public IEnumerable <Prediction> PredictTopN(RawDrawing drawing, int predictionsCount, double minActivation)
{
if (Evaluator == null)
{
return(null);
}
var normDrawing = dataProcessor.Normalize(drawing);
var img = rasterizer.Rasterize(normDrawing, TrainSettings.InputImgSizePx, TrainSettings.PenThicknessPerc / 100f, true, false);
var input = inputConvertor.ImageToVector(img);
return(Evaluator.PredictTopN(input, predictionsCount, minActivation));
}
public Matrix <double> CreateInputsMatrix(IEnumerable <RawDrawing> rawDrawings, Rasterizer rasterizer, bool normalize)
{
Contract.Requires(rawDrawings.Count() > 0);
var inputVectors = new List <Vector <double> >();
using (var img = new Bitmap(rasterizer.ImageSize, rasterizer.ImageSize, PixelFormat.Format24bppRgb)) {
int cols = img.Width * img.Height;
foreach (var drawing in rawDrawings)
{
RawDrawing rd = normalize ? dataProcessor.Normalize(drawing) : drawing;
rasterizer.Rasterize(img, rd);
inputVectors.Add(ImageToVector(img));
}
}
return(DenseMatrix.OfRowVectors(inputVectors));
}