tfep.analysis.estimator.fep_estimator

tfep.analysis.estimator.fep_estimator(data, kT=1.0, weights=None, vectorized=False)[source]

FEP estimator.

Computes the (weighted) free energy difference given a set of work values and, optionally, a set of associated sample log-weights (i.e. bias potential values).

Parameters:

  • data (torch.Tensor) – Shape (n_samples,) or (2, n_samples). In the first case, data is an tensor of work values (in kT units). In the second, data[0] contain the work values, and data[1] the log-weights for each samples (also in kT units).

    If vectorized is True, an extra dimension is expected so that the shape should be either (n_bootstraps, n_samples,) or (n_bootstraps, 2, n_samples).

  • kT (float, optional) – The function assumes that the work and log-weights values are already divided by kT. If this is not the case, this should be set to the value of kT in the same unit of energy passed in data.

  • weights (torch.Tensor, optional) – Shape (n_bootstraps, n_samples). The weights used for Bayesian bootstrapping.

  • vectorized (bool, optional) – Whether the estimate should be vectorized for bootstrap analysis.

Returns:

df – Shape (1,). The free energy difference in the same units of the work values.

If vectorized is True, this has shape (n_bootstraps,).

Return type:

torch.Tensor