def test_as_constraint():
from mystic.math.measures import mean, spread
def mean_constraint(x, target):
return mean(x) - target
def range_constraint(x, target):
return spread(x) - target
@quadratic_equality(condition=range_constraint, kwds={'target': 5.0})
@quadratic_equality(condition=mean_constraint, kwds={'target': 5.0})
def penalty(x):
return 0.0
ndim = 3
constraints = as_constraint(penalty) #, solver='fmin')
#XXX: this is expensive to evaluate, as there are nested optimizations
from numpy import arange
x = arange(ndim)
_x = constraints(x)
assert round(mean(_x)) == 5.0
assert round(spread(_x)) == 5.0
assert round(penalty(_x)) == 0.0
def cost(x):
return abs(sum(x) - 5.0)
npop = ndim * 3
from mystic.solvers import diffev
y = diffev(cost, x, npop, constraints=constraints, disp=False, gtol=10)
assert round(mean(y)) == 5.0
assert round(spread(y)) == 5.0
assert round(cost(y)) == 5.0 * (ndim - 1)
def test_as_constraint():
from mystic.math.measures import mean, spread
def mean_constraint(x, target):
return mean(x) - target
def range_constraint(x, target):
return spread(x) - target
@quadratic_equality(condition=range_constraint, kwds={'target':5.0})
@quadratic_equality(condition=mean_constraint, kwds={'target':5.0})
def penalty(x):
return 0.0
ndim = 3
constraints = as_constraint(penalty, solver='fmin')
#XXX: this is expensive to evaluate, as there are nested optimizations
from numpy import arange
x = arange(ndim)
_x = constraints(x)
assert round(mean(_x)) == 5.0
assert round(spread(_x)) == 5.0
assert round(penalty(_x)) == 0.0
def cost(x):
return abs(sum(x) - 5.0)
npop = ndim*3
from mystic.solvers import diffev
y = diffev(cost, x, npop, constraints=constraints, disp=False, gtol=10)
assert round(mean(y)) == 5.0
assert round(spread(y)) == 5.0
assert round(cost(y)) == 5.0*(ndim-1)
