jax-ml/jax · error · TypeError

expected x and y to have same length

Error message

expected x and y to have same length

What it means

polyfit builds vander(x, order) with len(x) rows, so y must have the same number of observations: y.shape[0] == x.shape[0]. A length mismatch means the design matrix and responses are inconsistent and the least-squares system cannot be formed.

Source

Thrown at jax/_src/numpy/polynomial.py:245

    ((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]
    if rhs.ndim == 2:
      rhs *= w_arr[:, np.newaxis]

View on GitHub (pinned to 1e1c6a8fc0)

Solutions

1. Apply identical filtering/slicing to both: jnp.polyfit(x[mask], y[mask], deg).
2. Verify shapes first: assert x.shape[0] == y.shape[0].
3. When using meshgrid outputs, ravel both arrays.

Example fix

// before
c = jnp.polyfit(x[keep], y, 3)
// after
c = jnp.polyfit(x[keep], y[keep], 3)

x, y = jnp.asarray(x), jnp.asarray(y)
assert x.shape[0] == y.shape[0], (x.shape, y.shape)
c = jnp.polyfit(x, y, deg)

def lengths_match(x, y) -> bool:
    return x.shape[0] == y.shape[0]

When it happens

Trigger: jnp.polyfit(x, y, deg) with len(y) != len(x); x masked/filtered but y not (or vice versa); x from ravel of a grid paired with an unflattened y.

Common situations: Applying the same mask to x but forgetting y (or slicing y's columns only); mixing flattened and unflattened arrays after grid operations; off-by-one trimming of one series.

Related errors

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

AI-assisted analysis of jax-ml/jax@1e1c6a8fc0 (2026-08-27). Data as JSON: /api/errors/a021dfd7dde5c12c. Report an issue: GitHub.