redux-scraper/sorbet/rbi/gems/rumale-naive_bayes@1.0.0.rbi

# typed: true

# DO NOT EDIT MANUALLY
# This is an autogenerated file for types exported from the `rumale-naive_bayes` gem.
# Please instead update this file by running `bin/tapioca gem rumale-naive_bayes`.


# Rumale is a machine learning library in Ruby.
#
# source://rumale-naive_bayes//lib/rumale/naive_bayes/base_naive_bayes.rb#7
module Rumale; end

# This module consists of the classes that implement naive bayes models.
#
# source://rumale-naive_bayes//lib/rumale/naive_bayes/base_naive_bayes.rb#8
module Rumale::NaiveBayes; end

# BaseNaiveBayes is a class that has methods for common processes of naive bayes classifier.
# This class is used internally.
#
# source://rumale-naive_bayes//lib/rumale/naive_bayes/base_naive_bayes.rb#11
class Rumale::NaiveBayes::BaseNaiveBayes < ::Rumale::Base::Estimator
  include ::Rumale::Base::Classifier

  # @return [BaseNaiveBayes] a new instance of BaseNaiveBayes
  #
  # source://rumale-naive_bayes//lib/rumale/naive_bayes/base_naive_bayes.rb#14
  def initialize; 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.
  #
  # source://rumale-naive_bayes//lib/rumale/naive_bayes/base_naive_bayes.rb#22
  def predict(x); 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.
  #
  # source://rumale-naive_bayes//lib/rumale/naive_bayes/base_naive_bayes.rb#34
  def predict_log_proba(x); 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.
  #
  # source://rumale-naive_bayes//lib/rumale/naive_bayes/base_naive_bayes.rb#46
  def predict_proba(x); end
end

# BernoulliNB is a class that implements Bernoulli Naive Bayes classifier.
#
# *Reference*
# - Manning, C D., Raghavan, P., and Schutze, H., "Introduction to Information Retrieval," Cambridge University Press., 2008.
#
# @example
#   require 'rumale/naive_bayes/bernoulli_nb'
#
#   estimator = Rumale::NaiveBayes::BernoulliNB.new(smoothing_param: 1.0, bin_threshold: 0.0)
#   estimator.fit(training_samples, training_labels)
#   results = estimator.predict(testing_samples)
#
# source://rumale-naive_bayes//lib/rumale/naive_bayes/bernoulli_nb.rb#18
class Rumale::NaiveBayes::BernoulliNB < ::Rumale::NaiveBayes::BaseNaiveBayes
  # Create a new classifier with Bernoulli Naive Bayes.
  #
  # @param smoothing_param [Float] The Laplace smoothing parameter.
  # @param bin_threshold [Float] The threshold for binarizing of features.
  # @return [BernoulliNB] a new instance of BernoulliNB
  #
  # source://rumale-naive_bayes//lib/rumale/naive_bayes/bernoulli_nb.rb#35
  def initialize(smoothing_param: T.unsafe(nil), bin_threshold: T.unsafe(nil)); end

  # Return the prior probabilities of the classes.
  #
  # @return [Numo::DFloat] (shape: [n_classes])
  #
  # source://rumale-naive_bayes//lib/rumale/naive_bayes/bernoulli_nb.rb#25
  def class_priors; end

  # Return the class labels.
  #
  # @return [Numo::Int32] (size: n_classes)
  #
  # source://rumale-naive_bayes//lib/rumale/naive_bayes/bernoulli_nb.rb#21
  def classes; end

  # Calculate confidence scores for samples.
  #
  # @param x [Numo::DFloat] (shape: [n_samples, n_features]) The samples to compute the scores.
  # @return [Numo::DFloat] (shape: [n_samples, n_classes]) Confidence scores per sample for each class.
  #
  # source://rumale-naive_bayes//lib/rumale/naive_bayes/bernoulli_nb.rb#71
  def decision_function(x); end

  # Return the conditional probabilities for features of each class.
  #
  # @return [Numo::DFloat] (shape: [n_classes, n_features])
  #
  # source://rumale-naive_bayes//lib/rumale/naive_bayes/bernoulli_nb.rb#29
  def feature_probs; end

  # Fit the model with given training data.
  #
  # @param x [Numo::DFloat] (shape: [n_samples, n_features]) The training data to be used for fitting the model.
  # @param y [Numo::Int32] (shape: [n_samples]) The categorical variables (e.g. labels)
  #   to be used for fitting the model.
  # @return [BernoulliNB] The learned classifier itself.
  #
  # source://rumale-naive_bayes//lib/rumale/naive_bayes/bernoulli_nb.rb#49
  def fit(x, y); end
end

# ComplementNB is a class that implements Complement Naive Bayes classifier.
#
# *Reference*
# - Rennie, J. D. M., Shih, L., Teevan, J., and Karger, D. R., "Tackling the Poor Assumptions of Naive Bayes Text Classifiers," ICML' 03, pp. 616--623, 2013.
#
# @example
#   require 'rumale/naive_bayes/complement_nb'
#
#   estimator = Rumale::NaiveBayes::ComplementNB.new(smoothing_param: 1.0)
#   estimator.fit(training_samples, training_labels)
#   results = estimator.predict(testing_samples)
#
# source://rumale-naive_bayes//lib/rumale/naive_bayes/complement_nb.rb#18
class Rumale::NaiveBayes::ComplementNB < ::Rumale::NaiveBayes::BaseNaiveBayes
  # Create a new classifier with Complement Naive Bayes.
  #
  # @param smoothing_param [Float] The smoothing parameter.
  # @param norm [Boolean] The flag indicating whether to normlize the weight vectors.
  # @return [ComplementNB] a new instance of ComplementNB
  #
  # source://rumale-naive_bayes//lib/rumale/naive_bayes/complement_nb.rb#35
  def initialize(smoothing_param: T.unsafe(nil), norm: T.unsafe(nil)); end

  # Return the prior probabilities of the classes.
  #
  # @return [Numo::DFloat] (shape: [n_classes])
  #
  # source://rumale-naive_bayes//lib/rumale/naive_bayes/complement_nb.rb#25
  def class_priors; end

  # Return the class labels.
  #
  # @return [Numo::Int32] (size: n_classes)
  #
  # source://rumale-naive_bayes//lib/rumale/naive_bayes/complement_nb.rb#21
  def classes; end

  # Calculate confidence scores for samples.
  #
  # @param x [Numo::DFloat] (shape: [n_samples, n_features]) The samples to compute the scores.
  # @return [Numo::DFloat] (shape: [n_samples, n_classes]) Confidence scores per sample for each class.
  #
  # source://rumale-naive_bayes//lib/rumale/naive_bayes/complement_nb.rb#75
  def decision_function(x); end

  # Return the conditional probabilities for features of each class.
  #
  # @return [Numo::DFloat] (shape: [n_classes, n_features])
  #
  # source://rumale-naive_bayes//lib/rumale/naive_bayes/complement_nb.rb#29
  def feature_probs; end

  # Fit the model with given training data.
  #
  # @param x [Numo::DFloat] (shape: [n_samples, n_features]) The training data to be used for fitting the model.
  # @param y [Numo::Int32] (shape: [n_samples]) The categorical variables (e.g. labels)
  #   to be used for fitting the model.
  # @return [ComplementNB] The learned classifier itself.
  #
  # source://rumale-naive_bayes//lib/rumale/naive_bayes/complement_nb.rb#49
  def fit(x, y); end

  private

  # @return [Boolean]
  #
  # source://rumale-naive_bayes//lib/rumale/naive_bayes/complement_nb.rb#83
  def normalize?; end
end

# GaussianNB is a class that implements Gaussian Naive Bayes classifier.
#
# @example
#   require 'rumale/naive_bayes/gaussian_nb'
#
#   estimator = Rumale::NaiveBayes::GaussianNB.new
#   estimator.fit(training_samples, training_labels)
#   results = estimator.predict(testing_samples)
#
# source://rumale-naive_bayes//lib/rumale/naive_bayes/gaussian_nb.rb#15
class Rumale::NaiveBayes::GaussianNB < ::Rumale::NaiveBayes::BaseNaiveBayes
  # Create a new classifier with Gaussian Naive Bayes.
  #
  # @return [GaussianNB] a new instance of GaussianNB
  #
  # source://rumale-naive_bayes//lib/rumale/naive_bayes/gaussian_nb.rb#33
  def initialize; end

  # Return the prior probabilities of the classes.
  #
  # @return [Numo::DFloat] (shape: [n_classes])
  #
  # source://rumale-naive_bayes//lib/rumale/naive_bayes/gaussian_nb.rb#22
  def class_priors; end

  # Return the class labels.
  #
  # @return [Numo::Int32] (size: n_classes)
  #
  # source://rumale-naive_bayes//lib/rumale/naive_bayes/gaussian_nb.rb#18
  def classes; end

  # Calculate confidence scores for samples.
  #
  # @param x [Numo::DFloat] (shape: [n_samples, n_features]) The samples to compute the scores.
  # @return [Numo::DFloat] (shape: [n_samples, n_classes]) Confidence scores per sample for each class.
  #
  # source://rumale-naive_bayes//lib/rumale/naive_bayes/gaussian_nb.rb#61
  def decision_function(x); end

  # Fit the model with given training data.
  #
  # @param x [Numo::DFloat] (shape: [n_samples, n_features]) The training data to be used for fitting the model.
  # @param y [Numo::Int32] (shape: [n_samples]) The categorical variables (e.g. labels)
  #   to be used for fitting the model.
  # @return [GaussianNB] The learned classifier itself.
  #
  # source://rumale-naive_bayes//lib/rumale/naive_bayes/gaussian_nb.rb#44
  def fit(x, y); end

  # Return the mean vectors of the classes.
  #
  # @return [Numo::DFloat] (shape: [n_classes, n_features])
  #
  # source://rumale-naive_bayes//lib/rumale/naive_bayes/gaussian_nb.rb#26
  def means; end

  # Return the variance vectors of the classes.
  #
  # @return [Numo::DFloat] (shape: [n_classes, n_features])
  #
  # source://rumale-naive_bayes//lib/rumale/naive_bayes/gaussian_nb.rb#30
  def variances; end
end

# MultinomialNB is a class that implements Multinomial Naive Bayes classifier.
#
# *Reference*
# - Manning, C D., Raghavan, P., and Schutze, H., "Introduction to Information Retrieval," Cambridge University Press., 2008.
#
# @example
#   require 'rumale/naive_bayes/multinomial_nb'
#
#   estimator = Rumale::NaiveBayes::MultinomialNB.new(smoothing_param: 1.0)
#   estimator.fit(training_samples, training_labels)
#   results = estimator.predict(testing_samples)
#
# source://rumale-naive_bayes//lib/rumale/naive_bayes/multinomial_nb.rb#18
class Rumale::NaiveBayes::MultinomialNB < ::Rumale::NaiveBayes::BaseNaiveBayes
  # Create a new classifier with Multinomial Naive Bayes.
  #
  # @param smoothing_param [Float] The Laplace smoothing parameter.
  # @return [MultinomialNB] a new instance of MultinomialNB
  #
  # source://rumale-naive_bayes//lib/rumale/naive_bayes/multinomial_nb.rb#34
  def initialize(smoothing_param: T.unsafe(nil)); end

  # Return the prior probabilities of the classes.
  #
  # @return [Numo::DFloat] (shape: [n_classes])
  #
  # source://rumale-naive_bayes//lib/rumale/naive_bayes/multinomial_nb.rb#25
  def class_priors; end

  # Return the class labels.
  #
  # @return [Numo::Int32] (size: n_classes)
  #
  # source://rumale-naive_bayes//lib/rumale/naive_bayes/multinomial_nb.rb#21
  def classes; end

  # Calculate confidence scores for samples.
  #
  # @param x [Numo::DFloat] (shape: [n_samples, n_features]) The samples to compute the scores.
  # @return [Numo::DFloat] (shape: [n_samples, n_classes]) Confidence scores per sample for each class.
  #
  # source://rumale-naive_bayes//lib/rumale/naive_bayes/multinomial_nb.rb#64
  def decision_function(x); end

  # Return the conditional probabilities for features of each class.
  #
  # @return [Numo::DFloat] (shape: [n_classes, n_features])
  #
  # source://rumale-naive_bayes//lib/rumale/naive_bayes/multinomial_nb.rb#29
  def feature_probs; end

  # Fit the model with given training data.
  #
  # @param x [Numo::DFloat] (shape: [n_samples, n_features]) The training data to be used for fitting the model.
  # @param y [Numo::Int32] (shape: [n_samples]) The categorical variables (e.g. labels)
  #   to be used for fitting the model.
  # @return [MultinomialNB] The learned classifier itself.
  #
  # source://rumale-naive_bayes//lib/rumale/naive_bayes/multinomial_nb.rb#45
  def fit(x, y); end
end

# NegationNB is a class that implements Negation Naive Bayes classifier.
#
# *Reference*
# - Komiya, K., Sato, N., Fujimoto, K., and Kotani, Y., "Negation Naive Bayes for Categorization of Product Pages on the Web," RANLP' 11, pp. 586--592, 2011.
#
# @example
#   require 'rumale/naive_bayes/negation_nb'
#
#   estimator = Rumale::NaiveBayes::NegationNB.new(smoothing_param: 1.0)
#   estimator.fit(training_samples, training_labels)
#   results = estimator.predict(testing_samples)
#
# source://rumale-naive_bayes//lib/rumale/naive_bayes/negation_nb.rb#18
class Rumale::NaiveBayes::NegationNB < ::Rumale::NaiveBayes::BaseNaiveBayes
  # Create a new classifier with Complement Naive Bayes.
  #
  # @param smoothing_param [Float] The smoothing parameter.
  # @return [NegationNB] a new instance of NegationNB
  #
  # source://rumale-naive_bayes//lib/rumale/naive_bayes/negation_nb.rb#34
  def initialize(smoothing_param: T.unsafe(nil)); end

  # Return the prior probabilities of the classes.
  #
  # @return [Numo::DFloat] (shape: [n_classes])
  #
  # source://rumale-naive_bayes//lib/rumale/naive_bayes/negation_nb.rb#25
  def class_priors; end

  # Return the class labels.
  #
  # @return [Numo::Int32] (size: n_classes)
  #
  # source://rumale-naive_bayes//lib/rumale/naive_bayes/negation_nb.rb#21
  def classes; end

  # Calculate confidence scores for samples.
  #
  # @param x [Numo::DFloat] (shape: [n_samples, n_features]) The samples to compute the scores.
  # @return [Numo::DFloat] (shape: [n_samples, n_classes]) Confidence scores per sample for each class.
  #
  # source://rumale-naive_bayes//lib/rumale/naive_bayes/negation_nb.rb#66
  def decision_function(x); end

  # Return the conditional probabilities for features of each class.
  #
  # @return [Numo::DFloat] (shape: [n_classes, n_features])
  #
  # source://rumale-naive_bayes//lib/rumale/naive_bayes/negation_nb.rb#29
  def feature_probs; end

  # Fit the model with given training data.
  #
  # @param x [Numo::DFloat] (shape: [n_samples, n_features]) The training data to be used for fitting the model.
  # @param y [Numo::Int32] (shape: [n_samples]) The categorical variables (e.g. labels)
  #   to be used for fitting the model.
  # @return [ComplementNB] The learned classifier itself.
  #
  # source://rumale-naive_bayes//lib/rumale/naive_bayes/negation_nb.rb#45
  def fit(x, y); end
end

# source://rumale-naive_bayes//lib/rumale/naive_bayes/version.rb#8
Rumale::NaiveBayes::VERSION = T.let(T.unsafe(nil), String)