public void Store(string filepath)
{
StreamWriter fs = new StreamWriter(filepath, false);
string csvStr = "";
Int64 avg = count / corpus.Count + 2;
int csvC = 0;
foreach (var entry in corpus)
{
nGramDist ngd = entry.Value;
if (ngd.isSalient && ngd.total > avg)
{
csvStr = "'" + entry.Key + "',";
csvStr += ngd.negCnt + ",";
csvStr += ngd.posCnt + ",";
csvStr += ngd.nutCnt + ",";
csvStr += ngd.bias + ",";
csvStr += ngd.objective + ",";
csvStr += ngd.total;
csvC++;
fs.WriteLine(csvStr);
}
}
Console.WriteLine(csvC);
fs.Close();
}
public double score(string line)
{
double score = 0.0;
line = line.ToLower();
line = line.Replace(",", "");
line = line.Replace(".", "");
line = line.Replace("!", "");
line = line.Replace("?", "");
line = line.Replace("\"", "");
line = line.Replace("'", "");
line = line.Replace("#", "");
string[] ngrams = NGramify(line);
double negS = 0.0000001, posS = 0.0000001, objS = 0.0000001;
foreach (string ng in ngrams)
{
if (corpus.ContainsKey(ng))
{
nGramDist ngd = corpus[ng];
//if (ngd.isSalient)
{
if (ngd.objective > 0.5)
{
objS += ngd.objective;
}
else
{
double bias = ngd.bias;
if (bias < 0)
{
negS -= bias;
}
else
{
posS += bias;
}
}
}
}
}
if (objS > negS && objS > posS)
{
return(0);
}
if (negS > posS)
{
return(-negS / (negS + posS + objS));
}
else if (negS < posS)
{
return(posS / (negS + posS + objS));
}
score = (posS - negS) / (negS + posS + objS);
return(score);
}
