public AnomalyDetection(IConfigurationRoot config)
{
var kMeansClusters = Convert.ToInt32(config["kMeansClusters"]);
GetProxy <IOrchestrator>().Detection.Subscribe(this, async signal =>
{
try
{
var cluster = 0;
if (_kMeansScoring != null)
{
lock (_syncClustering)
{
if (_kMeansScoring != null)
{
cluster = _kMeansScoring.Score(new[] { signal.Value });
}
}
}
if (cluster < 0)
{
await Anomaly.PublishAsync(new Anomaly {
Temperature = signal
});
}
}
catch (Exception ex)
{
Logger.LogError(ex, $"Error processing {signal.ToString()}");
}
return(MessageResult.Ok);
});
GetProxy <IOrchestrator>().Model.Subscribe(this, model =>
{
//if the messages have been stored and forwarded, but the file has been deleted (e.g. a restart)
//then the message can be empty (null)
if (model == null)
{
return(Task.FromResult(MessageResult.Ok));
}
try
{
lock (_syncClustering)
{
switch (model.Algorithm)
{
case Algorithm.kMeans:
_kMeansScoring = new KMeansScoring(kMeansClusters);
_kMeansScoring.DeserializeModel(model.DataJson);
break;
case Algorithm.LSTM:
break;
}
}
}
catch (Exception ex)
{
Logger.LogError(ex, $"Error processing {model.ToString()}");
}
return(Task.FromResult(MessageResult.Ok));
});
}
public AnomalyDetection(IOrchestrator orcherstratorProxy, IModelTraining modelTrainingProxy)
{
orcherstratorProxy.Detection.Subscribe(this, async signal =>
{
try
{
int cluster = 0;
if (_kMeansScoring != null)
{
lock (_syncClustering)
if (_kMeansScoring != null)
{
cluster = _kMeansScoring.Score(new double[] { signal.Value });
}
}
if (cluster < 0)
{
await Anomaly.PublishAsync(new Anomaly()
{
Temperature = signal
});
}
}
catch (Exception ex)
{
Console.WriteLine(ex);
}
return(MessageResult.Ok);
});
modelTrainingProxy.Model.Subscribe(this, async(model) =>
{
//if the messages has been stored and forwarded, but the file has been deleted (e.g. a restart)
//then the message can be empty (null)
if (model == null)
{
return(MessageResult.Ok);
}
try
{
lock (_syncClustering)
switch (model.Algorithm)
{
case ThermostatApplication.Algorithm.kMeans:
_kMeansScoring = new KMeansScoring(_numClusters);
_kMeansScoring.DeserializeModel(model.DataJson);
break;
case ThermostatApplication.Algorithm.LSTM:
break;
default:
break;
}
}
catch (Exception ex)
{
Console.WriteLine(ex);
}
return(MessageResult.Ok);
});
}
