Distance
Distance metrics for comparing simulated and observed data in ABC.
Standard Distance Metrics
l1
L1 (Manhattan) distance between observations.
approxbayescomp.distance.l1(x, y)
l2
L2 (Euclidean) distance between observations.
approxbayescomp.distance.l2(x, y)
Distribution Distances
wasserstein
1D Wasserstein (Earth Mover's) distance between empirical distributions.
approxbayescomp.distance.wasserstein = (sorted, l1_scaled)
module-attribute
wasserstein2D
2D Wasserstein distance for bivariate observations.
approxbayescomp.distance.wasserstein2D(x, y)
Compute the Wasserstein using the Hungarian algorithm.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
x
|
n*2 arrays, where n is the sample size
|
|
required |
y
|
n*2 arrays, where n is the sample size
|
|
required |
Returns:
| Type | Description |
|---|---|
scalar
|
|
The Wasserstein distance between the 2D samples
|
|
Specialized Distances
wrap_ss_curve_matching
Wrapper for curve matching distance using summary statistics.