public void TestDatasetRegressionPreprocess()
{
double[,] x = { { 0, 1 }, { 2, 3 }, { 0, 4 } };
AreEqual(
new[]
{
new[] { new double[] { 0, 0, 2 }, new double[] { 0, 2, 1 } },
new[] { new double[] { 1, 3, 4 }, new double[] { 0, 1, 2 } }
}, AcceleratedGradientBoostingClassifier.RegressDataset(x, 3));
}
DatasetBenchmarkAgbcTest()
{
const string path = BasePath +
"\\heart.csv";
var lineCount = File.ReadLines(path).Count();
var reader = new StreamReader(File.OpenRead(path));
var properties = new double[lineCount, 14];
for (var i2 = 0; i2 < lineCount; i2++)
{
var line = reader.ReadLine();
for (var i = 0; i < 14; i++)
{
if (line == null)
{
continue;
}
var values = line.Split(',');
properties[i2, i] = Convert.ToDouble(values[i]);
}
}
var xTrain = new double[lineCount, 13];
var yTrain = new int[lineCount];
for (var i = 0; i < lineCount; i++)
{
for (var j = 0; j < 13; j++)
{
xTrain[i, j] = properties[i, j];
}
yTrain[i] = (int)properties[i, 13];
}
var model = new AcceleratedGradientBoostingClassifier(epochs: 100, shrinkage: 0.9);
model.Fit(xTrain, yTrain);
WriteLine("Training data:");
var predicted = model.Predict(xTrain);
WriteLine("Accuracy:");
var accuracy = Accuracy(predicted, yTrain);
WriteLine(accuracy);
PrintDataset(yTrain);
PrintDataset(predicted);
Greater(accuracy, 0.65);
}
public void TestAcceleratedGradientBoostingClassifier()
{
AcceleratedGradientBoostingClassifier agbc = new AcceleratedGradientBoostingClassifier(50, 0.8);
double[,] x = { { 0, 0, 0 }, { 1, 1, 1 }, { 2, 2, 2 }, { 3, 3, 3 } };
int[] y = { 1, 2, 3, 4 };
agbc.Fit(x, y);
PrintDataset(agbc.Predict(x));
PrintDataset(agbc.Regress(x));
var prediction = agbc.Predict(x);
WriteLine("Accuracy:");
var accuracy = Accuracy(prediction, y);
WriteLine(accuracy);
AreEqual(accuracy, 1.0);
}
