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

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 of numpy.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 between 0 and 100.
• 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 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)