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tf.contrib.distributions.percentile

View source on GitHub

Compute the q-th percentile of x.

tf.contrib.distributions.percentile(
    x,
    q,
    axis=None,
    interpolation=None,
    keep_dims=False,
    validate_args=False,
    name=None
)

Given a vector x, the q-th percentile of x is the value q / 100 of the way from the minimum to the maximum in a sorted copy of x.

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.

# Get 30th percentile with default ('nearest') interpolation.
x = [1., 2., 3., 4.]
percentile(x, q=30.)
==> 2.0

# Get 30th percentile with 'lower' interpolation
x = [1., 2., 3., 4.]
percentile(x, q=30., interpolation='lower')
==> 1.0

# Get 100th percentile (maximum).  By default, this is computed over every dim
x = [[1., 2.]
     [3., 4.]]
percentile(x, q=100.)
==> 4.0

# Treat the leading dim as indexing samples, and find the 100th quantile (max)
# over all such samples.
x = [[1., 2.]
     [3., 4.]]
percentile(x, q=100., axis=[0])
==> [3., 4.]

Compare to numpy.percentile.

Args:

  • x: Floating point N-D Tensor with N > 0. If axis is not None, x must have statically known number of dimensions.
  • q: Scalar Tensor in [0, 100]. The percentile.
  • axis: Optional 0-D or 1-D integer Tensor with constant values. The axis that hold independent samples over which to return the desired percentile. If None (the default), treat every dimension as a sample dimension, returning a scalar.
  • interpolation: {"lower", "higher", "nearest"}. Default: "nearest" This optional parameter specifies the interpolation method to use when the desired quantile lies between two data points i < j:
    • lower: i.
    • higher: j.
    • nearest: i or j, whichever is nearest.
  • keep_dims: Python bool. If True, the last dimension is kept with size 1 If False, the last dimension is removed from the output shape.
  • validate_args: Whether to add runtime checks of argument validity. If False, and arguments are incorrect, correct behavior is not guaranteed.
  • name: A Python string name to give this Op. Default is "percentile"

Returns:

A (N - len(axis)) dimensional Tensor of same dtype as x, or, if axis is None, a scalar.

Raises:

  • ValueError: If argument 'interpolation' is not an allowed type.