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);
}
