/**
* input is a map of artifact.Id and processed text of each artifact
*
*/
public static TLDictionaryIndex build(TLArtifactsCollection setOfProcessedDocuments, ComponentLogger logger)
{
// Variables
TLTermEntry termEntry;
TLPosting posting;
double vectorLength;
// Stores the vector lenght of each document - this is used to normalize the term weights
// The vector length is the square root of the sum of the squares of all the term weights.
Dictionary <string, double> documentVectorLength = new Dictionary <string, double>();
// Creates the dictionary
TLDictionaryIndex dict = new TLDictionaryIndex();
// Iterates over all the documents
foreach (TLArtifact artifact in setOfProcessedDocuments.Values)
{
string[] terms = artifact.Text.Split(new char[] { ' ' }, StringSplitOptions.RemoveEmptyEntries);
if (terms.Length == 0)
{
logger.Warn(String.Format("Artifact Id {0} is empty.", artifact.Id));
}
// Iterates over all the terms
foreach (string t in terms)
{
// Checks if that term has already a posting
if (!dict.ContainsTermEntry(t))
{
// New term
termEntry = dict.AddTermEntry(t, 1, 1, 1.0);
posting = termEntry.AddPosting(artifact.Id, 1, 1.0);
}
else
{
// Existing term
termEntry = dict.GetTermEntry(t);
termEntry.TotalFrequencyAcrossArtifacts += 1;
termEntry.Weight = 1.0;
// Checks if there is already a posting for this document
if (!termEntry.ContainsPosting(artifact.Id))
{
// New posting
termEntry.NumberOfArtifactsContainingTerm += 1;
posting = termEntry.AddPosting(artifact.Id, 1, 1.0);
}
else
{
// Existing posting
posting = termEntry.GetPosting(artifact.Id);
posting.Frequency += 1;
posting.Weight += 1.0;
}
}
}
}
string artId;
// Now that all the counts are in, it calculates the document vector weights
foreach (TLTermEntry t in dict.TermEntries)
{
foreach (TLPosting p in t.Postings)
{
artId = p.ArtifactId;
vectorLength = Math.Pow(p.Frequency, 2);
if (documentVectorLength.ContainsKey(artId))
{
// The document has other terms
vectorLength += documentVectorLength[artId];
}
documentVectorLength[artId] = vectorLength;
}
}
// Finally, we need to get the square root of all entries in the document vector length
foreach (TLArtifact artifact in setOfProcessedDocuments.Values)
{
if (documentVectorLength.ContainsKey(artifact.Id))
{
vectorLength = Math.Sqrt(documentVectorLength[artifact.Id]);
// Here we update the document vector length of the dictionary - not the internal structure any more
dict.SetDocumentVectorWeight(artifact.Id, vectorLength);
}
}
return(dict);
}
public List <Result> search(String query, TLDictionaryIndex dict)
{
// Variables
List <Result> results;
Dictionary <string, double> intermediateResults;
Dictionary <string, int> queryTermFrequency;
Dictionary <string, double> queryTermWeight;
double queryVectorLength;
// Initializes the data structures
results = new List <Result>(); // Result array
intermediateResults = new Dictionary <string, double>(); // Where the intermediate results of the query are kept.
queryTermFrequency = new Dictionary <string, int>(); // Keeps track of term frequencies
queryTermWeight = new Dictionary <string, double>(); // Keeps track of term weights
queryVectorLength = 0.0; // The document vector length of the query
// The query is broken down into tokens
string[] queryTerms = query.Split(new char[] { ' ' });
// Iterates over each query term to compute the term frequency
foreach (string qterm in queryTerms)
{
// It only cares about those words that are in the dictionary
if (dict.ContainsTermEntry(qterm))
{
if (!queryTermFrequency.ContainsKey(qterm))
{
// First time the query word is encountered
queryTermFrequency.Add(qterm, 1);
}
else
{
// The query word is already there, so the frequency gets increased
queryTermFrequency[qterm] += 1;
}
}
}
// Iterates over the resulting query terms to compute their weights and the dot product of the query terms x the documents terms
foreach (string qterm in queryTermFrequency.Keys)
{
// Gets the Term from the dictionary
TLTermEntry term = dict.GetTermEntry(qterm);
// It computes the weight of a term - IE the frequency TIMES the term's specificity.
// Note: the specifity of the term is stored in the weight.
// For the basic dictionary this is just 1
// For the tf-idf dictionary this is the idf
// For the signal-noise this is the signal
double weight = queryTermFrequency[qterm] * term.Weight;
queryTermWeight.Add(qterm, weight);
// Updates the document vector length of the query
queryVectorLength += Math.Pow(weight, 2);
// It now iterates over all the postings that have this term
foreach (TLPosting posting in term.Postings)
{
string docId = posting.ArtifactId;
// Calculates the product of the query times the posting for this particular term
double r = queryTermWeight[qterm] * posting.Weight;
if (intermediateResults.ContainsKey(docId))
{
intermediateResults[docId] += r;
}
else
{
intermediateResults.Add(docId, r);
}
}
}
// The document vector lenght for the query is the square root of the sum of the squares of the term weights
queryVectorLength = Math.Sqrt(queryVectorLength);
// It iterates over the intermediate results to create the final array that is returned to the user
foreach (string docId in intermediateResults.Keys)
{
// Result r = new ResultObj(docId, intermediateResults.get(docId));
double similarity = _similarityMetric.ComputeSimilarity(intermediateResults[docId], queryVectorLength, dict.GetDocumentVectorWeight(docId));
Result r = new Result(docId, similarity);
results.Add(r);
}
// Sorts the results
results.Sort();
return(results);
}
