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module adabmDCA.statmech

function compute_energy

compute_energy(x: Tensor, params: Dict[str, Tensor]) → Tensor

Compute the DCA energy for a batch of sequences.

Args:

  • x (torch.Tensor): Tensor of shape (batch_size, L, q) - batch of one-hot encoded sequences.
  • params (Dict[str, torch.Tensor]): Parameters of the model. - "bias": Tensor of shape (L, q) - local biases. - "coupling_matrix": Tensor of shape (L, q, L, q) - coupling matrix.

Returns:

  • torch.Tensor: Tensor of shape (batch_size,) - DCA energy for each sequence in the batch.

function compute_log_likelihood

compute_log_likelihood(
    fi: Tensor,
    fij: Tensor,
    params: Dict[str, Tensor],
    logZ: float
) → float

Compute the log-likelihood of the model.

Args:

  • fi (torch.Tensor): Single-site frequencies of the data.
  • fij (torch.Tensor): Two-site frequencies of the data.
  • params (Dict[str, torch.Tensor]): Parameters of the model.
  • logZ (float): Log-partition function of the model.

Returns:

  • float: Log-likelihood of the model.

function enumerate_states

enumerate_states(L: int, q: int, device: device = device(type='cpu')) → Tensor

Enumerate all possible states of a system of L sites and q states.

Args:

  • L (int): Number of sites.
  • q (int): Number of states.
  • device (torch.device, optional): Device to store the states. Defaults to "cpu".

Returns:

  • torch.Tensor: All possible states.

function compute_logZ_exact

compute_logZ_exact(all_states: Tensor, params: Dict[str, Tensor]) → float

Compute the log-partition function of the model.

Args:

  • all_states (torch.Tensor): All possible states of the system.
  • params (Dict[str, torch.Tensor]): Parameters of the model.

Returns:

  • float: Log-partition function of the model.

function compute_entropy

compute_entropy(chains: Tensor, params: Dict[str, Tensor], logZ: float) → float

Compute the entropy of the DCA model.

Args:

  • chains (torch.Tensor): Chains that are supposed to be an equilibrium realization of the model.
  • params (Dict[str, torch.Tensor]): Parameters of the model.
  • logZ (float): Log-partition function of the model.

Returns:

  • float: Entropy of the model.

function iterate_tap

iterate_tap(
    mag: Tensor,
    params: Dict[str, Tensor],
    max_iter: int = 500,
    epsilon: float = 0.0001
) → Tensor

Iterates the TAP equations until convergence.

Args:

  • mag (torch.Tensor): Initial magnetizations.
  • params (Dict[str, torch.Tensor]): Parameters of the model.
  • max_iter (int, optional): Maximum number of iterations. Defaults to 500.
  • epsilon (float, optional): Convergence threshold. Defaults to 1e-4.

Returns:

  • torch.Tensor: Fixed point magnetizations of the TAP equations.

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