def solve(Xs_info, C, aa, f_cost, epsilon):
"""
Args:
Xs_info: map from distribution to tuple of type (probe cost, p_ij)
C: the cost budget
aa: the values taken on by the distribution
epsilon: the epsilon
"""
# Compute interval lengths. Note that I_i = [a_j, a_{j + 1}].
assert len(set(aa)) == len(aa), \
'The values taken on by the distribution must be distinct'
assert all(itertools.imap(lambda a: a >= 0, aa)), \
'The values taken on by the distribution must be nonnegative'
ls = [t - s for s, t in zip(aa, aa[1:])]
cs, ps = zip(*Xs_info.itervalues())
z, ys, problem = lp(cs, C, ls, ps)
print problem
problem.solve()
print 'z = {}, ys = {}'.format(z.value(), [y.value() for y in ys])
# The subset S corresponding to i s.t. y_i = 1 is feasible
s0 = [x for x, y in zip(Xs_info.iterkeys(), ys) if y.value() == 1]
# new cost budget C(log log m + log 1/e)
m = len(aa)
C_relaxed = C * (math.log(math.log(m)) + math.log(1.0 / epsilon))
X_cost = {k: c for k, (c, _) in Xs_info.iteritems()}
return minimum_element.minimum_element(X_cost, C_relaxed, f_cost, S_0=s0)
def do_uniform(C, n, m, sims):
"""
Args:
C: the cost budget
n: the number of random variables
m: the number of elements taken on by the distributions
"""
with open(OUTPUT_FILE, 'w') as f:
ties = 0
cip_victories = 0
basic_victories = 0
for _ in xrange(sims):
Xs_info = {}
elements = range(0, 500 * int(math.pow(m, 2)), 500)
for i in xrange(n):
x = stats.rv_discrete(
values=(elements,
[1.0 / len(elements) for _ in xrange(len(elements))]))
# x = stats.randint(0, m + 1)
# x = stats.geom(1. / 17)
# x = stats.poisson(0.6)
c = random.randint(1, C / (n / 2))
Xs_info['x' + str(i)] = (x, c, lambda a: x.sf(a))
f_cost = lambda S: len(S) + 1
Xs_dist = {k: d for k, (d, _, _) in Xs_info.iteritems()}
xs = simulate(Xs_dist)
Xs_cip_info = {k: (c, p) for k, (_, c, p) in Xs_info.iteritems()}
cip_result = cip_greedy.solve(Xs_cip_info, C, elements, f_cost, 0.01)
cip_cost = sum(map(lambda x: xs[x], cip_result))
print 'cip =', cip_cost,
X_cost = {k: c for k, (_, c, _) in Xs_info.iteritems()}
basic_result = minimum_element.minimum_element(X_cost, C, f_cost)
basic_cost = sum(map(lambda x: xs[x], basic_result))
print ', basic =', basic_cost
if cip_cost < basic_cost:
f.write('cip\n')
cip_victories += 1
elif cip_cost == basic_cost:
f.write('tie\n')
ties += 1
else:
f.write('basic\n')
basic_victories += 1
f.write('cip: {}, basic: {}, ties: {}\n'.format(
cip_victories, basic_victories, ties))
def test_general_case(self):
arr = [4, 3, 5, 2, 1, 6]
self.assertEqual(minimum_element(arr), 4)
def test_empty_list(self):
arr = []
with self.assertRaises(ValueError):
minimum_element(arr)
def test_singleton_list(self):
arr = [5]
self.assertEqual(minimum_element(arr), 0)
def test_duplicate_minimum_elements(self):
arr = [3, 4, 2, 5, 2, 6]
self.assertEqual(minimum_element(arr), 2)
def test_first_element_is_minimum(self):
arr = [1, 3, 6, 5, 3, 4]
self.assertEqual(minimum_element(arr), 0)
