Compute the quantile of the data along the specified axis.
JAX implementation of numpy.quantile().
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.
interpolation (DeprecatedArg | str) – deprecated alias of the method argument. Will result in a DeprecationWarning if used.
An array containing the specified quantiles along the specified axes.
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)
RetroSearch is an open source project built by @garambo | Open a GitHub Issue
Search and Browse the WWW like it's 1997 | Search results from DuckDuckGo
HTML:
3.2
| Encoding:
UTF-8
| Version:
0.7.4