Sha256: 93fe5bfab4a6e08b822fa8efece450499b59d2c20a21215d5ae54ef5a441beb4
Contents?: true
Size: 1.99 KB
Versions: 23
Compression:
Stored size: 1.99 KB
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