jax-ml/jax · error · TypeError

expected w and y to have the same length

Error message

expected w and y to have the same length

What it means

Weights in polyfit are applied elementwise per observation, so w must have exactly y.shape[0] entries — one weight per data point. A length mismatch would make the row-wise multiplication (w[:, None] * lhs) shape-invalid, and polyfit surfaces it as an explicit TypeError up front.

Source

Thrown at jax/_src/numpy/polynomial.py:260

  if y_arr.ndim < 1 or y_arr.ndim > 2:
    raise TypeError("expected 1D or 2D array for y")
  if x_arr.shape[0] != y_arr.shape[0]:
    raise TypeError("expected x and y to have same length")

  if rcond is None:
    rcond = len(x_arr) * float(finfo(x_arr.dtype).eps)
  rcond = core.concrete_or_error(float, rcond, "rcond must be float")
  # set up least squares equation for powers of x
  lhs = vander(x_arr, order)
  rhs = y_arr

  # apply weighting
  if w is not None:
    w_arr, = promote_dtypes_inexact(w)
    if w_arr.ndim != 1:
      raise TypeError("expected a 1-d array for weights")
    if w_arr.shape[0] != y_arr.shape[0]:
      raise TypeError("expected w and y to have the same length")
    lhs *= w_arr[:, np.newaxis]
    if rhs.ndim == 2:
      rhs *= w_arr[:, np.newaxis]
    else:
      rhs *= w_arr

  # scale lhs to improve condition number and solve
  scale = sqrt((lhs*lhs).sum(axis=0))
  lhs /= scale[np.newaxis, :]
  c, resids, rank, s = linalg.lstsq(lhs, rhs, rcond)

  # Broadcasting scale coefficients
  if c.ndim > 1:
    # For multi-dimensional output, make scale (1, order) to divide
    # across the c.T of shape (num_rhs, order)
    c = (c.T / scale[np.newaxis, :]).T
  else:
    # Simple case for 1D output

View on GitHub (pinned to 1e1c6a8fc0)

Solutions

1. Recompute/ravel weights on the same slice as y: w=w[mask] alongside y[mask].
2. Assert w.shape[0] == y.shape[0] before calling.
3. Regenerate weights whenever the underlying data length changes.

Example fix

// before
c = jnp.polyfit(x[mask], y[mask], 3, w=w)  # w unfiltered
// after
c = jnp.polyfit(x[mask], y[mask], 3, w=w[mask])

if w is not None:
    assert w.shape[0] == y.shape[0], (w.shape, y.shape)
c = jnp.polyfit(x, y, deg, w=w)

def weights_match(w, y) -> bool:
    return w is None or w.shape[0] == y.shape[0]

When it happens

Trigger: jnp.polyfit(x, y, deg, w=w) with len(w) != y.shape[0]; weights computed for the unfiltered dataset while y was filtered; weights from a different time window than y.

Common situations: Filtering x and y by a mask but computing weights before filtering (or vice versa); resampling one of the arrays independently; stale cached weights after data length changes.

Related errors

• expected a 1-d array for weights
• expected deg >= 0
• expected 1D vector for x
• expected non-empty vector for x
• expected 1D or 2D array for y

AI-assisted analysis of jax-ml/jax@1e1c6a8fc0 (2026-08-27). Data as JSON: /api/errors/2c2182e8693658a6. Report an issue: GitHub.