public DetectionResults Filter(DocumentClusters document)
{
List <ProcessingTextBlock> clusters = new List <ProcessingTextBlock>();
List <ProcessingTextBlock> withoutSentiment = new List <ProcessingTextBlock>();
foreach (var cluster in document.Clusters)
{
if (cluster.Sentences.Any(item => item.CalculateSentiment().HasValue))
{
clusters.Add(cluster);
}
else
{
withoutSentiment.Add(cluster);
}
}
return(new DetectionResults(clusters.ToArray(), withoutSentiment.ToArray()));
}
public DetectionResults Filter(DocumentClusters document)
{
if (document.Clusters.Length < 3)
{
logger.Info("Not enought text clusters for clustering");
return(new DetectionResults(document.Clusters));
}
double[][] observations = vectorSource.GetVectors(document.Clusters, NormalizationType.None);
var standardizer = Standardizer.GetNumericStandardizer(observations);
observations = standardizer.StandardizeAll(observations);
var data = observations.ToArray();
for (int i = 0; i < observations.Length; i++)
{
for (int j = 0; j < observations[i].Length; j++)
{
if (double.IsNaN(observations[i][j]))
{
observations[i][j] = 0;
}
}
}
var teacher = new OneclassSupportVectorLearning <Gaussian>
{
Kernel = Gaussian.FromGamma(1.0 / data.Length),
Nu = 0.5,
Shrinking = true,
Tolerance = 0.001
};
var svm = teacher.Learn(data);
double[] prediction = svm.Score(data);
Dictionary <int, List <double> > weights = new Dictionary <int, List <double> >();
for (int i = 0; i < prediction.Length; i++)
{
foreach (var sentenceItem in document.Clusters[i].Sentences)
{
if (!weights.TryGetValue(sentenceItem.Index, out var classType))
{
classType = new List <double>();
weights[sentenceItem.Index] = classType;
}
classType.Add(prediction[i]);
}
}
List <ProcessingTextBlock> anomaly = new List <ProcessingTextBlock>();
List <ProcessingTextBlock> resultData = new List <ProcessingTextBlock>();
List <SentenceItem> sentences = new List <SentenceItem>();
ProcessingTextBlock cluster;
bool?lastResult = null;
var cutoffIndex = (int)(weights.Count * 0.2);
var cutoff = weights.Select(item => item.Value.Sum()).OrderBy(item => item).Skip(cutoffIndex).First();
var allSentences = document.Clusters.SelectMany(item => item.Sentences)
.Distinct()
.OrderBy(item => item.Index)
.ToArray();
if (allSentences.Length != weights.Count)
{
throw new ArgumentOutOfRangeException(nameof(document), "Sentence length mismatch");
}
foreach (var sentence in allSentences)
{
var current = weights[sentence.Index].Sum();
var result = current > cutoff;
if (lastResult != null &&
result != lastResult)
{
cluster = new ProcessingTextBlock(sentences.ToArray());
sentences.Clear();
if (lastResult.Value)
{
resultData.Add(cluster);
}
else
{
anomaly.Add(cluster);
}
}
sentences.Add(sentence);
lastResult = result;
}
cluster = new ProcessingTextBlock(sentences.ToArray());
sentences.Clear();
if (lastResult.Value)
{
resultData.Add(cluster);
}
else
{
anomaly.Add(cluster);
}
StringBuilder builder = new StringBuilder();
foreach (var textCluster in anomaly)
{
foreach (var sentenceItem in textCluster.Sentences)
{
builder.AppendLine(sentenceItem.Text);
}
}
return(new DetectionResults(resultData.ToArray(), anomaly.ToArray()));
}