odatse.algorithm.montecarlo module

class odatse.algorithm.montecarlo.AlgorithmBase(info: Info, runner: Runner = None, domain=None, nwalkers: int = 1, run_mode: str = 'initial')[source]

Bases: AlgorithmBase

Base of Monte Carlo

nwalkers

the number of walkers (per one process)

Type:

int

x

current configurations (NxD array, N is the number of walkers and D is the dimension)

Type:

np.ndarray

fx

current “Energy”s

Type:

np.ndarray

istep

current step (or, the number of calculated energies)

Type:

int

best_x

best configuration

Type:

np.ndarray

best_fx

best “Energy”

Type:

float

best_istep

index of best configuration (step)

Type:

int

best_iwalker

index of best configuration (walker)

Type:

int

comm

MPI communicator

Type:

MPI.comm

rank

MPI rank

Type:

int

Ts

List of temperatures

Type:

np.ndarray

Tindex

Temperature index

Type:

np.ndarray

Initialize the AlgorithmBase class.

Parameters:

  • info (odatse.Info) – Information object containing algorithm parameters.

  • runner (odatse.Runner, optional) – Runner object for executing the algorithm (default is None).

  • domain (optional) – Domain object defining the problem space (default is None).

  • nwalkers (int, optional) – Number of walkers (default is 1).

  • run_mode (str, optional) – Mode of the run, e.g., “initial” (default is “initial”).

__init__(info: Info, runner: Runner = None, domain=None, nwalkers: int = 1, run_mode: str = 'initial') → None[source]

Initialize the AlgorithmBase class.

Parameters:

  • info (odatse.Info) – Information object containing algorithm parameters.

  • runner (odatse.Runner, optional) – Runner object for executing the algorithm (default is None).

  • domain (optional) – Domain object defining the problem space (default is None).

  • nwalkers (int, optional) – Number of walkers (default is 1).

  • run_mode (str, optional) – Mode of the run, e.g., “initial” (default is “initial”).

_evaluate(in_range: ndarray = None) → ndarray[source]

Evaluate the current “Energy”s.

This method overwrites self.fx with the result.

Parameters:

in_range (np.ndarray, optional) – Array indicating whether each walker is within the valid range (default is None).

Returns:

Array of evaluated energies for the current configurations.

Return type:

np.ndarray

_initialize()[source]

Initialize the algorithm state.

This method sets up the initial state of the algorithm, including the positions and energies of the walkers, and resets the counters for accepted and trial steps.

_setup_neighbour(info_param)[source]

Set up the neighbor list for the discrete problem.

Parameters:

info_param (dict) – Dictionary containing algorithm parameters, including the path to the neighbor list file.

Raises:

  • ValueError – If the neighbor list path is not specified in the parameters.

  • RuntimeError – If the transition graph made from the neighbor list is not connected or not bidirectional.

_write_result(fp, extra_info_to_write: List | Tuple = None) → None[source]

Write the result of the current step to the file.

Parameters:

  • fp (TextIO) – File pointer to the result file.

  • extra_info_to_write (Union[List, Tuple], optional) – Additional information to write for each walker (default is None).

_write_result_header(fp, extra_names=None) → None[source]

Write the header for the result file.

Parameters:

  • fp (TextIO) – File pointer to the result file.

  • extra_names (list of str, optional) – Additional column names to include in the header.

local_update(beta: float | ndarray, file_trial: TextIO, file_result: TextIO, extra_info_to_write: List | Tuple = None)[source]

one step of Monte Carlo

Parameters:

  • beta (np.ndarray) – inverse temperature for each walker

  • file_trial (TextIO) – log file for all trial points

  • file_result (TextIO) – log file for all generated samples

  • extra_info_to_write (List of np.ndarray or tuple of np.ndarray) – extra information to write

propose(current: ndarray) → ndarray[source]

Propose the next candidate positions for the walkers.

Parameters:

current (np.ndarray) – Current positions of the walkers.

Returns:

proposed – Proposed new positions for the walkers.

Return type:

np.ndarray

odatse.algorithm.montecarlo.read_Ts(info: dict, numT: int = None) → Tuple[bool, ndarray][source]

Read temperature or inverse-temperature values from the provided info dictionary.

Parameters:

  • info (dict) – Dictionary containing temperature or inverse-temperature parameters.

  • numT (int, optional) – Number of temperature or inverse-temperature values to generate (default is None).

Returns:

  • as_beta (bool) – True if using inverse-temperature, False if using temperature.

  • betas (np.ndarray) – Sequence of inverse-temperature values.

Raises:

  • ValueError – If numT is not specified, or if both Tmin/Tmax and bmin/bmax are defined, or if neither are defined, or if bmin/bmax or Tmin/Tmax values are invalid.

  • RuntimeError – If the mode is unknown (neither set_T nor set_b).