def __build_problem(self):
"""Build the matrix representation of the sampling problem."""
# Set up the mathematical problem
prob = constraint_matrices(self.model, zero_tol=feasibility_tol)
# check if there any non-zero equality constraints
equalities = prob.equalities
b = prob.b
homogeneous = all(np.abs(b) < feasibility_tol)
fixed_non_zero = np.abs(prob.variable_bounds[:, 1]) > feasibility_tol
fixed_non_zero &= prob.variable_fixed
# check if there are any non-zero fixed variables, add them as
# equalities to the stoichiometric matrix
if any(fixed_non_zero):
n_fixed = fixed_non_zero.sum()
rows = np.zeros((n_fixed, prob.equalities.shape[1]))
rows[range(n_fixed), np.where(fixed_non_zero)] = 1.0
equalities = np.vstack([equalities, rows])
var_b = prob.variable_bounds[:, 1]
b = np.hstack([b, var_b[fixed_non_zero]])
homogeneous = False
# Set up a projection that can cast point into the nullspace
nulls = nullspace(prob.equalities)
projection = nulls.dot(nulls.T)
# convert bounds to a matrix and add variable bounds as well
return Problem(equalities=equalities,
b=b,
inequalities=prob.inequalities,
bounds=np.atleast_2d(prob.bounds).T,
variable_fixed=prob.variable_fixed,
variable_bounds=np.atleast_2d(prob.variable_bounds).T,
projection=projection,
homogeneous=homogeneous)
def __build_problem(self) -> Problem:
"""Build the matrix representation of the sampling problem.
Returns
-------
Problem
The matrix representation in the form of a NamedTuple.
"""
# Set up the mathematical problem
prob = constraint_matrices(self.model, zero_tol=self.feasibility_tol)
# check if there any non-zero equality constraints
equalities = prob.equalities
b = prob.b
bounds = np.atleast_2d(prob.bounds).T
var_bounds = np.atleast_2d(prob.variable_bounds).T
homogeneous = all(np.abs(b) < self.feasibility_tol)
fixed_non_zero = np.abs(prob.variable_bounds[:,
1]) > self.feasibility_tol
fixed_non_zero &= prob.variable_fixed
# check if there are any non-zero fixed variables, add them as
# equalities to the stoichiometric matrix
if any(fixed_non_zero):
n_fixed = fixed_non_zero.sum()
rows = np.zeros((n_fixed, prob.equalities.shape[1]))
rows[range(n_fixed), np.where(fixed_non_zero)] = 1.0
equalities = np.vstack([equalities, rows])
var_b = prob.variable_bounds[:, 1]
b = np.hstack([b, var_b[fixed_non_zero]])
homogeneous = False
# Set up a projection that can cast point into the nullspace
nulls = nullspace(equalities)
# convert bounds to a matrix and add variable bounds as well
return Problem(
equalities=shared_np_array(equalities.shape, equalities),
b=shared_np_array(b.shape, b),
inequalities=shared_np_array(prob.inequalities.shape,
prob.inequalities),
bounds=shared_np_array(bounds.shape, bounds),
variable_fixed=shared_np_array(prob.variable_fixed.shape,
prob.variable_fixed,
integer=True),
variable_bounds=shared_np_array(var_bounds.shape, var_bounds),
nullspace=shared_np_array(nulls.shape, nulls),
homogeneous=homogeneous,
)
def __build_problem(self):
"""Build the matrix representation of the sampling problem."""
# Set up the mathematical problem
prob = constraint_matrices(self.model, zero_tol=self.feasibility_tol)
# check if there any non-zero equality constraints
equalities = prob.equalities
b = prob.b
bounds = np.atleast_2d(prob.bounds).T
var_bounds = np.atleast_2d(prob.variable_bounds).T
homogeneous = all(np.abs(b) < self.feasibility_tol)
fixed_non_zero = np.abs(prob.variable_bounds[:, 1]) > \
self.feasibility_tol
fixed_non_zero &= prob.variable_fixed
# check if there are any non-zero fixed variables, add them as
# equalities to the stoichiometric matrix
if any(fixed_non_zero):
n_fixed = fixed_non_zero.sum()
rows = np.zeros((n_fixed, prob.equalities.shape[1]))
rows[range(n_fixed), np.where(fixed_non_zero)] = 1.0
equalities = np.vstack([equalities, rows])
var_b = prob.variable_bounds[:, 1]
b = np.hstack([b, var_b[fixed_non_zero]])
homogeneous = False
# Set up a projection that can cast point into the nullspace
nulls = nullspace(equalities)
# convert bounds to a matrix and add variable bounds as well
return Problem(
equalities=shared_np_array(equalities.shape, equalities),
b=shared_np_array(b.shape, b),
inequalities=shared_np_array(prob.inequalities.shape,
prob.inequalities),
bounds=shared_np_array(bounds.shape, bounds),
variable_fixed=shared_np_array(prob.variable_fixed.shape,
prob.variable_fixed, integer=True),
variable_bounds=shared_np_array(var_bounds.shape, var_bounds),
nullspace=shared_np_array(nulls.shape, nulls),
homogeneous=homogeneous
)