Dirichlet
This notebook illustrates a regression task as a solution of the Dirichlet problem (heat diffusion with constraints).
[1]:
from IPython.display import SVG
[2]:
import numpy as np
[3]:
from sknetwork.data import karate_club, painters, movie_actor
from sknetwork.regression import Dirichlet
from sknetwork.visualization import svg_graph, svg_digraph, svg_bigraph
Graphs
[4]:
graph = karate_club(metadata=True)
adjacency = graph.adjacency
position = graph.position
labels_true = graph.labels
[5]:
# heat diffusion
dirichlet = Dirichlet()
seeds = {0: 0, 33: 1}
values = dirichlet.fit_transform(adjacency, seeds)
[6]:
image = svg_graph(adjacency, position, scores=values, seeds=seeds)
SVG(image)
[6]:
Directed graphs
[7]:
graph = painters(metadata=True)
adjacency = graph.adjacency
position = graph.position
names = graph.names
[8]:
picasso = 0
monet = 1
[9]:
dirichlet = Dirichlet()
seeds = {picasso: 0, monet: 1}
values = dirichlet.fit_transform(adjacency, seeds)
[10]:
image = svg_digraph(adjacency, position, names, scores=values, seeds=seeds)
SVG(image)
[10]:
Bipartite graphs
[11]:
graph = movie_actor(metadata=True)
biadjacency = graph.biadjacency
names_row = graph.names_row
names_col = graph.names_col
[12]:
dirichlet = Dirichlet()
[13]:
drive = 3
aviator = 9
[14]:
seeds_row = {drive: 0, aviator: 1}
dirichlet.fit(biadjacency, seeds_row)
values_row = dirichlet.values_row_
values_col = dirichlet.values_col_
[15]:
image = svg_bigraph(biadjacency, names_row, names_col, scores_row=values_row, scores_col=values_col,
seeds_row=seeds_row)
SVG(image)
[15]:
