| from common import TestCase, run_tests |
| import math |
| import torch |
| from torch.autograd import Variable, gradcheck |
| from torch.distributions import Bernoulli, Multinomial, Normal |
| |
| |
| class TestDistributions(TestCase): |
| def _gradcheck_log_prob(self, dist_ctor, ctor_params): |
| # performs gradient checks on log_prob |
| distribution = dist_ctor(*ctor_params) |
| s = distribution.sample() |
| |
| self.assertEqual(s.size(), distribution.log_prob(s).size()) |
| |
| def apply_fn(*params): |
| return dist_ctor(*params).log_prob(s) |
| |
| gradcheck(apply_fn, ctor_params, raise_exception=True) |
| |
| def _check_log_prob(self, dist, asset_fn): |
| # checks that the log_prob matches a reference function |
| s = dist.sample() |
| log_probs = dist.log_prob(s) |
| for i, (val, log_prob) in enumerate(zip(s.data.view(-1), log_probs.data.view(-1))): |
| asset_fn(i, val, log_prob) |
| |
| def test_bernoulli(self): |
| p = Variable(torch.Tensor([0.7, 0.2, 0.4]), requires_grad=True) |
| r = Variable(torch.Tensor([0.3]), requires_grad=True) |
| self.assertEqual(Bernoulli(p).sample_n(8).size(), (8, 3)) |
| self.assertEqual(Bernoulli(r).sample_n(8).size(), (8, 1)) |
| self.assertEqual(Bernoulli(r).sample().size(), (1,)) |
| self._gradcheck_log_prob(Bernoulli, (p,)) |
| |
| def ref_log_prob(idx, val, log_prob): |
| prob = p.data[idx] |
| self.assertEqual(log_prob, math.log(prob if val else 1 - prob)) |
| |
| self._check_log_prob(Bernoulli(p), ref_log_prob) |
| |
| def test_bernoulli_3d(self): |
| p = Variable(torch.Tensor(2, 3, 5).fill_(0.5), requires_grad=True) |
| self.assertEqual(Bernoulli(p).sample().size(), (2, 3, 5)) |
| self.assertEqual(Bernoulli(p).sample_n(2).size(), (2, 2, 3, 5)) |
| |
| def test_multinomial_1d(self): |
| p = Variable(torch.Tensor([0.1, 0.2, 0.3]), requires_grad=True) |
| # TODO: this should return a 0-dim tensor once we have Scalar support |
| self.assertEqual(Multinomial(p).sample().size(), (1,)) |
| self.assertEqual(Multinomial(p).sample_n(1).size(), (1, 1)) |
| self._gradcheck_log_prob(Multinomial, (p,)) |
| |
| def test_multinomial_2d(self): |
| probabilities = [[0.1, 0.2, 0.3], [0.5, 0.3, 0.2]] |
| p = Variable(torch.Tensor(probabilities), requires_grad=True) |
| self.assertEqual(Multinomial(p).sample().size(), (2,)) |
| self.assertEqual(Multinomial(p).sample_n(6).size(), (6, 2)) |
| self._gradcheck_log_prob(Multinomial, (p,)) |
| |
| def ref_log_prob(idx, val, log_prob): |
| sample_prob = p.data[idx][val] / p.data[idx].sum() |
| self.assertEqual(log_prob, math.log(sample_prob)) |
| |
| self._check_log_prob(Multinomial(p), ref_log_prob) |
| |
| def test_normal(self): |
| mean = Variable(torch.randn(5, 5), requires_grad=True) |
| std = Variable(torch.randn(5, 5).abs(), requires_grad=True) |
| mean_1d = Variable(torch.randn(1), requires_grad=True) |
| std_1d = Variable(torch.randn(1), requires_grad=True) |
| self.assertEqual(Normal(mean, std).sample().size(), (5, 5)) |
| self.assertEqual(Normal(mean, std).sample_n(7).size(), (7, 5, 5)) |
| self.assertEqual(Normal(mean_1d, std_1d).sample_n(1).size(), (1, 1)) |
| self.assertEqual(Normal(mean_1d, std_1d).sample().size(), (1,)) |
| self.assertEqual(Normal(0.2, .6).sample_n(1).size(), (1, 1)) |
| self.assertEqual(Normal(-0.7, 50.0).sample_n(1).size(), (1, 1)) |
| |
| self._gradcheck_log_prob(Normal, (mean, std)) |
| self._gradcheck_log_prob(Normal, (mean, 1.0)) |
| self._gradcheck_log_prob(Normal, (0.0, std)) |
| |
| def ref_log_prob(idx, x, log_prob): |
| m = mean.data.view(-1)[idx] |
| s = std.data.view(-1)[idx] |
| expected = (math.exp(-(x - m) ** 2 / (2 * s ** 2)) / |
| math.sqrt(2 * math.pi * s ** 2)) |
| self.assertAlmostEqual(log_prob, math.log(expected), places=3) |
| |
| self._check_log_prob(Normal(mean, std), ref_log_prob) |
| |
| |
| if __name__ == '__main__': |
| run_tests() |
