jax.numpy.percentile#

jax.numpy.percentile(a,q,axis=None,out=None,overwrite_input=False,method='linear',keepdims=False,*,interpolation=Deprecated)[source]#

Compute the percentile of the data along the specified axis.

JAX implementation ofnumpy.percentile().

Parameters:

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

• q (ArrayLike) – scalar or 1-dimensional array specifying the desired quantiles.qshould contain integer or floating point values between0 and100.

• 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 percentiles along the specified axes.

Return type:

Array

See also

• jax.numpy.quantile(): compute the quantile (0.0-1.0)

• jax.numpy.nanpercentile(): compute the percentile while ignoring NaNs

Examples

Computing the median and quartiles of a 1D array:

>>>x=jnp.array([0,1,2,3,4,5,6])>>>q=jnp.array([25,50,75])>>>jnp.percentile(x,q)Array([1.5, 3. , 4.5], dtype=float32)

Computing the same percentiles with nearest rather than linear interpolation:

>>>jnp.percentile(x,q,method='nearest')Array([1., 3., 4.], dtype=float32)