public void FitPredictLSQR_WithoutInterceptAndWithRandomData_ReturnsCorrectResult()
{
var(x, y, theta) = Generate(intercept: false);
var(xTrain, yTrain, xTest, yTest) = ModelValidation.TrainTestSplit(x, y, 0.5);
var estimator = new RidgeRegression(intercept: false, method: RidgeRegressionMethod.LSQR).Fit(x, y);
var yTrainPred = estimator.Predict(xTrain);
var yTestPred = estimator.Predict(xTest);
FloatingPointAssert.Equal(theta, estimator.Weights, 1e-3);
FloatingPointAssert.Equal(yTrain, yTrainPred, 1e-2);
FloatingPointAssert.Equal(yTest, yTestPred, 1e-2);
}
public void TestToyRidgeObject()
{
var x = DenseMatrix.OfArray(new double[, ] {
{ 1 }, { 2 }
});
var y = new DenseVector(new double[] { 1, 2 });
var clf = new RidgeRegression(alpha: 0.0);
clf.Fit(x, y);
var xTest = DenseMatrix.OfArray(new double[, ] {
{ 1 }, { 2 }, { 3 }, { 4 }
});
Assert.AreEqual((clf.Predict(xTest) -
DenseMatrix.OfArray(new double[, ] {
{ 1 }, { 2 }, { 3 }, { 4 }
})).FrobeniusNorm(), 0.0, 1e-10);
Assert.AreEqual(clf.Coef.RowCount, 1);
var y1 = DenseMatrix.OfColumns(y.Count, 2, new[] { y, y });
clf.Fit(x, y1);
//todo: what does this test do?
}
private double?TestMultiRidgeDiabetes(Func <Matrix <double>, Matrix <double> > filter)
{
// simulate several responses
Matrix y = yDiabetes.HStack(yDiabetes);
int nFeatures = xDiabetes.ColumnCount;
var ridge = new RidgeRegression(fitIntercept: false);
ridge.Fit(filter(xDiabetes), y);
Assert.AreEqual(ridge.Coef.Shape(), Tuple.Create(2, nFeatures));
Matrix <double> yPred = ridge.Predict(filter(xDiabetes));
ridge.Fit(filter(xDiabetes), yDiabetes);
var yPred1 = ridge.Predict(filter(xDiabetes));
Assert.IsTrue(yPred1.HStack(yPred1).AlmostEquals(yPred));
return(null);
}
public void RidgeRegressionSample()
{
var clf = new RidgeRegression(alpha: 0.5);
clf.Fit(new[, ] {
{ 0.0, 0.0 }, { 0.0, 0.0 }, { 1.0, 1.0 }
}, new[] { 0.0, 0.1, 1.0 });
Console.WriteLine(clf.Coef);
Console.WriteLine(clf.Intercept);
var prediction = clf.Predict(new[] { 5.0, 6.0 });
Console.WriteLine(prediction);
}
private double? TestMultiRidgeDiabetes(Func<Matrix<double>, Matrix<double>> filter)
{
// simulate several responses
Matrix y = yDiabetes.HStack(yDiabetes);
int nFeatures = xDiabetes.ColumnCount;
var ridge = new RidgeRegression(fitIntercept: false);
ridge.Fit(filter(xDiabetes), y);
Assert.AreEqual(ridge.Coef.Shape(), Tuple.Create(2, nFeatures));
Matrix<double> yPred = ridge.Predict(filter(xDiabetes));
ridge.Fit(filter(xDiabetes), yDiabetes);
var yPred1 = ridge.Predict(filter(xDiabetes));
Assert.IsTrue(yPred1.HStack(yPred1).AlmostEquals(yPred));
return null;
}
public void TestToyRidgeObject()
{
var x = DenseMatrix.OfArray(new double[,] { { 1 }, { 2 } });
var y = new DenseVector(new double[] { 1, 2 });
var clf = new RidgeRegression(alpha: 0.0);
clf.Fit(x, y);
var xTest = DenseMatrix.OfArray(new double[,] { { 1 }, { 2 }, { 3 }, { 4 } });
Assert.AreEqual((clf.Predict(xTest) -
DenseMatrix.OfArray(new double[,] { { 1 }, { 2 }, { 3 }, { 4 } })).FrobeniusNorm(), 0.0, 1e-10);
Assert.AreEqual(clf.Coef.RowCount, 1);
var y1 = DenseMatrix.OfColumns(y.Count, 2, new[] { y, y });
clf.Fit(x, y1);
//todo: what does this test do?
}
