# frozen_string_literal: true

require 'rumale/base/estimator'
require 'rumale/base/transformer'
require 'rumale/utils'
require 'rumale/validation'

module Rumale
  module KernelApproximation
    # Class for RBF kernel feature mapping.
    #
    # @example
    #   require 'rumale/kernel_approximation/rbf'
    #
    #   transformer = Rumale::KernelApproximation::RBF.new(gamma: 1.0, n_components: 128, random_seed: 1)
    #   new_training_samples = transformer.fit_transform(training_samples)
    #   new_testing_samples = transformer.transform(testing_samples)
    #
    # *Refernce*:
    # - Rahimi, A., and Recht, B., "Random Features for Large-Scale Kernel Machines," Proc. NIPS'07, pp.1177--1184, 2007.
    class RBF < ::Rumale::Base::Estimator
      include ::Rumale::Base::Transformer

      # Return the random matrix for transformation.
      # @return [Numo::DFloat] (shape: [n_features, n_components])
      attr_reader :random_mat

      # Return the random vector for transformation.
      # @return [Numo::DFloat] (shape: [n_components])
      attr_reader :random_vec

      # Return the random generator for transformation.
      # @return [Random]
      attr_reader :rng

      # Create a new transformer for mapping to RBF kernel feature space.
      #
      # @param gamma [Float] The parameter of RBF kernel: exp(-gamma * x^2).
      # @param n_components [Integer] The number of dimensions of the RBF kernel feature space.
      # @param random_seed [Integer] The seed value using to initialize the random generator.
      def initialize(gamma: 1.0, n_components: 128, random_seed: nil)
        super()
        @params = {
          gamma: gamma,
          n_components: n_components,
          random_seed: random_seed || srand
        }
        @rng = Random.new(@params[:random_seed])
      end

      # Fit the model with given training data.
      #
      # @overload fit(x) -> RBF
      #   @param x [Numo::NArray] (shape: [n_samples, n_features]) The training data to be used for fitting the model.
      #     This method uses only the number of features of the data.
      #   @return [RBF] The learned transformer itself.
      def fit(x, _y = nil)
        x = ::Rumale::Validation.check_convert_sample_array(x)

        n_features = x.shape[1]
        sub_rng = @rng.dup
        @params[:n_components] = 2 * n_features if @params[:n_components] <= 0
        @random_mat = ::Rumale::Utils.rand_normal([n_features, @params[:n_components]], sub_rng) * (2.0 * @params[:gamma])**0.5
        n_half_components = @params[:n_components] / 2
        @random_vec = Numo::DFloat.zeros(@params[:n_components] - n_half_components).concatenate(
          Numo::DFloat.ones(n_half_components) * (0.5 * Math::PI)
        )
        self
      end

      # Fit the model with training data, and then transform them with the learned model.
      #
      # @overload fit_transform(x) -> Numo::DFloat
      #   @param x [Numo::DFloat] (shape: [n_samples, n_features]) The training data to be used for fitting the model.
      #   @return [Numo::DFloat] (shape: [n_samples, n_components]) The transformed data
      def fit_transform(x, _y = nil)
        x = ::Rumale::Validation.check_convert_sample_array(x)

        fit(x).transform(x)
      end

      # Transform the given data with the learned model.
      #
      # @overload transform(x) -> Numo::DFloat
      #   @param x [Numo::DFloat] (shape: [n_samples, n_features]) The data to be transformed with the learned model.
      #   @return [Numo::DFloat] (shape: [n_samples, n_components]) The transformed data.
      def transform(x)
        x = ::Rumale::Validation.check_convert_sample_array(x)

        n_samples, = x.shape
        projection = x.dot(@random_mat) + @random_vec.tile(n_samples, 1)
        Numo::NMath.sin(projection) * ((2.0 / @params[:n_components])**0.5)
      end
    end
  end
end