dynadojo.systems.lv.competitive.CompetitiveLVSystem#

class dynadojo.systems.lv.competitive.CompetitiveLVSystem#

Bases: AbstractSystem

Methods

`__init__`([latent_dim, embed_dim, minK, ...])	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=2, embed_dim=2, minK=1, maxK=10, noise_scale=0.01, IND_range=(0.1, 0.5), OOD_range=(0.5, 0.9), R_range=(0.0, 0.5), interspecies_range=(0, 1), 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.