/// <summary>
/// Initializes a new instance of the <see cref="ColdUserItemParameterAlgorithm"/> class.
/// </summary>
/// <param name="userFeatureParameterPosteriors">The posteriors over the user feature related parameters learned during community training.</param>
/// <param name="itemFeatureParameterPosteriors">The posteriors over the item feature related parameters learned during community training.</param>
/// <param name="userParameterDistributionAverage">The average trait, bias, and threshold posterior over all users in training.</param>
/// <param name="itemParameterDistributionAverage">The average trait and bias posterior over all items in training.</param>
public ColdUserItemParameterAlgorithm(
FeatureParameterDistribution userFeatureParameterPosteriors,
FeatureParameterDistribution itemFeatureParameterPosteriors,
UserParameterDistribution userParameterDistributionAverage,
ItemParameterDistribution itemParameterDistributionAverage)
{
this.userFeatureParameterPosteriors = userFeatureParameterPosteriors;
this.itemFeatureParameterPosteriors = itemFeatureParameterPosteriors;
this.userParameterDistributionAverage = userParameterDistributionAverage;
this.itemParameterDistributionAverage = itemParameterDistributionAverage;
}
/// <summary>
/// Adds the feature contribution for a given user/item to its traits and bias.
/// </summary>
/// <param name="parameters">The user/item parameters.</param>
/// <param name="featureWeights">The feature weights.</param>
/// <param name="featureValues">The feature values.</param>
/// <param name="adjustedTraits">The resulting traits.</param>
/// <param name="adjustedBias">The resulting bias.</param>
public static void AddFeatureContribution(
EntityParameterDistribution parameters,
FeatureParameterDistribution featureWeights,
SparseFeatureVector featureValues,
out GaussianArray adjustedTraits,
out Gaussian adjustedBias)
{
adjustedTraits = new GaussianArray(
parameters.Traits.Count,
i => DoublePlusOp.SumAverageConditional(
parameters.Traits[i],
ComputeFeatureContribution(featureWeights.TraitWeights[i], featureValues.NonZeroFeatureValues, featureValues.NonZeroFeatureIndices)));
adjustedBias = DoublePlusOp.SumAverageConditional(
parameters.Bias,
ComputeFeatureContribution(featureWeights.BiasWeights, featureValues.NonZeroFeatureValues, featureValues.NonZeroFeatureIndices));
}