protected void CreateNeurons(int inputSize, int[] hiddenSizes, int outputSize)
{
for (var i = 0; i < inputSize; i++)
{
InputLayer.Add(new Neuron());
}
var firstHiddenLayer = new List <Neuron>();
for (var i = 0; i < hiddenSizes[0]; i++)
{
firstHiddenLayer.Add(new Neuron(InputLayer, HiddenActivationType));
}
HiddenLayers.Add(firstHiddenLayer);
for (var i = 1; i < hiddenSizes.Length; i++)
{
var hiddenLayer = new List <Neuron>();
for (var j = 0; j < hiddenSizes[i]; j++)
{
hiddenLayer.Add(new Neuron(HiddenLayers[i - 1], HiddenActivationType));
}
HiddenLayers.Add(hiddenLayer);
}
for (var i = 0; i < outputSize; i++)
{
OutputLayer.Add(new Neuron(HiddenLayers.Last(), OutputActivationType));
}
}
protected Network(Network initialNetwork)
{
var inputLayerSettings = LayerToLayerSettings(initialNetwork.InputLayer);
var inputLayer = new InputLayer(inputLayerSettings, this);
Layers = new List <Layer>(initialNetwork.Layers.Count)
{
inputLayer
};
foreach (var initialLayer in initialNetwork.HiddenLayers)
{
var prev = Layers.Last();
var layerSettings = LayerToLayerSettings(initialLayer);
Layers.Add(new HiddenLayer(layerSettings, this, prev));
}
var outputLayerSettings = LayerToLayerSettings(initialNetwork.OutputLayer);
Layers.Add(new OutputLayer(outputLayerSettings, this, Layers.Last()));
foreach (var(next, prev) in Layers.Zip(initialNetwork.Layers, (next, prev) => (next, prev)))
{
Parallel.ForEach(next.Neurons.Zip(prev.Neurons, (nextNeuron, prevNeuron) => new { nextNeuron, prevNeuron }), x =>
{
x.nextNeuron.Bias = x.prevNeuron.Bias;
x.nextNeuron.Weights = new List <double>(x.prevNeuron.Weights);
});
//foreach (var (nextNeuron, prevNeuron) in next.Neurons.Zip(prev.Neurons, (nextNeuron, prevNeuron) => (nextNeuron, prevNeuron)))
//{
// nextNeuron.Bias = prevNeuron.Bias;
// nextNeuron.Weights = new List<double>(prevNeuron.Weights);
//}
}
}
/// <summary>
/// Selects the first button
/// </summary>
/// <param name="key">the key</param>
/// <param name="strength">the strength</param>
/// <param name="layer">the layer</param>
public void OnKeyDown(Key key, float strength, InputLayer layer)
{
if (UI.Instance.EventSystem != null && UI.Instance.EventSystem.currentSelectedGameObject == null && (key == Key.Forward || key == Key.Backward || key == Key.Left || key == Key.Right) && layer == InputLayer.UI)
{
openSubmenus.Peek().SelectFirstButton();
}
}
private void PushLayer(CNNLayer layer)
{
if (Layers.Count != 0)
{
layer.PrevLayer = Layers.Last();
layer.InSize = Layers.Last().OutSize;
layer.InDepth = Layers.Last().OutDepth;
Layers.Last().NextLayer = layer;
if (layer is InputLayer)
{
throw new ArgumentException("You can't use more than 1 input layer");
}
if (layer is OutputLayer)
{
if (OutputLayer != null)
{
throw new ArgumentException("You can't use more than 1 output layer");
}
OutputLayer = (OutputLayer)layer;
}
}
else
{
if (!(layer is InputLayer))
{
throw new ArgumentException("First network layer should be " + nameof(InputLayer));
}
InputLayer = (InputLayer)layer;
}
Layers.Add(layer);
layer.Setup();
}
public OutputNeuronFactory(InputLayer inputLayer, ConnectionFactory connectionFactory)
{
this.inputLayer = inputLayer;
this.connectionFactory = connectionFactory;
weightUpperBound = L / (L - 1f + inputLayer.Size);
}
// Use this for initialization
void Start()
{
Input = new InputLayer();
Hidden = new Layer();
Output = new Layer();
for (int i = 0; i < 75; i++)
{
Input.Neurons.Add(new InputNeuron());
}
for (int i = 0; i < 30; i++)
{
Neuron n = new Neuron();
for (int x = 0; x < Input.Neurons.Count; x++)
{
n.Weights.Add(UnityEngine.Random.value);
}
n.Threshold = ThreshAdjust;
Hidden.Neurons.Add(n);
}
for (int i = 0; i < 15; i++)
{
Neuron n = new Neuron();
for (int x = 0; x < Hidden.Neurons.Count; x++)
{
n.Weights.Add(UnityEngine.Random.value);
}
n.Threshold = ThreshAdjust;
Output.Neurons.Add(n);
}
StartCoroutine("WaitForEverAThing");
}
public static Tensor[] Input(int[] batch_shape = null,
TF_DataType dtype = TF_DataType.DtInvalid,
string name = null,
bool sparse = false,
Tensor tensor = null)
{
var batch_size = batch_shape[0];
var shape = batch_shape.Skip(1).ToArray();
InputLayer input_layer = null;
if (batch_shape != null)
{
input_layer = new InputLayer(
batch_input_shape: batch_shape,
name: name,
dtype: dtype,
sparse: sparse,
input_tensor: tensor);
}
else
{
input_layer = new InputLayer(
input_shape: shape,
batch_size: batch_size,
name: name,
dtype: dtype,
sparse: sparse,
input_tensor: tensor);
}
var outputs = input_layer.inbound_nodes[0].output_tensors;
return(outputs);
}
//public Network(double[][] inputValues, double[] desiredOutput, int numberOfLayers, int[] numberOfNeuronsInLayers)
public Network(int inputWidth, int numberOfLayers, int[] numberOfNeuronsInLayers)
{
//_input = inputValues;
//_desiredOutput = desiredOutput;
_inputWidth = inputWidth;
_numberOfLayers = numberOfLayers;
Layers = new ILayer[_numberOfLayers + 2]; // nie liczymy warstwy wejściowej i wyjściowej
//najpierw warstwa wejciowa
Layers[0] = new InputLayer(_inputWidth, _inputWidth);
//Layers[0] = new InputLayer();
for (int i = 1; i < numberOfLayers+1; i++)
{
Layers[i] = new Layer(numberOfNeuronsInLayers[i - 1], Layers[i - 1].Neurons.Length);
}
//warstwa wyjściowa co ma tylko 1 neuron
Layers[numberOfLayers+1] = new Layer(1, Layers[numberOfLayers].Neurons.Length);
_totalNumberOfWeights = 0;
foreach (var layer in Layers)
{
_totalNumberOfWeights += layer.NumberOfWeights();
}
}
private NeuralNetwork.NeuralNetwork CreateNn()
{
NeuralNetwork.NeuralNetwork nn;
nn = new NeuralNetwork.NeuralNetwork();
InputLayer inputLayer = nn.CreateInputLayer();
//inHeading = inputLayer.CreateNeuron("heading");
//inVelocityAngle = inputLayer.CreateNeuron("v_angle");
//inVelocityLength = inputLayer.CreateNeuron("v_length");
inNextCheckpointDistance0 = inputLayer.CreateNeuron("c_dist0");
inNextCheckpointDistance1 = inputLayer.CreateNeuron("c_dist1");
inNextCheckpointDistance2 = inputLayer.CreateNeuron("c_dist2");
inNextCheckpointDistance3 = inputLayer.CreateNeuron("c_dist3");
inNextCheckpointAngle0 = inputLayer.CreateNeuron("c_angle0");
inNextCheckpointAngle1 = inputLayer.CreateNeuron("c_angle1");
inNextCheckpointAngle2 = inputLayer.CreateNeuron("c_angle2");
inNextCheckpointAngle3 = inputLayer.CreateNeuron("c_angle3");
inNextCheckpointAngle4 = inputLayer.CreateNeuron("c_angle4");
inNextCheckpointAngle5 = inputLayer.CreateNeuron("c_angle5");
inNextNextCheckpointAngle0 = inputLayer.CreateNeuron("nnc_angle0");
inNextNextCheckpointAngle1 = inputLayer.CreateNeuron("nnc_angle1");
inNextNextCheckpointAngle2 = inputLayer.CreateNeuron("nnc_angle2");
inNextNextCheckpointAngle3 = inputLayer.CreateNeuron("nnc_angle3");
inNextNextCheckpointAngle4 = inputLayer.CreateNeuron("nnc_angle4");
inNextNextCheckpointAngle5 = inputLayer.CreateNeuron("nnc_angle5");
//inNextCheckpointDistance = inputLayer.CreateNeuron("c_dist");
OutputLayer outputLayer = nn.CreateOutputLayer();
outHeading0 = outputLayer.CreateNeuron("o_heading0");
outHeading1 = outputLayer.CreateNeuron("o_heading1");
outHeading2 = outputLayer.CreateNeuron("o_heading2");
outHeading3 = outputLayer.CreateNeuron("o_heading3");
outHeading4 = outputLayer.CreateNeuron("o_heading4");
outHeading5 = outputLayer.CreateNeuron("o_heading5");
outThrust0 = outputLayer.CreateNeuron("o_thrust0");
outThrust1 = outputLayer.CreateNeuron("o_thrust1");
outThrust2 = outputLayer.CreateNeuron("o_thrust2");
outThrust3 = outputLayer.CreateNeuron("o_thrust3");
outThrust4 = outputLayer.CreateNeuron("o_thrust4");
outThrust5 = outputLayer.CreateNeuron("o_thrust5");
outThrust6 = outputLayer.CreateNeuron("o_thrust6");
for (int i = 0; i < 3; i++)
{
HiddenLayer hiddenLayer = nn.CreateHiddenLayer();
for (int j = 0; j < 32; j++)
{
HiddenNeuron hiddenNeuron = hiddenLayer.CreateNeuron(string.Format("hidden[{0}][{1}]", i, j));
}
}
nn.CreateFullConnections();
nn.InitWithRandomValues();
return(nn);
}
private static ITrainer CreateIrisTrainer(SigmaEnvironment sigma)
{
IDataset dataset = Defaults.Datasets.Iris();
ITrainer trainer = sigma.CreateTrainer("iris-trainer");
trainer.Network = new Network();
trainer.Network.Architecture = InputLayer.Construct(4)
+ FullyConnectedLayer.Construct(12)
+ FullyConnectedLayer.Construct(3)
+ OutputLayer.Construct(3)
+ SquaredDifferenceCostLayer.Construct();
//trainer.Network = Serialisation.ReadBinaryFileIfExists("iris.sgnet", trainer.Network);
trainer.TrainingDataIterator = new MinibatchIterator(50, dataset);
trainer.AddNamedDataIterator("validation", new UndividedIterator(dataset));
trainer.Optimiser = new GradientDescentOptimiser(learningRate: 0.06);
trainer.Operator = new CpuSinglethreadedOperator();
trainer.AddInitialiser("*.*", new GaussianInitialiser(standardDeviation: 0.1));
//trainer.AddGlobalHook(new StopTrainingHook(atEpoch: 100));
//trainer.AddLocalHook(new EarlyStopperHook("optimiser.cost_total", 20, target: ExtremaTarget.Min));
trainer.AddLocalHook(new AccumulatedValueReporter("optimiser.cost_total", TimeStep.Every(1, TimeScale.Epoch), reportEpochIteration: true));
//.On(new ExtremaCriteria("optimiser.cost_total", ExtremaTarget.Min)));
//trainer.AddLocalHook(new DiskSaviorHook<INetwork>("network.self", Namers.Dynamic("iris_epoch{0}.sgnet", "epoch"), verbose: true)
// .On(new ExtremaCriteria("optimiser.cost_total", ExtremaTarget.Min)));
trainer.AddHook(new MultiClassificationAccuracyReporter("validation", TimeStep.Every(1, TimeScale.Epoch), tops: 1));
return(trainer);
}
private static ITrainer CreateParkinsonsTrainer(SigmaEnvironment sigma)
{
IDataset dataset = Defaults.Datasets.Parkinsons();
ITrainer trainer = sigma.CreateTrainer("parkinsons-trainer");
trainer.Network = new Network
{
Architecture = InputLayer.Construct(22)
+ FullyConnectedLayer.Construct(140)
+ FullyConnectedLayer.Construct(20)
+ FullyConnectedLayer.Construct(1)
+ OutputLayer.Construct(1)
+ SquaredDifferenceCostLayer.Construct()
};
trainer.TrainingDataIterator = new MinibatchIterator(10, dataset);
trainer.AddNamedDataIterator("validation", new UndividedIterator(dataset));
trainer.Optimiser = new AdagradOptimiser(baseLearningRate: 0.01);
trainer.Operator = new CpuSinglethreadedOperator(new DebugHandler(new CpuFloat32Handler()));
trainer.AddInitialiser("*.*", new GaussianInitialiser(standardDeviation: 0.1));
trainer.AddLocalHook(new AccumulatedValueReporter("optimiser.cost_total", TimeStep.Every(1, TimeScale.Epoch)));
trainer.AddHook(new UniClassificationAccuracyReporter("validation", 0.5, TimeStep.Every(1, TimeScale.Epoch)));
return(trainer);
}
public FCSuperResolution()
{
superres_enc_front = InputLayer.Create(StartSide, 3);
superres_enc_back = ActivationLayer.Create <LeakyReLU>();
superres_enc_front.Append(
FCLayer.Create(16, 16).Append(
ActivationLayer.Create <LeakyReLU>().Append(
FCLayer.Create(8, 8).Append(
ActivationLayer.Create <LeakyReLU>().Append(
FCLayer.Create(4, 4).Append(
superres_enc_back
))))));
superres_dec_front = InputLayer.Create(4, 4);
superres_dec_back = ActivationLayer.Create <Tanh>();
superres_dec_front.Append(
FCLayer.Create(8, 8).Append(
ActivationLayer.Create <LeakyReLU>().Append(
FCLayer.Create(16, 8).Append(
ActivationLayer.Create <LeakyReLU>().Append(
FCLayer.Create(32, 3).Append(
superres_dec_back
))))));
superres_enc_back.Append(superres_dec_front);
//Initialize Weights
superres_enc_front.SetupInternalState();
superres_enc_front.InitializeWeights(new UniformWeightInitializer(0, 0.001f));
}
public void InputLayer_Backward()
{
var batchSize = 1;
var width = 28;
var height = 28;
var depth = 3;
var random = new Random(232);
var fanIn = width * height * depth;
var sut = new InputLayer(height, width, depth);
sut.Initialize(1, 1, 1, batchSize, Initialization.GlorotUniform, random);
var input = Matrix <float> .Build.Random(batchSize, fanIn, random.Next());
sut.Forward(input);
var delta = Matrix <float> .Build.Random(batchSize, fanIn, random.Next());
var actual = sut.Backward(delta);
var expected = delta;
MatrixAsserts.AreEqual(expected, actual);
}
private static void SampleXor()
{
SigmaEnvironment sigma = SigmaEnvironment.Create("logical");
sigma.SetRandomSeed(0);
sigma.Prepare();
RawDataset dataset = new RawDataset("xor");
dataset.AddRecords("inputs", new[] { 0, 0 }, new[] { 0, 1 }, new[] { 1, 0 }, new[] { 1, 1 });
dataset.AddRecords("targets", new[] { 0 }, new[] { 0 }, new[] { 0 }, new[] { 1 });
ITrainer trainer = sigma.CreateTrainer("xor-trainer");
trainer.Network.Architecture = InputLayer.Construct(2) + FullyConnectedLayer.Construct(2) + FullyConnectedLayer.Construct(1) + OutputLayer.Construct(1) + SquaredDifferenceCostLayer.Construct();
trainer.TrainingDataIterator = new MinibatchIterator(1, dataset);
trainer.AddNamedDataIterator("validation", new UndividedIterator(dataset));
trainer.Optimiser = new GradientDescentOptimiser(learningRate: 0.1);
trainer.Operator = new CudaSinglethreadedOperator();
trainer.AddInitialiser("*.*", new GaussianInitialiser(standardDeviation: 0.05));
trainer.AddLocalHook(new StopTrainingHook(atEpoch: 10000));
trainer.AddLocalHook(new AccumulatedValueReporter("optimiser.cost_total", TimeStep.Every(1, TimeScale.Epoch), averageValues: true));
trainer.AddLocalHook(new AccumulatedValueReporter("optimiser.cost_total", TimeStep.Every(1, TimeScale.Stop), averageValues: true));
trainer.AddLocalHook(new ValueReporter("network.layers.*<external_output>._outputs.default.activations", TimeStep.Every(1, TimeScale.Stop)));
trainer.AddLocalHook(new ValueReporter("network.layers.*-fullyconnected.weights", TimeStep.Every(1, TimeScale.Stop)));
trainer.AddLocalHook(new ValueReporter("network.layers.*-fullyconnected.biases", TimeStep.Every(1, TimeScale.Stop)));
sigma.Run();
}
private static void SampleHutter()
{
const long timeWindowSize = 10L;
SigmaEnvironment sigma = SigmaEnvironment.Create("recurrent");
IDataSource source = new MultiSource(new FileSource("enwik8"), new CompressedSource(new MultiSource(new FileSource("enwik8.zip"), new UrlSource("http://mattmahoney.net/dc/enwik8.zip"))));
IRecordExtractor extractor = new CharacterRecordReader(source, (int)(timeWindowSize + 1), Encoding.ASCII)
.Extractor(new ArrayRecordExtractor <short>(ArrayRecordExtractor <short>
.ParseExtractorParameters("inputs", new[] { 0L }, new[] { timeWindowSize }, "targets", new[] { 0L }, new[] { timeWindowSize }))
.Offset("targets", 1L))
.Preprocess(new PermutePreprocessor(0, 2, 1))
.Preprocess(new OneHotPreprocessor(0, 255));
IDataset dataset = new ExtractedDataset("hutter", ExtractedDataset.BlockSizeAuto, false, extractor);
ITrainer trainer = sigma.CreateTrainer("hutter");
trainer.Network.Architecture = InputLayer.Construct(256) + RecurrentLayer.Construct(256) + OutputLayer.Construct(256) + SoftMaxCrossEntropyCostLayer.Construct();
trainer.TrainingDataIterator = new MinibatchIterator(32, dataset);
trainer.AddNamedDataIterator("validation", new MinibatchIterator(100, dataset));
trainer.Optimiser = new AdagradOptimiser(baseLearningRate: 0.07);
trainer.Operator = new CudaSinglethreadedOperator();
trainer.AddInitialiser("*.*", new GaussianInitialiser(standardDeviation: 0.05));
trainer.AddLocalHook(new AccumulatedValueReporter("optimiser.cost_total", TimeStep.Every(1, TimeScale.Iteration), averageValues: true));
trainer.AddLocalHook(new RunningTimeReporter(TimeStep.Every(10, TimeScale.Iteration)));
sigma.PrepareAndRun();
}
private static void SampleParkinsons()
{
SigmaEnvironment sigma = SigmaEnvironment.Create("parkinsons");
IDataset dataset = Defaults.Datasets.Parkinsons();
ITrainer trainer = sigma.CreateGhostTrainer("parkinsons-trainer");
trainer.Network.Architecture = InputLayer.Construct(22)
+ FullyConnectedLayer.Construct(140)
+ FullyConnectedLayer.Construct(20)
+ FullyConnectedLayer.Construct(1)
+ OutputLayer.Construct(1)
+ SquaredDifferenceCostLayer.Construct();
trainer.TrainingDataIterator = new MinibatchIterator(10, dataset);
trainer.AddNamedDataIterator("validation", new UndividedIterator(dataset));
trainer.Optimiser = new AdagradOptimiser(baseLearningRate: 0.01);
trainer.AddInitialiser("*.*", new GaussianInitialiser(standardDeviation: 0.1));
trainer.AddLocalHook(new AccumulatedValueReporter("optimiser.cost_total", TimeStep.Every(1, TimeScale.Epoch)));
trainer.AddHook(new UniClassificationAccuracyReporter("validation", 0.5, TimeStep.Every(1, TimeScale.Epoch)));
sigma.AddTrainer(trainer);
sigma.PrepareAndRun();
}
public Network(List <LayerSettings> layersHyperParameters)
{
if (layersHyperParameters.Count < 2)
{
return;
}
var inputLayer = new InputLayer(layersHyperParameters.First(), this);
Layers = new List <Layer>(layersHyperParameters.Count)
{
inputLayer
};
foreach (var layerParams in layersHyperParameters.Skip(1).Take(layersHyperParameters.Count - 2))
{
var prev = Layers.Last();
Layers.Add(new HiddenLayer(layerParams, this, prev));
prev.InitNeuronsWeights();
}
var lastHidden = Layers.Last();
lastHidden.InitNeuronsWeights();
Layers.Add(new OutputLayer(layersHyperParameters.Last(), this, lastHidden));
Layers.Last().InitNeuronsWeights();
}
/// <summary>
/// Create a MNIST trainer (writing recognition) that will be added to an environemnt.
/// </summary>
/// <param name="sigma">The sigma environemnt this trainer will be assigned to.</param>
/// <returns>The newly created trainer.</returns>
private static ITrainer CreateMnistTrainer(SigmaEnvironment sigma)
{
IDataset dataset = Defaults.Datasets.Mnist();
ITrainer trainer = sigma.CreateTrainer("mnist-trainer");
trainer.Network = new Network();
trainer.Network.Architecture = InputLayer.Construct(28, 28)
+ DropoutLayer.Construct(0.2)
+ FullyConnectedLayer.Construct(1000, activation: "rel")
+ DropoutLayer.Construct(0.4)
+ FullyConnectedLayer.Construct(800, activation: "rel")
+ DropoutLayer.Construct(0.4)
+ FullyConnectedLayer.Construct(10, activation: "sigmoid")
+ OutputLayer.Construct(10)
+ SoftMaxCrossEntropyCostLayer.Construct();
trainer.TrainingDataIterator = new MinibatchIterator(100, dataset);
trainer.AddNamedDataIterator("validation", new UndividedIterator(Defaults.Datasets.MnistValidation()));
trainer.Optimiser = new AdagradOptimiser(baseLearningRate: 0.02);
trainer.Operator = new CudaSinglethreadedOperator();
trainer.AddInitialiser("*.weights", new GaussianInitialiser(standardDeviation: 0.1));
trainer.AddInitialiser("*.bias*", new GaussianInitialiser(standardDeviation: 0.05));
trainer.AddLocalHook(new ValueReporter("optimiser.cost_total", TimeStep.Every(1, TimeScale.Iteration), reportEpochIteration: true)
.On(new ExtremaCriteria("optimiser.cost_total", ExtremaTarget.Min)));
trainer.AddLocalHook(new RunningTimeReporter(TimeStep.Every(1, TimeScale.Epoch), 4));
return(trainer);
}
public void Convert()
{
var layers = _graph.GetOperators().Select(ConvertOperator).ToList();
foreach (var inputPair in _inputs)
{
if (_outputs.TryGetValue(inputPair.Value, out var output))
{
inputPair.Key.SetConnection(output);
}
}
var inputs = new List <InputLayer>();
foreach (var conn in _inputs.Keys.Where(o => o.Connection == null))
{
var input = new InputLayer(conn.Dimensions);
conn.SetConnection(input.Output);
inputs.Add(input);
}
var outputs = new List <OutputLayer>();
foreach (var conn in _outputs.Values.Where(o => !o.Connections.Any()))
{
var output = new OutputLayer(conn.Dimensions);
conn.AddConnection(output.Input);
outputs.Add(output);
}
Graph = new Graph(inputs, outputs);
}
public static ITrainer CreateTicTacToeTrainer(SigmaEnvironment sigma)
{
IDataset dataset = Defaults.Datasets.TicTacToe();
ITrainer trainer = sigma.CreateTrainer("tictactoe-trainer");
trainer.Network = new Network();
trainer.Network.Architecture = InputLayer.Construct(9)
+ FullyConnectedLayer.Construct(72, "tanh")
+ FullyConnectedLayer.Construct(99, "tanh")
+ FullyConnectedLayer.Construct(3, "tanh")
+ OutputLayer.Construct(3)
+ SoftMaxCrossEntropyCostLayer.Construct();
trainer.TrainingDataIterator = new MinibatchIterator(21, dataset);
trainer.AddNamedDataIterator("validation", new UndividedIterator(dataset));
trainer.Optimiser = new MomentumGradientOptimiser(learningRate: 0.01, momentum: 0.9);
trainer.Operator = new CpuSinglethreadedOperator();
trainer.AddInitialiser("*.*", new GaussianInitialiser(standardDeviation: 0.1));
trainer.AddLocalHook(new AccumulatedValueReporter("optimiser.cost_total", TimeStep.Every(1, TimeScale.Epoch)));
trainer.AddHook(new MultiClassificationAccuracyReporter("validation", TimeStep.Every(1, TimeScale.Epoch), tops: new[] { 1, 2 }));
trainer.AddGlobalHook(new DiskSaviorHook <INetwork>(TimeStep.Every(1, TimeScale.Epoch), "network.self", Namers.Static("tictactoe.sgnet"), verbose: true)
.On(new ExtremaCriteria("shared.classification_accuracy_top1", ExtremaTarget.Max)));
return(trainer);
}
/// <summary>
/// Инициализация слоёв нейронной сети.
/// </summary>
/// <param name="listOfPicturesmatrix">Список матриц изображений.</param>
/// <param name="layers">Список слоёв.</param>
/// <param name="filterCore">Ядро фильтра.</param>
/// <param name="inputLayerNeurons">Нейроны выходного слоя.</param>
/// <param name="convolutionalLayerNeurons">Нейроны свёрточного слоя.</param>
/// <param name="hiddenLayerNeurons">Нейроны скрытого слоя.</param>
/// <param name="outputNeuron">Нейроны выходного слоя.</param>
private static void LayersInitialize(List <double[, ]> listOfPicturesmatrix, List <Layer> layers,
double[,] filterCore, out Dictionary <string, double> inputLayerNeurons,
out List <NeuronModel> convolutionalLayerNeurons, out List <NeuronModel> hiddenLayerNeurons,
out NeuronModel outputNeuron)
{
var firstDataSet = listOfPicturesmatrix.First();
var inputLayer = new InputLayer(firstDataSet);
inputLayer.Initialize();
layers.Add(inputLayer);
inputLayerNeurons = inputLayer.GetLayerNeurons();
var convolutionalLayer = new ConvolutionalLayer(inputLayerNeurons);
convolutionalLayer.Initialize(filterCore);
layers.Add(convolutionalLayer);
convolutionalLayerNeurons = convolutionalLayer.GetLayerNeurons();
var hiddenLayer = new HiddenLayer(convolutionalLayerNeurons);
hiddenLayer.Initialize();
layers.Add(hiddenLayer);
hiddenLayerNeurons = hiddenLayer.GetLayerNeurons();
var outputLayer = new OutputLayer(hiddenLayerNeurons);
outputLayer.Initilize();
layers.Add(outputLayer);
outputNeuron = outputLayer.GetOutputNeuron();
}
public MainGameWindow()
: base(800, 600, GraphicsMode.Default, "Sea battles")
{
Resize += delegate(object sender, EventArgs e)
{
// Note that we cannot call any OpenGL methods directly. What we can do is set
// a flag and respond to it from the rendering thread.
lock (update_lock)
{
viewport_changed = true;
viewport_width = this.ClientRectangle.Width;
viewport_height = this.ClientRectangle.Height;
}
};
handlers.Add(typeof(TraceText), WriteTitle);
handlers.Add(typeof(ButtonUp), HandleButtonUp);
mainCamera = new Camera(0, 0, 1, 800, 600);
input = new InputLayer(this);
ship = Ship.Create(new PointF(0, 0), 40, 10);
ship2 = Ship.Create(new PointF(0, 0), 40, 1);
ship.Name = "player";
ship.Graphics.Name = "player graphics";
ship2.Graphics.Name = "enemy graphics";
//box = new TestBoundingObject(BoundShape.Ship, new PointF(0, 0), 10, 20, Color.Green, Color.Red, 0);
//box2 = new TestBoundingObject(BoundShape.Circle, new PointF(0, 0), 20, 40, Color.White, Color.Black, -0.5f);
//MessageDispatcher.RegisterHandler(typeof(ButtonDown), box);
//MessageDispatcher.RegisterHandler(typeof(SetPosition), box);
//MessageDispatcher.RegisterHandler(typeof(SetSpeed), box);
//MessageDispatcher.RegisterHandler(typeof(GetOwnerPosition), box);
//MessageDispatcher.RegisterHandler(typeof(InformPosition), box);
//MessageDispatcher.RegisterHandler(typeof(BoundSetCollision), box);
//MessageDispatcher.RegisterHandler(typeof(BoundSetNotCollision), box);
//---------------------------------------------------
//graphicsAspects.Add(ship.Graphics);
//только корабль игрока подписывается на приём пользовательского ввода
MessageDispatcher.RegisterHandler(typeof(ButtonDown), ship);
MessageDispatcher.RegisterHandler(typeof(ButtonHold), ship);
//MessageDispatcher.RegisterHandler(typeof(SetPosition), ship);
//MessageDispatcher.RegisterHandler(typeof(SetSpeed), ship);
//// нужно для определения координат и скорости корабля в момент выстрела
//// в данном случае owner-ом является ship
//MessageDispatcher.RegisterHandler(typeof(GetOwnerPosition), ship);
//MessageDispatcher.RegisterHandler(typeof(InformPosition), ship);
//MessageDispatcher.RegisterHandler(typeof(Shoot), ship);
//MessageDispatcher.RegisterHandler(typeof(BoundSetCollision), ship);
//MessageDispatcher.RegisterHandler(typeof(BoundSetNotCollision), ship);
////MessageDispatcher.RegisterHandler(typeof(SetPosition), anotherShip);
MessageDispatcher.RegisterHandler(typeof(TraceText), this);
MessageDispatcher.RegisterHandler(typeof(ButtonUp), this);
//timer = new System.Threading.Timer(new TimerCallback(timer_Tick), null, 1000, 1000);
}
/// <summary>
/// Switches to the next window
/// </summary>
public void OnKeyCodeDown(int key, float strength, InputLayer layer)
{
if (key == (int)KeyCode.Tab)
{
target.Select();
}
}
public void DeuxNeuronesPropagate()
{
var inputs =
new InputLayer(
new InputNeurone(1, 1),
new InputNeurone(2, 2)
);
var resultLayer =
new DeepLayer(
inputs,
new DeepNeurone(
1,
new SigmoidFnc(),
new Synapse(1, 1, 0.5),
new Synapse(2, 1, 0.5)
),
new DeepNeurone(
2,
new SigmoidFnc(),
new Synapse(1, 2, 0.5),
new Synapse(2, 2, 0.5)
)
);
Assert.AreEqual(
resultLayer.propagate().neuroneValue(1).value(),
new Sigmoid(1 * 0.5 + 2 * 0.5).value()
);
Assert.AreEqual(
resultLayer.propagate().neuroneValue(2).value(),
new Sigmoid(1 * 0.5 + 2 * 0.5).value()
);
}
/// <summary>
/// Instantiate a Keras tensor.
/// </summary>
/// <param name="shape"></param>
/// <param name="batch_size"></param>
/// <param name="dtype"></param>
/// <param name="name"></param>
/// <param name="sparse">
/// A boolean specifying whether the placeholder to be created is sparse.
/// </param>
/// <param name="ragged">
/// A boolean specifying whether the placeholder to be created is ragged.
/// </param>
/// <param name="tensor">
/// Optional existing tensor to wrap into the `Input` layer.
/// If set, the layer will not create a placeholder tensor.
/// </param>
/// <returns></returns>
public Tensor Input(TensorShape shape = null,
int batch_size = -1,
TensorShape batch_input_shape = null,
TF_DataType dtype = TF_DataType.DtInvalid,
string name = null,
bool sparse = false,
bool ragged = false,
Tensor tensor = null)
{
if (batch_input_shape != null)
{
shape = batch_input_shape.dims.Skip(1).ToArray();
}
var args = new InputLayerArgs
{
Name = name,
InputShape = shape,
BatchInputShape = batch_input_shape,
BatchSize = batch_size,
DType = dtype,
Sparse = sparse,
Ragged = ragged,
InputTensor = tensor
};
var layer = new InputLayer(args);
return(layer.InboundNodes[0].Outputs);
}
public NeuralNet(NeuronMode mode, bool evolveActivationThreshold, int inputCount, int outputCount, int middleCount, int hiddenLayerCount)
{
this.mode = mode;
inputLayer = new InputLayer(inputCount);
outputLayer = new NeuronLayer(mode, outputCount, defaultActivationThreshold, evolveActivationThreshold);
NeuronLayer prevLayer = null;
for (int i = 0; i < hiddenLayerCount; ++i)
{
NeuronLayer newLayer = new NeuronLayer(mode, middleCount, defaultActivationThreshold, evolveActivationThreshold);
hiddenLayers.Add(newLayer);
if (prevLayer != null)
{
prevLayer.setOutputLayer(newLayer);
}
prevLayer = newLayer;
}
if (prevLayer != null)
{
prevLayer.setOutputLayer(outputLayer);
inputLayer.setOutputLayer(hiddenLayers[0]);
}
else
{
inputLayer.setOutputLayer(outputLayer);
}
}
public static BPNetwork Create(int inputLayerNeuronCount, int[] hiddenLayerNeuronCount, int outputLayerNeuronCount, ActivationFunction hiddenLayerActivationFunction = ActivationFunction.SIGMOID, ActivationFunction outputLayerActivationFunction = ActivationFunction.SIGMOID)
{
BPNetwork network = new BPNetwork();
var inputLayer = new InputLayer();
inputLayer.InitNeurons(inputLayerNeuronCount, false, ActivationFunction.LINEAR);
network.InputLayer = inputLayer;
network.HiddenLayers = new List <HiddenLayer>();
if (hiddenLayerNeuronCount != null)
{
hiddenLayerNeuronCount.ToList().ForEach(count => {
network.HiddenLayers.Add(new HiddenLayer());
});
for (var i = 0; i < hiddenLayerNeuronCount.Length; i++)
{
network.HiddenLayers[i].InitNeurons(hiddenLayerNeuronCount[i], true, hiddenLayerActivationFunction);
}
}
var outputLayer = new OutputLayer();
outputLayer.InitNeurons(outputLayerNeuronCount, true, outputLayerActivationFunction);
network.OutputLayer = outputLayer;
network.ConstructConnectionsAndRandonizeWeights();
return(network);
}
//Sends the inputs once through the network
public void Train(params double[] inputs)
{
int i = 0;
InputLayer.ForEach(a => a.Value = inputs[i++]); //Assign input data to input-neurons
HiddenLayer.ForEach(a => a.Calc_Value()); //Hidden Calc
OutputLayer.ForEach(a => a.Calc_Value()); //Outuput Calc
}
public void Export(string path, string fileName)
{
var fullPath = Path.Combine(path, fileName);
if (File.Exists(fullPath))
{
File.Delete(Path.Combine(path, fullPath));
}
var jsonObject = new List <string>();
JsonSerializer serializer = new JsonSerializer();
for (int i = 0; i < Weights.Length; i++)
{
jsonObject.Add(JsonConvert.SerializeObject(this.Weights[i].Data));
jsonObject.Add(JsonConvert.SerializeObject(this.Weights[i].Rows));
jsonObject.Add(JsonConvert.SerializeObject(this.Weights[i].Columns));
}
jsonObject.Add("#");
for (int i = 0; i < Bias.Length; i++)
{
jsonObject.Add(JsonConvert.SerializeObject(this.Bias[i].Data));
jsonObject.Add(JsonConvert.SerializeObject(this.Bias[i].Rows));
jsonObject.Add(JsonConvert.SerializeObject(this.Bias[i].Columns));
}
jsonObject.Add("#");
jsonObject.Add(JsonConvert.SerializeObject(this.HiddenLayer));
jsonObject.Add((ActivationFuntion.Method.Name));
jsonObject.Add(InputLayer.ToString());
jsonObject.Add(OutputLayer.ToString());
File.WriteAllLines(fullPath, jsonObject);
}
private static NeuralNetwork InitializeNeuralNetwork(int seed)
{
Random random = new Random(seed == 0 ? new Random().Next() : seed);
float RandomWeight() => (float)(random.NextDouble() * 2 - 1);
Layer prevLayer;
InputLayer li = new InputLayer(3, 5);
prevLayer = li;
ConvolutionalLayer l0 = new ConvolutionalLayer(8, 2, 1, 0, prevLayer, ActivationFunctions.ReLU(true));
prevLayer = l0;
prevLayer.InitializeWeights(RandomWeight);
ConvolutionalLayer l2 = new ConvolutionalLayer(16, 2, 1, 0, prevLayer, ActivationFunctions.ReLU(true));
prevLayer = l2;
prevLayer.InitializeWeights(RandomWeight);
FullyConnectedLayer l7 = new FullyConnectedLayer(16, prevLayer, ActivationFunctions.Sigmoid(1));
prevLayer = l7;
prevLayer.InitializeWeights(RandomWeight);
FullyConnectedLayer l8 = new FullyConnectedLayer(10, prevLayer, ActivationFunctions.SoftMax(1));
prevLayer = l8;
prevLayer.InitializeWeights(RandomWeight);
return(new NeuralNetwork(li, l0, l2, l7, l8));
}
public TensorModel ReadModel(List <string> _RawData)
{
TensorModel Model = new TensorModel();
try
{
// Doc dong dau tien, tao lop InputLayer va set ten cho model
var _stringtemp = _RawData[0].Split(new char[] { '(', ')' });
Model.ModelName = _stringtemp[0].Replace("def", string.Empty);
InputLayer _inputlayer;
Model.Layers.Add(_inputlayer = new InputLayer(_stringtemp[1]));
// Doc cac dong tiep theo la thu tu cac lop cua Model
for (int i = 1; i < _RawData.Count; i++)
{
Layer _layertemp = new Layer();
Model.Layers.Add(LayerReader(_RawData[i]));
}
}
catch
{
}
// Tra ve Tensorflow Model
return(Model);
}
private void Create(InputLayer inputLayer, INodeLayer[] layers)
{
NodeLayers = new INodeLayer[layers.Length + 1];
ConnectionLayers = new List <IConnectionLayer>();
var nodes = new List <List <INode> >();
NumberOfInputs = inputLayer.Input.Length;
NumberOfOutputs = layers.Last().Output.Length;
NodeLayers[0] = inputLayer;
nodes.Add(inputLayer.Nodes);
for (int i = 0; i < layers.Length; i++)
{
var ih = layers[i];
NodeLayers[i + 1] = ih;
nodes.Add(ih.Nodes);
}
for (var i = 0; i < NodeLayers.Length - 1; i++)
{
var cl = new LinearWeightLayer(NodeLayers[i], NodeLayers[i + 1]);
ConnectionLayers.Add(cl);
}
StagedNodes = nodes.Select(n => n.ToArray()).ToArray();
Nodes = nodes.SelectMany(s => s).ToList();
}
/// <summary>
/// Create a MNIST trainer (writing recognition) will be added to an environemnt.
/// </summary>
/// <param name="sigma">The sigma environemnt this trainer will be assigned to.</param>
/// <returns>The newly created trainer.</returns>
private static ITrainer CreateMnistTrainer(SigmaEnvironment sigma)
{
ByteRecordReader mnistImageReader = new ByteRecordReader(headerLengthBytes: 16, recordSizeBytes: 28 * 28, source: new CompressedSource(new MultiSource(new FileSource("train-images-idx3-ubyte.gz"), new UrlSource("http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz"))));
IRecordExtractor mnistImageExtractor = mnistImageReader.Extractor("inputs", new[] { 0L, 0L }, new[] { 28L, 28L }).Preprocess(new NormalisingPreprocessor(0, 255));
ByteRecordReader mnistTargetReader = new ByteRecordReader(headerLengthBytes: 8, recordSizeBytes: 1, source: new CompressedSource(new MultiSource(new FileSource("train-labels-idx1-ubyte.gz"), new UrlSource("http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz"))));
IRecordExtractor mnistTargetExtractor = mnistTargetReader.Extractor("targets", new[] { 0L }, new[] { 1L }).Preprocess(new OneHotPreprocessor(minValue: 0, maxValue: 9));
IDataset dataset = new Dataset("mnist-training", Dataset.BlockSizeAuto, mnistImageExtractor, mnistTargetExtractor);
ITrainer trainer = sigma.CreateTrainer("test");
trainer.Network = new Network
{
Architecture = InputLayer.Construct(28, 28)
+ 2 * FullyConnectedLayer.Construct(28 * 28)
+ FullyConnectedLayer.Construct(10)
+ OutputLayer.Construct(10)
+ SoftMaxCrossEntropyCostLayer.Construct()
};
trainer.TrainingDataIterator = new MinibatchIterator(8, dataset);
trainer.Optimiser = new AdagradOptimiser(baseLearningRate: 0.02);
trainer.Operator = new CpuSinglethreadedOperator();
trainer.AddInitialiser("*.weights", new GaussianInitialiser(standardDeviation: 0.05f));
trainer.AddInitialiser("*.bias*", new GaussianInitialiser(standardDeviation: 0.01f, mean: 0.03f));
trainer.AddGlobalHook(new CurrentEpochIterationReporter(TimeStep.Every(1, TimeScale.Iteration)));
return(trainer);
}
/// <summary>
/// Initializes a new instance of the <see cref="FirstPersonControl"/> class.
/// </summary>
/// <param name="camera">The camera to use</param>
/// <param name="inputLayer">The input layer that the controls will add input triggers to</param>
public FirstPersonControl(Camera camera, InputLayer inputLayer)
{
_cam = camera;
inputLayer.RegisterTrigger(new InputTrigger(new KeyPressedCondition(Keys.A, true),
new InputAction(delegate(GameTime time) {
Move(-_cam.Right, time);
})));
inputLayer.RegisterTrigger(new InputTrigger(new KeyPressedCondition(Keys.D, true),
new InputAction(delegate(GameTime time) {
Move(_cam.Right, time);
})));
inputLayer.RegisterTrigger(new InputTrigger(new KeyPressedCondition(Keys.W, true),
new InputAction(delegate(GameTime time) {
Move(_cam.Direction, time);
})));
inputLayer.RegisterTrigger(new InputTrigger(new KeyPressedCondition(Keys.S, true),
new InputAction(delegate(GameTime time) {
Move(-_cam.Direction, time);
})));
inputLayer.RegisterTrigger(new InputTrigger(new PassThroughCondition(),
new InputAction(delegate(GameTime time) {
RotateCamera(time);
})));
}
public void SerializationTest()
{
// Create a InputLayer
var layer = new InputLayer(5, 5, 3);
layer.Init(10, 10, 3);
InputLayer deserialized;
using (var ms = new MemoryStream())
{
// Serialize
IFormatter formatter = new BinaryFormatter();
formatter.Serialize(ms, layer);
// Deserialize
ms.Position = 0;
deserialized = formatter.Deserialize(ms) as InputLayer;
}
Assert.AreEqual(layer.InputDepth, deserialized.InputDepth);
Assert.AreEqual(layer.InputHeight, deserialized.InputHeight);
Assert.AreEqual(layer.InputWidth, deserialized.InputWidth);
Assert.AreEqual(layer.OutputDepth, deserialized.OutputDepth);
Assert.AreEqual(layer.OutputHeight, deserialized.OutputHeight);
Assert.AreEqual(layer.OutputWidth, deserialized.OutputWidth);
}
/// <summary>
/// Used for scrolling through the keys
/// </summary>
public void OnKeyCode(int axis, float strength, InputLayer layer)
{
if (axis == (int)AxisKey.MouseWheelp && scrollbar.value < 1 && layer == inputLayer)
{
scrollbar.value += 0.1f * scrollFactor * strength;
}
else if (axis == (int)AxisKey.MouseWheelm && scrollbar.value > 0 && layer == inputLayer)
{
scrollbar.value -= 0.1f * scrollFactor * strength;
}
}
/// <summary>
/// Opens and closes the pausemenu
/// </summary>
/// <param name="key">the key that was pressed</param>
/// <param name="strength">if the key is an AxisKey, the value is the analog strength</param>
/// <param name="layer">the actual InputLayer</param>
public void OnKeyDown(Key key, float strength, InputLayer layer)
{
if (!UIController.Instance.IsUIWindowOpen(UIWindow.PauseMenu) && layer == InputLayer.Default && key == Key.Menu)
{
UIController.Instance.OpenUIWindow(UIWindow.PauseMenu);
}
else if (UIController.Instance.IsUIWindowOpen(UIWindow.PauseMenu) && layer == InputLayer.UI && key == Key.Menu)
{
UIController.Instance.CloseUIWindow(UIWindow.PauseMenu);
}
}
public InputLayer Build(int inputSize)
{
IList<InputNeuron> neurons = new List<InputNeuron>(inputSize);
for (int i = 0; i < inputSize; ++i)
{
neurons.Add(inputNeuronFactory.Create());
}
InputLayer inputLayer = new InputLayer(neurons);
return inputLayer;
}
public Network(Activation activation, TrainingInfo trainInfo, int inputSize, int[] hiddenSizes, int outputSize)
{
this.InputSize = inputSize;
this.HiddenSizes = hiddenSizes;
this.OutputSize = outputSize;
this.activation = activation;
this.inputLayer = new InputLayer(inputSize);
this.hiddenLayers = hiddenSizes
.Select(size => new HiddenLayer(activation, trainInfo, size))
.ToArray();
this.outputLayer = new OutputLayer(activation, trainInfo, outputSize);
ConnectLayers();
}
/// <summary>
/// Sends and Returns the pokemon
/// </summary>
/// <param name="key">the key</param>
/// <param name="strength">the strength</param>
/// <param name="layer">the actual layer</param>
public void OnKeyDown(Key key, float strength, InputLayer layer)
{
if (!isOut && key == Key.SendPokemon && layer == InputLayer.Default)
{
isOut = true;
Character.Main.ActivePokemon = new Pokemon();
HUDController.Instance.HUD = HUD.PokemonHUD;
}
else if (isOut && key == Key.ReturnPokemon && layer == InputLayer.Default)
{
isOut = false;
HUDController.Instance.HUD = HUD.PlayerHUD;
Character.Main.ActivePokemon = null;
}
}
/// <summary>
/// Changes the import-to layer name to IMP_importFileName.
/// If the importFileName is longer than LayerNameLength, truncate it to LayerNameLength.
/// If the importFileName contain characters other than alphabet, number, replace it with '_'
/// </summary>
/// <returns>Returns true if succeed.</returns>
public bool ChangeLayerName(InputLayer layer, ref string layerName, bool isToLayer0)
{
LayerNameType layerNameType = LayerNameType.LayerNameDirect;
try
{
if (isToLayer0)
{
layerName = "0";
}
else
{
string existingName = null;
// Truncate the layer name if it's too long
existingName = layer.Name;
// Replace illegal characters
foreach (Char tmp in existingName)
{
if (!(tmp >= 'A' && tmp <= 'Z'))
{
if (!(tmp >= 'a' && tmp <= 'z'))
{
if (!(tmp >='0' && tmp <='9'))
{
existingName.Replace(tmp, '_');
}
}
}
}
// Prefix "IMP_"
layerName = string.Concat("IMP_", existingName);
}
layer.SetLayerName(layerNameType, layerName);
return true;
}
catch
{
return false;
}
}
/// <summary>
/// Deselects the currently selected button
/// </summary>
/// <param name="axis">the axis</param>
/// <param name="strength">the strength</param>
/// <param name="layer">the layer</param>
public void OnKeyCode(int axis, float strength, InputLayer layer)
{
if (UI.Instance.EventSystem != null && UI.Instance.EventSystem.currentSelectedGameObject != null && (axis == (int)AxisKey.MouseXm || axis == (int)AxisKey.MouseXp || axis == (int)AxisKey.MouseYm || axis == (int)AxisKey.MouseYp) && layer == InputLayer.UI)
{
UI.Instance.EventSystem.SetSelectedGameObject(null);
}
}
public OrientingSubsystem(InputLayer inputLayer, OutputLayer outputLayer)
{
this.dataFromInputLayer = inputLayer;
this.outputLayer = outputLayer;
}
public void OnKeyCodeDown(int key, float strength, InputLayer layer)
{
}
/// <summary>
/// This function set the OD table name to layerName_OD.
/// </summary>
public bool SetTableName(InputLayer layer, string layerName)
{
ImportDataMapping tableType;
tableType = ImportDataMapping.NewObjectDataOnly;
string tableName = string.Concat(layerName, "_OD");
string newTableName = null;
// If the table name already exists, append a number after the table name,
// until no OD table with the same name found.
if (TableNameExist(tableName))
{
int index = 1;
do
{
newTableName = string.Concat(tableName, index.ToString());
index++;
}
while (TableNameExist(newTableName));
layer.SetDataMapping(tableType, newTableName);
}
else
{
layer.SetDataMapping(tableType, tableName);
}
return true;
}
public void OnKeyCode(int key, float strength, InputLayer layer)
{
// Position
if (Highlighted && key == (int)AxisKey.RSYp && layer == InputLayer.UI)
{
if (RectTransform.anchorMin.y > 0 && Time.deltaTime * movementFactor * strength <= RectTransform.anchorMin.y)
{
RectTransform.anchorMin = new Vector2(RectTransform.anchorMin.x, RectTransform.anchorMin.y - (Time.deltaTime * movementFactor * strength));
RectTransform.anchorMax = new Vector2(RectTransform.anchorMax.x, RectTransform.anchorMax.y - (Time.deltaTime * movementFactor * strength));
}
else if (RectTransform.anchorMin.y > 0)
{
RectTransform.anchorMax = new Vector2(RectTransform.anchorMax.x, RectTransform.anchorMax.y - RectTransform.anchorMin.y);
RectTransform.anchorMin = new Vector2(RectTransform.anchorMin.x, 0);
}
}
else if (Highlighted && key == (int)AxisKey.RSYm && layer == InputLayer.UI)
{
if (RectTransform.anchorMax.y < 1 && Time.deltaTime * movementFactor * strength <= 1 - RectTransform.anchorMax.y)
{
RectTransform.anchorMin = new Vector2(RectTransform.anchorMin.x, RectTransform.anchorMin.y + (Time.deltaTime * movementFactor * strength));
RectTransform.anchorMax = new Vector2(RectTransform.anchorMax.x, RectTransform.anchorMax.y + (Time.deltaTime * movementFactor * strength));
}
else if (RectTransform.anchorMax.y < 1)
{
RectTransform.anchorMin = new Vector2(RectTransform.anchorMin.x, RectTransform.anchorMin.y + 1 - RectTransform.anchorMax.y);
RectTransform.anchorMax = new Vector2(RectTransform.anchorMax.x, 1);
}
}
if (Highlighted && key == (int)AxisKey.RSXp && layer == InputLayer.UI)
{
if (RectTransform.anchorMax.x < 1 && Time.deltaTime * movementFactor * strength <= 1 - RectTransform.anchorMax.x)
{
RectTransform.anchorMin = new Vector2(RectTransform.anchorMin.x + (Time.deltaTime * movementFactor * strength), RectTransform.anchorMin.y);
RectTransform.anchorMax = new Vector2(RectTransform.anchorMax.x + (Time.deltaTime * movementFactor * strength), RectTransform.anchorMax.y);
}
else if (RectTransform.anchorMax.x < 1)
{
RectTransform.anchorMin = new Vector2(RectTransform.anchorMin.x + 1 - RectTransform.anchorMax.x, RectTransform.anchorMin.y);
RectTransform.anchorMax = new Vector2(1, RectTransform.anchorMax.y);
}
}
else if (Highlighted && key == (int)AxisKey.RSXm && layer == InputLayer.UI)
{
if (RectTransform.anchorMin.x > 0 && Time.deltaTime * movementFactor * strength <= RectTransform.anchorMin.x)
{
RectTransform.anchorMin = new Vector2(RectTransform.anchorMin.x - (Time.deltaTime * movementFactor * strength), RectTransform.anchorMin.y);
RectTransform.anchorMax = new Vector2(RectTransform.anchorMax.x - (Time.deltaTime * movementFactor * strength), RectTransform.anchorMax.y);
}
else if (RectTransform.anchorMin.x > 0)
{
RectTransform.anchorMax = new Vector2(RectTransform.anchorMax.x - RectTransform.anchorMin.x, RectTransform.anchorMax.y);
RectTransform.anchorMin = new Vector2(0, RectTransform.anchorMin.y);
}
}
// Scale
if (Highlighted && (key == (int)KeyCode.W || key == (int)AxisKey.HatYp) && layer == InputLayer.UI)
{
if (RectTransform.anchorMin.y > 0 && RectTransform.anchorMax.y < 1 && Time.deltaTime * scaleFactor * strength <= RectTransform.anchorMin.y && Time.deltaTime * scaleFactor * strength <= 1 - RectTransform.anchorMax.y)
{
RectTransform.anchorMin = new Vector2(RectTransform.anchorMin.x, RectTransform.anchorMin.y - (Time.deltaTime * scaleFactor * strength));
RectTransform.anchorMax = new Vector2(RectTransform.anchorMax.x, RectTransform.anchorMax.y + (Time.deltaTime * scaleFactor * strength));
}
else if (RectTransform.anchorMin.y > 0 && Time.deltaTime * scaleFactor * strength <= RectTransform.anchorMin.y)
{
RectTransform.anchorMin = new Vector2(RectTransform.anchorMin.x, RectTransform.anchorMin.y - (Time.deltaTime * scaleFactor * strength));
}
else if (RectTransform.anchorMax.y < 1 && Time.deltaTime * scaleFactor * strength <= 1 - RectTransform.anchorMax.y)
{
RectTransform.anchorMax = new Vector2(RectTransform.anchorMax.x, RectTransform.anchorMax.y + (Time.deltaTime * scaleFactor * strength));
}
else
{
RectTransform.anchorMin = new Vector2(RectTransform.anchorMin.x, 0);
RectTransform.anchorMax = new Vector2(RectTransform.anchorMax.x, 1);
}
}
else if (Highlighted && (key == (int)KeyCode.S || key == (int)AxisKey.HatYm) && layer == InputLayer.UI)
{
if (RectTransform.anchorMin.y + 0.01f < RectTransform.anchorMax.y && Time.deltaTime * scaleFactor * 2 * strength <= RectTransform.anchorMax.y - RectTransform.anchorMin.y)
{
RectTransform.anchorMin = new Vector2(RectTransform.anchorMin.x, RectTransform.anchorMin.y + (Time.deltaTime * scaleFactor * strength));
RectTransform.anchorMax = new Vector2(RectTransform.anchorMax.x, RectTransform.anchorMax.y - (Time.deltaTime * scaleFactor * strength));
}
else
{
RectTransform.anchorMin = new Vector2(RectTransform.anchorMin.x, RectTransform.anchorMin.y);
RectTransform.anchorMax = new Vector2(RectTransform.anchorMax.x, RectTransform.anchorMin.y + 0.01f);
}
}
if (Highlighted && (key == (int)KeyCode.A || key == (int)AxisKey.HatXm) && layer == InputLayer.UI)
{
if (RectTransform.anchorMin.x + 0.01f < RectTransform.anchorMax.x && Time.deltaTime * scaleFactor * 2 * strength <= RectTransform.anchorMax.x - RectTransform.anchorMin.x)
{
RectTransform.anchorMin = new Vector2(RectTransform.anchorMin.x + (Time.deltaTime * scaleFactor * strength), RectTransform.anchorMin.y);
RectTransform.anchorMax = new Vector2(RectTransform.anchorMax.x - (Time.deltaTime * scaleFactor * strength), RectTransform.anchorMax.y);
}
else
{
RectTransform.anchorMin = new Vector2(RectTransform.anchorMin.x, RectTransform.anchorMin.y);
RectTransform.anchorMax = new Vector2(RectTransform.anchorMin.x + 0.01f, RectTransform.anchorMax.y);
}
}
else if (Highlighted && (key == (int)KeyCode.D || key == (int)AxisKey.HatXp) && layer == InputLayer.UI)
{
if (RectTransform.anchorMin.x > 0 && RectTransform.anchorMax.x < 1 && Time.deltaTime * scaleFactor * strength <= RectTransform.anchorMin.x && Time.deltaTime * scaleFactor <= 1 - RectTransform.anchorMax.x)
{
RectTransform.anchorMin = new Vector2(RectTransform.anchorMin.x - (Time.deltaTime * scaleFactor * strength), RectTransform.anchorMin.y);
RectTransform.anchorMax = new Vector2(RectTransform.anchorMax.x + (Time.deltaTime * scaleFactor * strength), RectTransform.anchorMax.y);
}
else if (RectTransform.anchorMin.x > 0 && Time.deltaTime * scaleFactor * strength <= RectTransform.anchorMin.x)
{
RectTransform.anchorMin = new Vector2(RectTransform.anchorMin.x - (Time.deltaTime * scaleFactor * strength), RectTransform.anchorMin.y);
}
else if (RectTransform.anchorMax.x < 1 && Time.deltaTime * scaleFactor * strength <= 1 - RectTransform.anchorMax.x)
{
RectTransform.anchorMax = new Vector2(RectTransform.anchorMax.x + (Time.deltaTime * scaleFactor * strength), RectTransform.anchorMax.y);
}
else
{
RectTransform.anchorMin = new Vector2(0, RectTransform.anchorMin.y);
RectTransform.anchorMax = new Vector2(1, RectTransform.anchorMax.y);
}
}
}
public AttentionalSubsystem(OrientingSubsystem orientingSubsystem, InputLayer inputLayer, OutputLayer outputLayer)
{
this.orientingSubsystem = orientingSubsystem;
this.inputLayer = inputLayer;
this.outputLayer = outputLayer;
}