private ActionResult plot(IFunctionWithDerivative f, PlotViewModel model)
{
List <Tuple <Color, List <Vector <double> > > > points = new List <Tuple <Color, List <Vector <double> > > >();
double minDeriv = model.MinDerivCompMaxMagn;
var result = new DenseVector(f.DimensionsCount);
{
result[0] = model.InitialX;
result[1] = model.InitialY;
var ps = new List <Vector <double> >();
points.Add(new Tuple <Color, List <Vector <double> > >(Color.Lime, ps));
var sdImpl = new SteepestDescentBasicOptmizer(model.BasicStep, model.MaxIters);
sdImpl.Optimize(result, f, model.MinDerivCompMaxMagn, (i, pFunc) => ps.Add(pFunc()), CancellationToken.None);
}
{
result[0] = model.InitialX;
result[1] = model.InitialY;
var ps = new List <Vector <double> >();
points.Add(new Tuple <Color, List <Vector <double> > >(Color.Red, ps));
var sdImpl = new SteepestDescentBasicOptmizer(model.MomentumStep,
model.MomentumStart, model.MomentumEnd, model.MaxIters);
sdImpl.Optimize(result, f, model.MinDerivCompMaxMagn, (i, pFunc) => ps.Add(pFunc()), CancellationToken.None);
}
{
result[0] = model.InitialX;
result[1] = model.InitialY;
var ps = new List <Vector <double> >();
points.Add(new Tuple <Color, List <Vector <double> > >(Color.Blue, ps));
var sdImpl = new SteepestDescentAdvancedOptmizer(model.MomentumStep,
model.MomentumStart, model.MomentumEnd, model.MaxIters);
sdImpl.Optimize(result, f, model.MinDerivCompMaxMagn, (i, pFunc) => ps.Add(pFunc()), CancellationToken.None);
}
{
result[0] = model.InitialX;
result[1] = model.InitialY;
var ps = new List <Vector <double> >();
points.Add(new Tuple <Color, List <Vector <double> > >(Color.Magenta, ps));
var sdImpl = new RpropPlusOptmizer(model.BasicStep, 10, 1.2, 0.5, model.MaxIters);
sdImpl.Optimize(result, f, model.MinDerivCompMaxMagn, (i, pFunc) => ps.Add(pFunc()), CancellationToken.None);
}
{
result[0] = model.InitialX;
result[1] = model.InitialY;
var ps = new List <Vector <double> >();
points.Add(new Tuple <Color, List <Vector <double> > >(Color.Cyan, ps));
var sdImpl = new ImprovedRpropMinusOptmizer(model.BasicStep, 10, 1.2, 0.5, model.MaxIters);
sdImpl.Optimize(result, f, model.MinDerivCompMaxMagn, (i, pFunc) => ps.Add(pFunc()), CancellationToken.None);
}
var fp = new FunctionPlotter();
var img = fp.AutoContourPlot(f, points, 0.1f, 0, 1, model.Width, model.Height,
model.ContoursCount, model.ScalePower);
var ms = new MemoryStream();
img.Save(ms, ImageFormat.Png);
ms.Seek(0, SeekOrigin.Begin);
return(File(ms, "image/png"));
}
private void performTrainTest(Net net, Matrix <double> inputs, int[] outputIndices,
double learningRate, double regularizationLambda, int maxIters)
{
var optimizer = new SteepestDescentAdvancedOptmizer(learningRate, 0.6, 0.99, maxIters);
var trainer = new NetTrainer(net, optimizer, regularizationLambda);
bool converged = trainer.Train(inputs, outputIndices, 1e-4, 1, null, CancellationToken.None);
var actualOutputIndices = trainer.Predict(inputs);
CollectionAssert.AreEqual(outputIndices, actualOutputIndices.Select(x => x.OutputId).ToArray());
}
public void OptimizeNoMomentumTest()
{
var instance = new SteepestDescentAdvancedOptmizer(0.05, 0, 0, 256);
OptimizerTestsHelper.PerformOptimizerTestOnSinCosCrazyFunction(instance);
}
