jax-ml/jax · error · ValueError

x must be a one-dimensional array

Error message

x must be a one-dimensional array

What it means

Raised by jnp.vander when the input x is not one-dimensional; the Vandermonde matrix is only defined for a 1-D vector of roots.

Source

Thrown at jax/_src/numpy/lax_numpy.py:8149

    >>> jnp.vander(x, N=2)
    Array([[1, 1],
           [2, 1],
           [3, 1],
           [4, 1]], dtype=int32)

    Generates the Vandermonde matrix in increasing order of powers, when
    ``increasing=True``.

    >>> jnp.vander(x, increasing=True)
    Array([[ 1,  1,  1,  1],
           [ 1,  2,  4,  8],
           [ 1,  3,  9, 27],
           [ 1,  4, 16, 64]], dtype=int32)
  """
  x = util.ensure_arraylike("vander", x)
  if x.ndim != 1:
    raise ValueError("x must be a one-dimensional array")
  N = x.shape[0] if N is None else core.concrete_or_error(
    operator.index, N, "'N' argument of jnp.vander()")
  if N < 0:
    raise ValueError("N must be nonnegative")

  iota = lax.iota(x.dtype, N)
  if not increasing:
    iota = lax.sub(lax._const(iota, N - 1), iota)

  return ufuncs.power(x[..., None], expand_dims(iota, tuple(range(x.ndim))))


### Misc

@export
def argwhere(
    a: ArrayLike,
    *,

View on GitHub (pinned to 1e1c6a8fc0)

Solutions

1. Flatten input: jnp.vander(x.ravel(), N)
2. Use jax.vmap over rows if you need per-row Vandermonde matrices
3. Fix upstream slicing to produce 1-D (x[:, 0] instead of x[:, :1])

Example fix

// before
jnp.vander(x[:, :1])  # shape (n,1)
// after
jnp.vander(x[:, 0])   # shape (n,)

x = jnp.asarray(x)
if x.ndim != 1: x = x.ravel()

def is_1d(x):
    return jnp.asarray(x).ndim == 1

When it happens

Trigger: jnp.vander on a 2-D matrix, a (n,1) column, or a list that asarray promotes to >1-D.

Common situations: Passing a column vector from slicing (shape (n,1)) instead of ravel; feeding a batch of points where per-row vander via vmap was intended.

Related errors

• scan got `length` argument of {} which disagrees with leadin
• conv_general_dilated batch_group_count must divide lhs batch
• conv_general_dilated rhs output feature dimension size must
• conv_general_dilated window and window_strides must have the
• Wrong number of explicit pads for convolution: expected {},

AI-assisted analysis of jax-ml/jax@1e1c6a8fc0 (2026-08-27). Data as JSON: /api/errors/5d02329f25dad593. Report an issue: GitHub.