Developer Guide

Contents

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
.
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:
initialization
Procedure
Not applicable
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
.

Product and Performance Information

1

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Notice revision #20110804