freealg.kde#
- freealg.kde(eig, xs, lam_m, lam_p, h, kernel='beta', plot=False)#
Kernel density estimation of eigenvalues.
- Parameters:
- eignumpy.array
1D array of samples of size n.
- xsnumpy.array
1D array of evaluation grid (must lie within
[lam_m, lam_p])- lam_mfloat
Lower end of the support endpoints with
lam_m < lam_p.- lam_pfloat
Upper end of the support endpoints with
lam_m < lam_p.- hfloat
Kernel bandwidth in rescaled units where
0 < h < 1.- kernel{
'gaussian','beta'}, default='beta' Kernel function using either Gaussian or Beta distribution.
- plotbool, default=False
If True, the KDE is plotted.
- Returns:
- pdfnumpy.ndarray
Probability distribution function with the same length as
xs.
See also