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 ofnumpy.quantile().

Parameters:

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

• q (ArrayLike) – scalar or 1-dimensional array specifying the desired quantiles.qshould 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 ofdimensions 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)