From aa52dee8dcaba963faa0ec4e36a85d97bbc8e2de Mon Sep 17 00:00:00 2001 From: Kevin Sheppard Date: Sat, 13 Nov 2021 04:17:50 +0000 Subject: ENH: random: Add broadcast support to Generator.multinomial (#16740) xref github issue #15201 --- numpy/random/tests/test_generator_mt19937.py | 64 +++++++++++++++++++++++++--- 1 file changed, 58 insertions(+), 6 deletions(-) (limited to 'numpy/random/tests') diff --git a/numpy/random/tests/test_generator_mt19937.py b/numpy/random/tests/test_generator_mt19937.py index d057122f1..e5411b8ef 100644 --- a/numpy/random/tests/test_generator_mt19937.py +++ b/numpy/random/tests/test_generator_mt19937.py @@ -136,12 +136,6 @@ class TestMultinomial: contig = random.multinomial(100, pvals=np.ascontiguousarray(pvals)) assert_array_equal(non_contig, contig) - def test_multidimensional_pvals(self): - assert_raises(ValueError, random.multinomial, 10, [[0, 1]]) - assert_raises(ValueError, random.multinomial, 10, [[0], [1]]) - assert_raises(ValueError, random.multinomial, 10, [[[0], [1]], [[1], [0]]]) - assert_raises(ValueError, random.multinomial, 10, np.array([[0, 1], [1, 0]])) - def test_multinomial_pvals_float32(self): x = np.array([9.9e-01, 9.9e-01, 1.0e-09, 1.0e-09, 1.0e-09, 1.0e-09, 1.0e-09, 1.0e-09, 1.0e-09, 1.0e-09], dtype=np.float32) @@ -2361,6 +2355,64 @@ class TestBroadcast: [2, 3, 6, 4, 2, 3]], dtype=np.int64) assert_array_equal(actual, desired) + random = Generator(MT19937(self.seed)) + actual = random.multinomial([5, 20], [[1 / 6.] * 6] * 2) + desired = np.array([[0, 0, 2, 1, 2, 0], + [2, 3, 6, 4, 2, 3]], dtype=np.int64) + assert_array_equal(actual, desired) + + random = Generator(MT19937(self.seed)) + actual = random.multinomial([[5], [20]], [[1 / 6.] * 6] * 2) + desired = np.array([[[0, 0, 2, 1, 2, 0], + [0, 0, 2, 1, 1, 1]], + [[4, 2, 3, 3, 5, 3], + [7, 2, 2, 1, 4, 4]]], dtype=np.int64) + assert_array_equal(actual, desired) + + @pytest.mark.parametrize("n", [10, + np.array([10, 10]), + np.array([[[10]], [[10]]]) + ] + ) + def test_multinomial_pval_broadcast(self, n): + random = Generator(MT19937(self.seed)) + pvals = np.array([1 / 4] * 4) + actual = random.multinomial(n, pvals) + n_shape = tuple() if isinstance(n, int) else n.shape + expected_shape = n_shape + (4,) + assert actual.shape == expected_shape + pvals = np.vstack([pvals, pvals]) + actual = random.multinomial(n, pvals) + expected_shape = np.broadcast_shapes(n_shape, pvals.shape[:-1]) + (4,) + assert actual.shape == expected_shape + + pvals = np.vstack([[pvals], [pvals]]) + actual = random.multinomial(n, pvals) + expected_shape = np.broadcast_shapes(n_shape, pvals.shape[:-1]) + assert actual.shape == expected_shape + (4,) + actual = random.multinomial(n, pvals, size=(3, 2) + expected_shape) + assert actual.shape == (3, 2) + expected_shape + (4,) + + with pytest.raises(ValueError): + # Ensure that size is not broadcast + actual = random.multinomial(n, pvals, size=(1,) * 6) + + def test_invalid_pvals_broadcast(self): + random = Generator(MT19937(self.seed)) + pvals = [[1 / 6] * 6, [1 / 4] * 6] + assert_raises(ValueError, random.multinomial, 1, pvals) + assert_raises(ValueError, random.multinomial, 6, 0.5) + + def test_empty_outputs(self): + random = Generator(MT19937(self.seed)) + actual = random.multinomial(np.empty((10, 0, 6), "i8"), [1 / 6] * 6) + assert actual.shape == (10, 0, 6, 6) + actual = random.multinomial(12, np.empty((10, 0, 10))) + assert actual.shape == (10, 0, 10) + actual = random.multinomial(np.empty((3, 0, 7), "i8"), + np.empty((3, 0, 7, 4))) + assert actual.shape == (3, 0, 7, 4) + class TestThread: # make sure each state produces the same sequence even in threads -- cgit v1.2.1