public static NeuralNetwork<Matrix> Create(IDataSet<Matrix, Vector> dataSet)
{
var count = 5;
var a = new ISingleLayer<Matrix, Matrix>[count];
for (var i = 0; i < count; ++i)
a[i] = new MatrixConvolutor(28, 28, 24, 24, new Tanh());
var b = new ISingleLayer<Matrix, Matrix>[count];
for (var i = 0; i < count; ++i)
b[i] = new MatrixSubsampler(24, 24, 12, 12, new Tanh());
var c = new ISingleLayer<Matrix, Matrix>[count];
for (var i = 0; i < count; ++i)
c[i] = new MatrixConvolutor(12, 12, 8, 8, new Tanh());
var d = new ISingleLayer<Matrix, Matrix>[count];
for (var i = 0; i < count; ++i)
d[i] = new MatrixSubsampler(8, 8, 4, 4, new Tanh());
var splitter = new Splitter<Matrix, Matrix>(a);
var applicator1 = new Applicator<Matrix, Matrix>(b);
var applicator2 = new Applicator<Matrix, Matrix>(c);
var merger = new MatrixMerger<Matrix>(d);
var classif = new FullyConnectedLayer(16 * count, 10, new Tanh());
var comp = CompositeLayer<Vector, Vector[], Vector>.Compose(splitter,
applicator1,
applicator2,
merger,
classif);
return new NeuralNetwork<Matrix>(comp);
}
public ApplicatorViewModel(Applicator applicator)
{
Applicator = applicator;
IsBack = true;
CreateTankCommand = new Command(CreateApplicatorTank);
DeleteTankCommand = new Command(DeleteApplicatorTank);
SaveTankCommand = new Command(SaveApplicatorTank);
BackCommand = new Command(Back);
}
public Trap(string creator, Frame fr, Applicator app, Gear g, Trigger tr)
{
itemName = "Trap";
frame = fr;
applicator = app;
gear = g;
trigger = tr;
setInventoryTextureName("Trap");
creatorId = creator;
}
public Turret(string creator, Frame fr, Applicator app, Gear g, EnergySource es)
{
itemName = "Turret";
itemStackType = ItemStackType.Turret;
frame = fr;
applicator = app;
gear = g;
energySource = es;
setInventoryTextureName("Turret"); //"Units/Turrets/TurretPlaceholder");
creatorId = creator;
}
public ObservableCollection <ApplicatorTank> GetApplicatorTanks(Applicator applicator)
{
if (applicator == null)
{
return(null);
}
else
{
using (AppDbContext db = App.GetContext())
{
var applicatorTanksDB = db.ApplicatorTanks.Where(at => at.ApplicatorId == applicator.ApplicatorId);
return(new ObservableCollection <ApplicatorTank>(applicatorTanksDB.ToList()));
}
}
}
public Trap(string itemData, string delim) : base(itemData, delim)
{
string[] split = itemData.Split(delim.ToCharArray());
int curr = numSplit;
ItemCode frameCode = (ItemCode)int.Parse(split[curr++]);
if (frameCode != ItemCode.None)
{
frame = (Frame)Item.deserializeItem(frameCode, split[curr++], otherDelimiter);
}
applicator = (Applicator)Item.deserializeItem((ItemCode)int.Parse(split[curr++]), split[curr++], otherDelimiter);
gear = (Gear)Item.deserializeItem((ItemCode)int.Parse(split[curr++]), split[curr++], otherDelimiter);
trigger = (Trigger)Item.deserializeItem((ItemCode)int.Parse(split[curr++]), split[curr++], otherDelimiter);
if (curr < split.Length)
{
creatorId = split[curr++];
}
}
public static NeuralNetwork<Matrix> CreateNorb(IDataSet<Matrix, Vector> dataSet)
{
var count = 12;
var branch = 5;
var a = new ISingleLayer<Matrix, Matrix>[count];
for (var i = 0; i < count; ++i)
a[i] = new MatrixConvolutor(96, 96, 92, 92, new Tanh());
var b = new ISingleLayer<Matrix, Matrix>[count];
for (var i = 0; i < count; ++i)
b[i] = new MatrixSubsampler(92, 92, 46, 46, new Tanh());
var c = new ISingleLayer<Matrix, Matrix>[count];
for (var i = 0; i < count; ++i)
c[i] = new MatrixSubsampler(46, 46, 23, 23, new Tanh());
var splitter = new Splitter<Matrix, Matrix>(a);
var applicator1 = new Applicator<Matrix, Matrix>(b);
var merger = new MatrixMerger<Matrix>(c);
var classif = new FullyConnectedLayer(23 * 23 * count, 5, new Tanh());
var comp = CompositeLayer<Vector, Vector[], Vector>.Compose(splitter,
applicator1,
merger,
classif
);
return new NeuralNetwork<Matrix>(comp);
}
public TennisRule(string category, Applicator applier)
: base(category)
{
apllicator = applier;
}
public static float[,] AddHeat(this float[,] array, Vector2Int position, Heater function, Applicator applicator)
{
float[] values = new float[Mathf.Max(array.GetLength(0) - position.x, position.x) + Mathf.Max(array.GetLength(1) - position.y, position.y) + 1];
for (int i = 0; i < values.Length; i++)
{
values[i] = function(i);
}
for (int x = 0; x < array.GetLength(0); x++)
{
for (int y = 0; y < array.GetLength(1); y++)
{
int distance = Mathf.Abs(position.x - x) + Mathf.Abs(position.y - y);
array[x, y] = applicator(array[x, y], values[distance]);
}
}
return(array);
}
public CurveMenuRule(string category, Applicator applier) : base(category) {
apllicator = applier;
}
