private MovingDataset[] LoadMovingDatasets()
{
List <MovingDataset> datasetList = new List <MovingDataset>();
string[] movingVecFilenames = Directory.GetFiles(MovingVectorDirectoryName, "*.vec");
foreach (string movingVecFilename in movingVecFilenames)
{
byte[] data = File.ReadAllBytes(movingVecFilename);
MovingDataset dataset = new MovingDataset();
dataset.AddBytesData(data);
datasetList.Add(dataset);
}
return(datasetList.ToArray());
}
public unsafe Vector2 PredictMoving(ChampIndicator indicator)
{
float[] outputDataF;
try {
float[] inputDataF = MovingDataset.GetInputData(indicator.positionHistoryList
.Skip(indicator.positionHistoryList.Count - MovingDataset.InputVectorCount - 1)
.Take(MovingDataset.InputVectorCount + 1).ToArray());
for (int i = 0; i < inputDataF.Length; ++i)
{
inputDatas[0, i] = inputDataF[i];
}
var outputData = model.PredictOnBatch(inputDatas);
outputDataF = outputData.GetData <float>();
} catch {
outputDataF = new float[2];
}
return(new Vector2(outputDataF[0], outputDataF[1]));
}
public async void TrainMovingData()
{
try {
// Compile
var optimizer = new Adam(lr: 0.001f);
model.Compile(optimizer: optimizer, loss: "mse", metrics: new string[] { "accuracy" });
// Train
MovingDataset[] movingTrainDatasets = LoadMovingDatasets();
int dataCount = 0;
List <float[, ]> inputList = new List <float[, ]>();
List <float[, ]> outputList = new List <float[, ]>();
for (int i = 0; i < movingTrainDatasets.Length; ++i)
{
MovingDataset datasetPart = movingTrainDatasets[i];
TrainData trainData = datasetPart.GetTrainData();
inputList.Add(trainData.inputs);
outputList.Add(trainData.outputs);
dataCount += trainData.inputs.GetLength(0);
}
float[,] inputs = new float[dataCount, MovingDataset.InputFloatCount];
float[,] outputs = new float[dataCount, MovingDataset.OutputFloatCount];
int inputIndex = 0;
foreach (float[,] input in inputList)
{
for (int i = 0; i < input.GetLength(0); ++i)
{
for (int f = 0; f < MovingDataset.InputFloatCount; ++f)
{
inputs[inputIndex, f] = input[i, f];
}
++inputIndex;
}
}
int outputIndex = 0;
foreach (float[,] output in outputList)
{
for (int i = 0; i < output.GetLength(0); ++i)
{
for (int f = 0; f < MovingDataset.OutputFloatCount; ++f)
{
outputs[outputIndex, f] = output[i, f];
}
++outputIndex;
}
}
for (; ;)
{
model.Fit(inputs, outputs, batch_size: 100, epochs: 100, verbose: 1);
SaveModel();
await Task.Delay(10);
}
//float[] predicts = model.Predict(x).GetData<float>();
//Stopwatch watch = new Stopwatch();
//watch.Start();
//for (int i = 0; i < 1; ++i) {
// predicts = model.Predict(x, verbose: 0).GetData<float>();
// //Debug.WriteLine($"Result: ({string.Join(",", predicts)})");
//}
//watch.Stop();
//MainWindow.Instance.Dispatcher.BeginInvoke(new Action(() => {
// MainWindow.Instance.DebugTextBox.Text = watch.GetElapsedMilliseconds().ToString();
//}));
} catch (Exception ex) {
MainWindow.Instance.Dispatcher.BeginInvoke(new Action(() => {
MainWindow.Instance.DebugTextBox.Text = ex.ToString();
}));
}
}