#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);
}