public void RoundTripFiles()
{
var testData = @"{ claimData: ""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""}";
var dataFilePath = Path.GetTempFileName();
var injectedImagePath = Path.Join(Path.GetTempPath(), Path.GetRandomFileName() + ".png");
File.WriteAllText(dataFilePath, testData, Encoding.UTF8);
var imageMaker = new ImageMaker()
{
ImgInputFile = "test_input.png",
ImgOutputFile = injectedImagePath,
DataFile = dataFilePath
};
imageMaker.Header.BitsPerPixel = 2;
imageMaker.Header.SetWritableRange(105, 150, 165, 200);
imageMaker.Run();
var dataFilePath2 = Path.GetTempFileName();
var dataMaker = new DataMaker()
{
ImgInputFile = injectedImagePath,
DataFile = dataFilePath2
};
dataMaker.Run();
var resultData = File.ReadAllText(dataFilePath2, Encoding.UTF8);
Assert.AreEqual(testData, resultData);
}
public bool AddNewSensore(string id, IEnumerable <decimal> feedData)
{
var intId = Int32.Parse(id);
if (forestDict.ContainsKey(intId))
{
return(false);
}
else
{
try
{
var f = new Forest(treeCount, samplingRatio);
var m = new DataMaker();
f.BuildForest(m.NormalizePoints(feedData).ToList());
forestDict[intId] = f;
dataMakerDict[intId] = m;
return(true);
}
catch (Exception)
{
return(false);
}
}
}
public static void Run()
{
DataMaker dataMaker = new DataMaker(STEPS_PER_CYCLE, NUMBER_OF_CYCLES);
NdArray trainData = dataMaker.Make();
FunctionStack model = new FunctionStack("Test8",
new Linear(true, 1, 5, name: "Linear l1"),
new LSTM(true, 5, 5, name: "LSTM l2"),
new Linear(true, 5, 1, name: "Linear l3")
);
model.SetOptimizer(new Adam());
RILogManager.Default?.SendDebug("Training...");
for (int epoch = 0; epoch < TRAINING_EPOCHS; epoch++)
{
NdArray[] sequences = dataMaker.MakeMiniBatch(trainData, MINI_BATCH_SIZE, LENGTH_OF_SEQUENCE);
Real loss = ComputeLoss(model, sequences);
model.Update();
model.ResetState();
if (epoch != 0 && epoch % DISPLAY_EPOCH == 0)
{
RILogManager.Default?.SendDebug("[{0}]training loss:\t{1}", epoch, loss);
}
}
RILogManager.Default?.SendDebug("Testing...");
NdArray[] testSequences = dataMaker.MakeMiniBatch(trainData, MINI_BATCH_SIZE, LENGTH_OF_SEQUENCE);
int sample_index = 45;
predict(testSequences[sample_index], model, PREDICTION_LENGTH);
}
public void Setup()
{
_imageMaker = new ImageMaker()
{
ImgInputFile = "../../test_input.png",
DataFile = "../../test_data.txt"
};
_imageMaker.Header.BitsPerPixel = 2;
_dataMaker = new DataMaker()
{
ImgInputFile = "../../test_input.png",
DataFile = "../../test_data.txt"
};
_dataMaker.Header.BitsPerPixel = 2;
}
public static void Run()
{
DataMaker dataMaker = new DataMaker(STEPS_PER_CYCLE, NUMBER_OF_CYCLES);
NdArray <Real> trainData = dataMaker.Make();
//ネットワークの構成は FunctionStack に書き連ねる
FunctionStack <Real> model = new FunctionStack <Real>(
new Linear <Real>(1, 5, name: "Linear l1"),
new LSTM <Real>(5, 5, name: "LSTM l2"),
new Linear <Real>(5, 1, name: "Linear l3")
);
//optimizerを宣言
Adam <Real> adam = new Adam <Real>();
adam.SetUp(model);
//訓練ループ
Console.WriteLine("Training...");
for (int epoch = 0; epoch < TRAINING_EPOCHS; epoch++)
{
NdArray <Real>[] sequences = dataMaker.MakeMiniBatch(trainData, MINI_BATCH_SIZE, LENGTH_OF_SEQUENCE);
Real loss = ComputeLoss(model, sequences);
adam.Update();
model.ResetState();
if (epoch != 0 && epoch % DISPLAY_EPOCH == 0)
{
Console.WriteLine("[{0}]training loss:\t{1}", epoch, loss);
}
}
Console.WriteLine("Testing...");
NdArray <Real>[] testSequences = dataMaker.MakeMiniBatch(trainData, MINI_BATCH_SIZE, LENGTH_OF_SEQUENCE);
int sample_index = 45;
predict(testSequences[sample_index], model, PREDICTION_LENGTH);
}
public void RoundTripDataReadsHeader()
{
var testData = new byte[] { 1, 2, 3, 4 };
var dataToInject = new BitArray(testData);
var bmp = _FilledSquare(Color.White, 10, 10);
var whitePixels = _imageMaker.PixelsFromImage(bmp);
var imageMaker = new ImageMaker();
imageMaker.Header.BitsPerPixel = 2;
var dataMaker = new DataMaker();
dataMaker.Header.BitsPerPixel = -1;
var injectedPixels = imageMaker.InjectData(whitePixels, dataToInject, 10, 10);
byte[] extractedData = dataMaker.ExtractData(injectedPixels, 10, 10);
Assert.AreEqual(testData, extractedData);
}
public static void Run()
{
DataMaker dataMaker = new DataMaker(STEPS_PER_CYCLE, NUMBER_OF_CYCLES);
NdArray trainData = dataMaker.Make();
//Network configuration is written in FunctionStack
FunctionStack model = new FunctionStack(
new Linear(1, 5, name: "Linear l1"),
new LSTM(5, 5, name: "LSTM l2"),
new Linear(5, 1, name: "Linear l3")
);
//Declare optimizer
model.SetOptimizer(new Adam());
//Training loop
Console.WriteLine("Training...");
for (int epoch = 0; epoch < TRAINING_EPOCHS; epoch++)
{
NdArray[] sequences = dataMaker.MakeMiniBatch(trainData, MINI_BATCH_SIZE, LENGTH_OF_SEQUENCE);
Real loss = ComputeLoss(model, sequences);
model.Update();
model.ResetState();
if (epoch != 0 && epoch % DISPLAY_EPOCH == 0)
{
Console.WriteLine("[{0}]training loss:\t{1}", epoch, loss);
}
}
Console.WriteLine("Testing...");
NdArray[] testSequences = dataMaker.MakeMiniBatch(trainData, MINI_BATCH_SIZE, LENGTH_OF_SEQUENCE);
int sample_index = 45;
predict(testSequences[sample_index], model, PREDICTION_LENGTH);
}
public void RoundTripDataWritablePixels()
{
var testData = new byte[] { 213, 214, 215, 216 };
var dataToInject = new BitArray(testData);
var bmp = _FilledSquare(Color.White, 10, 10);
var whitePixels = _imageMaker.PixelsFromImage(bmp);
var imageMaker = new ImageMaker();
imageMaker.Header.BitsPerPixel = 8;
imageMaker.Header.SetWritableRange(4, 4, 8, 8);
var dataMaker = new DataMaker();
var injectedPixels = imageMaker.InjectData(whitePixels, dataToInject, 10, 10);
Dump(injectedPixels, "Injected pixels");
byte[] extractedData = dataMaker.ExtractData(injectedPixels, 10, 10);
Assert.AreEqual(testData, extractedData);
}
/** 添加构造器 */
public void addResponseMaker(DataMaker maker)
{
BytesControl.addDataMaker(maker);
_maker.addDic(maker);
}
/** 添加发送消息构造 */
public void addRequestMaker(DataMaker maker)
{
BytesControl.addRequestMaker(maker);
}
/// <summary>
/// 添加一个
/// </summary>
protected void add(DataMaker maker)
{
BytesControl.addDataMaker(maker);
}
