smautner / ubergauss

knee point detection, sigma plot, 1d 'bayesian' optimization

ubergauss

kneepoint detection (using 2 gaussians or max dist to diagonal) and sklearn gmm wrapper

import ubergauss as ug

# kneepoint gaussians:
>>>ug.between_gaussians([.1,.1,.2,.2,.4,.7])
4


# max dist to diagonal
>>>ug.diag_maxdist([.1,.1,.2,.2,.4,.7])
3


# trian gmm
ug.get_model(X, poolsize = -1,
                nclust_min = 4,
                nclust_max = 20,
                n_init = 30,
                covariance_type = 'tied',
                kneepoint_detection = diag_maxdist,
                **kwargs)

sigma boxplot

optimization:

blackboxBORE is the one that works best