Distributions

Login Register

SkewNorm_gen.nnlf()

statsmodels.sandbox.distributions.extras.SkewNorm_gen.nnlf

2017-01-18 16:16:31

ACSkewT_gen.var()

statsmodels.sandbox.distributions.extras.ACSkewT_gen.var

2017-01-18 16:06:28

Transf_gen.logsf()

statsmodels.sandbox.distributions.transformed.Transf_gen.logsf

2017-01-18 16:20:41

SkewNorm2_gen.cdf()

statsmodels.sandbox.distributions.extras.SkewNorm2_gen.cdf

2017-01-18 16:16:16

TransfTwo_gen.freeze()

statsmodels.sandbox.distributions.transformed.TransfTwo_gen.freeze

2017-01-18 16:20:30

ExpTransf_gen.est_loc_scale()

statsmodels.sandbox.distributions.transformed.ExpTransf_gen.est_loc_scale

2017-01-18 16:08:37

TransfTwo_gen.var()

statsmodels.sandbox.distributions.transformed.TransfTwo_gen.var

2017-01-18 16:20:36

ExpTransf_gen.rvs()

statsmodels.sandbox.distributions.transformed.ExpTransf_gen.rvs

2017-01-18 16:08:43

SkewNorm_gen.stats()

statsmodels.sandbox.distributions.extras.SkewNorm_gen.stats

2017-01-18 16:16:32

ExpTransf_gen.ppf()

statsmodels.sandbox.distributions.transformed.ExpTransf_gen.ppf

2017-01-18 16:08:43

Popular Articles

neighbors.LSHForest() class sklearn.neighbors.LSHForest(n_estimators=10, radius=1.0, n_candidates=50, n_neighbors=5, min_hash_match=4, radius_cutoff_ratio=0.9, random_st

How to parallelize loops How to parallelize loops In image processing, we frequently apply the same algorithm on a large batch of images. In this paragraph, we propose to use

projections projections matplotlib.projections class matplotlib.projections.ProjectionRegistry Bases: object Manages the set of projections available to the

API Reference This is the class and function reference of scikit-learn. Please refer to the full user guide for further details, as the class and function raw spe

sklearn.feature_selection.mutual_info_classif() sklearn.feature_selection.mutual_info_classif(X, y, discrete_features='auto', n_neighbors=3, copy=True, random_state=None) [source] Estimate mutu

Page 21 of 22

|< < Prev 18 19 20 21 22 Next > >|