def distance_L2(d1, d2): # deprecated
""" Distance that does not take into account the uncertainty. """
warnings.warn('deprected function, use Diffeo2Distance objects instead')
assert isinstance(d1, Diffeomorphism2D)
assert isinstance(d2, Diffeomorphism2D)
from diffeo2d import diffeo_distance_L2
return diffeo_distance_L2(d1.d, d2.d)
def distance_L2(d1, d2): # deprecated
""" Distance that does not take into account the uncertainty. """
warnings.warn(
'deprected function, use Diffeo2Distance objects instead')
assert isinstance(d1, Diffeomorphism2D)
assert isinstance(d2, Diffeomorphism2D)
from diffeo2d import diffeo_distance_L2
return diffeo_distance_L2(d1.d, d2.d)
def similarity(a1, a2):
''' Returns a similarity score between two actions
1=same
0=uncorrelated
-1=inverse
'''
# These are distances, in [0, max]
d_max = np.sqrt(2) / 2
same_d = diffeo_distance_L2(a1.d, a2.d)
oppo_d = 0.5 * (diffeo_distance_L2(a1.d, a2.d_inv) +
diffeo_distance_L2(a2.d, a1.d_inv))
# we have to normalize [0,max] -> [1,0]
# we go through an "angle" representation
same_d = same_d / d_max * (np.pi / 2)
oppo_d = oppo_d / d_max * (np.pi / 2)
if same_d < oppo_d: # more similar than inverse
return np.cos(same_d)
else:
return -np.cos(oppo_d)
def distance_to_inverse(a1, a2):
oppo_d = 0.5 * (diffeo_distance_L2(a1.d, a2.d_inv) +
diffeo_distance_L2(a2.d, a1.d_inv))
return oppo_d
def distance(a1, a2):
return diffeo_distance_L2(a1.d, a2.d)