mxnet.np.quantile

quantile(a, q, axis=None, out=None, overwrite_input=None, interpolation='linear', keepdims=False)

Compute the q-th quantile of the data along the specified axis. New in version 1.15.0.

Parameters

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

  • q (ndarray) – 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.

  • out (ndarray, optional) – Alternative output array in which to place the result. It must have the same shape and buffer length as the expected output, but the type (of the output) will be cast if necessary.

  • 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 out is specified, that array is returned instead.

Return type

ndarray

See also

mean()

()

Given a vector V of length N, the q-th quantile of V is the value q of the way from the minimum to the maximum in a sorted copy of V. The values and distances of the two nearest neighbors as well as the interpolation parameter will determine the quantile if the normalized ranking does not match the location of q exactly. This function is the same as the median if q=0.5, the same as the minimum if q=0.0 and the same as the maximum if q=1.0. This function differs from the original numpy.quantile in the following aspects: * q must be ndarray type even if it is a scalar * do not support overwrite_input

Examples

>>> a = np.array([[10, 7, 4], [3, 2, 1]])
>>> a
array([[10., 7., 4.],
       [3., 2., 1.]])
>>> q = np.array(0.5)
>>> q
array(0.5)
>>> np.quantile(a, q)
array(3.5)
>>> np.quantile(a, q, axis=0)
array([6.5, 4.5, 2.5])
>>> np.quantile(a, q, axis=1)
array([7., 2.])
>>> np.quantile(a, q, axis=1, keepdims=True)
array([[7.],
       [2.]])
>>> m = np.quantile(a, q, axis=0)
>>> out = np.zeros_like(m)
>>> np.quantile(a, q, axis=0, out=out)
array([6.5, 4.5, 2.5])
>>> out
array([6.5, 4.5, 2.5])