GaussianKdeFitter

Class for fitting a gaussian kernel density to a point cloud

Basically a wrapper for scipy.stats.gaussian_kde.
A gaussian kernel density can be thought of as a sum of little (potentially multidimensional) gaussians, each one centered at one of the points in the cloud. Hence, the resulting distribution is a sort of smoothed version of the discrete point cloud.

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[class] GaussianKdeFitter

comments:

class for fitting a gaussian kernel density to a point cloud  
basically a wrapper for scipy.stats.gaussian_kde.  
parameters  
- kernel: scipy.stats.gaussian_kde object  
- cov: covariance matrix   
(use np.cov for now, maybe later replace by internal kernel.covariance)  

⤷ __init__

full signature:

def __init__(self)  

comments:

empty constructor  

⤷ fit

full signature:

def fit(self, points, bw_method='scott', bw_scott_factor=None)  

comments:

fit to a set of points  
input arguments:  
- points: a np array of shape (npoints,ndims)  
- bw_method: method to calculate the bandwidth of the gaussians,  
  see https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.gaussian_kde.html  
- bw_scott_factor: additional multiplication factor applied to bandwidth in case it is set to 'scott'  

⤷ pdf

full signature:

def pdf(self,points)  

comments:

get pdf at points  

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