def create_true_params_2x3():
params = kalman.SSMParameters()
params.F = np.array([
[99, 20],
[-4, -80],
])/ 100
print(np.linalg.eig(params.F))
params.H = np.array([
[ -1, 1],
[ 0.5, -0.1],
[ 1.5, 5.0],
])
params.Q = np.array([
[ 10, 0.001],
[0.001, 1],
])
params.R = np.array([
[ 0.1, 0.007, 0.01],
[0.007, 5, 0.01],
[ 0.01, 0.01, 0.1],
])
params.X0 = np.array([
[87],
[0],
])
params.P0 = np.array([
[ 0.9, 0.001],
[0.001, 0.5],
])
params.set_dimensions(3, 2)
np.random.seed(0)
T = 200
X, Y = params.simulate(T)
return params, X, Y
def create_true_params_enders_2x2_true():
params = kalman.SSMParameters()
params.F = np.array([
[0.7, 0.2],
[0.2, 0.7],
])
params.Q = np.array([
[0.1, 0.01],
[0.01, 0.1],
])
params.X0 = np.array([100, 80]).reshape(-1, 1)
params.P0 = np.array([
[0.1, 0.01],
[0.01, 0.1],
])
params.H = np.array([
[10.0, 0.0],
[0.1, 1],
])
params.R = np.array([
[0.01, 0.01],
[0.01, 0.01],
])
print("Stability of F:", np.linalg.eig(params.F)[0])
params.set_dimensions(2, 2)
np.random.seed(0)
T = 300
X, Y = params.simulate(T)
return params, X, Y
def create_true_params_1x3():
params = kalman.SSMParameters()
params.F = np.array([
[1 - 1e-3],
])
print(np.linalg.eig(params.F))
params.H = np.array([
[ -1],
[ 0.1],
[ 1.0],
])
params.Q = np.array([
[ 100],
])
params.R = np.array([
[ 100, 0.07, 0.01],
[0.07, 4, 0.01],
[ 0.01, 0.01, 500],
])
params.X0 = np.array([
[7],
])
params.P0 = np.array([
[ 0.1 ],
])
params.set_dimensions(3, 1)
np.random.seed(0)
T = 300
X, Y = params.simulate(T)
return params, X, Y
def noising_params(params, factor=0.2):
np.random.seed(123456)
noise_params = kalman.SSMParameters()
noise_params.F = params.F + factor * np.random.randn(*params.F.shape)
noise_params.H = params.H + factor * np.random.randn(*params.H.shape)
noise_params.Q = params.Q + factor * np.random.randn(*params.Q.shape)
noise_params.R = params.R + factor * np.random.randn(*params.R.shape)
noise_params.X0 = params.X0 + factor * np.random.randn(*params.X0.shape)
noise_params.P0 = params.P0 + factor * np.random.randn(*params.P0.shape)
noise_params.set_dimensions(3, 2)
return noise_params