Developer Guide for Intel® Data Analytics Acceleration Library 2019 Update 5
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. |
The univariate outlier detection algorithm has the following parameters:
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