alibi.utils.kernel module
- class alibi.utils.kernel.EuclideanDistance[source]
Bases:
object
- __call__(x, y)[source]
Computes the kernel distance matrix between x and y.
- Parameters:
x (
ndarray
) – The first array of data instances.y (
ndarray
) – The second array of data instances.
- Return type:
ndarray
- Returns:
Kernel distance matrix between x and y having the size of Nx x Ny, where Nx is the number of instances in x and y is the number of instances in y.
- class alibi.utils.kernel.GaussianRBF(sigma=None)[source]
Bases:
object
- __call__(x, y, infer_sigma=False)[source]
Computes the kernel matrix between x and y.
- Parameters:
x (
ndarray
) – The first array of data instances.y (
ndarray
) – The second array of data instances.infer_sigma (
bool
) – Whether to infer sigma automatically. The sigma value is computed based on the median distance value between the instances from x and y.
- Return type:
ndarray
- Returns:
Kernel matrix between x and y having the size of Nx x Ny where Nx is the number of instances in x and y is the number of instances in y.
- __init__(sigma=None)[source]
Gaussian RBF kernel: \(k(x,y) = \exp(-\frac{||x-y||^2}{2\sigma^2})\). A forward pass takes a batch of instances x of size Nx x f1 x f2 x … and y of size Ny x f1 x f2 x … ` and returns the kernel matrix of size `Nx x Ny.
- property sigma: ndarray
- class alibi.utils.kernel.GaussianRBFDistance(sigma=None)[source]
Bases:
object
- __init__(sigma=None)[source]
Gaussian RBF kernel dissimilarity/distance: \(k(x, y) = 1 - \exp(-\frac{||x-y||^2}{2\sigma^2})\). A forward pass takes a batch of instances x of size Nx x f1 x f2 x … and y of size Ny x f1 x f2 x … and returns the kernel matrix of size Nx x Ny.
- Parameters:
sigma (
Union
[float
,ndarray
,None
]) – Seealibi.utils.kernel.GaussianRBF.__init__()
.