#pragma warning restore 612, 618 /// <summary> /// Applies the transformation to an input, producing an associated output. /// </summary> /// <param name="input">The input data to which the transformation should be applied.</param> /// <returns> /// The output generated by applying this transformation to the given input. /// </returns> public override double Transform(double input) { var polynomial = new double[this.Degree]; for (int j = 0; j < polynomial.Length; j++) { polynomial[j] = Math.Pow(input, this.Degree - j); } return(regression.Transform(polynomial)); }
public double Compute(double input) { // Creates the polynomial int order = this.Degree + 1; double[] polynomial = new double[order]; for (int j = 0; j < order; j++) { polynomial[j] = Math.Pow(input, order - j - 1); } return(regression.Transform(polynomial)); }
public void RegressTest7() { double[][] example2 = { new double[] { -0.47, 1.16, -1.25 }, new double[] { 0.55, 1.15, -0.78 }, new double[] { 1.38, 0.63, -0.84 }, new double[] { 0.99, 0.63, -0.81 }, new double[] { 1.72, 0.62, -1.59 }, new double[] { 1.05, 0.62, -1.05 }, new double[] { -0.51, 0.62, -0.98 }, new double[] { 1.83, 0.61, 0.86 }, new double[] { 1.16, 0.61, 0.15 }, new double[] { 0.59, 0.61, -0.28 }, new double[] { 0.40, 0.60, -0.30 }, new double[] { 0.48, 0.60, -0.41 }, new double[] { 1.28, 0.53, -0.31 }, new double[] { 0.36, 0.53, -0.41 }, new double[] { 0.93, 0.16, -0.19 }, new double[] { -0.61, 0.16, -0.32 }, new double[] { -0.58, 0.16, -0.01 }, new double[] { 0.53, 0.16, -0.13 }, new double[] { 1.48, 0.16, 1.12 }, new double[] { -0.34, 0.15, -0.10 }, new double[] { 0.81, 0.15, 0.14 }, new double[] { 0.85, 0.15, -0.02 }, new double[] { 0.69, 0.15, -0.16 }, new double[] { 0.39, 0.15, -0.33 }, new double[] { 0.70, 0.00, 2.00 }, new double[] { 0.25, 0.00, -0.01 }, new double[] { -0.96, 0.85, -0.19 }, new double[] { 1.04, 0.84, 0.35 }, new double[] { 0.30, 0.83, 0.05 }, new double[] { 0.28, 0.83, 0.84 }, new double[] { 0.18, 0.82, 0.06 }, new double[] { 0.49, 0.81, 0.41 }, new double[] { 0.40, 0.81, 0.50 }, new double[] { 0.41, 0.80, 0.00 }, new double[] { 0.06, 0.79, 0.39 }, new double[] { 0.55, 0.79, 0,55 }, }; double[][] inputs = example2.GetColumns(new[] { 1, 2 }); double[] outputs = example2.GetColumn(0); bool thrown = false; MultipleLinearRegressionAnalysis target; try { target = new MultipleLinearRegressionAnalysis(inputs, outputs, new string[0], "Test", false); } catch (ArgumentException) { thrown = true; } Assert.IsTrue(thrown); target = new MultipleLinearRegressionAnalysis(inputs, outputs, new string[2], "Test", false); target.Compute(); Assert.AreEqual(2, target.NumberOfInputs); Assert.AreEqual(1, target.NumberOfOutputs); Assert.AreEqual(target.Array, inputs); Assert.AreEqual(target.Outputs, outputs); Assert.AreEqual(-0.19371930561139417, target.RSquared, 1e-5); Assert.AreEqual(-0.26606593019390279, target.RSquareAdjusted, 1e-5); Assert.AreEqual(2, target.Table[0].DegreesOfFreedom); Assert.AreEqual(33, target.Table[1].DegreesOfFreedom); Assert.AreEqual(35, target.Table[2].DegreesOfFreedom); Assert.AreEqual(-2.9165797494934651, target.Table[0].SumOfSquares, 1e-10); Assert.AreEqual(17.972279749493463, target.Table[1].SumOfSquares, 1e-10); Assert.AreEqual(15.055699999999998, target.Table[2].SumOfSquares, 1e-10); Assert.IsFalse(Double.IsNaN(target.Table[0].SumOfSquares)); Assert.IsFalse(Double.IsNaN(target.Table[1].SumOfSquares)); Assert.IsFalse(Double.IsNaN(target.Table[2].SumOfSquares)); Assert.AreEqual(-1.4582898747467326, target.Table[0].MeanSquares, 1e-10); Assert.AreEqual(0.54461453786343827, target.Table[1].MeanSquares, 1e-10); Assert.IsFalse(Double.IsNaN(target.Table[0].MeanSquares)); Assert.IsFalse(Double.IsNaN(target.Table[1].MeanSquares)); Assert.AreEqual(-2.6776550630978524, target.Table[0].Statistic.Value, 1e-10); Assert.AreEqual(1, target.Table[0].Significance.PValue, 1e-16); Assert.IsFalse(Double.IsNaN(target.Table[0].Significance.PValue)); Assert.AreEqual(0.72195200211671728, target.Coefficients[0].Value, 1e-10); Assert.AreEqual(0.15872233321508125, target.Coefficients[1].Value, 1e-10); Assert.IsFalse(Double.IsNaN(target.Coefficients[0].Value)); Assert.IsFalse(Double.IsNaN(target.Coefficients[1].Value)); Assert.IsFalse(target.Coefficients[0].IsIntercept); Assert.IsFalse(target.Coefficients[1].IsIntercept); Assert.AreEqual(0.20506051379737225, target.Coefficients[0].StandardError, 1e-10); Assert.AreEqual(0.18842330299464302, target.Coefficients[1].StandardError, 1e-10); Assert.IsFalse(Double.IsNaN(target.Coefficients[0].StandardError)); Assert.IsFalse(Double.IsNaN(target.Coefficients[1].StandardError)); Assert.AreEqual(3.5206778172325479, target.Coefficients[0].TTest.Statistic, 1e-10); Assert.AreEqual(0.84237103740609942, target.Coefficients[1].TTest.Statistic, 1e-10); Assert.IsFalse(Double.IsNaN(target.Coefficients[0].TTest.Statistic)); Assert.IsFalse(Double.IsNaN(target.Coefficients[1].TTest.Statistic)); DoubleRange range; range = target.Coefficients[0].TTest.GetConfidenceInterval(0.9); Assert.AreEqual(0.37491572761667513, range.Min, 1e-10); Assert.AreEqual(1.0689882766167593, range.Max, 1e-10); range = target.Coefficients[1].TTest.GetConfidenceInterval(0.9); Assert.AreEqual(-0.16015778606945111, range.Min, 1e-10); Assert.AreEqual(0.47760245249961364, range.Max, 1e-10); MultipleLinearRegression mlr = new MultipleLinearRegression(2, false); mlr.Regress(inputs, outputs); Assert.AreEqual(2, target.NumberOfInputs); Assert.AreEqual(1, target.NumberOfOutputs); double[] actual = target.Transform(inputs); double[] expected = mlr.Transform(inputs); Assert.IsTrue(Matrix.IsEqual(actual, expected, 1e-8)); double r2 = mlr.CoefficientOfDetermination(inputs, outputs, false); double r2a = mlr.CoefficientOfDetermination(inputs, outputs, true); Assert.AreEqual(r2, target.RSquared, 1e-6); Assert.AreEqual(r2a, target.RSquareAdjusted, 1e-6); }
public void RegressTest3() { // We will try to model a plane as an equation in the form // "ax + by + c = z". We have two input variables (x and y) // and we will be trying to find two parameters a and b and // an intercept term c. // Create a multiple linear regression for two input and an intercept var target = new MultipleLinearRegression(2, true); // Now suppose you have some points double[][] inputs = { new double[] { 1, 1 }, new double[] { 0, 1 }, new double[] { 1, 0 }, new double[] { 0, 0 }, }; // located in the same Z (z = 1) double[] outputs = { 1, 1, 1, 1 }; // Now we will try to fit a regression model double error = target.Regress(inputs, outputs); Assert.AreEqual(2, target.NumberOfInputs); Assert.AreEqual(1, target.NumberOfOutputs); // As result, we will be given the following: double a = target.Coefficients[0]; // a = 0 double b = target.Coefficients[1]; // b = 0 double c = target.Coefficients[2]; // c = 1 // This is the plane described by the equation // ax + by + c = z => 0x + 0y + 1 = z => 1 = z. Assert.AreEqual(0.0, a, 1e-6); Assert.AreEqual(0.0, b, 1e-6); Assert.AreEqual(1.0, c, 1e-6); Assert.AreEqual(0.0, error, 1e-6); double[] expected = target.Compute(inputs); double[] actual = target.Transform(inputs); Assert.IsTrue(expected.IsEqual(actual, 1e-10)); double r = target.CoefficientOfDetermination(inputs, outputs); Assert.AreEqual(1.0, r); }