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# frozen_string_literal: true
require 'rumale/base/evaluator'
module Rumale
module EvaluationMeasure
# CalinskiHarabaszScore is a class that calculates the Calinski and Harabasz score.
#
# @example
# evaluator = Rumale::EvaluationMeasure::CalinskiHarabaszScore.new
# puts evaluator.score(x, predicted)
#
# *Reference*
# - T. Calinski and J. Harabsz, "A dendrite method for cluster analysis," Communication in Statistics, Vol. 3 (1), pp. 1--27, 1972.
class CalinskiHarabaszScore
include Base::Evaluator
# Calculates the Calinski and Harabasz 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 Calinski and Harabasz score.
def score(x, y)
x = check_convert_sample_array(x)
y = check_convert_label_array(y)
check_sample_label_size(x, y)
labels = y.to_a.uniq.sort
n_clusters = labels.size
n_dimensions = x.shape[1]
centroids = Numo::DFloat.zeros(n_clusters, n_dimensions)
within_group = 0.0
n_clusters.times do |n|
cls_samples = x[y.eq(labels[n]), true]
cls_centroid = cls_samples.mean(0)
centroids[n, true] = cls_centroid
within_group += ((cls_samples - cls_centroid)**2).sum
end
return 1.0 if within_group.zero?
mean_vec = x.mean(0)
between_group = 0.0
n_clusters.times do |n|
sz_cluster = y.eq(labels[n]).count
between_group += sz_cluster * ((centroids[n, true] - mean_vec)**2).sum
end
n_samples = x.shape[0]
(between_group / (n_clusters - 1)) / (within_group / (n_samples - n_clusters))
end
end
end
end
Version data entries
19 entries across 19 versions & 1 rubygems