jax.numpy.quantile

jax.numpy.quantile(a, q, axis=None, out=None, overwrite_input=False, method='linear', keepdims=False, *, interpolation=Deprecated)

Compute the quantile of the data along the specified axis.

JAX implementation of numpy.quantile().

Parameters:

• a (ArrayLike) – N-dimensional array input.

• q (ArrayLike) – scalar or 1-dimensional array specifying the desired quantiles. q should contain floating-point values between 0.0 and 1.0.

• axis (int | tuple[int, ...] | None) – optional axis or tuple of axes along which to compute the quantile

• out (None) – not implemented by JAX; will error if not None

• overwrite_input (bool) – not implemented by JAX; will error if not False

• method (str) – specify the interpolation method to use. Options are one of ["linear", "lower", "higher", "midpoint", "nearest"]. default is linear.

• keepdims (bool) – if True, then the returned array will have the same number of dimensions as the input. Default is False.

• interpolation (DeprecatedArg | str) – deprecated alias of the method argument. Will result in a DeprecationWarning if used.

Returns:

An array containing the specified quantiles along the specified axes.

Return type:

Array

See also

• jax.numpy.nanquantile(): compute the quantile while ignoring NaNs

• jax.numpy.percentile(): compute the percentile (0-100)

Examples

Computing the median and quartiles of an array, with linear interpolation:

>>> x = jnp.arange(10)
>>> q = jnp.array([0.25, 0.5, 0.75])
>>> jnp.quantile(x, q)
Array([2.25, 4.5 , 6.75], dtype=float32)

Computing the quartiles using nearest-value interpolation:

>>> jnp.quantile(x, q, method='nearest')
Array([2., 4., 7.], dtype=float32)