public override float Predict(ModelDataSet input)
{
if (TrainedModel == null)
{
throw new Exception("Must initialize the model before calling");
}
lock (TrainedModel)
{
// cache for reuse
if (PredictInput == null)
{
PredictInput = new float[input.Features()];
}
for (int i = 0; i < PredictInput.Length; i++)
{
PredictInput[i] = input.Feature(i);
}
using (var arr = InputArray.Create <float>(PredictInput))
{
return(TrainedModel.Predict(arr));
}
}
}
public override float Predict(float[] data)
{
if (TrainedModel == null)
{
throw new InvalidOperationException("Must train/load a model before predicting");
}
lock (TrainedModel)
{
using (var arr = InputArray.Create <float>(data))
{
return(TrainedModel.Predict(arr));
}
}
}
public override float Predict(DataSet data)
{
if (TrainedModel == null)
{
throw new InvalidOperationException("Must train/load a model before evaluating");
}
lock (TrainedModel)
{
#if ML_LEGACY
var result = TrainedModel.Predict(data);
return(result.Score);
#else
var result = PredictFunc.Predict(data);
return(result.Score);
#endif
}
}
public override float Predict(ModelDataSet data)
{
if (TrainedModel == null)
{
throw new Exception("Must initialize the model before calling");
}
lock (TrainedModel)
{
#if ML_LEGACY
var result = TrainedModel.Predict(data);
return(result.Score);
#else
var result = PredictFunc.Predict(data);
return(result.Score);
#endif
}
}