Bone-Market-Solver/bonemarketsolver/objects/bone_market_model.py

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__author__ = "Jeremy Saklad"
from functools import cache, partialmethod, reduce, singledispatch
from ortools.sat.python import cp_model
class BoneMarketModel(cp_model.CpModel):
"""A CpModel with additional functions for common constraints and enhanced enforcement literal support."""
__slots__ = ()
def AddAllowedAssignments(self, variables, tuples_list):
intermediate_variables, constraints = zip(*(self.NewIntermediateIntVar(variable, f'{repr((variables, tuples_list))}: {variable}') for variable in variables))
super().AddAllowedAssignments(intermediate_variables, tuples_list)
return constraints
def AddApproximateExponentiationEquality(self, target, var, exp, upto):
"""Add an approximate exponentiation equality using a lookup table.
Set `upto` to a value that is unlikely to come into play.
Each parameter is interpreted as a BoundedLinearExpression, and a layer of indirection is applied such that each Constraint in the returned tuple can accept an enforcement literal."""
return self.AddAllowedAssignments((target, var), ((int(base**exp), base) for base in range(upto + 1)))
def AddIf(self, variable, *constraints):
"""Add constraints to the model, only enforced if the specified variable is true.
Each item in `constraints` must be either a BoundedLinearExpression, a Constraint compatible with OnlyEnforceIf, a 0-arity partial method of CpModel returning a valid item, or an iterable containing valid items."""
@singledispatch
def Add(constraint):
if constraint_iterator := iter(constraint):
return frozenset((Add(element) for element in constraint_iterator))
else:
raise TypeError(f"Invalid constraint: {repr(constraint)}")
@Add.register
def _(constraint: cp_model.Constraint):
return constraint.OnlyEnforceIf(variable)
@Add.register
def _(constraint: cp_model.BoundedLinearExpression):
return Add(self.Add(constraint))
@Add.register
def _(constraint: partialmethod):
return Add(constraint.__get__(self)())
return frozenset((Add(constraint) for constraint in constraints))
def AddMultiplicationEquality(self, target, variables):
"""Adds `target == variables[0] * .. * variables[n]`.
Each parameter is interpreted as a BoundedLinearExpression, and a layer of indirection is applied such that each Constraint in the returned tuple can accept an enforcement literal."""
superclass = super()
def Multiply(end, stack):
intermediate_variable, variable_constraint = self.NewIntermediateIntVar(stack.pop(), f'{repr(end)} == {"*".join((repr(variable) for variable in stack))}: last variable')
partial_target = self.NewIntVar(f'{repr(end)} == {"*".join((repr(variable) for variable in stack))}: partial target')
recursive_constraints = self.AddMultiplicationEquality(partial_target, stack) if len(stack) > 1 else (self.Add(partial_target == stack.pop()),)
intermediate_target, target_constraint = self.NewIntermediateIntVar(end, f'{repr(end)} == {"*".join((repr(variable) for variable in stack))}: target')
superclass.AddMultiplicationEquality(intermediate_target, (partial_target, intermediate_variable))
return (variable_constraint, *recursive_constraints, target_constraint)
# Avoid mutating parameter directly
return Multiply(target, variables.copy() if isinstance(variables, list) else list(variables))
@cache
def BoolExpression(self, bounded_linear_exp):
"""Add a fully-reified implication using an intermediate Boolean variable."""
intermediate = self.NewBoolVar(str(bounded_linear_exp))
linear_exp = bounded_linear_exp.Expression()
domain = cp_model.Domain(*bounded_linear_exp.Bounds())
self.AddLinearExpressionInDomain(linear_exp, domain).OnlyEnforceIf(intermediate)
self.AddLinearExpressionInDomain(linear_exp, domain.Complement()).OnlyEnforceIf(intermediate.Not())
return intermediate
def NewIntermediateIntVar(self, linear_exp, name, *, lb = cp_model.INT_MIN//8, ub = cp_model.INT_MAX//8):
"""Creates an integer variable equivalent to the given expression and returns a tuple consisting of the variable and constraint for use with enforcement literals."""
intermediate = super().NewIntVar(lb, ub, name)
return (intermediate, self.Add(intermediate == linear_exp))
def NewIntVar(self, name, *, lb = cp_model.INT32_MIN, ub = cp_model.INT32_MAX):
return super().NewIntVar(lb, ub, name)