def test_unbounded_active_variable_domain(self):
np.random.seed(42)
df = np.random.normal(size=(10, 3))
sub = ss.Subspaces()
sub.compute(df=df)
uavd = dom.UnboundedActiveVariableDomain(sub)
def test_rs_0(self):
np.random.seed(42)
df0 = np.random.normal(size=(10, 2))
sub = ss.Subspaces()
sub.compute(df=df0)
avd = dom.UnboundedActiveVariableDomain(sub)
avm = dom.UnboundedActiveVariableMap(avd)
asm.ActiveSubspaceResponseSurface(avm)
def test_fun_ubnd_int(self):
np.random.seed(42)
X0 = np.random.normal(size=(50,3))
f0 = np.random.normal(size=(50,1))
df0 = np.random.normal(size=(50,3))
sub = ss.Subspaces()
sub.compute(df=df0)
sub.partition(1)
avd = dom.UnboundedActiveVariableDomain(sub)
avm = dom.UnboundedActiveVariableMap(avd)
mu, lb, ub = asi.integrate(self.quad_fun, avm, 10)
def test_unbounded_active_variable_map_1(self):
np.random.seed(42)
df0 = np.random.normal(size=(10, 3))
sub = ss.Subspaces()
sub.compute(df=df0)
m, n = sub.W1.shape
uavd = dom.UnboundedActiveVariableDomain(sub)
uavm = dom.UnboundedActiveVariableMap(uavd)
X = np.random.normal(size=(100, m))
Y, Z = uavm.forward(X)
X0 = np.dot(Y, sub.W1.T) + np.dot(Z, sub.W2.T)
np.testing.assert_almost_equal(X0, X)
def test_sample_z(self):
np.random.seed(42)
df0 = np.random.normal(size=(10, 3))
sub = ss.Subspaces()
sub.compute(df=df0)
W1, W2 = sub.W1, sub.W2
m, n = W1.shape
np.random.seed(43)
x = np.random.uniform(-1.0, 1.0, size=(1, m))
y = np.dot(x, W1).reshape((n, ))
N = 10
np.random.seed(42)
Z = dom.sample_z(N, y, W1, W2)
def test_rs_bnd_2d_int(self):
np.random.seed(42)
X0 = np.random.uniform(-1.0,1.0,size=(50,3))
f0 = np.random.normal(size=(50,1))
df0 = np.random.normal(size=(50,3))
sub = ss.Subspaces()
sub.compute(df=df0)
sub.partition(2)
avd = dom.BoundedActiveVariableDomain(sub)
avm = dom.BoundedActiveVariableMap(avd)
asrs = asm.ActiveSubspaceResponseSurface(avm)
asrs.train_with_data(X0, f0)
I = asi.av_integrate(asrs, avm, 10)
def test_unbounded_active_variable_map_3(self):
np.random.seed(42)
df0 = np.random.normal(size=(10, 3))
sub = ss.Subspaces()
sub.compute(df=df0)
m, n = sub.W1.shape
uavd = dom.UnboundedActiveVariableDomain(sub)
uavm = dom.UnboundedActiveVariableMap(uavd)
X = np.random.normal(size=(100, m))
Y, Z = uavm.forward(X)
X0 = uavm.inverse(Y, N=10)[0]
np.testing.assert_almost_equal(np.dot(X0, sub.W1),
np.kron(Y, np.ones((10, 1))))
def test_bounded_active_variable_map_3(self):
np.random.seed(42)
df0 = np.random.normal(size=(10, 3))
sub = ss.Subspaces()
sub.compute(df=df0)
m, n = sub.W1.shape
bavd = dom.BoundedActiveVariableDomain(sub)
bavm = dom.BoundedActiveVariableMap(bavd)
X = np.random.uniform(-1.0, 1.0, size=(10, m))
Y, Z = bavm.forward(X)
X0 = bavm.inverse(Y, N=10)[0]
np.testing.assert_almost_equal(np.dot(X0, sub.W1),
np.kron(Y, np.ones((10, 1))))
np.testing.assert_equal(np.floor(np.abs(X0)), np.zeros(X0.shape))
def test_rs_data_train_pr_bnd(self):
np.random.seed(42)
X0 = np.random.uniform(-1.0, 1.0, size=(50, 3))
f0 = np.random.normal(size=(50, 1))
df0 = np.random.normal(size=(50, 3))
sub = ss.Subspaces()
sub.compute(df=df0)
avd = dom.BoundedActiveVariableDomain(sub)
avm = dom.BoundedActiveVariableMap(avd)
pr = rs.PolynomialApproximation()
asrs = asm.ActiveSubspaceResponseSurface(avm, pr)
asrs.train_with_data(X0, f0)
XX = np.random.uniform(-1.0, 1.0, size=(10, 3))
ff, dff = asrs.predict(XX, compgrad=True)
def test_rs_data_train_gp_ubnd(self):
np.random.seed(42)
X0 = np.random.normal(size=(50, 3))
f0 = np.random.normal(size=(50, 1))
df0 = np.random.normal(size=(50, 3))
sub = ss.Subspaces()
sub.compute(df=df0)
avd = dom.UnboundedActiveVariableDomain(sub)
avm = dom.UnboundedActiveVariableMap(avd)
asrs = asm.ActiveSubspaceResponseSurface(avm)
asrs.train_with_data(X0, f0)
np.random.seed(43)
XX = np.random.normal(size=(10, 3))
ff, dff = asrs.predict(XX, compgrad=True)
def test_rs_fun_train_gp_bnd(self):
np.random.seed(42)
X0 = np.random.uniform(-1.0, 1.0, size=(50, 3))
f0 = np.random.normal(size=(50, 1))
df0 = np.random.normal(size=(50, 3))
sub = ss.Subspaces()
sub.compute(df=df0)
avd = dom.BoundedActiveVariableDomain(sub)
avm = dom.BoundedActiveVariableMap(avd)
asrs = asm.ActiveSubspaceResponseSurface(avm)
asrs.train_with_interface(self.quad_fun, 10)
XX = np.random.uniform(-1.0, 1.0, size=(10, 3))
ff, dff = asrs.predict(XX, compgrad=True)
def test_rs_fun_train_pr_ubnd_2d(self):
np.random.seed(42)
X0 = np.random.normal(size=(50, 3))
f0 = np.random.normal(size=(50, 1))
df0 = np.random.normal(size=(50, 3))
sub = ss.Subspaces()
sub.compute(df=df0)
sub.partition(2)
avd = dom.UnboundedActiveVariableDomain(sub)
avm = dom.UnboundedActiveVariableMap(avd)
pr = rs.PolynomialApproximation()
asrs = asm.ActiveSubspaceResponseSurface(avm, pr)
asrs.train_with_interface(self.quad_fun, 10)
XX = np.random.normal(size=(10, 3))
ff, dff = asrs.predict(XX, compgrad=True)
def test_rs_bnd_2d_int(self):
np.random.seed(42)
X0 = np.random.uniform(-1.,1.,size=(50,3))
f0 = np.zeros((50,1))
df0 = np.zeros((50,3))
for i in range(50):
x = X0[i,:]
f0[i,0] = self.quad_fun(x)
df0[i,:] = self.quad_dfun(x).reshape((3, ))
sub = ss.Subspaces()
sub.compute(df=df0)
sub.partition(2)
avd = dom.BoundedActiveVariableDomain(sub)
avm = dom.BoundedActiveVariableMap(avd)
asrs = asm.ActiveSubspaceResponseSurface(avm)
asrs.train_with_data(X0, f0)
xstar, fstar = aso.minimize(asrs, X0, f0)
def test_subspace_class(self):
np.random.seed(42)
X = np.random.normal(size=(50,3))
f = np.random.normal(size=(50,1))
df = np.random.normal(size=(50,3))
weights = np.ones((50,1)) / 50
sub = ss.Subspaces()
sub.compute(X, f, df, weights)
sub.compute(X, f, df, weights, sstype='AS')
sub.compute(X, f, df, weights, sstype='OLS')
sub.compute(X, f, df, weights, sstype='QPHD')
sub.compute(X, f, df, weights, sstype='OPG')
sub.compute(X, f, df, weights, sstype='AS', nboot=10)
sub.compute(X, f, df, weights, sstype='OLS', nboot=10)
sub.compute(X, f, df, weights, sstype='QPHD', nboot=10)
sub.compute(X, f, df, weights, sstype='OPG', nboot=10)
sub.compute(X, f, df, weights, sstype='AS', ptype='EVG', nboot=100)
sub.compute(X, f, df, weights, sstype='AS', ptype='RS', nboot=100)
sub.compute(X, f, df, weights, sstype='AS', ptype='LI', nboot=100)
