works ish
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20c484809a
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87
h2/mbc.py
87
h2/mbc.py
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@ -75,16 +75,38 @@ def solve_1dlp(c: float, constraints: List[Tuple[float, float]]):
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:param constraints: [(ai, bi), ...]
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:return: x1
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"""
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min_ = -10000
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max_ = 10000
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for constraint in constraints:
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(a, b) = constraint
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if a == 0:
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assert(b >= 0)
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if a > 0:
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max_ = min(b/a, max_)
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else:
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min_ = max(b/a, min_)
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if c > 0:
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return max(b/a for a, b in constraints if a < 0)
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return min(b/a for a, b in constraints if a > 0)
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return min_
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else:
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return max_
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assert solve_1dlp(1, [(-1, -2)]) == 2
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assert solve_1dlp(1, [(-1, -2), (-1, -3)]) == 3
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assert solve_1dlp(1, [(-1, -3), (-1, -2)]) == 3
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assert solve_1dlp(-1, [(1, 3), (1, 2)]) == 2
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assert solve_1dlp(1, [(-1, 3), (-1, 2)]) == -2
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# if c > 0:
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# return max(b/a for a, b in constraints if a < 0)
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# return min(b/a for a, b in constraints if a > 0)
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#assert solve_1dlp(1, [(-1, -2)]) == 2
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#assert solve_1dlp(1, [(-1, -2), (-1, -3)]) == 3
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#assert solve_1dlp(1, [(-1, -3), (-1, -2)]) == 3
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#assert solve_1dlp(-1, [(1, 3), (1, 2)]) == 2
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#assert solve_1dlp(1, [(-1, 3), (-1, 2)]) == -2
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def solve_2dlp(c: Tuple[float, float], constraints: List[Tuple[Tuple[float, float], float]]):
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@ -93,44 +115,51 @@ def solve_2dlp(c: Tuple[float, float], constraints: List[Tuple[Tuple[float, floa
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:param constraints: [(ai1, ai2, bi), ...]
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:return: x1, x2
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"""
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x1, x2 = -inf, -inf # TODO: something other than -inf (?)
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if c[0] > 0:
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x1 = -10000
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else:
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x1 = 10000
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if c[1] > 0:
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x2 = -10000
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else:
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x2 = 10000
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our_constraints = []
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for (a1, a2), b in constraints: # TODO: random.shuffle()
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print("x1 and x2", x1, x2)
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if len(our_constraints) == 0:
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our_constraints.append(((a1, a2), b))
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continue
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print("{} + {} <= {}".format(a1*x1, a2*x2, b))
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if not (a1*x1 + a2*x2 <= b):
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if not a1*x1 + a2*x2 <= b:
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print("rrgeerg"*30)
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constraint_for_1d = []
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# a_prime = a1 - (a2*a1)/a2
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# b_prime = b - (a2*b)/a2
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# print("a,b prime", a_prime, b_prime)
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# constraint_for_1d.append((a_prime, b_prime))
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# Fix this
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new_obj = c[0] - (c[1]*a1)/a2
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new_obj = c[0] - ((c[1]*a1)/a2)
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for constraint in our_constraints:
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(a_i1, a_i2), b_i = constraint
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print("lel", a_i1, a_i2, b_i)
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a_prime = a_i1 - ((a_i2*a1)/a2)
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b_prime = b_i - ((a_i2*b)/a2)
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constraint_for_1d.append((a_prime, b_prime))
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print("lal", a1, a2, b)
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print(new_obj)
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print(constraint_for_1d)
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print("obj", new_obj)
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print("const", constraint_for_1d)
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print("lol",[cons[0] for cons in constraint_for_1d])
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# res = linprog([new_obj], [[cons[0]] for cons in constraint_for_1d], [[cons[1]] for cons in constraint_for_1d], bounds=[(None, None)])
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x1 = solve_1dlp(new_obj, constraint_for_1d)
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x2 = ((b/a2) - (a1/a2))*x1
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# x1 = res.x[0]
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x2 = ((b/a2) - (a1/a2)*x1)
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our_constraints.append(((a1, a2), b))
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return x1, x2
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@ -146,7 +175,7 @@ constraints = [
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result = solve_2dlp(c, constraints)
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print(result)
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exit()
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#exit()
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def mbc_ch(points: Set[Point], linprog_flipper: callable) -> Set[Point]:
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@ -165,8 +194,12 @@ def mbc_ch(points: Set[Point], linprog_flipper: callable) -> Set[Point]:
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A = [[linprog_flipper(-p.x), linprog_flipper(-1)] for p in points]
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b = [linprog_flipper(-p.y) for p in points]
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result = linprog(c, A, b, bounds=[(None, None), (None, None)], options={"tol": 0.00001})
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slope, intercept = result.x[0], result.x[1]
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#result = linprog(c, A, b, bounds=[(None, None), (None, None)], options={"tol": 0.00001})
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#slope, intercept = result.x[0], result.x[1]
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slope, intercept = solve_2dlp(c, list(zip(A, b)))
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#display_line_only(points, slope, intercept, [])
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# Find the two points which are on the line, should work
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left_point = next(p for p in pl if sidedness(slope, intercept, p, linprog_flipper) == Side.ON)
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