Batch Processing

Algorithm Input

The univariate outlier detection 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 `n` x `p` numeric table with the data for outlier detection. The input can be an object of any class derived from the NumericTable class.

Algorithm Parameters

The univariate outlier detection algorithm has the following parameters:

Parameter	Default Value	Description
algorithmFPType	double	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.
initializationProcedure	Not applicable	The procedure for setting initial parameters of the algorithm (the vector of means, vectors of standard deviations, and vector of limits that defines the outlier region). You must define the initialization procedure. For a possible implementation, refer to the examples.

Algorithm Output

The univariate outlier detection 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
weights	Pointer to the `n` x `p` numeric table of zeros and ones. One in the position (`i`, `j`) indicates an outlier in the `i`-th observation of the `j`-th feature. By default, the 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 СSRNumericTable.

Examples

C++: outlier_detection_univariate_batch.cpp

Java*: OutlierDetectionUnivariateBatch.java