def test_noncentral_chisquare_for_invalid_params(self, param_dtype):
df = cupy.full(self.df_shape, -1, dtype=param_dtype)
nonc = cupy.full(self.nonc_shape, 1, dtype=param_dtype)
with self.assertRaises(ValueError):
_distributions.noncentral_chisquare(df, nonc, size=self.shape)
df = cupy.full(self.df_shape, 1, dtype=param_dtype)
nonc = cupy.full(self.nonc_shape, -1, dtype=param_dtype)
with self.assertRaises(ValueError):
_distributions.noncentral_chisquare(df, nonc, size=self.shape)
def test_triangular_for_invalid_params(self, param_dtype):
left = cupy.full(self.left_shape, 1, dtype=param_dtype)
mode = cupy.full(self.mode_shape, 0, dtype=param_dtype)
right = cupy.full(self.right_shape, 2, dtype=param_dtype)
with self.assertRaises(ValueError):
_distributions.triangular(left, mode, right, size=self.shape)
left = cupy.full(self.left_shape, -2, dtype=param_dtype)
mode = cupy.full(self.mode_shape, 0, dtype=param_dtype)
right = cupy.full(self.right_shape, -1, dtype=param_dtype)
with self.assertRaises(ValueError):
_distributions.triangular(left, mode, right, size=self.shape)
left = cupy.full(self.left_shape, 0, dtype=param_dtype)
mode = cupy.full(self.mode_shape, 0, dtype=param_dtype)
right = cupy.full(self.right_shape, 0, dtype=param_dtype)
with self.assertRaises(ValueError):
_distributions.triangular(left, mode, right, size=self.shape)
def test_lo_hi_nonrandom(self):
a = random.randint(-0.9, 1.1, size=3)
numpy.testing.assert_array_equal(a, cupy.full((3,), 0))
a = random.randint(-1.1, -0.9, size=(2, 2))
numpy.testing.assert_array_equal(a, cupy.full((2, 2), -1))
def test_object(self):
d = [1.] * 100
k = [1.] * 3
numpy.testing.assert_array_almost_equal(
dpnp.convolve(d, k)[2:-2], dpnp.full(98, 3))
d1 = halfs + halfs * np.erf(w1)
d2 = halfs + halfs * 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.full(SIZE, 0, dtype=DTYPE)
put = np.full(SIZE, -1, dtype=DTYPE)
mrs = np.full(SIZE, -RISK_FREE, dtype=DTYPE)
vol = VOLATILITY
vol_vol_twos = np.full(SIZE, vol * vol * 2, dtype=DTYPE)
quarters = np.full(SIZE, 0.25, dtype=DTYPE)
ones = np.full(SIZE, 1, dtype=DTYPE)
halfs = np.full(SIZE, 0.5, dtype=DTYPE)
black_scholes(price, strike, t, mrs, vol_vol_twos, quarters, ones, halfs, call,
put)
print(call[:10])
print(put[:10])
def check_distribution(self, dist_func, df_dtype, dtype):
df = cupy.full(self.df_shape, 5, 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, lam_dtype, dtype):
lam = cupy.full(self.lam_shape, 5, dtype=lam_dtype)
out = dist_func(lam, self.shape, dtype)
self.assertEqual(self.shape, out.shape)
self.assertEqual(out.dtype, dtype)
def check_distribution(self, dist_func, df_dtype):
df = cupy.full(self.df_shape, 5, dtype=df_dtype)
out = dist_func(df, 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, lam_dtype):
lam = cupy.full(self.lam_shape, 5, dtype=lam_dtype)
out = dist_func(lam, 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_full_like_reshape_cupy_only(self, dtype):
a = testing.shaped_arange((2, 3, 4), cupy, dtype)
b = cupy.full_like(a, 1, shape=self.shape)
c = cupy.full(self.shape, 1, dtype=dtype)
testing.assert_array_equal(b, c)