def test_calcrule_16():
loss_in = np.array([0., 10., 20., 30., 40., 50., 60.])
loss_out = np.empty_like(loss_in)
policy = np.array([(0, 16, 1/4, 0, 0, 10, 0.6, 0.5, 0, 0)], dtype=fm_profile_dtype)[0]
calc(policy, loss_out, loss_in, None)
loss_expected = np.array([0., 7.5, 15., 22.5, 30., 37.5, 45.])
assert_array_almost_equal(loss_out, loss_expected)
def test_calcrule_24():
loss_in = np.array([0., 10., 20., 30., 40., 50., 60.])
loss_out = np.empty_like(loss_in)
policy = np.array([(0, 24, 0, 0, 0, 5, 20, 1/2, 3/4, 4/5)], dtype=fm_profile_dtype)[0]
calc(policy, loss_out, loss_in, None)
loss_expected = np.array([0., 0., 3., 6., 9., 12., 12.])
assert_array_almost_equal(loss_out, loss_expected)
def test_calcrule_34():
"""as there is shares, deductible won't be use later on so no need to compute it"""
loss_in = np.array([0., 10., 20., 30., 40., 50., 60.])
loss_out = np.empty_like(loss_in)
policy = np.array([(0, 34, 15, 0, 0, 10, 30, 0.5, 0, 0)], dtype=fm_profile_dtype)[0]
calc(policy, loss_out, loss_in, None)
loss_expected = np.array([0., 0., 0., 2.5, 7.5, 12.5, 17.5])
assert_array_almost_equal(loss_out, loss_expected)
def test_calcrule_20():
loss_in = np.array([0., 10., 20., 30., 40., 50., 60.])
deductible = np.ones_like(loss_in) * 5
over_limit = np.ones_like(loss_in) * 5
under_limit = np.ones_like(loss_in) * 5
loss_out = np.empty_like(loss_in)
policy = np.array([(0, 20, 25, 0, 0, 10, 30, 0.5, 0, 0)], dtype=fm_profile_dtype)[0]
calc(policy, loss_out, loss_in, deductible, over_limit, under_limit, None)
loss_expected = np.array([0., 10., 20., 0., 0., 0., 0.])
assert_array_almost_equal(loss_out, loss_expected)
def test_calcrule_36():
loss_in = np.array([20., 20., 20., 20., 20., 20., 1., 20, 60])
deductible = np.array([0., 0., 0., 30., 30., 30., 16., 10, 10])
over_limit = np.array([0., 3., 10., 10., 10., 0., 0., 10, 10])
under_limit = np.array([0., 10., 10., 0., 5., 15., 0., 10, 10])
loss_out = np.empty_like(loss_in)
policy = np.array([(0, 36, 5, 10, 20, 0, 4/5, 0, 0, 0)], dtype=fm_profile_dtype)[0]
calc(policy, loss_out, loss_in, deductible, over_limit, under_limit, None)
loss_expected = np.array([10., 13., 16., 16., 16., 16., 0., 15., 48])
deductible_expected = np.array([10., 10., 10., 20., 20., 20., 17, 15., 15])
over_limit_expected = np.array([0., 0., 9., 24., 24., 14., 0., 10., 17])
under_limit_expected = np.array([5., 3., 0., 0., 0., 0., 0.8, 1., 0.])
assert_array_almost_equal(loss_out, loss_expected)
assert_array_almost_equal(deductible, deductible_expected)
assert_array_almost_equal(over_limit, over_limit_expected)
assert_array_almost_equal(under_limit, under_limit_expected)
def test_calcrule_33():
loss_in = np.array([0., 10., 20., 30., 40., 50., 60.])
deductible = np.ones_like(loss_in) * 5
over_limit = np.ones_like(loss_in) * 5
under_limit = np.ones_like(loss_in) * 5
loss_out = np.empty_like(loss_in)
policy = np.array([(0, 33, 1/4, 0, 0, 10, 30, 0, 0, 0)], dtype=fm_profile_dtype)[0]
calc(policy, loss_out, loss_in, deductible, over_limit, under_limit, None)
loss_expected = np.array([0., 7.5, 15., 22.5, 30., 30., 30.])
deductible_expected = np.array([5., 7.5, 10., 12.5, 15., 17.5, 20.])
over_limit_expected = np.array([5., 5., 5., 5., 5., 12.5, 20.])
under_limit_expected = np.array([5., 7.5, 10., 7.5, 0., 0., 0.])
assert_array_almost_equal(loss_out, loss_expected)
assert_array_almost_equal(deductible, deductible_expected)
assert_array_almost_equal(over_limit, over_limit_expected)
assert_array_almost_equal(under_limit, under_limit_expected, decimal=4)
def test_calcrule_26():
loss_in = np.array([20., 20., 20., 20., 20., 20., 1., 20, 60])
deductible = np.array([0., 0., 0., 30., 30., 30., 16., 10, 10])
over_limit = np.array([0., 3., 10., 10., 10., 0., 0., 10, 10])
under_limit = np.array([0., 10., 10., 0., 5., 15., 0., 10, 10])
loss_out = np.empty_like(loss_in)
policy = np.array([(0, 26, 1/4, 10, 20, 0, 30, 0.5, 0, 0)], dtype=fm_profile_dtype)[0]
calc(policy, loss_out, loss_in, deductible, over_limit, under_limit, None)
loss_expected = np.array([10., 13., 20., 20., 25., 30., 0.75, 15., 30])
deductible_expected = np.array([10., 10., 10., 20., 20., 20., 16.25, 15., 20])
over_limit_expected = np.array([0., 0., 5., 20., 15., 0., 0., 10., 30])
under_limit_expected = np.array([5., 12., 10., 0., 5., 0., 0.25, 15., 0.])
assert_array_almost_equal(loss_out, loss_expected)
assert_array_almost_equal(deductible, deductible_expected)
assert_array_almost_equal(over_limit, over_limit_expected)
assert_array_almost_equal(under_limit, under_limit_expected)
def test_calcrule_25():
loss_in = np.array([0., 10., 20., 30., 40., 50., 60.])
deductible = np.ones_like(loss_in) * 5
over_limit = np.ones_like(loss_in) * 5
under_limit = np.ones_like(loss_in) * 5
loss_out = np.empty_like(loss_in)
policy = np.array([(0, 25, 0, 0, 0, 0, 0, 1/2, 3/4, 4/5)], dtype=fm_profile_dtype)[0]
calc(policy, loss_out, loss_in, deductible, over_limit, under_limit, None)
loss_expected = np.array([0., 3., 6., 9., 12., 15., 18.])
deductible_expected = np.array([5., 5., 5., 5., 5., 5., 5.])
over_limit_expected = np.array([5., 5., 5., 5., 5., 5., 5.])
under_limit_expected = np.array([5., 5., 5., 5., 5., 5., 5.])
assert_array_almost_equal(loss_out, loss_expected)
assert_array_almost_equal(deductible, deductible_expected)
assert_array_almost_equal(over_limit, over_limit_expected)
assert_array_almost_equal(under_limit, under_limit_expected)
def test_calcrule_5():
# ded + limit > 1
loss_in = np.array([0., 10., 20., 30., 40., 50., 60.])
loss_out = np.empty_like(loss_in)
policy = np.array([(0, 5, 0.25, 0, 0, 10, 0.8, 0.5, 0, 0)], dtype=fm_profile_dtype)[0]
calc(policy, loss_out, loss_in, None)
loss_expected = np.array([0., 7.5, 15., 22.5, 30., 37.5, 45.])
# ded + limit < 1
assert_array_almost_equal(loss_out, loss_expected)
loss_out = np.empty_like(loss_in)
policy = np.array([(0, 5, 0.25, 0, 0, 10, 0.5, 0.5, 0, 0)], dtype=fm_profile_dtype)[0]
calc(policy, loss_out, loss_in, None)
loss_expected = np.array([0., 5., 10., 15., 20., 25., 30.])
assert_array_almost_equal(loss_out, loss_expected)
def test_calcrule_5():
# ded + limit > 1
loss_in = np.array([0., 10., 20., 30., 40., 50., 60.])
deductible = np.ones_like(loss_in) * 5
over_limit = np.ones_like(loss_in) * 5
under_limit = np.ones_like(loss_in) * 5
loss_out = np.empty_like(loss_in)
policy = np.array([(0, 5, 0.25, 0, 0, 10, 0.8, 0.5, 0, 0)],
dtype=fm_profile_dtype)[0]
calc(policy, loss_out, loss_in, deductible, over_limit, under_limit)
loss_expected = np.array([0., 7.5, 15., 22.5, 30., 37.5, 45.])
deductible_expected = np.array([5., 7.5, 10., 12.5, 15., 17.5, 20.])
over_limit_expected = np.array([5., 5., 5., 5., 5., 5., 5.])
under_limit_expected = np.array([0., 0.5, 1., 1.5, 2., 2.5, 3.])
# ded + limit < 1
assert_array_almost_equal(loss_out, loss_expected)
assert_array_almost_equal(deductible, deductible_expected)
assert_array_almost_equal(over_limit, over_limit_expected)
assert_array_almost_equal(under_limit, under_limit_expected)
deductible = np.ones_like(loss_in) * 5
over_limit = np.ones_like(loss_in) * 5
under_limit = np.ones_like(loss_in) * 5
loss_out = np.empty_like(loss_in)
policy = np.array([(0, 5, 0.25, 0, 0, 10, 0.5, 0.5, 0, 0)],
dtype=fm_profile_dtype)[0]
calc(policy, loss_out, loss_in, deductible, over_limit, under_limit)
loss_expected = np.array([0., 5., 10., 15., 20., 25., 30.])
deductible_expected = np.array([5., 7.5, 10., 12.5, 15., 17.5, 20.])
over_limit_expected = np.array([5., 7.5, 10., 12.5, 15., 17.5, 20.])
under_limit_expected = np.array([0., 0., 0., 0., 0., 0., 0.])
assert_array_almost_equal(loss_out, loss_expected)
assert_array_almost_equal(deductible, deductible_expected)
assert_array_almost_equal(over_limit, over_limit_expected)
assert_array_almost_equal(under_limit, under_limit_expected)
