/// <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>
/// Initializes a new instance of the <see cref="ParameterDistributions"/> class.
/// Each contained distribution is initialized to uniform.
/// </summary>
/// <param name="metaData">The instance meta data.</param>
/// <param name="traitCount">The number of traits.</param>
public ParameterDistributions(InstanceMetadata metaData, int traitCount)
{
// Check input data
Debug.Assert(metaData.UserCount > 0, "The number of users must be positive.");
Debug.Assert(metaData.ItemCount > 0, "The number of items must be positive.");
Debug.Assert(metaData.RatingCount + 1 > 0, "The number of user thresholds must be positive.");
Debug.Assert(metaData.UserFeatures.FeatureCount >= 0, "The number of user features must be non-negative.");
Debug.Assert(metaData.ItemFeatures.FeatureCount >= 0, "The number of item features must be non-negative.");
Debug.Assert(traitCount >= 0, "The number of traits must be non-negative.");
// The value to which each element of the contained distributions will be initialized
var initialValue = Gaussian.Uniform();
// Initialize contained distributions.
this.UserTraitDistribution = new GaussianMatrix(new GaussianArray(initialValue, traitCount), metaData.UserCount);
this.UserBiasDistribution = new GaussianArray(initialValue, metaData.UserCount);
this.UserThresholdDistribution = new GaussianMatrix(
new GaussianArray(initialValue, metaData.RatingCount + 1), metaData.UserCount);
this.ItemTraitDistribution = new GaussianMatrix(new GaussianArray(initialValue, traitCount), metaData.ItemCount);
this.ItemBiasDistribution = new GaussianArray(initialValue, metaData.ItemCount);
this.UserFeature = new FeatureParameterDistribution(traitCount, metaData.UserFeatures.FeatureCount, initialValue);
this.ItemFeature = new FeatureParameterDistribution(traitCount, metaData.ItemFeatures.FeatureCount, initialValue);
}
