public void AnalyzeWords(List <string> words, ComboSource <string, ThreeTuple <ContinuousDistribution, ContinuousDistribution, ContinuousDistribution> > combo,
Dictionary <string, List <KeyValuePair <double, double> > > sentencesV,
Dictionary <string, List <KeyValuePair <double, double> > > sentencesA,
Dictionary <string, List <KeyValuePair <double, double> > > sentencesD)
{
List <KeyValuePair <double, double> > wordsV = new List <KeyValuePair <double, double> >(),
wordsA = new List <KeyValuePair <double, double> >(),
wordsD = new List <KeyValuePair <double, double> >();
foreach (string word in words)
{
if (word.StartsWith(" ") || word.Length <= 2)
{
continue;
}
ThreeTuple <ContinuousDistribution, ContinuousDistribution, ContinuousDistribution> vad;
if (!TryGetWordOrStem(combo, word, out vad))
{
continue;
}
wordsV.Add(new KeyValuePair <double, double>(vad.one.Mean, 1 / vad.one.Variance));
wordsA.Add(new KeyValuePair <double, double>(vad.two.Mean, 1 / vad.two.Variance));
wordsD.Add(new KeyValuePair <double, double>(vad.three.Mean, 1 / vad.three.Variance));
}
double vmean = WeightedStatistics.Mean(wordsV), amean = WeightedStatistics.Mean(wordsA), dmean = WeightedStatistics.Mean(wordsD);
double vvar = WeightedStatistics.Variance(wordsV, vmean, true), avar = WeightedStatistics.Variance(wordsA, amean, true), dvar = WeightedStatistics.Variance(wordsD, dmean, true);
if (double.IsNaN(vmean) || double.IsNaN(amean) || double.IsNaN(dmean))
{
return;
}
foreach (string word in words)
{
if (word.StartsWith(" ") || word.Length <= 2)
{
continue;
}
ThreeTuple <ContinuousDistribution, ContinuousDistribution, ContinuousDistribution> vad;
if (TryGetWordOrStem(source, word, out vad))
{
continue;
}
string stem = stemmer.stemTerm(word);
AddTextNumerDenom(stem, sentencesV, vmean, vvar);
AddTextNumerDenom(stem, sentencesA, amean, avar);
AddTextNumerDenom(stem, sentencesD, dmean, dvar);
}
}
//-------------------------------------------------
// コンストラクタ
//-------------------------------------------------
/// <summary>
/// コンストラクタ
/// </summary>
/// <param name="dispatcher"></param>
public MainViewModel(Dispatcher dispatcher)
{
base.Dispatcher = dispatcher;
//-------------------------------------------------
// コンボボックスの設定値
//-------------------------------------------------
// UIスレッド渡す
//ComboSource = new BindingListAsync<MainViewModelCombo>(dispatcher);
ComboSource.Add(new MainViewModelCombo(0, "PID_HOGE"));
ComboSource.Add(new MainViewModelCombo(1, "PID_FUGA"));
ComboSource.Add(new MainViewModelCombo(2, "PID_HEGE"));
}
//-------------------------------------------------
// コンストラクタ
//-------------------------------------------------
/// <summary>
/// コンストラクタ
/// </summary>
/// <param name="dispatcher"></param>
public MainViewModel(Dispatcher dispatcher)
{
//-------------------------------------------------
// 画面コントロールの初期値
//-------------------------------------------------
BtnStartServiceEnabled = true;
BtnStopServiceEnabled = false;
//-------------------------------------------------
// コンボボックスの設定値
//-------------------------------------------------
// UIスレッド渡す
//ComboSource = new BindingListAsync<MainViewModelCombo>(dispatcher);
ComboSource.Add(new MainViewModelCombo(0, "PID_HOGE"));
ComboSource.Add(new MainViewModelCombo(1, "PID_FUGA"));
ComboSource.Add(new MainViewModelCombo(2, "PID_HEGE"));
//-------------------------------------------------
// コールバック登録受付通知受信のための準備
// ※NuGetでMVVMLightLibs取得
//-------------------------------------------------
Messenger.Default.Register <CallbackRegistNty>(this, onReceivedMessage);
}
ImputeEmotionalContentFromFile(string filename, uint column, uint repeats, string imputesave)
{
MemorySource <string, ThreeTuple <ContinuousDistribution, ContinuousDistribution, ContinuousDistribution> > imputed = new MemorySource <string, ThreeTuple <ContinuousDistribution, ContinuousDistribution, ContinuousDistribution> >();
ComboSource <string, ThreeTuple <ContinuousDistribution, ContinuousDistribution, ContinuousDistribution> > combo = new ComboSource <string, ThreeTuple <ContinuousDistribution, ContinuousDistribution, ContinuousDistribution> >(source, imputed);
// Check for existing imputed file
DataReader imputereader = new DataReader(imputesave);
uint kk = 0;
for (string[] row = imputereader.ReadRow(); row != null; row = imputereader.ReadRow())
{
kk++;
if (kk % 1000 == 0)
{
Console.WriteLine("#" + kk);
}
double meanv = double.Parse(row[1]), varv = double.Parse(row[2]), meana = double.Parse(row[3]), vara = double.Parse(row[4]), meand = double.Parse(row[5]), vard = double.Parse(row[6]);
ContinuousDistribution valence = new ClippedGaussianDistribution(meanv, varv, 0, 1);
ContinuousDistribution arousal = new ClippedGaussianDistribution(meana, vara, 0, 1);
ContinuousDistribution dominance = new ClippedGaussianDistribution(meand, vard, 0, 1);
imputed[row[0]] = new ThreeTuple <ContinuousDistribution, ContinuousDistribution, ContinuousDistribution>(valence, arousal, dominance);
}
imputereader.Close();
for (uint ii = 0; ii < repeats; ii++)
{
Dictionary <string, List <KeyValuePair <double, double> > >
sentencesV = new Dictionary <string, List <KeyValuePair <double, double> > >(),
sentencesA = new Dictionary <string, List <KeyValuePair <double, double> > >(),
sentencesD = new Dictionary <string, List <KeyValuePair <double, double> > >();
DataReader reader = new DataReader(filename);
uint jj = 0;
for (string[] row = reader.ReadRow(); row != null; row = reader.ReadRow())
{
jj++;
if (jj % 1000 == 0)
{
Console.WriteLine("#" + jj + ": " + sentencesV.Count + ", " + imputed.Count);
}
List <string> words = TwitterUtilities.SplitWords(row[column].ToLower());
AnalyzeWords(words, combo, sentencesV, sentencesA, sentencesD);
}
reader.Close();
AnalyzeSentences(imputed, sentencesV, sentencesA, sentencesD, imputesave);
}
source = combo;
return(imputed);
}
/*public IDataSource<string, ThreeTuple<ContinuousDistribution, ContinuousDistribution, ContinuousDistribution>> ImputeEmotionalContent(List<List<string>> texts, uint repeats) {
* MemorySource<string, ThreeTuple<ContinuousDistribution, ContinuousDistribution, ContinuousDistribution>> inputed = new MemorySource<string, ThreeTuple<ContinuousDistribution, ContinuousDistribution, ContinuousDistribution>>();
* ComboSource<string, ThreeTuple<ContinuousDistribution, ContinuousDistribution, ContinuousDistribution>> combo = new ComboSource<string, ThreeTuple<ContinuousDistribution, ContinuousDistribution, ContinuousDistribution>>(source, inputed);
*
* for (uint ii = 0; ii < repeats; ii++) {
* Dictionary<string, double> wordVnumers = new Dictionary<string, double>(), wordVdenoms = new Dictionary<string, double>(),
* wordAnumers = new Dictionary<string, double>(), wordAdenoms = new Dictionary<string, double>(),
* wordDnumers = new Dictionary<string, double>(), wordDdenoms = new Dictionary<string, double>(),
* wordVsumvar = new Dictionary<string, double>(), wordVcounts = new Dictionary<string, double>(),
* wordAsumvar = new Dictionary<string, double>(), wordAcounts = new Dictionary<string, double>(),
* wordDsumvar = new Dictionary<string, double>(), wordDcounts = new Dictionary<string, double>();
*
* uint jj = 0;
* foreach (List<string> words in texts) {
* jj++;
* if (jj % 1000 == 0)
* Console.WriteLine("#" + jj);
*
* double textVnumer = 0, textVdenom = 0, textAnumer = 0, textAdenom = 0, textDnumer = 0, textDdenom = 0;
* double textVsumvar = 0, textVcount = 0, textAsumvar = 0, textAcount = 0, textDsumvar = 0, textDcount = 0;
* foreach (string word in words) {
* if (word.StartsWith(" ") || word.Length <= 2)
* continue;
*
* ThreeTuple<ContinuousDistribution, ContinuousDistribution, ContinuousDistribution> vad;
* if (!TryGetWordOrStem(combo, word, out vad))
* continue;
*
* textVnumer += vad.one.Mean / vad.one.Variance;
* textVdenom += 1 / vad.one.Variance;
* textVsumvar += vad.one.Variance;
* textVcount++;
* textAnumer += vad.two.Mean / vad.two.Variance;
* textAdenom += 1 / vad.two.Variance;
* textAsumvar += vad.two.Variance;
* textAcount++;
* textDnumer += vad.three.Mean / vad.three.Variance;
* textDdenom += 1 / vad.three.Variance;
* textDsumvar += vad.three.Variance;
* textDcount++;
* }
*
* double vmean = textVnumer / textVdenom, amean = textAnumer / textAdenom, dmean = textDnumer / textDdenom;
* double vvar = textVsumvar / textVcount, avar = textAsumvar / textAcount, dvar = textDsumvar / textDcount;
*
* if (double.IsNaN(vmean) || double.IsNaN(amean) || double.IsNaN(dmean))
* continue;
*
* foreach (string word in words) {
* if (word.StartsWith(" ") || word.Length <= 2)
* continue;
*
* ThreeTuple<ContinuousDistribution, ContinuousDistribution, ContinuousDistribution> vad;
* if (TryGetWordOrStem(source, word, out vad))
* continue;
*
* string stem = stemmer.stemTerm(word);
*
* AddTextNumerDenom(stem, wordVnumers, wordVdenoms, wordVsumvar, wordVcounts, vmean, vvar);
* AddTextNumerDenom(stem, wordAnumers, wordAdenoms, wordAsumvar, wordAcounts, amean, avar);
* AddTextNumerDenom(stem, wordDnumers, wordDdenoms, wordDsumvar, wordDcounts, dmean, dvar);
* }
* }
*
* foreach (string stem in wordVnumers.Keys) {
* ContinuousDistribution valence = new ClippedGaussianDistribution(wordVnumers[stem] / wordVdenoms[stem], wordVsumvar[stem] / wordVcounts[stem], 0, 1);
* ContinuousDistribution arousal = new ClippedGaussianDistribution(wordAnumers[stem] / wordAdenoms[stem], wordAsumvar[stem] / wordAcounts[stem], 0, 1);
* ContinuousDistribution dominance = new ClippedGaussianDistribution(wordDnumers[stem] / wordDdenoms[stem], wordDsumvar[stem] / wordDcounts[stem], 0, 1);
*
* inputed[stem] = new ThreeTuple<ContinuousDistribution, ContinuousDistribution, ContinuousDistribution>(valence, arousal, dominance);
* }
* }
*
* source = combo;
* return inputed;
* }
*
* public void AddTextNumerDenom(string stem, Dictionary<string, double> wordXnumers,
* Dictionary<string, double> wordXdenoms, Dictionary<string, double> wordXsumvar,
* Dictionary<string, double> wordXcounts, double xmean, double xvar) {
* double numer, denom, sumvar, count;
* if (!wordXnumers.TryGetValue(stem, out numer)) {
* numer = 0;
* denom = 0;
* sumvar = 0;
* count = 0;
* } else {
* wordXdenoms.TryGetValue(stem, out denom);
* wordXsumvar.TryGetValue(stem, out sumvar);
* wordXcounts.TryGetValue(stem, out count);
* xvar += (xmean - numer / denom) * (xmean - numer / denom);
* }
*
* numer += xmean / xvar;
* denom += 1 / xvar;
* sumvar += xvar;
* count++;
*
* wordXnumers[stem] = numer;
* wordXdenoms[stem] = denom;
* wordXsumvar[stem] = sumvar;
* wordXcounts[stem] = count;
* }*/
public IDataSource <string, ThreeTuple <ContinuousDistribution, ContinuousDistribution, ContinuousDistribution> > ImputeEmotionalContent(List <List <string> > texts, uint repeats, string imputesave)
{
MemorySource <string, ThreeTuple <ContinuousDistribution, ContinuousDistribution, ContinuousDistribution> > imputed = new MemorySource <string, ThreeTuple <ContinuousDistribution, ContinuousDistribution, ContinuousDistribution> >();
ComboSource <string, ThreeTuple <ContinuousDistribution, ContinuousDistribution, ContinuousDistribution> > combo = new ComboSource <string, ThreeTuple <ContinuousDistribution, ContinuousDistribution, ContinuousDistribution> >(source, imputed);
for (uint ii = 0; ii < repeats; ii++)
{
Dictionary <string, List <KeyValuePair <double, double> > >
sentencesV = new Dictionary <string, List <KeyValuePair <double, double> > >(),
sentencesA = new Dictionary <string, List <KeyValuePair <double, double> > >(),
sentencesD = new Dictionary <string, List <KeyValuePair <double, double> > >();
uint jj = 0;
foreach (List <string> words in texts)
{
jj++;
if (jj % 1000 == 0)
{
Console.WriteLine("#" + jj);
}
AnalyzeWords(words, combo, sentencesV, sentencesA, sentencesD);
}
AnalyzeSentences(imputed, sentencesV, sentencesA, sentencesD, imputesave);
}
source = combo;
return(imputed);
}
public IDataSource<string, ThreeTuple<ContinuousDistribution, ContinuousDistribution, ContinuousDistribution>> ImputeEmotionalContentFromFile(string filename, uint column, uint repeats, string imputesave)
{
MemorySource<string, ThreeTuple<ContinuousDistribution, ContinuousDistribution, ContinuousDistribution>> imputed = new MemorySource<string, ThreeTuple<ContinuousDistribution, ContinuousDistribution, ContinuousDistribution>>();
ComboSource<string, ThreeTuple<ContinuousDistribution, ContinuousDistribution, ContinuousDistribution>> combo = new ComboSource<string, ThreeTuple<ContinuousDistribution, ContinuousDistribution, ContinuousDistribution>>(source, imputed);
// Check for existing imputed file
DataReader imputereader = new DataReader(imputesave);
uint kk = 0;
for (string[] row = imputereader.ReadRow(); row != null; row = imputereader.ReadRow()) {
kk++;
if (kk % 1000 == 0)
Console.WriteLine("#" + kk);
double meanv = double.Parse(row[1]), varv = double.Parse(row[2]), meana = double.Parse(row[3]), vara = double.Parse(row[4]), meand = double.Parse(row[5]), vard = double.Parse(row[6]);
ContinuousDistribution valence = new ClippedGaussianDistribution(meanv, varv, 0, 1);
ContinuousDistribution arousal = new ClippedGaussianDistribution(meana, vara, 0, 1);
ContinuousDistribution dominance = new ClippedGaussianDistribution(meand, vard, 0, 1);
imputed[row[0]] = new ThreeTuple<ContinuousDistribution, ContinuousDistribution, ContinuousDistribution>(valence, arousal, dominance);
}
imputereader.Close();
for (uint ii = 0; ii < repeats; ii++) {
Dictionary<string, List<KeyValuePair<double, double>>>
sentencesV = new Dictionary<string, List<KeyValuePair<double, double>>>(),
sentencesA = new Dictionary<string, List<KeyValuePair<double, double>>>(),
sentencesD = new Dictionary<string, List<KeyValuePair<double, double>>>();
DataReader reader = new DataReader(filename);
uint jj = 0;
for (string[] row = reader.ReadRow(); row != null; row = reader.ReadRow()) {
jj++;
if (jj % 1000 == 0)
Console.WriteLine("#" + jj + ": " + sentencesV.Count + ", " + imputed.Count);
List<string> words = TwitterUtilities.SplitWords(row[column].ToLower());
AnalyzeWords(words, combo, sentencesV, sentencesA, sentencesD);
}
reader.Close();
AnalyzeSentences(imputed, sentencesV, sentencesA, sentencesD, imputesave);
}
source = combo;
return imputed;
}
/*public IDataSource<string, ThreeTuple<ContinuousDistribution, ContinuousDistribution, ContinuousDistribution>> ImputeEmotionalContent(List<List<string>> texts, uint repeats) {
MemorySource<string, ThreeTuple<ContinuousDistribution, ContinuousDistribution, ContinuousDistribution>> inputed = new MemorySource<string, ThreeTuple<ContinuousDistribution, ContinuousDistribution, ContinuousDistribution>>();
ComboSource<string, ThreeTuple<ContinuousDistribution, ContinuousDistribution, ContinuousDistribution>> combo = new ComboSource<string, ThreeTuple<ContinuousDistribution, ContinuousDistribution, ContinuousDistribution>>(source, inputed);
for (uint ii = 0; ii < repeats; ii++) {
Dictionary<string, double> wordVnumers = new Dictionary<string, double>(), wordVdenoms = new Dictionary<string, double>(),
wordAnumers = new Dictionary<string, double>(), wordAdenoms = new Dictionary<string, double>(),
wordDnumers = new Dictionary<string, double>(), wordDdenoms = new Dictionary<string, double>(),
wordVsumvar = new Dictionary<string, double>(), wordVcounts = new Dictionary<string, double>(),
wordAsumvar = new Dictionary<string, double>(), wordAcounts = new Dictionary<string, double>(),
wordDsumvar = new Dictionary<string, double>(), wordDcounts = new Dictionary<string, double>();
uint jj = 0;
foreach (List<string> words in texts) {
jj++;
if (jj % 1000 == 0)
Console.WriteLine("#" + jj);
double textVnumer = 0, textVdenom = 0, textAnumer = 0, textAdenom = 0, textDnumer = 0, textDdenom = 0;
double textVsumvar = 0, textVcount = 0, textAsumvar = 0, textAcount = 0, textDsumvar = 0, textDcount = 0;
foreach (string word in words) {
if (word.StartsWith(" ") || word.Length <= 2)
continue;
ThreeTuple<ContinuousDistribution, ContinuousDistribution, ContinuousDistribution> vad;
if (!TryGetWordOrStem(combo, word, out vad))
continue;
textVnumer += vad.one.Mean / vad.one.Variance;
textVdenom += 1 / vad.one.Variance;
textVsumvar += vad.one.Variance;
textVcount++;
textAnumer += vad.two.Mean / vad.two.Variance;
textAdenom += 1 / vad.two.Variance;
textAsumvar += vad.two.Variance;
textAcount++;
textDnumer += vad.three.Mean / vad.three.Variance;
textDdenom += 1 / vad.three.Variance;
textDsumvar += vad.three.Variance;
textDcount++;
}
double vmean = textVnumer / textVdenom, amean = textAnumer / textAdenom, dmean = textDnumer / textDdenom;
double vvar = textVsumvar / textVcount, avar = textAsumvar / textAcount, dvar = textDsumvar / textDcount;
if (double.IsNaN(vmean) || double.IsNaN(amean) || double.IsNaN(dmean))
continue;
foreach (string word in words) {
if (word.StartsWith(" ") || word.Length <= 2)
continue;
ThreeTuple<ContinuousDistribution, ContinuousDistribution, ContinuousDistribution> vad;
if (TryGetWordOrStem(source, word, out vad))
continue;
string stem = stemmer.stemTerm(word);
AddTextNumerDenom(stem, wordVnumers, wordVdenoms, wordVsumvar, wordVcounts, vmean, vvar);
AddTextNumerDenom(stem, wordAnumers, wordAdenoms, wordAsumvar, wordAcounts, amean, avar);
AddTextNumerDenom(stem, wordDnumers, wordDdenoms, wordDsumvar, wordDcounts, dmean, dvar);
}
}
foreach (string stem in wordVnumers.Keys) {
ContinuousDistribution valence = new ClippedGaussianDistribution(wordVnumers[stem] / wordVdenoms[stem], wordVsumvar[stem] / wordVcounts[stem], 0, 1);
ContinuousDistribution arousal = new ClippedGaussianDistribution(wordAnumers[stem] / wordAdenoms[stem], wordAsumvar[stem] / wordAcounts[stem], 0, 1);
ContinuousDistribution dominance = new ClippedGaussianDistribution(wordDnumers[stem] / wordDdenoms[stem], wordDsumvar[stem] / wordDcounts[stem], 0, 1);
inputed[stem] = new ThreeTuple<ContinuousDistribution, ContinuousDistribution, ContinuousDistribution>(valence, arousal, dominance);
}
}
source = combo;
return inputed;
}
public void AddTextNumerDenom(string stem, Dictionary<string, double> wordXnumers,
Dictionary<string, double> wordXdenoms, Dictionary<string, double> wordXsumvar,
Dictionary<string, double> wordXcounts, double xmean, double xvar) {
double numer, denom, sumvar, count;
if (!wordXnumers.TryGetValue(stem, out numer)) {
numer = 0;
denom = 0;
sumvar = 0;
count = 0;
} else {
wordXdenoms.TryGetValue(stem, out denom);
wordXsumvar.TryGetValue(stem, out sumvar);
wordXcounts.TryGetValue(stem, out count);
xvar += (xmean - numer / denom) * (xmean - numer / denom);
}
numer += xmean / xvar;
denom += 1 / xvar;
sumvar += xvar;
count++;
wordXnumers[stem] = numer;
wordXdenoms[stem] = denom;
wordXsumvar[stem] = sumvar;
wordXcounts[stem] = count;
}*/
public IDataSource<string, ThreeTuple<ContinuousDistribution, ContinuousDistribution, ContinuousDistribution>> ImputeEmotionalContent(List<List<string>> texts, uint repeats, string imputesave)
{
MemorySource<string, ThreeTuple<ContinuousDistribution, ContinuousDistribution, ContinuousDistribution>> imputed = new MemorySource<string, ThreeTuple<ContinuousDistribution, ContinuousDistribution, ContinuousDistribution>>();
ComboSource<string, ThreeTuple<ContinuousDistribution, ContinuousDistribution, ContinuousDistribution>> combo = new ComboSource<string, ThreeTuple<ContinuousDistribution, ContinuousDistribution, ContinuousDistribution>>(source, imputed);
for (uint ii = 0; ii < repeats; ii++) {
Dictionary<string, List<KeyValuePair<double, double>>>
sentencesV = new Dictionary<string, List<KeyValuePair<double, double>>>(),
sentencesA = new Dictionary<string, List<KeyValuePair<double, double>>>(),
sentencesD = new Dictionary<string, List<KeyValuePair<double, double>>>();
uint jj = 0;
foreach (List<string> words in texts) {
jj++;
if (jj % 1000 == 0)
Console.WriteLine("#" + jj);
AnalyzeWords(words, combo, sentencesV, sentencesA, sentencesD);
}
AnalyzeSentences(imputed, sentencesV, sentencesA, sentencesD, imputesave);
}
source = combo;
return imputed;
}
public void AnalyzeWords(List<string> words, ComboSource<string, ThreeTuple<ContinuousDistribution, ContinuousDistribution, ContinuousDistribution>> combo,
Dictionary<string, List<KeyValuePair<double, double>>> sentencesV,
Dictionary<string, List<KeyValuePair<double, double>>> sentencesA,
Dictionary<string, List<KeyValuePair<double, double>>> sentencesD)
{
List<KeyValuePair<double, double>> wordsV = new List<KeyValuePair<double, double>>(),
wordsA = new List<KeyValuePair<double, double>>(),
wordsD = new List<KeyValuePair<double, double>>();
foreach (string word in words) {
if (word.StartsWith(" ") || word.Length <= 2)
continue;
ThreeTuple<ContinuousDistribution, ContinuousDistribution, ContinuousDistribution> vad;
if (!TryGetWordOrStem(combo, word, out vad))
continue;
wordsV.Add(new KeyValuePair<double, double>(vad.one.Mean, 1/vad.one.Variance));
wordsA.Add(new KeyValuePair<double, double>(vad.two.Mean, 1/vad.two.Variance));
wordsD.Add(new KeyValuePair<double, double>(vad.three.Mean, 1/vad.three.Variance));
}
double vmean = WeightedStatistics.Mean(wordsV), amean = WeightedStatistics.Mean(wordsA), dmean = WeightedStatistics.Mean(wordsD);
double vvar = WeightedStatistics.Variance(wordsV, vmean, true), avar = WeightedStatistics.Variance(wordsA, amean, true), dvar = WeightedStatistics.Variance(wordsD, dmean, true);
if (double.IsNaN(vmean) || double.IsNaN(amean) || double.IsNaN(dmean))
return;
foreach (string word in words) {
if (word.StartsWith(" ") || word.Length <= 2)
continue;
ThreeTuple<ContinuousDistribution, ContinuousDistribution, ContinuousDistribution> vad;
if (TryGetWordOrStem(source, word, out vad))
continue;
string stem = stemmer.stemTerm(word);
AddTextNumerDenom(stem, sentencesV, vmean, vvar);
AddTextNumerDenom(stem, sentencesA, amean, avar);
AddTextNumerDenom(stem, sentencesD, dmean, dvar);
}
}
