evaluate

class CalculatedExpression

CalculatedExpression(objective: 'float', constraint: 'dict[str, dict[tuple[int, ...], float]]', penalty: 'dict[str, dict[tuple[int, ...], float]]')

---

class CompiledInstance

CompiledInstance: object return compile_model method.

Attributes

• sense(jijmodeling.ProblemSense) : problem sense minimize or maximize.
• objective(SubstitutedExpression) : objective expression.
• constraint(dict[str, dict[tuple[int, ...], SubstitutedExpression]]) : constraints. str key represents name of constraint. tuple[int,...] is values of forall index.
• penalty : dict[str, dict[tuple[int, ...], SubstitutedExpression]]
• var_map : VariableMap
• data : InstanceData
• problem : jijmodeling.InstanceData

Examples

import jijmodeling as jm
import jijmodeling_transpiler as jmt
n = jm.Placeholder("n")
x = jm.Binary("x", (n, n))
i = jm.Element("i", n)
problem = jm.Problem("sample")
problem += x[:, :]
problem += jm.Constraint("onehot", x[:, i], forall=i)
compiled_instance = jmt.core.compile_model(problem, {"n": 2}, {})
compiled_instance

CompiledInstance(
    objective=SubstitutedExpression(
        linear=LinearSubstitutedExpr(coeff={0: 1.0, 1: 1.0, 2: 1.0, 3: 1.0}, constant=0.0),
        nonlinear=None),
    constraint={
        'onehot': {
            (0,): SubstitutedExpression(
                    linear=LinearSubstitutedExpr(coeff={0: 1.0, 1: 1.0}, constant=0.0),
                    nonlinear=None),
            (1,): SubstitutedExpression(
                    linear=LinearSubstitutedExpr(coeff={2: 1.0, 3: 1.0}, constant=0.0),
                    nonlinear=None)}
            },
    penalty={},
    var_map=VariableMap(var_map={'x': {(0, 0): 0, (0, 1): 2, (1, 0): 1, (1, 1): 3}},
                        var_num=4,
                        integer_bound={}),
    ...
)

add (self, source) -> -

---

calc_expr (sample, compiled_model) -> -

---

calc_value (deci_var, subs_expr) -> -

---

eval_constraint (sample, compield_model) -> -

---

evaluate (samples, compiled_model) -> -