jax.numpy.quantile#
- jax.numpy.quantile(a, q, axis=None, out=None, overwrite_input=False, method='linear', keepdims=False, *, interpolation=Deprecated)[source]#
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 between0.0
and1.0
.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 islinear
.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 aDeprecationWarning
if used.
- Returns:
An array containing the specified quantiles along the specified axes.
- Return type:
See also
jax.numpy.nanquantile()
: compute the quantile while ignoring NaNsjax.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)