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# frozen_string_literal: true
require 'svmkit/validation'
require 'svmkit/base/base_estimator'
require 'svmkit/base/transformer'
module SVMKit
# This module consists of the classes that perform preprocessings.
module Preprocessing
# Normalize samples to unit L2-norm.
#
# @example
# normalizer = SVMKit::Preprocessing::StandardScaler.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)
SVMKit::Validation.check_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)
SVMKit::Validation.check_sample_array(x)
fit(x)
x / @norm_vec.tile(x.shape[1], 1).transpose
end
end
end
end
Version data entries
15 entries across 15 versions & 1 rubygems