public static void LoadData()
{
DataFrame frame = new DataFrame();
Downloader.DownloadSample(SampleDataset.HousingRegression);
var samplePath = Downloader.GetSamplePath(SampleDataset.HousingRegression);
frame.LoadFromCsv(samplePath.Train);
var xy = frame.SplitXY(14, new[] { 1, 13 });
traintest = xy.SplitTrainTest(0.25);
}
private static void RunTest()
{
Random Rnd = new Random();
DataFrame trnX_fin = new DataFrame();
DataFrame trnY_fin = new DataFrame();
for (int cc = 0; (cc < 100); cc++)
{
float[] sngLst = new float[100];
for (int indx = 0; (indx < 100); indx++)
{
sngLst[indx] = (float)Rnd.NextDouble();
}
trnX_fin.Add(sngLst);
}
for (int cc = 0; (cc < 100); cc++)
{
float[] sngLst = new float[3];
// fake one hot just for check
sngLst[0] = 0;
sngLst[1] = 1;
sngLst[2] = 0;
trnY_fin.Add(sngLst);
}
XYFrame XYfrm = new XYFrame();
XYfrm.XFrame = trnX_fin;
XYfrm.YFrame = trnY_fin;
// Split
TrainTestFrame trainTestFrame = XYfrm.SplitTrainTest(0.3);
// init some values
int shape_of_input = XYfrm.XFrame.Shape[1];
int embval = 100;
int seed = 2;
Sequential model = new Sequential();
model.Add(new Reshape(Shape.Create(1, embval), Shape.Create(shape_of_input)));
model.Add(new LSTM(64, returnSequence: false, cellDim: 4, weightInitializer: new SiaNet.Model.Initializers.GlorotUniform(0.05, seed), recurrentInitializer: new SiaNet.Model.Initializers.GlorotUniform(0.05, seed), biasInitializer: new SiaNet.Model.Initializers.GlorotUniform(0.05, seed)));
model.Add(new Dense(3, act: "sigmoid", useBias: true, weightInitializer: new SiaNet.Model.Initializers.GlorotUniform(0.05, seed)));
model.Compile(OptOptimizers.Adam, OptLosses.MeanSquaredError, OptMetrics.Accuracy);
model.Train(trainTestFrame.Train, 200, 8, trainTestFrame.Test);
}