public void TestPredictionWindowSize()
{
using (StreamWriter sw = new StreamWriter(@"E:\repos\RBFNetwork\windowSize.txt"))
{
for (int i = 10; i <= 10; i++)
{
var data = GetTemperatureDataAsync(95, i).Result;
int accuracy = 0;
var topology = new Topology(data.trainData.ClassesNames?.Count ?? 1, 0.01, data.trainData.ColumnCount);
var network = new RBFNeuralNetwork(topology);
double error = network.Train(data.trainData, 100, false);
double predictError = 0;
foreach (Record record in data.testData.Records)
{
var expected = record.Expected[0];
var output = network.Predict(record.InputData)[0];
var diff = expected - output;
predictError += diff * diff;
if ((expected * data.inputsSum - output * data.inputsSum) <= 2)
{
accuracy++;
}
}
predictError = Math.Sqrt(predictError / (data.testData.RowCount - 1));
Trace.WriteLine($"Current accuracy: {accuracy} of {data.testData.Records.Count} = {accuracy * 100 / data.testData.Records.Count}%");
var predicted = accuracy * 100 / data.testData.RowCount;
sw.WriteLine($"{i} {predictError} {predicted}");
}
}
}
public NeuralNetworkModel(Dataset dataset, Dataset testSet)
{
for (int i = 0; i < dataset.RowCount; i++)
{
trainDataset.Records.Add(dataset.Records[i]);
}
for (int i = 0; i < testSet.RowCount; i++)
{
testDataset.Records.Add(testSet.Records[i]);
}
// Топология
var topology = new Topology(trainDataset.ClassesNames?.Count ?? 1, 0.01, trainDataset.ColumnCount);
neuralNetwork = new RBFNeuralNetwork(topology);
}
