public void NipalsComputeTest3_new_method()
{
// Example data from P. Geladi and B.R. Kowalski, "An example of 2-block
// predictive partial least-squares regression with simulated data",
// Analytica Chemica Acta, 185(1996) 19--32, as given by Yi Cao in his
// excellent PLS tutorial.
double[][] x =
{
new double[] { 4, 9, 6, 7, 7, 8, 3, 2 },
new double[] { 6, 15, 10, 15, 17, 22, 9, 4 },
new double[] { 8, 21, 14, 23, 27, 36, 15, 6 },
new double[] { 10, 21, 14, 13, 11, 10, 3, 4 },
new double[] { 12, 27, 18, 21, 21, 24, 9, 6 },
new double[] { 14, 33, 22, 29, 31, 38, 15, 8 },
new double[] { 16, 33, 22, 19, 15, 12, 3, 6 },
new double[] { 18, 39, 26, 27, 25, 26, 9, 8 },
new double[] { 20, 45, 30, 35, 35, 40, 15, 10 }
};
double[][] y =
{
new double[] { 1, 1 },
new double[] { 3, 1 },
new double[] { 5, 1 },
new double[] { 1, 3 },
new double[] { 3, 3 },
new double[] { 5, 3 },
new double[] { 1, 5 },
new double[] { 3, 5 },
new double[] { 5, 5 }
};
var pls = new PartialLeastSquaresAnalysis(
AnalysisMethod.Center, PartialLeastSquaresAlgorithm.NIPALS);
var regression = pls.Learn(x, y);
double[,] eYL =
{
{ 0.808248528018965, -0.588841504103759},
{ 0.588841504103759, 0.808248528018964}
};
double[,] eYS =
{
{ -2.79418006424545, -0.438814047830411 },
{ -1.17768300820752, -1.61649705603793 },
{ 0.438814047830411, -2.79418006424545 },
{ -1.61649705603793, 1.17768300820752 },
{ 0.00000000000000, 0.00000000000 },
{ 1.61649705603793, -1.17768300820752 },
{ -0.438814047830411, 2.79418006424545 },
{ 1.17768300820752, 1.61649705603793 },
{ 2.79418006424545, 0.438814047830411 }
};
double[] eProportionsX = { 0.82623088878551032, 0.17376911121448976, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00 };
double[] eProportionsY = { 0.50000000000000033, 0.50000000000000011, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00 };
double[,] aXL = pls.Predictors.FactorMatrix.ToMatrix();
double[,] aYL = pls.Dependents.FactorMatrix.ToMatrix();
double[,] aXS = pls.Predictors.Result;
double[,] aYS = pls.Dependents.Result;
double[][] aW = pls.Weights;
double[,] aB = regression.Coefficients;
double[] aC = regression.Intercepts;
for (int i = 0; i < eProportionsX.Length; i++)
{
Assert.AreEqual(eProportionsX[i], pls.Predictors.FactorProportions[i], 0.01);
Assert.AreEqual(eProportionsY[i], pls.Dependents.FactorProportions[i], 0.01);
}
for (int i = 0; i < eYL.GetLength(0); i++)
for (int j = 0; j < eYL.GetLength(1); j++)
Assert.AreEqual(aYL[i, j], eYL[i, j], 0.01);
for (int i = 0; i < eYS.GetLength(0); i++)
for (int j = 0; j < eYS.GetLength(1); j++)
Assert.AreEqual(aYS[i, j], eYS[i, j], 0.01);
}