def test_predict(self):
"""Test _core.InterpolatedInferredDiscreteROM.predict()."""
model = roi.InterpolatedInferredDiscreteROM("cAH")
# Get data for fitting.
n, m, k, r = 50, 10, 100, 5
X1, _, U1 = _get_data(n, k, m)
X2, U2 = X1+1, U1+1
Xs = [X1, X2]
Us = [U1, U2]
ps = [1, 2]
Vr = la.svd(np.hstack(Xs))[0][:,:r]
# Parameters for predicting.
x0 = np.random.random(n)
niters = 5
U = np.ones((m,niters))
# Fit / predict with no inputs.
model.fit(Vr, ps, Xs)
model.predict(1, x0, niters)
model.predict(1.5, x0, niters)
# Fit / predict with inputs.
model.modelform = "cAHB"
model.fit(Vr, ps, Xs, Us)
model.predict(1, x0, niters, U)
model.predict(1.5, x0, niters, U)
def test_fit(self):
"""Test
_core._interpolate._inferred.InterpolatedInferredDiscreteROM.fit().
"""
model = roi.InterpolatedInferredDiscreteROM("cAH")
# Get data for fitting.
n, m, k, r = 50, 10, 100, 5
X1, _, U1 = _get_data(n, k, m)
X2, U2 = X1 + 1, U1 + 1
Xs = [X1, X2]
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)
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])
assert ex.value.args[0] == \
"num parameter samples != num state snapshot sets (2 != 3)"
# Try with varying input sizes.
model.modelform = "cAHB"
with pytest.raises(ValueError) as ex:
model.fit(Vr, ps, Xs, [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)
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 = "cAHGB"
model.fit(Vr, ps, Xs, Us)
assert len(model) == len(ps)
# Test again with Vr = None and projected inputs.
Xs_ = [Vr.T @ X for X in Xs]
model.fit(None, ps, Xs_, Us)
assert len(model) == len(ps)
assert model.Vr is None
assert model.n is None
def test_fit(self, n=20, m=4, k=500, r=3):
"""Test
_core._interpolate._inferred.InterpolatedInferredDiscreteROM.fit().
"""
model = roi.InterpolatedInferredDiscreteROM("cAH")
# Get data for fitting.
X1, _, U1 = _get_data(n, k, m)
X2, U2 = X1 + 1, U1 + 1
Xs = [X1, X2]
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)
# 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])
assert ex.value.args[0] == "num parameter samples != num state " \
"snapshot training sets (2 != 3)"
# Fit correctly with no inputs.
model.modelform = "cAH"
model.fit(Vr, ps, Xs)
for attr in ["models_", "fs_"]:
assert hasattr(model, attr)
assert len(getattr(model, attr)) == len(model.models_)
# Fit correctly with inputs.
model.modelform = "cAHGB"
model.fit(Vr, ps, Xs, Us)
assert len(model) == len(ps)
# Test again with Vr = None and projected inputs.
Xs_ = [Vr.T @ X for X in Xs]
model.fit(None, ps, Xs_, Us)
assert len(model) == len(ps)
assert model.Vr is None
assert model.n is None
def test_fit(self):
"""Test _core.InterpolatedInferredDiscreteROM.fit()."""
model = roi.InterpolatedInferredDiscreteROM("cAH")
# Get data for fitting.
n, m, k, r = 50, 10, 100, 5
X1, _, U1 = _get_data(n, k, m)
X2, U2 = X1+1, U1+1
Xs = [X1, X2]
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)
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])
assert ex.value.args[0] == \
"num parameter samples != num state snapshot sets (2 != 3)"
# Try with varying input sizes.
model.modelform = "cAHB"
with pytest.raises(ValueError) as ex:
model.fit(Vr, ps, Xs, [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)
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, Us)
assert len(model) == len(ps)