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_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_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)
