def _affinexp_pic2scip(self, picosExpression):
"""
Tranforms a PICOS (multidimensional) affine expression into a list of
SCIP (scalar) expressions.
:returns: A :class:`list` of :class:`SCIP expressions
<pyscipopt.scip.Expr>`.
"""
from pyscipopt.scip import Expr
if picosExpression is None:
return [Expr()]
length = len(picosExpression)
# Convert linear part.
scipExpressions = [Expr() for _ in range(length)]
for picosVar, picosCoefs in picosExpression.factors.items():
startIndex = picosVar.startIndex
for localIndex, localVarIndex, coefficient in zip(
picosCoefs.I, picosCoefs.J, picosCoefs.V):
scipVar = self._scipVar[startIndex + localVarIndex]
scipExpressions[localIndex] += coefficient * scipVar
# Convert constant part.
if picosExpression.constant:
for i in range(length):
scipExpressions[i] += picosExpression.constant[i]
return scipExpressions
def encode_reward(model, A, S, Aux, x, y, v, d, reward, horizon):
sum_expr = Expr()
empty = True
for t in range(horizon):
for t_index, term in enumerate(reward[0]):
coef = "1.0"
variables = term.split("*")
if variables[0] not in A + S + Aux:
coef = variables[0]
variables = variables[1:]
if set(A).isdisjoint(variables) or t < horizon:
prod_expr = ProdExpr()
for var in variables:
if var in A:
prod_expr *= VarExpr(x[(var,t)])
elif var in S:
prod_expr *= VarExpr(y[(var,t)])
else:
prod_expr *= VarExpr(v[(var,t)])
d[(t_index,t)] = model.addVar("rew_" + str(t_index) + "_" + str(t))
model.addCons(prod_expr == d[(t_index,t)])
sum_expr += float(coef) * d[(t_index,t)]
empty = False
if not empty:
#print(sum_expr)
model.setObjective(sum_expr, "maximize")
return model
def test_degree(model):
m, x, y, z = model
expr = Expr()
assert expr.degree() == 0
expr = Expr() + 3.0
assert expr.degree() == 0
expr = x + 1
assert expr.degree() == 1
expr = x * x + y - 2
assert expr.degree() == 2
expr = (x + 1) * (y + 1) * (x - 1)
assert expr.degree() == 3
def encode_initial_constraints(model, S, y, initials):
for init in initials:
variables = init[:-2]
init_expr = Expr()
for var in variables:
coef = "1.0"
if "*" in var:
coef, var = var.split("*")
init_expr += float(coef) * y[(var,0)]
model.addCons(init_expr == float(init[len(init)-1]))
return model
def test_degree(model):
m, x, y, z = model
expr = Expr()
assert expr.degree() == 0
expr = Expr() + 3.0
assert expr.degree() == 0
expr = x + 1
assert expr.degree() == 1
expr = x*x + y - 2
assert expr.degree() == 2
expr = (x + 1)*(y + 1)*(x - 1)
assert expr.degree() == 3