159 lines
7.2 KiB
Ruby
Generated
159 lines
7.2 KiB
Ruby
Generated
# typed: true
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# DO NOT EDIT MANUALLY
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# This is an autogenerated file for types exported from the `rumale-nearest_neighbors` gem.
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# Please instead update this file by running `bin/tapioca gem rumale-nearest_neighbors`.
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# Rumale is a machine learning library in Ruby.
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#
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# source://rumale-nearest_neighbors//lib/rumale/nearest_neighbors/k_neighbors_classifier.rb#8
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module Rumale; end
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# This module consists of the classes that implement estimators based on nearest neighbors rule.
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#
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# source://rumale-nearest_neighbors//lib/rumale/nearest_neighbors/k_neighbors_classifier.rb#10
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module Rumale::NearestNeighbors; end
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# KNeighborsClassifier is a class that implements the classifier with the k-nearest neighbors rule.
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# The current implementation uses the Euclidean distance for finding the neighbors.
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#
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# @example
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# require 'rumale/nearest_neighbors/k_neighbors_classifier'
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#
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# estimator =
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# Rumale::NearestNeighbors::KNeighborsClassifier.new(n_neighbors: 5)
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# estimator.fit(training_samples, traininig_labels)
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# results = estimator.predict(testing_samples)
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#
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# source://rumale-nearest_neighbors//lib/rumale/nearest_neighbors/k_neighbors_classifier.rb#22
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class Rumale::NearestNeighbors::KNeighborsClassifier < ::Rumale::Base::Estimator
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include ::Rumale::Base::Classifier
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# Create a new classifier with the nearest neighbor rule.
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#
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# @param n_neighbors [Integer] The number of neighbors.
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# @param metric [String] The metric to calculate the distances.
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# If metric is 'euclidean', Euclidean distance is calculated for distance between points.
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# If metric is 'precomputed', the fit and predict methods expect to be given a distance matrix.
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# @return [KNeighborsClassifier] a new instance of KNeighborsClassifier
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#
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# source://rumale-nearest_neighbors//lib/rumale/nearest_neighbors/k_neighbors_classifier.rb#44
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def initialize(n_neighbors: T.unsafe(nil), metric: T.unsafe(nil)); end
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# Return the class labels.
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#
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# @return [Numo::Int32] (size: n_classes)
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#
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# source://rumale-nearest_neighbors//lib/rumale/nearest_neighbors/k_neighbors_classifier.rb#36
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def classes; end
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# Calculate confidence scores for samples.
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#
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# @param x [Numo::DFloat] (shape: [n_testing_samples, n_features]) The samples to compute the scores.
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# If the metric is 'precomputed', x must be a square distance matrix (shape: [n_testing_samples, n_training_samples]).
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# @return [Numo::DFloat] (shape: [n_testing_samples, n_classes]) Confidence scores per sample for each class.
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#
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# source://rumale-nearest_neighbors//lib/rumale/nearest_neighbors/k_neighbors_classifier.rb#77
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def decision_function(x); end
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# Fit the model with given training data.
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#
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# @param x [Numo::DFloat] (shape: [n_training_samples, n_features]) The training data to be used for fitting the model.
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# If the metric is 'precomputed', x must be a square distance matrix (shape: [n_training_samples, n_training_samples]).
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# @param y [Numo::Int32] (shape: [n_training_samples]) The labels to be used for fitting the model.
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# @return [KNeighborsClassifier] The learned classifier itself.
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#
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# source://rumale-nearest_neighbors//lib/rumale/nearest_neighbors/k_neighbors_classifier.rb#58
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def fit(x, y); end
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# Return the labels of the prototypes
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#
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# @return [Numo::Int32] (size: n_training_samples)
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#
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# source://rumale-nearest_neighbors//lib/rumale/nearest_neighbors/k_neighbors_classifier.rb#32
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def labels; end
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# Predict class labels for samples.
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#
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# @param x [Numo::DFloat] (shape: [n_testing_samples, n_features]) The samples to predict the labels.
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# If the metric is 'precomputed', x must be a square distance matrix (shape: [n_testing_samples, n_training_samples]).
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# @return [Numo::Int32] (shape: [n_testing_samples]) Predicted class label per sample.
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#
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# source://rumale-nearest_neighbors//lib/rumale/nearest_neighbors/k_neighbors_classifier.rb#103
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def predict(x); end
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# Return the prototypes for the nearest neighbor classifier.
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# If the metric is 'precomputed', that returns nil.
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#
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# @return [Numo::DFloat] (shape: [n_training_samples, n_features])
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#
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# source://rumale-nearest_neighbors//lib/rumale/nearest_neighbors/k_neighbors_classifier.rb#28
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def prototypes; end
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end
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# KNeighborsRegressor is a class that implements the regressor with the k-nearest neighbors rule.
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# The current implementation uses the Euclidean distance for finding the neighbors.
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#
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# @example
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# require 'rumale/nearest_neighbors/k_neighbors_regressor'
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#
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# estimator =
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# Rumale::NearestNeighbors::KNeighborsRegressor.new(n_neighbors: 5)
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# estimator.fit(training_samples, traininig_target_values)
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# results = estimator.predict(testing_samples)
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#
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# source://rumale-nearest_neighbors//lib/rumale/nearest_neighbors/k_neighbors_regressor.rb#21
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class Rumale::NearestNeighbors::KNeighborsRegressor < ::Rumale::Base::Estimator
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include ::Rumale::Base::Regressor
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# Create a new regressor with the nearest neighbor rule.
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#
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# @param n_neighbors [Integer] The number of neighbors.
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# @param metric [String] The metric to calculate the distances.
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# If metric is 'euclidean', Euclidean distance is calculated for distance between points.
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# If metric is 'precomputed', the fit and predict methods expect to be given a distance matrix.
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# @return [KNeighborsRegressor] a new instance of KNeighborsRegressor
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#
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# source://rumale-nearest_neighbors//lib/rumale/nearest_neighbors/k_neighbors_regressor.rb#40
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def initialize(n_neighbors: T.unsafe(nil), metric: T.unsafe(nil)); end
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# Fit the model with given training data.
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#
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# @param x [Numo::DFloat] (shape: [n_training_samples, n_features]) The training data to be used for fitting the model.
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# If the metric is 'precomputed', x must be a square distance matrix (shape: [n_training_samples, n_training_samples]).
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# @param y [Numo::DFloat] (shape: [n_training_samples, n_outputs]) The target values to be used for fitting the model.
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# @return [KNeighborsRegressor] The learned regressor itself.
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#
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# source://rumale-nearest_neighbors//lib/rumale/nearest_neighbors/k_neighbors_regressor.rb#54
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def fit(x, y); end
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# Predict values for samples.
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#
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# @param x [Numo::DFloat] (shape: [n_testing_samples, n_features]) The samples to predict the values.
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# If the metric is 'precomputed', x must be a square distance matrix (shape: [n_testing_samples, n_training_samples]).
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# @return [Numo::DFloat] (shape: [n_testing_samples, n_outputs]) Predicted values per sample.
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#
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# source://rumale-nearest_neighbors//lib/rumale/nearest_neighbors/k_neighbors_regressor.rb#72
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def predict(x); end
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# Return the prototypes for the nearest neighbor regressor.
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# If the metric is 'precomputed', that returns nil.
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# If the algorithm is 'vptree', that returns Rumale::NearestNeighbors::VPTree.
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#
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# @return [Numo::DFloat] (shape: [n_training_samples, n_features])
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#
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# source://rumale-nearest_neighbors//lib/rumale/nearest_neighbors/k_neighbors_regressor.rb#28
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def prototypes; end
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# Return the values of the prototypes
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#
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# @return [Numo::DFloat] (shape: [n_training_samples, n_outputs])
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#
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# source://rumale-nearest_neighbors//lib/rumale/nearest_neighbors/k_neighbors_regressor.rb#32
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def values; end
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end
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# source://rumale-nearest_neighbors//lib/rumale/nearest_neighbors/version.rb#8
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Rumale::NearestNeighbors::VERSION = T.let(T.unsafe(nil), String)
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