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Contents
# frozen_string_literal: true
require 'svmkit/validation'
require 'svmkit/base/evaluator'
module SVMKit
# This module consists of the classes for model evaluation.
module EvaluationMeasure
# Accuracy is a class that calculates the accuracy of classifier from the predicted labels.
#
# @example
# evaluator = SVMKit::EvaluationMeasure::Accuracy.new
# puts evaluator.score(ground_truth, predicted)
class Accuracy
include Base::Evaluator
# Calculate mean accuracy.
#
# @param y_true [Numo::Int32] (shape: [n_samples]) Ground truth labels.
# @param y_pred [Numo::Int32] (shape: [n_samples]) Predicted labels.
# @return [Float] Mean accuracy
def score(y_true, y_pred)
SVMKit::Validation.check_label_array(y_true)
SVMKit::Validation.check_label_array(y_pred)
(y_true.to_a.map.with_index { |label, n| label == y_pred[n] ? 1 : 0 }).inject(:+) / y_true.size.to_f
end
end
end
end
Version data entries
17 entries across 17 versions & 1 rubygems