def test_ar_model_predict(self):
m1 = Monomial(1, 0, [1], [], [1], [])
m2 = Monomial(1, 0, [1], [], [2], [])
m = [m1, m2]
mdl = Polynomial(m)
y = [[0.500000000000000],
[0.925000000000000],
[0.256687500000000],
[0.705956401171875],
[0.768053255020420],
[0.659145574149943],
[0.831288939045395],
[0.518916263804854],
[0.923676047365561],
[0.260844845488171],
[0.713377804660565],
[0.756538676169480],
[0.681495258228080],
[0.803120043590673],
[0.585037484942277],
[0.898243916772361],
[0.338186596189094],
[0.828120762684376],
[0.526646030853067],
[0.922372959447176]]
u = np.reshape([], (20, 0))
params = [3.7, -3.7]
y1 = predict(mdl, y[:-1], u[:-1], params)
assert_array_almost_equal(y1, y[1:])
def test_fir_model_predict(self):
m1 = Monomial(0, 1, [], [1], [], [1])
m2 = Monomial(0, 1, [], [1], [], [2])
m = [m1, m2]
mdl = Polynomial(m)
y = [[0],
[-1],
[-6],
[-15],
[-28],
[-45],
[-66],
[-91],
[-120],
[-153],
[-190],
[-231],
[-276],
[-325],
[-378],
[-435],
[-496],
[-561],
[-630],
[-703]]
u = np.arange(1, 21).reshape((20, 1))
params = [1, -2]
y1 = predict(mdl, y[:-1], u[:-1], params)
assert_array_almost_equal(y1, y[1:])
def test_mimo_prediction(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]
y1 = predict(mdl, y[:-1], u[:-1], params)
assert_array_almost_equal(y1, y[3:])
def test_siso_prediction(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]
y1 = predict(mdl, y[:-1], u[:-1], params)
assert_array_almost_equal(y1, y[3:])
def fun(x):
return predict(mdl, y, u, x).flatten()