jax-ml/jax · error · TypeError

expected 1D or 2D array for y

Error message

expected 1D or 2D array for y

What it means

polyfit accepts y either as a 1-D array of responses or a 2-D array where columns are separate responses to fit simultaneously. Any other rank (scalar after promotion, or 3-D+) breaks the shape contract with the Vandermonde design matrix and is rejected with a TypeError.

Source

Thrown at jax/_src/numpy/polynomial.py:243

    >>> p, C = jnp.polyfit(x, y, 2, cov=True)
    >>> p.shape, C.shape
    ((3, 3), (3, 3, 3))
  """
  if w is None:
    x_arr, y_arr = ensure_arraylike("polyfit", x, y)
  else:
    x_arr, y_arr, w = ensure_arraylike("polyfit", x, y, w)
  del x, y
  deg = core.concrete_or_error(int, deg, "deg must be int")
  order = deg + 1
  if deg < 0:
    raise ValueError("expected deg >= 0")
  if x_arr.ndim != 1:
    raise TypeError("expected 1D vector for x")
  if x_arr.size == 0:
    raise TypeError("expected non-empty vector for x")
  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]

View on GitHub (pinned to 1e1c6a8fc0)

Solutions

1. Reshape y to (len(x), n_targets): y.reshape(len(x), -1).
2. Squeeze accidental extra axes (e.g. y[:, :, 0]).
3. For higher-rank data, vmap polyfit over the extra axes.

Example fix

// before
c = jnp.polyfit(x, Y, 3)  # Y.shape == (T, C, B)
// after
c = jax.vmap(lambda y: jnp.polyfit(x, y, 3))(Y.reshape(len(x), C * B).reshape(len(x), -1))
// or simply: c = jnp.polyfit(x, Y.reshape(len(x), -1), 3)

y = jnp.asarray(y)
if y.ndim not in (1, 2):
    y = y.reshape(len(x), -1)
c = jnp.polyfit(x, y, deg)

def y_rank_ok(y) -> bool:
    return y.ndim in (1, 2)

When it happens

Trigger: jnp.polyfit(x, Y, deg) with Y.ndim == 3 (e.g. images or batched series); a 0-d y scalar.

Common situations: Fitting multiple channels that arrive as (T, C, B) instead of (T, C); passing a batched array of targets from a training pipeline without first squeezing the batch axis.

Related errors

• expected 1D vector for x
• expected a 1-d array for weights
• Input must be a rank-1 array.
• expected deg >= 0
• expected non-empty vector for x

AI-assisted analysis of jax-ml/jax@1e1c6a8fc0 (2026-08-27). Data as JSON: /api/errors/d89b0888e21e10a8. Report an issue: GitHub.