我们从Python开源项目中，提取了以下14个代码示例，用于说明如何使用numpy.irr()。
def test_irr(self): v = [-150000, 15000, 25000, 35000, 45000, 60000] assert_almost_equal(np.irr(v), 0.0524, 2) v = [-100, 0, 0, 74] assert_almost_equal(np.irr(v), -0.0955, 2) v = [-100, 39, 59, 55, 20] assert_almost_equal(np.irr(v), 0.28095, 2) v = [-100, 100, 0, -7] assert_almost_equal(np.irr(v), -0.0833, 2) v = [-100, 100, 0, 7] assert_almost_equal(np.irr(v), 0.06206, 2) v = [-5, 10.5, 1, -8, 1] assert_almost_equal(np.irr(v), 0.0886, 2) # Test that if there is no solution then np.irr returns nan # Fixes gh-6744 v = [-1, -2, -3] assert_equal(np.irr(v), np.nan)
def test_irr(self): v = [-150000, 15000, 25000, 35000, 45000, 60000] assert_almost_equal(np.irr(v), 0.0524, 2) v = [-100, 0, 0, 74] assert_almost_equal(np.irr(v), -0.0955, 2) v = [-100, 39, 59, 55, 20] assert_almost_equal(np.irr(v), 0.28095, 2) v = [-100, 100, 0, -7] assert_almost_equal(np.irr(v), -0.0833, 2) v = [-100, 100, 0, 7] assert_almost_equal(np.irr(v), 0.06206, 2) v = [-5, 10.5, 1, -8, 1] assert_almost_equal(np.irr(v), 0.0886, 2)
def run_many(case): @functools.wraps(case) def wrapped(): for test in range(1000): d, r = case() assert irr.irr(d) == pytest.approx(r) return wrapped
def test_performance(): us_times = [] np_times = [] ns = [10, 20, 50, 100] for n in ns: k = 100 sums = [0.0, 0.0] for j in range(k): r = math.exp(random.gauss(0, 1.0 / n)) - 1 x = random.gauss(0, 1) d = [x] + [0.0] * (n-2) + [-x * (1+r)**(n-1)] results = [] for i, f in enumerate([irr.irr, numpy.irr]): t0 = time.time() results.append(f(d)) sums[i] += time.time() - t0 if not numpy.isnan(results[1]): assert results[0] == pytest.approx(results[1]) for times, sum in zip([us_times, np_times], sums): times.append(sum/k) try: from matplotlib import pyplot import seaborn except ImportError: return pyplot.plot(ns, us_times, label='Our library') pyplot.plot(ns, np_times, label='Numpy') pyplot.xlabel('n') pyplot.ylabel('time(s)') pyplot.yscale('log') pyplot.savefig('plot.png')
def npv(rate, values): """ Returns the NPV (Net Present Value) of a cash flow series. Parameters ---------- rate : scalar The discount rate. values : array_like, shape(M, ) The values of the time series of cash flows. The (fixed) time interval between cash flow "events" must be the same as that for which `rate` is given (i.e., if `rate` is per year, then precisely a year is understood to elapse between each cash flow event). By convention, investments or "deposits" are negative, income or "withdrawals" are positive; `values` must begin with the initial investment, thus `values[0]` will typically be negative. Returns ------- out : float The NPV of the input cash flow series `values` at the discount `rate`. Notes ----- Returns the result of: [G]_ .. math :: \\sum_{t=0}^{M-1}{\\frac{values_t}{(1+rate)^{t}}} References ---------- .. [G] L. J. Gitman, "Principles of Managerial Finance, Brief," 3rd ed., Addison-Wesley, 2003, pg. 346. Examples -------- >>> np.npv(0.281,[-100, 39, 59, 55, 20]) -0.0084785916384548798 (Compare with the Example given for numpy.lib.financial.irr) """ values = np.asarray(values) return (values / (1+rate)**np.arange(0, len(values))).sum(axis=0)
