def test_gp_population_full():
Add.nargs = 2
Mul.nargs = 2
from EvoDAG.gp import Population
from EvoDAG import EvoDAG
fs = EvoDAG()._function_set
class Population2(Population):
def __init__(self, *args, **kwargs):
super(Population2, self).__init__(*args, **kwargs)
self._funcs = [Add, Sin]
self._terms = [2, 0]
def random_function(self):
func = self._funcs.pop()
if func.nargs == 1:
return func(0, weight=1)
return func(range(func.nargs), weight=np.ones(func.nargs))
def random_terminal(self):
return Variable(self._terms.pop(), 1)
pop = Population2(fs, nterminals=3)
ind = pop.create_random_ind_full(depth=2)
assert len(pop._funcs) == 0 and len(pop._terms) == 0
assert isinstance(ind[0], Sin) and isinstance(ind[1], Add)
assert ind[2].variable == 0 and ind[3].variable == 2
ind = Individual(ind)
print(X.shape, ind.individual)
hy = ind.decision_function(X)
assert hy.isfinite()
default_nargs()
def test_gp_population_full():
Add.nargs = 2
Mul.nargs = 2
from EvoDAG.gp import Population
from EvoDAG import EvoDAG
fs = EvoDAG()._function_set
class Population2(Population):
def __init__(self, *args, **kwargs):
super(Population2, self).__init__(*args, **kwargs)
self._funcs = [Add, Sin]
self._terms = [2, 0]
def random_function(self):
func = self._funcs.pop()
if func.nargs == 1:
return func(0, weight=1)
return func(range(func.nargs), weight=np.ones(func.nargs))
def random_terminal(self):
return Variable(self._terms.pop(), 1)
pop = Population2(fs, nterminals=3)
ind = pop.create_random_ind_full(depth=2)
assert len(pop._funcs) == 0 and len(pop._terms) == 0
assert isinstance(ind[0], Sin) and isinstance(ind[1], Add)
assert ind[2].variable == 0 and ind[3].variable == 2
ind = Individual(ind)
print(X.shape, ind.individual)
hy = ind.decision_function(X)
assert hy.isfinite()
def test_indindividual_decision_function():
Add.nargs = 2
Mul.nargs = 2
vars = Model.convert_features(X)
for x in vars:
x._eval_ts = x._eval_tr.copy()
vars = [Variable(k, weight=1) for k in range(len(vars))]
for i in range(len(vars)):
ind = Individual([vars[i]])
ind.decision_function(X)
hy = ind._ind[0].hy.tonparray()
[assert_almost_equals(a, b) for a, b in zip(X[:, i], hy)]
ind = Individual([Sin(0, weight=1),
Add(range(2), np.ones(2)), vars[0], vars[-1]])
ind.decision_function(X)
hy = ind._ind[0].hy.tonparray()
y = np.sin(X[:, 0] + X[:, -1])
[assert_almost_equals(a, b) for a, b in zip(y, hy)]
y = np.sin((X[:, 0] + X[:, 1]) * X[:, 0] + X[:, 2])
ind = Individual([Sin(0, weight=1), Add(range(2), weight=np.ones(2)),
Mul(range(2), weight=1),
Add(range(2), weight=np.ones(2)),
vars[0], vars[1], vars[0], vars[2]])
ind.decision_function(X)
# assert v.hy.SSE(v.hy_test) == 0
hy = ind._ind[0].hy.tonparray()
[assert_almost_equals(a, b) for a, b in zip(hy, y)]
def test_indindividual_decision_function():
Add.nargs = 2
Mul.nargs = 2
vars = Model.convert_features(X)
for x in vars:
x._eval_ts = x._eval_tr.copy()
vars = [Variable(k, weight=np.ones(1)) for k in range(len(vars))]
for i in range(len(vars)):
ind = Individual([vars[i]])
ind.decision_function(X)
hy = tonparray(ind._ind[0].hy)
[assert_almost_equals(a, b) for a, b in zip(X[:, i], hy)]
ind = Individual([
Sin(0, weight=np.ones(1)),
Add(range(2), np.ones(2)), vars[0], vars[-1]
])
ind.decision_function(X)
print(ind._ind[0].hy, ind._ind[1].hy)
hy = tonparray(ind._ind[0].hy)
y = np.sin(X[:, 0] + X[:, -1])
[assert_almost_equals(a, b) for a, b in zip(y, hy)]
y = np.sin((X[:, 0] + X[:, 1]) * X[:, 0] + X[:, 2])
ind = Individual([
Sin(0, weight=1),
Add(range(2), weight=np.ones(2)),
Mul(range(2), weight=1),
Add(range(2), weight=np.ones(2)), vars[0], vars[1], vars[0], vars[2]
])
ind.decision_function(X)
# assert v.hy.SSE(v.hy_test) == 0
hy = tonparray(ind._ind[0].hy)
[assert_almost_equals(a, b) for a, b in zip(hy, y)]
default_nargs()
