def calculate_collect_(ind,
context,
x,
y,
terminals_and_constants_repr,
gro_ter_con,
dim_ter_con_list,
dim_type,
fuzzy,
cv=1,
refit=True,
scoring=None,
score_pen=(1, ),
add_coef=True,
filter_warning=True,
inter_add=True,
inner_add=False,
vector_add=False,
out_add=False,
flat_add=False,
np_maps=None,
classification=False,
dim_maps=None,
cal_dim=True,
score_object="y",
details=False):
expr01 = compile_context(ind, context, gro_ter_con)
if cal_dim:
dim, dim_score = calcualte_dim_score(expr01,
terminals_and_constants_repr,
dim_ter_con_list,
dim_type,
fuzzy,
dim_maps=dim_maps)
else:
dim, dim_score = dless, 1
score, expr01, pre_y = calculate_cv_score(expr01,
x,
y,
terminals_and_constants_repr,
cv=cv,
refit=refit,
add_coef=add_coef,
inter_add=inter_add,
inner_add=inner_add,
vector_add=vector_add,
out_add=out_add,
flat_add=flat_add,
scoring=scoring,
score_pen=score_pen,
filter_warning=filter_warning,
np_maps=np_maps,
classification=classification,
score_object=score_object,
details=details)
return score, dim, dim_score, expr01, pre_y
for si in s:
if isinstance(si, sympy.Symbol):
ss.append(si)
else:
ss.extend(si)
target = (ss[0] + ss[1]) * (ss[2] - ss[3])
target = sympy.simplify(target)
# a = time.time()
random.seed(4)
population = [
SymbolTree.genFull(pset, int(height - 1),
int(height - 1) + 1) for _ in range(5000)
]
for n, i in enumerate(population):
i = compile_context(i, pset.context, pset.gro_ter_con, simplify=True)
expr = general_expr_dict(i,
pset.expr_init_map,
pset.free_symbol,
pset.gsym_map,
simplifying=True)
# print(expr)
if expr == target:
print(n)
break
####GP
pset = SymbolSet()
pset.add_features(x, y)
pset.add_operations(categories=("Add", "Mul", "Sub", "Div"))
x_u = [kg] * 13
y_u = kg
c_u = [dless, dless, dless]
x, x_dim = Dim.convert_x(x, x_u, target_units=None, unit_system="SI")
y, y_dim = Dim.convert_xi(y, y_u)
c, c_dim = Dim.convert_x(c, c_u)
t = time.time()
# symbolset
pset0 = SymbolSet()
pset0.add_features(x,
y,
x_dim=x_dim,
y_dim=y_dim,
x_group=[[1, 2], [3, 4, 5]])
pset0.add_constants(c, c_dim=c_dim, c_prob=None)
pset0.add_operations(power_categories=(2, 3, 0.5),
categories=("Add", "Mul", "Sub", "Div", "exp"),
self_categories=None)
random.seed(0)
z = time.time()
sl = [SymbolTree.genGrow(pset0, 3, 4) for _ in range(100)]
a = time.time()
sl = [compile_context(sli, pset0.context, pset0.gro_ter_con) for sli in sl]
b = time.time()
print(b - a, a - z)
dim_type=None,
fuzzy=False,
add_coef=False,
inter_add=True,
inner_add=False,
n_jobs=1,
batch_size=20,
tq=True)
from sys import getsizeof
T100 = getsizeof(sl)
psize = getsizeof(pset0)
# print(T100,psize)
for i in sl:
b = time.time()
i0 = compile_context(i, pset0.context, pset0.gro_ter_con)
r2 = time.time()
# pprint(i0,pset0)
calculate_y(i0,
pset0.data_x,
pset0.y,
pset0.terminals_and_constants_repr,
add_coef=True,
filter_warning=True,
inter_add=True,
inner_add=True,
np_maps=np_map())
# i = pset0.calculate_detail(i)
# j = simple(i.coef_expr, pset0.gro_ter_con)
# print(i)
# print(repr(i))
pset0 = CalculatePrecisionSet(pset0,
scoring=None,
score_pen=(1, ),
filter_warning=True,
cal_dim=False,
dim_type=None,
fuzzy=False,
add_coef=False,
inter_add=True,
inner_add=False,
n_jobs=1,
batch_size=20,
tq=True)
sl = [SymbolTree.genGrow(pset0, 3, 4) for _ in range(50)]
sll = [
compile_context(o, pset0.context, pset0.gro_ter_con, simplify=True)
for o in sl
]
slll = [
try_add_coef(i,
pset0.data_x,
pset0.y,
pset0.terminals_and_constants_repr,
filter_warning=True,
inter_add=True,
inner_add=False,
vector_add=True,
np_maps=np_map())[1] for i in sll
]
sllll = [general_expr(o, pset0, simplifying=False) for o in slll]