def test_siso_ynext_derivatives(self):
mdl = Linear(N=2, M=2)
dy, du, dparams = mdl.derivatives(y=[1, 2],
u=[2, 3],
params=[10, 20, 30, 40])
assert_array_almost_equal(dy, [[[10], [20]]])
assert_array_almost_equal(du, [[[30], [40]]])
assert_array_almost_equal(dparams, [[1, 2, 2, 3]])
def test_ar_model_derivatives(self):
mdl = Linear(N=3, M=0, Ny=1, Nu=0)
y = [1, 2, 3]
u = []
params = [1, 2, 3]
dy, du, dparams = mdl.derivatives(y, u, params)
assert_array_almost_equal(dy, [[[1], [2], [3]]])
assert_array_almost_equal(du, np.reshape([], (1, 0, 0)))
assert_array_almost_equal(dparams, [[1, 2, 3]])
def test_fir_model_derivatives(self):
mdl = Linear(N=0, M=2, Ny=1, Nu=1)
y = []
u = [2, 1]
params = [1, 2]
dy, du, dparams = mdl.derivatives(y, u, params)
assert_array_almost_equal(du, [[[1], [2]]])
assert_array_almost_equal(dy, np.reshape([], (1, 0, 1)))
assert_array_almost_equal(dparams, [[2, 1]])
def test_tito_ynext_derivatives(self):
mdl = Linear(N=2, M=2, Ny=2, Nu=2)
y = [[1, 2], [2, 3]]
u = [[2, 3], [3, 4]]
dy, du, dparams = mdl.derivatives(
y=y,
u=u,
params=[10, 20, 0, 0, 0, 0, 50, 60, 30, 40, 0, 0, 0, 0, 70, 80])
assert_array_almost_equal(dy, [[[10, 20], [0, 0]], [[0, 0], [50, 60]]])
assert_array_almost_equal(du, [[[30, 40], [0, 0]], [[0, 0], [70, 80]]])
assert_array_almost_equal(
dparams, [[1, 2, 2, 3, 0, 0, 0, 0, 2, 3, 3, 4, 0, 0, 0, 0],
[0, 0, 0, 0, 1, 2, 2, 3, 0, 0, 0, 0, 2, 3, 3, 4]])
def test_fir_model_numerical_derivatives(self):
mdl = Linear(N=0, M=2, Ny=1, Nu=1)
y = []
u = [2, 1]
params = [1, 2]
dy, du, dparams = mdl.derivatives(y, u, params)
dy_numeric, du_numeric, \
dparams_numeric = mdl._numeric_derivatives(y, u, params)
assert_array_almost_equal(dy, dy_numeric)
assert_array_almost_equal(du, du_numeric)
assert_array_almost_equal(dparams, dparams_numeric)
def test_check_too_small_Nd(self):
mdl = Linear(N=2, M=2, Ny=2, Nu=2)
y = [[1, 2], [2, 3]]
u = [[2, 3]]
params = [1, 2, 0, 0, 0, 0, 5, 6, 3, 4, 0, 0, 0, 0, 1, 2]
assert_raises(ValueError, simulate, mdl, y, u, params)
def test_check_wrong_u_size(self):
mdl = Linear(N=2, M=2, Ny=2, Nu=2)
y0 = [[1, 2], [2, 3]]
u = [[2, 3, 3], [3, 4, 2], [5, 6, 4]]
params = [1, 2, 0, 0, 0, 0, 5, 6, 3, 4, 0, 0, 0, 0, 1, 2]
assert_raises(ValueError, simulate, mdl, y0, u, params)
def test_mimo_prediction_error(self):
mdl = Linear(N=3, M=3, Ny=2, Nu=2)
y = [[0, 0], [4, 8], [13, 26], [24.8, 49.6], [30.2, 60.4],
[27.96, 63.92], [11.76, 61.52], [-16.568, 55.464],
[-50.384, 34.032], [-64.7776, 8.9648], [-57.784, -10.1680],
[-26.03872, -5.53344], [22.23296, 34.39392],
[76.507136, 112.787072], [98.513024, 180.165248],
[86.7539072, 216.8143744], [96.2449152, 243.2395904],
[154.54439424, 260.82114048], [244.89924352, 271.59234304],
[290.512711168, 231.583348736]]
u = [[1 + 0.5, 1 + 0.5], [1 + 0.5, 1 + 0.5], [1 + 0.5, 1 + 0.5],
[1 + 0.5, 1 + 0.5], [1 + 0.5, 3 + 0.5], [-2 + 0.3, 3 + 0.3],
[-2 + 0.3, 3 + 0.3], [-2 + 0.3, 3 + 0.3], [-2 + 0.3, 1 + 0.3],
[-2 + 0.3, 1 + 0.3], [3 + 0.3, 1 + 0.3], [3 + 0.3, 1 + 0.3],
[3 + 0.3, 5 + 0.3], [3 + 0.3, 5 + 0.3], [3 + 0.3, 5 + 0.3],
[10 + 1, 5 + 1], [10 + 1, -2 + 1], [10 + 1, -2 + 1],
[10 + 1, -2 + 1], [10 + 1, -2 + 1]]
params = [
1, 0, -0.8, 0, 0.2, 0, 0, 1, 0, -0.8, 0, 0.2, 4, 0, 5, 0, 6, 0, 4,
4, 5, 5, 6, 6
]
expected_error = [
7.5, 15, 7.5, 15, 7.5, 15, 6.7, 13.4, 5.7, 11.4, 4.5, 9, 4.5, 9,
4.5, 9, 4.5, 9, 4.5, 9, 4.5, 9, 4.5, 9, 4.5, 9, 7.3, 14.6, 10.8,
21.6, 15, 30, 15, 30
]
error = PredictionError(mdl, y, u)(params)
assert_array_almost_equal(error, expected_error)
def test_mimo_estimate_parameters_single_shoot(self):
mdl = Linear(N=3, M=3, Ny=2, Nu=2)
y = [[0, 0], [4, 8], [13, 26], [24.8, 49.6], [30.2, 60.4],
[27.96, 63.92], [11.76, 61.52], [-16.568, 55.464],
[-50.384, 34.032], [-64.7776, 8.9648], [-57.784, -10.1680],
[-26.03872, -5.53344], [22.23296, 34.39392],
[76.507136, 112.787072], [98.513024, 180.165248],
[86.7539072, 216.8143744], [96.2449152, 243.2395904],
[154.54439424, 260.82114048], [244.89924352, 271.59234304],
[290.512711168, 231.583348736]]
u = [[1, 1], [1, 1], [1, 1], [1, 1], [1, 3], [-2, 3], [-2, 3], [-2, 3],
[-2, 1], [-2, 1], [3, 1], [3, 1], [3, 5], [3, 5], [3, 5], [10, 5],
[10, -2], [10, -2], [10, -2], [10, -2]]
true_params = [
1, 0, -0.8, 0, 0.2, 0, 0, 1, 0, -0.8, 0, 0.2, 4, 0, 5, 0, 6, 0, 4,
4, 5, 5, 6, 6
]
initial_guess = [
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0
]
error = SimulationError(mdl, y, u, maxlength=20)
params, info = error.lsq_estimate_parameters(initial_guess,
use_sparse=False)
multiple_shoots = info["multiple_shoots"]
initial_conditions = info["initial_conditions"]
assert_array_almost_equal(params, true_params)
assert_array_almost_equal(multiple_shoots, [(0, 20)], decimal=3)
assert_array_almost_equal(initial_conditions,
[[[0, 0], [4, 8], [13, 26]]],
decimal=3)
def test_mimo_simulation_error_one_shoot_derivatives(self):
mdl = Linear(N=3, M=3, Ny=2, Nu=2)
y = [[0, 0], [4, 8], [13, 26], [24.8, 49.6], [30.2, 60.4],
[27.96, 63.92], [11.76, 61.52], [-16.568, 55.464],
[-50.384, 34.032], [-64.7776, 8.9648], [-57.784, -10.1680],
[-26.03872, -5.53344], [22.23296, 34.39392],
[76.507136, 112.787072], [98.513024, 180.165248],
[86.7539072, 216.8143744], [96.2449152, 243.2395904],
[154.54439424, 260.82114048], [244.89924352, 271.59234304],
[290.512711168, 231.583348736]]
u = [[1 + 0.5, 1 + 0.5], [1 + 0.5, 1 + 0.5], [1 + 0.5, 1 + 0.5],
[1 + 0.5, 1 + 0.5], [1 + 0.5, 3 + 0.5], [-2 + 0.3, 3 + 0.3],
[-2 + 0.3, 3 + 0.3], [-2 + 0.3, 3 + 0.3], [-2 + 0.3, 1 + 0.3],
[-2 + 0.3, 1 + 0.3], [3 + 0.3, 1 + 0.3], [3 + 0.3, 1 + 0.3],
[3 + 0.3, 5 + 0.3], [3 + 0.3, 5 + 0.3], [3 + 0.3, 5 + 0.3],
[10 + 1, 5 + 1], [10 + 1, -2 + 1], [10 + 1, -2 + 1],
[10 + 1, -2 + 1], [10 + 1, -2 + 1]]
params = [
1, 0, -0.8, 0, 0.2, 0, 0, 1, 0, -0.8, 0, 0.2, 4, 0, 5, 0, 6, 0, 4,
4, 5, 5, 6, 6
]
extended_params = params + [0, 0, 4, 8, 13, 26]
error = SimulationError(mdl, y, u)
dparams = error.derivatives(extended_params)
dparams_numeric \
= error_numeric_derivatives(error, extended_params)
assert_array_almost_equal(dparams, dparams_numeric)
def test_mimo_simulation_derivative(self):
mdl = Linear(N=3, M=3, Ny=2, Nu=2)
y = [[0, 0], [4, 8], [13, 26], [24.8, 49.6], [30.2, 60.4],
[27.96, 63.92], [11.76, 61.52], [-16.568, 55.464],
[-50.384, 34.032], [-64.7776, 8.9648], [-57.784, -10.1680],
[-26.03872, -5.53344], [22.23296, 34.39392],
[76.507136, 112.787072], [98.513024, 180.165248],
[86.7539072, 216.8143744], [96.2449152, 243.2395904],
[154.54439424, 260.82114048], [244.89924352, 271.59234304],
[290.512711168, 231.583348736]]
u = [[1, 1], [1, 1], [1, 1], [1, 1], [1, 3], [-2, 3], [-2, 3], [-2, 3],
[-2, 1], [-2, 1], [3, 1], [3, 1], [3, 5], [3, 5], [3, 5], [10, 5],
[10, -2], [10, -2], [10, -2], [10, -2]]
params = [
1, 0, -0.8, 0, 0.2, 0, 0, 1, 0, -0.8, 0, 0.2, 4, 0, 5, 0, 6, 0, 4,
4, 5, 5, 6, 6
]
jac_params, jac_y0 = simulate_derivatives(mdl, y[:3], u[:-1], params)
jac_params_numeric, jac_y0_numeric \
= simulate_numeric_derivatives(mdl, y[:3], u[:-1], params)
assert_array_almost_equal(jac_params, jac_params_numeric, decimal=6)
assert_array_almost_equal(jac_y0, jac_y0_numeric, decimal=6)
def test_siso_estimate_parameters_multiple_shoot(self):
mdl = Linear(N=3, M=3)
y = [[0], [4], [13], [24.8], [30.2], [27.96], [11.76], [-16.568],
[-50.384], [-64.7776], [-57.784], [-26.03872], [22.23296],
[76.507136], [98.513024], [86.7539072], [96.2449152],
[154.54439424], [244.89924352], [290.512711168]]
u = [[1], [1], [1], [1], [1], [-2], [-2], [-2], [-2], [-2], [3], [3],
[3], [3], [3], [10], [10], [10], [10], [10]]
true_params = [1, -0.8, 0.2, 4, 5, 6]
initial_guess = [0, 0, 0, 0, 0, 0]
error = SimulationError(mdl, y, u, maxlength=5)
params, info = error.lsq_estimate_parameters(initial_guess)
multiple_shoots = info["multiple_shoots"]
initial_conditions = info["initial_conditions"]
assert_array_almost_equal(params, true_params, decimal=3)
assert_array_almost_equal(multiple_shoots, [(0, 8), (5, 13), (10, 18),
(15, 20)],
decimal=3)
assert_array_almost_equal(
initial_conditions,
[[[0], [4], [13]], [[27.96], [11.76], [-16.568]],
[[-57.784], [-26.03872], [22.23296]],
[[86.7539072], [96.2449152], [154.54439424]]],
decimal=3)
def test_check_parameters_size(self):
mdl = Linear(N=2, M=2, Ny=2, Nu=2)
y0 = [[1, 2], [2, 3]]
u = [[2, 3], [3, 4], [5, 6]]
params = [1, 2, 0, 0, 0, 0, 5, 6, 3, 4, 0, 0]
assert_raises(ValueError, simulate_derivatives, mdl, y0, u, params)
def test_mimo_simulation_error_one_shoot(self):
mdl = Linear(N=3, M=3, Ny=2, Nu=2)
y = [[0, 0], [4, 8], [13, 26], [24.8 - 1.23, 49.6 - 2],
[30.2 - 1.23, 60.4 - 2], [27.96 - 1.23, 63.92 - 2],
[11.76 - 1.23, 61.52 - 2], [-16.568 - 1.23, 55.464 - 2],
[-50.384 - 1.23, 34.032 - 2], [-64.7776 - 1.23, 8.9648 - 2],
[-57.784 - 1.23, -10.1680 - 2], [-26.03872 - 1.23, -5.53344 - 2],
[22.23296 - 12, 34.39392 - 2],
[76.507136 - 12, 112.787072 - 2.45],
[98.513024 - 12, 180.165248 - 2.45],
[86.7539072 - 12, 216.8143744 - 2.45],
[96.2449152 - 12, 243.2395904 - 2.45],
[154.54439424 - 12, 260.82114048 - 2.45],
[244.89924352 - 12, 271.59234304 - 2.45],
[290.512711168 - 12, 231.583348736 - 2.45]]
u = [[1, 1], [1, 1], [1, 1], [1, 1], [1, 3], [-2, 3], [-2, 3], [-2, 3],
[-2, 1], [-2, 1], [3, 1], [3, 1], [3, 5], [3, 5], [3, 5], [10, 5],
[10, -2], [10, -2], [10, -2], [10, -2]]
params = [
1, 0, -0.8, 0, 0.2, 0, 0, 1, 0, -0.8, 0, 0.2, 4, 0, 5, 0, 6, 0, 4,
4, 5, 5, 6, 6
]
expected_error = [
1.23, 2, 1.23, 2, 1.23, 2, 1.23, 2, 1.23, 2, 1.23, 2, 1.23, 2,
1.23, 2, 1.23, 2, 12, 2, 12, 2.45, 12, 2.45, 12, 2.45, 12, 2.45,
12, 2.45, 12, 2.45, 12, 2.45
]
error = SimulationError(mdl, y, u)(params + [0, 0, 4, 8, 13, 26])
assert_array_almost_equal(error, expected_error)
def test_mimo_estimate_parameters(self):
mdl = Linear(N=3, M=3, Ny=2, Nu=2)
y = [[0, 0], [4, 8], [13, 26], [24.8, 49.6], [30.2, 60.4],
[27.96, 63.92], [11.76, 61.52], [-16.568, 55.464],
[-50.384, 34.032], [-64.7776, 8.9648], [-57.784, -10.1680],
[-26.03872, -5.53344], [22.23296, 34.39392],
[76.507136, 112.787072], [98.513024, 180.165248],
[86.7539072, 216.8143744], [96.2449152, 243.2395904],
[154.54439424, 260.82114048], [244.89924352, 271.59234304],
[290.512711168, 231.583348736]]
u = [[1, 1], [1, 1], [1, 1], [1, 1], [1, 3], [-2, 3], [-2, 3], [-2, 3],
[-2, 1], [-2, 1], [3, 1], [3, 1], [3, 5], [3, 5], [3, 5], [10, 5],
[10, -2], [10, -2], [10, -2], [10, -2]]
true_params = [
1, 0, -0.8, 0, 0.2, 0, 0, 1, 0, -0.8, 0, 0.2, 4, 0, 5, 0, 6, 0, 4,
4, 5, 5, 6, 6
]
initial_guess = [
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0
]
error = PredictionError(mdl, y, u)
error = PredictionError(mdl, y, u)
params, info = error.lsq_estimate_parameters(initial_guess)
assert_array_almost_equal(params, true_params)
def test_ynext_tito(self):
mdl = Linear(N=2, M=2, Ny=2, Nu=2)
y = [[1, 2], [2, 3]]
u = [[2, 3], [3, 4]]
ynext = mdl(y=y,
u=u,
params=[1, 2, 0, 0, 0, 0, 5, 6, 3, 4, 0, 0, 0, 0, 7, 8])
assert_array_almost_equal(ynext, [23, 81])
def test_check_parameters_size(self):
mdl = Linear(N=2, M=2, Ny=2, Nu=2)
y = [[1, 2], [2, 3], [3, 4]]
u = [[2, 3], [3, 4], [5, 6]]
params = [1, 2, 0, 0,
0, 0, 5, 6,
3, 4, 0, 0]
assert_raises(ValueError, predict, mdl, y, u, params)
def test_check_diferent_Nd(self):
mdl = Linear(N=2, M=2, Ny=2, Nu=2)
y = [[1, 2], [2, 3], [3, 4]]
u = [[2, 3], [3, 3], [4, 2], [5, 6]]
params = [1, 2, 0, 0,
0, 0, 5, 6,
3, 4, 0, 0,
0, 0, 1, 2]
assert_raises(ValueError, predict, mdl, y, u, params)
def test_check_wrong_u_size(self):
mdl = Linear(N=2, M=2, Ny=2, Nu=2)
y = [[1, 2], [2, 3], [3, 4]]
u = [[2, 3, 3], [3, 4, 2], [5, 6, 4]]
params = [1, 2, 0, 0,
0, 0, 5, 6,
3, 4, 0, 0,
0, 0, 1, 2]
assert_raises(ValueError, predict_derivatives, mdl, y, u, params)
def test_siso_simulation(self):
mdl = Linear(N=3, M=3)
y = [[0], [4], [13], [24.8], [30.2], [27.96], [11.76], [-16.568],
[-50.384], [-64.7776], [-57.784], [-26.03872], [22.23296],
[76.507136], [98.513024], [86.7539072], [96.2449152],
[154.54439424], [244.89924352], [290.512711168]]
u = [[1], [1], [1], [1], [1], [-2], [-2], [-2], [-2], [-2], [3], [3],
[3], [3], [3], [10], [10], [10], [10], [10]]
params = [1, -0.8, 0.2, 4, 5, 6]
ys = simulate(mdl, y[:3], u[:-1], params)
assert_array_almost_equal(ys, y[3:])
def test_siso_predict_numerical_derivativesative(self):
mdl = Linear(N=3, M=3)
y = [[0],
[4],
[13],
[24.8],
[30.2],
[27.96],
[11.76],
[-16.568],
[-50.384],
[-64.7776],
[-57.784],
[-26.03872],
[22.23296],
[76.507136],
[98.513024],
[86.7539072],
[96.2449152],
[154.54439424],
[244.89924352],
[290.512711168]]
u = [[1],
[1],
[1],
[1],
[1],
[-2],
[-2],
[-2],
[-2],
[-2],
[3],
[3],
[3],
[3],
[3],
[10],
[10],
[10],
[10],
[10]]
params = [1, -0.8, 0.2, 4, 5, 6]
jac = predict_derivatives(mdl, y[:-1], u[:-1], params)
jac_numeric = predict_numeric_derivatives(mdl, y[:-1], u[:-1], params)
assert_array_almost_equal(jac, jac_numeric)
def test_mimo_numerical_approx_twoinputs_threeoutputs(self):
# Define model
N = 3
M = 2
Ny = 2
Nu = 3
mdl = Linear(N, M, Ny, Nu)
Nparams = mdl.Nparams
# Define ynext and derivatives
y = np.array([[12, 2.41], [3, 1.23], [2.41, 5]])
u = np.array([[4.5, 6.4, 10], [2.23, 2.41, 521]])
params = np.arange(Nparams)
dy, du, dparams = mdl.derivatives(y, u, params)
# Compute the same derivatives numerically
dy_numeric, du_numeric, \
dparams_numeric = mdl._numeric_derivatives(y, u,
params)
# Check
assert_array_almost_equal(dy, dy_numeric)
assert_array_almost_equal(du, du_numeric)
assert_array_almost_equal(dparams, dparams_numeric)
def test_siso_numerical_approx(self):
# Define model
N = 2
M = 2
Ny = 1
Nu = 1
mdl = Linear(N, M)
Nparams = mdl.Nparams
# Define ynext and derivatives
y = np.array([1.23, 2.41])
u = np.array([2.54, 23])
params = [1, 5.46, 3.23, 4]
dy, du, dparams = mdl.derivatives(y, u, params)
# Compute the same derivatives numerically
dy_numeric, du_numeric, \
dparams_numeric = mdl._numeric_derivatives(y, u,
params)
# Check
assert_array_almost_equal(dy, dy_numeric)
assert_array_almost_equal(du, du_numeric)
assert_array_almost_equal(dparams, dparams_numeric)
def test_siso_estimate_parameters(self):
mdl = Linear(N=3, M=3)
y = [[0], [4], [13], [24.8], [30.2], [27.96], [11.76], [-16.568],
[-50.384], [-64.7776], [-57.784], [-26.03872], [22.23296],
[76.507136], [98.513024], [86.7539072], [96.2449152],
[154.54439424], [244.89924352], [290.512711168]]
u = [[1], [1], [1], [1], [1], [-2], [-2], [-2], [-2], [-2], [3], [3],
[3], [3], [3], [10], [10], [10], [10], [10]]
true_params = [1, -0.8, 0.2, 4, 5, 6]
initial_guess = [0, 0, 0, 0, 0, 0]
error = PredictionError(mdl, y, u)
params, info = error.lsq_estimate_parameters(initial_guess)
assert_array_almost_equal(params, true_params)
def test_siso_simulation_error_multiple_shoot(self):
mdl = Linear(N=3, M=3)
y = [[0], [4], [13], [24.8 - 1.34], [30.2 - 1.34], [27.96 - 1.34],
[11.76 - 1.34], [-16.568 - 1.34], [-50.384 - 1.34],
[-64.7776 - 1.34], [-57.784 - 1.34], [-26.03872 - 5.34],
[22.23296 - 5.34], [76.507136 - 5.34], [98.513024 - 5.34],
[86.7539072 - 5.34], [96.2449152 - 5.34], [154.54439424 - 5.34],
[244.89924352 - 5.34], [290.512711168 - 5.34]]
u = [[1], [1], [1], [1], [1], [-2], [-2], [-2], [-2], [-2], [3], [3],
[3], [3], [3], [10], [10], [10], [10], [10]]
params = [1, -0.8, 0.2, 4, 5, 6]
expected_error = [
1.34,
1.34,
1.34,
1.34,
1.34,
0, # overlap error
0, # overlap error
0, # overlap error
1.34,
1.34,
1.34,
5.34,
5.34,
0, # overlap error
0, # overlap error
0, # overlap error
5.34,
5.34,
5.34,
5.34,
5.34,
0, # overlap error
0, # overlap error
0, # overlap error
5.34,
5.34
]
e = SimulationError(mdl, y, u, 5)
error = e(params + [0, 4, 13] + [27.96, 11.76, -16.568] +
[-57.784, -26.03872, 22.23296] +
[86.7539072, 96.2449152, 154.54439424])
assert_array_almost_equal(error, expected_error)
def test_siso_prediction_error_derivatives(self):
mdl = Linear(N=3, M=3)
y = [[0], [4], [13], [24.8], [30.2], [27.96], [11.76], [-16.568],
[-50.384], [-64.7776], [-57.784], [-26.03872], [22.23296],
[76.507136], [98.513024], [86.7539072], [96.2449152],
[154.54439424], [244.89924352], [290.512711168]]
u = [[1 + 0.5], [1 + 0.5], [1 + 0.5], [1 + 0.5], [1 + 0.5], [-2 + 0.3],
[-2 + 0.3], [-2 + 0.3], [-2 + 0.3], [-2 + 0.3], [3 + 0.3],
[3 + 0.3], [3 + 0.3], [3 + 0.3], [3 + 0.3], [10 + 1], [10 + 1],
[10 + 1], [10 + 1], [10 + 1]]
params = [1, -0.8, 0.2, 4, 5, 6]
error = PredictionError(mdl, y, u)
jac = error.derivatives(params)
jac_numeric \
= error_numeric_derivatives(error, params)
assert_array_almost_equal(jac, jac_numeric)
def test_siso_simulation_error_one_shoot_derivatives(self):
mdl = Linear(N=3, M=3)
y = [[0], [4], [13], [24.8], [30.2], [27.96], [11.76], [-16.568],
[-50.384], [-64.7776], [-57.784], [-26.03872], [22.23296],
[76.507136], [98.513024], [86.7539072], [96.2449152],
[154.54439424], [244.89924352], [290.512711168]]
u = [[1 + 0.5], [1 + 0.5], [1 + 0.5], [1 + 0.5], [1 + 0.5], [-2 + 0.3],
[-2 + 0.3], [-2 + 0.3], [-2 + 0.3], [-2 + 0.3], [3 + 0.3],
[3 + 0.3], [3 + 0.3], [3 + 0.3], [3 + 0.3], [10 + 1], [10 + 1],
[10 + 1], [10 + 1], [10 + 1]]
params = [1, -0.8, 0.2, 4, 5, 6]
extended_params = params + [0, 4, 13]
error = SimulationError(mdl, y, u)
dparams = error.derivatives(extended_params)
dparams_numeric \
= error_numeric_derivatives(error, extended_params)
assert_array_almost_equal(dparams, dparams_numeric)
def test_siso_prediction_error(self):
mdl = Linear(N=3, M=3)
y = [[0], [4], [13], [24.8], [30.2], [27.96], [11.76], [-16.568],
[-50.384], [-64.7776], [-57.784], [-26.03872], [22.23296],
[76.507136], [98.513024], [86.7539072], [96.2449152],
[154.54439424], [244.89924352], [290.512711168]]
u = [[1 + 0.5], [1 + 0.5], [1 + 0.5], [1 + 0.5], [1 + 0.5], [-2 + 0.3],
[-2 + 0.3], [-2 + 0.3], [-2 + 0.3], [-2 + 0.3], [3 + 0.3],
[3 + 0.3], [3 + 0.3], [3 + 0.3], [3 + 0.3], [10 + 1], [10 + 1],
[10 + 1], [10 + 1], [10 + 1]]
params = [1, -0.8, 0.2, 4, 5, 6]
expected_error = [
7.5, 7.5, 7.5, 6.7, 5.7, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5,
7.3, 10.8, 15, 15
]
e = PredictionError(mdl, y, u)
error = e(params)
assert_array_almost_equal(error, expected_error)
def test_check_diferent_Nd(self):
mdl = Linear(N=2, M=2, Ny=2, Nu=2)
y = [[1, 2], [2, 3], [3, 4]]
u = [[2, 3], [3, 3], [4, 2], [5, 6]]
assert_raises(ValueError, PredictionError, mdl, y, u)
def test_check_wrong_u_size(self):
mdl = Linear(N=2, M=2, Ny=2, Nu=2)
y = [[1, 2], [2, 3], [3, 4]]
u = [[2, 3, 3], [3, 4, 2], [5, 6, 4]]
assert_raises(ValueError, PredictionError, mdl, y, u)