Developer Guide for Intel® Data Analytics Acceleration Library 2018 Update 3
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. |
location |
Set of 0.0 |
scatter |
Set of 1.0 |
threshold |
Set of 3.0 |
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 |
NoteThis 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:
Results of the initialization procedure are:
If you do not set this parameter, the library uses the default initialization, which sets:
|
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. |
C++: out_detect_uni_dense_batch.cpp
Java*: OutDetectUniDenseBatch.java
Python*: out_detect_uni_dense_batch.py