tf.contrib.distributions.percentile(
x,
q,
axis=None,
interpolation=None,
keep_dims=False,
validate_args=False,
name=None
)
Defined in tensorflow/contrib/distributions/python/ops/sample_stats.py
.
Compute the q
-th percentile of x
.
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 pointN-D
Tensor
withN > 0
. Ifaxis
is notNone
,x
must have statically known number of dimensions.q
: ScalarTensor
in[0, 100]
. The percentile.axis
: Optional0-D
or1-D
integerTensor
with constant values. The axis that hold independent samples over which to return the desired percentile. IfNone
(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 pointsi < j
:- lower:
i
. - higher:
j
. - nearest:
i
orj
, whichever is nearest.
- lower:
keep_dims
: Pythonbool
. IfTrue
, the last dimension is kept with size 1 IfFalse
, 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 thisOp
. 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.