Developer Guide for Intel® Data Analytics Acceleration Library 2019 Update 5
The multivariate 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 p x p numeric table that contains the variance-covariance matrix. The input can be an object of any class derived from NumericTable except PackedTriangularMatrix. |
|
threshold |
Pointer to the 1 x 1 numeric table with the non-negative number that defines the outlier region. The input can be an object of any class derived from NumericTable except PackedSymmetricMatrix and PackedTriangularMatrix. |
The multivariate outlier detection algorithm has the following parameters, which depend on the computation method parameter method:
The multivariate 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 1 numeric table of zeros and ones. Zero in the i-th position indicates that the i-th feature vector is an outlier. 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 the PackedSymmetricMatrix, PackedTriangularMatrix, and CSRNumericTable. |
C++:
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