/// <summary>
/// Create a <see cref="T:Util.PriorityQueue{object[]}"/> from a word->tf map.
/// </summary>
/// <param name="words"> a map of words keyed on the word(<see cref="string"/>) with <see cref="Int32"/> objects as the values. </param>
/// <exception cref="IOException"/>
private Util.PriorityQueue <object[]> CreateQueue(IDictionary <string, Int32> words)
{
// have collected all words in doc and their freqs
int numDocs = ir.NumDocs;
FreqQ res = new FreqQ(words.Count); // will order words by score
foreach (string word in words.Keys) // for every word
{
int tf = words[word].x; // term freq in the source doc
if (MinTermFreq > 0 && tf < MinTermFreq)
{
continue; // filter out words that don't occur enough times in the source
}
// go through all the fields and find the largest document frequency
string topField = FieldNames[0];
int docFreq = 0;
foreach (string fieldName in FieldNames)
{
int freq = ir.DocFreq(new Term(fieldName, word));
topField = (freq > docFreq) ? fieldName : topField;
docFreq = (freq > docFreq) ? freq : docFreq;
}
if (MinDocFreq > 0 && docFreq < MinDocFreq)
{
continue; // filter out words that don't occur in enough docs
}
if (docFreq > MaxDocFreq)
{
continue; // filter out words that occur in too many docs
}
if (docFreq == 0)
{
continue; // index update problem?
}
float idf = Similarity.Idf(docFreq, numDocs);
float score = tf * idf;
// only really need 1st 3 entries, other ones are for troubleshooting
res.InsertWithOverflow(new object[] { word, topField, score, idf, docFreq, tf }); // freq in all docs - idf - overall score - the top field - the word
}
return(res);
}
