/// <summary>Creates new search object</summary>
/// <param name="size">The size of the beam (k)</param>
/// <param name="contextGenerator">the context generator for the model</param>
/// <param name="model">the model for assigning probabilities to the sequence outcomes</param>
/// <param name="cacheSize">size of the cache to use for performance</param>
public BeamSearch(int size, IBeamSearchContextGenerator contextGenerator, SharpEntropy.IMaximumEntropyModel model, int cacheSize)
{
Size = size;
ContextGenerator = contextGenerator;
Model = model;
_probabilities = new double[model.OutcomeCount];
if (cacheSize > 0)
{
_contextsCache = new Cache(cacheSize);
}
}
/// <summary>
/// Creates new search object.
/// </summary>
/// <param name="size">
/// The size of the beam (k).
/// </param>
/// <param name="contextGenerator">
/// the context generator for the model.
/// </param>
/// <param name="model">
/// the model for assigning probabilities to the sequence outcomes.
/// </param>
/// <param name="cacheSize">
/// size of the cache to use for performance.
/// </param>
public BeamSearch(int size, IBeamSearchContextGenerator contextGenerator, SharpEntropy.IMaximumEntropyModel model, int cacheSize)
{
Size = size;
ContextGenerator = contextGenerator;
Model = model;
mProbabilities = new double[model.OutcomeCount];
if (cacheSize > 0)
{
mContextsCache = new Cache(cacheSize);
}
}
/// <summary>
/// Finds the sequence with the highest probability.
/// </summary>
/// <param name="sequence">The sequence.</param>
/// <param name="additionalContext">The additional context.</param>
/// <param name="beamSearch">The beam search.</param>
/// <param name="validator">The validator.</param>
/// <returns>The sequence with the highest probability.</returns>
public Sequence BestSequence(T[] sequence, object[] additionalContext,
IBeamSearchContextGenerator <T> beamSearch,
ISequenceValidator <T> validator)
{
var sequences = BestSequences(1, sequence, additionalContext, beamSearch, validator);
if (sequence.Length > 0)
{
return(sequences[0]);
}
return(null);
}
/// <summary>
/// Creates new search object.
/// </summary>
/// <param name="size">The size of the beam (k).</param>
/// <param name="cg">The context generator for the model.</param>
/// <param name="model">The model for assigning probabilities to the sequence outcomes.</param>
/// <param name="validator">The sequence validator.</param>
/// <param name="cacheSize">Size of the cache.</param>
public BeamSearch(int size, IBeamSearchContextGenerator <T> cg, IMaxentModel model, ISequenceValidator <T> validator, int cacheSize)
{
this.cg = cg;
this.size = size;
this.model = model;
this.validator = validator;
if (cacheSize > 0)
{
contextsCache = new Cache(cacheSize);
}
probs = new double[model.GetNumOutcomes()];
}
/// <summary>Creates new search object</summary>
/// <param name="size">The size of the beam (k)</param>
/// <param name="contextGenerator">the context generator for the model</param>
/// <param name="model">the model for assigning probabilities to the sequence outcomes</param>
/// <param name="cacheSizeInMegaBytes">size of the cache to use for performance</param>
public BeamSearch(int size, IBeamSearchContextGenerator contextGenerator,
SharpEntropy.IMaximumEntropyModel model, int cacheSizeInMegaBytes)
{
Size = size;
ContextGenerator = contextGenerator;
Model = model;
if (cacheSizeInMegaBytes > 0)
{
var properties = new NameValueCollection
{
{ "cacheMemoryLimitMegabytes", cacheSizeInMegaBytes.ToString() }
};
contextsCache = new MemoryCache("beamSearchContextCache", properties);
}
}
/// <summary>Creates new search object</summary>
/// <param name="size">The size of the beam (k)</param>
/// <param name="contextGenerator">the context generator for the model</param>
/// <param name="model">the model for assigning probabilities to the sequence outcomes</param>
/// <param name="cacheSizeInMegaBytes">size of the cache to use for performance</param>
public BeamSearch(int size, IBeamSearchContextGenerator contextGenerator,
SharpEntropy.IMaximumEntropyModel model, int cacheSizeInMegaBytes)
{
Size = size;
ContextGenerator = contextGenerator;
Model = model;
if (cacheSizeInMegaBytes > 0)
{
var properties = new NameValueCollection
{
{"cacheMemoryLimitMegabytes", cacheSizeInMegaBytes.ToString()}
};
contextsCache = new MemoryCache("beamSearchContextCache", properties);
}
}
/// <summary>
/// Creates new search object.
/// </summary>
/// <param name="size">
/// The size of the beam (k).
/// </param>
/// <param name="contextGenerator">
/// the context generator for the model.
/// </param>
/// <param name="model">
/// the model for assigning probabilities to the sequence outcomes.
/// </param>
public BeamSearch(int size, IBeamSearchContextGenerator contextGenerator, SharpEntropy.IMaximumEntropyModel model)
: this(size, contextGenerator, model, 0)
{
}
// Constructors -----------------
/// <summary>Creates new search object</summary>
/// <param name="size">The size of the beam (k)</param>
/// <param name="contextGenerator">the context generator for the model</param>
/// <param name="model">the model for assigning probabilities to the sequence outcomes</param>
public BeamSearch(int size, IBeamSearchContextGenerator contextGenerator,
SharpEntropy.IMaximumEntropyModel model) :
this(size, contextGenerator, model, 0)
{
}
/// <summary>
/// Finds the n most probable sequences.
/// </summary>
/// <param name="numSequences">The number sequences.</param>
/// <param name="sequence">The sequence.</param>
/// <param name="additionalContext">The additional context.</param>
/// <param name="minSequenceScore">The minimum sequence score.</param>
/// <param name="beamSearch">The beam search.</param>
/// <param name="validator">The validator.</param>
public Sequence[] BestSequences(int numSequences, T[] sequence, object[] additionalContext,
double minSequenceScore,
IBeamSearchContextGenerator <T> beamSearch, ISequenceValidator <T> validator)
{
IHeap <Sequence> prev = new ListHeap <Sequence>(size);
IHeap <Sequence> next = new ListHeap <Sequence>(size);
prev.Add(new Sequence());
if (additionalContext == null)
{
additionalContext = new object[] {}; // EMPTY_ADDITIONAL_CONTEXT
}
for (var i = 0; i < sequence.Length; i++)
{
var sz = Math.Min(size, prev.Size());
for (var sc = 0; prev.Size() > 0 && sc < sz; sc++)
{
var top = prev.Extract();
var tmpOutcomes = top.Outcomes;
var outcomes = tmpOutcomes.ToArray();
var contexts = beamSearch.GetContext(i, sequence, outcomes, additionalContext);
double[] scores;
if (contextsCache != null)
{
scores = (double[])contextsCache.Get(contexts);
if (scores == null)
{
scores = model.Eval(contexts, probs);
contextsCache.Put(contexts, scores);
}
}
else
{
scores = model.Eval(contexts, probs);
}
var tempScores = new double[scores.Length];
for (var c = 0; c < scores.Length; c++)
{
tempScores[c] = scores[c];
}
Array.Sort(tempScores);
var min = tempScores[Math.Max(0, scores.Length - size)];
for (var p = 0; p < scores.Length; p++)
{
if (scores[p] < min)
{
continue; //only advance first "size" outcomes
}
var outcome = model.GetOutcome(p);
if (validator.ValidSequence(i, sequence, outcomes, outcome))
{
var ns = new Sequence(top, outcome, scores[p]);
if (ns.Score > minSequenceScore)
{
next.Add(ns);
}
}
}
if (next.Size() == 0)
{
//if no advanced sequences, advance all valid
for (var p = 0; p < scores.Length; p++)
{
var outcome = model.GetOutcome(p);
if (validator.ValidSequence(i, sequence, outcomes, outcome))
{
var ns = new Sequence(top, outcome, scores[p]);
if (ns.Score > minSequenceScore)
{
next.Add(ns);
}
}
}
}
}
// make prev = next; and re-init next (we reuse existing prev set once we clear it)
prev.Clear();
var tmp = prev;
prev = next;
next = tmp;
}
var numSeq = Math.Min(numSequences, prev.Size());
var topSequences = new Sequence[numSeq];
for (var seqIndex = 0; seqIndex < numSeq; seqIndex++)
{
topSequences[seqIndex] = prev.Extract();
}
return(topSequences);
}
/// <summary>
/// Finds the n most probable sequences.
/// </summary>
/// <param name="numSequences">The number sequences.</param>
/// <param name="sequence">The sequence.</param>
/// <param name="additionalContext">The additional context.</param>
/// <param name="beamSearch">The beam search.</param>
/// <param name="validator">The validator.</param>
/// <returns>The n most probable sequences.</returns>
public Sequence[] BestSequences(int numSequences, T[] sequence, object[] additionalContext,
IBeamSearchContextGenerator <T> beamSearch, ISequenceValidator <T> validator)
{
return(BestSequences(numSequences, sequence, additionalContext, zeroLog, beamSearch, validator));
}
/// <summary>
/// Creates new search object.
/// </summary>
/// <param name="size">The size of the beam (k).</param>
/// <param name="cg">The context generator for the model.</param>
/// <param name="model">The model for assigning probabilities to the sequence outcomes.</param>
/// <param name="cacheSize">Size of the cache.</param>
public BeamSearch(int size, IBeamSearchContextGenerator <T> cg, IMaxentModel model, int cacheSize)
: this(size, cg, model, null, cacheSize)
{
}
/// <summary>
/// Creates new search object.
/// </summary>
/// <param name="size">
/// The size of the beam (k).
/// </param>
/// <param name="contextGenerator">
/// the context generator for the model.
/// </param>
/// <param name="model">
/// the model for assigning probabilities to the sequence outcomes.
/// </param>
public BeamSearch(int size, IBeamSearchContextGenerator contextGenerator, MaxEntropyModel_Interface model) : this(size, contextGenerator, model, 0)
{
}
