// Return the value for vector c which best fits the linear model y = Xc.
// X is a matrix given by its row vectors: each row corresponds to a y value;
// columns within the row correspond to coefficients for parameters in c.
func Linear(y vec.Vector, X []vec.Vector) vec.Vector {
n := C.size_t(len(y)) // number of observations
p := C.size_t(len(X[0])) // number of parameters
c, gslY := C.gsl_vector_alloc(p), C.gsl_vector_alloc(n)
cov, gslX := C.gsl_matrix_alloc(p, p), C.gsl_matrix_alloc(n, p)
vecToGSL(y, gslY)
matrixToGSL(X, gslX)
chisq := C.double(0)
work := C.gsl_multifit_linear_alloc(n, p)
C.gsl_multifit_linear(gslX, gslY, c, cov, &chisq, work)
C.gsl_multifit_linear_free(work)
result := vecFromGSL(c)
C.gsl_matrix_free(gslX)
C.gsl_matrix_free(cov)
C.gsl_vector_free(gslY)
C.gsl_vector_free(c)
return result
}
// Return an ordered slice of the eigenvalues of sym, and a slice of the
// eigenvectors in the same order.
func (sym *SymmetricMatrix) Eigensystem() ([]float64, [][]float64) {
originalSize := sym.length
reduced, convert := sym.RemoveEmptyRows()
size := C.size_t(reduced.length)
eigenvalues := C.gsl_vector_alloc(size)
eigenvectors := C.gsl_matrix_alloc(size, size)
matrix := reduced.toMatrix()
work := C.gsl_eigen_symmv_alloc(size)
err := C.gsl_eigen_symmv(matrix, eigenvalues, eigenvectors, work)
if err != 0 {
// handle it
}
goEigenvalues := vectorToSlice(eigenvalues)
goEigenvectors := matrixColumnsToSlices(eigenvectors)
C.gsl_vector_free(eigenvalues)
C.gsl_matrix_free(eigenvectors)
C.gsl_matrix_free(matrix)
C.gsl_eigen_symmv_free(work)
retEigenvectors := InsertEmptyRows(goEigenvectors, convert, originalSize)
return goEigenvalues, retEigenvectors
}