/**
* Create a PriorityQueue from a word->tf map.
*
* @param words a map of words keyed on the word(String) with Int objects as the values.
* @param fieldNames an array of field names to override defaults.
*/
private PriorityQueue<ScoreTerm> createQueue(Map<String, Int> words, String... fieldNames)
throws IOException {
// have collected all words in doc and their freqs
int numDocs = ir.numDocs();
final int limit = Math.min(maxQueryTerms, words.size());
FreqQ queue = new FreqQ(limit); // will order words by score
for (String word : words.keySet()) { // for every word
int tf = words.get(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;
for (String fieldName : 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;
if (queue.size() < limit) {
// there is still space in the queue
queue.add(new ScoreTerm(word, topField, score, idf, docFreq, tf));
} else {
ScoreTerm term = queue.top();
if (term.score < score) { // update the smallest in the queue in place and update the queue.
term.update(word, topField, score, idf, docFreq, tf);
queue.updateTop();
}
}
}
return queue;
}
/**
* Create a PriorityQueue from a word->tf map.
*
* @param perFieldTermFrequencies a per field map of words keyed on the word(String) with Int
* objects as the values.
*/
private PriorityQueue<ScoreTerm> createQueue(
Map<String, Map<String, Int>> perFieldTermFrequencies) throws IOException {
// have collected all words in doc and their freqs
int numDocs = ir.numDocs();
final int limit = Math.min(maxQueryTerms, this.getTermsCount(perFieldTermFrequencies));
FreqQ queue = new FreqQ(limit); // will order words by score
for (Map.Entry<String, Map<String, Int>> entry : perFieldTermFrequencies.entrySet()) {
Map<String, Int> perWordTermFrequencies = entry.getValue();
String fieldName = entry.getKey();
for (Map.Entry<String, Int> tfEntry : perWordTermFrequencies.entrySet()) { // for every word
String word = tfEntry.getKey();
int tf = tfEntry.getValue().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
}
int docFreq = ir.docFreq(new Term(fieldName, word));
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;
if (queue.size() < limit) {
// there is still space in the queue
queue.add(new ScoreTerm(word, fieldName, score, idf, docFreq, tf));
} else {
ScoreTerm term = queue.top();
if (term.score
< score) { // update the smallest in the queue in place and update the queue.
term.update(word, fieldName, score, idf, docFreq, tf);
queue.updateTop();
}
}
}
}
return queue;
}
