It is relatively easy to extend caracas
by calling
SymPy
functions directly.
This can be achived using sympy_func(x, fun, ...)
that
calls a member function on the object provided,
i.e. x$fun(...)
, or if that fails it calls a function from
the global namespace fun(x, ...)
.
As an example consider inverting a regular matrix A: Let B be the inverse of A. Then, using cofactors, Bij=Cji/det(A). The cofactor Cij is given as Cij=(−1)i+jMij where Mij is the determinant of the submatrix of A obtained by deleting the ith row and the jth column of A.
A quick search https://docs.sympy.org/latest/modules/matrices/matrices.html
shows that there are two relevant functions in SymPy
:
cofactor
and cofactor_matrix
.
If these functions are not available in caracas
they can
be made so using sympy_func
:
cofactor_matrix <- function(x) {
sympy_func(x, "cofactor_matrix")
}
cofactor <- function(x, i, j) {
# Python indexing starts at 0 - thus subtract 1 to convert from R indexing
# to Python indexing
sympy_func(x, "cofactor", i - 1, j - 1)
}
CC <- cofactor_matrix(A)
CC
#> c: ⎡a₂₂⋅a₃₃ - a₂₃⋅a₃₂ -a₂₁⋅a₃₃ + a₂₃⋅a₃₁ a₂₁⋅a₃₂ - a₂₂⋅a₃₁ ⎤
#> ⎢ ⎥
#> ⎢-a₁₂⋅a₃₃ + a₁₃⋅a₃₂ a₁₁⋅a₃₃ - a₁₃⋅a₃₁ -a₁₁⋅a₃₂ + a₁₂⋅a₃₁⎥
#> ⎢ ⎥
#> ⎣a₁₂⋅a₂₃ - a₁₃⋅a₂₂ -a₁₁⋅a₂₃ + a₁₃⋅a₂₁ a₁₁⋅a₂₂ - a₁₂⋅a₂₁ ⎦
cc <- cofactor(A, 1, 1)
cc
#> c: a₂₂⋅a₃₃ - a₂₃⋅a₃₂
We get the right answer