def DefaultBernoulliLDS(D_obs, D_latent, D_input=0,
mu_init=None, sigma_init=None,
A=None, B=None, sigma_states=None,
C=None, D=None
):
model = LaplaceApproxBernoulliLDS(
init_dynamics_distn=
Gaussian(mu_0=np.zeros(D_latent), sigma_0=np.eye(D_latent),
kappa_0=1.0, nu_0=D_latent + 1),
dynamics_distn=Regression(
nu_0=D_latent + 1,
S_0=D_latent * np.eye(D_latent),
M_0=np.zeros((D_latent, D_latent + D_input)),
K_0=D_latent * np.eye(D_latent + D_input)),
emission_distn=
BernoulliRegression(D_obs, D_latent + D_input, verbose=False))
set_default = \
lambda prm, val, default: \
model.__setattr__(prm, val if val is not None else default)
set_default("mu_init", mu_init, np.zeros(D_latent))
set_default("sigma_init", sigma_init, np.eye(D_latent))
set_default("A", A, 0.99 * random_rotation(D_latent))
set_default("B", B, 0.1 * np.random.randn(D_latent, D_input))
set_default("sigma_states", sigma_states, 0.1 * np.eye(D_latent))
set_default("C", C, np.random.randn(D_obs, D_latent))
set_default("D", D, 0.1 * np.random.randn(D_obs, D_input))
return model
def DefaultLDS(D_obs, D_latent, D_input=0,
mu_init=None, sigma_init=None,
A=None, B=None, sigma_states=None,
C=None, D=None, sigma_obs=None):
model = LDS(
dynamics_distn=Regression(
nu_0=D_latent + 1,
S_0=D_latent * np.eye(D_latent),
M_0=np.zeros((D_latent, D_latent + D_input)),
K_0=D_latent * np.eye(D_latent + D_input)),
emission_distn=Regression(
nu_0=D_obs + 1,
S_0=D_obs * np.eye(D_obs),
M_0=np.zeros((D_obs, D_latent + D_input)),
K_0=D_obs * np.eye(D_latent + D_input)))
set_default = \
lambda prm, val, default: \
model.__setattr__(prm, val if val is not None else default)
set_default("mu_init", mu_init, np.zeros(D_latent))
set_default("sigma_init", sigma_init, np.eye(D_latent))
set_default("A", A, 0.99 * random_rotation(D_latent))
set_default("B", B, 0.1 * np.random.randn(D_latent, D_input))
set_default("sigma_states", sigma_states, 0.1 * np.eye(D_latent))
set_default("C", C, np.random.randn(D_obs, D_latent))
set_default("D", D, 0.1 * np.random.randn(D_obs, D_input))
set_default("sigma_obs", sigma_obs, 0.1 * np.eye(D_obs))
return model
def DefaultPoissonLDS(
D_obs,
D_latent,
D_input=0,
mu_init=None,
sigma_init=None,
A=None,
B=None,
sigma_states=None,
C=None,
d=None,
):
assert D_input == 0, "Inputs are not yet supported for Poisson LDS"
model = LaplaceApproxPoissonLDS(
init_dynamics_distn=Gaussian(mu_0=np.zeros(D_latent),
sigma_0=np.eye(D_latent),
kappa_0=1.0,
nu_0=D_latent + 1),
dynamics_distn=Regression(A=0.9 * np.eye(D_latent),
sigma=np.eye(D_latent),
nu_0=D_latent + 1,
S_0=D_latent * np.eye(D_latent),
M_0=np.zeros((D_latent, D_latent)),
K_0=D_latent * np.eye(D_latent)),
emission_distn=PoissonRegression(D_obs, D_latent, verbose=False))
set_default = \
lambda prm, val, default: \
model.__setattr__(prm, val if val is not None else default)
set_default("mu_init", mu_init, np.zeros(D_latent))
set_default("sigma_init", sigma_init, np.eye(D_latent))
set_default("A", A, 0.99 * random_rotation(D_latent))
set_default("B", B, 0.1 * np.random.randn(D_latent, D_input))
set_default("sigma_states", sigma_states, 0.1 * np.eye(D_latent))
set_default("C", C, np.random.randn(D_obs, D_latent))
set_default("d", d, np.zeros((D_obs, 1)))
return model
def make_rslds_parameters(C_init):
init_dynamics_distns = [
Gaussian(
mu=np.zeros(D_latent),
sigma=3 * np.eye(D_latent),
nu_0=D_latent + 2,
sigma_0=3. * np.eye(D_latent),
mu_0=np.zeros(D_latent),
kappa_0=1.0,
) for _ in range(K)
]
ths = np.random.uniform(np.pi / 30., 1.0, size=K)
As = [random_rotation(D_latent, th) for th in ths]
As = [np.hstack((A, np.ones((D_latent, 1)))) for A in As]
dynamics_distns = [
Regression(
A=As[k],
sigma=np.eye(D_latent),
nu_0=D_latent + 1000,
S_0=np.eye(D_latent),
M_0=np.hstack((np.eye(D_latent), np.zeros((D_latent, 1)))),
K_0=np.eye(D_latent + 1),
) for k in range(K)
]
if C_init is not None:
emission_distns = \
DiagonalRegression(D_obs, D_latent + 1,
A=C_init.copy(), sigmasq=np.ones(D_obs),
alpha_0=2.0, beta_0=2.0)
else:
emission_distns = \
DiagonalRegression(D_obs, D_latent + 1,
alpha_0=2.0, beta_0=2.0)
return init_dynamics_distns, dynamics_distns, emission_distns
from pylds.util import random_rotation
from pyslds.models import DefaultSLDS
npr.seed(0)
# Set parameters
K = 5
D_obs = 100
D_latent = 2
D_input = 1
T = 1000
# Make an LDS with known parameters
true_mu_inits = [np.ones(D_latent) for _ in range(K)]
true_sigma_inits = [np.eye(D_latent) for _ in range(K)]
true_As = [.9 * random_rotation(D_latent)
for k in range(K)]
true_Bs = [3 * npr.randn(D_latent, D_input) for k in range(K)]
true_sigma_states = [np.eye(D_latent) for _ in range(K)]
true_C = np.random.randn(D_obs, D_latent)
true_Ds = np.zeros((D_obs, D_input))
true_sigma_obs = np.eye(D_obs)
true_model = DefaultSLDS(
K, D_obs, D_latent, D_input=D_input,
mu_inits=true_mu_inits, sigma_inits=true_sigma_inits,
As=true_As, Bs=true_Bs, sigma_statess=true_sigma_states,
Cs=true_C, Ds=true_Ds, sigma_obss=true_sigma_obs)
# Simulate some data with a given discrete state sequence
inputs = np.ones((T, D_input))
z = np.arange(K).repeat(T // K)
from pylds.util import random_rotation
from pyslds.models import DefaultSLDS
npr.seed(0)
# Set parameters
K = 5
D_obs = 100
D_latent = 2
D_input = 1
T = 1000
# Make an LDS with known parameters
true_mu_inits = [np.ones(D_latent) for _ in range(K)]
true_sigma_inits = [np.eye(D_latent) for _ in range(K)]
true_As = [.9 * random_rotation(D_latent) for k in range(K)]
true_Bs = [3 * npr.randn(D_latent, D_input) for k in range(K)]
true_sigma_states = [np.eye(D_latent) for _ in range(K)]
true_C = np.random.randn(D_obs, D_latent)
true_Ds = np.zeros((D_obs, D_input))
true_sigma_obs = np.eye(D_obs)
true_model = DefaultSLDS(K,
D_obs,
D_latent,
D_input=D_input,
mu_inits=true_mu_inits,
sigma_inits=true_sigma_inits,
As=true_As,
Bs=true_Bs,
sigma_statess=true_sigma_states,
Cs=true_C,
from pylds.util import random_rotation
from pyslds.models import DefaultSLDS
npr.seed(0)
# Set parameters
K = 5
D_obs = 10
D_latent = 2
D_input = 1
T = 1000
# Make an LDS with known parameters
true_mu_inits = [np.ones(D_latent) for _ in range(K)]
true_sigma_inits = [np.eye(D_latent) for _ in range(K)]
true_As = [.99 * random_rotation(D_latent, theta=np.pi/((k+1) * 4)) for k in range(K)]
true_Bs = [3 * npr.randn(D_latent, D_input) for k in range(K)]
true_sigma_states = [np.eye(D_latent) for _ in range(K)]
true_C = np.random.randn(D_obs, D_latent)
true_Ds = np.zeros((D_obs, D_input))
true_sigma_obs = 0.05 * np.eye(D_obs)
true_model = DefaultSLDS(
K, D_obs, D_latent, D_input=D_input,
mu_inits=true_mu_inits, sigma_inits=true_sigma_inits,
As=true_As, Bs=true_Bs, sigma_statess=true_sigma_states,
Cs=true_C, Ds=true_Ds, sigma_obss=true_sigma_obs)
# Simulate some data with a given discrete state sequence
inputs = np.ones((T, D_input))
z = np.arange(K).repeat(T // K)
y, x, _ = true_model.generate(T, inputs=inputs, stateseq=z)
def simulate_nascar():
assert K_true == 4
As = [
random_rotation(D_latent, np.pi / 24.),
random_rotation(D_latent, np.pi / 48.)
]
# Set the center points for each system
centers = [np.array([+2.0, 0.]), np.array([-2.0, 0.])]
bs = [
-(A - np.eye(D_latent)).dot(center) for A, center in zip(As, centers)
]
# Add a "right" state
As.append(np.eye(D_latent))
bs.append(np.array([+0.1, 0.]))
# Add a "right" state
As.append(np.eye(D_latent))
bs.append(np.array([-0.25, 0.]))
# Construct multinomial regression to divvy up the space
w1, b1 = np.array([+1.0, 0.0]), np.array([-2.0]) # x + b > 0 -> x > -b
w2, b2 = np.array([-1.0, 0.0]), np.array([-2.0]) # -x + b > 0 -> x < b
w3, b3 = np.array([0.0, +1.0]), np.array([0.0]) # y > 0
w4, b4 = np.array([0.0, -1.0]), np.array([0.0]) # y < 0
reg_W = np.column_stack((100 * w1, 100 * w2, 10 * w3, 10 * w4))
reg_b = np.concatenate((100 * b1, 100 * b2, 10 * b3, 10 * b4))
# Make a recurrent SLDS with these params #
dynamics_distns = [
Regression(
A=np.column_stack((A, b)),
sigma=1e-4 * np.eye(D_latent),
nu_0=D_latent + 2,
S_0=1e-4 * np.eye(D_latent),
M_0=np.zeros((D_latent, D_latent + 1)),
K_0=np.eye(D_latent + 1),
) for A, b in zip(As, bs)
]
init_dynamics_distns = [
Gaussian(mu=np.array([0.0, 1.0]), sigma=1e-3 * np.eye(D_latent))
for _ in range(K)
]
C = np.hstack((npr.randn(D_obs, D_latent), np.zeros((D_obs, 1))))
emission_distns = \
DiagonalRegression(D_obs, D_latent+1,
A=C, sigmasq=1e-5 *np.ones(D_obs),
alpha_0=2.0, beta_0=2.0)
model = SoftmaxRecurrentOnlySLDS(trans_params=dict(W=reg_W, b=reg_b),
init_state_distn='uniform',
init_dynamics_distns=init_dynamics_distns,
dynamics_distns=dynamics_distns,
emission_distns=emission_distns,
alpha=3.)
#########################
# Sample from the model #
#########################
inputs = np.ones((T, 1))
y, x, z = model.generate(T=T, inputs=inputs)
# Maks off some data
if mask_start == mask_stop:
mask = None
else:
mask = np.ones((T, D_obs), dtype=bool)
mask[mask_start:mask_stop] = False
# Print the true parameters
np.set_printoptions(precision=2)
print("True W:\n{}".format(model.trans_distn.W))
print("True logpi:\n{}".format(model.trans_distn.logpi))
return model, inputs, z, x, y, mask
