/// <summary>
/// Classify an unknown document using network knowledge from training documents
/// </summary>
/// <param name="doc">The unknown document to classify</param>
/// <returns>Category of document based on prior knowledge</returns>
public Dictionary <string, ClassificationResults> ClassifyUnknownDocument(Doc doc)
{
Dictionary <string, ClassificationResults> returnResults = new Dictionary <string, ClassificationResults>();
Dictionary <Category, double> overallProbs = new Dictionary <Category, double>();
Dictionary <Category, double> overallProbsNGrams = new Dictionary <Category, double>();
Dictionary <Category, double> overallProbsTfIdf = new Dictionary <Category, double>();
foreach (PartyData p in Data)
{
// Calculate probability using words from document
foreach (Word word in doc.Words)
{
Word match = p.Words.Where(x => x.Key == word.Key).FirstOrDefault();
if (match != null)
{
if (overallProbs.TryGetValue(p.GetCategory(), out double prob))
{
overallProbs[p.GetCategory()] = prob + Math.Log(match.RelativeFrequency);
}
else
{
overallProbs[p.GetCategory()] = Math.Log(match.RelativeFrequency);
}
}
}
overallProbs[p.GetCategory()] += Math.Log(p.Probability);
// Calculate probability, this time using N-Grams not words.
foreach (Word ngram in doc.NGrams)
{
Word match = p.NGrams.Where(x => x.Key == ngram.Key).FirstOrDefault();
if (match != null)
{
if (overallProbsNGrams.TryGetValue(p.GetCategory(), out double prob))
{
overallProbsNGrams[p.GetCategory()] = prob + Math.Log(match.RelativeFrequency);
}
else
{
overallProbsNGrams[p.GetCategory()] = Math.Log(match.RelativeFrequency);
}
}
}
overallProbsNGrams[p.GetCategory()] += Math.Log(p.Probability);
// Same as above, instead using inverse document frequency instead of relative frequency
foreach (Word word in doc.Words)
{
Word match = p.Words.Where(x => x.Key == word.Key).FirstOrDefault();
if (match != null)
{
if (overallProbsTfIdf.TryGetValue(p.GetCategory(), out double prob))
{
overallProbsTfIdf[p.GetCategory()] = prob + Math.Log(match.Frequency * (TotalDocs / match.DocumentFrequency));
}
else
{
overallProbsTfIdf[p.GetCategory()] = Math.Log(match.Frequency * (TotalDocs / match.DocumentFrequency));
}
}
}
overallProbsTfIdf[p.GetCategory()] += Math.Log(p.Probability);
}
// Create results object and set all relevant variables
ClassificationResults results = new ClassificationResults();
ClassificationResults nGramResults = new ClassificationResults();
ClassificationResults tfIdfResults = new ClassificationResults();
// Calculate percentages of certainty
results.SetConservativePercentage(((overallProbs.Where(x => x.Key == Category.CONSERVATIVE).FirstOrDefault().Value * -1) / overallProbs.Sum(x => x.Value) * -1) * 100);
results.SetCoalitionPercentage(((overallProbs.Where(x => x.Key == Category.COALITION).FirstOrDefault().Value * -1) / overallProbs.Sum(x => x.Value) * -1) * 100);
results.SetLabourPercentage(((overallProbs.Where(x => x.Key == Category.LABOUR).FirstOrDefault().Value * -1) / overallProbs.Sum(x => x.Value) * -1) * 100);
nGramResults.SetConservativePercentage(((overallProbsNGrams.Where(x => x.Key == Category.CONSERVATIVE).FirstOrDefault().Value * -1) / overallProbsNGrams.Sum(x => x.Value) * -1) * 100);
nGramResults.SetCoalitionPercentage(((overallProbsNGrams.Where(x => x.Key == Category.COALITION).FirstOrDefault().Value * -1) / overallProbsNGrams.Sum(x => x.Value) * -1) * 100);
nGramResults.SetLabourPercentage(((overallProbsNGrams.Where(x => x.Key == Category.LABOUR).FirstOrDefault().Value * -1) / overallProbsNGrams.Sum(x => x.Value) * -1) * 100);
tfIdfResults.SetConservativePercentage(((overallProbsTfIdf.Where(x => x.Key == Category.CONSERVATIVE).FirstOrDefault().Value * -1) / overallProbsTfIdf.Sum(x => x.Value) * -1) * 100);
tfIdfResults.SetCoalitionPercentage(((overallProbsTfIdf.Where(x => x.Key == Category.COALITION).FirstOrDefault().Value * -1) / overallProbsTfIdf.Sum(x => x.Value) * -1) * 100);
tfIdfResults.SetLabourPercentage(((overallProbsTfIdf.Where(x => x.Key == Category.LABOUR).FirstOrDefault().Value * -1) / overallProbsTfIdf.Sum(x => x.Value) * -1) * 100);
// Add results to dictionary ready for return
returnResults["normal"] = results;
returnResults["ngram"] = nGramResults;
returnResults["tfidf"] = tfIdfResults;
return(returnResults);
}