C-Optimized MoG

C-optimized routines for MultiMin.

multimin.cmog.mog_c(X, weights, mus, Sigmas)

Compute full MoG PDF using C library (sum of components).

Parameters:

• X (array-like (n_points, k) or (k,))

• weights (array-like (n_comps,))

• mus (array-like (n_comps, k))

• Sigmas (array-like (n_comps, k, k))

multimin.cmog.nmd_c(X, mu, Sigma)

Compute Normal Multivariate Distribution PDF using C library.

Parameters:

• X (array-like) – Points (n_points, k) or (k,).

• mu (array-like) – Mean vector (k,).

• Sigma (array-like) – Covariance matrix (k, k).

Returns:

pdf – PDF values.

Return type:

ndarray

multimin.cmog.tmog_c(X, weights, mus, Sigmas, a, b, Zs)

Compute full Truncated MoG PDF using C library (sum of components).

Parameters:

• X (array-like (n_points, k) or (k,))

• weights (array-like (n_comps,))

• mus (array-like (n_comps, k))

• Sigmas (array-like (n_comps, k, k))

• a (array-like (k,) - bounds)

• b (array-like (k,) - bounds)

• Zs (array-like (n_comps,) - normalization constants per component)

multimin.cmog.tnmd_c(X, mu, Sigma, a, b, Z=1.0)

Compute Truncated Normal Multivariate Distribution PDF using C library.

Parameters:

• X (array-like) – Points (n_points, k).

• mu (array-like) – Mean (k,).

• Sigma (array-like) – Covariance (k, k).

• a (array-like) – Lower bounds (k,).

• b (array-like) – Upper bounds (k,).

• Z (float) – Normalization constant.

Returns:

pdf

Return type:

ndarray