.NumberOfMessages.ObservedValue = instances.Select(ia => ia.Count).ToArray();
this.NumberOfFeatures.ObservedValue = mode == InputMode.CommunityTraining
? featureSet.SharedFeatureVectorLength
: featureSet.FeatureVectorLength;
this.NoiseVariance.ObservedValue = communityPriors.NoiseVariance;
if (mode == InputMode.CommunityTraining || mode == InputMode.Training)
{
this.RepliedTo.ObservedValue = instances.Select(inner => inner.Select(ia => ia.Label).ToArray()).ToArray();
}
this.WeightMeanPriors.ObservedValue = DistributionArrayHelpers.Copy(communityPriors.WeightMeans.Values);
this.WeightPrecisionPriors.ObservedValue = DistributionArrayHelpers.Copy(communityPriors.WeightPrecisions.Values);
// If this is community training, we only want shared features
Func <KeyValuePair <FeatureBucket, double>, bool> predicate =
fbv => mode != InputMode.CommunityTraining || fbv.Key.Feature.IsShared;
this.FeatureIndices.ObservedValue =
instances.Select(
inner =>
inner.Select(
ia => ia.FeatureValues.Where(predicate).Select(fbv => featureSet.FeatureBuckets.IndexOf(fbv.Key)).ToArray())
.ToArray()).ToArray();
this.FeatureValue.ObservedValue =
instances.Select(
inner => inner.Select(ia => ia.FeatureValues.Where(predicate).Select(fbv => fbv.Value).ToArray()).ToArray()).ToArray();
this.FeatureCounts.ObservedValue =
this.FeatureValue.ObservedValue.Select(inner => inner.Select(ia => ia.Length).ToArray()).ToArray();
}
