def boundary_slow(self):
"""
This is the brute force version
Returns:
attract repel vector
"""
returnA = np.empty([len(self.maze_path), 2])
R1 = 2.0 * self.R0_B
for i in range(0,
len(self.maze_path)):
fi = np.array([0., 0.])
pi = np.array(self.maze_path[i])
for j in range(0,
len(self.boundary_seg) - 1):
j_pt = self.boundary_seg[j]
jp1_pt = self.boundary_seg[j + 1]
pi2xij, xij = TSPopt.distABtoP(j_pt, jp1_pt, pi)
self.minDist = min(self.minDist, pi2xij)
if pi2xij < R1:
fij = (pi - xij) / max(0.00001, pi2xij)
fij *= _repulse(self.R0_B / pi2xij) * self.Fo
fi += fij
returnA[i] = fi
return returnA
def runTest(self):
d1, (x, y) = TSPopt.distABtoP(self.segList1[0], self.segList1[1],
self.segList1[2])
self.assertEqual(d1, 1)
self.assertEqual(x, 0.)
self.assertEqual(y, 2.)
d2, (x, y) = TSPopt.distABtoP(self.segList1[0], self.segList1[1],
self.segList1[3])
self.assertEqual(d2, 2)
self.assertEqual(x, 0.)
self.assertEqual(y, 2.)
d3, (x, y) = TSPopt.distABtoP(self.segList1[0], self.segList1[1],
(1., 1.))
self.assertEqual(d3, 1)
self.assertEqual(x, 0.)
self.assertEqual(y, 1.)
d4, (x, y) = TSPopt.distABtoP(self.segList1[0], self.segList1[2],
(1., 1.))
self.assertEqual(d4, 1)
self.assertEqual(x, 0.)
self.assertEqual(y, 1.)
d5, (x, y) = TSPopt.distABtoP(self.segList1[0], self.segList1[2],
(-5., 2.))
self.assertEqual(d5, 5)
self.assertEqual(x, 0.)
self.assertEqual(y, 2.)
d6, (x, y) = TSPopt.distABtoP(self.segList1[0], self.segList1[2],
(4., 6.))
self.assertEqual(d6, 5)
self.assertEqual(x, 0.)
self.assertEqual(y, 3.)