works ish

h2/mbc.py

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