def check_distribution(self, dist_func, dfnum_dtype, dfden_dtype):
dfnum = cupy.ones(self.dfnum_shape, dtype=dfnum_dtype)
dfden = cupy.ones(self.dfden_shape, dtype=dfden_dtype)
out = dist_func(dfnum, dfden, self.shape)
self.assertEqual(self.shape, out.shape)
# numpy and dpdp output dtype is float64
self.assertEqual(out.dtype, numpy.float64)
def check_distribution(self, dist_func, shape_dtype, scale_dtype):
shape = cupy.ones(self.shape_shape, dtype=shape_dtype)
scale = cupy.ones(self.scale_shape, dtype=scale_dtype)
out = dist_func(shape, scale, self.shape)
self.assertEqual(self.shape, out.shape)
# numpy and dpdp output dtype is float64
self.assertEqual(out.dtype, numpy.float64)
def check_distribution(self, dist_func, mu_dtype, kappa_dtype):
mu = cupy.ones(self.mu_shape, dtype=mu_dtype)
kappa = cupy.ones(self.kappa_shape, dtype=kappa_dtype)
out = dist_func(mu, kappa, self.shape)
self.assertEqual(self.shape, out.shape)
# numpy and dpdp output dtype is float64
self.assertEqual(out.dtype, numpy.float64)
def check_distribution(self, dist_func, ngood_dtype, nbad_dtype,
nsample_dtype, dtype):
ngood = cupy.ones(self.ngood_shape, dtype=ngood_dtype)
nbad = cupy.ones(self.nbad_shape, dtype=nbad_dtype)
nsample = cupy.ones(self.nsample_shape, dtype=nsample_dtype)
out = dist_func(ngood, nbad, nsample, self.shape, dtype)
self.assertEqual(self.shape, out.shape)
self.assertEqual(out.dtype, dtype)
def check_distribution(self, dist_func, ngood_dtype, nbad_dtype,
nsample_dtype):
ngood = cupy.ones(self.ngood_shape, dtype=ngood_dtype)
nbad = cupy.ones(self.nbad_shape, dtype=nbad_dtype)
nsample = cupy.ones(self.nsample_shape, dtype=nsample_dtype)
out = dist_func(ngood, nbad, nsample, self.shape)
self.assertEqual(self.shape, out.shape)
# numpy output dtype is int64, dpnp output is int32
self.assertEqual(out.dtype, numpy.int64)
def test_mode(self):
d = dpnp.ones(100)
k = dpnp.ones(3)
default_mode = dpnp.convolve(d, k, mode='full')
full_mode = dpnp.convolve(d, k, mode='f')
numpy.testing.assert_array_equal(full_mode, default_mode)
# integer mode
with numpy.testing.assert_raises(ValueError):
dpnp.convolve(d, k, mode=-1)
numpy.testing.assert_array_equal(dpnp.convolve(d, k, mode=2),
full_mode)
# illegal arguments
with numpy.testing.assert_raises(TypeError):
dpnp.convolve(d, k, mode=None)
def test_multiply_scalar2(shape, dtype):
a = numpy.ones(shape, dtype=dtype)
ia = inp.ones(shape, dtype=dtype)
result = 0.5 * ia
expected = 0.5 * a
numpy.testing.assert_array_equal(result, expected)
def test_logseries_for_invalid_p(self, p_dtype, dtype):
with self.assertRaises(ValueError):
cp_params = {'p': cupy.zeros(self.p_shape, dtype=p_dtype)}
_distributions.logseries(size=self.shape, dtype=dtype, **cp_params)
with self.assertRaises(ValueError):
cp_params = {'p': cupy.ones(self.p_shape, dtype=p_dtype)}
_distributions.logseries(size=self.shape, dtype=dtype, **cp_params)
def test_multiply_scalar2(shape, dtype):
np_a = numpy.ones(shape, dtype=dtype)
dpnp_a = dpnp.ones(shape, dtype=dtype)
result = 0.5 * dpnp_a
expected = 0.5 * np_a
numpy.testing.assert_array_equal(result, expected)
def test_histogram_weights_basic(self):
v = cupy.random.rand(100)
w = cupy.ones(100) * 5
a, b = cupy.histogram(v)
na, nb = cupy.histogram(v, density=True)
wa, wb = cupy.histogram(v, weights=w)
nwa, nwb = cupy.histogram(v, weights=w, density=True)
testing.assert_array_almost_equal(a * 5, wa)
testing.assert_array_almost_equal(na, nwa)
def test_cumprod_huge_array(self):
size = 2**32
# Free huge memory for slow test
cupy.get_default_memory_pool().free_all_blocks()
a = cupy.ones(size, 'b')
result = cupy.cumprod(a, dtype='b')
del a
self.assertTrue((result == 1).all())
# Free huge memory for slow test
del result
cupy.get_default_memory_pool().free_all_blocks()
def test_no_overwrite(self):
d = dpnp.ones(100)
k = dpnp.ones(3)
dpnp.convolve(d, k)
numpy.testing.assert_array_equal(d, dpnp.ones(100))
numpy.testing.assert_array_equal(k, dpnp.ones(3))
def check_distribution(self, dist_func, mu_dtype, kappa_dtype, dtype):
mu = cupy.ones(self.mu_shape, dtype=mu_dtype)
kappa = cupy.ones(self.kappa_shape, dtype=kappa_dtype)
out = dist_func(mu, kappa, self.shape, dtype)
self.assertEqual(self.shape, out.shape)
self.assertEqual(out.dtype, dtype)
def check_distribution(self, dist_func, df_dtype, dtype):
df = cupy.ones(self.df_shape, dtype=df_dtype)
out = dist_func(df, self.shape, dtype)
self.assertEqual(self.shape, out.shape)
self.assertEqual(out.dtype, dtype)
def check_distribution(self, dist_func, a_dtype, dtype):
a = cupy.ones(self.a_shape, dtype=a_dtype)
out = dist_func(a, self.shape, dtype)
self.assertEqual(self.shape, out.shape)
self.assertEqual(out.dtype, dtype)
def check_distribution(self, dist_func, p_dtype, dtype):
p = 0.5 * cupy.ones(self.p_shape, dtype=p_dtype)
out = dist_func(p, self.shape, dtype)
self.assertEqual(self.shape, out.shape)
self.assertEqual(out.dtype, dtype)
def check_distribution(self, dist_func, a_dtype):
a = cupy.ones(self.a_shape, dtype=a_dtype)
out = dist_func(a, self.shape)
self.assertEqual(self.shape, out.shape)
# numpy and dpdp output dtype is float64
self.assertEqual(out.dtype, numpy.float64)
def test_real_inplace(self, dtype):
x = cupy.zeros((2, 3), dtype=dtype)
x.real[:] = 1
expected = cupy.ones((2, 3), dtype=dtype)
assert cupy.all(x == expected)
def check_distribution(self, dist_func, p_dtype):
p = 0.5 * cupy.ones(self.p_shape, dtype=p_dtype)
out = dist_func(p, self.shape)
self.assertEqual(self.shape, out.shape)
# numpy output dtype is int64, dpnp output is int32
self.assertEqual(out.dtype, numpy.int64)
def check_distribution(self, dist_func, dfnum_dtype, dfden_dtype, dtype):
dfnum = cupy.ones(self.dfnum_shape, dtype=dfnum_dtype)
dfden = cupy.ones(self.dfden_shape, dtype=dfden_dtype)
out = dist_func(dfnum, dfden, self.shape, dtype)
self.assertEqual(self.shape, out.shape)
self.assertEqual(out.dtype, dtype)
def check_distribution(self, dist_func, shape_dtype, scale_dtype, dtype):
shape = cupy.ones(self.shape_shape, dtype=shape_dtype)
scale = cupy.ones(self.scale_shape, dtype=scale_dtype)
out = dist_func(shape, scale, self.shape, dtype)
self.assertEqual(self.shape, out.shape)
self.assertEqual(out.dtype, dtype)
b = T * mr
z = T * sig_sig_two
c = 0.25 * z
y = 1./np.sqrt(z)
w1 = (a - b + c) * y
w2 = (a - b - c) * y
d1 = 0.5 + 0.5 * np.erf(w1)
d2 = 0.5 + 0.5 * np.erf(w2)
Se = np.exp(b) * S
r = P * d1 - Se * d2
call[:] = r # temporary `r` is necessary for faster `put` computation
put[:] = r - P + Se
np.random.seed(SEED)
price = np.random.uniform(PL, PH, SIZE)
strike = np.random.uniform(SL, SH, SIZE)
t = np.random.uniform(TL, TH, SIZE)
call = np.zeros(SIZE, dtype=DTYPE)
put = -np.ones(SIZE, dtype=DTYPE)
black_scholes(price, strike, t, RISK_FREE, VOLATILITY, call, put)
print(call[:10])
print(put[:10])
def test_ones_like_reshape_cupy_only(self, dtype):
a = testing.shaped_arange((2, 3, 4), cupy, dtype)
b = cupy.ones_like(a, shape=self.shape)
c = cupy.ones(self.shape, dtype=dtype)
testing.assert_array_equal(b, c)
