def test_rand_min_max(self):
m = (rand(self.sds,
rows=shape[0],
cols=shape[1],
min=min_max[0],
max=min_max[1]).compute())
self.assertTrue((m.min() >= min_max[0]) and (m.max() <= min_max[1]))
def test_rand_sparsity(self):
m = rand(self.sds,
rows=shape[0],
cols=shape[1],
sparsity=sparsity,
seed=0).compute()
non_zero_value_percent = np.count_nonzero(m) * 100 / np.prod(m.shape)
self.assertTrue(
math.isclose(non_zero_value_percent, sparsity * 100, rel_tol=5))
def test_rand_normal_distribution(self):
m = (rand(self.sds,
rows=dist_shape[0],
cols=dist_shape[1],
pdf="normal",
min=min_max[0],
max=min_max[1],
seed=0).compute())
dist = find_best_fit_distribution(m.flatten("F"), distributions)
self.assertTrue(dist == "norm")
def test_rand_shape(self):
m = rand(self.sds, rows=shape[0], cols=shape[1]).compute()
self.assertTrue(m.shape == shape)
def test_rand_invalid_pdf(self):
with self.assertRaises(ValueError) as context:
rand(self.sds, rows=1, cols=10, pdf="norm").compute()
def test_rand_invalid_shape(self):
with self.assertRaises(ValueError) as context:
rand(self.sds, rows=1, cols=-10).compute()
def test_rand_zero_shape(self):
m = rand(self.sds, rows=0, cols=0).compute()
self.assertTrue(np.allclose(m, np.array([[]])))