Quantile
Quantile is an algorithm to analyze the distribution of observations.
Quantiles are the values that divide the distribution so that a given portion of observations is below the quantile.
Details
Given a set 
and the quantile orders 
,
the problem is to compute 
that meets the following conditions:
X
of p
features
and the quantile orders ,
the problem is to compute that meets the following conditions:
In the equations above:
are observations of a random variable 
that represents the i-th feature- Pis the probability measure
Batch Processing
Algorithm Input
The quantile algorithm accepts the input described below.
Pass the
Input ID
as a parameter to the methods that provide input for your algorithm.
For more details, see Algorithms.Input ID | Input |
|---|---|
data | Pointer to the  numeric table that contains the input data set.
This table can be an object of any class derived from NumericTable . |
Algorithm Parameters
The quantile algorithm has the following parameters:
Parameter | Default Value | Description |
|---|---|---|
algorithmFPType | float | The floating-point type that the algorithm uses for intermediate computations. Can be float or double . |
method | defaultDense | Performance-oriented computation method, the only method supported by the algorithm. |
quantileOrders | 0.5 | The  numeric table with quantile orders. |
Algorithm Output
The quantile algorithm calculates the result described below.
Pass the
Result ID
as a parameter to the methods that access the results of your algorithm.
For more details, see Algorithms.Result ID | Result |
|---|---|
quantiles | Pointer to the  numeric table with the quantiles.By default, this result is an object of the HomogenNumericTable class, but you can define the result as an object of any class
derived from NumericTable except PackedSymmetricMatrix , PackedTriangularMatrix , and CSRNumericTable . |
Examples
C++ (CPU)
Batch Processing:
Java*
There is no support for Java on GPU.
Batch Processing:
Python*
Batch Processing: