public SparseVector <double> ProcessDocument(string document, string language)
{
Dictionary <int, int> tfVec = new Dictionary <int, int>();
ArrayList <WordStem> nGrams = new ArrayList <WordStem>(mMaxNGramLen);
mTokenizer.Text = document;
IStemmer stemmer;
Set <string> .ReadOnly stopWords;
if (stemmers.ContainsKey(language))
{
stemmer = stemmers[language];
stopWords = stopWordDict[language];
}
else
{
Language lang = TextMiningUtils.GetLanguage(language);
try
{
TextMiningUtils.GetLanguageTools(lang, out stopWords, out stemmer);
}
catch (ArgumentNotSupportedException) // Language tools to not exist, so fallback to english
{
TextMiningUtils.GetLanguageTools(Language.English, out stopWords, out stemmer);
mLogger.Error("Initialize", "Missing language tools for language code {0}.", language);
}
stemmers[language] = stemmer;
stopWordDict[language] = stopWords;
}
foreach (string token in mTokenizer)
{
string word = token.Trim().ToLower();
if (stopWords == null || !stopWords.Contains(word))
{
string stem = stemmer == null ? word : stemmer.GetStem(word).Trim().ToLower();
if (nGrams.Count < mMaxNGramLen)
{
WordStem wordStem = new WordStem();
wordStem.Word = word;
wordStem.Stem = stem;
nGrams.Add(wordStem);
if (nGrams.Count < mMaxNGramLen)
{
continue;
}
}
else
{
WordStem wordStem = nGrams[0];
wordStem.Word = word;
wordStem.Stem = stem;
for (int i = 0; i < mMaxNGramLen - 1; i++)
{
nGrams[i] = nGrams[i + 1];
}
nGrams[mMaxNGramLen - 1] = wordStem;
}
ProcessDocumentNGrams(nGrams, 0, tfVec);
}
}
int startIdx = nGrams.Count == mMaxNGramLen ? 1 : 0;
for (int i = startIdx; i < nGrams.Count; i++)
{
ProcessDocumentNGrams(nGrams, i, tfVec);
}
SparseVector <double> docVec = new SparseVector <double>();
if (mWordWeightType == WordWeightType.TermFreq)
{
foreach (KeyValuePair <int, int> tfItem in tfVec)
{
docVec.InnerIdx.Add(tfItem.Key);
docVec.InnerDat.Add(tfItem.Value);
}
}
else if (mWordWeightType == WordWeightType.TfIdf)
{
foreach (KeyValuePair <int, int> tfItem in tfVec)
{
double tfIdf = (double)tfItem.Value * mIdxInfo[tfItem.Key].mIdf;
if (tfIdf > 0)
{
docVec.InnerIdx.Add(tfItem.Key);
docVec.InnerDat.Add(tfIdf);
}
}
}
else if (mWordWeightType == WordWeightType.LogDfTfIdf)
{
foreach (KeyValuePair <int, int> tfItem in tfVec)
{
double tfIdf = (double)tfItem.Value * mIdxInfo[tfItem.Key].mIdf;
if (tfIdf > 0)
{
docVec.InnerIdx.Add(tfItem.Key);
docVec.InnerDat.Add(Math.Log(1 + mIdxInfo[tfItem.Key].mDocFreq) * tfIdf);
}
}
}
docVec.Sort();
CutLowWeights(ref docVec);
if (mNormalizeVectors)
{
Utils.TryNrmVecL2(docVec);
}
return(docVec);
}
public ArrayList <SparseVector <double> > Initialize(IEnumerable <KeyDat <string, string> > documents, bool keepBowVectors)
{
Utils.ThrowException(documents == null ? new ArgumentNullException("documents") : null);
Debug.Assert(documents != null, "Documents are always passed");
mWordInfo.Clear();
mIdxInfo.Clear();
mBowVectors.Clear();
ArrayList <SparseVector <double> > bows = keepBowVectors ? null : new ArrayList <SparseVector <double> >();
// build vocabulary
mLogger.Info("Initialize", "Building vocabulary ...");
int docCount = 0;
foreach (var document in documents)
{
docCount++;
mLogger.ProgressFast(this, "Initialize", "Document {0} ...", docCount, /*numSteps=n*/ -1);
Set <string> docWords = new Set <string>();
ArrayList <WordStem> nGrams = new ArrayList <WordStem>(mMaxNGramLen);
mTokenizer.Text = document.First;
IStemmer stemmer;
Set <string> .ReadOnly stopWords;
// Setup stopwords and stemmer
if (stemmers.ContainsKey(document.Second))
{
stemmer = stemmers[document.Second];
stopWords = stopWordDict[document.Second];
}
else
{
Language lang = TextMiningUtils.GetLanguage(document.Second);
try
{
TextMiningUtils.GetLanguageTools(lang, out stopWords, out stemmer);
}
catch (ArgumentNotSupportedException) // Language tools to not exist, so fallback to english
{
TextMiningUtils.GetLanguageTools(Language.English, out stopWords, out stemmer);
mLogger.Error("Initialize", "Missing language tools for language code {0}.", document.Second);
}
stemmers[document.Second] = stemmer;
stopWordDict[document.Second] = stopWords;
}
foreach (string token in mTokenizer)
{
string word = token.Trim().ToLower();
if (stopWords == null || !stopWords.Contains(word))
{
string stem = stemmer == null ? word : stemmer.GetStem(word).Trim().ToLower();
if (nGrams.Count < mMaxNGramLen)
{
WordStem wordStem = new WordStem();
wordStem.Word = word;
wordStem.Stem = stem;
nGrams.Add(wordStem);
if (nGrams.Count < mMaxNGramLen)
{
continue;
}
}
else
{
WordStem wordStem = nGrams[0];
wordStem.Word = word;
wordStem.Stem = stem;
for (int i = 0; i < mMaxNGramLen - 1; i++)
{
nGrams[i] = nGrams[i + 1];
}
nGrams[mMaxNGramLen - 1] = wordStem;
}
ProcessNGramsPass1(nGrams, 0, docWords);
}
}
int startIdx = nGrams.Count == mMaxNGramLen ? 1 : 0;
for (int i = startIdx; i < nGrams.Count; i++)
{
ProcessNGramsPass1(nGrams, i, docWords);
}
}
mLogger.ProgressFast(this, "Initialize", "Document {0} ...", docCount, docCount);
// remove unfrequent words and n-grams, precompute IDF
ArrayList <string> removeList = new ArrayList <string>();
foreach (KeyValuePair <string, Word> wordInfo in mWordInfo)
{
if (wordInfo.Value.mFreq < mMinWordFreq)
{
removeList.Add(wordInfo.Key);
}
else
{
wordInfo.Value.mIdf = Math.Log((double)docCount / (double)wordInfo.Value.mDocFreq);
}
}
foreach (string key in removeList)
{
mWordInfo.Remove(key);
}
// determine most frequent word and n-gram forms
foreach (Word wordInfo in mWordInfo.Values)
{
int max = 0;
foreach (KeyValuePair <string, int> wordForm in wordInfo.mForms)
{
if (wordForm.Value > max)
{
max = wordForm.Value;
wordInfo.mMostFrequentForm = wordForm.Key;
}
}
if (!mKeepWordForms)
{
wordInfo.mForms.Clear();
}
}
// compute bag-of-words vectors
mLogger.Info("Initialize", "Computing bag-of-words vectors ...");
int docNum = 1;
foreach (var document in documents)
{
Set <string> .ReadOnly stopWords = stopWordDict[document.Second];
IStemmer stemmer = stemmers[document.Second];
mLogger.ProgressFast(this, "Initialize", "Document {0} / {1} ...", docNum++, docCount);
Dictionary <int, int> tfVec = new Dictionary <int, int>();
ArrayList <WordStem> nGrams = new ArrayList <WordStem>(mMaxNGramLen);
mTokenizer.Text = document.First;
foreach (string token in mTokenizer)
{
string word = token.Trim().ToLower();
if (stopWords == null || !stopWords.Contains(word))
{
string stem = stemmer == null ? word : stemmer.GetStem(word).Trim().ToLower();
if (nGrams.Count < mMaxNGramLen)
{
WordStem wordStem = new WordStem();
wordStem.Word = word;
wordStem.Stem = stem;
nGrams.Add(wordStem);
if (nGrams.Count < mMaxNGramLen)
{
continue;
}
}
else
{
WordStem wordStem = nGrams[0];
wordStem.Word = word;
wordStem.Stem = stem;
for (int i = 0; i < mMaxNGramLen - 1; i++)
{
nGrams[i] = nGrams[i + 1];
}
nGrams[mMaxNGramLen - 1] = wordStem;
}
ProcessNGramsPass2(nGrams, 0, tfVec);
}
}
int startIdx = nGrams.Count == mMaxNGramLen ? 1 : 0;
for (int i = startIdx; i < nGrams.Count; i++)
{
ProcessNGramsPass2(nGrams, i, tfVec);
}
SparseVector <double> docVec = new SparseVector <double>();
if (mWordWeightType == WordWeightType.TermFreq)
{
foreach (KeyValuePair <int, int> tfItem in tfVec)
{
docVec.InnerIdx.Add(tfItem.Key);
docVec.InnerDat.Add(tfItem.Value);
}
}
else if (mWordWeightType == WordWeightType.TfIdf)
{
foreach (KeyValuePair <int, int> tfItem in tfVec)
{
double tfIdf = (double)tfItem.Value * mIdxInfo[tfItem.Key].mIdf;
if (tfIdf > 0)
{
docVec.InnerIdx.Add(tfItem.Key);
docVec.InnerDat.Add(tfIdf);
}
}
}
else if (mWordWeightType == WordWeightType.LogDfTfIdf)
{
foreach (KeyValuePair <int, int> tfItem in tfVec)
{
double tfIdf = (double)tfItem.Value * mIdxInfo[tfItem.Key].mIdf;
if (tfIdf > 0)
{
docVec.InnerIdx.Add(tfItem.Key);
docVec.InnerDat.Add(Math.Log(1 + mIdxInfo[tfItem.Key].mDocFreq) * tfIdf);
}
}
}
docVec.Sort();
CutLowWeights(ref docVec);
if (mNormalizeVectors)
{
Utils.TryNrmVecL2(docVec);
}
if (keepBowVectors)
{
mBowVectors.Add(docVec);
}
else
{
bows.Add(docVec);
}
}
return(bows);
}
