def test_hyp_hyp_linking_case_2(self):
T = np.arange(0, 20, eps)
M = 10
f = TradeoffFunction(5, 6, 4, 4, 1)
g = TradeoffFunction(3, 4, 1, 1, -0.5)
numeric_opt_d, numeric_h = numeric.link_consumption(T, f, g, M)
analytic_opt_d, analytic_h = analytic.link_consumption(f, g)
self.assertTrue(equal("opt_d", T, numeric_opt_d, analytic_opt_d))
self.assertTrue(equal("h", T, numeric_h, analytic_h))
numeric_opt_d, numeric_h = numeric.link_consumption(T, g, f, M)
analytic_opt_d, analytic_h = analytic.link_consumption(g, f)
self.assertTrue(equal("opt_d", T, numeric_opt_d, analytic_opt_d))
self.assertTrue(equal("h", T, numeric_h, analytic_h))
def test_hyp_hyp_linking_case_3(self):
T = np.arange(0, 20, eps)
M = 10
f = TradeoffFunction(2, 4, 1, 1, -0.5)
g = TradeoffFunction(5, 10, 4, 4, 1)
numeric_opt_d, numeric_h = numeric.link_consumption(T, f, g, M)
analytic_opt_d, analytic_h = analytic.link_consumption(f, g)
self.assertTrue(equal("opt_d", T, numeric_opt_d, analytic_opt_d))
self.assertTrue(equal("h", T, numeric_h, analytic_h))
# should also work in the symmetric case
numeric_opt_d, numeric_h = numeric.link_consumption(T, g, f, M)
analytic_opt_d, analytic_h = analytic.link_consumption(g, f)
self.assertTrue(equal("opt_d", T, numeric_opt_d, analytic_opt_d))
self.assertTrue(equal("h", T, numeric_h, analytic_h))
def test_lin_lin_same_linking(self):
T = np.arange(0, 20, eps)
M = 10
f = PiecewiseFunction([0], [LinearFunction(0, 5)])
g = PiecewiseFunction([0], [LinearFunction(0, 5)])
numeric_opt_d, numeric_h = numeric.link_consumption(T, f, g, M)
analytic_opt_d, analytic_h = analytic.link_consumption(f, g)
self.assertTrue(equal("opt_d", T, numeric_opt_d, analytic_opt_d))
self.assertTrue(equal("h", T, numeric_h, analytic_h))
self.assertEqual(analytic_h(0), 10)
self.assertEqual(analytic_h(20), 10)
def test_linking_numeric(self):
T = np.arange(0, 20, 0.1)
link_consumption = lambda f, g, M: numeric.link_consumption(T, f, g, M)
link_charging = lambda f, cf, M: numeric.link_charging(T, f, cf, M)
f = PiecewiseFunction([0], [LinearFunction(0, 5)])
cf = PiecewiseFunction(
[0, 10],
[LinearFunction(1, 0), LinearFunction(0, 10)])
graph = Graph([(0, 1, (False, f)), (1, 2, (True, cf))])
ds, h = link_path(graph, [0, 1, 2], 10, link_consumption,
link_charging)
self.assertEqual(h(0), 5)
self.assertEqual(h(5), 0)
def test_hyp_lin_linking(self):
T = np.arange(0, 20, eps)
M = 10
f = PiecewiseFunction([0, 2, 6], [
LinearFunction(0, float('inf')),
HypLinFunction(5, 1, 1, 0),
LinearFunction(0, 6 / 5.)
])
self.assertEqual(f(2), 6)
self.assertEqual(f(6), 1.2)
self.assertEqual(f(10), 1.2)
g = PiecewiseFunction([0, 4, 9], [
LinearFunction(0, float('inf')),
LinearFunction(-1, 9),
LinearFunction(0, 0)
])
numeric_opt_d, numeric_h = numeric.link_consumption(T, f, g, M)
analytic_opt_d, analytic_h = analytic.link_consumption(f, g)
self.assertTrue(equal("opt_d", T, numeric_opt_d, analytic_opt_d))
self.assertTrue(equal("h", T, numeric_h, analytic_h))
self.assertEqual(analytic_h(6), 11)
self.assertEqual(analytic_h(16), 6 / 5.)