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# frozen_string_literal: true
require 'rumale/base/evaluator'
require 'rumale/pairwise_metric'
module Rumale
module EvaluationMeasure
# DaviesBouldinScore is a class that calculates the Davies-Bouldin score.
#
# @example
# require 'rumale/evaluation_measure/davies_bouldin_score'
#
# evaluator = Rumale::EvaluationMeasure::DaviesBouldinScore.new
# puts evaluator.score(x, predicted)
#
# *Reference*
# - Davies, D L., and Bouldin, D W., "A Cluster Separation Measure," IEEE Trans. Pattern Analysis and Machine Intelligence, Vol. PAMI-1, No. 2, pp. 224--227, 1979.
class DaviesBouldinScore
# Calculates the Davies-Bouldin score.
#
# @param x [Numo::DFloat] (shape: [n_samples, n_features]) The samples to be used for calculating score.
# @param y [Numo::Int32] (shape: [n_samples]) The predicted labels for each sample.
# @return [Float] The Davies-Bouldin score.
def score(x, y)
labels = y.to_a.uniq.sort
n_clusters = labels.size
n_dimensions = x.shape[1]
dist_cluster = Numo::DFloat.zeros(n_clusters)
centroids = Numo::DFloat.zeros(n_clusters, n_dimensions)
n_clusters.times do |n|
cls_samples = x[y.eq(labels[n]), true]
cls_centroid = cls_samples.mean(0)
centroids[n, true] = cls_centroid
dist_cluster[n] = Rumale::PairwiseMetric.euclidean_distance(cls_samples, cls_centroid.expand_dims(0)).mean
end
dist_centroid = Rumale::PairwiseMetric.euclidean_distance(centroids)
dist_centroid[dist_centroid.eq(0)] = Float::INFINITY
dist_mat = (dist_cluster.expand_dims(1) + dist_cluster) / dist_centroid
dist_mat[dist_mat.diag_indices] = -Float::INFINITY
dist_mat.max(0).mean
end
end
end
end
Version data entries
7 entries across 7 versions & 1 rubygems