public SplitData GetData()
{
var inputs = new double[2, 4] {
{ 0, 0, 1, 1 }, { 0, 1, 1, 0 }
};
var mathInputs = Matrix <double> .Build.DenseOfArray(inputs);
var outputs = new double[1, 4] {
{ 0, 0, 1, 0 }
};
var mathOutputs = Matrix <double> .Build.DenseOfArray(outputs);
var data = new MathData(mathInputs, mathOutputs);
return(new SplitData(data, data, data));
}
private SplitData ConvertToSplitData(string inputPath, string outputPath)
{
var input = ReadToCsv(inputPath);
var inputMatrix = DenseMatrix.OfColumnArrays(input);
var output = ReadToCsv(outputPath);
var outputMatrix = DenseMatrix.OfColumnArrays(output);
var inputSize = inputMatrix.RowCount;
var outputSize = outputMatrix.RowCount;
var dataSize = inputMatrix.ColumnCount;
var trainingSize = 3 * dataSize / 5;
var validationSize = dataSize / 5;
var trainingInputs = inputMatrix.SubMatrix(0, inputSize, 0, trainingSize);
var trainingOutputs = outputMatrix.SubMatrix(0, outputSize, 0, trainingSize);
var training = new MathData(trainingInputs, trainingOutputs);
var validationInputs = inputMatrix.SubMatrix(0, inputSize, trainingSize, validationSize);
var validationOutputs = outputMatrix.SubMatrix(0, outputSize, trainingSize, validationSize);
var validation = new MathData(validationInputs, validationOutputs);
var testingInputs = inputMatrix.SubMatrix(0, inputSize, trainingSize + validationSize, dataSize - (trainingSize + validationSize));
var testingOutputs = outputMatrix.SubMatrix(0, outputSize, trainingSize + validationSize, dataSize - (trainingSize + validationSize));
var testing = new MathData(testingInputs, testingOutputs);
return(new SplitData(training, validation, testing));
}
public SplitData(MathData trainingData, MathData validationData, MathData testData)
{
TrainingData = trainingData;
ValidationData = validationData;
TestData = testData;
}
