def generate_random(self):
# Generate random vectors
v1 = RandomNumpyVector(self.N)
v2 = RandomNumpyVector(self.N)
# Generate random matrix
A = RandomNumpyMatrix(self.N, self.N)
# Return
return (v1, v2, A)
def generate_random(self):
af_product = OnlineAffineExpansionStorage(self.Qa, self.Qf)
af_product_legacy = legacy_tensor((self.Qa, self.Qf, self.N))
for i in range(self.Qa):
for j in range(self.Qf):
# Generate random vector
af_product[i, j] = RandomNumpyVector(self.N)
for n in range(self.N):
af_product_legacy[i, j, n] = af_product[i, j][n]
# Genereate random theta
theta_a = RandomTuple(self.Qa)
theta_f = RandomTuple(self.Qf)
# Generate random solution
u = RandomNumpyVector(self.N)
# Return
return (theta_a, theta_f, af_product, af_product_legacy, u)
def generate_random(self):
aa_product = OnlineAffineExpansionStorage(self.Q, self.Q)
aa_product_legacy = legacy_tensor((self.Q, self.Q, self.N, self.N))
for i in range(self.Q):
for j in range(self.Q):
# Generate random matrix
aa_product[i, j] = RandomNumpyMatrix(self.N, self.N)
for n in range(self.N):
for m in range(self.N):
aa_product_legacy[i, j, n, m] = aa_product[i, j][n, m]
# Genereate random theta
theta = RandomTuple(self.Q)
# Generate random solution
u = RandomNumpyVector(self.N)
v = RandomNumpyVector(self.N)
# Return
return (theta, aa_product, aa_product_legacy, u, v)
def generate_random(self):
# Generate random vectors
S = FunctionsList(self.V)
for _ in range(self.N):
b = RandomDolfinFunction(self.V)
S.enrich(b)
uN = RandomNumpyVector(self.N)
# Return
return (S, uN)
def generate_random(self):
F = OnlineAffineExpansionStorage(self.Q)
for i in range(self.Q):
# Generate random vector
F[i] = RandomNumpyVector(self.N)
# Genereate random theta
theta = RandomTuple(self.Q)
# Return
return (theta, F)
def generate_random(self):
# Generate random vectors
Z = BasisFunctionsMatrix(self.V)
Z.init("u")
for _ in range(self.N):
b = RandomDolfinFunction(self.V)
Z.enrich(b)
uN = RandomNumpyVector(self.N)
# Return
return (Z, uN)
def generate_random(self):
F = OnlineAffineExpansionStorage(self.Q)
for i in range(self.Q):
# Generate random vector
F[i] = RandomNumpyVector(self.Nmax)
# Genereate random theta
theta = RandomTuple(self.Q)
# Generate slice
N_stop = RandomSize(1, self.Nmax + 1)
N_start = RandomSize(0, N_stop)
# Return
return (theta, F, slice(N_start, N_stop))
def generate_random(self):
aa_product = OnlineAffineExpansionStorage(self.Qa, self.Qa)
aa_product_legacy = legacy_tensor((self.Qa, self.Qa, self.N, self.N))
for i in range(self.Qa):
for j in range(self.Qa):
# Generate random matrix
aa_product[i, j] = RandomNumpyMatrix(self.N, self.N)
for n in range(self.N):
for m in range(self.N):
aa_product_legacy[i, j, n, m] = aa_product[i, j][n, m]
af_product = OnlineAffineExpansionStorage(self.Qa, self.Qf)
af_product_legacy = legacy_tensor((self.Qa, self.Qf, self.N))
for i in range(self.Qa):
for j in range(self.Qf):
# Generate random matrix
af_product[i, j] = RandomNumpyVector(self.N)
for n in range(self.N):
af_product_legacy[i, j, n] = af_product[i, j][n]
ff_product = OnlineAffineExpansionStorage(self.Qf, self.Qf)
ff_product_legacy = legacy_tensor((self.Qf, self.Qf))
for i in range(self.Qf):
for j in range(self.Qf):
# Generate random matrix
ff_product[i, j] = RandomNumber()
ff_product_legacy[i, j] = ff_product[i, j]
# Genereate random theta
theta_a = []
theta_f = []
for t in range(self.Ntrain):
theta_a.append(RandomTuple(self.Qa))
theta_f.append(RandomTuple(self.Qf))
# Generate random solution
u = []
v = []
for t in range(self.Ntrain):
u.append(RandomNumpyVector(self.N))
v.append(RandomNumpyVector(self.N))
# Return
return (theta_a, theta_f, aa_product, af_product, ff_product,
aa_product_legacy, af_product_legacy, ff_product_legacy, u, v)
def generate_random(self):
# Generate random vectors
v1 = RandomNumpyVector(self.N)
v2 = RandomNumpyVector(self.N)
# Return
return (v1, v2)
