jax-ml/jax · error · RuntimeError

incompatible numbers of samples and aweights

Error message

incompatible numbers of samples and aweights

What it means

Observation weights (aweights) passed to jnp.cov must have length equal to the number of observations X.shape[1]. If np.shape(aweights)[0] != X.shape[1], RuntimeError('incompatible numbers of samples and aweights') is raised.

Source

Thrown at jax/_src/numpy/lax_numpy.py:9224

    ddof = 1 if bias == 0 else 0

  w: Array | None = None
  if fweights is not None:
    fweights = util.ensure_arraylike("cov", fweights)
    if np.ndim(fweights) > 1:
      raise RuntimeError("cannot handle multidimensional fweights")
    if np.shape(fweights)[0] != X.shape[1]:
      raise RuntimeError("incompatible numbers of samples and fweights")
    if not issubdtype(fweights.dtype, np.integer):
      raise TypeError("fweights must be integer.")
    # Ensure positive fweights; note that numpy raises an error on negative fweights.
    w = abs(fweights)
  if aweights is not None:
    aweights = util.ensure_arraylike("cov", aweights)
    if np.ndim(aweights) > 1:
      raise RuntimeError("cannot handle multidimensional aweights")
    if np.shape(aweights)[0] != X.shape[1]:
      raise RuntimeError("incompatible numbers of samples and aweights")
    # Ensure positive aweights: note that numpy raises an error for negative aweights.
    aweights = abs(aweights)
    w = asarray(aweights if w is None else w * aweights)

  if dtype is not None:
    X = X.astype(dtype)
    w = w.astype(dtype) if w is not None else w

  avg, w_sum = reductions.average(X, axis=1, weights=w, returned=True)
  w_sum = w_sum[0]

  if w is None:
    f = X.shape[1] - ddof
  elif ddof == 0:
    f = w_sum
  elif aweights is None:
    f = w_sum - ddof
  else:

View on GitHub (pinned to 1e1c6a8fc0)

Solutions

1. Match weight length to observation count: len(aweights) == m.shape[1] (or m.shape[0] with rowvar=False)
2. Slice or recompute weights after filtering data
3. Add an assertion before the call

Example fix

// before
jnp.cov(m, aweights=w)  # len(w) != n_obs
// after
assert w.shape[0] == m.shape[-1]
jnp.cov(m, aweights=w)

n_obs = m.shape[-1]
assert jnp.shape(aweights)[0] == n_obs, 'aweights length must equal number of samples'
jnp.cov(m, aweights=aweights)

When it happens

Trigger: jnp.cov(m, y=None, aweights=np.ones(4)) with m of shape (2, 3) (3 observations); weights sized to variables rather than samples.

Common situations: rowvar confusion (weights computed for rows when columns are observations); subsampling data without adjusting weights.

Related errors

• incompatible numbers of samples and fweights
• cannot handle multidimensional fweights
• fweights must be integer.
• cannot handle multidimensional aweights
• expected w and y to have the same length

AI-assisted analysis of jax-ml/jax@1e1c6a8fc0 (2026-08-27). Data as JSON: /api/errors/13cc92e7beecc25a. Report an issue: GitHub.