qmctorch.sampler package

Submodules

qmctorch.sampler.generalized_metropolis module

class qmctorch.sampler.generalized_metropolis.GeneralizedMetropolis(nwalkers: int = 100, nstep: int = 1000, step_size: float = 3, ntherm: int = -1, ndecor: int = 1, nelec: int = 1, ndim: int = 1, init: Dict[str, Any] = {'max': 5, 'min': -5, 'type': 'uniform'}, cuda: bool = False)[source]

Bases: SamplerBase

Generalized Metropolis Hasting sampler

Parameters:

  • nwalkers (int, optional) – number of walkers. Defaults to 100.

  • nstep (int, optional) – number of steps. Defaults to 1000.

  • step_size (float, optional) – size of the steps. Defaults to 3.

  • ntherm (int, optional) – number of steps for thermalization. Defaults to -1.

  • ndecor (int, optional) – number of steps for decorelation. Defaults to 1.

  • nelec (int, optional) – number of electron. Defaults to 1.

  • ndim (int, optional) – number of dimensions. Defaults to 1.

  • init (dict, optional) – method to initialize the walkers. Defaults to {‘type’: ‘uniform’, ‘min’: -5, ‘max’: 5}.

  • cuda (bool, optional) – use cuda. Defaults to False.

Returns:

None

move(drift: torch.Tensor) → torch.Tensor[source]

Move electron one at a time in a vectorized way.

Parameters:

drift (torch.Tensor) – drift velocity of the walkers

Returns:

new positions of the walkers

Return type:

torch.Tensor

trans(xf: torch.Tensor, xi: torch.Tensor, drifti: torch.Tensor) → torch.Tensor[source]

Transform the positions

Parameters:
Returns:

Transition probabilities

Return type:

torch.Tensor

get_drift(pdf: Callable[[torch.Tensor], torch.Tensor], x: torch.Tensor) → torch.Tensor[source]

Compute the drift velocity

Parameters:

  • pdf (callable) – function that returns the density

  • x (torch.tensor) – positions of the walkers

Returns:

drift velocity

Return type:

torch.tensor

qmctorch.sampler.hamiltonian module

class qmctorch.sampler.hamiltonian.Hamiltonian(nwalkers: int = 100, nstep: int = 100, step_size: float = 0.2, L: int = 10, ntherm: int = -1, ndecor: int = 1, nelec: int = 1, ndim: int = 3, init: Dict = {'max': 5, 'min': -5}, cuda: bool = False)[source]

Bases: SamplerBase

Hamiltonian Monte Carlo Sampler.

Parameters:

  • nwalkers (int, optional) – Number of walkers. Defaults to 100.

  • nstep (int, optional) – Number of steps. Defaults to 100.

  • step_size (int, optional) – length of the step. Defaults to 0.2.

  • L (int, optional) – length of the trajectory . Defaults to 10.

  • nelec (int, optional) – total number of electrons. Defaults to 1.

  • ntherm (int, optional) – number of mc step to thermalize. Defaults to -1, i.e. keep only last position

  • ndecor (int, optional) – number of mc step for decorrelation. Defaults to 1.

  • ndim (int, optional) – total number of dimension. Defaults to 3.

  • init (dict, optional) – method to init the positions of the walkers. See Molecule.domain()

  • cuda (bool, optional) – turn CUDA ON/OFF. Defaults to False.

static get_grad(func: Callable[[torch.Tensor], torch.Tensor], inp: torch.Tensor) → torch.Tensor[source]

get the gradient of the pdf using autograd

Parameters:

  • func – function to compute the pdf

  • inp – input of the function

Returns:

gradient of the wavefunction

static log_func(func: Callable[[torch.Tensor], torch.Tensor])[source]

Compute the negative log of a function

Parameters:

func (callable) – input function

Returns:

negative log of the function

Return type:

callable

qmctorch.sampler.metropolis module

class qmctorch.sampler.metropolis.Metropolis(nwalkers: int = 100, nstep: int = 1000, step_size: float = 0.2, ntherm: int = -1, ndecor: int = 1, nelec: int = 1, ndim: int = 3, init: Dict = {'max': 5, 'min': -5}, move: Dict = {'proba': 'normal', 'type': 'all-elec'}, logspace: bool = False, symmetry=None, cuda: bool = False)[source]

Bases: SamplerBase

Metropolis Hasting generator

Parameters:

  • nwalkers (int, optional) – Number of walkers. Defaults to 100.

  • nstep (int, optional) – Number of steps. Defaults to 1000.

  • step_size (int, optional) – length of the step. Defaults to 0.2.

  • nelec (int, optional) – total number of electrons. Defaults to 1.

  • ntherm (int, optional) – number of mc step to thermalize. Defaults to -1, i.e. keep only the last position

  • ndecor (int, optional) – number of mc step for decorelation. Defauts to 1.

  • ndim (int, optional) – total number of dimension. Defaults to 3.

  • init (dict, optional) – method to init the positions of the walkers. See Molecule.domain()

  • move (dict, optional) –

    method to move the electrons. default(‘all-elec’,’normal’)

    ’type’:

    ’one-elec’: move a single electron per iteration

    ’all-elec’: move all electrons at the same time

    ’all-elec-iter’: move all electrons by iterating through single elec moves

    ’proba’ :

    ’uniform’: uniform in a cube

    ’normal’: gussian in a sphere

  • cuda (bool, optional) – turn CUDA ON/OFF. Defaults to False.

Examples::
>>> mol = Molecule('h2.xyz')
>>> wf = SlaterJastrow(mol)
>>> sampler = Metropolis(nwalkers=100, nelec=wf.nelec)
>>> pos = sampler(wf.pdf)
log_data() → None[source]

log data about the sampler.

static log_func(func)[source]

Compute the negative log of a function

Parameters:

func (callable) – input function

Returns:

negative log of the function

Return type:

callable

configure_move(move: Dict) → None[source]

Configure the electron moves

Parameters:

move (dict, optional) –

method to move the electrons. default(‘all-elec’,’normal’)

’type’:

’one-elec’: move a single electron per iteration

’all-elec’: move all electrons at the same time

’all-elec-iter’: move all electrons by iterating through single elec moves

’proba’ :

’uniform’: uniform ina cube

’normal’: gussian in a sphere

Raises:

ValueError – If moves are not recognized

move(pdf: Callable, id_elec: int) → torch.Tensor[source]

Move electron one at a time in a vectorized way.

Parameters:

  • pdf (callable) – function to sample

  • id_elec (int) – index f the electron to move

Returns:

new positions of the walkers

Return type:

torch.tensor

qmctorch.sampler.metropolis_hasting module

qmctorch.sampler.pints_sampler module

qmctorch.sampler.sampler_base module

class qmctorch.sampler.sampler_base.SamplerBase(nwalkers: int, nstep: int, step_size: float, ntherm: int, ndecor: int, nelec: int, ndim: int, init: Dict, cuda: bool)[source]

Bases: object

Base class for the sampler

Parameters:

  • nwalkers (int) – number of walkers

  • nstep (int) – number of MC steps

  • step_size (float) – size of the steps in bohr

  • ntherm (int) – number of MC steps to thermalize

  • ndecor (int) – number of MC steps to decorellate

  • nelec (int) – number of electrons in the system

  • ndim (int) – number of cartesian dimension

  • init (dict) – method to initialize the walkers

  • cuda (bool) – turn CUDA ON/OFF

get_sampling_size() → int[source]

evaluate the number of sampling point we’ll have.

qmctorch.sampler.symmetry module

qmctorch.sampler.walkers module

class qmctorch.sampler.walkers.Walkers(nwalkers: int = 100, nelec: int = 1, ndim: int = 3, init: Dict | None = None, cuda: bool = False)[source]

Bases: object

Creates Walkers for the sampler.

Parameters:

  • nwalkers (int, optional) – Number of walkers. Defaults to 100.

  • nelec (int, optional) – number of electron. Defaults to 1.

  • ndim (int, optional) – Number of dimensions. Defaults to 3.

  • init (dict, optional) – method to initialize the walkers. Defaults to None. (see Molecule.domain())

  • cuda (bool, optional) – turn cuda ON/OFF. Defaults to False

initialize(pos: None | torch.Tensor = None) → None[source]

Initalize the position of the walkers

Parameters:

  • method (str, optional) – how to initialize the positions. Defaults to ‘uniform’.

  • pos ([type], optional) – existing position of the walkers. Defaults to None.

Raises:

ValueError – if the method is not recognized

Module contents