public void TestRidge()
{
var rng = new Random(0);
const double alpha = 1.0;
foreach (var solver in new[] { RidgeSolver.Svd, RidgeSolver.DenseCholesky, RidgeSolver.Lsqr })
{
// With more samples than features
int nSamples = 6;
int nFeatures = 5;
var normal = new Normal { RandomSource = rng };
Vector y = DenseVector.CreateRandom(nSamples, normal);
Matrix x = DenseMatrix.CreateRandom(nSamples, nFeatures, normal);
var ridge = new RidgeRegression(alpha: alpha, solver: solver);
ridge.Fit(x, y);
Assert.AreEqual(ridge.Coef.Row(0).Count, x.ColumnCount);
Assert.IsTrue(ridge.Score(x, y) > 0.47);
ridge.Fit(x, y, sampleWeight: DenseVector.Create(nSamples, i => 1.0));
Assert.IsTrue(ridge.Score(x, y) > 0.47);
// With more features than samples
nSamples = 5;
nFeatures = 10;
y = DenseVector.CreateRandom(nSamples, normal);
x = DenseMatrix.CreateRandom(nSamples, nFeatures, normal);
ridge = new RidgeRegression(alpha: alpha, solver: solver);
ridge.Fit(x, y);
Assert.IsTrue(ridge.Score(x, y) > 0.9);
ridge.Fit(x, y, sampleWeight: DenseVector.Create(nSamples, i => 1.0));
Assert.IsTrue(ridge.Score(x, y) > 0.9);
}
}
private double?TestRidgeDiabetes(Func <Matrix <double>, Matrix <double> > filter)
{
var ridge = new RidgeRegression(fitIntercept: false);
ridge.Fit(filter(xDiabetes), yDiabetes);
return(Math.Round(ridge.Score(filter(xDiabetes), yDiabetes), 5));
}
public void TestRidge()
{
var rng = new Random(0);
const double alpha = 1.0;
foreach (var solver in new[] { RidgeSolver.Svd, RidgeSolver.DenseCholesky, RidgeSolver.Lsqr })
{
// With more samples than features
int nSamples = 6;
int nFeatures = 5;
var normal = new Normal {
RandomSource = rng
};
Vector y = DenseVector.CreateRandom(nSamples, normal);
Matrix x = DenseMatrix.CreateRandom(nSamples, nFeatures, normal);
var ridge = new RidgeRegression(alpha: alpha, solver: solver);
ridge.Fit(x, y);
Assert.AreEqual(ridge.Coef.Row(0).Count, x.ColumnCount);
Assert.IsTrue(ridge.Score(x, y) > 0.47);
ridge.Fit(x, y, sampleWeight: DenseVector.Create(nSamples, i => 1.0));
Assert.IsTrue(ridge.Score(x, y) > 0.47);
// With more features than samples
nSamples = 5;
nFeatures = 10;
y = DenseVector.CreateRandom(nSamples, normal);
x = DenseMatrix.CreateRandom(nSamples, nFeatures, normal);
ridge = new RidgeRegression(alpha: alpha, solver: solver);
ridge.Fit(x, y);
Assert.IsTrue(ridge.Score(x, y) > 0.9);
ridge.Fit(x, y, sampleWeight: DenseVector.Create(nSamples, i => 1.0));
Assert.IsTrue(ridge.Score(x, y) > 0.9);
}
}
private double?TestTolerance(Func <Matrix <double>, Matrix <double> > filter)
{
var ridge = new RidgeRegression(tol: 1e-5);
ridge.Fit(filter(xDiabetes), yDiabetes);
double score = ridge.Score(filter(xDiabetes), yDiabetes);
var ridge2 = new RidgeRegression(tol: 1e-3);
ridge2.Fit(filter(xDiabetes), yDiabetes);
double score2 = ridge2.Score(filter(xDiabetes), yDiabetes);
Assert.IsTrue(score >= score2);
return(null);
}
private double? TestTolerance(Func<Matrix<double>, Matrix<double>> filter)
{
var ridge = new RidgeRegression(tol: 1e-5);
ridge.Fit(filter(xDiabetes), yDiabetes);
double score = ridge.Score(filter(xDiabetes), yDiabetes);
var ridge2 = new RidgeRegression(tol: 1e-3);
ridge2.Fit(filter(xDiabetes), yDiabetes);
double score2 = ridge2.Score(filter(xDiabetes), yDiabetes);
Assert.IsTrue(score >= score2);
return null;
}
private double? TestRidgeDiabetes(Func<Matrix<double>, Matrix<double>> filter)
{
var ridge = new RidgeRegression(fitIntercept: false);
ridge.Fit(filter(xDiabetes), yDiabetes);
return Math.Round(ridge.Score(filter(xDiabetes), yDiabetes), 5);
}
