vslSSEditOutliersDetection
vslSSEditOutliersDetection
Modifies array pointers
related to multivariate outliers detection.
Syntax
status
=
vslsSSEditOutliersDetection
(
task
,
nparams
,
params
,
w
);
status
=
vsldSSEditOutliersDetection
(
task
,
nparams
,
params
,
w
);
Include Files
- mkl.h
Input Parameters
Name | Type | Description |
---|---|---|
task | VSLSSTaskPtr | Descriptor of the task |
nparams | const MKL_INT* | Pointer to the number of method parameters |
params | const float* for vslsSSEditOutliersDetection const double* for vsldSSEditOutliersDetection | Pointer to the array of method parameters |
w | float* for vslsSSEditOutliersDetection double* for vsldSSEditOutliersDetection | Pointer to an array of size n . The array holds the weights of observations to be marked as outliers. |
Output Parameters
Name | Type | Description |
---|---|---|
status | int | Current status of the task |
Description
The
vslSSEditOutliersDetection
routine uses the parameters passed to replace - the pointers to the number of method parameters and to the array of the method parameters of sizenparams
- the pointer to the array that holds the calculated weights of the observations
If you pass a value of
NULL
for a specific input parameter, the value of that parameter in the task descriptor is unchanged.Intel® oneAPI Math Kernel Library
params
array and pass them into the editor. Table "Structure of the Array of BACON Parameters"
describes the params
structure.Array Position | Algorithm Parameter | Description |
---|---|---|
0 | Method to start the algorithm | The parameter takes one of the following possible values: VSL_SS_METHOD_BACON_MEDIAN_INIT , if the algorithm is started using the median estimate. This is the default value of the parameter.VSL_SS_METHOD_BACON_MAHALANOBIS_INIT , if the algorithm is started using the Mahalanobis distances. |
1 | α | One-tailed probability that defines the (1 - ) quantile
of α χ 2
distribution with p degrees of freedom. The recommended value is α / n n is the number of observations. By default, the value is 0.05. |
2 | δ | Stopping criterion; the algorithm is terminated if the size of the basic subset is changed less than δ . By default, the value is 0.005. |
Output of the algorithm is the vector of weights,
BaconWeights
, such that BaconWeights
(i
) = 0 if i
-th observation is detected as an outlier. Otherwise BaconWeights
(i
) = w
(i
), where w
is the vector of input weights. If you do not provide the vector of input weights, BaconWeights
(i
) is set to 1 if the i
-th observation is not detected as an outlier.See additional details about usage model of the algorithm in the Summary Statistics Application Notes document [SS Notes].
Intel® oneAPI Math Kernel Library