def test_predict(self, n=50, m=10, k=100, r=3):
"""Test
_core._interpolate._inferred.InterpolatedInferredContinuousROM.predict().
"""
# Get data for fitting.
X1, Xdot1, U1 = _get_data(n, k, m)
X2, Xdot2, U2 = X1 + 1, Xdot1.copy(), U1 + 1
Xs = [X1, X2]
Xdots = [Xdot1, Xdot2]
Us = [U1, U2]
ps = [1, 2]
Vr = la.svd(np.hstack(Xs))[0][:, :r]
# Parameters for predicting.
x0 = np.zeros(n)
nt = 5
t = np.linspace(0, .01 * nt, nt)
u = lambda t: np.zeros(m)
# Fit / predict with no inputs.
model = roi.InterpolatedInferredContinuousROM("AH")
model.fit(Vr, ps, Xs, Xdots)
model.predict(1, x0, t)
model.predict(1.5, x0, t)
# Fit / predict with inputs.
model = roi.InterpolatedInferredContinuousROM("AHB")
model.fit(Vr, ps, Xs, Xdots, Us)
model.predict(1, x0, t, u)
model.predict(1.5, x0, t, u)
def test_predict(self):
"""Test _core.InterpolatedInferredContinuousROM.predict()."""
model = roi.InterpolatedInferredContinuousROM("cAH")
# Get data for fitting.
n, m, k, r = 50, 10, 100, 5
X1, Xdot1, U1 = _get_data(n, k, m)
X2, Xdot2, U2 = X1+1, Xdot1.copy(), U1+1
Xs = [X1, X2]
Xdots = [Xdot1, Xdot2]
Us = [U1, U2]
ps = [1, 2]
Vr = la.svd(np.hstack(Xs))[0][:,:r]
# Parameters for predicting.
x0 = np.random.random(n)
nt = 5
t = np.linspace(0, .01*nt, nt)
u = lambda t: np.ones(10)
# Fit / predict with no inputs.
model.fit(Vr, ps, Xs, Xdots)
model.predict(1, x0, t)
model.predict(1.5, x0, t)
# Fit / predict with inputs.
model.modelform = "cAHB"
model.fit(Vr, ps, Xs, Xdots, Us)
model.predict(1, x0, t, u)
model.predict(1.5, x0, t, u)
def test_str(self):
"""Test _core._base._ParametricMixin.__str__()
(string representation).
"""
# Continuous ROMs
model = roi.InterpolatedInferredContinuousROM("A")
assert str(model) == \
"Reduced-order model structure: dx / dt = Ax(t)"
model.c_ = lambda t: t
model.A_ = lambda t: t
model.modelform = "cA"
assert str(model) == \
"Reduced-order model structure: dx / dt = c(µ) + A(µ)x(t)"
model.Hc_ = None
model.Gc_ = lambda t: t
model.B_ = None
model.modelform = "HB"
assert str(model) == \
"Reduced-order model structure: dx / dt = H(x(t) ⊗ x(t)) + Bu(t)"
model.modelform = "G"
assert str(model) == \
"Reduced-order model structure: dx / dt = G(µ)(x(t) ⊗ x(t) ⊗ x(t))"
# Discrete ROMs
model = roi.AffineIntrusiveDiscreteROM("cH")
assert str(model) == \
"Reduced-order model structure: x_{j+1} = c + H(x_{j} ⊗ x_{j})"
model.c_ = lambda t: t
model.Hc_ = None
assert str(model) == \
"Reduced-order model structure: x_{j+1} = c(µ) + H(x_{j} ⊗ x_{j})"
def test_fit(self):
"""Test
_core._interpolate._inferred.InterpolatedInferredContinuousROM.fit().
"""
model = roi.InterpolatedInferredContinuousROM("cAH")
# Get data for fitting.
n, m, k, r = 50, 10, 100, 5
X1, Xdot1, U1 = _get_data(n, k, m)
X2, Xdot2, U2 = X1 + 1, Xdot1.copy(), U1 + 1
Xs = [X1, X2]
Xdots = [Xdot1, Xdot2]
Us = [U1, U2]
ps = [1, 2]
Vr = la.svd(np.hstack(Xs))[0][:, :r]
# Try with non-scalar parameters.
with pytest.raises(ValueError) as ex:
model.fit(Vr, [np.array([1, 1]), np.array([2, 2])], Xs, Xdots)
assert ex.value.args[0] == "only scalar parameter values are supported"
# Try with bad number of Xs.
with pytest.raises(ValueError) as ex:
model.fit(Vr, ps, [X1, X2, X2 + 1], Xdots)
assert ex.value.args[0] == \
"num parameter samples != num state snapshot sets (2 != 3)"
# Try with bad number of Xdots.
with pytest.raises(ValueError) as ex:
model.fit(Vr, ps, Xs, Xdots + [Xdot1])
assert ex.value.args[0] == \
"num parameter samples != num velocity snapshot sets (2 != 3)"
# Try with varying input sizes.
model.modelform = "cAHB"
with pytest.raises(ValueError) as ex:
model.fit(Vr, ps, Xs, Xdots, [U1, U2[:-1]])
assert ex.value.args[0] == "control inputs not aligned"
# Fit correctly with no inputs.
model.modelform = "cAH"
model.fit(Vr, ps, Xs, Xdots)
for attr in ["models_", "fs_"
] + [s[:-1] + "s_" for s in _LSTSQ_REPORTS]:
assert hasattr(model, attr)
assert len(getattr(model, attr)) == len(model.models_)
# Fit correctly with inputs.
model.modelform = "cAHB"
model.fit(Vr, ps, Xs, Xdots, Us)
assert len(model) == len(ps)
# Test again with Vr = None and projected inputs.
Xs_ = [Vr.T @ X for X in Xs]
Xdots_ = [Vr.T @ Xdot for Xdot in Xdots]
model.fit(None, ps, Xs_, Xdots_, Us)
assert len(model) == len(ps)
assert model.Vr is None
assert model.n is None
def test_fit(self):
"""Test _core.InterpolatedInferredContinuousROM.fit()."""
model = roi.InterpolatedInferredContinuousROM("cAH")
# Get data for fitting.
n, m, k, r = 50, 10, 100, 5
X1, Xdot1, U1 = _get_data(n, k, m)
X2, Xdot2, U2 = X1+1, Xdot1.copy(), U1+1
Xs = [X1, X2]
Xdots = [Xdot1, Xdot2]
Us = [U1, U2]
ps = [1, 2]
Vr = la.svd(np.hstack(Xs))[0][:,:r]
# Try with non-scalar parameters.
with pytest.raises(ValueError) as ex:
model.fit(Vr, [np.array([1,1]), np.array([2,2])], Xs, Xdots)
assert ex.value.args[0] == "only scalar parameter values are supported"
# Try with bad number of Xs.
with pytest.raises(ValueError) as ex:
model.fit(Vr, ps, [X1, X2, X2+1], Xdots)
assert ex.value.args[0] == \
"num parameter samples != num state snapshot sets (2 != 3)"
# Try with bad number of Xdots.
with pytest.raises(ValueError) as ex:
model.fit(Vr, ps, Xs, Xdots + [Xdot1])
assert ex.value.args[0] == \
"num parameter samples != num velocity snapshot sets (2 != 3)"
# Try with varying input sizes.
model.modelform = "cAHB"
with pytest.raises(ValueError) as ex:
model.fit(Vr, ps, Xs, Xdots, [U1, U2[:-1]])
assert ex.value.args[0] == \
"shape of 'U' inconsistent across samples"
# Fit correctly with no inputs.
model.modelform = "cAH"
model.fit(Vr, ps, Xs, Xdots)
for attr in ["models_", "dataconds_", "residuals_", "fs_"]:
assert hasattr(model, attr)
assert len(getattr(model, attr)) == len(model.models_)
# Fit correctly with inputs.
model.modelform = "cAHB"
model.fit(Vr, ps, Xs, Xdots, Us)
assert len(model) == len(ps)