public void TestFitIntercept()
{
var x2 = DenseMatrix.OfArray(new[,]
{
{0.38349978, 0.61650022},
{0.58853682, 0.41146318}
});
var x3 = DenseMatrix.OfArray(new[,]
{
{0.27677969, 0.70693172, 0.01628859},
{0.08385139, 0.20692515, 0.70922346}
});
var y = DenseVector.OfEnumerable(new double[] {1, 1});
var lr2WithoutIntercept = new LinearRegression(fitIntercept: false);
lr2WithoutIntercept.Fit(x2, y);
var lr2WithIntercept = new LinearRegression(fitIntercept: true);
lr2WithIntercept.Fit(x2, y);
var lr3WithoutIntercept = new LinearRegression(fitIntercept: false);
lr3WithoutIntercept.Fit(x3, y);
var lr3WithIntercept = new LinearRegression(fitIntercept: true);
lr3WithIntercept.Fit(x3, y);
Assert.AreEqual(lr2WithIntercept.Coef.Column(0).Count,
lr2WithoutIntercept.Coef.Column(0).Count);
Assert.AreEqual(lr3WithIntercept.Coef.Column(0).Count,
lr3WithoutIntercept.Coef.Column(0).Count);
}
public void TestLinearRegression()
{
// Control.LinearAlgebraProvider = new MklLinearAlgebraProvider();
// a simple dataset
var x = DenseMatrix.OfArray(new double[,] {{1}, {2}});
var y = DenseVector.OfEnumerable(new double[] {1, 2});
var clf = new LinearRegression();
clf.Fit(x, y);
Assert.AreEqual(1.0, clf.Coef.Column(0)[0], 1E-5);
//Assert.AreEqual(0.0, clf.Intercept[0]);
Assert.IsTrue(DenseVector.OfEnumerable(new double[] {1, 2}).AlmostEquals(clf.Predict(x).Column(0)));
// test it also for degenerate input
x = DenseMatrix.OfArray(new double[,] {{1}});
y = DenseVector.OfEnumerable(new double[] {0});
clf = new LinearRegression(fitIntercept: false);
clf.Fit(x, y);
Assert.AreEqual(0.0, clf.Coef.Column(0)[0]);
//assert_array_almost_equal(clf.intercept_, [0])
Assert.AreEqual(0.0, clf.Predict(x).Column(0)[0]);
}
public void TestLinearRegressionSparse()
{
const int n = 100;
Matrix x = SparseMatrix.Identity(n);
var beta = DenseVector.CreateRandom(n, new Normal());
var y = x*beta;
var ols = new LinearRegression(fitIntercept: true);
ols.Fit(x, y);
Assert.IsTrue((ols.Coef.Row(0) + ols.Intercept[0]).AlmostEquals(beta));
}
public void TestLinearRegressionSparseMultipleOutcome()
{
var random = new Random(0);
var r = SampleGenerator.MakeSparseUncorrelated(random : random);
Matrix x = SparseMatrix.OfMatrix(r.X);
Vector<double> y = r.Y.Column(0);
Matrix y1 = DenseMatrix.OfColumns(y.Count, 2, new[] {y, y});
int nFeatures = x.ColumnCount;
var ols = new LinearRegression();
ols.Fit(x, y1);
Assert.AreEqual(Tuple.Create(2, nFeatures), ols.Coef.Shape());
Assert.AreEqual(Tuple.Create(2, nFeatures), ols.Coef.Shape());
Matrix<double> yPred = ols.Predict(x);
ols.Fit(x, y);
Matrix<double> yPred1 = ols.Predict(x);
Assert.IsTrue(yPred1.Column(0).AlmostEquals(yPred.Column(0)));
Assert.IsTrue(yPred1.Column(0).AlmostEquals(yPred.Column(1)));
}
public void TestLinearRegressionMultipleOutcome()
{
var result = SampleGenerator.MakeRegression(shuffle:false, random : new Random(0));
Matrix y = DenseMatrix.OfColumns(
result.Y.RowCount,
2,
new[]{ result.Y.Column(0), result.Y.Column(0) });
var numFeatures = result.X.RowCount;
var clf = new LinearRegression(fitIntercept: true);
clf.Fit(result.X, y);
Assert.AreEqual(Tuple.Create(2, numFeatures), clf.Coef.Shape());
Matrix<double> yPred = clf.Predict(result.X);
clf.Fit(result.X, result.Y);
Matrix<double> yPred1 = clf.Predict(result.X);
Assert.IsTrue(yPred1.Column(0).AlmostEquals(yPred.Column(0)));
Assert.IsTrue(yPred1.Column(0).AlmostEquals(yPred.Column(1)));
}