public NeuralNetworkDefaultData(NeuralNetworkDefault nrlNet)
{
Id = nrlNet.Id;
ActivationFuncName = FuncDictionary.GetFuncName(nrlNet.ActivationFunc) ?? "Unknown function";
Layers = nrlNet.Layers;
Weights = nrlNet.Weigths;
}
/// <summary>
/// Initializes a new instance of the <see cref="MediumSolverFactory"/> class.
/// </summary>
/// <param name="timeStep">The time step.</param>
public MediumSolverFactory(double timeStep)
{
this.timeStep = timeStep;
this.mediumFactors = new MemoDictionary <string, BaseMediumFactor>();
this.mediumSolvers = new FuncDictionary();
this.initSolvers();
this.initFactors();
}
public static void EqualTest(bool shouldBeEqual, int[] xs, string[] xv, int[] ys, string[] yv)
{
var expected = FuncDictionary.Make(xs.Zip(xv));
var actual = FuncDictionary.Make(ys.Zip(yv));
if (shouldBeEqual)
{
Assert.True(actual.Equals(expected));
}
else
{
Assert.False(actual.Equals(expected));
}
}
public NetworkDataModel GetNetworkData()
{
var data = new NetworkDataModel
{
ActivationFuncName = FuncDictionary.GetFuncName(ActivationFunc),
Id = Id,
Layers = Layers,
LearningRate = LearningRate,
Name = Name,
Weights = Weigths,
Generation = Generation,
StorageId = StorageId
};
return(data);
}
public NetworkVM GetViewModel()
{
NetworkVM networkVM = new NetworkVM(this)
{
Id = Id.ToString(),
Layers = new ObservableCollection <NetworkLayerVM>(Layers.ToLayerViewModels()),
CurrentFunc = FuncDictionary.GetFuncName(ActivationFunc),
LearningRate = LearningRate,
Name = Name,
IsPrototype = false,
Generation = Generation,
Storageid = StorageId.ToString()
};
return(networkVM);
}
public static void FuncDictionaryAdd(int[] xs, string[] ys, int key, string value)
{
var intComp = new FuncComparer <int>((x, y) => x.CompareTo(y), x => x.GetHashCode());
var fd = new FuncDictionary <int, string>(intComp, (xs ?? new int[0]).Zip(ys ?? new string[0]));
var origLength = fd.Count;
fd.Add(key, value);
var dict = new FuncDictionary <int, string>(intComp, (xs ?? new int[0]).Zip(ys ?? new string[0]))
{
{ key, value }
};
Assert.Equal(dict.OrderBy(kv => kv.Key), fd.OrderBy(kv => kv.Key));
Assert.Equal(origLength + 1, fd.Count);
}
public NeuralNetworkDefault(NeuralNetworkDefaultData nrlNetData)
{
Id = nrlNetData.Id ?? Guid.NewGuid();
Layers = nrlNetData.Layers;
Weigths = nrlNetData.Weights ?? GetDefaultWeigths();
LearningRate = nrlNetData.LearningRate;
AllOutputs = new Matrix2D[Layers.Length];
if (!string.IsNullOrEmpty(nrlNetData.ActivationFuncName) && FuncDictionary.TryGetFunc(nrlNetData.ActivationFuncName, out Func <float, float> activationFunc))
{
ActivationFunc = activationFunc;
}
else
{
ActivationFunc = MathFuncs.Sigmoid;
}
}
/// <summary>
/// Equivalent to <see cref="IFunctor{TSource}.Map{TResult}(Func{TSource, TResult})"/>, but restricted to <see cref="FuncDictionary{TKey, TValue}"/>. Offers LINQ query support with one <c>from</c>-clause.
/// </summary>
/// <typeparam name="TKey">The type of the source's key.</typeparam>
/// <typeparam name="TSource">The type of the source's value.</typeparam>
/// <typeparam name="TResult">The type of the result's value.</typeparam>
/// <param name="source">The source.</param>
/// <param name="f">The function to apply.</param>
public static FuncDictionary <TKey, TResult> Select <TKey, TSource, TResult>(this FuncDictionary <TKey, TSource> source, Func <TSource, TResult> f)
=> (FuncDictionary <TKey, TResult>)source.Map(f);