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t_equivalent_posynomials.py
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t_equivalent_posynomials.py
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from gpkit import Variable, Monomial
import numpy as np
from robust.equivalent_posynomials import EquivalentPosynomials
def test_merge_intersected_lists():
for _ in xrange(10000):
number_of_lists = int(10*np.random.random()) + 1
list_of_lists = []
for l in xrange(number_of_lists):
number_of_elements = int(5*np.random.random()) + 1
list_of_lists.append([int(20*np.random.random()) for _ in xrange(number_of_elements)])
partition = EquivalentPosynomials.merge_intersected_lists(list_of_lists)
for i in xrange(len(partition) - 1):
assert (all(not (set(partition[i]) & set(partition[j])) for j in range(i+1, len(partition))))
for from_list in list_of_lists:
assert(any(set(from_list) <= set(from_partition) for from_partition in partition))
return
def test_same_sign():
for _ in xrange(100):
number_of_elements = int(50*np.random.random()) + 1
a = [int(20*np.random.random()) for _ in xrange(number_of_elements)]
assert(EquivalentPosynomials.same_sign(a))
a = [int(20*np.random.random()) - 19 for _ in xrange(number_of_elements)]
assert(EquivalentPosynomials.same_sign(a))
a = [int(20*np.random.random()) - 19 for _ in xrange(number_of_elements)]
neg_flag = False
for element in a:
if element < 0:
neg_flag = True
break
pos_flag = False
for element in a:
if element > 0:
pos_flag = True
break
if neg_flag and pos_flag:
assert(not EquivalentPosynomials.same_sign(a))
else:
assert(EquivalentPosynomials.same_sign(a))
return
def test_correlated_monomials():
for _ in xrange(100):
number_of_monomials = int(50*np.random.random())+1
number_of_gp_variables = int(np.random.rand()*20) + 1
number_of_uncertain_variables = int(np.random.rand()*5) + 1
vector_to_choose_from = [0, 0, 0, 0, 0, 0, 0, 0, 1, -1]
m = number_of_monomials*[1]
p_uncertain_vars = []
correlations = []
dependent_theoretical_partition = []
for j in xrange(number_of_monomials):
for i in xrange(number_of_gp_variables):
x = Variable('x_%s' % i)
m[j] *= x**(np.random.rand()*10 - 5)
for i in xrange(number_of_uncertain_variables):
u = Variable('u_%s' % i, np.random.random(), pr=100*np.random.random())
p_uncertain_vars.append(u.key)
neg_pos_neutral_powers = [vector_to_choose_from[int(10*np.random.random())] for _ in xrange(number_of_monomials)]
same_sign = EquivalentPosynomials.same_sign(neg_pos_neutral_powers)
if not same_sign:
related_monomials = [i for i in xrange(number_of_monomials) if neg_pos_neutral_powers[i] != 0]
correlations.append(related_monomials)
for j in xrange(number_of_monomials):
m[j] *= u**(np.random.rand()*5*neg_pos_neutral_powers[j])
if neg_pos_neutral_powers[j] != 0:
dependent_theoretical_partition.append(j)
theoretical_partition = EquivalentPosynomials.merge_intersected_lists(correlations)
p = sum(m)
equivalent_posynomial = EquivalentPosynomials(p, 0, False, False)
equivalent_posynomial.main_p = p
actual_partition = equivalent_posynomial.correlated_monomials()
theoretical_posynomials = []
actual_posynomials = []
for theo_list, act_list in zip(theoretical_partition, actual_partition):
theoretical_posynomials.append(sum([m[i] for i in theo_list]))
actual_posynomials.append(sum([Monomial(p.exps[j], p.cs[j]) for j in act_list]))
assert (set(actual_posynomials) == set(theoretical_posynomials))
# dependent uncertainties
temp = []
for part in theoretical_partition:
temp += part
theoretical_partition = list(set(dependent_theoretical_partition))
equivalent_posynomial = EquivalentPosynomials(p, 0, False, True)
equivalent_posynomial.main_p = p
actual_partition = equivalent_posynomial.correlated_monomials()
theoretical_posynomials = []
actual_posynomials = []
theoretical_posynomials.append(sum([m[i] for i in theoretical_partition]))
actual_posynomials.append(sum([Monomial(p.exps[j], p.cs[j]) for j in actual_partition[0]]))
assert (set(actual_posynomials) == set(theoretical_posynomials))
return
def test_check_if_in_list_of_lists():
for _ in xrange(100):
number_of_lists = int(10*np.random.random()) + 1
list_of_lists = []
number_of_elements = []
for l in xrange(number_of_lists):
number_of_elements.append(int(5*np.random.random()) + 1)
list_of_lists.append([int(200*np.random.random()) for _ in xrange(number_of_elements[l])])
element_list = int(number_of_lists*np.random.random())
element = list_of_lists[element_list][int(number_of_elements[element_list]*np.random.random())]
non_element = int(200*np.random.random()) + 200
assert (EquivalentPosynomials.check_if_in_list_of_lists(element, list_of_lists))
assert (not EquivalentPosynomials.check_if_in_list_of_lists(non_element, list_of_lists))
return
def test():
test_same_sign()
test_merge_intersected_lists()
test_correlated_monomials()
test_check_if_in_list_of_lists()