def random_model(n, p, T):
data = np.random.randn(T, p)
model = DefaultLDS(n, p)
model.A = 0.99 * random_rotation(n, 0.01)
model.C = np.random.randn(p, n)
J, h = lds_to_dense_infoparams(model, data)
model.add_data(data)
return model, (J, h)
def random_model(n, p, T):
data = np.random.randn(T, p)
model = DefaultLDS(n, p)
model.A = 0.99 * random_rotation(n, 0.01)
model.C = np.random.randn(p, n)
J, h = lds_to_dense_infoparams(model, data)
model.add_data(data)
return model, (J, h)
def random_model(n, p, d, T):
data = np.random.randn(T, p)
inputs = np.random.randn(T, d)
model = DefaultLDS(p, n, d)
model.A = 0.99 * random_rotation(n, 0.01)
model.B = 0.1 * np.random.randn(n, d)
model.C = np.random.randn(p, n)
model.D = 0.1 * np.random.randn(p, d)
J, h = lds_to_dense_infoparams(model, data, inputs)
model.add_data(data, inputs=inputs)
return model, (J, h)
