dynadojo.systems.fbsnn_pde.bsb.BSBSystem

class dynadojo.systems.fbsnn_pde.bsb.BSBSystem

Bases: FBSNNSystem

Methods

__init__([latent_dim, embed_dim, ...])	Initialize the class.
calc_control_cost(control)	Calculate the control cost for each control trajectory (i.e., calculates the costs for every control matrix, not for the whole tensor).
calc_error(x, y)	Calculates the error between two tensors of trajectories.
make_data(init_conds, control, timesteps[, ...])	Makes trajectories from initial conditions.
make_init_conds(n[, in_dist])	Generate initial conditions..

Attributes

embed_dim	The embedded dimension for the system.
latent_dim	The latent dimension for the system.
seed	The random seed for the system.

__init__(latent_dim=1, embed_dim=1, noise_scale=0.001, IND_range=(0, 0.8), OOD_range=(0.8, 1), layers=None, T=1.0, seed=None)

Initialize the class.

Parameters:

• latent_dim (int) – Dimension of the latent space.

• embed_dim (int) – Embedded dimension of the system.

• seed (int or None, optional) – Seed for random number generation.

• **kwargs – Additional keyword arguments.

calc_control_cost(control)

Calculate the control cost for each control trajectory (i.e., calculates the costs for every control matrix, not for the whole tensor).

Parameters:

control (numpy.ndarray) – (n, timesteps, embed_dim) Controls tensor.

Returns:

(n,) Control costs vector.

Return type:

numpy.ndarray

calc_error(x, y)

Calculates the error between two tensors of trajectories.

Parameters:

• x (numpy.ndarray) – (n, timesteps, embed_dim) Trajectories tensor.

• y (numpy.ndarray) – (n, timesteps, embed_dim) Trajectories tensor.

Returns:

The error between x and y.

Return type:

float

property embed_dim

The embedded dimension for the system.

property latent_dim

The latent dimension for the system.

make_data(init_conds, control, timesteps, noisy=False)

Makes trajectories from initial conditions.

Parameters:

• init_conds (numpy.ndarray) – (n, embed_dim) Initial conditions matrix.

• control (numpy.ndarray) – (n, timesteps, embed_dim) Controls tensor.

• timesteps (int) – Timesteps per training trajectory (per action horizon).

• noisy (bool, optional) – If True, add noise to trajectories. Defaults to False. If False, no noise is added.

Returns:

(n, timesteps, embed_dim) Trajectories tensor.

Return type:

numpy.ndarray

make_init_conds(n, in_dist=True)

Generate initial conditions..

Note

Systems developers determine what counts as in vs out-of-distribution. DynaDojo doesn’t provide any verification that this distinction makes sense or even exists. See LDSystem for a principled example.

Parameters:

• n (int) – Number of initial conditions.

• in_dist (bool, optional) – If True, generate in-distribution initial conditions. Defaults to True. If False, generate out-of-distribution initial conditions.

Returns:

(n, embed_dim) Initial conditions matrix.

Return type:

numpy.ndarray

property seed

The random seed for the system.