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 \(X\) of \(p\) features \(x_1 = (x_{11}, \ldots, x_{1p}), \ldots x_n = (x_{n1}, \ldots, x_{np})\) and the quantile orders \(\beta = \beta_1, \ldots, \beta_m\), the problem is to compute \(z_{ik}\) that meets the following conditions:
In the equations above:
\(x_i = (x_{1i}, \ldots, x_{ni})\) are observations of a random variable \(\xi_i\) that represents the \(i\)-th feature
\(P\) is the probability measure
\(i = 1, \ldots, p\)
\(k = 1, \ldots, m\)
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 |
---|---|
|
Pointer to the \(n \times p\) numeric table that contains the input data set.
This table can be an object of any class derived from |
Algorithm Parameters¶
The quantile algorithm has the following parameters:
Parameter |
Default Value |
Description |
---|---|---|
|
|
The floating-point type that the algorithm uses for intermediate computations. Can be |
|
|
Performance-oriented computation method, the only method supported by the algorithm. |
|
\(0.5\) |
The \(1 \times m\) 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 |
---|---|
|
Pointer to the \(p \times m\) numeric table with the quantiles. By default, this result is an object of the |
Examples¶
Batch Processing: