public SimpleLinearRegression Regress(double[] inputs, double[] outputs)
{
var newInputs = new double[inputs.Length][];
for (int i = 0; i < inputs.Length; i++)
{
newInputs[i] = new double[] { inputs[i] };
}
var result = _multipleLinearRegression.Regress(newInputs, outputs);
_linearRegression = result.LinearRegression;
return(this);
}
public MultipleLinearRegression Regress(Matrix matrix, Vector output, bool addOne = true)
{
if (addOne)
{
matrix = matrix.AddColumn(1);
}
output.SetVertical();
if (_alg == MatrixDecompositionAlgs.NAIVE)
{
_singularValueDecomposition.DecomposeNaive(matrix);
}
else
{
_singularValueDecomposition.DecomposeGolubReinsch(matrix);
}
var coefficients = _singularValueDecomposition.Inverse()
.Multiply((Matrix)output)
.Evaluate()
.GetColumnVector(0);
var result = new LinearRegressionResult();
for (int i = 0; i < coefficients.Length; i++)
{
var coefficient = coefficients[i].GetNumber();
var coefficientResult = new CoefficientResult
{
Value = coefficient
};
if (i == 0)
{
result.Intercept = coefficientResult;
}
else
{
result.SlopeLst.Add(coefficientResult);
}
}
_linearRegressionResult = result;
UpdateInformation(matrix, output, addOne);
return(this);
}