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);
}
}
public void AnalyzeSentences(MemorySource <string, ThreeTuple <ContinuousDistribution, ContinuousDistribution, ContinuousDistribution> > inputed,
Dictionary <string, List <KeyValuePair <double, double> > > sentencesV,
Dictionary <string, List <KeyValuePair <double, double> > > sentencesA,
Dictionary <string, List <KeyValuePair <double, double> > > sentencesD, string imputesave)
{
using (var stream = File.CreateText(imputesave)) {
foreach (string stem in sentencesV.Keys)
{
double vmean = WeightedStatistics.Mean(sentencesV[stem]), amean = WeightedStatistics.Mean(sentencesA[stem]), dmean = WeightedStatistics.Mean(sentencesD[stem]);
ClippedGaussianDistribution valence = new ClippedGaussianDistribution(vmean, WeightedStatistics.Variance(sentencesV[stem], vmean, true), 0, 1);
ClippedGaussianDistribution arousal = new ClippedGaussianDistribution(amean, WeightedStatistics.Variance(sentencesA[stem], amean, true), 0, 1);
ClippedGaussianDistribution dominance = new ClippedGaussianDistribution(dmean, WeightedStatistics.Variance(sentencesD[stem], dmean, true), 0, 1);
inputed[stem] = new ThreeTuple <ContinuousDistribution, ContinuousDistribution, ContinuousDistribution>(valence, arousal, dominance);
stream.WriteLine(stem + "," + valence.InternalMean + "," + valence.InternalVariance + "," + arousal.InternalMean + "," + arousal.InternalVariance + "," + dominance.InternalMean + "," + dominance.InternalVariance);
}
}
}
