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([[]])))