include("Engine.jl")

using SparseArrays
using AbstractAlgebra
using PolynomialRoots
using Random

# initialize the partial gram matrix for an arrangement of seven spheres in
# which spheres 1 through 5 are mutually tangent, and spheres 3 through 7 are
# also mutually tangent
J = Int64[]
K = Int64[]
values = BigFloat[]
for j in 1:7
  for k in 1:7
    if (j <= 5 && k <= 5) || (j >= 3 && k >= 3)
      push!(J, j)
      push!(K, k)
      push!(values, j == k ? 1 : -1)
    end
  end
end
gram = sparse(J, K, values)

# set the independent variable
#
# using gram[6, 2] or gram[7, 1] as the independent variable seems to stall
# convergence, even if its value comes from a known solution, like
#
#   gram[6, 2] = 0.9936131705272925
#
indep_val = -9//5
gram[6, 1] = BigFloat(indep_val)
gram[1, 6] = gram[6, 1]

# in this initial guess, the mutual tangency condition is satisfied for spheres
# 1 through 5
Random.seed!(50793)
guess = let
  a = sqrt(BigFloat(3)/2)
  hcat(
    sqrt(1/BigFloat(2)) * BigFloat[
      1    1   -1   -1    0
      1   -1    1   -1    0
      1   -1   -1    1    0
      0.5  0.5  0.5  0.5  1+a
      0.5  0.5  0.5  0.5  1-a
    ] + 0.2*Engine.rand_on_shell(fill(BigFloat(-1), 5)),
    Engine.rand_on_shell(fill(BigFloat(-1), 2))
  )
end

# complete the gram matrix using Newton's method with backtracking
L, success, history = Engine.realize_gram(gram, guess)
completed_gram = L'*Engine.Q*L
println("Completed Gram matrix:\n")
display(completed_gram)
if success
  println("\nTarget accuracy achieved!")
else
  println("\nFailed to reach target accuracy")
end
println("Steps: ", size(history.scaled_loss, 1))
println("Loss: ", history.scaled_loss[end], "\n")

# === algebraic check ===

#=
R, gens = polynomial_ring(AbstractAlgebra.Rationals{BigInt}(), ["x", "t₁", "t₂", "t₃"])
x = gens[1]
t = gens[2:4]

S, u = polynomial_ring(AbstractAlgebra.Rationals{BigInt}(), "u")

M = matrix_space(R, 7, 7)
gram_symb = M(R[
   1    -1    -1 -1 -1  t[1] t[2];
  -1     1    -1 -1 -1  x    t[3]
  -1    -1     1 -1 -1 -1   -1;
  -1    -1    -1  1 -1 -1   -1;
  -1    -1    -1 -1  1 -1   -1;
   t[1]  x    -1 -1 -1  1   -1;
   t[2]  t[3] -1 -1 -1 -1    1
])
rank_constraints = det.([
  gram_symb[1:6, 1:6],
  gram_symb[2:7, 2:7],
  gram_symb[[1, 3, 4, 5, 6, 7], [1, 3, 4, 5, 6, 7]]
])

# solve for x and t
x_constraint = 25//16 * to_univariate(S, evaluate(rank_constraints[1], [2], [indep_val]))
t₂_constraint = 25//16 * to_univariate(S, evaluate(rank_constraints[3], [2], [indep_val]))
x_vals = PolynomialRoots.roots(x_constraint.coeffs)
t₂_vals = PolynomialRoots.roots(t₂_constraint.coeffs)
=#