jax-ml/jax · error · TypeError

expected 1D vector for x

Error message

expected 1D vector for x

What it means

polyfit fits a polynomial along a 1-D independent variable: it builds vander(x, order), which is only defined for a 1-D x. Passing multi-dimensional x (2-D grid, batched samples) is a TypeError because the Vandermonde construction and the least-squares system would be ambiguous.

Source

Thrown at jax/_src/numpy/polynomial.py:239

    If ``cov=True`` and ``full=False``, returns a tuple of arrays having
    polynomial coefficients and covariance matrix.

    >>> 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:

View on GitHub (pinned to 1e1c6a8fc0)

Solutions

1. Flatten x (and correspondingly y) so both are 1-D and length-matched: jnp.polyfit(X.ravel(), Y.ravel(), deg).
2. For multivariate fitting, use jax.scipy or a custom linear solve on stacked basis features, not polyfit.
3. Check x.ndim == 1 before the call.

Example fix

// before
c = jnp.polyfit(X, Z, 2)  # X from meshgrid, shape (m, n)
// after
c = jnp.polyfit(X.ravel(), Z.ravel(), 2)

x = jnp.asarray(x)
if x.ndim != 1:
    x = x.ravel()
y = y.reshape(-1) if x.size == y.size else y
c = jnp.polyfit(x, y, deg)

def is_1d(x) -> bool:
    return getattr(x, 'ndim', 0) == 1

When it happens

Trigger: jnp.polyfit(X, y, deg) where X has shape (m, n) (e.g. a meshgrid output); passing paired (x, y) points as a 2-D array to the first argument.

Common situations: Confusing polyfit's signature with sklearn-style fit(features, target); using the X output of np.meshgrid directly instead of a flattened coordinate array.

Related errors

• expected 1D or 2D array for y
• 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/d7fba7338f04fa87. Report an issue: GitHub.