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Contents
# frozen_string_literal: true require 'rumale/base/base_estimator' require 'rumale/base/transformer' module Rumale # This module consists of the classes that perform preprocessings. module Preprocessing # Normalize samples to unit L2-norm. # # @example # normalizer = Rumale::Preprocessing::L2Normalizer.new # new_samples = normalizer.fit_transform(samples) class L2Normalizer include Base::BaseEstimator include Base::Transformer # Return the vector consists of L2-norm for each sample. # @return [Numo::DFloat] (shape: [n_samples]) attr_reader :norm_vec # :nodoc: # Create a new normalizer for normaliing to unit L2-norm. def initialize @params = {} @norm_vec = nil end # Calculate L2-norms of each sample. # # @overload fit(x) -> L2Normalizer # # @param x [Numo::DFloat] (shape: [n_samples, n_features]) The samples to calculate L2-norms. # @return [L2Normalizer] def fit(x, _y = nil) x = check_convert_sample_array(x) @norm_vec = Numo::NMath.sqrt((x**2).sum(1)) self end # Calculate L2-norms of each sample, and then normalize samples to unit L2-norm. # # @overload fit_transform(x) -> Numo::DFloat # # @param x [Numo::DFloat] (shape: [n_samples, n_features]) The samples to calculate L2-norms. # @return [Numo::DFloat] The normalized samples. def fit_transform(x, _y = nil) x = check_convert_sample_array(x) fit(x) x / @norm_vec.tile(x.shape[1], 1).transpose end # Calculate L2-norms of each sample, and then normalize samples to unit L2-norm. # This method calls the fit_transform method. This method exists for the Pipeline class. # # @param x [Numo::DFloat] (shape: [n_samples, n_features]) The samples to calculate L2-norms. # @return [Numo::DFloat] The normalized samples. def transform(x) fit_transform(x) end end end end
Version data entries
23 entries across 23 versions & 1 rubygems
Version | Path |
---|---|
rumale-0.14.2 | lib/rumale/preprocessing/l2_normalizer.rb |
rumale-0.14.1 | lib/rumale/preprocessing/l2_normalizer.rb |
rumale-0.14.0 | lib/rumale/preprocessing/l2_normalizer.rb |