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.

location

Pointer to the 1 x p numeric table with the vector of means. The input can be an object of any class derived from NumericTable except PackedSymmetricMatrix and PackedTriangularMatrix.

scatter

Pointer to the 1 x p numeric table with the vector of standard deviations. The input can be an object of any class derived from NumericTable except PackedSymmetricMatrix and PackedTriangularMatrix.

threshold

Pointer to the 1 x p numeric table with non-negative numbers that define the outlier region. The input can be an object of any class derived from NumericTable except PackedSymmetricMatrix and PackedTriangularMatrix.

Note

If you do not provide at least one of the location, scatter, threshold inputs, the library will initialize all of them with the following default values:
location

Set of 0.0

scatter

Set of 1.0

threshold

Set of 3.0

Algorithm Parameters

The univariate outlier detection 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.

DEPRECATED: initializationProcedure

Not applicable

Note

This parameter is deprecated and will be removed in a future release. To initialize the algorithm, use tables in the input class.

The procedure for setting initial parameters of the algorithm. It is your responsibility to define the procedure.

Input objects for the initialization procedure are:

  • data - numeric table of size n x p that contains input data of the univariate outlier detection algorithm

Results of the initialization procedure are:

  • location - numeric table of size 1 x p that contains the vector of means
  • scatter - numeric table of size 1 x p that contains the vector of deviations
  • threshold - numeric table of size 1 x p with the non-negative numbers that define the outlier region

If you do not set this parameter, the library uses the default initialization, which sets:

  • location to 0.0
  • scatter to 1.0
  • threshold to 3.0

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. Zero 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++: out_detect_uni_dense_batch.cpp

Java*: OutDetectUniDenseBatch.java

Python*: out_detect_uni_dense_batch.py

For more complete information about compiler optimizations, see our Optimization Notice.
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