kde_scikitlearn#

agabpylib.densityestimation.kde.kde_scikitlearn(data, N=100, lims=None, evalOnData=False, kde_bandwidth=1.0, **kwargs)#

Provide a kernel density estimate for a set of data points (d_i).

Make use of the sklearn.neighbours.KernelDensity class.

Parameters:

  • data (float array) – 1D array of values of d_i

  • lims (tuple) – Limits on data to use (dmin, dmax)

  • N (int) – Number of KDE samples in d (regular grid between dmin and dmax)

  • evalOndata (boolean) – If true return the log(density) evaluated on the data (instead of the regular grid)

  • kde_bandwidth (float) – Bandwith for density estimator

  • **kwargs (dict) – Extra arguments for KernelDensity class initializer

Returns:

Dsamples, log_dens – Dsamples, log_dens: The log(density) evaluated on the regular grid Dsamples (both shape (N,)) If evalOnData is True return only log_dens evaluated for the data points (shape (data.size,)).

Return type:

float array