@info(" ... derivatives")
_rrval(x::ACE.XState) = x.rr
for ntest = 1:30
Us = randn(SMatrix{3, 3, Float64,9 }, length(Xs))
C = randn(typeof(φ.val), length(basis))
F = t -> sum( sum(c .* b.val)
for (c, b) in zip(C, ACE.evaluate(basis, ACEConfig(Xs + t[1] * Us))) )
dF = t -> [ sum( sum(c .* db)
for (c, db) in zip(C, _rrval.(ACE.evaluate_d(basis, ACEConfig(Xs + t[1] * Us))) * Us) ) ]
print_tf(@test fdtest(F, dF, [0.0], verbose=false))
end
println()
needs to be adapted for EuclideanMatrix ? That is, if we want to implement/use derivatives of matrix-valued equivariant functions w.r.t. to position ...
The test
needs to be adapted for
EuclideanMatrix
? That is, if we want to implement/use derivatives of matrix-valued equivariant functions w.r.t. to position ...