def g_directed_hausdorff(t1, t2):
"""
Usage
-----
directed hausdorff distance from trajectory t1 to trajectory t2.
Parameters
----------
param t1 : len(t1)x2 numpy_array
param t2 : len(t2)x2 numpy_array
Returns
-------
dh : float, directed hausdorff from trajectory t1 to trajectory t2
"""
n0 = len(t1)
n1 = len(t2)
dh = 0
for j in range(n1):
dist_j0 = 9e100
for i in range(n0 - 1):
dist_j0 = min(
dist_j0,
point_to_path(t1[i][0], t1[i][1], t1[i + 1][0], t1[i + 1][1],
t2[j][0], t2[j][1]))
dh = max(dh, dist_j0)
return dh
def g_spd(t1, t2):
"""
Usage
-----
The spd-distance of trajectory t2 from trajectory t1
The spd-distance is the sum of the all the point-to-path distance of points of t1 from trajectory t2
Parameters
----------
param t1 : len(t1)x2 numpy_array
param t2 : len(t2)x2 numpy_array
Returns
-------
spd : float
spd-distance of trajectory t2 from trajectory t1
"""
n0 = len(t1)
n1 = len(t2)
lats0 = t1[:, 1]
lons0 = t1[:, 0]
lats1 = t2[:, 1]
lons1 = t2[:, 0]
dist = 0
for j in range(n1):
dist_j0 = 9e100
for i in range(n0 - 1):
dist_j0 = np.min((dist_j0, point_to_path(lons0[i], lats0[i], lons0[i + 1], lats0[i + 1], lons1[j],
lats1[j])))
dist = dist + dist_j0
dist = float(dist) / n1
return dist
def g_directed_hausdorff (t1, t2):
"""
Usage
-----
directed hausdorff distance from trajectory t1 to trajectory t2.
Parameters
----------
param t1 : len(t1)x2 numpy_array
param t2 : len(t2)x2 numpy_array
Returns
-------
dh : float, directed hausdorff from trajectory t1 to trajectory t2
"""
n0=len(t1)
n1=len(t2)
dh=0
for j in range(n1) :
dist_j0=9e100
for i in range(n0-1):
dist_j0=min(dist_j0,point_to_path(t1[i][0],t1[i][1],t1[i+1][0],t1[i+1][1],t2[j][0],t2[j][1]))
dh=max(dh,dist_j0)
return dh
