jax.numpy.quantile — JAX documentation (original) (raw)

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. qshould 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)

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)