deepmd.utils.out_stat

deepmd.utils.out_stat#

Output statistics.

Functions#

`compute_stats_from_redu`(→ tuple[numpy.ndarray, ...)	Compute the output statistics.
`compute_stats_from_atomic`(→ tuple[numpy.ndarray, ...)	Compute the output statistics.
`compute_stats_do_not_distinguish_types`(...)	Compute element-independent statistics for property fitting.

Module Contents#

deepmd.utils.out_stat.compute_stats_from_redu(output_redu: numpy.ndarray, natoms: numpy.ndarray, assigned_bias: numpy.ndarray | None = None, rcond: float | None = None) → tuple[numpy.ndarray, numpy.ndarray][source]#

Compute the output statistics.

Given the reduced output value and the number of atoms for each atom, compute the least-squares solution as the atomic output bias and std.

Parameters:

output_redu: The reduced output value, shape is [nframes, *(odim0, odim1, …)].
natoms: The number of atoms for each atom, shape is [nframes, ntypes].
assigned_bias: The assigned output bias, shape is [ntypes, *(odim0, odim1, …)]. Set to a tensor of shape (odim0, odim1, …) filled with nan if the bias of the type is not assigned.
rcond: Cut-off ratio for small singular values of a.

Returns:

np.ndarray: The computed output bias, shape is [ntypes, *(odim0, odim1, …)].
np.ndarray: The computed output std, shape is [*(odim0, odim1, …)].

deepmd.utils.out_stat.compute_stats_from_atomic(output: numpy.ndarray, atype: numpy.ndarray) → tuple[numpy.ndarray, numpy.ndarray][source]#

Compute the output statistics.

Given the output value and the type of atoms, compute the atomic output bias and std.

Parameters:

output: The output value, shape is [nframes, nloc, ndim].
atype: The type of atoms, shape is [nframes, nloc].

Returns:

np.ndarray: The computed output bias, shape is [ntypes, ndim].
np.ndarray: The computed output std, shape is [ntypes, ndim].

deepmd.utils.out_stat.compute_stats_do_not_distinguish_types(output_redu: numpy.ndarray, natoms: numpy.ndarray, assigned_bias: numpy.ndarray | None = None, intensive: bool = False) → tuple[numpy.ndarray, numpy.ndarray][source]#

Compute element-independent statistics for property fitting.

Computes mean and standard deviation of the output, treating all elements equally. For extensive properties, the output is normalized by the total number of atoms before computing statistics.

Parameters:

output_redu: The reduced output value, shape is [nframes, *(odim0, odim1, …)].
natoms: The number of atoms for each atom, shape is [nframes, ntypes]. Used for normalization of extensive properties and generating uniform bias.
assigned_bias: The assigned output bias, shape is [ntypes, *(odim0, odim1, …)]. Set to a tensor of shape (odim0, odim1, …) filled with nan if the bias of the type is not assigned.
intensive: Whether the output is intensive or extensive. If False, the output will be normalized by the total number of atoms before computing statistics.

Returns:

np.ndarray: The computed output mean(fake bias), shape is [ntypes, *(odim0, odim1, …)]. The same bias is used for all atom types.
np.ndarray: The computed output standard deviation, shape is [ntypes, *(odim0, odim1, …)]. The same standard deviation is used for all atom types.