/// <summary>
///
/// </summary>
/// <param name="dataModel"><see cref="taste.Model.DataModel">DataModel</see> which provides <see cref="taste.mode.User">User</see>s</param>
/// <param name="clusterSimilarity"><see cref="taste.Recommender.ClusterSimilarity">ClusterSimilarity</see> used to compute cluster similarity</param>
/// <param name="numClusters">desired number of clusters to create</param>
/// <param name="samplingPercentage">
/// percentage of all cluster-cluster pairs to consider when finding
/// next-most-similar clusters. Decreasing this value from 1.0 can increase performance at the
/// cost of accuracy
/// </param>
public TreeClusteringRecommender(DataModel dataModel,
ClusterSimilarity clusterSimilarity,
int numClusters,
double samplingPercentage)
: base(dataModel)
{
if (clusterSimilarity == null)
{
throw new ArgumentNullException("clusterSimilarity is null");
}
if (numClusters < 2)
{
throw new ArgumentException("numClusters must be at least 2");
}
if (double.IsNaN(samplingPercentage) || samplingPercentage <= 0.0 || samplingPercentage > 1.0)
{
throw new ArgumentException("samplingPercentage is invalid: " + samplingPercentage);
}
this.clusterSimilarity = clusterSimilarity;
this.numClusters = numClusters;
this.clusteringThreshold = Double.NaN;
this.clusteringByThreshold = false;
this.samplingPercentage = samplingPercentage;
this.refreshLock = new ReentrantLock();
this.buildClustersLock = new ReentrantLock();
}
/// <summary>
/// </summary>
/// <param name="dataModel">
/// <see cref="taste.Model.DataModel">DataModel</see> which provides <see cref="taste.Model.User">User</see>s
/// </param>
/// <param name="clusterSimilarity">
/// <see cref="taste.Recommender.ClusterSimilarity">ClusterSimilarity</see> used to compute cluster similarity
/// </param>
/// <param name="clusteringThreshold">clustering similarity threshold; clusters will be aggregated
/// into larger clusters until the next two nearest clusters' similarity drops below this threshold
/// </param>
/// <param name="samplingPercentage">
/// percentage of all cluster-cluster pairs to consider when finding next-most-similar
/// clusters. Decreasing this value from 1.0 can increase performance at the cost of accuracy
/// </param>
/// <remarks>
/// Throws ArgumentException if arguments are <code>null</code>, or <code>clusteringThreshold</code> is
/// {@link Double#NaN}, or samplingPercentage is {@link Double#NaN} or nonpositive or greater than 1.0
/// </remarks>
public TreeClusteringRecommender(DataModel dataModel,
ClusterSimilarity clusterSimilarity,
double clusteringThreshold,
double samplingPercentage)
: base(dataModel)
{
if (clusterSimilarity == null)
{
throw new ArgumentNullException("clusterSimilarity is null");
}
if (double.IsNaN(clusteringThreshold))
{
throw new ArgumentException("clusteringThreshold must not be NaN");
}
if (double.IsNaN(samplingPercentage) || samplingPercentage <= 0.0 || samplingPercentage > 1.0)
{
throw new ArgumentException("samplingPercentage is invalid: " + samplingPercentage);
}
this.clusterSimilarity = clusterSimilarity;
this.numClusters = int.MinValue;
this.clusteringThreshold = clusteringThreshold;
this.clusteringByThreshold = true;
this.samplingPercentage = samplingPercentage;
this.refreshLock = new ReentrantLock();
this.buildClustersLock = new ReentrantLock();
}
/**
* @param dataModel
* {@link DataModel} which provides users
* @param clusterSimilarity
* {@link ClusterSimilarity} used to compute cluster similarity
* @param numClusters
* desired number of clusters to create
* @throws IllegalArgumentException
* if arguments are {@code null}, or {@code numClusters} is less than 2
*/
public TreeClusteringRecommender2(DataModel dataModel, ClusterSimilarity clusterSimilarity, int numClusters)
: base(dataModel)
{
if (numClusters < 2)
{
throw new Exception("numClusters must be at least 2");
}
//Preconditions.checkArgument(numClusters >= 2, "numClusters must be at least 2");
this.clusterSimilarity = clusterSimilarity;//Preconditions.checkNotNull(clusterSimilarity);
this.numClusters = numClusters;
this.clusteringThreshold = Double.NaN;
this.clusteringByThreshold = false;
this.refreshHelper = new RefreshHelper(buildClusters);
refreshHelper.addDependency(dataModel);
refreshHelper.addDependency(clusterSimilarity);
buildClusters();
}
/**
* @param dataModel
* {@link DataModel} which provides users
* @param clusterSimilarity
* {@link ClusterSimilarity} used to compute cluster
* similarity
* @param clusteringThreshold
* clustering similarity threshold; clusters will be aggregated into larger clusters until the next
* two nearest clusters' similarity drops below this threshold
* @throws IllegalArgumentException
* if arguments are {@code null}, or {@code clusteringThreshold} is {@link Double#NaN}
*/
public TreeClusteringRecommender2(DataModel dataModel,
ClusterSimilarity clusterSimilarity,
double clusteringThreshold)
: base(dataModel)
{
// Preconditions.checkArgument(!Double.isNaN(clusteringThreshold), "clusteringThreshold must not be NaN");
if (Double.IsNaN(clusteringThreshold))
{
throw new Exception("clusteringThreshold must not be NaN");
}
this.clusterSimilarity = clusterSimilarity; //Preconditions.checkNotNull(clusterSimilarity);
this.numClusters = int.MinValue; // Integer.MIN_VALUE;
this.clusteringThreshold = clusteringThreshold;
this.clusteringByThreshold = true;
this.refreshHelper = new RefreshHelper(buildClusters);
refreshHelper.addDependency(dataModel);
refreshHelper.addDependency(clusterSimilarity);
buildClusters();
}
/**
* @param dataModel
* {@link DataModel} which provides users
* @param clusterSimilarity
* {@link ClusterSimilarity} used to compute cluster similarity
* @param clusteringThreshold
* clustering similarity threshold; clusters will be aggregated into larger clusters until the next
* two nearest clusters' similarity drops below this threshold
* @param samplingRate
* percentage of all cluster-cluster pairs to consider when finding next-most-similar clusters.
* Decreasing this value from 1.0 can increase performance at the cost of accuracy
* @throws IllegalArgumentException
* if arguments are {@code null}, or {@code clusteringThreshold} is {@link Double#NaN},
* or samplingRate is {@link Double#NaN} or nonpositive or greater than 1.0
*/
public TreeClusteringRecommender(DataModel dataModel,
ClusterSimilarity clusterSimilarity,
double clusteringThreshold,
double samplingRate)
: base(dataModel)
{
//Preconditions.checkArgument(!Double.IsNaN(clusteringThreshold), "clusteringThreshold must not be NaN");
//Preconditions.checkArgument(samplingRate > 0.0 && samplingRate <= 1.0, "samplingRate is invalid: %f", samplingRate);
random = RandomUtils.getRandom();
this.clusterSimilarity = clusterSimilarity; //Preconditions.checkNotNull(clusterSimilarity);
this.numClusters = int.MinValue; //Integer.MIN_VALUE;
this.clusteringThreshold = clusteringThreshold;
this.clusteringByThreshold = true;
this.samplingRate = samplingRate;
this.refreshHelper = new RefreshHelper(buildClusters);
refreshHelper.addDependency(dataModel);
refreshHelper.addDependency(clusterSimilarity);
buildClusters();
}
/**
* @param dataModel
* {@link DataModel} which provdes users
* @param clusterSimilarity
* {@link ClusterSimilarity} used to compute cluster similarity
* @param numClusters
* desired number of clusters to create
* @param samplingRate
* percentage of all cluster-cluster pairs to consider when finding next-most-similar clusters.
* Decreasing this value from 1.0 can increase performance at the cost of accuracy
* @throws IllegalArgumentException
* if arguments are {@code null}, or {@code numClusters} is less than 2, or samplingRate
* is {@link Double#NaN} or nonpositive or greater than 1.0
*/
public TreeClusteringRecommender(DataModel dataModel,
ClusterSimilarity clusterSimilarity,
int numClusters,
double samplingRate)
: base(dataModel)
{
//Preconditions.checkArgument(numClusters >= 2, "numClusters must be at least 2");
//Preconditions.checkArgument(samplingRate > 0.0 && samplingRate <= 1.0,
// "samplingRate is invalid: %f", samplingRate);
random = RandomUtils.getRandom();
this.clusterSimilarity = clusterSimilarity;//Preconditions.checkNotNull(clusterSimilarity);
this.numClusters = numClusters;
this.clusteringThreshold = Double.NaN;
this.clusteringByThreshold = false;
this.samplingRate = samplingRate;
this.refreshHelper = new RefreshHelper(buildClusters);
refreshHelper.addDependency(dataModel);
refreshHelper.addDependency(clusterSimilarity);
buildClusters();
}
/**
* @param dataModel {@link taste.Model.DataModel} which provdes {@link taste.Model.User}s
* @param clusterSimilarity {@link taste.Recommender.ClusterSimilarity} used to compute
* cluster similarity
* @param numClusters desired number of clusters to create
* @throws IllegalArgumentException if arguments are <code>null</code>, or <code>numClusters</code> is
* less than 2
*/
public TreeClusteringRecommender2(DataModel dataModel,
ClusterSimilarity clusterSimilarity,
int numClusters)
: base(dataModel)
{
if (clusterSimilarity == null)
{
throw new ArgumentNullException("clusterSimilarity is null");
}
if (numClusters < 2)
{
throw new ArgumentException("numClusters must be at least 2");
}
this.clusterSimilarity = clusterSimilarity;
this.numClusters = numClusters;
this.clusteringThreshold = Double.NaN;
this.clusteringByThreshold = false;
this.refreshLock = new ReentrantLock();
this.buildClustersLock = new ReentrantLock();
}
/**
* @param dataModel {@link taste.Model.DataModel} which provdes {@link taste.Model.User}s
* @param clusterSimilarity {@link taste.Recommender.ClusterSimilarity} used to compute
* cluster similarity
* @param clusteringThreshold clustering similarity threshold; clusters will be aggregated into larger
* clusters until the next two nearest clusters' similarity drops below this threshold
* @throws IllegalArgumentException if arguments are <code>null</code>, or <code>clusteringThreshold</code> is
* {@link Double#NaN}
* @since 1.1.1
*/
public TreeClusteringRecommender2(DataModel dataModel,
ClusterSimilarity clusterSimilarity,
double clusteringThreshold)
: base(dataModel)
{
if (clusterSimilarity == null)
{
throw new ArgumentNullException("clusterSimilarity is null");
}
if (double.IsNaN(clusteringThreshold))
{
throw new ArgumentException("clusteringThreshold must not be NaN");
}
this.clusterSimilarity = clusterSimilarity;
this.numClusters = int.MinValue;
this.clusteringThreshold = clusteringThreshold;
this.clusteringByThreshold = true;
this.refreshLock = new ReentrantLock();
this.buildClustersLock = new ReentrantLock();
}
/**
* @param dataModel
* {@link DataModel} which provdes users
* @param clusterSimilarity
* {@link ClusterSimilarity} used to compute cluster similarity
* @param clusteringThreshold
* clustering similarity threshold; clusters will be aggregated into larger clusters until the next
* two nearest clusters' similarity drops below this threshold
* @throws IllegalArgumentException
* if arguments are {@code null}, or {@code clusteringThreshold} is {@link Double#NaN}
*/
public TreeClusteringRecommender(DataModel dataModel,
ClusterSimilarity clusterSimilarity,
double clusteringThreshold) :
this(dataModel, clusterSimilarity, clusteringThreshold, 1.0)
{
}
/**
* @param dataModel
* {@link DataModel} which provdes users
* @param clusterSimilarity
* {@link ClusterSimilarity} used to compute cluster similarity
* @param numClusters
* desired number of clusters to create
* @throws IllegalArgumentException
* if arguments are {@code null}, or {@code numClusters} is less than 2
*/
public TreeClusteringRecommender(DataModel dataModel, ClusterSimilarity clusterSimilarity, int numClusters)
: this(dataModel, clusterSimilarity, numClusters, 1.0)
{
}
