/// <summary>
/// Priors from posteriors with possible missing features (e.g. for community training).
/// </summary>
/// <param name="posteriors">The posteriors.</param>
/// <param name="featureSet">The feature set.</param>
/// <param name="point">The point.</param>
/// <param name="thresholdAndNoiseVariance">The threshold and noise variance.</param>
/// <param name="userNames">The user names.</param>
/// <returns>The <see cref="CommunityPriors" />.</returns>
internal static CommunityPriors FromPosteriors(
CommunityPosteriors posteriors,
FeatureSet featureSet,
double point,
double thresholdAndNoiseVariance,
IList <string> userNames)
{
return(new CommunityPriors
{
WeightMeans =
featureSet.FeatureBuckets.ToDictionary(
ia => ia,
ia =>
posteriors.WeightMeans.ContainsKey(ia)
? posteriors.WeightMeans[ia]
: Gaussian.FromMeanAndVariance(0.0, 1.0)),
WeightPrecisions =
featureSet.FeatureBuckets.ToDictionary(
ia => ia,
ia =>
posteriors.WeightPrecisions.ContainsKey(ia)
? posteriors.WeightPrecisions[ia]
: Gamma.PointMass(point)),
Thresholds =
userNames.ToDictionary(
ia => ia,
ia =>
posteriors.Thresholds != null && posteriors.Thresholds.ContainsKey(ia)
? posteriors.Thresholds[ia]
: Gaussian.FromMeanAndVariance(0.0, thresholdAndNoiseVariance)),
NoiseVariance = thresholdAndNoiseVariance,
});
}
/// <summary>
/// Priors from the community posteriors, using the means of the weight mean and weight precision posteriors.
/// </summary>
/// <param name="posteriors">The posteriors.</param>
/// <param name="featureSet">The feature set.</param>
/// <param name="thresholdAndNoiseVariance">The threshold and noise variance.</param>
/// <returns>The <see cref="Priors" /></returns>
internal static Priors FromCommunityPosteriors(CommunityPosteriors posteriors, FeatureSet featureSet, double thresholdAndNoiseVariance)
{
return(new Priors
{
Weights =
featureSet.FeatureBuckets.ToDictionary(
ia => ia,
ia =>
posteriors.WeightMeans.ContainsKey(ia) && posteriors.WeightPrecisions.ContainsKey(ia)
? Gaussian.FromMeanAndPrecision(
posteriors.WeightMeans[ia].GetMean(),
posteriors.WeightPrecisions[ia].GetMean())
: Gaussian.FromMeanAndVariance(0.0, thresholdAndNoiseVariance)),
Threshold = Gaussian.FromMeanAndVariance(0.0, thresholdAndNoiseVariance),
NoiseVariance = thresholdAndNoiseVariance
});
}
