#!/usr/bin/env python3
from bitten import *
import numpy as np

constraints, tol = (
    lambda *p: 2.0 * p[0] + 2.0 * p[1] + p[9] + p[10] - 10.0,
    lambda *p: 2.0 * p[0] + 2.0 * p[2] + p[9] + p[11] - 10.0,
    lambda *p: 2.0 * p[1] + 2.0 * p[2] + p[10] + p[11] - 10.0,
    lambda *p: -8.0 * p[0] + p[9],
    lambda *p: -8.0 * p[1] + p[10],
    lambda *p: -8.0 * p[2] + p[11],
    lambda *p: -2.0 * p[3] - p[4] + p[9],
    lambda *p: -2.0 * p[5] - p[6] + p[10],
    lambda *p: -2.0 * p[7] - p[8] + p[11],
), 1e-15


@bite(Minimize(), Constrain(*constraints, tol=tol), _debug=True)
def fun(
    p0=Bound(0.0, 1.0),
    p1=Bound(0.0, 1.0),
    p2=Bound(0.0, 1.0),
    p3=Bound(0.0, 1.0),
    p4=Bound(0.0, 1.0),
    p5=Bound(0.0, 1.0),
    p6=Bound(0.0, 1.0),
    p7=Bound(0.0, 1.0),
    p8=Bound(0.0, 1.0),
    p9=Bound(0.0, 100.0),
    pA=Bound(0.0, 100.0),
    pB=Bound(0.0, 100.0),
    pC=Bound(0.0, 1.0),
):
    p = (p0, p1, p2, p3, p4, p5, p6, p7, p8, p9, pA, pB, pC)
    s1, s2, s3 = 0.0, 0.0, 0.0
    for i in range(4):
        s1 += p[i]
        s2 += np.square(p[i])
    for i in range(4, 13):
        s3 += p[i]
    return 5.0 * s1 - 5.0 * s2 - s3


print("solution:", fun())
for k, v in fun.__optimized__.items():
    print(f"{k:>4}: {v:.8f}")