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Implement jenson shannon divergence

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This commit is contained in:
Johannes Fischer
2021-08-03 17:03:37 +02:00
parent 1916a8fe69
commit 1ee46214a7
2 changed files with 62 additions and 17 deletions
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21
tests/test_metrics.py
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@@ -25,10 +25,10 @@ def test_kl_divergence():
d5 = divergence(q, p, type='kl', n_components=-1)
assert isinstance(d5, float)
p = 1000000 * torch.randn(1000)
p = 1000 * torch.randn(1000)
q = torch.randn(1000)
d6 = divergence(p, q, type='kl', n_components=-1)
assert np.isnan(d6)
assert ~np.isfinite(d6)
def test_evaluate_histogram():
N = 10000
@@ -47,6 +47,23 @@ def test_evaluate_histogram():
assert qx.shape == q.shape
def test_js_divergence():
N = 1000
p = torch.randn(N)
q = 1.0 + 2.0 * torch.randn(2*N)
d1 = divergence(p, q, type='js')
d2 = divergence(q, p, type='js')
assert d1 == d2
p = 1000 * torch.randn(1000)
q = torch.randn(1000)
d1 = divergence(p, q, type='js')
d2 = divergence(q, p, type='js')
assert d1 == d2
assert np.isfinite(d1)
if __name__ == '__main__':
p = torch.randn(10)
q = 1.0 + 2.0 * torch.randn(10)