private void _UpdateUI(DocumentTermModel model)
{
progressBar.Visibility = Visibility.Collapsed;
browserFrame.Visibility = Visibility.Visible;
NNMFMatrix termDocument = model.GetNormalised();
termDocument.Factorise(FEATURE_COUNT, out _weightMatrix, out _featureMatrix);
// calculate the maximum weight across all documents
_topWeight = 0;
for (int i = 0; i < _weightMatrix.ShapeX; i++)
{
for (int j = 0; j < _weightMatrix.ShapeY; j++)
{
float val = _weightMatrix[i, j];
if (val > _topWeight)
{
_topWeight = val;
}
}
}
// build the list of clusters
for (int i = 0; i < FEATURE_COUNT; i++)
{
StringBuilder sb = new StringBuilder();
List <int> wordList = _RankFeatureWordList(_featureMatrix, i);
foreach (int index in wordList)
{
string word = model.GetTerm(index);
if (sb.Length > 0)
{
sb.Append(", ");
}
sb.Append(word);
}
Cluster cluster = new Cluster(sb.ToString(), _colourList[i % 7], i);
cluster.Clicked += new Cluster.ClickedDelegate(Cluster_Clicked);
tagCloudContainer.Children.Add(cluster);
}
_ShowResults(_resultList);
}
public NNMFMatrix GetNormalised()
{
int numDocs = _documentList.Count;
int numTerms = _termToIndex.Count;
double nd = numDocs;
NNMFMatrix ret = new NNMFMatrix(numTerms, numDocs);
int x = 0;
foreach (Document doc in _documentList)
{
double docTermCount = doc.totalTermCount;
foreach (KeyValuePair <int, uint> item in doc.termCount)
{
int termIndex = item.Key;
double count = item.Value;
double termFrequency = count / docTermCount;
double inverseDocumentFrequency = Math.Log(nd / _keywordDocumentOccurence[termIndex].Count);
double weight = inverseDocumentFrequency * termFrequency;
ret.Set(termIndex, x, (float)weight);
}
++x;
}
return(ret);
}
