public void Create_RebonatoJaeckelExample2_BenchmarkResult()
{
int n = 3;
var rawCorrelationMatrix = new DenseMatrix(n, n, new[] { 1.0, 0.9, 0.7, 0.9, 1.0, 0.3, 0.7, 0.3, 1.0 });
OrdinaryMultiDimOptimizer multiDimOptimizer = new PowellOptimizer();
var matrixDecomposer = new SapMatrixDecomposer(multiDimOptimizer);
PseudoSqrtMatrixDecomposer.State state;
var b = matrixDecomposer.Create(rawCorrelationMatrix, out state);
int expectedRank = 2;
Assert.That(state.Rank, Is.EqualTo(expectedRank), String.Format("Rank should be {0}, but was {1}.", expectedRank, state.Rank));
var actual = b * b.T;
var expected = new DenseMatrix(n, n, new[] {
1.0, 0.89458, 0.69662,
0.89458, 1.0, 0.30254,
0.69662, 0.30254, 1.0
});
Assert.That(actual.Data, Is.EqualTo(expected.Data).AsCollection.Within(1E-4));
}
public void Create_RebonatoJaeckelExample1_BenchmarkResult()
{
int n = 3;
var rawCorrelationMatrix = new DenseMatrix(n, n, new[] {
1.0, 0.9, 0.7,
0.9, 1.0, 0.4,
0.7, 0.4, 1.0
});
OrdinaryMultiDimOptimizer multiDimOptimizer = new PowellOptimizer();
var matrixDecomposer = new SapMatrixDecomposer(multiDimOptimizer);
PseudoSqrtMatrixDecomposer.State state;
var actual = matrixDecomposer.Create(rawCorrelationMatrix, out state);
Assert.That(state.Rank, Is.EqualTo(n), String.Format("Rank should be {0}, but was {1}.", n, state.Rank));
var expected = new DenseMatrix(n, n, new[] { // the values taken from the reference are re-ordered and some columns are multiplied with -1.0
0.13192, -0.10021, -0.05389,
-0.08718, -0.45536, 0.63329,
-0.98742, -0.88465, -0.77203
});
Assert.That(actual.Data, Is.EqualTo(expected.Data).AsCollection.Within(1E-4));
}