Promote matrix to the bundled gems

1 files changed, 0 insertions, 219 deletions

diff --git a/lib/matrix/lup_decomposition.rb b/lib/matrix/lup_decomposition.rb
deleted file mode 100644
index e37def75f6..0000000000
--- a/lib/matrix/lup_decomposition.rb
+++ /dev/null
@@ -1,219 +0,0 @@
-# frozen_string_literal: false
-class Matrix
-  # Adapted from JAMA: http://math.nist.gov/javanumerics/jama/
-
-  #
-  # For an m-by-n matrix A with m >= n, the LU decomposition is an m-by-n
-  # unit lower triangular matrix L, an n-by-n upper triangular matrix U,
-  # and a m-by-m permutation matrix P so that L*U = P*A.
-  # If m < n, then L is m-by-m and U is m-by-n.
-  #
-  # The LUP decomposition with pivoting always exists, even if the matrix is
-  # singular, so the constructor will never fail.  The primary use of the
-  # LU decomposition is in the solution of square systems of simultaneous
-  # linear equations.  This will fail if singular? returns true.
-  #
-
-  class LUPDecomposition
-    # Returns the lower triangular factor +L+
-
-    include Matrix::ConversionHelper
-
-    def l
-      Matrix.build(@row_count, [@column_count, @row_count].min) do |i, j|
-        if (i > j)
-          @lu[i][j]
-        elsif (i == j)
-          1
-        else
-          0
-        end
-      end
-    end
-
-    # Returns the upper triangular factor +U+
-
-    def u
-      Matrix.build([@column_count, @row_count].min, @column_count) do |i, j|
-        if (i <= j)
-          @lu[i][j]
-        else
-          0
-        end
-      end
-    end
-
-    # Returns the permutation matrix +P+
-
-    def p
-      rows = Array.new(@row_count){Array.new(@row_count, 0)}
-      @pivots.each_with_index{|p, i| rows[i][p] = 1}
-      Matrix.send :new, rows, @row_count
-    end
-
-    # Returns +L+, +U+, +P+ in an array
-
-    def to_ary
-      [l, u, p]
-    end
-    alias_method :to_a, :to_ary
-
-    # Returns the pivoting indices
-
-    attr_reader :pivots
-
-    # Returns +true+ if +U+, and hence +A+, is singular.
-
-    def singular?
-      @column_count.times do |j|
-        if (@lu[j][j] == 0)
-          return true
-        end
-      end
-      false
-    end
-
-    # Returns the determinant of +A+, calculated efficiently
-    # from the factorization.
-
-    def det
-      if (@row_count != @column_count)
-        raise Matrix::ErrDimensionMismatch
-      end
-      d = @pivot_sign
-      @column_count.times do |j|
-        d *= @lu[j][j]
-      end
-      d
-    end
-    alias_method :determinant, :det
-
-    # Returns +m+ so that <tt>A*m = b</tt>,
-    # or equivalently so that <tt>L*U*m = P*b</tt>
-    # +b+ can be a Matrix or a Vector
-
-    def solve b
-      if (singular?)
-        raise Matrix::ErrNotRegular, "Matrix is singular."
-      end
-      if b.is_a? Matrix
-        if (b.row_count != @row_count)
-          raise Matrix::ErrDimensionMismatch
-        end
-
-        # Copy right hand side with pivoting
-        nx = b.column_count
-        m = @pivots.map{|row| b.row(row).to_a}
-
-        # Solve L*Y = P*b
-        @column_count.times do |k|
-          (k+1).upto(@column_count-1) do |i|
-            nx.times do |j|
-              m[i][j] -= m[k][j]*@lu[i][k]
-            end
-          end
-        end
-        # Solve U*m = Y
-        (@column_count-1).downto(0) do |k|
-          nx.times do |j|
-            m[k][j] = m[k][j].quo(@lu[k][k])
-          end
-          k.times do |i|
-            nx.times do |j|
-              m[i][j] -= m[k][j]*@lu[i][k]
-            end
-          end
-        end
-        Matrix.send :new, m, nx
-      else # same algorithm, specialized for simpler case of a vector
-        b = convert_to_array(b)
-        if (b.size != @row_count)
-          raise Matrix::ErrDimensionMismatch
-        end
-
-        # Copy right hand side with pivoting
-        m = b.values_at(*@pivots)
-
-        # Solve L*Y = P*b
-        @column_count.times do |k|
-          (k+1).upto(@column_count-1) do |i|
-            m[i] -= m[k]*@lu[i][k]
-          end
-        end
-        # Solve U*m = Y
-        (@column_count-1).downto(0) do |k|
-          m[k] = m[k].quo(@lu[k][k])
-          k.times do |i|
-            m[i] -= m[k]*@lu[i][k]
-          end
-        end
-        Vector.elements(m, false)
-      end
-    end
-
-    def initialize a
-      raise TypeError, "Expected Matrix but got #{a.class}" unless a.is_a?(Matrix)
-      # Use a "left-looking", dot-product, Crout/Doolittle algorithm.
-      @lu = a.to_a
-      @row_count = a.row_count
-      @column_count = a.column_count
-      @pivots = Array.new(@row_count)
-      @row_count.times do |i|
-         @pivots[i] = i
-      end
-      @pivot_sign = 1
-      lu_col_j = Array.new(@row_count)
-
-      # Outer loop.
-
-      @column_count.times do |j|
-
-        # Make a copy of the j-th column to localize references.
-
-        @row_count.times do |i|
-          lu_col_j[i] = @lu[i][j]
-        end
-
-        # Apply previous transformations.
-
-        @row_count.times do |i|
-          lu_row_i = @lu[i]
-
-          # Most of the time is spent in the following dot product.
-
-          kmax = [i, j].min
-          s = 0
-          kmax.times do |k|
-            s += lu_row_i[k]*lu_col_j[k]
-          end
-
-          lu_row_i[j] = lu_col_j[i] -= s
-        end
-
-        # Find pivot and exchange if necessary.
-
-        p = j
-        (j+1).upto(@row_count-1) do |i|
-          if (lu_col_j[i].abs > lu_col_j[p].abs)
-            p = i
-          end
-        end
-        if (p != j)
-          @column_count.times do |k|
-            t = @lu[p][k]; @lu[p][k] = @lu[j][k]; @lu[j][k] = t
-          end
-          k = @pivots[p]; @pivots[p] = @pivots[j]; @pivots[j] = k
-          @pivot_sign = -@pivot_sign
-        end
-
-        # Compute multipliers.
-
-        if (j < @row_count && @lu[j][j] != 0)
-          (j+1).upto(@row_count-1) do |i|
-            @lu[i][j] = @lu[i][j].quo(@lu[j][j])
-          end
-        end
-      end
-    end
-  end
-end