maxframe.tensor.percentile#

maxframe.tensor.percentile(a, q, axis=None, out=None, overwrite_input=False, interpolation='linear', keepdims=False)[source]#

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

Returns the q-th percentile(s) of the array elements.

Parameters:

  • a (array_like) – Input tensor or object that can be converted to a tensor.

  • q (array_like of float) – Percentile or sequence of percentiles to compute, which must be between 0 and 100 inclusive.

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

  • 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.

  • overwrite_input (bool, optional) – Just for compatibility with Numpy, would not take effect.

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

    This optional parameter specifies the interpolation method to use when the desired percentile 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:

percentile – If q is a single percentile and axis=None, then the result is a scalar. If multiple percentiles are given, first axis of the result corresponds to the percentiles. 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

See also

mean

median

equivalent to percentile(..., 50)

nanpercentile

quantile

equivalent to percentile, except with q in the range [0, 1].

Notes

Given a vector V of length N, the q-th percentile of V is the value q/100 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 percentile if the normalized ranking does not match the location of q exactly. This function is the same as the median if q=50, the same as the minimum if q=0 and the same as the maximum if q=100.

Examples

>>> import maxframe.tensor as mt
>>> a = mt.array([[10, 7, 4], [3, 2, 1]])
>>> a.execute()
array([[10,  7,  4],
       [ 3,  2,  1]])
>>> mt.percentile(a, 50).execute()
3.5
>>> mt.percentile(a, 50, axis=0).execute()
array([6.5, 4.5, 2.5])
>>> mt.percentile(a, 50, axis=1).execute()
array([7.,  2.])
>>> mt.percentile(a, 50, axis=1, keepdims=True).execute()
array([[7.],
       [2.]])

>>> m = mt.percentile(a, 50, axis=0)
>>> out = mt.zeros_like(m)
>>> mt.percentile(a, 50, axis=0, out=out).execute()
array([6.5, 4.5, 2.5])
>>> m.execute()
array([6.5, 4.5, 2.5])

The different types of interpolation can be visualized graphically:

(Source code)