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Contents
# frozen_string_literal: true
require 'rumale/base/estimator'
module Rumale
# This module consists of the classes that implement generalized linear models.
module LinearModel
# BaseEstimator is an abstract class for implementation of linear model. This class is used internally.
class BaseEstimator < Rumale::Base::Estimator
# Return the weight vector.
# @return [Numo::DFloat] (shape: [n_outputs/n_classes, n_features])
attr_reader :weight_vec
# Return the bias term (a.k.a. intercept).
# @return [Numo::DFloat] (shape: [n_outputs/n_classes])
attr_reader :bias_term
# Create an initial linear model.
private
def expand_feature(x)
n_samples = x.shape[0]
Numo::NArray.hstack([x, Numo::DFloat.ones([n_samples, 1]) * @params[:bias_scale]])
end
def split_weight(w)
if w.ndim == 1
if fit_bias?
[w[0...-1].dup, w[-1]]
else
[w, 0.0]
end
elsif fit_bias?
[w[true, 0...-1].dup, w[true, -1].dup]
else
[w, Numo::DFloat.zeros(w.shape[0])]
end
end
def fit_bias?
@params[:fit_bias] == true
end
end
end
end
Version data entries
6 entries across 6 versions & 1 rubygems