示例#1
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def test_nmf_mask(simulate):
  x, eta = simulate
  mask = np.random.uniform(size=x.shape) < 0.25
  y = np.ma.masked_array(x, mask=mask)
  res = nmf(y, 1)
  res0 = nmf(x, 1)
  assert (res != res0).any()
示例#2
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def imputation_score_nmf(x, rank):
    try:
        from wlra.nmf import nmf
        res = nmf(x, rank, atol=1e-3)
        return loss(res[x.mask], x.data[x.mask])
    except RuntimeError:
        return [np.nan for f in losses]
示例#3
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def generalization_score_nmf(train, test, rank=10, **kwargs):
    from wlra.nmf import nmf
    lam = nmf(train, rank=rank)
    return pois_llik(lam, train, test)
示例#4
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def training_score_nmf(x, rank=10):
    from wlra.nmf import nmf
    return st.poisson(mu=nmf(x, rank)).logpmf(x).sum()
示例#5
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def test_nmf_shape(simulate):
  x, eta = simulate
  res = nmf(x, 1)
  assert res.shape == x.shape
示例#6
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def test_nmf_return_lf(simulate):
  x, eta = simulate
  l, f = nmf(x, 1, return_lf=True)
  assert l.shape == (x.shape[0], 1)
  assert f.shape == (1, x.shape[1])
示例#7
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def test_nmf_not_frob(simulate):
  x, eta = simulate
  res = nmf(x, 1, frob=False)
示例#8
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def test_nmf_objective(simulate):
  x, eta = simulate
  res = nmf(x, 1)
  obj = np.linalg.norm(x - res)
  res0 = skd.NMF(n_components=1).fit(x)
  assert np.isclose(obj, res0.reconstruction_err_)
示例#9
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def test_nmf_rank(simulate):
  x, eta = simulate
  res = nmf(x, 1)
  u, d, vt = np.linalg.svd(res)
  assert (~np.isclose(d, 0)).sum() == 1