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Contents
# frozen_string_literal: true
require 'rumale/base/estimator'
require 'rumale/base/classifier'
require 'rumale/validation'
module Rumale
module NaiveBayes
# BaseNaiveBayes is a class that has methods for common processes of naive bayes classifier.
# This class is used internally.
class BaseNaiveBayes < ::Rumale::Base::Estimator
include ::Rumale::Base::Classifier
def initialize # rubocop:disable Lint/UselessMethodDefinition
super()
end
# Predict class labels for samples.
#
# @param x [Numo::DFloat] (shape: [n_samples, n_features]) The samples to predict the labels.
# @return [Numo::Int32] (shape: [n_samples]) Predicted class label per sample.
def predict(x)
x = ::Rumale::Validation.check_convert_sample_array(x)
n_samples = x.shape.first
decision_values = decision_function(x)
Numo::Int32.asarray(Array.new(n_samples) { |n| @classes[decision_values[n, true].max_index] })
end
# Predict log-probability for samples.
#
# @param x [Numo::DFloat] (shape: [n_samples, n_features]) The samples to predict the log-probailities.
# @return [Numo::DFloat] (shape: [n_samples, n_classes]) Predicted log-probability of each class per sample.
def predict_log_proba(x)
x = ::Rumale::Validation.check_convert_sample_array(x)
n_samples, = x.shape
log_likelihoods = decision_function(x)
log_likelihoods - Numo::NMath.log(Numo::NMath.exp(log_likelihoods).sum(axis: 1)).reshape(n_samples, 1)
end
# Predict probability for samples.
#
# @param x [Numo::DFloat] (shape: [n_samples, n_features]) The samples to predict the probailities.
# @return [Numo::DFloat] (shape: [n_samples, n_classes]) Predicted probability of each class per sample.
def predict_proba(x)
x = ::Rumale::Validation.check_convert_sample_array(x)
Numo::NMath.exp(predict_log_proba(x)).abs
end
end
end
end
Version data entries
7 entries across 7 versions & 1 rubygems