/// <summary>
/// Sets the observed values of the user and item metadata - both the features and their corresponding counts.
/// </summary>
/// <param name="metadata">The metadata to observe.</param>
public void SetObservedMetadata(InstanceMetadata metadata)
{
Debug.Assert(
metadata.UserCount > 0 && metadata.ItemCount > 0,
"The training set must contain at least one user and item.");
Debug.Assert(
metadata.UserFeatures.EntityCount == metadata.UserCount,
"Features should be provided for all users in the training set, and only for them.");
Debug.Assert(
metadata.ItemFeatures.EntityCount == metadata.ItemCount,
"Features should be provided for all items in the training set, and only for them.");
var thresholdCount = metadata.RatingCount + 1;
this.inferenceAlgorithm.UserCount = metadata.UserCount;
this.inferenceAlgorithm.ItemCount = metadata.ItemCount;
this.inferenceAlgorithm.UserThresholdCount = thresholdCount;
this.inferenceAlgorithm.MiddleUserThresholdIndex = thresholdCount / 2;
this.inferenceAlgorithm.UserThresholdPriorMean = AlgorithmUtils.GetUserThresholdPriorMeans(thresholdCount);
this.inferenceAlgorithm.UserFeatureCount = metadata.UserFeatures.FeatureCount;
this.inferenceAlgorithm.ItemFeatureCount = metadata.ItemFeatures.FeatureCount;
this.inferenceAlgorithm.NonZeroUserFeatureValues = metadata.UserFeatures.NonZeroFeatureValues;
this.inferenceAlgorithm.NonZeroUserFeatureIndices = metadata.UserFeatures.NonZeroFeatureIndices;
this.inferenceAlgorithm.NonZeroUserFeatureCounts = metadata.UserFeatures.NonZeroFeatureCounts;
this.inferenceAlgorithm.NonZeroItemFeatureValues = metadata.ItemFeatures.NonZeroFeatureValues;
this.inferenceAlgorithm.NonZeroItemFeatureIndices = metadata.ItemFeatures.NonZeroFeatureIndices;
this.inferenceAlgorithm.NonZeroItemFeatureCounts = metadata.ItemFeatures.NonZeroFeatureCounts;
}
/// <summary>
/// Infers parameters for a given cold user.
/// </summary>
/// <param name="userFeatures">The user features.</param>
/// <returns>A distribution over the user parameters.</returns>
public UserParameterDistribution InferUserParameters(SparseFeatureVector userFeatures)
{
UserParameterDistribution result;
if (this.userFeatureParameterPosteriors.FeatureCount == 0)
{
Debug.Assert(
userFeatures.FeatureCount == 0,
"The number of user features passed must be equal to the number of user features learned.");
result = this.userParameterDistributionAverage;
}
else
{
GaussianArray traits;
Gaussian bias;
AlgorithmUtils.AddFeatureContribution(
this.userParameterDistributionAverage,
this.userFeatureParameterPosteriors,
userFeatures,
out traits,
out bias);
result = new UserParameterDistribution(traits, bias, this.userParameterDistributionAverage.Thresholds);
}
return(result);
}
