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c8c66bcf92
git-svn-id: svn+ssh://ci.ruby-lang.org/ruby/trunk@65504 b2dd03c8-39d4-4d8f-98ff-823fe69b080e
882 lines
22 KiB
Ruby
882 lines
22 KiB
Ruby
# frozen_string_literal: false
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class Matrix
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# Adapted from JAMA: http://math.nist.gov/javanumerics/jama/
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# Eigenvalues and eigenvectors of a real matrix.
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#
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# Computes the eigenvalues and eigenvectors of a matrix A.
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#
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# If A is diagonalizable, this provides matrices V and D
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# such that A = V*D*V.inv, where D is the diagonal matrix with entries
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# equal to the eigenvalues and V is formed by the eigenvectors.
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#
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# If A is symmetric, then V is orthogonal and thus A = V*D*V.t
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class EigenvalueDecomposition
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# Constructs the eigenvalue decomposition for a square matrix +A+
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#
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def initialize(a)
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# @d, @e: Arrays for internal storage of eigenvalues.
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# @v: Array for internal storage of eigenvectors.
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# @h: Array for internal storage of nonsymmetric Hessenberg form.
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raise TypeError, "Expected Matrix but got #{a.class}" unless a.is_a?(Matrix)
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@size = a.row_count
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@d = Array.new(@size, 0)
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@e = Array.new(@size, 0)
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if (@symmetric = a.symmetric?)
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@v = a.to_a
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tridiagonalize
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diagonalize
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else
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@v = Array.new(@size) { Array.new(@size, 0) }
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@h = a.to_a
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@ort = Array.new(@size, 0)
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reduce_to_hessenberg
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hessenberg_to_real_schur
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end
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end
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# Returns the eigenvector matrix +V+
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#
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def eigenvector_matrix
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Matrix.send(:new, build_eigenvectors.transpose)
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end
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alias_method :v, :eigenvector_matrix
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# Returns the inverse of the eigenvector matrix +V+
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#
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def eigenvector_matrix_inv
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r = Matrix.send(:new, build_eigenvectors)
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r = r.transpose.inverse unless @symmetric
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r
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end
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alias_method :v_inv, :eigenvector_matrix_inv
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# Returns the eigenvalues in an array
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#
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def eigenvalues
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values = @d.dup
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@e.each_with_index{|imag, i| values[i] = Complex(values[i], imag) unless imag == 0}
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values
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end
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# Returns an array of the eigenvectors
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#
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def eigenvectors
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build_eigenvectors.map{|ev| Vector.send(:new, ev)}
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end
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# Returns the block diagonal eigenvalue matrix +D+
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#
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def eigenvalue_matrix
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Matrix.diagonal(*eigenvalues)
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end
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alias_method :d, :eigenvalue_matrix
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# Returns [eigenvector_matrix, eigenvalue_matrix, eigenvector_matrix_inv]
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#
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def to_ary
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[v, d, v_inv]
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end
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alias_method :to_a, :to_ary
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private def build_eigenvectors
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# JAMA stores complex eigenvectors in a strange way
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# See http://web.archive.org/web/20111016032731/http://cio.nist.gov/esd/emaildir/lists/jama/msg01021.html
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@e.each_with_index.map do |imag, i|
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if imag == 0
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Array.new(@size){|j| @v[j][i]}
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elsif imag > 0
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Array.new(@size){|j| Complex(@v[j][i], @v[j][i+1])}
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else
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Array.new(@size){|j| Complex(@v[j][i-1], -@v[j][i])}
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end
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end
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end
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# Complex scalar division.
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private def cdiv(xr, xi, yr, yi)
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if (yr.abs > yi.abs)
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r = yi/yr
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d = yr + r*yi
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[(xr + r*xi)/d, (xi - r*xr)/d]
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else
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r = yr/yi
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d = yi + r*yr
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[(r*xr + xi)/d, (r*xi - xr)/d]
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end
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end
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# Symmetric Householder reduction to tridiagonal form.
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private def tridiagonalize
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# This is derived from the Algol procedures tred2 by
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# Bowdler, Martin, Reinsch, and Wilkinson, Handbook for
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# Auto. Comp., Vol.ii-Linear Algebra, and the corresponding
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# Fortran subroutine in EISPACK.
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@size.times do |j|
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@d[j] = @v[@size-1][j]
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end
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# Householder reduction to tridiagonal form.
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(@size-1).downto(0+1) do |i|
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# Scale to avoid under/overflow.
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scale = 0.0
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h = 0.0
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i.times do |k|
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scale = scale + @d[k].abs
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end
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if (scale == 0.0)
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@e[i] = @d[i-1]
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i.times do |j|
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@d[j] = @v[i-1][j]
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@v[i][j] = 0.0
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@v[j][i] = 0.0
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end
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else
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# Generate Householder vector.
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i.times do |k|
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@d[k] /= scale
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h += @d[k] * @d[k]
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end
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f = @d[i-1]
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g = Math.sqrt(h)
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if (f > 0)
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g = -g
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end
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@e[i] = scale * g
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h -= f * g
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@d[i-1] = f - g
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i.times do |j|
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@e[j] = 0.0
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end
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# Apply similarity transformation to remaining columns.
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i.times do |j|
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f = @d[j]
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@v[j][i] = f
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g = @e[j] + @v[j][j] * f
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(j+1).upto(i-1) do |k|
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g += @v[k][j] * @d[k]
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@e[k] += @v[k][j] * f
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end
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@e[j] = g
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end
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f = 0.0
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i.times do |j|
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@e[j] /= h
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f += @e[j] * @d[j]
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end
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hh = f / (h + h)
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i.times do |j|
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@e[j] -= hh * @d[j]
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end
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i.times do |j|
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f = @d[j]
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g = @e[j]
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j.upto(i-1) do |k|
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@v[k][j] -= (f * @e[k] + g * @d[k])
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end
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@d[j] = @v[i-1][j]
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@v[i][j] = 0.0
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end
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end
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@d[i] = h
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end
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# Accumulate transformations.
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0.upto(@size-1-1) do |i|
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@v[@size-1][i] = @v[i][i]
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@v[i][i] = 1.0
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h = @d[i+1]
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if (h != 0.0)
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0.upto(i) do |k|
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@d[k] = @v[k][i+1] / h
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end
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0.upto(i) do |j|
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g = 0.0
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0.upto(i) do |k|
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g += @v[k][i+1] * @v[k][j]
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end
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0.upto(i) do |k|
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@v[k][j] -= g * @d[k]
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end
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end
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end
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0.upto(i) do |k|
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@v[k][i+1] = 0.0
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end
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end
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@size.times do |j|
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@d[j] = @v[@size-1][j]
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@v[@size-1][j] = 0.0
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end
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@v[@size-1][@size-1] = 1.0
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@e[0] = 0.0
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end
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# Symmetric tridiagonal QL algorithm.
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private def diagonalize
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# This is derived from the Algol procedures tql2, by
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# Bowdler, Martin, Reinsch, and Wilkinson, Handbook for
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# Auto. Comp., Vol.ii-Linear Algebra, and the corresponding
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# Fortran subroutine in EISPACK.
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1.upto(@size-1) do |i|
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@e[i-1] = @e[i]
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end
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@e[@size-1] = 0.0
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f = 0.0
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tst1 = 0.0
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eps = Float::EPSILON
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@size.times do |l|
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# Find small subdiagonal element
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tst1 = [tst1, @d[l].abs + @e[l].abs].max
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m = l
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while (m < @size) do
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if (@e[m].abs <= eps*tst1)
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break
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end
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m+=1
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end
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# If m == l, @d[l] is an eigenvalue,
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# otherwise, iterate.
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if (m > l)
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iter = 0
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begin
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iter = iter + 1 # (Could check iteration count here.)
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# Compute implicit shift
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g = @d[l]
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p = (@d[l+1] - g) / (2.0 * @e[l])
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r = Math.hypot(p, 1.0)
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if (p < 0)
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r = -r
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end
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@d[l] = @e[l] / (p + r)
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@d[l+1] = @e[l] * (p + r)
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dl1 = @d[l+1]
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h = g - @d[l]
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(l+2).upto(@size-1) do |i|
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@d[i] -= h
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end
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f += h
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# Implicit QL transformation.
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p = @d[m]
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c = 1.0
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c2 = c
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c3 = c
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el1 = @e[l+1]
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s = 0.0
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s2 = 0.0
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(m-1).downto(l) do |i|
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c3 = c2
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c2 = c
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s2 = s
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g = c * @e[i]
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h = c * p
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r = Math.hypot(p, @e[i])
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@e[i+1] = s * r
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s = @e[i] / r
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c = p / r
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p = c * @d[i] - s * g
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@d[i+1] = h + s * (c * g + s * @d[i])
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# Accumulate transformation.
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@size.times do |k|
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h = @v[k][i+1]
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@v[k][i+1] = s * @v[k][i] + c * h
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@v[k][i] = c * @v[k][i] - s * h
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end
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end
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p = -s * s2 * c3 * el1 * @e[l] / dl1
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@e[l] = s * p
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@d[l] = c * p
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# Check for convergence.
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end while (@e[l].abs > eps*tst1)
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end
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@d[l] = @d[l] + f
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@e[l] = 0.0
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end
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# Sort eigenvalues and corresponding vectors.
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0.upto(@size-2) do |i|
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k = i
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p = @d[i]
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(i+1).upto(@size-1) do |j|
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if (@d[j] < p)
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k = j
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p = @d[j]
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end
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end
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if (k != i)
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@d[k] = @d[i]
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@d[i] = p
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@size.times do |j|
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p = @v[j][i]
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@v[j][i] = @v[j][k]
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@v[j][k] = p
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end
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end
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end
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end
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# Nonsymmetric reduction to Hessenberg form.
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private def reduce_to_hessenberg
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# This is derived from the Algol procedures orthes and ortran,
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# by Martin and Wilkinson, Handbook for Auto. Comp.,
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# Vol.ii-Linear Algebra, and the corresponding
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# Fortran subroutines in EISPACK.
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low = 0
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high = @size-1
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(low+1).upto(high-1) do |m|
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# Scale column.
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scale = 0.0
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m.upto(high) do |i|
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scale = scale + @h[i][m-1].abs
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end
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if (scale != 0.0)
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# Compute Householder transformation.
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h = 0.0
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high.downto(m) do |i|
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@ort[i] = @h[i][m-1]/scale
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h += @ort[i] * @ort[i]
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end
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g = Math.sqrt(h)
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if (@ort[m] > 0)
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g = -g
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end
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h -= @ort[m] * g
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@ort[m] = @ort[m] - g
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# Apply Householder similarity transformation
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# @h = (I-u*u'/h)*@h*(I-u*u')/h)
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m.upto(@size-1) do |j|
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f = 0.0
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high.downto(m) do |i|
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f += @ort[i]*@h[i][j]
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end
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f = f/h
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m.upto(high) do |i|
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@h[i][j] -= f*@ort[i]
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end
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end
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0.upto(high) do |i|
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f = 0.0
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high.downto(m) do |j|
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f += @ort[j]*@h[i][j]
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end
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f = f/h
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m.upto(high) do |j|
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@h[i][j] -= f*@ort[j]
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end
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end
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@ort[m] = scale*@ort[m]
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@h[m][m-1] = scale*g
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end
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end
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# Accumulate transformations (Algol's ortran).
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@size.times do |i|
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@size.times do |j|
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@v[i][j] = (i == j ? 1.0 : 0.0)
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end
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end
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(high-1).downto(low+1) do |m|
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if (@h[m][m-1] != 0.0)
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(m+1).upto(high) do |i|
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@ort[i] = @h[i][m-1]
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end
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m.upto(high) do |j|
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g = 0.0
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m.upto(high) do |i|
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g += @ort[i] * @v[i][j]
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end
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# Double division avoids possible underflow
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g = (g / @ort[m]) / @h[m][m-1]
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m.upto(high) do |i|
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@v[i][j] += g * @ort[i]
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end
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end
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end
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end
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end
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# Nonsymmetric reduction from Hessenberg to real Schur form.
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private def hessenberg_to_real_schur
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# This is derived from the Algol procedure hqr2,
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# by Martin and Wilkinson, Handbook for Auto. Comp.,
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# Vol.ii-Linear Algebra, and the corresponding
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# Fortran subroutine in EISPACK.
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# Initialize
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nn = @size
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n = nn-1
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low = 0
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high = nn-1
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eps = Float::EPSILON
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exshift = 0.0
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p = q = r = s = z = 0
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# Store roots isolated by balanc and compute matrix norm
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norm = 0.0
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nn.times do |i|
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if (i < low || i > high)
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@d[i] = @h[i][i]
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@e[i] = 0.0
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end
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([i-1, 0].max).upto(nn-1) do |j|
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norm = norm + @h[i][j].abs
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end
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end
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# Outer loop over eigenvalue index
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iter = 0
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while (n >= low) do
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# Look for single small sub-diagonal element
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l = n
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while (l > low) do
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s = @h[l-1][l-1].abs + @h[l][l].abs
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if (s == 0.0)
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s = norm
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end
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if (@h[l][l-1].abs < eps * s)
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break
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end
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l-=1
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end
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# Check for convergence
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# One root found
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if (l == n)
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@h[n][n] = @h[n][n] + exshift
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@d[n] = @h[n][n]
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@e[n] = 0.0
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n-=1
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iter = 0
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# Two roots found
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elsif (l == n-1)
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w = @h[n][n-1] * @h[n-1][n]
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p = (@h[n-1][n-1] - @h[n][n]) / 2.0
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q = p * p + w
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z = Math.sqrt(q.abs)
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@h[n][n] = @h[n][n] + exshift
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@h[n-1][n-1] = @h[n-1][n-1] + exshift
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x = @h[n][n]
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# Real pair
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if (q >= 0)
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if (p >= 0)
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z = p + z
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else
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z = p - z
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end
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@d[n-1] = x + z
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@d[n] = @d[n-1]
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if (z != 0.0)
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@d[n] = x - w / z
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end
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@e[n-1] = 0.0
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@e[n] = 0.0
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x = @h[n][n-1]
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s = x.abs + z.abs
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p = x / s
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q = z / s
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r = Math.sqrt(p * p+q * q)
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p /= r
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q /= r
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# Row modification
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(n-1).upto(nn-1) do |j|
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z = @h[n-1][j]
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@h[n-1][j] = q * z + p * @h[n][j]
|
|
@h[n][j] = q * @h[n][j] - p * z
|
|
end
|
|
|
|
# Column modification
|
|
|
|
0.upto(n) do |i|
|
|
z = @h[i][n-1]
|
|
@h[i][n-1] = q * z + p * @h[i][n]
|
|
@h[i][n] = q * @h[i][n] - p * z
|
|
end
|
|
|
|
# Accumulate transformations
|
|
|
|
low.upto(high) do |i|
|
|
z = @v[i][n-1]
|
|
@v[i][n-1] = q * z + p * @v[i][n]
|
|
@v[i][n] = q * @v[i][n] - p * z
|
|
end
|
|
|
|
# Complex pair
|
|
|
|
else
|
|
@d[n-1] = x + p
|
|
@d[n] = x + p
|
|
@e[n-1] = z
|
|
@e[n] = -z
|
|
end
|
|
n -= 2
|
|
iter = 0
|
|
|
|
# No convergence yet
|
|
|
|
else
|
|
|
|
# Form shift
|
|
|
|
x = @h[n][n]
|
|
y = 0.0
|
|
w = 0.0
|
|
if (l < n)
|
|
y = @h[n-1][n-1]
|
|
w = @h[n][n-1] * @h[n-1][n]
|
|
end
|
|
|
|
# Wilkinson's original ad hoc shift
|
|
|
|
if (iter == 10)
|
|
exshift += x
|
|
low.upto(n) do |i|
|
|
@h[i][i] -= x
|
|
end
|
|
s = @h[n][n-1].abs + @h[n-1][n-2].abs
|
|
x = y = 0.75 * s
|
|
w = -0.4375 * s * s
|
|
end
|
|
|
|
# MATLAB's new ad hoc shift
|
|
|
|
if (iter == 30)
|
|
s = (y - x) / 2.0
|
|
s *= s + w
|
|
if (s > 0)
|
|
s = Math.sqrt(s)
|
|
if (y < x)
|
|
s = -s
|
|
end
|
|
s = x - w / ((y - x) / 2.0 + s)
|
|
low.upto(n) do |i|
|
|
@h[i][i] -= s
|
|
end
|
|
exshift += s
|
|
x = y = w = 0.964
|
|
end
|
|
end
|
|
|
|
iter = iter + 1 # (Could check iteration count here.)
|
|
|
|
# Look for two consecutive small sub-diagonal elements
|
|
|
|
m = n-2
|
|
while (m >= l) do
|
|
z = @h[m][m]
|
|
r = x - z
|
|
s = y - z
|
|
p = (r * s - w) / @h[m+1][m] + @h[m][m+1]
|
|
q = @h[m+1][m+1] - z - r - s
|
|
r = @h[m+2][m+1]
|
|
s = p.abs + q.abs + r.abs
|
|
p /= s
|
|
q /= s
|
|
r /= s
|
|
if (m == l)
|
|
break
|
|
end
|
|
if (@h[m][m-1].abs * (q.abs + r.abs) <
|
|
eps * (p.abs * (@h[m-1][m-1].abs + z.abs +
|
|
@h[m+1][m+1].abs)))
|
|
break
|
|
end
|
|
m-=1
|
|
end
|
|
|
|
(m+2).upto(n) do |i|
|
|
@h[i][i-2] = 0.0
|
|
if (i > m+2)
|
|
@h[i][i-3] = 0.0
|
|
end
|
|
end
|
|
|
|
# Double QR step involving rows l:n and columns m:n
|
|
|
|
m.upto(n-1) do |k|
|
|
notlast = (k != n-1)
|
|
if (k != m)
|
|
p = @h[k][k-1]
|
|
q = @h[k+1][k-1]
|
|
r = (notlast ? @h[k+2][k-1] : 0.0)
|
|
x = p.abs + q.abs + r.abs
|
|
next if x == 0
|
|
p /= x
|
|
q /= x
|
|
r /= x
|
|
end
|
|
s = Math.sqrt(p * p + q * q + r * r)
|
|
if (p < 0)
|
|
s = -s
|
|
end
|
|
if (s != 0)
|
|
if (k != m)
|
|
@h[k][k-1] = -s * x
|
|
elsif (l != m)
|
|
@h[k][k-1] = -@h[k][k-1]
|
|
end
|
|
p += s
|
|
x = p / s
|
|
y = q / s
|
|
z = r / s
|
|
q /= p
|
|
r /= p
|
|
|
|
# Row modification
|
|
|
|
k.upto(nn-1) do |j|
|
|
p = @h[k][j] + q * @h[k+1][j]
|
|
if (notlast)
|
|
p += r * @h[k+2][j]
|
|
@h[k+2][j] = @h[k+2][j] - p * z
|
|
end
|
|
@h[k][j] = @h[k][j] - p * x
|
|
@h[k+1][j] = @h[k+1][j] - p * y
|
|
end
|
|
|
|
# Column modification
|
|
|
|
0.upto([n, k+3].min) do |i|
|
|
p = x * @h[i][k] + y * @h[i][k+1]
|
|
if (notlast)
|
|
p += z * @h[i][k+2]
|
|
@h[i][k+2] = @h[i][k+2] - p * r
|
|
end
|
|
@h[i][k] = @h[i][k] - p
|
|
@h[i][k+1] = @h[i][k+1] - p * q
|
|
end
|
|
|
|
# Accumulate transformations
|
|
|
|
low.upto(high) do |i|
|
|
p = x * @v[i][k] + y * @v[i][k+1]
|
|
if (notlast)
|
|
p += z * @v[i][k+2]
|
|
@v[i][k+2] = @v[i][k+2] - p * r
|
|
end
|
|
@v[i][k] = @v[i][k] - p
|
|
@v[i][k+1] = @v[i][k+1] - p * q
|
|
end
|
|
end # (s != 0)
|
|
end # k loop
|
|
end # check convergence
|
|
end # while (n >= low)
|
|
|
|
# Backsubstitute to find vectors of upper triangular form
|
|
|
|
if (norm == 0.0)
|
|
return
|
|
end
|
|
|
|
(nn-1).downto(0) do |k|
|
|
p = @d[k]
|
|
q = @e[k]
|
|
|
|
# Real vector
|
|
|
|
if (q == 0)
|
|
l = k
|
|
@h[k][k] = 1.0
|
|
(k-1).downto(0) do |i|
|
|
w = @h[i][i] - p
|
|
r = 0.0
|
|
l.upto(k) do |j|
|
|
r += @h[i][j] * @h[j][k]
|
|
end
|
|
if (@e[i] < 0.0)
|
|
z = w
|
|
s = r
|
|
else
|
|
l = i
|
|
if (@e[i] == 0.0)
|
|
if (w != 0.0)
|
|
@h[i][k] = -r / w
|
|
else
|
|
@h[i][k] = -r / (eps * norm)
|
|
end
|
|
|
|
# Solve real equations
|
|
|
|
else
|
|
x = @h[i][i+1]
|
|
y = @h[i+1][i]
|
|
q = (@d[i] - p) * (@d[i] - p) + @e[i] * @e[i]
|
|
t = (x * s - z * r) / q
|
|
@h[i][k] = t
|
|
if (x.abs > z.abs)
|
|
@h[i+1][k] = (-r - w * t) / x
|
|
else
|
|
@h[i+1][k] = (-s - y * t) / z
|
|
end
|
|
end
|
|
|
|
# Overflow control
|
|
|
|
t = @h[i][k].abs
|
|
if ((eps * t) * t > 1)
|
|
i.upto(k) do |j|
|
|
@h[j][k] = @h[j][k] / t
|
|
end
|
|
end
|
|
end
|
|
end
|
|
|
|
# Complex vector
|
|
|
|
elsif (q < 0)
|
|
l = n-1
|
|
|
|
# Last vector component imaginary so matrix is triangular
|
|
|
|
if (@h[n][n-1].abs > @h[n-1][n].abs)
|
|
@h[n-1][n-1] = q / @h[n][n-1]
|
|
@h[n-1][n] = -(@h[n][n] - p) / @h[n][n-1]
|
|
else
|
|
cdivr, cdivi = cdiv(0.0, -@h[n-1][n], @h[n-1][n-1]-p, q)
|
|
@h[n-1][n-1] = cdivr
|
|
@h[n-1][n] = cdivi
|
|
end
|
|
@h[n][n-1] = 0.0
|
|
@h[n][n] = 1.0
|
|
(n-2).downto(0) do |i|
|
|
ra = 0.0
|
|
sa = 0.0
|
|
l.upto(n) do |j|
|
|
ra = ra + @h[i][j] * @h[j][n-1]
|
|
sa = sa + @h[i][j] * @h[j][n]
|
|
end
|
|
w = @h[i][i] - p
|
|
|
|
if (@e[i] < 0.0)
|
|
z = w
|
|
r = ra
|
|
s = sa
|
|
else
|
|
l = i
|
|
if (@e[i] == 0)
|
|
cdivr, cdivi = cdiv(-ra, -sa, w, q)
|
|
@h[i][n-1] = cdivr
|
|
@h[i][n] = cdivi
|
|
else
|
|
|
|
# Solve complex equations
|
|
|
|
x = @h[i][i+1]
|
|
y = @h[i+1][i]
|
|
vr = (@d[i] - p) * (@d[i] - p) + @e[i] * @e[i] - q * q
|
|
vi = (@d[i] - p) * 2.0 * q
|
|
if (vr == 0.0 && vi == 0.0)
|
|
vr = eps * norm * (w.abs + q.abs +
|
|
x.abs + y.abs + z.abs)
|
|
end
|
|
cdivr, cdivi = cdiv(x*r-z*ra+q*sa, x*s-z*sa-q*ra, vr, vi)
|
|
@h[i][n-1] = cdivr
|
|
@h[i][n] = cdivi
|
|
if (x.abs > (z.abs + q.abs))
|
|
@h[i+1][n-1] = (-ra - w * @h[i][n-1] + q * @h[i][n]) / x
|
|
@h[i+1][n] = (-sa - w * @h[i][n] - q * @h[i][n-1]) / x
|
|
else
|
|
cdivr, cdivi = cdiv(-r-y*@h[i][n-1], -s-y*@h[i][n], z, q)
|
|
@h[i+1][n-1] = cdivr
|
|
@h[i+1][n] = cdivi
|
|
end
|
|
end
|
|
|
|
# Overflow control
|
|
|
|
t = [@h[i][n-1].abs, @h[i][n].abs].max
|
|
if ((eps * t) * t > 1)
|
|
i.upto(n) do |j|
|
|
@h[j][n-1] = @h[j][n-1] / t
|
|
@h[j][n] = @h[j][n] / t
|
|
end
|
|
end
|
|
end
|
|
end
|
|
end
|
|
end
|
|
|
|
# Vectors of isolated roots
|
|
|
|
nn.times do |i|
|
|
if (i < low || i > high)
|
|
i.upto(nn-1) do |j|
|
|
@v[i][j] = @h[i][j]
|
|
end
|
|
end
|
|
end
|
|
|
|
# Back transformation to get eigenvectors of original matrix
|
|
|
|
(nn-1).downto(low) do |j|
|
|
low.upto(high) do |i|
|
|
z = 0.0
|
|
low.upto([j, high].min) do |k|
|
|
z += @v[i][k] * @h[k][j]
|
|
end
|
|
@v[i][j] = z
|
|
end
|
|
end
|
|
end
|
|
|
|
end
|
|
end
|