public IAnomalyDetector Load(string path)
{
logger.LogInformation("Loading <{0}>...", path);
(string postiveFile, string negativeFile, string modelFile)files = GetFiles(path);
if (File.Exists(files.postiveFile))
{
logger.LogDebug("Loading <{0}> positive documents", files.postiveFile);
Document[] positiveDocs = JsonConvert.DeserializeObject <Document[]>(File.ReadAllText(files.postiveFile));
positive = GetDocuments(positiveDocs);
}
if (File.Exists(files.negativeFile))
{
logger.LogDebug("Loading <{0}> negative documents", files.negativeFile);
Document[] negativeDocs = JsonConvert.DeserializeObject <Document[]>(File.ReadAllText(files.negativeFile));
negative = GetDocuments(negativeDocs);
}
SupportVectorMachine <Linear> model = null;
if (File.Exists(files.modelFile))
{
logger.LogDebug("Loading <{0}> model", files.modelFile);
model = Serializer.Load <SupportVectorMachine <Linear> >(files.modelFile);
}
logger.LogDebug("Loaded <{0}> positive and <{1}> negative", positive.Count, negative.Count);
current = new SvmAnomalyDetector(vectorSource, factory, model);
return(Current);
}
public void Reset()
{
logger.LogDebug("Reset");
negative.Clear();
positive.Clear();
duplicate.Clear();
current = null;
}
public async Task <SvmAnomalyDetector> Train(CancellationToken token)
{
logger.LogDebug("Train");
if (positive.Count < 5 || negative.Count < 5)
{
throw new InvalidOperationException("Not enough training samples");
}
SvmAnomalyDetector detector = new SvmAnomalyDetector(vectorSource, factory, null);
DataSet dataset = new DataSet
{
Positive = positive.Select(item => new ProcessingTextBlock(item.Sentences.ToArray())).ToArray(),
Negative = negative.Select(item => new ProcessingTextBlock(item.Sentences.ToArray())).ToArray()
};
await detector.Train(dataset, token).ConfigureAwait(false);
current = detector;
return(detector);
}
