狄利克雷

本笔记本说明了将回归任务作为狄利克雷问题（带约束的热扩散）的解决方案。

[1]:

from IPython.display import SVG

[2]:

from sknetwork.data import karate_club, painters, movie_actor
from sknetwork.regression import Dirichlet
from sknetwork.visualization import visualize_graph, visualize_bigraph

图

[3]:

graph = karate_club(metadata=True)
adjacency = graph.adjacency
position = graph.position
labels_true = graph.labels

[4]:

# heat diffusion
dirichlet = Dirichlet()
values = {0: 0, 33: 1}
values_pred = dirichlet.fit_predict(adjacency, values)

[5]:

image = visualize_graph(adjacency, position, scores=values_pred, seeds=values)
SVG(image)

[5]:

../../_images/tutorials_regression_dirichlet_7_0.svg

有向图

[6]:

graph = painters(metadata=True)
adjacency = graph.adjacency
position = graph.position
names = graph.names

[7]:

picasso = 0
monet = 1

[8]:

dirichlet = Dirichlet()
values = {picasso: 0, monet: 1}
values_pred = dirichlet.fit_predict(adjacency, values)

[9]:

image = visualize_graph(adjacency, position, names, scores=values_pred, seeds=values)
SVG(image)

[9]:

../../_images/tutorials_regression_dirichlet_12_0.svg

二部图

[10]:

graph = movie_actor(metadata=True)
biadjacency = graph.biadjacency
names_row = graph.names_row
names_col = graph.names_col

[11]:

dirichlet = Dirichlet()

[12]:

drive = 3
aviator = 9

[13]:

values_row = {drive: 0, aviator: 1}
dirichlet.fit(biadjacency, values_row)
values_row_pred = dirichlet.values_row_
values_col_pred = dirichlet.values_col_

[14]:

image = visualize_bigraph(biadjacency, names_row, names_col, scores_row=values_row_pred, scores_col=values_col_pred,
                    seeds_row=values_row)
SVG(image)

[14]:

../../_images/tutorials_regression_dirichlet_18_0.svg