jax.numpy.quantile

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jax.numpy.quantile#

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

  • weights (ArrayLike | None) – keyword-only. optional array of weights associated with the values in a. Each value in a contributes to the quantile according to its associated weight. The weights array must be broadcastable to the same shape as a. Only works with method="inverted_cdf".

Returns:

An array containing the specified quantiles along the specified axes.

Return type:

Array

See also

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)

Computing weighted quantiles:

>>> x = jnp.array([1, 2, 3, 4, 5])
>>> weights = jnp.array([1, 1, 2, 1, 1])
>>> jnp.quantile(x, 0.5, weights=weights, method='inverted_cdf')
Array(3., dtype=float32)
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