576 lines
22 KiB
Ruby
Generated
576 lines
22 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-core` gem.
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# Please instead update this file by running `bin/tapioca gem rumale-core`.
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# Rumale is a machine learning library in Ruby.
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#
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# source://rumale-core//lib/rumale/core/version.rb#4
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module Rumale; end
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# This module consists of basic mix-in classes.
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#
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# source://rumale-core//lib/rumale/base/estimator.rb#7
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module Rumale::Base; end
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# Module for all classifiers in Rumale.
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#
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# source://rumale-core//lib/rumale/base/classifier.rb#10
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module Rumale::Base::Classifier
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# An abstract method for fitting a model.
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#
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# @raise [NotImplementedError]
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#
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# source://rumale-core//lib/rumale/base/classifier.rb#12
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def fit; end
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# An abstract method for predicting labels.
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#
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# @raise [NotImplementedError]
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#
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# source://rumale-core//lib/rumale/base/classifier.rb#17
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def predict; end
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# Calculate the mean accuracy of the given testing data.
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#
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# @param x [Numo::DFloat] (shape: [n_samples, n_features]) Testing data.
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# @param y [Numo::Int32] (shape: [n_samples]) True labels for testing data.
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# @return [Float] Mean accuracy
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#
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# source://rumale-core//lib/rumale/base/classifier.rb#26
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def score(x, y); end
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end
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# Module for all clustering algorithms in Rumale.
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#
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# source://rumale-core//lib/rumale/base/cluster_analyzer.rb#8
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module Rumale::Base::ClusterAnalyzer
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# An abstract method for analyzing clusters and predicting cluster indices.
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#
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# @raise [NotImplementedError]
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#
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# source://rumale-core//lib/rumale/base/cluster_analyzer.rb#10
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def fit_predict; end
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# Calculate purity of clustering result.
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#
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# @param x [Numo::DFloat] (shape: [n_samples, n_features]) Testing data.
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# @param y [Numo::Int32] (shape: [n_samples]) True labels for testing data.
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# @return [Float] Purity
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#
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# source://rumale-core//lib/rumale/base/cluster_analyzer.rb#19
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def score(x, y); end
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end
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# Base class for all estimators in Rumale.
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#
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# source://rumale-core//lib/rumale/base/estimator.rb#9
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class Rumale::Base::Estimator
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# Return parameters about an estimator.
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#
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# @return [Hash]
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#
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# source://rumale-core//lib/rumale/base/estimator.rb#12
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def params; end
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private
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# @return [Boolean]
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#
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# source://rumale-core//lib/rumale/base/estimator.rb#16
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def enable_linalg?(warning: T.unsafe(nil)); end
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# @return [Boolean]
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#
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# source://rumale-core//lib/rumale/base/estimator.rb#34
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def enable_parallel?(warning: T.unsafe(nil)); end
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# source://rumale-core//lib/rumale/base/estimator.rb#47
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def n_processes; end
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# source://rumale-core//lib/rumale/base/estimator.rb#53
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def parallel_map(n_outputs, &block); end
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end
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# Module for all evaluation measures in Rumale.
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#
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# source://rumale-core//lib/rumale/base/evaluator.rb#8
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module Rumale::Base::Evaluator
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# An abstract method for evaluation of model.
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#
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# @raise [NotImplementedError]
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#
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# source://rumale-core//lib/rumale/base/evaluator.rb#10
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def score; end
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end
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# Module for all regressors in Rumale.
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#
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# source://rumale-core//lib/rumale/base/regressor.rb#8
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module Rumale::Base::Regressor
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# An abstract method for fitting a model.
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#
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# @raise [NotImplementedError]
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#
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# source://rumale-core//lib/rumale/base/regressor.rb#10
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def fit; end
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# An abstract method for predicting labels.
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#
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# @raise [NotImplementedError]
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#
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# source://rumale-core//lib/rumale/base/regressor.rb#15
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def predict; end
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# Calculate the coefficient of determination for the given testing data.
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#
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# @param x [Numo::DFloat] (shape: [n_samples, n_features]) Testing data.
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# @param y [Numo::DFloat] (shape: [n_samples, n_outputs]) Target values for testing data.
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# @return [Float] Coefficient of determination
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#
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# source://rumale-core//lib/rumale/base/regressor.rb#24
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def score(x, y); end
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end
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# Module for all validation methods in Rumale.
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#
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# source://rumale-core//lib/rumale/base/splitter.rb#8
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module Rumale::Base::Splitter
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# Return the number of splits.
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#
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# @return [Integer]
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#
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# source://rumale-core//lib/rumale/base/splitter.rb#11
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def n_splits; end
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# An abstract method for splitting dataset.
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#
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# @raise [NotImplementedError]
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#
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# source://rumale-core//lib/rumale/base/splitter.rb#14
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def split; end
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end
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# Module for all transfomers in Rumale.
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#
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# source://rumale-core//lib/rumale/base/transformer.rb#8
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module Rumale::Base::Transformer
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# An abstract method for fitting a model.
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#
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# @raise [NotImplementedError]
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#
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# source://rumale-core//lib/rumale/base/transformer.rb#10
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def fit; end
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# An abstract method for fitting a model and transforming given data.
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#
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# @raise [NotImplementedError]
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#
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# source://rumale-core//lib/rumale/base/transformer.rb#15
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def fit_transform; end
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end
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# source://rumale-core//lib/rumale/core/version.rb#6
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module Rumale::Core; end
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# source://rumale-core//lib/rumale/core/version.rb#8
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Rumale::Core::VERSION = T.let(T.unsafe(nil), String)
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# Module for loading and saving a dataset file.
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#
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# source://rumale-core//lib/rumale/dataset.rb#9
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module Rumale::Dataset
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class << self
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# Dump the dataset with the libsvm file format.
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#
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# @param data [Numo::NArray] (shape: [n_samples, n_features]) matrix consisting of feature vectors.
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# @param labels [Numo::NArray] (shape: [n_samples]) matrix consisting of labels or target values.
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# @param filename [String] A path to the output libsvm file.
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# @param zero_based [Boolean] Whether the column index starts from 0 (true) or 1 (false).
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#
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# source://rumale-core//lib/rumale/dataset.rb#43
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def dump_libsvm_file(data, labels, filename, zero_based: T.unsafe(nil)); end
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# Load a dataset with the libsvm file format into Numo::NArray.
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#
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# @param filename [String] A path to a dataset file.
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# @param n_features [Integer/Nil] The number of features of data to load.
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# If nil is given, it will be detected automatically from given file.
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# @param zero_based [Boolean] Whether the column index starts from 0 (true) or 1 (false).
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# @param dtype [Numo::NArray] Data type of Numo::NArray for features to be loaded.
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# @return [Array<Numo::NArray>] Returns array containing the (n_samples x n_features) matrix for feature vectors
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# and (n_samples) vector for labels or target values.
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#
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# source://rumale-core//lib/rumale/dataset.rb#22
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def load_libsvm_file(filename, n_features: T.unsafe(nil), zero_based: T.unsafe(nil), dtype: T.unsafe(nil)); end
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# Generate Gaussian blobs.
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#
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# @param n_samples [Integer] The total number of samples.
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# @param n_features [Integer] The number of features.
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# If "centers" parameter is given as a Numo::DFloat array, this parameter is ignored.
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# @param centers [Integer/Numo::DFloat/Nil] The number of cluster centroids or the fixed cluster centroids.
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# If nil is given, the number of cluster centroids is set to 3.
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# @param cluster_std [Float] The standard deviation of the clusters.
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# @param center_box [Array] The bounding box for each cluster centroids.
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# If "centers" parameter is given as a Numo::DFloat array, this parameter is ignored.
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# @param shuffle [Boolean] The flag indicating whether to shuffle the dataset
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# @param random_seed [Integer] The seed value using to initialize the random generator.
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#
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# source://rumale-core//lib/rumale/dataset.rb#134
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def make_blobs(n_samples = T.unsafe(nil), n_features = T.unsafe(nil), centers: T.unsafe(nil), cluster_std: T.unsafe(nil), center_box: T.unsafe(nil), shuffle: T.unsafe(nil), random_seed: T.unsafe(nil)); end
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# Generate a two-dimensional data set consisting of an inner circle and an outer circle.
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#
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# @param n_samples [Integer] The number of samples.
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# @param shuffle [Boolean] The flag indicating whether to shuffle the dataset
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# @param noise [Float] The standard deviaion of gaussian noise added to the data.
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# If nil is given, no noise is added.
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# @param factor [Float] The scale factor between inner and outer circles. The interval of factor is (0, 1).
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# @param random_seed [Integer] The seed value using to initialize the random generator.
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#
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# source://rumale-core//lib/rumale/dataset.rb#65
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def make_circles(n_samples, shuffle: T.unsafe(nil), noise: T.unsafe(nil), factor: T.unsafe(nil), random_seed: T.unsafe(nil)); end
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# Generate a two-dimensional data set consisting of two half circles shifted.
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#
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# @param n_samples [Integer] The number of samples.
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# @param shuffle [Boolean] The flag indicating whether to shuffle the dataset
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# @param noise [Float] The standard deviaion of gaussian noise added to the data.
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# If nil is given, no noise is added.
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# @param random_seed [Integer] The seed value using to initialize the random generator.
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#
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# source://rumale-core//lib/rumale/dataset.rb#97
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def make_moons(n_samples, shuffle: T.unsafe(nil), noise: T.unsafe(nil), random_seed: T.unsafe(nil)); end
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private
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# source://rumale-core//lib/rumale/dataset.rb#196
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def convert_to_matrix(data, n_features, dtype); end
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# source://rumale-core//lib/rumale/dataset.rb#206
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def detect_dtype(data); end
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# source://rumale-core//lib/rumale/dataset.rb#224
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def dump_label(label, label_type_str); end
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# source://rumale-core//lib/rumale/dataset.rb#215
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def dump_libsvm_line(label, ftvec, label_type, value_type, zero_based); end
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# source://rumale-core//lib/rumale/dataset.rb#191
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def parse_label(label); end
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# source://rumale-core//lib/rumale/dataset.rb#176
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def parse_libsvm_line(line, zero_based); end
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end
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end
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# Module for calculating pairwise distances, similarities, and kernels.
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#
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# source://rumale-core//lib/rumale/pairwise_metric.rb#7
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module Rumale::PairwiseMetric
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private
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# Calculate the pairwise cosine distances between x and y.
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#
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# @param x [Numo::DFloat] (shape: [n_samples_x, n_features])
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# @param y [Numo::DFloat] (shape: [n_samples_y, n_features])
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# @return [Numo::DFloat] (shape: [n_samples_x, n_samples_x] or [n_samples_x, n_samples_y] if y is given)
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#
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# source://rumale-core//lib/rumale/pairwise_metric.rb#74
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def cosine_distance(x, y = T.unsafe(nil)); end
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# Calculate the pairwise cosine simlarities between x and y.
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#
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# @param x [Numo::DFloat] (shape: [n_samples_x, n_features])
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# @param y [Numo::DFloat] (shape: [n_samples_y, n_features])
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# @return [Numo::DFloat] (shape: [n_samples_x, n_samples_x] or [n_samples_x, n_samples_y] if y is given)
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#
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# source://rumale-core//lib/rumale/pairwise_metric.rb#55
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def cosine_similarity(x, y = T.unsafe(nil)); end
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# Calculate the pairwise euclidean distances between x and y.
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#
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# @param x [Numo::DFloat] (shape: [n_samples_x, n_features])
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# @param y [Numo::DFloat] (shape: [n_samples_y, n_features])
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# @return [Numo::DFloat] (shape: [n_samples_x, n_samples_x] or [n_samples_x, n_samples_y] if y is given)
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#
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# source://rumale-core//lib/rumale/pairwise_metric.rb#15
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def euclidean_distance(x, y = T.unsafe(nil)); end
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# Calculate the linear kernel between x and y.
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#
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# @param x [Numo::DFloat] (shape: [n_samples_x, n_features])
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# @param y [Numo::DFloat] (shape: [n_samples_y, n_features])
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# @return [Numo::DFloat] (shape: [n_samples_x, n_samples_x] or [n_samples_x, n_samples_y] if y is given)
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#
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# source://rumale-core//lib/rumale/pairwise_metric.rb#97
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def linear_kernel(x, y = T.unsafe(nil)); end
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# Calculate the pairwise manhattan distances between x and y.
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#
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# @param x [Numo::DFloat] (shape: [n_samples_x, n_features])
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# @param y [Numo::DFloat] (shape: [n_samples_y, n_features])
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# @return [Numo::DFloat] (shape: [n_samples_x, n_samples_x] or [n_samples_x, n_samples_y] if y is given)
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#
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# source://rumale-core//lib/rumale/pairwise_metric.rb#24
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def manhattan_distance(x, y = T.unsafe(nil)); end
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# Calculate the polynomial kernel between x and y.
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#
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# @param x [Numo::DFloat] (shape: [n_samples_x, n_features])
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# @param y [Numo::DFloat] (shape: [n_samples_y, n_features])
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# @param degree [Integer] The parameter of polynomial kernel.
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# @param gamma [Float] The parameter of polynomial kernel, if nil it is 1 / n_features.
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# @param coef [Integer] The parameter of polynomial kernel.
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# @return [Numo::DFloat] (shape: [n_samples_x, n_samples_x] or [n_samples_x, n_samples_y] if y is given)
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#
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# source://rumale-core//lib/rumale/pairwise_metric.rb#110
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def polynomial_kernel(x, y = T.unsafe(nil), degree = T.unsafe(nil), gamma = T.unsafe(nil), coef = T.unsafe(nil)); end
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# Calculate the rbf kernel between x and y.
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#
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# @param x [Numo::DFloat] (shape: [n_samples_x, n_features])
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# @param y [Numo::DFloat] (shape: [n_samples_y, n_features])
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# @param gamma [Float] The parameter of rbf kernel, if nil it is 1 / n_features.
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# @return [Numo::DFloat] (shape: [n_samples_x, n_samples_x] or [n_samples_x, n_samples_y] if y is given)
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#
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# source://rumale-core//lib/rumale/pairwise_metric.rb#86
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def rbf_kernel(x, y = T.unsafe(nil), gamma = T.unsafe(nil)); end
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# Calculate the sigmoid kernel between x and y.
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#
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# @param x [Numo::DFloat] (shape: [n_samples_x, n_features])
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# @param y [Numo::DFloat] (shape: [n_samples_y, n_features])
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# @param gamma [Float] The parameter of polynomial kernel, if nil it is 1 / n_features.
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# @param coef [Integer] The parameter of polynomial kernel.
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# @return [Numo::DFloat] (shape: [n_samples_x, n_samples_x] or [n_samples_x, n_samples_y] if y is given)
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#
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# source://rumale-core//lib/rumale/pairwise_metric.rb#123
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def sigmoid_kernel(x, y = T.unsafe(nil), gamma = T.unsafe(nil), coef = T.unsafe(nil)); end
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# Calculate the pairwise squared errors between x and y.
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#
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# @param x [Numo::DFloat] (shape: [n_samples_x, n_features])
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# @param y [Numo::DFloat] (shape: [n_samples_y, n_features])
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# @return [Numo::DFloat] (shape: [n_samples_x, n_samples_x] or [n_samples_x, n_samples_y] if y is given)
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#
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# source://rumale-core//lib/rumale/pairwise_metric.rb#40
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def squared_error(x, y = T.unsafe(nil)); end
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class << self
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# Calculate the pairwise cosine distances between x and y.
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#
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# @param x [Numo::DFloat] (shape: [n_samples_x, n_features])
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# @param y [Numo::DFloat] (shape: [n_samples_y, n_features])
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# @return [Numo::DFloat] (shape: [n_samples_x, n_samples_x] or [n_samples_x, n_samples_y] if y is given)
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#
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# source://rumale-core//lib/rumale/pairwise_metric.rb#74
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def cosine_distance(x, y = T.unsafe(nil)); end
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# Calculate the pairwise cosine simlarities between x and y.
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#
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# @param x [Numo::DFloat] (shape: [n_samples_x, n_features])
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# @param y [Numo::DFloat] (shape: [n_samples_y, n_features])
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# @return [Numo::DFloat] (shape: [n_samples_x, n_samples_x] or [n_samples_x, n_samples_y] if y is given)
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#
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# source://rumale-core//lib/rumale/pairwise_metric.rb#55
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def cosine_similarity(x, y = T.unsafe(nil)); end
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# Calculate the pairwise euclidean distances between x and y.
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#
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# @param x [Numo::DFloat] (shape: [n_samples_x, n_features])
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# @param y [Numo::DFloat] (shape: [n_samples_y, n_features])
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# @return [Numo::DFloat] (shape: [n_samples_x, n_samples_x] or [n_samples_x, n_samples_y] if y is given)
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#
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# source://rumale-core//lib/rumale/pairwise_metric.rb#15
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def euclidean_distance(x, y = T.unsafe(nil)); end
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# Calculate the linear kernel between x and y.
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#
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# @param x [Numo::DFloat] (shape: [n_samples_x, n_features])
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# @param y [Numo::DFloat] (shape: [n_samples_y, n_features])
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# @return [Numo::DFloat] (shape: [n_samples_x, n_samples_x] or [n_samples_x, n_samples_y] if y is given)
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#
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# source://rumale-core//lib/rumale/pairwise_metric.rb#97
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def linear_kernel(x, y = T.unsafe(nil)); end
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# Calculate the pairwise manhattan distances between x and y.
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#
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# @param x [Numo::DFloat] (shape: [n_samples_x, n_features])
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# @param y [Numo::DFloat] (shape: [n_samples_y, n_features])
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# @return [Numo::DFloat] (shape: [n_samples_x, n_samples_x] or [n_samples_x, n_samples_y] if y is given)
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#
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# source://rumale-core//lib/rumale/pairwise_metric.rb#24
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def manhattan_distance(x, y = T.unsafe(nil)); end
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# Calculate the polynomial kernel between x and y.
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#
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# @param x [Numo::DFloat] (shape: [n_samples_x, n_features])
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# @param y [Numo::DFloat] (shape: [n_samples_y, n_features])
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# @param degree [Integer] The parameter of polynomial kernel.
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# @param gamma [Float] The parameter of polynomial kernel, if nil it is 1 / n_features.
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# @param coef [Integer] The parameter of polynomial kernel.
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# @return [Numo::DFloat] (shape: [n_samples_x, n_samples_x] or [n_samples_x, n_samples_y] if y is given)
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#
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# source://rumale-core//lib/rumale/pairwise_metric.rb#110
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def polynomial_kernel(x, y = T.unsafe(nil), degree = T.unsafe(nil), gamma = T.unsafe(nil), coef = T.unsafe(nil)); end
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# Calculate the rbf kernel between x and y.
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#
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# @param x [Numo::DFloat] (shape: [n_samples_x, n_features])
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# @param y [Numo::DFloat] (shape: [n_samples_y, n_features])
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# @param gamma [Float] The parameter of rbf kernel, if nil it is 1 / n_features.
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# @return [Numo::DFloat] (shape: [n_samples_x, n_samples_x] or [n_samples_x, n_samples_y] if y is given)
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#
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# source://rumale-core//lib/rumale/pairwise_metric.rb#86
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def rbf_kernel(x, y = T.unsafe(nil), gamma = T.unsafe(nil)); end
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# Calculate the sigmoid kernel between x and y.
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#
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# @param x [Numo::DFloat] (shape: [n_samples_x, n_features])
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# @param y [Numo::DFloat] (shape: [n_samples_y, n_features])
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# @param gamma [Float] The parameter of polynomial kernel, if nil it is 1 / n_features.
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# @param coef [Integer] The parameter of polynomial kernel.
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# @return [Numo::DFloat] (shape: [n_samples_x, n_samples_x] or [n_samples_x, n_samples_y] if y is given)
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#
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# source://rumale-core//lib/rumale/pairwise_metric.rb#123
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def sigmoid_kernel(x, y = T.unsafe(nil), gamma = T.unsafe(nil), coef = T.unsafe(nil)); end
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# Calculate the pairwise squared errors between x and y.
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#
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# @param x [Numo::DFloat] (shape: [n_samples_x, n_features])
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# @param y [Numo::DFloat] (shape: [n_samples_y, n_features])
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# @return [Numo::DFloat] (shape: [n_samples_x, n_samples_x] or [n_samples_x, n_samples_y] if y is given)
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#
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# source://rumale-core//lib/rumale/pairwise_metric.rb#40
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def squared_error(x, y = T.unsafe(nil)); end
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end
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end
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# Module for calculating posterior class probabilities with SVM outputs.
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# This module is used for internal processes.
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#
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# *Reference*
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# - Platt, J C., "Probabilistic Outputs for Support Vector Machines and Comparisons to Regularized Likelihood Methods," Adv. Large Margin Classifiers, pp. 61--74, 2000.
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# - Lin, H-T., Lin, C-J., and Weng, R C., "A Note on Platt's Probabilistic Outputs for Support Vector Machines," J. Machine Learning, Vol. 63 (3), pp. 267--276, 2007.
|
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#
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|
# @example
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# estimator = Rumale::LinearModel::SVC.new
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# estimator.fit(x, bin_y)
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# df = estimator.decision_function(x)
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# params = Rumale::ProbabilisticOutput.fit_sigmoid(df, bin_y)
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# probs = 1 / (Numo::NMath.exp(params[0] * df + params[1]) + 1)
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#
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# source://rumale-core//lib/rumale/probabilistic_output.rb#19
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module Rumale::ProbabilisticOutput
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|
class << self
|
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# Fit the probabilistic model for binary SVM outputs.
|
|
#
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|
# @param df [Numo::DFloat] (shape: [n_samples]) The outputs of decision function to be used for fitting the model.
|
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# @param bin_y [Numo::Int32] (shape: [n_samples]) The binary labels to be used for fitting the model.
|
|
# @param max_iter [Integer] The maximum number of iterations.
|
|
# @param min_step [Float] The minimum step of Newton's method.
|
|
# @param sigma [Float] The parameter to avoid hessian matrix from becoming singular matrix.
|
|
# @return [Numo::DFloat] (shape: 2) The parameters of the model.
|
|
#
|
|
# source://rumale-core//lib/rumale/probabilistic_output.rb#29
|
|
def fit_sigmoid(df, bin_y, max_iter = T.unsafe(nil), min_step = T.unsafe(nil), sigma = T.unsafe(nil)); end
|
|
|
|
private
|
|
|
|
# source://rumale-core//lib/rumale/probabilistic_output.rb#109
|
|
def directions(grad_vec, hess_mat); end
|
|
|
|
# source://rumale-core//lib/rumale/probabilistic_output.rb#76
|
|
def error_function(target_probs, df, alpha, beta); end
|
|
|
|
# source://rumale-core//lib/rumale/probabilistic_output.rb#96
|
|
def gradient(target_probs, probs, df); end
|
|
|
|
# source://rumale-core//lib/rumale/probabilistic_output.rb#101
|
|
def hessian_matrix(probs, df, sigma); end
|
|
|
|
# source://rumale-core//lib/rumale/probabilistic_output.rb#86
|
|
def predicted_probs(df, alpha, beta); end
|
|
end
|
|
end
|
|
|
|
# source://rumale-core//lib/rumale/utils.rb#7
|
|
module Rumale::Utils
|
|
private
|
|
|
|
# source://rumale-core//lib/rumale/utils.rb#45
|
|
def binarize_labels(labels); end
|
|
|
|
# source://rumale-core//lib/rumale/utils.rb#11
|
|
def choice_ids(size, probs, rng = T.unsafe(nil)); end
|
|
|
|
# source://rumale-core//lib/rumale/utils.rb#56
|
|
def normalize(x, norm); end
|
|
|
|
# source://rumale-core//lib/rumale/utils.rb#37
|
|
def rand_normal(shape, rng = T.unsafe(nil), mu = T.unsafe(nil), sigma = T.unsafe(nil)); end
|
|
|
|
# source://rumale-core//lib/rumale/utils.rb#26
|
|
def rand_uniform(shape, rng = T.unsafe(nil)); end
|
|
|
|
class << self
|
|
# source://rumale-core//lib/rumale/utils.rb#45
|
|
def binarize_labels(labels); end
|
|
|
|
# source://rumale-core//lib/rumale/utils.rb#11
|
|
def choice_ids(size, probs, rng = T.unsafe(nil)); end
|
|
|
|
# source://rumale-core//lib/rumale/utils.rb#56
|
|
def normalize(x, norm); end
|
|
|
|
# source://rumale-core//lib/rumale/utils.rb#37
|
|
def rand_normal(shape, rng = T.unsafe(nil), mu = T.unsafe(nil), sigma = T.unsafe(nil)); end
|
|
|
|
# source://rumale-core//lib/rumale/utils.rb#26
|
|
def rand_uniform(shape, rng = T.unsafe(nil)); end
|
|
end
|
|
end
|
|
|
|
# source://rumale-core//lib/rumale/validation.rb#5
|
|
module Rumale::Validation
|
|
private
|
|
|
|
# source://rumale-core//lib/rumale/validation.rb#17
|
|
def check_convert_label_array(y); end
|
|
|
|
# source://rumale-core//lib/rumale/validation.rb#9
|
|
def check_convert_sample_array(x); end
|
|
|
|
# source://rumale-core//lib/rumale/validation.rb#25
|
|
def check_convert_target_value_array(y); end
|
|
|
|
# source://rumale-core//lib/rumale/validation.rb#33
|
|
def check_sample_size(x, y); end
|
|
|
|
class << self
|
|
# @raise [ArgumentError]
|
|
#
|
|
# source://rumale-core//lib/rumale/validation.rb#17
|
|
def check_convert_label_array(y); end
|
|
|
|
# @raise [ArgumentError]
|
|
#
|
|
# source://rumale-core//lib/rumale/validation.rb#9
|
|
def check_convert_sample_array(x); end
|
|
|
|
# @raise [ArgumentError]
|
|
#
|
|
# source://rumale-core//lib/rumale/validation.rb#25
|
|
def check_convert_target_value_array(y); end
|
|
|
|
# @raise [ArgumentError]
|
|
#
|
|
# source://rumale-core//lib/rumale/validation.rb#33
|
|
def check_sample_size(x, y); end
|
|
end
|
|
end
|