def g_erp(t0, t1, g):
"""
Usage
-----
The Edit distance with Real Penalty between trajectory t0 and t1.
Parameters
----------
param t0 : len(t0)x2 numpy_array
param t1 : len(t1)x2 numpy_array
Returns
-------
dtw : float
The Dynamic-Time Warping distance between trajectory t0 and t1
"""
n0 = len(t0)
n1 = len(t1)
C = np.zeros((n0 + 1, n1 + 1))
C[1:, 0] = sum(
map(lambda x: abs(great_circle_distance(g[0], g[1], x[0], x[1])), t0))
C[0, 1:] = sum(
map(lambda y: abs(great_circle_distance(g[0], g[1], y[0], y[1])), t1))
for i in np.arange(n0) + 1:
for j in np.arange(n1) + 1:
derp0 = C[i - 1, j] + great_circle_distance(
t0[i - 1][0], t0[i - 1][1], g[0], g[1])
derp1 = C[i, j - 1] + great_circle_distance(
g[0], g[1], t1[j - 1][0], t1[j - 1][1])
derp01 = C[i - 1, j - 1] + great_circle_distance(
t0[i - 1][0], t0[i - 1][1], t1[j - 1][0], t1[j - 1][1])
C[i, j] = min(derp0, derp1, derp01)
erp = C[n0, n1]
return erp
def g_erp(t0,t1,g):
"""
Usage
-----
The Edit distance with Real Penalty between trajectory t0 and t1.
Parameters
----------
param t0 : len(t0)x2 numpy_array
param t1 : len(t1)x2 numpy_array
Returns
-------
dtw : float
The Dynamic-Time Warping distance between trajectory t0 and t1
"""
n0 = len(t0)
n1 = len(t1)
C=np.zeros((n0+1,n1+1))
C[1:,0]=sum(map(lambda x : abs(great_circle_distance(g[0],g[1],x[0],x[1])),t0))
C[0,1:]=sum(map(lambda y : abs(great_circle_distance(g[0],g[1],y[0],y[1])),t1))
for i in np.arange(n0)+1:
for j in np.arange(n1)+1:
derp0 = C[i-1,j] + great_circle_distance(t0[i-1][0],t0[i-1][1],g[0],g[1])
derp1 = C[i,j-1] + great_circle_distance(g[0],g[1],t1[j-1][0],t1[j-1][1])
derp01 = C[i-1,j-1] + great_circle_distance(t0[i-1][0],t0[i-1][1],t1[j-1][0],t1[j-1][1])
C[i,j] = min(derp0,derp1,derp01)
erp = C[n0,n1]
return erp
def g_dtw(t0, t1):
"""
Usage
-----
The Dynamic-Time Warping distance between trajectory t0 and t1.
Parameters
----------
param t0 : len(t0)x2 numpy_array
param t1 : len(t1)x2 numpy_array
Returns
-------
dtw : float
The Dynamic-Time Warping distance between trajectory t0 and t1
"""
n0 = len(t0)
n1 = len(t1)
C = np.zeros((n0 + 1, n1 + 1))
C[1:, 0] = float('inf')
C[0, 1:] = float('inf')
for i in np.arange(n0) + 1:
for j in np.arange(n1) + 1:
C[i, j] = great_circle_distance(
t0[i - 1][0], t0[i - 1][1], t1[j - 1][0], t1[j - 1][1]) + min(
C[i, j - 1], C[i - 1, j - 1], C[i - 1, j])
dtw = C[n0, n1]
return dtw
def g_edr(t0, t1,eps):
"""
Usage
-----
The Edit Distance on Real sequence between trajectory t0 and t1.
Parameters
----------
param t0 : len(t0)x2 numpy_array
param t1 : len(t1)x2 numpy_array
eps : float
Returns
-------
edr : float
The Longuest-Common-Subsequence distance between trajectory t0 and t1
"""
n0 = len(t0)
n1 = len(t1)
# An (m+1) times (n+1) matrix
C = [[0] * (n1+1) for _ in range(n0+1)]
for i in range(1, n0+1):
for j in range(1, n1+1):
if great_circle_distance(t0[i-1][0],t0[i-1][1],t1[j-1][0],t1[j-1][1])<eps:
subcost = 0
else:
subcost = 1
C[i][j] = min(C[i][j-1]+1, C[i-1][j]+1,C[i-1][j-1]+subcost)
edr = float(C[n0][n1])/max([n0,n1])
return edr
def g_lcss(t0, t1, eps):
"""
Usage
-----
The Longuest-Common-Subsequence distance between trajectory t0 and t1.
Parameters
----------
param t0 : len(t0)x2 numpy_array
param t1 : len(t1)x2 numpy_array
eps : float
Returns
-------
lcss : float
The Longuest-Common-Subsequence distance between trajectory t0 and t1
"""
n0 = len(t0)
n1 = len(t1)
# An (m+1) times (n+1) matrix
C = [[0] * (n1 + 1) for _ in range(n0 + 1)]
for i in range(1, n0 + 1):
for j in range(1, n1 + 1):
if great_circle_distance(t0[i - 1, 0], t0[i - 1, 1], t1[j - 1, 0],
t1[j - 1, 1]) < eps:
C[i][j] = C[i - 1][j - 1] + 1
else:
C[i][j] = max(C[i][j - 1], C[i - 1][j])
lcss = 1 - float(C[n0][n1]) / min([n0, n1])
return lcss
def g_dtw(t0,t1):
"""
Usage
-----
The Dynamic-Time Warping distance between trajectory t0 and t1.
Parameters
----------
param t0 : len(t0)x2 numpy_array
param t1 : len(t1)x2 numpy_array
Returns
-------
dtw : float
The Dynamic-Time Warping distance between trajectory t0 and t1
"""
n0 = len(t0)
n1 = len(t1)
C=np.zeros((n0+1,n1+1))
C[1:,0]=float('inf')
C[0,1:]=float('inf')
for i in np.arange(n0)+1:
for j in np.arange(n1)+1:
C[i,j]=great_circle_distance(t0[i-1][0],t0[i-1][1],t1[j-1][0],t1[j-1][1]) + min(C[i,j-1],C[i-1,j-1],C[i-1,j])
dtw = C[n0,n1]
return dtw
def g_lcss(t0, t1,eps):
"""
Usage
-----
The Longuest-Common-Subsequence distance between trajectory t0 and t1.
Parameters
----------
param t0 : len(t0)x2 numpy_array
param t1 : len(t1)x2 numpy_array
eps : float
Returns
-------
lcss : float
The Longuest-Common-Subsequence distance between trajectory t0 and t1
"""
n0 = len(t0)
n1 = len(t1)
# An (m+1) times (n+1) matrix
C = [[0] * (n1+1) for _ in range(n0+1)]
for i in range(1, n0+1):
for j in range(1, n1+1):
if great_circle_distance(t0[i-1,0],t0[i-1,1],t1[j-1,0],t1[j-1,1])<eps:
C[i][j] = C[i-1][j-1] + 1
else:
C[i][j] = max(C[i][j-1], C[i-1][j])
lcss = 1-float(C[n0][n1])/min([n0,n1])
return lcss
def g_edr(t0, t1, eps):
"""
Usage
-----
The Edit Distance on Real sequence between trajectory t0 and t1.
Parameters
----------
param t0 : len(t0)x2 numpy_array
param t1 : len(t1)x2 numpy_array
eps : float
Returns
-------
edr : float
The Longuest-Common-Subsequence distance between trajectory t0 and t1
"""
n0 = len(t0)
n1 = len(t1)
# An (m+1) times (n+1) matrix
C = [[0] * (n1 + 1) for _ in range(n0 + 1)]
for i in range(1, n0 + 1):
for j in range(1, n1 + 1):
if great_circle_distance(t0[i - 1][0], t0[i - 1][1], t1[j - 1][0],
t1[j - 1][1]) < eps:
subcost = 0
else:
subcost = 1
C[i][j] = min(C[i][j - 1] + 1, C[i - 1][j] + 1,
C[i - 1][j - 1] + subcost)
edr = float(C[n0][n1]) / max([n0, n1])
return edr
