jax.numpy.quantileΒΆ

jax.numpy.quantile(a, q, axis=None, out=None, overwrite_input=False, interpolation='linear', keepdims=False)[source]ΒΆ

Compute the q-th quantile of the data along the specified axis.

LAX-backend implementation of quantile().

Original docstring below.

New in version 1.15.0.

Parameters
  • a (array_like) – Input array or object that can be converted to an array.

  • q (array_like of float) – Quantile or sequence of quantiles to compute, which must be between 0 and 1 inclusive.

  • axis ({int, tuple of int, None}, optional) – Axis or axes along which the quantiles are computed. The default is to compute the quantile(s) along a flattened version of the array.

  • interpolation ({'linear', 'lower', 'higher', 'midpoint', 'nearest'}) –

    This optional parameter specifies the interpolation method to use when the desired quantile lies between two data points i < j:

    • linear: i + (j - i) * fraction, where fraction is the fractional part of the index surrounded by i and j.

    • lower: i.

    • higher: j.

    • nearest: i or j, whichever is nearest.

    • midpoint: (i + j) / 2.

  • keepdims (bool, optional) – If this is set to True, the axes which are reduced are left in the result as dimensions with size one. With this option, the result will broadcast correctly against the original array a.

Returns

quantile – If q is a single quantile and axis=None, then the result is a scalar. If multiple quantiles are given, first axis of the result corresponds to the quantiles. The other axes are the axes that remain after the reduction of a. If the input contains integers or floats smaller than float64, the output data-type is float64. Otherwise, the output data-type is the same as that of the input. If out is specified, that array is returned instead.

Return type

scalar or ndarray