jpmckinney/tf-idf-similarity

module TfIdfSimilarity
  module MatrixMethods
  private

    # @return [GSL::Matrix,NArray,NMatrix,Matrix] all document vectors as unit vectors
    #
    # @note Lucene normalizes document length differently.
    def normalize
      case @library
      when :gsl
        @matrix.clone.each_col do |column|
          unless column.isnull?
            column.normalize!
          end
        end
      when :narray # @see https://github.com/masa16/narray/issues/21
        norm = NMath.sqrt((@matrix ** 2).sum(1).reshape(@matrix.shape[0], 1))
        norm[norm.where2[1]] = 1.0 # avoid division by zero
        NMatrix.refer(@matrix / norm) # must be NMatrix for matrix multiplication
      when :numo
        norm = Numo::NMath.sqrt((@matrix ** 2).sum(0).reshape(1, @matrix.shape[1]))
        norm[(norm.eq 0).where] = 1.0 # avoid division by zero
        (@matrix / norm)
      when :nmatrix # @see https://github.com/SciRuby/nmatrix/issues/38
        normal = NMatrix.new(:dense, @matrix.shape, 0, :float64)
        (0...@matrix.shape[1]).each do |j|
          column = @matrix.column(j)
          norm = Math.sqrt(column.transpose.dot(column)[0, 0])
          (0...@matrix.shape[0]).each do |i|
            normal[i, j] = norm.zero? ? 0 : @matrix[i, j] / norm
          end
        end
        normal
      else
        Matrix.columns(@matrix.column_vectors.map do |column|
          if column.to_a.all?(&:zero?)
            column
          elsif column.respond_to?(:normalize)
            column.normalize
          else
            column / Math.sqrt(column.inner_product(column))
          end
        end)
      end
    end

    # @param [Integer] row index
    # @param [Integer] column index
    def get(i, j)
      case @library
      when :narray, :numo
        @matrix[j, i]
      else
        @matrix[i, j]
      end
    end

    # @param [Integer] index the row index
    # @return [GSL::Vector::View,NArray,NMatrix,Vector] a row
    def row(index)
      case @library
      when :narray
        @matrix[true, index]
      when :numo
        @matrix[index, true]
      else
        @matrix.row(index)
      end
    end

    # @param [Integer] index the column index
    # @return [GSL::Vector::View,NArray,NMatrix,Vector] a column
    def column(index)
      case @library
      when :narray, :numo
        @matrix[index, true]
      else
        @matrix.column(index)
      end
    end

    # @return [Float] the number of rows in the matrix
    def row_size
      case @library
      when :gsl, :nmatrix
        @matrix.shape[0]
      when :narray, :numo
        @matrix.shape[1]
      else
        @matrix.row_size
      end
    end

    # @return [Float] the number of columns in the matrix
    def column_size
      case @library
      when :gsl, :nmatrix
        @matrix.shape[1]
      when :narray, :numo
        @matrix.shape[0]
      else
        @matrix.column_size
      end
    end

    # @return [Array<Float>] the matrix's values
    def values
      case @library
      when :nmatrix
        @matrix.each.to_a # faster than NMatrix's `to_a` and `to_flat_a`
      else
        @matrix.to_a.flatten
      end
    end

    # @return [Float] the sum of all values in the matrix
    def sum
      case @library
      when :narray, :numo
        @matrix.sum
      else
        values.reduce(0, :+)
      end
    end

    # @param [Array<Array>] array matrix rows
    # @return [GSL::Matrix,NArray,NMatrix,Matrix] a matrix
    def initialize_matrix(array)
      case @library
      when :gsl
        GSL::Matrix[*array]
      when :narray
        NArray[*array]
      when :numo
        Numo::DFloat[*array]
      when :nmatrix # @see https://github.com/SciRuby/nmatrix/issues/91#issuecomment-18870619
        NMatrix.new(:dense, [array.size, array.empty? ? 0 : array[0].size], array.flatten, :float64)
      else
        Matrix[*array]
      end
    end

    # @param [GSL::Matrix,NArray,NMatrix,Matrix] matrix a matrix
    # @return [GSL::Matrix,NArray,NMatrix,Matrix] the product
    def multiply_self(matrix)
      case @library
      when :nmatrix, :numo
        matrix.transpose.dot(matrix)
      else
        matrix.transpose * matrix
      end
    end

    def log(number)
      case @library
      when :gsl
        GSL::Sf::log(number)
      when :narray
        NMath.log(number)
      when :numo
        Numo::NMath.log(number)
      else
        Math.log(number)
      end
    end

    def sqrt(number)
      case @library
      when :narray
        NMath.sqrt(number)
      when :numo
        Numo::NMath.sqrt(number)
      else
        Math.sqrt(number)
      end
    end
  end
end