private static Liblinear.Train prepare(int mode)
{
Accord.Math.Random.Generator.Seed = 0;
Liblinear.Train train = new Liblinear.Train();
train.Main(String.Format("-s {0} -c 4.0 -e 1e-06 Resources/liblinear/a9a.train result.txt", mode).Split(' '));
return(train);
}
private static Liblinear.Train prepare(int mode)
{
Accord.Math.Random.Generator.Seed = 0;
Liblinear.Train train = new Liblinear.Train();
train.Main("-s", mode.ToString(), "-c", "4.0", "-e", "1e-06",
Path.Combine(TestContext.CurrentContext.TestDirectory, "Resources", "liblinear", "a9a.train"),
"result.txt");
return(train);
}
private static void check(Liblinear.Train train, LibSvmModel model)
{
var svm = train.Machine;
//Assert.IsTrue(svm.SupportVectors.Length > 1);
svm.Compress();
Assert.AreEqual(svm.Threshold, model.Weights[0]);
Assert.AreEqual(svm.SupportVectors.Length, 1);
double[] expected = model.Weights.Get(1, 0);
double[] actual = svm.SupportVectors[0].ToDense(model.Dimension);
if (expected.Length < actual.Length)
expected = Vector.Create(actual.Length, expected);
Assert.IsTrue(expected.IsEqual(actual, 1e-10));
}
