def setup_class(cls):
grid = dt._Grid([91, 101])
cop_tr = tra.TransfFrank
args = (2, )
ca = ArchimedeanCopula(cop_tr())
distr1 = stats.beta(4, 3)
distr2 = stats.beta(4, 4) # (5, 2)
cad = CopulaDistribution(ca, [distr1, distr2], cop_args=args)
cdfv = cad.cdf(grid.x_flat, args)
cdf_g = cdfv.reshape(grid.k_grid)
cls.grid = grid
cls.cdfv = cdfv
cls.distr = cad
cls.bpd = BernsteinDistribution(cdf_g)
def test_bernstein_distribution_2d():
grid = dt._Grid([51, 51])
cop_tr = tra.TransfFrank
args = (2, )
ca = ArchimedeanCopula(cop_tr())
distr1 = stats.uniform
distr2 = stats.uniform
cad = CopulaDistribution(ca, [distr1, distr2], cop_args=args)
cdfv = cad.cdf(grid.x_flat, args)
cdf_g = cdfv.reshape(grid.k_grid)
bpd = BernsteinDistribution(cdf_g)
cdf_bp = bpd.cdf(grid.x_flat)
assert_allclose(cdf_bp, cdfv, atol=0.005)
assert_array_less(np.median(np.abs(cdf_bp - cdfv)), 0.001)
grid_eps = dt._Grid([51, 51], eps=1e-8)
pdfv = cad.pdf(grid_eps.x_flat)
pdf_bp = bpd.pdf(grid_eps.x_flat)
assert_allclose(pdf_bp, pdfv, atol=0.01, rtol=0.04)
assert_array_less(np.median(np.abs(pdf_bp - pdfv)), 0.05)
# check marginal cdfs
# get marginal cdf
xx = np.column_stack((np.linspace(0, 1, 5), np.ones(5)))
cdf_m1 = bpd.cdf(xx)
assert_allclose(cdf_m1, xx[:, 0], atol=1e-13)
xx = np.column_stack((np.ones(5), np.linspace(0, 1, 5)))
cdf_m2 = bpd.cdf(xx)
assert_allclose(cdf_m2, xx[:, 1], atol=1e-13)
xx_ = np.linspace(0, 1, 5)
xx = xx_[:, None] # currently requires 2-dim
bpd_m1 = bpd.get_marginal(0)
cdf_m1 = bpd_m1.cdf(xx)
assert_allclose(cdf_m1, xx_, atol=1e-13)
pdf_m1 = bpd_m1.pdf(xx)
assert_allclose(pdf_m1, np.ones(len(xx)), atol=1e-13)
bpd_m = bpd.get_marginal(1)
cdf_m = bpd_m.cdf(xx)
assert_allclose(cdf_m, xx_, atol=1e-13)
pdf_m = bpd_m.pdf(xx)
assert_allclose(pdf_m, np.ones(len(xx)), atol=1e-13)