public void TestRidgeShapes() { var rng = new Random(0); const int nSamples = 5; const int nFeatures = 10; Matrix x = DenseMatrix.CreateRandom(nSamples, nFeatures, new Normal { RandomSource = rng }); Vector y = DenseVector.CreateRandom(nSamples, new Normal { RandomSource = rng }); Matrix Y = DenseMatrix.OfColumns(y.Count, 2, new[] { y, y.Add(1) }); var ridge = new RidgeRegression(); ridge.Fit(x, y); Assert.AreEqual(ridge.Coef.Shape(), Tuple.Create(1, nFeatures)); Assert.AreEqual(ridge.Intercept.Count, 1); ridge.Fit(x, MatrixExtensions.ToColumnMatrix(y)); Assert.AreEqual(ridge.Coef.Shape(), Tuple.Create(1, nFeatures)); Assert.AreEqual(ridge.Intercept.Count, 1); ridge.Fit(x, Y); Assert.AreEqual(ridge.Coef.Shape(), Tuple.Create(2, nFeatures)); Assert.AreEqual(ridge.Intercept.Count, 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?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); } }
public void TestRidgeSampleWeights() { var rng = new Random(0); const double alpha = 1.0; foreach (var solver in new[] { RidgeSolver.Svd, RidgeSolver.DenseCholesky, RidgeSolver.Lsqr }) { var tests = new[] { new[] { 6, 5 }, new[] { 5, 10 } }; foreach (var test in tests) { int nSamples = test[0]; int nFeatures = test[1]; Vector y = DenseVector.CreateRandom(nSamples, new Normal { RandomSource = rng }); Matrix x = DenseMatrix.CreateRandom(nSamples, nFeatures, new Normal { RandomSource = rng }); Vector sampleWeight = DenseVector.CreateRandom(nSamples, new ContinuousUniform(1, 2)); var ridgeRegression = new RidgeRegression(alpha: alpha, solver: solver, fitIntercept: false); ridgeRegression.Fit(x, y, sampleWeight); var coefs = ridgeRegression.Coef.Column(0); // Sample weight can be implemented via a simple rescaling // for the square loss var regression2 = new RidgeRegression(alpha, solver: solver, fitIntercept: false); regression2.Fit( x.MulColumnVector(sampleWeight.Sqrt()), y.PointwiseMultiply(sampleWeight.Sqrt())); var coefs2 = regression2.Coef.Column(0); Assert.IsTrue(coefs.AlmostEquals(coefs2)); } } }
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 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?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?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 TestRidgeIntercept() { var rng = new Random(0); const int nSamples = 5; const int nFeatures = 10; Matrix x = DenseMatrix.CreateRandom(nSamples, nFeatures, new Normal { RandomSource = rng }); Vector y = DenseVector.CreateRandom(nSamples, new Normal { RandomSource = rng }); Matrix y1 = DenseMatrix.OfColumns(y.Count, 2, new[] { y, y.Add(1) }); var ridge = new RidgeRegression(); ridge.Fit(x, y); var intercept = ridge.Intercept; ridge.Fit(x, y1); Assert.AreEqual(ridge.Intercept[0], intercept[0], 1e-10); Assert.AreEqual(ridge.Intercept[1], intercept[0] + 1, 1e-10); }
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); } }
public void TestRidgeVsLstsq() { var random = new Random(0); // we need more samples than features const int nSamples = 5; const int nFeatures = 4; var y = DenseVector.CreateRandom(nSamples, new Normal { RandomSource = random }); var x = DenseMatrix.CreateRandom(nSamples, nFeatures, new Normal { RandomSource = random }); var ridge = new RidgeRegression(alpha: 0.0, fitIntercept: false); var ols = new LinearRegression(fitIntercept: false); ridge.Fit(x, y); ols.Fit(x, y); Assert.IsTrue(ridge.Coef.AlmostEquals(ols.Coef)); ridge.Fit(x, y); ols.Fit(x, y); Assert.IsTrue(ridge.Coef.AlmostEquals(ols.Coef)); }
public void TestRidgeShapes() { var rng = new Random(0); const int nSamples = 5; const int nFeatures = 10; Matrix x = DenseMatrix.CreateRandom(nSamples, nFeatures, new Normal { RandomSource = rng }); Vector y = DenseVector.CreateRandom(nSamples, new Normal { RandomSource = rng }); Matrix Y = DenseMatrix.OfColumns(y.Count, 2, new[] { y, y.Add(1) }); var ridge = new RidgeRegression(); ridge.Fit(x, y); Assert.AreEqual(ridge.Coef.Shape(), Tuple.Create(1, nFeatures)); Assert.AreEqual(ridge.Intercept.Count, 1); ridge.Fit(x, MatrixExtensions.ToColumnMatrix(y)); Assert.AreEqual(ridge.Coef.Shape(), Tuple.Create(1, nFeatures)); Assert.AreEqual(ridge.Intercept.Count, 1); ridge.Fit(x, Y); Assert.AreEqual(ridge.Coef.Shape(), Tuple.Create(2, nFeatures)); Assert.AreEqual(ridge.Intercept.Count, 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? }
public void TestRidgeVsLstsq() { var random = new Random(0); // we need more samples than features const int nSamples = 5; const int nFeatures = 4; var y = DenseVector.CreateRandom(nSamples, new Normal { RandomSource = random }); var x = DenseMatrix.CreateRandom(nSamples, nFeatures, new Normal { RandomSource = random }); var ridge = new RidgeRegression(alpha: 0.0, fitIntercept: false); var ols = new LinearRegression(fitIntercept: false); ridge.Fit(x, y); ols.Fit(x, y); Assert.IsTrue(ridge.Coef.AlmostEquals(ols.Coef)); ridge.Fit(x, y); ols.Fit(x, y); Assert.IsTrue(ridge.Coef.AlmostEquals(ols.Coef)); }
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 TestRidgeIntercept() { var rng = new Random(0); const int nSamples = 5; const int nFeatures = 10; Matrix x = DenseMatrix.CreateRandom(nSamples, nFeatures, new Normal { RandomSource = rng }); Vector y = DenseVector.CreateRandom(nSamples, new Normal { RandomSource = rng }); Matrix y1 = DenseMatrix.OfColumns(y.Count, 2, new[] { y, y.Add(1) }); var ridge = new RidgeRegression(); ridge.Fit(x, y); var intercept = ridge.Intercept; ridge.Fit(x, y1); Assert.AreEqual(ridge.Intercept[0], intercept[0], 1e-10); Assert.AreEqual(ridge.Intercept[1], intercept[0] + 1, 1e-10); }
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 TestRidgeSampleWeights() { var rng = new Random(0); const double alpha = 1.0; foreach (var solver in new[] { RidgeSolver.Svd, RidgeSolver.DenseCholesky, RidgeSolver.Lsqr }) { var tests = new[] { new[] { 6, 5 }, new[] { 5, 10 } }; foreach (var test in tests) { int nSamples = test[0]; int nFeatures = test[1]; Vector y = DenseVector.CreateRandom(nSamples, new Normal { RandomSource = rng }); Matrix x = DenseMatrix.CreateRandom(nSamples, nFeatures, new Normal { RandomSource = rng }); Vector sampleWeight = DenseVector.CreateRandom(nSamples, new ContinuousUniform(1, 2)); var ridgeRegression = new RidgeRegression(alpha: alpha, solver: solver, fitIntercept: false); ridgeRegression.Fit(x, y, sampleWeight); var coefs = ridgeRegression.Coef.Column(0); // Sample weight can be implemented via a simple rescaling // for the square loss var regression2 = new RidgeRegression(alpha, solver: solver, fitIntercept: false); regression2.Fit( x.MulColumnVector(sampleWeight.Sqrt()), y.PointwiseMultiply(sampleWeight.Sqrt())); var coefs2 = regression2.Coef.Column(0); Assert.IsTrue(coefs.AlmostEquals(coefs2)); } } }
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; }