public void TestSgd()
{
var model = new SgdClassifier(epochs: 10000, learningRate: 0.01);
var xTrain = new double[, ] {
{ 2, 0 }, { 1, 1 }, { 2, 3 }, { 6, 7 }, { 8, 9 }, { 1, 1 }
};
var yTrain = new[] { 0, 0, 1, 1, 1, 0 };
model.Fit(xTrain, yTrain);
WriteLine("Training data:");
WriteLine("Prediction:");
foreach (var item in model.Predict(xTrain))
{
WriteLine(item.ToString());
}
WriteLine("Actual:");
foreach (var item in yTrain)
{
WriteLine(item.ToString());
}
WriteLine("Beta params:");
foreach (var item in model.BetaParam)
{
WriteLine(item.ToString(CultureInfo.InvariantCulture));
}
AreEqual(model.Predict(xTrain), yTrain);
}
public void DatasetBenchmarkSgdTest3()
{
const string path = BasePath +
"\\ldp.csv";
var lineCount = File.ReadLines(path).Count();
var reader = new StreamReader(File.OpenRead(path));
var properties = new double[lineCount, 22];
for (var i2 = 0; i2 < lineCount; i2++)
{
var line = reader.ReadLine();
for (var i = 0; i < 22; i++)
{
if (line == null)
{
continue;
}
var values = line.Split(',');
properties[i2, i] = Convert.ToDouble(values[i]);
}
}
var xTrain = new double[lineCount, 21];
var yTrain = new int[lineCount];
for (var i = 0; i < lineCount; i++)
{
for (var j = 0; j < 21; j++)
{
xTrain[i, j] = properties[i, j];
}
yTrain[i] = (int)properties[i, 21];
}
var model = new SgdClassifier(epochs: 10000, learningRate: 0.005);
model.Fit(xTrain, yTrain);
WriteLine("Training data:");
var predicted = model.Predict(xTrain);
WriteLine("Accuracy:");
var accuracy = Accuracy(predicted, yTrain);
WriteLine(accuracy);
WriteLine("Beta params:");
foreach (var item in model.BetaParam)
{
WriteLine(item.ToString(CultureInfo.InvariantCulture));
}
Greater(accuracy, 0.75);
}
