Index

A

AUASE() (in module pyemb.embedding), [1]

B

branch_lengths() (in module pyemb.hc), [1]

C

children_ (pyemb.hc.DotProductAgglomerativeClustering attribute), [1] collapsed_branches (pyemb.hc.ConstructTree attribute), [1]

ConstructTree (class in pyemb.hc), [1] cophenetic_distances() (in module pyemb.hc), [1]

D

degree_correction() (in module pyemb.tools), [1] distances_ (pyemb.hc.DotProductAgglomerativeClustering attribute), [1]

DotProductAgglomerativeClustering (class in pyemb.hc), [1] dyn_embed() (in module pyemb.embedding), [1]

E

eigen_decomp() (in module pyemb.embedding), [1]

embed() (in module pyemb.embedding), [1] epsilon (pyemb.hc.ConstructTree attribute), [1]

F

find_connected_components() (in module pyemb.preprocessing), [1] find_descendents() (in module pyemb.hc), [1]

find_subgraph() (in module pyemb.preprocessing), [1] fit() (pyemb.hc.ConstructTree method), [1] (pyemb.hc.DotProductAgglomerativeClustering method), [1]

G

get_fig_legend_handles_labels() (in module pyemb.plotting), [1]

get_ranking() (in module pyemb.hc), [1] graph_from_dataframes() (in module pyemb.preprocessing), [1]

I

iid_SBM() (in module pyemb.simulation), [1]

ISE() (in module pyemb.embedding), [1]

K

kendalltau_similarity() (in module pyemb.hc), [1]

L

labels_ (pyemb.hc.DotProductAgglomerativeClustering attribute), [1] largest_cc_of() (in module pyemb.preprocessing), [1] linkage (pyemb.hc.ConstructTree attribute), [1]

linkage_matrix() (in module pyemb.hc), [1] load_lyon() (in module pyemb.datasets), [1] load_newsgroup() (in module pyemb.datasets), [1] load_planaria() (in module pyemb.datasets), [1]

M

model (pyemb.hc.ConstructTree attribute), [1] module pyemb pyemb.datasets, [1] pyemb.embedding, [1] pyemb.hc, [1] pyemb.plotting, [1] pyemb.preprocessing, [1] pyemb.simulation, [1] pyemb.temp pyemb.tools, [1]

N

n_clusters_ (pyemb.hc.DotProductAgglomerativeClustering attribute), [1] n_connected_components_ (pyemb.hc.DotProductAgglomerativeClustering attribute), [1]

n_features_in_ (pyemb.hc.DotProductAgglomerativeClustering attribute), [1] n_leaves_ (pyemb.hc.DotProductAgglomerativeClustering attribute), [1]

O

OMNI() (in module pyemb.embedding), [1]

P

plot() (pyemb.hc.ConstructTree method), [1] plot_dendrogram() (in module pyemb.hc), [1] point_cloud (pyemb.hc.ConstructTree attribute), [1] pyemb module pyemb.datasets module, [1] pyemb.embedding module, [1] pyemb.hc module, [1]

pyemb.plotting module, [1] pyemb.preprocessing module, [1] pyemb.simulation module, [1] pyemb.temp module pyemb.tools module, [1]

Q

quick_plot() (in module pyemb.plotting), [1]

R

recover_subspaces() (in module pyemb.tools), [1]

regularised_ULSE() (in module pyemb.embedding), [1]

S

sample_hyperbolicity() (in module pyemb.hc), [1] SBM() (in module pyemb.simulation), [1] select() (in module pyemb.tools), [1]

snapshot_plot() (in module pyemb.plotting), [1] (in module pyemb.temp) symmetrises() (in module pyemb.simulation), [1]

T

time_series_matrix_and_attributes() (in module pyemb.preprocessing), [1] to_laplacian() (in module pyemb.tools), [1]

to_networkx() (in module pyemb.preprocessing), [1] tree (pyemb.hc.ConstructTree attribute), [1]

U

UASE() (in module pyemb.embedding), [1]

V

varimax() (in module pyemb.tools), [1]