Animals
Table of contents
Animal dataset
In this animal dataset (B. M. Lake an J. B. Tenenbaum 2010), there are 102 features which are binary answers to questions such as “Does it has feathers?”, “Is it warm-blooded?”, etc. There are in total 33 animals to be clustered.
library(viridis)
library(spectralGraphTopology)
library(igraph)
# read data
df <- read.csv("animals.txt", header = FALSE)
names <- matrix(unlist(read.csv("animals_names.txt", header = FALSE)))
Y <- matrix(as.numeric(unlist(df)), nrow = nrow(df))
n <- nrow(Y)
# estimate graph
graph <- learn_k_component_graph(cov(t(Y)) + diag(1/3, n, n), w0 = "qp",
beta = 1, k = 10, verbose = FALSE)
# build network
net <- graph_from_adjacency_matrix(graph$adjacency, mode = "undirected", weighted = TRUE)
# colorify edges
colors <- viridis(50, begin = 0, end = 1, direction = -1)
c_scale <- colorRamp(colors)
E(net)$color = apply(c_scale(abs(E(net)$weight) / max(abs(E(net)$weight))), 1,
function(x) rgb(x[1]/255, x[2]/255, x[3]/255))
V(net)$color = "pink"
# plot network
plot(net, vertex.label = names,
vertex.size = 4,
vertex.label.dist = 1,
vertex.label.family = "Helvetica",
vertex.label.cex = .8,
vertex.label.color = "black")
- Lake, Brendan and Joshua Tenenbaum. “Discovering Structure by Learning Sparse Graphs.” Proceedings of the 32nd Annual Meeting of the Cognitive Science Society CogSci 2010, Portland, Oregon, United States, 11-14 August, 2010, Cognitive Science Society, Inc., 2010. pp. 778-784.