public void TestLoad()
{
var res = DiabetesDataset.Load();
Assert.AreEqual(res.Data.Shape(), Tuple.Create(442, 10));
Assert.AreEqual(res.Target.Count, 442);
}
public void TestInitialize()
{
var diabetes = DiabetesDataset.Load();
xDiabetes = diabetes.Data.SubMatrix(0, 200, 0, diabetes.Data.ColumnCount);
yDiabetes = MatrixExtensions.ToColumnMatrix(diabetes.Target.SubVector(0, 200));
//ind = np.arange(X_diabetes.shape[0])
//Random rng = new Random(0);
//rng.shuffle(ind)
//ind = ind[:200]
//X_diabetes, y_diabetes = X_diabetes[ind], y_diabetes[ind]
var iris = IrisDataset.Load();
xIris = SparseMatrix.OfMatrix(iris.Data);
yIris = iris.Target;
}
[Ignore] //This test is ignored in scikit-learn also.
public void TestBayesianOnDiabetes()
{
//raise SkipTest("XFailed Test")
var diabetes = DiabetesDataset.Load();
var x = diabetes.Data;
var y = diabetes.Target;
var clf = new BayesianRidgeRegression(computeScore: true);
// Test with more samples than features
clf.Fit(x, y);
// Test that scores are increasing at each iteration
Assert.AreEqual(clf.Scores.Count - 1, clf.Scores.Diff().Count(v => v > 0));
// Test with more features than samples
x = x.SubMatrix(0, 5, 0, x.ColumnCount);
y = y.SubVector(0, 5);
clf.Fit(x, y);
// Test that scores are increasing at each iteration
Assert.AreEqual(clf.Scores.Count - 1, clf.Scores.Diff().Count(v => v > 0));
}
